Coal remains one of the key raw materials used in the energy industry to generate electricity and heat. As a result, diagnostics of the combustion process is still an important topic of scientific research. Correct implementation of the process allows the emission of pollutants into the atmosphere to be kept at a compliant level. Therefore, it is important to conduct the process in a manner that will not exceed these standards. A preliminary analysis of the measurement signals was carried out, and signal predictions of flame intensity changes were determined using the autoregressive moving average (ARMA) model. Different fuzzy neural network architectures have been investigated. Binary and multi-class classifications of flame states were conducted. The best results were obtained from the ANFIS_grid partition model, producing an accuracy of 95.46% for binary classification and 79.08% for multi-class classification. The accuracy of the recognition of flame states and the high convergence of the determined predictions with measurement signals validate the application of the proposed approach in diagnosing or controlling the combustion process of pulverized coal and its mixtures with biomass. Expert decisions determine the range of acceptable states.

The aim of this study is a comparative evaluation of the effectiveness of YOLO and RT-DETR family models for the automatic recognition and localization of meniscus tears in knee joint MRI images. The experiments were conducted on a proprietary annotated dataset consisting of 2000 images from 2242 patients from various clinics. Based on key performance metrics, the most effective representatives from each family, YOLOv8-x and RT-DETR-l, were selected. Comparative analysis based on training, validation, and testing results showed that YOLOv8-x delivered more stable and accurate outcomes than RT-DETR-l. The YOLOv8-x model achieved high values across key metrics: accuracy—0.958, recall—0.961; F1-score—0.960; mAP@50—0.975; and mAP@50–95—0.616. These results demonstrate the potential of modern object detection models for clinical application, providing accurate, interpretable, and reproducible diagnosis of meniscal injuries.

This article is devoted to creating an intellectual model for assessing the level of formation of students’ competencies based on learning outcomes, taking into account the requirements of the professional environment. The authors of the article proposed a method for constructing a fuzzy cognitive map and a model for assessing the level of competence formation based on a fuzzy approach. The main concepts of the cognitive map are defined as disciplines, learning outcomes, and competencies. The article provides an example of a cognitive map for the educational program in the Information Technology direction. The determination of the link weights between the concepts of the map was implemented in an expert way. The model for assessing the level of competence formation is based on the fuzzy set theory. The article defines the following parameters of the model: linguistic variables, membership functions and similarity measures. A universal five-level Dreyfus model was used to describe the level of competence formation. Based on the proposed model, the “Digital Profile” information system was developed and implemented as a web application. The article presents the main mechanisms of information system design: information objects are defined, the logical scheme of the database is described, the scheme of the algorithm for creating a digital profile is described and the functional windows of the web application are described. The developed “Digital Profile” information system makes it possible to assess the achievements of mastering the competencies of educational programs, as well as to assess the quality of student training.

This article details a matrix-based mathematical method to calculate power flows and transmission losses in an electric grid specifically attributable to low-carbon and renewable energy sources (LCRES) (wind, solar, nuclear). The goal is to improve the transparency and reliability of Guarantees of Origin (GO) certificates. Current GO schemes rely on contractual accounting and neglect physical power losses, undermining consumers’ confidence that they receive “clean” energy. The method uses steady-state power flow analysis to derive a power-loss distribution coefficient matrix. This matrix accurately allocates grid losses back to the LCRES generating nodes, complying strictly with electrical engineering principles. It accommodates both time-varying renewable output and stable nuclear generation. The results offer highly accurate loss-attribution data, supporting more verifiable GOs, ensuring fair compensation for losses, and enhancing energy balance accuracy in hybrid power systems.