Olena M. Stankevych

The paper substantiates the factors that give rise to crisis situations and conflicts during the management of energy-active facilities – situations that frequently escalate into emergencies requiring rapid response and effective decision-making. In such contexts, it is crucial to examine the psychological and intellectual resilience of operators, as this resilience underpins the capacity of systems and agents to function within the scope of defined objectives and authorities. Such resilience serves as a key factor in maintaining functional efficiency when countering various types of threats and attacks. A significant aspect of functional resilience involves analysing the logic behind decision-making aimed at achieving system-oriented outcomes. These decisions are grounded in principles of coherence, multi-level analysis, and risk assessment. Identifying the structure of this management logic and the associated decision-making procedures enables the development of efficient algorithms for addressing crisis situations – algorithms applicable across diverse fields, ranging from security management to socio-economic systems. The challenge of making efficient decisions – whether by an intelligent agent (a team player), an operational team, or a managerial/administrative structure – is determined by a complex interplay of psycho-neurophysiological and intellectual factors, operating at both the individual and team levels. They are participants in a game structure that shapes the strategy of permissible actions. To address this problem, the paper develops a cognitive-logical concept for the operational resilience of cyber-physical infrastructure, which integrates a “human-agent” into a unified situational control loop. Based on the category algebra, a category-theoretic information model is constructed for the decomposition of a hierarchical production structure, accounting for 16 threat-resilient personnel levels (P1–P16). By integrating H. Gardner’s theory of multiple intelligences and R. Sternberg’s triarchic theory, a cognitive map for operator decision-making during laser-based environmental monitoring is formalized. A mathematical framework for the quantitative assessment of personnel actions is developed, incorporating cognitive coefficients (KF), confidence levels (K(B)), and error risk factors (Ri). Practical testing of the system at the Burshtyn Power Station (Ukraine) and glass-manufacturing companies has demonstrated its efficiency under extreme operating conditions (1200°C–1500°C). Implementation of this method has reduced the reaction time of operational and dispatch personnel during pre-emergency and emergency conditions (Alarm/Avaria) and minimized the overall risk of error decisions stemming from the human factor.

Unmanned aerial vehicles (UAVs) have become an indispensable tool for solving a wide range of complex tasks. To improve and increase the efficiency of unmanned aerial vehicles, it is important to optimize their structural elements. Reducing weight and strategically distributing loads while maintaining structural strength and rigidity directly impacts flight endurance, stability, payload capacity, and overall airframe integrity. Integrating topological optimization with artificial intelligence enables the automated design of components tailored to specific operational requirements. The application of an integrated approach to automate the design of small-type UAV structures and the combination of classical methods of topological optimization with artificial intelligence and additive manufacturing technologies were considered. A microdrone airframe, fabricated via 3D printing using carbon fiber-reinforced nylon (Nylon PA12 + CF15), was analyzed both before and after generative design optimization. The effectiveness of the optimization and the changes in the structure’s rigidity and stability were assessed. Optimization resulted in an approximately 12% reduction in structural mass relative to the baseline model. Structural modeling and load simulations demonstrated that the maximum displacement of the optimized model does not exceed 3 mm, compared to 9 mm for the baseline structure. The results from virtual load simulations and physical testing of the printed models confirm that structural mass can be significantly reduced while preserving the integrity and core functional performance of the airframe. The study is based on simplified loading conditions and is limited to the number of experimental samples. Furthermore, dynamic and vibrational loads were not accounted for in the current study. Future research will focus on expanding the experimental framework and conducting an in-depth analysis of combined loading conditions that significantly affect the aerodynamic performance and real-world flight envelopes of drones.

Цифровізація освітнього процесу супроводжується накопиченням значних масивів даних про успішність та поведінку здобувачів освіти. Однак практичне застосування методів інтелектуального аналізу таких даних зустрічається з низкою викликів, зумовлених складною природою освітнього середовища та етичними ризиками.

The stress-strain state of a two-layer composite containing a penny-shaped crack under the influence of SH-waves generated by time-harmonic torsional loads is investigated. The problem is solved using the boundary integral equations method, with the wave field solutions represented through Helmholtz potentials. A system of boundary integral equations is derived to determine the unknown functions of tangential displacement along the crack surfaces. The frequency-dependent behavior of dynamic stress intensity factors near the defect contour is analyzed with respect to the mechanical properties and geometric parameters of the composite layers.

The article presents a developed 3D model of a vibration separator with an unbalanced drive and pneumatic suspension. The created model is parametric, allowing for modifications to the design of the vibration separator based on the area of application. The welded separator housing was selected as the main assembly unit of the model. Using Autodesk Inventor Professional, a strength analysis of the vibration separator was carried out to confirm its operational reliability. The study evaluated whether the designed structure can withstand the applied operational loads, and parametric optimization was performed to reduce the mass of the vibrating part of the separator while maintaining its structural strength. This approach enables engineers to tailor the separator design to various industrial applications and optimize material utilization without compromising durability.

Fracture of commercially pure aluminum in the as-supplied state and after annealing during quasi-static tension is investigated. The wavelet transform parameters identify the acoustic emission (AE) signals from dislocation processes and microcrack formation. The architecture of the multilayer perceptron is optimized. AE signals from different sources are classified with an accuracy of up to 92.5% for training data and 87.6% for test data. The energy parameter and the frequency of the local AE event are the most important information for the proposed neural network.

Computer vision systems are increasingly expanding their application in visual data analysis. Model training methods are undergoing the greatest development and improvement as the results of this stage significantly affect the final classification of objects and the interpretation of input information. Typically, computer vision systems use convolutional neural networks for training (Convolution Neural Network, CNN). The disadvantages of such systems are significant limitations in cross-modal learning, multimodality implementation, labeling of large amounts of data, etc. One of the ways to overcome these problems is to use Vision Transformers (ViT), which, compared to classical CNNs, have higher performance due to reduced inductive biases and high parallel computing efficiency. Introducing Self-Supervised Learning (SSL) technologies can significantly reduce the dependence on manually labeled data, contributing to the formation of generalized representations of images. Cross-Modal Learning (CML) expands the possibilities of processing them by combining data of different types. The development of the new approach, combined with the capabilities of cross-modal learning and self-learning in ViT in a single architecture, will ensure adaptability, efficiency, and system scalability in various applications. The research aims to provide a detailed overview of ViTs, approaches to their architecture, and methods for improving their efficiency. The mathematical foundations of the key concepts of ViT, cross-modal learning and self-learning, the main modifications of ViT, and their integration with SSL and CML technologies are considered. A comparison of methods using characteristics, performance, and efficiency is provided. The key challenges and prospects facing researchers and developers while creating universal models in computer vision are outlined. ViTs change computer vision by capturing global dependencies on images. Despite some challenges, ViTs provide excellent scalability and performance for large datasets. The active search for methods to overcome their limitations makes ViTs a key tool for improving image classification, object detection, and other computer vision tasks.