Recent advances in large language models have substantially improved natural language understanding and enabled their application across a wide range of domains. However, highly specialized fields such as law and medicine remain challenging because their documents often contain complex structures, domain-specific terminology, and dense logical dependencies. In such settings, large language models may produce errors when important structural information is not explicitly preserved in the document representation. To address this limitation, we propose a novel approach for document decomposition into graph-based representations that better capture the structural and semantic relationships within complex texts. We develop a method for processing raw legal documents from the Ukrainian domain using an LLM-based decomposition pipeline, transforming them into structured graph representations that can reinforce contextual retrieval and support retrieval-augmented generation. The proposed method improves document understanding by preserving key contextual dependencies and enhancing the representation of legal knowledge in downstream tasks.

The article deals with the problem of automatic control of the roll angle of an unmanned aerial vehicle. Based on the complete mathematical model of the unmanned aerial vehicle and modal control methods, two variants of the automatic control system are considered: an autopilot implemented as a closed-loop feedback system and a system with an observer device. The controllability and observability conditions of the system are analyzed, and the autopilot equation with a structure corresponding to a PID controller is obtained. Numerical simulation results are presented in the form of time dependences of the roll angle, roll rate, and aileron deflection angle for different natural frequencies. Analytical verification confirms the steady-state value of the roll angle obtained by simulation. The parameters of the autopilot for different transient processes are determined. The roots of the characteristic equations for the binomial and Butterworth standard forms are calculated, and analytical expressions for the gain matrix of the Luenberger observer are obtained. The results show that the use of an observer device significantly changes the transient response of the system. The proposed approach based on modal control methods can be applied not only to roll control but also to the design of automatic control systems for pitch and yaw angles of unmanned aerial vehicles and other aircraft.

The article examines the role of modern computing architectures in enabling the digital transformation of the energy sector, particularly in the context of the development of decentralised electricity markets. The advantages and limitations of on-premises, cloud, and hybrid infrastructure solutions for building distributed, scalable computing systems are analysed. Based on this analysis, requirements for the communication and information system of interaction among participants in a decentralised electricity market are formulated. The key design principles of such a system are identified, including scalability, architectural unification, high availability, and fault tolerance. Particular attention is given to processing large data volumes, ensuring data sovereignty, integrating blockchain technologies and smart contracts, and using consensus mechanisms suitable for industrial systems. Requirements for network infrastructure, geographical distribution of computing nodes, and cyber-resilience are also considered, particularly in the context of cascading risk effects. The study demonstrates that a hybrid computing architecture provides the most balanced approach by combining control over critical infrastructure components with the flexibility and scalability of cloud technologies, thereby creating a technological foundation for a resilient communication and information system supporting a decentralised electricity market.

Short Message Service (SMS) remains a critical signaling-layer communication mechanism in 4G and 5G mobile networks, particularly within Application-to-Person (A2P) ecosystems supporting authentication, financial notifications, public services, and IoT fallback signaling. The transition to LTE packet-switched delivery and further to 5G Service-Based Architecture (SBA) introduces dynamic routing, SLA differentiation, multi-channel fallback, and cost variability. Traditional Quality of Service (QoS) indicators are insufficient to assess operational efficiency in such environments. Furthermore, Artificially Inflated Traffic (AIT) distorts economic performance without necessarily degrading technical KPIs. This paper proposes an integrated techno-economic framework for SMS delivery optimization in next-generation networks. The framework introduces the Price Delivery Gap (PDG) as an economic deviation metric and develops the Integrated Gap-Delivery-Performance (IGDP) model combining QoS, Quality of Experience (QoE), and PDG into a unified optimization function. An intelligent architecture incorporating message categorization, AIT detection, adaptive routing, and closed-loop monitoring is presented. Scenario-based evaluation under mixed A2P traffic conditions demonstrates reduced economic deviation and improved integrated efficiency compared to static and partially adaptive approaches.

The article addresses the problem of identifying the key features of a MicroGrid as a complex energy system, which is a necessary prerequisite for the development of an intelligent control system. The widespread introduction of distributed generation, renewable energy sources, and energy storage systems necessitates adaptive control methods capable of operating under conditions of uncertainty and variable electrical network modes. Existing approaches to MicroGrid control remain limited due to the lack of a unified and scientifically grounded set of structural, operational, dynamic, and informational features that should be taken into account in intelligent systems. The paper analyzes contemporary scientific approaches to the classification, modeling, and real-time control of microgrids, which makes it possible to identify insufficiently studied aspects related to the formation of feature sets and their influence on decision-making processes. The objective of the study is to systematize and substantiate a comprehensive set of microgrid features that can serve as a basis for the development of intelligent control algorithms. A conceptual approach to structuring microgrid features into the following groups is proposed: architectural, energy-technical, operational, features related to the stability of dynamic operating modes, and data-oriented features. Their significance for forecasting, optimization, state estimation, and autonomous operation of local energy systems is demonstrated. The obtained results form a methodological foundation that enhances the effectiveness of developing intelligent microgrid control systems and contributes to improving the flexibility and reliability of local energy networks.

The scientific work is devoted to the development of a method for intelligent extraction of textural features of vestibular schwannomas based on magnetic resonance imaging images for predicting tumor growth. The VS-MC-RC2 dataset was analyzed (421 timepoints, 189 patients, 1990–1999). The ML dataset consists of 211 samples (74 growing, 137 stable, imbalance 1.85:1). Gray Level Co-occurrence Matrix and Gray Level Size Zone Matrix matrices, shape features, wavelet transform, and the PyRadiomics v3.0.1 library were used to extract features from T1C images (priority) and T1 images (fallback) with the following parameters: bins = 32, δ = 1 voxel, 13 3D directions. Model v2 (107 original features) achieved an AUC of 0.618. Model v3 (851 features + 8 wavelet decompositions) achieved an AUC of 0.712 (+15.2%). Validation was performed using 10-fold cross-validation with an 80/20 train/test split. Among the top 15 features, 73% were wavelet features (LHH, LLH, HLH). The best feature, original_glszm_ZoneEntropy (F = 12.67, threshold = 4.51), correlates with the Antoni A/B tissue ratio and the proliferative activity of the tumor.

The article presents the results of a study aimed at improving the stability, reproducibility, and structural consistency of computer vision pipelines for multispectral UAV imagery of winter wheat canopies. A new adaptive preprocessing model is introduced, incorporating illumination normalization based on a modified Retinex/MSRCR algorithm, entropy-regulated spatial-spectral filtering for noise suppression, and instability-driven spectral feature fusion to obtain stable multispectral descriptors. The model is formulated as a multi-objective preprocessing framework, jointly optimizing illumination invariance, noise robustness, structural preservation, and information richness. Experiments conducted on the open-access UAV dataset of nine winter-wheat fields (Switzerland) demonstrated a reduction of the coefficient of variation to 0.12 and RMSE to 0.089, together with improvements in structural similarity (SSIM = 0.923) and spectral entropy (H = 5.9), significantly outperforming classical normalization methods. The results confirm the effectiveness of the proposed approach in mitigating illumination heterogeneity and sensor-induced distortions, ensuring stable and phenologically consistent feature extraction. The developed framework can be integrated into computer-integrated and robotic precision-farming systems to enhance the reliability of automated monitoring and decision-support processes in winter-wheat production.

The article is devoted to the intelligent diagnosis of thyroid tumors, the diagnosis of papillary thyroid cancer based on general information, ultrasound images, and pathohistological images. It examines modern approaches to the intelligent diagnosis of thyroid tumors using machine learning and artificial intelligence methods. The types of medical intelligent systems, their architecture, accuracy, and the set of tasks they perform for the classification of thyroid cancer are considered. The problems of papillary thyroid cancer are considered, the specifics of the disease and the signs by which it is diagnosed are described. The main tasks of an intelligent system capable of automatically analyzing patient medical data and supporting clinical decision-making by an endocrinologist, segmenting and classifying thyroid tumors are outlined. The equipment used to form the training sample is described, and the process of data collection for building an intelligent medical system is described. The task to be solved is set. The metrics by which the accuracy of the intelligent medical system will be evaluated are characterized. The architecture of the intelligent medical system is presented, its main blocks are characterized, and the functionality of each block is described. A UML diagram is presented, according to which the intelligent medical system will operate. The data that will be used to form the training sample is presented, indicating its type, dimension, how the data is collected, and how this data will be used to train the intelligent medical system. The results of the study are aimed at improving the effectiveness of early detection of thyroid pathologies and reducing the number of false diagnoses, creating a convenient tool that will reduce the time it takes for a doctor to diagnose the disease and increase the accuracy of diagnosing papillary thyroid cancer.

The article explores the use of information technology to monitor the condition of building facades based on visual data obtained from unmanned aerial vehicles. The study highlights the growing role of unmanned aerial vehicles in structural inspections, noting their key advantages, including increased safety, efficiency, and accuracy, compared to traditional methods. The study is structured into three main sections. The first section provides an overview of existing approaches to facade monitoring, comparing traditional inspection methods with UAV-based methods. The second section discusses the technological aspects of data collection, processing, and analysis, focusing on artificial intelligence, computer vision, and photogrammetry. The final section presents the practical application of these technologies, an overview of relevant software tools, examples, and economic benefits. The results show that unmanned aerial vehicles, combined with advanced image processing technologies, significantly increase the efficiency and reliability of building facade assessments.

This scientific work is dedicated to the development of an intelligent system for diagnosing vestibular schwannoma. A new approach to texture analysis of magnetic resonance imaging images of schwanomas has been proposed as a method for assessing the growth of swelling. The use of this approach will help to avoid the risks of the progression of the neoplasm and immediately eliminate the need for surgical intervention. At the boundaries of the research, a number of classes of texture descriptors were put together, including: first-order statistics (intensity histograms), grey-color consistency matrix, dovzhin sequence matrix Gray Rivne, zone size matrix, Gray Rivn deposit matrix, as well as hvillet-transformed signs. The comprehensive analysis of these descriptors made it possible to formalize the internal microstructure of the fluff and implement an effective model for predicting its growth.