The problem of processing large amounts of unstructured data obtained from open web sources in conditions of limited storage resources and a growing proportion of spam content is considered. The aim of the research is to develop architectural and technological solutions for effective management of unstructured data flows, including maintaining the current state of the core of domain-specific documents. Options for implementing technologies for proactive data storage and deferred web scanning are offered. Preferential storage allows you to manage data in systems with a fixed amount of memory, using criteria for the importance of documents: creation time, compliance with the subject area and the level of duplication. Deferred analysis technology is designed to enrich data by supplementing and clarifying information from open sources without creating a peak load on external resources. A solution to the problem of maintaining an up-todate core of documents related to the state of the subject area is proposed. The architecture of a proactive storage and deferred web scraping system is proposed, which allows efficient data management with exponential content growth. The results obtained can be used to improve the methods of processing aggregated and synthetic content obtained from open sources.

The directions of using ontologies to ensure the requirements of interoperability in the creation of information systems (IS) using artificial intelligence technologies are analyzed. It is shown that it is advisable to integrate these technologies with traditional analytical and simulation modeling technologies. At the same time, the semantics of the transmitted information, knowledge about the order of its processing and application, as well as knowledge about the goals and objectives in a given context of using IS are of the greatest importance in the functional compatibility of IS for various purposes. The analysis shows that in order to ensure the interoperability of existing and legacy IS without interfering with the existing order of their separate operation, the technological process of data processing, architectural and technical solutions, it is necessary to create an ontologically controlled superstructure over them, allowing for the interoperability of IS. At the same time, the functional compatibility of IS can be ensured both on the basis of expert intermediary subsystems implemented in the form of semantic mediators, and analytical and simulation models, subject to the conditions of homomorphism of the relations underlying them.

Quadcopters are one of the most widely used types of unmanned aerial vehicles due to their simple design, high maneuverability, versatility and cost-effectiveness. However, controlling the movement of the quadcopter along a given trajectory is a serious problem due to non-linearities, external disturbances and drive limitations. The proportionalintegral-differential (PID) controller is a convenient control tool when using modern optimization methods. The aim of the work is to increase the efficiency of controlling the movement of a quadcopter along a given trajectory using a PID controller, the coefficients of which are optimized by the 3S Optimizer metaheuristic algorithm. The quadcopter is controlled by generating signals for four engines, which provide the appropriate angular velocities to achieve a given position and orientation in space. The quadcopter’s control scheme is designed according to a hierarchical model that includes three nested control circuits. The design of PID controllers using 3S Optimizer is formulated as an optimization problem with limitations on overshoot, rise time, and transition time. Two types of experiments are conducted: 1) checking the response of the control system to input signals in the form of single jumps; 2) checking the ability of the control system to follow a given trajectory. In both experiments, control systems with settings using the 3S Optimizer algorithm shows the best results by almost all criteria.

Methods of modeling difficult-to-formalize objects are considered. The complexity of formalization is due to the manifestation of non-factors, which differ in the signs of manifestation when constructing models, and this is expressed in the weak predictability of the object’s behavior, a large number of influencing factors (often described qualitatively), and the difficulties of their quantitative measurement. As an example, the technological process of feed production is analyzed, and the process formalization is complicated by various types of non-factors (uncertainty, vagueness, etc.). The example of this object demonstrates the possibility of using a fuzzy-possibility approach in constructing models with uncertainty compensation.

A multi-level digital twin concept for adaptive manufacturing of small spacecraft of the CubeSat type is developed. The architecture of the digital twin is formalized, providing end-to-end data synchronization based on the structural and functional model of the product, the assembly route and the production system. A stochastic calendarnetwork model with dynamic routing of assembly operations is used as a methodological basis. In the MatLab/Simulink environment, a simulation of the proposed model is performed with calculation of the production cycle time and the overall efficiency coefficient of the equipment. A mathematical model of adaptive production cycle planning for small CubeSattype spacecraft is formulated, in which the minimization objective function combines calendar time deviation, integrated equipment efficiency and rejection rate, and the simulation is carried out taking into account network constraints, the precedence matrix and load limits by resource type. The developed architecture can be used to organize automated control, planning and adaptation of the production process during the serial assembly of small spacecraft.

A mathematical model of wear of a steel–polymer friction pair without lubrication is proposed. The model is based on the equations of thermal balance and wear kinetics, which take into account energy dissipation during friction and heat exchange with the environment. Unlike traditional approaches, the proposed model includes critical points corresponding to temperature transitions in the polymer, which makes it possible to predict their position based on experimental dependences with high accuracy. Special attention is paid to the destruction of polymer properties under the influence of temperature and mechanical loads, which significantly affects the tribological characteristics of the friction pair. The model is implemented in MATLAB CAD using the finite element method for the numerical solution of the equations of thermal conductivity and wear. The simulation results, compared with experimental data, demonstrate the adequacy of the model and the possibility of its application to optimize the parameters of tribological systems steel–polymer.

An analytical model of a system for reading information from a pseudorandom code scale using the window method is proposed. The model is based on the theory of linear feedback shift registers and finite Galois fields. Special attention is paid to establishing a connection between the recursive structure of binary sequences of maximum length and their algebraic representation in the space of finite fields, where primitive polynomials generate multiplicative cyclic groups.

Modeling the dynamics of underwater robots is a complex task due to the presence of both parametric and functional uncertainties. These arise from interactions with a viscous medium, a priori uncertainty, and variability in the system’s dynamic parameters, as well as the complexity and computational cost of first-principles models and the challenges of identification procedures. This paper proposes the use of neural network parameterization of ordinary differential equations based on the port-Hamiltonian formalism to develop accurate and computationally efficient dynamic models of underwater robots. These models can be used for trajectory prediction, integration with onboard sensor data for localization systems, and controller synthesis. The proposed approach captures both the physical structure of the system and the impact of uncertainties, enabling the creation of physically grounded, data-driven representations of complex nonlinear dynamics. Comparative experiments with classical identification and modeling methods using real-world data from an underwater robot demonstrate advantages of the proposed method in prediction accuracy and its robustness over long-horizon prediction.

Localized quantitative assessment of myocardial fibrosis is used to predict outcomes of cardiovascular diseases, select therapeutic and surgical strategies. In widespread practice, localized quantitative assessment of myocardial fibrosis is complicated by the need for manual or semiautomatic image preprocessing. A software package based on the use of deep learning technologies for analyzing MR images of the heart with late gadolinium enhancement and quantifying myocardial fibrosis is proposed. The results of statistical analysis of the prepared dataset indicate the presence of stable correlations of localized relative fibrosis volume with clinical data on ejection fractions and the degree of heart failure in patients. A pronounced negative correlation can be noted between the degree of heart failure and the left ventricular ejection fraction (p <0.001). There is also a small correlation between the LV ejection fraction and the relative volume of fibrosis for two basins - RCA and LCx (p < 0.01). The coefficient of structural similarity with the reference marking of the myocardium is 0.87 / 0.87 / 0.88 according to the DSC / Precision / Recall metrics for the automatic Cascade-U-Net-based solution. The accuracy of classification of the left ventricle level on the slice by the trained U-NetLoc model is 83% / 86% / 95% in the classification of basal / medial / apical myocardial slices. The quality of myocardial localization by the trained U-NetBull model ensures the Sorensen–Dice similarity coefficient 0,827/0,778/0,734 in individual segments of the basal / medial / apical level of the left ventricle level. The developed software package for localized quantitative assessment of myocardial fibrosis can be used as an auxiliary tool by cardiologists and radiologists to increase the diagnostic accuracy.