Purpose of the study is to obtain numerical values for the occurrence of resonance of the studied structure and to evaluate the influence of the number of welds on the resonance of the impeller of an industrial fan in the APM FEM software package.

Methods. This article uses the finite element method (FEM) analysis of a welded industrial fan impeller structure using the APM FEM software package for KOMPAS-3D v23.0.0.8. The structure was modeled using KOMPAS V23, and the welds were modeled using the "Permanent Joints" application in accordance with GOST 14771–76-T3. Weldto-solid conversion was applied to the weld locations to account for them when generating the finite element mesh.

Results. Based on the analysis, it can be concluded that the design has a high vibration resistance margin under nominal loads. Increasing the number of welds from two to four per blade of the industrial fan impeller during design slightly increases the resonance risk. Therefore, the design can be recommended for manufacturing with a minimum number of welds, namely two per blade.

Conclusion. An analysis of the welded structure of an industrial fan impeller using the APM FEM software package showed that when examined for the presence of natural frequencies, the structure retains its strength and geometric stability, and the obtained numerical values of resonance occurrence significantly exceed the values possible during operation. For the variant with two welds per blade, the natural frequencies of the first five modes were 357.42; 363.01; 363.36; 365.73 and 367.13 Hz. For the variant with four welds, the corresponding frequencies were 383.33; 391.77; 394.39; 396.63 and 397.18 Hz. The obtained numerical values of the expected occurrence of resonance comply with the requirements of regulatory documents.

Purpose of research. The aim of this research is to develop a constructive solution that ensures the earthquake resistance of a modular building in an area with a seismicity of 9 points on the MSK-64 scale. 

Methods. The research uses a FEM computational model of a building with a modular structural system with reinforced concrete modules. Numerical analysis was performed using finite element modeling (FEM) in ANSYS and SCAD. A single computational model of the entire building was developed in SCAD, incorporating the monolithic part, modular part, and inter-module connections. The inter-module connections were modeled using special finite-stiffness elements. The stiffness of inter-module connections was determined through a 3D finite element model in ANSYS, which was also used to analyze stress distribution in the connections. The model accounted for all operational loads. In addition seismic loads were used, in compliance with valid Russian design codes for Vladikavkaz. The analysis assumed linear elastic material behavior for all structural components. During the calculations, the intensity of reinforcement in the building's load-bearing structures and the stress distribution in the most loaded inter-module joint are investigated. 

Results. The calculation results show that the use of seismic insulation significantly reduces the impact of seismic effects on the building, reducing the intensity of reinforcement to 55% (by more than 2 times) in the monolithic part and up to 60% in the modular part compared to a similar building without a seismic insulation system. Additionally, a reinforced inter-module connection design was proposed, which ensures compliance with strength requirements under seismic loading.

Conclusion. This study demonstrates that the required level of earthquake resistance is achieved because of the use of a seismic insulation system, thus, constructive solutions can be preserved. This study has an important practical benefit, which consists in preparing the calculation base for the construction of prefabricated modular reinforced concrete buildings in seismic areas. In subsequent research phases, it is necessary to account for the post-elastic behavior of inter-module connections, and the strongly nonlinear response of sliding seismic isolators. This will enable more accurate quantitative assessment of seismic performance calculations for modular buildings with base isolation systems.

Purpose of research. The article provides a mathematical description of the heat transfer process during the combined utilization of low-potential waste heat and ventilation emissions in the channels of a multilayer plate heat exchanger.

Methods. To carry out a comparative analysis of the Eulerian criteria based on the theory of similarity in a hot channel for various configurations of turbulators in combined low-potential heat recovery systems, to determine how high-speed modes of air flows affect the heat transfer coefficient, as well as thermal and electrical efficiency in the processes of recycling secondary and renewable energy resources with associated thermoelectric generation.

Results. The advantage of the staggered configuration of cylindrical turbulators in the recuperator under study is established in comparison with the corridor and rib schemes of their arrangement, for which a comparative analysis of Euler criteria based on similarity theory was used. A mathematical model of thermal processes with a filling arrangement of cylindrical turbulators (checkerboard, corridor) in a plate heat exchanger in a quasi-stationary thermal regime has been created. A method for determining the coefficients of heat transfer and heat transfer of a complex multilayer plate heat exchanger with increased turbulence of heat carriers is proposed.

Conclusion. The study showed that the staggered configuration of cylindrical turbulators significantly increases the efficiency of heat transfer compared to other schemes, which makes it preferable for use in hot channels of plate heat recuperators of combined low-potential heat recovery systems. The developed mathematical model makes it possible to predict and optimize thermal processes, taking into account the influence of turbulators. The method of determining heat transfer coefficients ensures high accuracy of calculations, and a comparative analysis of Euler criteria based on similarity theory has confirmed the advantage of the chess scheme. The areas of operating parameters and the influence of the geometric characteristics of the turbulators on the efficiency of the heat recovery process were also determined, which underlines the importance of their optimization.

Purpose of research. The development of a hybrid, two-level method to enhance both the accuracy and robustness of detecting operator face spoofing in images, which is a pressing issue given the constant growth and sophistication of threats from deepfake technologies. 

Methods. A novel architecture is proposed, combining the EfficientNet convolutional neural network for deep pattern extraction with an ensemble of four classifiers. These classifiers specifically analyze distinct feature groups: expertbased, textural, statistical, and those based on facial landmark coordinates, enabling the detection of specific synthesis artifacts. For training and testing, an extensive and representative dataset of 34,000 images was compiled, including deepfakes generated by several modern tools as well as public datasets. 

Results. The high efficacy of the proposed method was experimentally confirmed: accuracy reached 0.921 and the F1-score was 0.914. These metrics significantly surpass the performance of any of the individual models used separately, demonstrating a pronounced and practically significant synergistic effect from their combination. 

Conclusion. This work demonstrates that the synergy between deep learning and classical feature-based models allows for the creation of a genuinely more reliable and precise detector. The proposed method improves overall accuracy and enhances system robustness by effectively compensating for the individual weaknesses of separate classifiers. This validates the hypothesis that combining a neural network's ability to extract complex, implicit patterns with feature-based models' capacity to analyze specific, predefined artifacts (such as geometric distortions) leads to a more powerful and resilient detector.

Purpose. Investigation of the relationship between input data error of a neuron intended for use in an artificial neural network implemented on FPGA, and computational error, as well as development of a methodology for selecting the bit width of neuron components aimed at reducing hardware costs while maintaining computational accuracy consistent with the accuracy of the input data.

Methods. The study employed methods of digital circuit design based on the VHDL hardware description language, error analysis of computations relative to a floating-point reference model, as well as device synthesis and FPGA resource utilization estimation methods integrated into Xilinx ISE. Mathematical statistics techniques, including the construction of regression models describing the dependence of accuracy and hardware costs on input data bit width, were applied to process the experimental results.

Results. A method has been proposed for estimating the bit width of the processing unit, enabling its precision to be matched with the inherent error level of the input data. The impact of the bit width of input data and weight coefficients on computational accuracy and the amount of FPGA hardware resources consumed by the implemented neuron was investigated. Based on the VHDL description of the device, a parameterized model was developed that enables coordinated adjustment of the neuron’s internal component bit widths as the bit width of input signals is varied. To assess the effect of bit width on computational accuracy, a floating-point-based reference model was used. For each bit-width configuration, comparative computations of the device’s output were performed, and the resulting error was quantified. The influence of bit width on FPGA resource utilization — specifically the number of LUTs and flip-flops (FFs) — was also analyzed. The proposed methodology was validated on the Xilinx Spartan-3E XC3S500E (xc3s500e-4pq208) FPGA platform using the ISE Design Suite 14.7 environment. Multiple versions of the digital neuron were implemented, with input data bit widths ranging from 4 to 12 bits (including the sign bit). For each variant, the operating clock frequency, utilized FPGA resources, and computational accuracy were recorded. As a case study using 12-bit input data, an experimental evaluation determined that a sigmoid function lookup table with 8,192 entries achieves an optimal trade-off between computational accuracy (maximum relative error — 0.12%) and hardware cost (occupying only 1% of the FPGA’s available resources).

Conclusion. This paper presents a description of a neuron circuit with a sigmoid activation function, implemented in the VHDL hardware description language and suitable for integration into neural network solutions on FieldProgrammable Gate Arrays (FPGAs). The device accepts signed integer input values of fixed bit width, computes the weighted sum of inputs and bias, and generates the neuron’s output using a precomputed lookup table stored in block RAM. The operation, scaling, and optimization of the module are described in detail.

The proposed method enables determination of the optimal bit width for the processing unit, ensuring that computational error remains consistent with the error level of the input data while minimizing hardware resource consumption. The obtained relationships can be utilized during the design phase to select parameters for digital processing modules in real-time systems and embedded devices.

Purpose of reseach. Mathematical modeling of the dynamics of controlled motion of a spherical magnetically active object in a curved channel by means of an external magnetic field created by a movable permanent magnet

Tasks. Development of a system of differential equations describing the controlled motion of a magnetically active object in a curved elastic channel. Development of algorithms for localization of a magnetically active object inside a channel, calculation of the normal and magnitude of deformation during contact interaction. Setting up computational experiments in order to determine the nature of the movement of a magnetically active object in a curved channel and obtain the maximum values of the system parameters that ensure the controllability of the microrobot due to the movement of a permanent magnet.

Methods. When modeling the motion of a magnetically active microrobot inside a biologically inspired curved channel, a system of differential equations and equations for an external inhomogeneous magnetic field is used. The model takes into account magnetic forces, environmental resistance forces, inertia forces, and gravity. Numerical integration methods are used to solve the equations of system dynamics. In the framework of this study, the model was implemented using the MATLAB.

Results. The paper presents a mathematical model of the motion of a controlled magnetically active spherical object in a curved elastic channel simulating a blood vessel. The developed model takes into account hydrodynamic resistance, interaction with deformable channel walls and external magnetic influence. The numerical experiments performed demonstrate the possibility of predicting the trajectory of an object and reveal the limiting values of the system parameters at which controllability by a magnetically active microrobot is maintained.

Conclusions. The movement of the particle along the sinusoidal channel is effectively ensured by the action of a permanent magnet. The resulting normal reaction of the canal wall does not exceed the values allowed for vascular structures, amounting to 5 mN at the peak and about 1 mN with prolonged exposure. Taking into account key physical and geometric parameters, such as the channel shape, magnetically active object properties, viscosity of the medium, friction forces and ponderomotor action, ensures the versatility of the model. The proposed methodology can be used to optimize magnetic navigation algorithms for endovascular embolization, targeted drug delivery, and other promising medical techniques.

Purpose of research. Parallel data processing based on the residue number system allows to reduce the hardware costs of digital signal filtering devices, which is one of the key problems of digital signal processing. Parallelization of calculations allowed to develop a method of digital signal filtering based on the use of truncated blocks of multiplication with accumulation in the residue number system. This article presents the advantages of using the developed approach and its limitations.

Methods. The study used a method for organizing calculations in a system of residual classes with ranges of 32 and 48 bits and using balanced sets of modules of the form {2 − 1, 2 , 2 + 1}, an analytical assessment of the complexity of the device calculation, and hardware modeling in the Synopsys Design Compiler environment using the standard library.

Results. The hardware cost reduction was recorded when using special modules {2 − 1, 2 , 2 + 1}, which allowed them to be reduced to 16,139.30  for 3-rd order filters, 31,152.99  for 7-th order filters, 62,507.06  for 15th order filters, and 126,564.46  for 31-st order filters with the organization of 32-bits arithmetic data processing in the residue number system. Thus, the hardware costs were reduced by 21.5%-23% relative to filters based on parallel-prefix adders using the Kogge-Stone method and by 20.6%-22.2% based on parallel carry adders with propagation. For 48-bit digital filters with arithmetic processing of data in the residue number system, the simulation results showed a reduction in hardware costs from 9.45% to 14% depending on their order.

Conclusion. Carrying out calculations in the system of residual classes allows improving the operational characteristics of digital signal processing devices, for which the primary task is to minimize hardware costs.

Purpose of the work was to substantiate the effectiveness of applying artificial intelligence techniques (machine learning and deep learning) for the timely detection of destructive information-technical impacts on critical infrastructure objects. 

Methods. An analysis of scientific sources has been conducted scientific sources, including cybersecurity surveys and standards, and conducted an experiment on a public network attack dataset (UNSW-NB15) using machine learning (Random Forest) and a deep neural network. Evaluation was based on metrics such as accuracy, detection recall, F1-score, etc. 

Results. ML/DL methods show significantly higher attack detection accuracy compared to traditional signature-based tools: ~96% accuracy was achieved on the UNSW-NB15 dataset using a neural network, versus ~70% for the signature approach. We demonstrate that deep learning enables discovery of previously unknown attacks (including sophisticated multi-vector APTs) by recognizing hidden anomalies, and that ensemble and federated approaches improve detection reliability and speed.

Conclusion. Integrating AI techniques into security monitoring systems considerably increases the protection efficiency of critical systems by proactively identifying cyberattacks with minimal false alarms. The experimental results confirm the practical applicability of the chosen methods for securing network infrastructure (energy, communications, industrial IoT). However, further work is needed to ensure robustness against adversarial attacks and to uphold AI reliability principles.

Рurpose of research is to develop a method for obtaining a generalized wrist movement trajectory in Nordic walking for integration into the automatic control system of the upper limb rehabilitation stand.

Methods. The article discusses and analyzes in detail the method of obtaining, subsequent processing and mathematical modeling of the generalized trajectory of wrist movement during the step cycle in Nordic walking. To do this, based on experimental data obtained from subjects with various anthropometric data based on semi-automatic video analysis and subsequent approximation of the obtained trajectories, for subsequent integration into the automatic control system of the upper limb rehabilitation stand based on the principle of continuous passive movement (CPM) in order to mobilize joints along the trajectories of natural movement. To digitize the laws of motion of the analytical description of the generalized trajectory of wrist movement, approximation by polynomials of the 5th order was applied.

Results. In the article, individual wrist movement trajectories of groups of subjects with different anthropometric parameters were obtained and visualized. Based on them, a generalized trajectory of wrist movement in the process of Nordic walking was constructed, which became possible thanks to the use of semi-automatic video analysis techniques. By conducting a thorough statistical analysis of the collected data, significant biomechanical deviations that occur with incorrect and irrational stick length selection for Nordic walking were identified and quantified. During the mathematical modeling process, specific numerical coefficients for fifth-order polynomials were calculated and obtained, which were used in the approximation to describe the generalized trajectory of motion.

Conclusion. The results obtained confirm the effectiveness of the semi-automatic video analysis method for modeling the trajectory of wrist movement and allow optimizing the parameters of rehabilitation stands, providing natural biomechanics of movements. The importance of correct stick length selection for Nordic walking is confirmed. 

Purpose of reseach solves the current applied problem of developing a unified information system for managing supply chains of a retail company. The relevance of the study is due to the need to simplify the transfer of information between departments of a retail company and increase the interaction between departments in the managing supply chains. Market analysis has shown that the need to replace foreign software remains a key issue in the Russian Federation. Replacement and adaptation of software modules and settings, user and administrator support, as well as information security and data integrity are all components of a unified, reliable system for a modern enterprise to operate smoothly in a world of transformational processes. Development of a unified information system to improve the interaction between departments of a retail company in the supply chain management 

Methods. The study uses the ArchiMate architecture modeling language to design the functional architecture of a unified supply chain management information system. The unified modeling language UML is used to model the functionality of the designed modules of the unified information system. An object-oriented approach is used to develop software for the supply chain management information system.

Results. In the course of the study, a model of the functional architecture of the information system for monitoring prices of trading enterprises was built. An analysis of the functional capabilities of the designed system was performed and an object-oriented design of the static structure of the information system for monitoring prices of trading enterprises was carried out. A web application for the information system for monitoring prices of trading enterprises was developed, allowing the collection of information on prices of goods of trading enterprises through online resources and through retail outlets.

In the course of the study, a model of the functional architecture of a unified information system for supply chain management was built. An analysis of the functional capabilities of the designed system was performed and an object-oriented design of the information system static structure for supply chain management was carried out. A software for a unified information system was developed, which allows for an increase the interaction between departments in the supply chain management, as well as simplification of the information transfer between departments of a retail company.

Conclusion. The developed system, by automating the entire supply chain management circuit, makes it easier to transfer information between the departments of a retail company, which reduces the likelihood of errors when interacting between departments.

Purpose of research. The aim of this study is to develop a conceptual framework for allocating quotas for catches of aquatic biological resources (ABR) in the Russian Federation, taking into account the strengths and weaknesses of the existing system and its key operational challenges. Furthermore, the study formalizes a new, improved model for managing the quota allocation process, the application of which will improve efficiency and address the identified limitations.

Methods. The research employs a systematic analysis of regulatory frameworks and operational mechanisms governing ABR quota allocation, including the «historical principle», auction-based distribution, and «investment quotas». It utilizes official statistical data on ABR catch dynamics across various fishery basins (2023-2024) to illustrate the system's performance. The paper also develops formal mathematical models to describe the algorithms and constraints of each allocation method and to define a conceptual framework for an adaptive quota management system.

Results. The analysis reveals that while the current ABR quota system is sophisticated and aims to balance economic, environmental, and social objectives, its inherent static nature and reliance on historical precedence lead to limitations. Specifically, it often fails to adequately incentivize the full and rational utilization of allocated resources, resulting in systematic under-utilization of quotas and missed economic benefits for the state. To address these issues, the study introduces a theoretical model for an advanced, adaptive quota management process, which extends existing states and actions to incorporate intelligent decision-making support, aiming for Pareto dominance over the current system in terms of efficiency.

Conclusion. In conclusion, despite significant advancements and the integration of digital technologies, the Russian ABR management system requires further evolution. Moving beyond purely static allocation towards an adaptive model, capable of incorporating feedback on resource utilization, is crucial for improving overall operational and economic efficiency. This research highlights a critical gap in existing literature concerning the detailed operational and economic analysis of quota distribution, transfer, and pricing mechanisms, particularly in fostering comprehensive resource exploitation.

Purpose of research. Traditional methods based on visual observation and manual counting not only have obvious limitations in terms of time and human resource costs but also yield insufficiently accurate results due to the subjective human factor involved in the process. These inaccuracies, even minor ones, can lead to erroneous management decisions, which negatively impact production efficiency in aquaculture. 

Methods. To eliminate these shortcomings, this paper presents an automated solution that utilizes the YOLOv9t neural network model for the task of detecting and counting fish in underwater images. Thanks to the optimized architecture of the YOLOv9t neural model, which contains only 2 million parameters, it demonstrated high performance in identifying fish in images from the DeepFish dataset, with the following evaluation metrics: Precision - 0.928, Recall - 0.91, mAP50 - 0.961, and mAP50-95 - 0.584. The Non-Maximum Suppression method was used to eliminate duplicate detections of fish in the same area, while the application of the DeepSORT algorithm enabled the continuous tracking of each individual across video frame sequences by assigning unique identifiers. 

Results. The research results confirmed that the YOLOv9t neural model is suitable for creating automated video analytics systems in aquaculture for monitoring fish behavior and managing activation devices. This enables the transition of key control processes to a fully automated basis, thereby optimizing resource utilization. The proposed architecture provided high accuracy and reliability across various environmental conditions-from clear to murky wateropening prospects for application in real-world production environments. 

Conclusion. This operational stability makes the system ready for industrial-scale implementation with the aim of enhancing farm management efficiency.

Purpose of research.  In the modern digital era, the role of video cameras as high‑quality sources of primary data is increasing. However, raw video data by themselves carry low informational value without subsequent analysis. The key tasks that allow extracting semantic information from a video sequence are object localization and motion detection. The relevance of this task is determined by its critical importance for a wide range of applied and research disciplines. Despite its long history, detecting moving objects remains a relevant scientific problem due to the following challenges: variability of lighting conditions, dynamic background, occlusion effects, and the need to operate in real time. The aim of this work is to reduce computational load when solving object motion analysis tasks in real time by developing and testing a method for extracting motion vectors from compressed video streams.

Methods. To achieve this goal, the framework of motion vectors was used as the basis for compensating temporal redundancy, along with computer vision algorithms and motion compensation algorithms in video data.

Results.  A software module has been developed that allows extracting motion vectors directly from a video stream. An experimental evaluation of the proposed method’s effectiveness was carried out, demonstrating its efficiency in various applied fields, including video surveillance, agriculture, and robotics, with a significant reduction in computational costs.

Conclusion. The experimental evaluations have shown that using motion vectors already contained in compressed video data allows effectively solving motion analysis tasks without the need to recalculate them. This is especially relevant for systems with limited computational resources.

Purpose of research: improving the efficiency and control of production processes by identifying potential risks of incidents and product defects during post-production stage.

Methods. This article examines the analysis of service requests and complaints automated processing at an enterprise. This process includes the analysis of incoming data on product incidents and defects and the search for causeand-effect relationships with production processes at various stages of the product life cycle. Requests about defects and product incidents are pre-registered in the unified database through the CRM-system and are available for further analysis. To carry out the research, a mathematical model for analyzing service requests and complaints using factor analysis was developed.

Results. Based on the mathematical model, a practical analysis of the enterprise's data was carried out and the main factors have been identified. The calculations were performed using the STATISTICA data processing package. The physical interpretation of the obtained factors makes it possible to explain the cause of incidents and defects, provide an expert opinion and to take measures on further production processes adjustments. The practical application of the developed mathematical model is carried out as the software module, which is implemented into the CRM system at an enterprise as an additional component for automated data processing.

Conclusion. The pre-prepared data of service requests and complaints is sent from the CRM system for analysis. The developed software module performs an automated analysis of the received data. The results of the analysis are physically interpreted and used to make management decisions at the enterprise, which allows regular monitoring of current production processes of the enterprise and make appropriate adjustments to improve the efficiency of production processes.