Numerical analysis of stress-strain state of monolithic slab with account of corrosion damage of concrete and reinforcement of compressed and tensile zones in the span part of the slab in PC LIRA-SAPR is carried out. 6 variants of corrosion damage depending on the area of spreading and degree of degradation are considered. The calculations have been carried out taking into account physical and geometrical nonlinearity. The peculiarities of structural deflections changes at different variants of corrosion damage and loading levels of the floor slab have been revealed. Redistributions of forces in spans and on supports arising at local changes of concrete and rebars stiffnesses are analyzed. No structural failure stage has been identified for the adopted design characteristics and damage variants.

Keywords: monolithic slab, corrosion damage of reinforced concrete, numerical analysis, redistribution of forces, bearing capacity, deformation capacity

Deviation of forestry equipment from the designated route leads to environmental, legal, and economic issues, such as soil damage, tree destruction, and fines. Autonomous route correction systems are essential to address these problems. The aim of this study is to develop a system for deviation detection and trajectory calculation to return to the designated route. The system determines the current position of the equipment using global positioning sensors and an inertial measurement unit. The Kalman filter ensures positioning accuracy, while the A* algorithm and trajectory smoothing methods are used to compute efficient routes considering obstacles and turning radii. The proposed solution effectively detects deviations and calculates a trajectory for returning to the route.

Keywords: deviation detection, route correction, mobile application, Kalman filter, logging operations

The article is devoted to numerical modeling of corrosion-damaged reinforced concrete columns under low-cycle horizontal loading by static load in LS DYNA software package. The comparison of numerical calculation and experimental data on research of strength of reinforced concrete columns with corrosion damage of reinforcement under low-cycle horizontal loading is carried out.

Keywords: corrosion, reinforcement, seismics, reinforced concrete, corrosion damage, low-cycle strength, numerical modeling

The article considers the issues of imitation modeling of fibrous material mixing processes using Markov processes. The correct combination and redistribution of components in a two-component mixture significantly affects their physical properties, and the developed model makes it possible to optimize this process. The authors propose an algorithm for modeling transitions between mixture states based on Markov processes.

Keywords: modeling, imitation, mixture, mixing, fibrous materials

The use of recurrent neural networks to predict the water level in the Amur River are consider. The advantages of using such networks in comparison with traditional machine learning methods are described. Various architectures of recurrent networks are compared, and hyperparameters of the model are optimized. The developed model based on long-term short-term memory (LSTM) has demonstrated high prediction accuracy, surpassing traditional methods. The results obtained can be used to improve the effectiveness of monitoring water resources and flood prevention.

Keywords: time series analysis, Amur, water level, forecasting, neural networks, recurrent network

The relevance of the research is determined by the need to solve complex optimization problems under conditions of high dimensionality, noisy data, and dynamically changing environments. Classical methods, such as genetic algorithms, often encounter the problem of premature convergence and fail to effectively adapt to changes in the problem. Therefore, this article focuses on identifying opportunities to enhance the flexibility and efficiency of evolutionary algorithms through integration with artificial neural networks, which allow for dynamically adjusting search parameters during the evolutionary process. The leading approach to addressing this problem is the development of a hybrid system that combines genetic algorithms with neural networks. This approach enables the neural network to adaptively regulate mutation and crossover probabilities based on the analysis of the current state of the population, preventing premature convergence and accelerating the search for the global extremum. The article presents methods for dynamic adjustment of evolutionary parameters using a neural network approach, reveals the principles of the hybrid system's operation, and provides results from testing on the Rastrigin function. The materials of the article hold practical value for further research in the field of optimization, particularly in solving problems with many local minima, where traditional methods may be ineffective. The application of the proposed hybrid model opens new perspectives for developing adaptive algorithms that can be used in various fields of science and engineering, where high accuracy and robustness to environmental changes are required.

Keywords: genetic algorithm, artificial neural network, dynamic tuning, hybrid method, global optimization, adaptive algorithm

The article explores the use of computer vision technologies to automate the process of observing animals in open spaces, with the aim of counting and identifying species. It discusses advanced methods of animal detection and recognition through the use of highly accurate neural networks. A significant challenge addressed in the study is the issue of duplicate animal counts in image data. To overcome this, two approaches are proposed: the analysis of video data sequences and the individual recognition of animals. The advantages and limitations of each method are analyzed in detail, alongside the potential benefits of combining both techniques to enhance the system's accuracy. The study also describes the process of training a neural network using a specialized dataset. Particular attention is given to the steps involved in data preparation, augmentation, and the application of neural networks like YOLO for efficient detection and classification. Testing results highlight the system's success in detecting animals, even under challenging conditions. Moreover, the article emphasizes the practical applications and potential of these technologies in monitoring animal populations and improving livestock management. It is noted that these advancements could contribute significantly to the development of similar systems in agriculture. The integration of such technologies is presented as a promising solution for tracking animal movement, assessing their health, and minimizing livestock losses across vast grazing areas.

Keywords: algorithm, computer vision, monitoring, pasture-based, livestock farming

This article will present the mlreflect package, written in Python, which is an optimized data pipeline for automated analysis of reflectometry data using machine learning. This package combines several methods of training and data processing. The predictions made by the neural network are accurate and reliable enough to serve as good starting parameters for subsequent data fitting using the least-mean-squares (LSC) method. For a large dataset consisting of 250 reflectivity curves of various thin films on silicon substrates, it was demonstrated that the analytical data pipeline with high accuracy finds the minimum of the film, which is very close to the set by the researcher using physical knowledge and carefully selected boundary conditions.

Keywords: neural network, radiography, thin films, data pipeline, machine learning

This paper is devoted to the application of the Winograd method to perform the wavelet transform in the problem of image compression. The application of this method reduces the computational complexity and also increases the speed of computation due to group processing of pixels. In this paper, the minimum number of bits at which high quality of processed images is achieved as a result of performing discrete wavelet transform in fixed-point computation format is determined. The experimental results showed that for processing fragments of 2 and 3 pixels without loss of accuracy using the Winograd method it is enough to use 2 binary decimal places for calculations. To obtain a high-quality image when processing groups of 4 and 5 pixels, it is sufficient to use 4 and 7 binary decimal places, respectively. Development of hardware accelerators of the proposed method of image compression is a promising direction for further research.

Keywords: wavelet transform, Winograd method, image processing, digital filtering, convolution with step

This paper presents the results of a study aimed at developing a method for semantic segmentation of thermal images using a modified neural network algorithm that differs from the original neural network algorithm by a higher speed or processing graphic information. As part of the study, a modification of the DeepLabv3+ semantic segmentation neural network algorithm was carried out by reducing the number of parameters of the neural network model, which made it possible to increase the speed of processing graphic information by 48% – from 27 to 40 frames per second. A training method is also presented that allows to increase the accuracy of the modified neural network algorithm; the accuracy value obtained was 5% lower than the accuracy of the original neural network algorithm.

Keywords: neural network algorithms, semantic segmentation, machine learning, data augmentation

This study presents a method for recognizing and classifying micro-expressions using optical flow analysis and the YOLOv11 architecture. Unlike previous binary detection approaches, this research enables multi-class classification while considering gender differences, as facial expressions may vary between males and females. A novel optical flow algorithm and a discretization technique improve classification stability, while the Micro ROC-AUC metric addresses class imbalance. Experimental results show that the proposed method achieves competitive accuracy, with gender-specific models further enhancing performance. Future work will explore ethnic variations and advanced learning strategies for improved recognition.

Keywords: microexpressions, pattern recognition, optical flow, YOLOv11

This article discusses the basic concepts and practical aspects of programming using the actor model on the Akka platform. The actor model is a powerful tool for creating parallel and distributed systems, providing high performance, fault tolerance and scalability. The article describes in detail the basic principles of how actors work, their lifecycle, and messaging mechanisms, as well as provides examples of typical patterns such as Master/Worker and Proxy. Special attention is paid to clustering and remote interaction of actors, which makes the article useful for developers working on distributed systems.

Keywords: actor model, akka, parallel programming, distributed systems, messaging, clustering, fault tolerance, actor lifecycle, programming patterns, master worker, proxy actor, synchronization, asynchrony, scalability, error handling

Drilling of hardened steel 40 HRC 24...32 is investigated using numerical simulation in Abaqus/Explicit. The stress-strain state is analyzed. It has been found that optimization of cutting modes (feed speeds and revolutions) reduces Mises stresses to 55% of the ultimate strength, increasing tool durability. The results show the dependence of stress distribution on cutting parameters and the influence of drill geometry on the machining process.

Keywords: drilling, hardened steel, numerical modeling, finite element method, Mises stresses, tool durability, optimization of cutting modes, drill geometry

Mathematical modeling, numerical methods and program complexes (technical sciences). Geopolitical situation analysis of a number of episodes of the American Revolution in the context of applying structural balance and mathematical modeling methods. Structural balance management can help to find the most optimal strategies for interacting parties. This approach is used in a set of disciplines. In this article, the author analyzes examples of actors' interaction in the context of the American Revolution, which allows us to evaluate the state of affairs at this historical stage in an illustrative form. This approach is universal and is able to emphasize the management of structural balance in systems with actors, each of which has its own features and interests. A number of specific historical episodes serves as an example of the balanced and unbalanced systems. Each episode has its explanation in the frame of history. During the American Revolution, actors (countries and specific politicians, as well as indigenous peoples) had their own goals and interests, and their positive or negative interactions shaped the course of history in many ways.

Keywords: mathematical modeling, structural balance, discrete models, sign graph, U.S. history

The article is devoted to the development of a tool for automated generation of time constraints in the context of circuit development in the basis of programmable logic integrated circuits (FPGAs). The paper analyzes current solutions in the field of interface tools for generating design constraints. The data structure for the means of generating design constraints and algorithms for reading and writing Synopsys Design Constraints format files have been developed. Based on the developed structures and algorithms, a software module was implemented, which was subsequently implemented into the circuit design flow in the FPGA basis - X-CAD.

Keywords: computer-aided design, field programmable gate array, automation, design constraints, development, design route, interface, algorithm, tool, static timing analysis

The article presents an analysis of the application of the Socratic method for selecting machine learning models in corporate information systems. The study aims to explore the potential of utilizing the modular architecture of Socratic Models for integrating pretrained models without the need for additional training. The methodology relies on linguistic interactions between modules, enabling the combination of data from various domains, including text, images, and audio, to address multimodal tasks. The results demonstrate that the proposed approach holds significant potential for optimizing model selection, accelerating decision-making processes, and reducing the costs associated with implementing artificial intelligence in corporate environments.

Keywords: Socratic method, machine learning, corporate information systems, multimodal data, linguistic interaction, business process optimization, artificial intelligence

The article examines the modular structure of interactions between various models based on the Socratic dialogue. The research aims to explore the possibilities of synthesizing neural networks and system analysis using Socratic methods for managing corporate IT projects. The application of these methods enables the integration of knowledge stored in pre – trained models without additional training, facilitating the resolution of complex management tasks. The research methodology is based on analyzing the capabilities of multimodal models, their integration through linguistic interactions, and system analysis of key aspects of IT project management. The results include the development of a structured framework for selecting suitable models and generating recommendations, thereby improving the efficiency of project management in corporate environments. The scientific significance of the study lies in the integration of modern artificial intelligence approaches to implement system analysis using multi – agent solutions.

Keywords: neural networks, system analysis, Socratic method, corporate IT projects, multimodal models, project management

The article discusses the problem of heating the wall in connection with the occurrence of a fire source. The conditions of convective heat exchange with the environment are considered on the wall surface. At a known ignition temperature of wood, the time it takes for the wall surface to reach this temperature is found. The problem is solved for a homogeneous wall made of a single material, as well as for an inhomogeneous wall in which a thin layer of wood is followed by a thick thermal insulation layer. The problem is solved analytically, as well as by the finite element method. The solution of the problem by the finite difference method is also considered.

Keywords: wood, thermal insulation layer, ignition temperature, convection, finite element method, finite difference method, thermal conductivity problem

This article investigates the application of a digital operational model to enhance the efficiency of maintenance and repair processes for capital construction projects. The study focuses on the operational phase within the building lifecycle, analyzing maintenance procedures and categorizing structural defects. The research identifies limitations in traditional defect reports, which lack quantitative data and spatial referencing to the building structure. These limitations hinder their effectiveness in organizational and technical planning for repair works. The proposed digital model optimizes building condition management, improves the accuracy of technical assessments, and facilitates precise quantification of repair scopes. It also enhances collaboration between facility management and contracting entities. A case study of the educational and laboratory building at the Northern (Arctic) Federal University demonstrates the model's implementation. Key benefits include cost reduction, improved maintenance quality, and streamlined operational workflows. The findings highlight the potential of digital tools to transform building maintenance practices, offering a data-driven approach to facility management in the construction sector.

Keywords: maintenance and repair, operational digital model, defect, defect list, technical condition

The construction of a mathematical model for solving the problem of planning construction excavation, which is interpreted as a problem with a linear objective function and constraints, is considered. The calculation algorithm is implemented by software in Python using the scipy.optimize.linprog library, which provides effective methods for solving linear programming problems. The developed program visualizes the results, making the allocation of time for the operation of machines. When testing the program, scenarios with different input data were considered, allowing us to conclude that the developed tool helps to make the best decision when planning construction work and analyze the impact of changes in input parameters on the result.

Keywords: organization of construction, linear programming, distribution tasks, optimization, planning, mathematical modeling, simplex method

The paper presents a calculation model for assessing the wear resistance of radial plain bearings with a polymer coating and a groove, taking into account inertial effects and nonlinear properties of the medium under steady-state friction. Clarified analytical dependencies have been developed to improve the accuracy of calculations of the hydrodynamic characteristics of the bearing. The main objective of the study is to create a multifactorial model that takes into account the influence of the bearing geometric parameters (the presence and configuration of the groove), the properties of the polymer coating and the inertial force. The model allows predicting the bearing life in real operating conditions, taking into account the influence of various factors, which increases the accuracy of design and optimization of the design. The results of the work are aimed at improving the operational reliability of plain bearings due to more accurate prediction of their wear resistance and optimization of design parameters.

Keywords: modified design, nonlinear factors, polymer coating, axial groove, load capacity, friction coefficient, increased wear resistance

The article presents the results of a numerical experiment comparing the accuracy of neural network recognition of objects in images using various types of data set extensions. It describes the need to expand data sets using adaptive approaches in order to minimize the use of image transformations that may reduce the accuracy of object recognition. The author considers such approaches to data set expansion as random and automatic augmentation, as they are common, as well as the developed method of adaptive data set expansion using a reinforcement learning algorithm. The algorithms of operation of each of the approaches, their advantages and disadvantages of the methods are given. The work and main parameters of the developed method of expanding the dataset using the Deep-Q-Network algorithm are described from the point of view of the algorithm and the main module of the software package. Attention is being paid to one of the machine learning approaches, namely reinforcement learning. The application of a neural network for approximating the Q-function and updating it in the learning process, which is based on the developed method, is described. The experimental results show the advantage of using data set expansion using a reinforcement learning algorithm using the example of the Squeezenet v1.1 classification model. The comparison of recognition accuracy using data set expansion methods was carried out using the same parameters of a neural network classifier with and without the use of pre-trained weights. Thus, the increase in accuracy in comparison with other methods varies from 2.91% to 6.635%.

Keywords: dataset, extension, neural network models, classification, image transformation, data replacement

This article examines the support structures of a wind turbine designed for operation in the extreme climatic conditions of the Russian High North. The relevance of the study is driven by the strategic objectives of developing the Arctic zone of Russia and the necessity to account for specific environmental and climatic factors in the design of energy infrastructure. A modular structural system is proposed, taking into consideration transportation and technological constraints associated with Arctic wind turbines. A CAD-model of the structural system has been developed, comprising a three-section tubular conical tower and a compound pile cap with a three-point support configuration. CAE-based simulations were conducted to evaluate the load-bearing capacity of the structural system under extreme load combination. The results demonstrate that the proposed structural configuration meets transportation limitations while ensuring the strength and stability of the Arctic wind turbine under critical load combination. The proposed design solution is suitable for simplifying transportation and on-site assembly of Arctic wind turbine in remote northern energy infrastructure projects.

Keywords: Arctic wind turbine, modular structures, supporting structures, CAD modeling, CAE simulation, permafrost

Modern intelligent control systems (ICS) are complex software and hardware systems that use artificial intelligence, machine learning, and big data processing to automate decision-making processes. The article discusses the main tools and technologies used in the development of ICS, such as neural networks, deep learning algorithms, expert systems and decision support systems. Special attention is paid to the role of cloud computing, the Internet of Things and cyber-physical systems in improving the efficiency of intelligent control systems. The prospects for the development of this field are analyzed, as well as challenges related to data security and interpretability of models. Examples of the successful implementation of ICS in industry, medicine and urban management are given.

Keywords: intelligent control systems, artificial intelligence, machine learning, neural networks, big data, Internet of things, cyber-physical systems, deep learning, expert systems, automation

The technology of applying the variational principle in problems of development and testing of complex technical systems is described. Let there be a certain set of restrictions imposed on random variables in the form of given statistical moments and/or in the form of a restriction by some estimates from above and below the range of possible values of these random variables. The task is set: without knowing anything except these restrictions, to construct for further research, ultimately, for assessing the efficiency of the complex technical system being developed, the probability distribution function of its determining parameter. By varying the functional, including Shannon entropy and typical restrictions on the distribution density function of the determining parameter of a complex technical system, the main stages of constructing the distribution density function are described. It is shown that, depending on the type of restriction, the constructed distribution density function can have an analytical form, be expressed through special mathematical functions, or be calculated numerically. Examples of applying the variational principle to find the distribution density function are given. It is demonstrated that the variational principle allows obtaining both the distribution laws widely used in probability theory and mathematical statistics, and specific distributions characteristic of the problems of developing and testing complex technical systems. The technology of applying the variational principle presented in the article can be used in the model of managing the self-diagnostics process of intelligent control systems with machine consciousness.

Keywords: variational principle, distribution density function, Shannon entropy, complex technical system