The article considers the parameter identification issues of linear non-stationary dynamic systems adaptive models using the example of a linearized adjustable model of a DC motor with independent excitation. A new method for estimating the parameters of adjustable models from a small number of observations is developed based on projection identification and the apparatus of linear algebra and analytical geometry. To evaluate the developed identification method, a comparison of the transient processes of the adaptive model of a DC motor with independent excitation with the obtained parameter estimates with reference characteristics was carried out. The efficiency of the proposed identification method in problems of DC electric drive control is shown.

Keywords: DC motor, projection identification, dynamic system parameter estimation, adaptive model of non-stationary dynamic system

Analysis of a digital data transmission system through a noisy communication channel based on the Huffman compression method and encoding using cyclic Bose-Chowdhury-Hockingham codes This article examines the effectiveness of a digital data transmission system through a noisy communication channel using the Huffman compression method and cyclic BCH encoding (Bose-Chowdhury-Hockingham). Huffman compression reduces data redundancy, which increases the effective transmission rate, while BCH codes detect and correct errors caused by channel noise. The analysis likely includes evaluating parameters such as compression ratio, data transmission rate, error probability after decoding, and computational complexity of the algorithms. The results demonstrate the effectiveness of this combination of techniques in improving data transmission reliability in noisy environments.

Keywords: digital transmission system, cyclic coding, compression ratio, decoding, encoding

The article examines the aspects of using bionics as a basis for adaptive architecture and construction in the conditions of the North Caucasus. Modern architecture is undergoing a shift from static and resource-intensive models to dynamic, intelligent and environmentally oriented systems. This approach is particularly relevant for regions with difficult climatic and landscape conditions, such as the North Caucasus, where traditional construction is often unable to effectively withstand the challenges of sudden temperature fluctuations, seismic activity, strong winds and high insolation. This article substantiates the thesis that the integration of bionics principles into adaptive construction offers not just innovative solutions, but a holistic design philosophy based on centuries of experience in the evolution of natural systems. It is proved that bionics is not an alien concept for the region, but, on the contrary, finds deep parallels in traditional mountain architecture, such as in tower complexes demonstrating organic integration into the landscape and effective thermoregulation, similar to natural prototypes. The paper discusses specific areas of application of bionic principles, including the development of adaptive facades inspired by plant and animal thermoregulation systems, as well as the creation of earthquake-resistant structures similar to the flexible structures of bamboo stems or trees. Special attention is paid to the design methodology that combines the analysis of local conditions, biomimicry methods and the integration of modern digital technologies such as computer modeling and 3D printing to create unique, energy-efficient facilities. The need for synergy between traditional architectural knowledge and innovative technologies is emphasized, which makes it possible to create buildings that not only adapt to the environment, but also reflect the unique cultural identity of the North Caucasus. In conclusion, it is argued that bionics is a logical and promising direction for the architectural development of the region, ensuring the creation of a sustainable, safe and comfortable living environment through harmonization with the natural context.

Keywords: construction, bionics, modern trends, modern design technologies, construction industry, biomimicry, adaptive architecture, North Caucasus

This article examines the growing threat of web scraping (parsing) as a form of automated cyberattack, particularly aimed. Although scraping publicly available data is often legal, its misuse can lead to serious consequences, including server overload, data breaches and intellectual property infringement. Recent court cases against OpenAI and ChatGPT highlight the legal uncertainty associated with unauthorized data collection.
The study presents a dual approach to combat malicious scraping. Traffic Classification Model - a machine learning based solution using Random Forest algorithms results in performance that achieves 89% accuracy in distinguishing between legitimate and malicious bot traffic, enabling early detection of scraping attempts. Data Deception Technique - the countermeasure dynamically modifies HTML content to convey false information to scrapers while maintaining the original look of the page. This technique prevents data collection without affecting the user experience.
Performance results include real-time traffic monitoring, dynamic page obfuscation, and automatic response systems.
The proposed system demonstrates effectiveness in mitigating the risks associated with scraping and emphasizes the need for adaptive cybersecurity measures in evolving digital technologies.

 

Keywords: parsing, automated attacks, data protection, bot detection, traffic classification, machine learning, attack analysis, data spoofing, web security

The article explores the experience of adaptive reuse and the redevelopment of the former Vena Plant, located within the “Gray Belt” of St. Petersburg. Using this case study, it analyzes the fundamental principles of industrial site refunctionalization, as well as the strategies employed to preserve architectural heritage while integrating it into a contemporary urban context.

Keywords: adaptive reuse, urban greening, high-quality residential environment, Gray Belt, spirit of place (genius loci), industrial facilities, Vena Plant, residential complex, cultural heritage

The paper considers the effect of particle size on the dynamics of suspended sediments in a riverbed. The EcoGIS-Simulation computing complex is used to simulate the joint dynamics of surface waters and sediments in the Volga River model below the Volga hydroelectric dam. The most important factor in the variability of the riverbed is the spring releases of water from the Volgograd reservoir, when water consumption increases fivefold. Some integral and local characteristics of the riverbed are calculated depending on the particle size coefficient.

Keywords: suspended sediment, soil particle size, sediment dynamics, diffusion, bottom sediments, channel morphology, relief, particle gravitational settling velocity, EcoGIS-Simulation software and hardware complex, Wexler formula, water flow

The article examines the influence of the data processing direction on the results of the discrete cosine transform (DCT). Based on the theory of groups, the symmetries of the basic functions of the DCT are considered, and the changes that occur when the direction of signal processing is changed are analyzed. It is shown that the antisymmetric components of the basis change sign in the reverse order of counts, while the symmetric ones remain unchanged. Modified expressions for block PREP are proposed, taking into account the change in the processing direction. The invariance of the frequency composition of the transform to the data processing direction has been experimentally confirmed. The results demonstrate the possibility of applying the proposed approach to the analysis of arbitrary signals, including image processing and data compression.

Keywords: discrete transforms, basic functions, invariance, symmetry, processing direction, matrix representation, correlation

Modern engineering equipment operation necessitates solving optimal control problems based on measurement data from numerous physical and technological process parameters. The analysis of multidimensional data arrays for their approximation with analytical dependencies represents both current and practically significant challenges. Existing software solutions demonstrate limitations when working with multidimensional data or provide only fixed sets of basis functions.
Objectives. The aim of this study is to develop software for multidimensional regression based on the least squares method and a library of constructible basis functions, enabling users to create and utilize diverse basis functions for approximating multidimensional data.
Methods. The development employs a generalized least squares method model with loss function minimization in the form of a multidimensional elliptical paraboloid. LASSO (L1), ridge regression (L2), and Elastic Net regularization mechanisms enhance model generalization and numerical stability. A precomputation strategy reduces asymptotic complexity from O(b²·N·f·log₂(p)) to O(b·N·(b+f·log₂(p))). The software architecture includes recursive algorithms for basis function generation, WebAssembly for computationally intensive operations, and modern web technologies including Vue3, TypeScript, and visualization libraries.
Results. The developed web application provides efficient approximation of multidimensional data with 2D and 3D visualization capabilities. Quality assessment employs MSE, R², and AIC metrics. The software supports XLSX data loading and intuitive basis function construction through a user-friendly interface.
Conclusion. The practical value lies in creating a publicly accessible tool at https://datapprox.com for analyzing and modeling complex multidimensional dependencies without requiring additional software installation.

Keywords: approximation, least squares method, basic functions, multidimensional regression, L1/L2 regularization, web application, multidimensional elliptical paraboloid

A two-level hierarchically organized model of managing the interaction of the director with the personnel in a company related to the development of projects in the construction sector is presented. The director acts as the leader, and the company's employees act as followers. Both management entities strive to maximize their target functions, reflecting their income and expenses. The study of the model was conducted taking into account its hierarchical structure.  An algorithm for constructing a Stackelberg solution under inducement has been developed. A numerical study of the model has been conducted using the scenario method by partially enumerating the areas of admissible controls of subjects with a certain step. When conducting simulation experiments, all input parameters of the model varied in a fairly wide range. The results of the simulation experiments have been analyzed, and some patterns of system development have been identified.

Keywords: hierarchy, incentive, control system, solution algorithm, Stackelberg equilibrium, leader, follower, imitation, experiment, investment project

This article proposes a new concept of production management of a chemical industry holding as the basis for mathematical support of an automated control system. The concept is based on the use of both traditional methods of proactive management (preventive and predictive) and new generation proactive management, which not only prevents the occurrence of undesirable events, but also ensures a decrease in the frequency of occurrence of such events in the future. A system of basic concepts and principles of proactive production management has been developed. The proposed concept will allow developing the existing mathematical support of an automated production control system and increasing its efficiency.

Keywords: production, automated production control system, mathematical support, concept, proactivity, strategies, proactive management

In this paper, methods for estimating one's own position from a video image are considered. A robust two-stage algorithm for reconstructing the scene structure from its observed video images is proposed. In the proposed algorithm, at the feature extraction and matching stage, a random sample based on the neighborhood graph cuts is used to select the most probable matching feature pairs. At the nonlinear optimization stage, an improved optimization algorithm with an adaptive attenuation coefficient and dynamic adjustment of the trust region is used. Compared with the classical Levenberg-Marquard (LM) algorithm, global and local convergence can be better balanced. To simplify the system's decisions, the Schur complement method is used at the group tuning stage, which allows for a significant reduction in the amount of computation. The experiments confirmed the operability and effectiveness of the proposed algorithm.

Keywords: 3D reconstruction, graph-cut, Structure-from-Motion (SfM)，RANSAC，Bundle Adjustment optimization，Levenberg-Marquardt algorithm，Robust feature matching

The paper considers a lightweight modified version of the YOLO-v5 neural network, which is used to recognize road scene objects in the task of controlling an unmanned vehicle. In the proposed model, the pooling layer is replaced by the ADown module in order to reduce the complexity of the model. The C2f module is added as a feature extraction module to improve accuracy by combining features. Experiments using snowy road scenes are presented and the effectiveness of the proposed model for object recognition is demonstrated.

Keywords: Bobkov A. V., Du K., Dai I., Wang Z., Chen H.

The invention related to the field of construction is proposed, which can be used for trenchless linear laying of oil, gas and other pipelines under natural and artificial obstacles, linear and extended structures (highways, railroads, etc.) located, including in monolithic rocky soils with simultaneous formation of a case, development and arrangement of a pre-drilled well.

Keywords: pilot well, diamond wire rope, foundation pit, encasement, working body, circular sector

The characteristics of a submersible induction motor are described with sufficient reliability for practice by the theory of multi-motor electric drive. In this case, the classical circuit of a submersible induction motor is a coupled system of several equivalent-T circuits. In turn, this significantly increases its computational complexity and reduces the speed of ACS. It is proposed to construct a mathematical model of the submersible electric motor in the form of polynomials with significantly higher speed using the methods of experiment planning. In the area of applicability, the differences in the estimation of energy performance do not exceed 1.1%, between the proposed models and classical equivalent-T circuits.

Keywords: automated control system, mathematical model, polynomial, mean absolute percentage error, computational complexity, design of experiment, scatter diagram, modal interval, submersible electrical motor, rotor package

The article discusses the surface-active properties of casein and its potential for use as a bioorganic additive in repair and restoration compounds (RRC). An analysis of literary sources confirming the amphiphilic nature of casein, its ability to stabilize interfacial systems and form stable micelles is carried out. The results of an experimental study of the dependence of the surface tension of aqueous solutions of casein on its concentration, carried out by the hanging drop method on a DSA-100 tensiometer, are presented. It is shown that casein effectively reduces the surface tension of an aqueous solution, especially at concentrations of up to 4–5%, which confirms its active adsorption at the phase boundary. Based on the obtained surface tension isotherm, the predicted wettability is considered based on the Young equation. The data obtained emphasize the prospects of using casein as a natural surfactant for creating environmentally friendly and authentic restoration materials that meet the requirements of construction and restoration.

Keywords: casein, surfactants, repair and restoration compounds, surface tension, wettability.