The article discusses important urban planning and architectural approaches that determine the process of forming residential facilities in a modern urbanized urban environment. Special attention is paid to the tasks of ensuring sustainable development, improving the quality of life of the population and the possibility of preserving the unique appearance of cities. The basic principles of urban planning are also analyzed, which include in their content the influence of density and typology of building, the creation of a comfortable and functional urban environment. Architectural approaches are based on the ergonomics of living spaces, functionality, transformation and adaptability of buildings. An important aspect is the interaction of buildings with the surrounding built environment, which are based on the principles of energy efficiency and the use of innovative building materials.

Keywords: residential facilities, urban planning, urban environment, architectural organization, sustainable development, efficient use of the territory, comfort

Development of design documentation is one of the most important stages of the construction process, determining the successful result of the entire project. The effective proceeding of this stage largely depends on the choice of project management methods. The features (properties) inherent in project work and influencing the choice of project management method are formulated. Traditional, agile, and hybrid approaches to project management used in construction are reviewed and analysed, with an emphasis on their use in the development of design documentation. Particular attention is paid to the possibilities of integrating agile methodologies (Agile, Lean, Scrum, Kanban) within the project work.

Keywords: project management, construction, project documentation, agile methodology, hybrid methodology

The study is devoted to the analysis of modern approaches to the organization of body scanning processes using photogrammetric technologies. Various methods of digital reconstruction of the human body are being considered, including manual and robotic scanning systems. A comparative analysis of measurement accuracy, texture quality and time characteristics of various approaches was carried out. Particular attention is paid to the issues of rigidity of the structures of scanning systems and their impact on the quality of the resulting three-dimensional models. The results show the superiority of specialized hand-held scanners over photogrammetric methods in accuracy, but the greater versatility of the latter in various application conditions.

Keywords: photogrammetry, 3D scanning, digital reconstruction, measurement accuracy, structural rigidity, robotic systems

The article is devoted to the development of methodology for implementing digital quality control systems in asphalt concrete production. The main attention is paid to the analysis of existing approaches to digitalization of quality control, formation of integrated monitoring system structure and development of step-by-step implementation algorithm. The study includes analysis of traditional control methods problems, justification of digital technologies selection and evaluation of proposed methodology effectiveness. The results show the possibility of increasing technological process stability and reducing control operations time by 35-40%.

Keywords: production digitalization, asphalt concrete quality control, digital transformation, control automation, technological process, information technologies, quality management system

The article is devoted to a comparative analysis of classical interpreted machine learning algorithms for detecting phishing URLs. The introduction substantiates the relevance of the problem, notes the evolution of threats and the lack of research evaluating not only accuracy, but also practical criteria for performance and explainability of models. The literature review systematizes modern approaches: methods of URL feature analysis, semantic text analysis, and traditional non-ML solutions, and highlights a gap in the comprehensive evaluation of algorithms. The methodology describes the stages of working with a public dataset: data preprocessing, including removing constant features and scaling, and choosing three algorithms for comparison — logistic regression, decision tree, and random forest. The results section presents comparative quality metrics (Accuracy, Precision, Recall, F1-Score), error matrix analysis, training time measurements and predictions, as well as model interpretation through the importance of features, where the key indicators of phishing are the short age of the domain and signs of obfuscation. The discussion of the results includes comparing the effectiveness of Random Forest with neural network approaches from other studies, confirming the high accuracy of ensemble methods, and formulating practical recommendations for choosing an algorithm depending on the use case (prototyping, industrial deployment). In conclusion, the practical value and interpretability of classical methods are emphasized, as well as the limitations and prospects of creating hybrid systems.

Keywords: phishing, cybersecurity, information security, machine learning, Random Forest, detection of phishing attacks

The article examines the organizational and technological aspects of constructing offshore platform substructures and the factors influencing the pace of their erection. A comparison of foreign and Russian practice reveals key constraints related to the continuity of formwork-concrete operations and the applied form-shaping systems. The study presents classifications of materials and technologies, as well as a multicriteria efficiency assessment model that enables the comparison of alternative technological solutions. Based on the findings, the paper identifies promising directions for improving technological approaches to offshore platform construction.

Keywords: offshore platforms, supports, construction, sliding formwork, concreting, technological processes, multi-criteria assessment

The article considers the solution to the problem of increasing the autonomy and accuracy of controlling the movement of a transport trolley is inextricably linked to the accuracy of determining its current location. In this regard, a hardware and software system based on the integration of inertial and visual odometry data has been developed, which makes it possible to compensate for the disadvantages of some navigation methods with the advantages of others.

Keywords: trajectory of the control object, navigation system, inertial odometry, visual odometry, data aggregation, Kalman filter, location determination

Recent growth in online learning has created a need for reliable methods to monitor learner engagement, cognitive load, and fatigue. This study presents a deep learning framework that integrates eye-tracking data with electroencephalogram features to classify engagement levels in digital learning environments. Eye-tracking indicators of cognitive load, including pupil dilation, blink rate, fixation duration, and saccade velocity, were extracted from a publicly available dataset and combined with electroencephalography (EEG) measures. Engagement level was modelled as a three-class problem, including low, moderate, and high, using hybrid CNN-LSTM architecture designed to capture both spatial and temporal patterns. The model achieved an overall accuracy of approximately 89 percent with high precision and recall across categories. ANOVA analysis showed that no single feature could reliably distinguish engagement levels, underscoring the benefit of multimodal deep learning. The study highlights how combining eye-tracking measures with EEG signals can offer a clearer, real-time picture of learners’ cognitive states during e-learning activities. By detecting moments when attention declines or cognitive fatigue begins to set in, such systems can enable genuinely adaptive learning platforms, ones that know when to suggest brief breaks, adjust the pace of instruction, or provide timely, targeted support to help learners stay engaged.

Keywords: cognitive fatigue, deep learning, e-learning, eye-tracking, student engagement, EEG

This article examines the voting process in the UN Security Council. It describes the decision-making process from the perspective of cooperative voting games. A method for finding the distribution of payoffs between agents in a voting game is presented. An algorithm for formalizing a voting game with player vetoes and a variable number of agents is described. A comparison of player payoffs as a result of voting with different agent compositions is presented. An analysis of how voting would change in the event of a possible US withdrawal from the UN Security Council is conducted. Hypotheses are put forward regarding changes to the voting rules should the composition of the Council change. Conclusions are drawn regarding the use of cooperative games in analyzing the voting process. A conclusion is formulated regarding the consequences of a US withdrawal from the UN Security Council.

Keywords: game theory, cooperative games, Shapley value, coalition, C-core, voting, UN, division, veto

The article describes an experiment on the compilation of a training sample, training and testing of a neural network model of a computer vision system for detecting burns of a tundish nozzle at a continuous steel casting plant. The issue of validity of augmentation of data for training is considered. The obtained results are analyzed.

Keywords: computer vision, object detection, dataset, augmentation, steelmaking, continuous steel casting, burnout of a tundish nozzle

This article addresses the challenge of building Android applications within secure, network-isolated environments where no direct internet connection is available. The primary objective is to develop a reliable method for the continuous integration and delivery (CI/CD) of Android artifacts under these constraints. The proposed solution methodologically integrates Docker containerization to ensure a standardized build environment with the Nexus Repository Manager for creating a comprehensive local mirror of all external dependencies, such as those from Google Maven. This local repository cache is then made accessible inside the isolated network via a configured nginx proxy server. The implemented system successfully enables a complete and automated Android build pipeline, entirely eliminating the need for external access during compilation. The results demonstrate significant enhancements in security by mitigating risks associated with public repositories, while also ensuring build stability, reproducibility, and protection against upstream outages. In conclusion, this approach provides a practical and robust framework for secure mobile application development in high-security or restricted corporate network infrastructures.

Keywords: docker, containerization, android, flutter, ci/cd, nginx, proxying, network isolation, application building.

The design of automated control systems for the physical protection of facilities is one of the most sought-after area in the development of domestic software products. The article presents the architecture of a hardware-software system, an assessment of the development tools required to implement a web application based on the Astra Linux operating system, and a description of an experiment to create a system prototype. The following tools were used to build the system: the Angular framework for the client layer; the FastAPI framework, the SQLAlchemy library, and the WebSocket protocol for the server layer; and the object-relational PostgreSQL database management system for data storage. The result of the work is a technical means control system that demonstrates interaction with devices and the database. The implemented prototype will serve as a basis for developing a hardware-software complex for the physical protection of a facility.

Keywords: domestic operating system, web application, development tools, management system, database, sensor, monitoring

The article discusses the problem of feature selection when training machine learning (ML) models in the task of identifying fake (phishing) websites. As a solution, a set of key metrics is proposed: efficiency, reliability, fault tolerance, and retrieval speed. Efficiency measures impact of feature to prediction accuracy. Reliability measures how well feature distinct phishing from legitimate. Fault tolerance score measures empirical probability of feature to be valid and fulfilled. And retrieval speed is logarithmic time of feature extraction. This approach allows for the ranking of features into categories and their subsequent selection for training machine learning models, depending on the specific domain and constraints. In this article, 82 features was measured, and 6 fully-connected neural networks was trained to evaluate the effectiveness of metrics. Experiments has shown that proposed approach can increase the accuracy of models by 1-3%, precision by 0.03, and significantly reduce overall extraction time and so improve response rate.

Keywords: feature evaluation method, machine learning model, identification of phishing websites, metric, efficiency, reliability, fault tolerance, and retrieval speed

This paper discusses a method for countering Sybil attacks in distributed systems based on the analysis of electromagnetic power maps of the temporal characteristics of network traffic. The key hypothesis is that multiple Sybil identifiers controlled by a single attacker node exhibit statistically significant correlation in their network activity patterns, which can be identified using a correlogram. A method for detecting Sybil attacks in wireless networks is proposed based on the analysis of correlograms of electromagnetic signal power maps. The method exploits the statistical properties of power profiles arising from the correlation of network activity of Sybil nodes controlled by a single attacker. A protection system architecture has been developed, including modules for network activity monitoring, correlogram calculation, clustering, and anomaly detection. A set of 10 correlogram parameters is introduced for attack identification, including profile variance, randomness and periodicity coefficients, spectral density, and correlation characteristics. Experimental testing on a millimeter-wave radar station demonstrated detection accuracy ranging from 83.2% to 97.4%. To improve the method's effectiveness, the use of deep neural networks after accumulating a sufficient amount of data is proposed. The proposed method enables the identification and denial of compromised identifiers, increasing the resilience of P2P networks, blockchain systems, and distributed ledgers.

Keywords: Sybil attack, distributed systems security, correlogram, network traffic analysis, time series, autocorrelation, anomaly detection

This article proposes a hybrid method for speckle noise reduction in radar images based on a combination of the wavelet transform and the U-Net neural network (NN) architecture with enhancement of low-frequency components in high-frequency subbands. The wavelet transform decomposes the radar images into frequency subbands, allowing noise to be localized primarily in high-frequency components. These components are processed using a U-Net neural network, whose effectiveness stems from its symmetric structure and skip connections, which allow for the accurate preservation and restoration of important image details. Furthermore, enhancing the low-frequency component in high-frequency subbands to improve the signal-to-noise ratio allows the neural network to more accurately separate useful signal structures from the noise. The combined approach demonstrates high speckle noise reduction efficiency with minimal loss of structural information, outperforming traditional methods in terms of restoration quality and image clarity.

Keywords: speckle noise, noise reduction, wavelet transform, neural networks, U-Net, neural networks, frequency subbands

The article presents a hybrid neural network for estimating the mass of a car and the longitudinal/transverse slopes of a road, combining a square-root sigma-point Kalman filter and a neural network model based on a transformer encoder using cross-attention to the evaluation residuals. The proposed approach combines the physical interpretability of the filter with the high approximation capability of the neural network. To ensure implementation on embedded electronic control units, the model was simplified by converting knowledge into a compact network of long-term short-term memory. The results of experiments in various scenarios showed a reduction in the average error by more than 25% with a computational delay of less than 0.3 ms.

Keywords: vehicle condition assessment, road slope assessment, vehicle mass assessment, transformer neural network, cross-focus, adaptive filtering, knowledge distillation, square-root sigma-dot Kalman filter, intelligent vehicles, sensor fusion

The article is devoted to the method of formalizing indicators of compromise (IoC) using a Bayesian approach to classify and rank them based on probabilistic inference. The problem of detecting malicious indicators from a large volume of data found in various sources of threat information is critically important for assessing modern cybersecurity systems. Traditional heuristic approaches, based on simple aggregation or expert evaluation of IoCs, do not provide sufficient formalization and further ranking of their reliability regarding their association with a particular malicious campaign due to the incompleteness and uncertainty of the information received from various sources.

Keywords: indicators of compromise (IoC), Bayesian inference, cyber threats, probabilistic models, malicious activity analysis, threat intelligence, IoC classification, multi-source analysis

The article discusses modern approaches to forecasting and detecting forest fires using machine learning technologies and remote sensing data. Special attention is paid to the use of computer vision algorithms, such as convolutional neural networks and transformers, to detect and segment fires in images from unmanned aerial vehicles. The high efficiency of hybrid architectures and lightweight models for real-time operation is noted.

Keywords: forest fires, forecasting, unmanned aerial vehicles, deep learning, convolutional neural networks, transformers, image segmentation

The article examines the retrospective of the market’s emergence as an archaic element, its placement within the urban environment, and its value in contemporary conditions. It illustrates the interaction between the market square and the church. The differences between a shopping and entertainment centre and a traditional market are explored, along with the latter’s advantages and distinctive features. The focus is on markets as communicative spaces with their own character and originality, and on their influence on the identity of the urban environment. The study emphasises the architectural and spatial environment of the market square within the context of the urban planning framework.

Keywords: market, market square, retail space, commercial space, shopping space, shopping and entertainment centre, shopping and entertainment center, modern retail space, contemporary shopping space, trading rows, market stalls, covered market arcade

Abstract: The scope of applying fiber-reinforced concrete in critical load-bearing structures, such as aerodrome pavements, is often limited by insufficient information regarding material behavior and life-cycle economics. This study addresses this gap by developing and evaluating an optimal hybrid mix of micro and macro-basalt fibers consisting of 1.5% and 0.5% of cement mass, respectively for high-performance airfield concrete, followed by a 30-year Life-Cycle Cost Analysis (LCCA). Mechanical testing confirmed the technical feasibility, showing significant performance gains over baseline concrete: 14.5% increase in compressive strength 72.8MPa and 18.2% increase in flexural strength 10.4MPa. These gains are attributed to enhanced durability, multi-scale crack control, and superior post-crack load-carrying capacity. The LCCA, conducted using a 6% discount rate, revealed that the hybrid option, which incurs a 13.03% higher upfront material cost, is economically viable only under the optimistic scenario where the improved durability eliminates the need for major rehabilitation over 30 years. This scenario yields a marginal LCC saving of 4% compared to the baseline. In conservative and moderate scenarios, the upfront cost outweighed the delayed or reduced rehabilitation costs. Overall, Hybrid Basalt Fiber Reinforced Concrete is a promising high-performance material that achieves cost parity if its durability benefits are maximized to prevent major rehabilitation. Future work should involve field trials and expanded LCCA incorporating operational downtime and risk-based performance modeling.

Keywords: aerodrome pavement, basalt fiber, hybrid fiber-reinforced concrete, life-cycle cost analysis, rehabilitation, discount rate, microfiber, macrofiber, net present value

This article examines the design of efficient nodal connections for steel frame elements, taking into account international experience. A comparative analysis of the calculation approaches adopted in the European standard EN 1993-1-8, the Chinese standard GB 50017-2017, and domestic regulatory documents is provided. Particular attention is paid to the impact of calculation assumptions and consideration of the deformative rigidity of nodal connections on the performance of frame systems. To verify the validity of the adopted models, numerical simulation of flange connections was performed using the finite element method. The results of the study confirm that the use of modern regulatory approaches allows for a more accurate consideration of the actual performance of nodal connections and contributes to a more rational design.

Keywords: nodal connections, steel frames, flange connections, nodal calculations, normative approaches, numerical modeling, international experience

The article examines the environmental impact of ferrous metallurgy waste, with particular emphasis on its contribution to the formation of the fine particulate fraction PM10. The analysis shows that dispersed materials generated during waste handling participate in the formation of stable aerosol structures, the role of which has long been underestimated due to fragmented data and limitations in monitoring. Previously unrecognized mechanisms of metal-containing particles contributing to the overall aerosol load were identified. Based on the synthesized information, a methodological approach is proposed that enables a new perspective on assessing the influence of metallurgical waste on ambient air quality.

Keywords: fine particulate matter, PM10, metallurgical waste, dust formation, aerosol load, metal-containing aerosols, technogenic impact, ambient air quality, secondary resuspension, environmental monitoring, assessment methodology