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

This article analyzes the impact of artificial intelligence (AI) and machine learning technologies on the development and transformation of cyberthreats and the creation of highly effective cyberdefense systems. Key trends in AI evolution are discussed, including data-, model-, application-, and human-centric approaches, and their role in shaping both defensive and offensive capabilities. It is shown that attackers actively use AI to automate reconnaissance, personalize attacks, evade detection systems, and conduct complex multi-stage cyberattacks. The main types of impact on machine learning systems are analyzed: data manipulation, adversarial examples, attacks on models and their infrastructure. Modern defense methods that improve model robustness, data security, and the resilience of AI systems are presented. The idea of ​​​​the need to integrate intelligent approaches at all levels of the cyberdefense architecture and develop trusted, interpretable, and resilient machine learning models to counter new classes of threats is put forward.

Keywords: artificial intelligence, cybersecurity, cyberattack, machine learning, innovation, security, information, protection

The article presents a comprehensive study of the possibilities of applying mathematical optimization methods to improve the efficiency of managing organizational systems, using the example of forensic expert institutions. The work focuses on a critical analysis and adaptation of classical assignment theory to the realities of managing expert activities, which are characterized by high responsibility, diverse tasks, and strict procedural limitations. The evolution from simple linear assignment models to complex, multi-criteria, and generalized formulations that can adequately reflect the requirements for quality, urgency, and workload balance is analyzed in detail. The theoretical foundations and limits of applicability of key optimization paradigms, such as scheduling theory, the transportation problem, and the traveling salesman problem, have been investigated.

Keywords: management of organizational systems, resource allocation optimization, assignment theory, multi-criteria optimization, generalized assignment problem, load balancing, expert activity management, scheduling algorithms, decision support system

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 evolution of graph-based methods for describing technological data processing in industrial control systems (ICS) and substantiates the feasibility of transitioning from the traditional paradigm of functional block diagrams to architectures based on directed acyclic graphs (DAGs). It is shown that the use of the DAG model provides enhanced capabilities for formalization, dynamic configuration, and distributed execution of computational processes, thereby overcoming the limitations inherent in classical programming tools for programmable logic controllers. The applicability of modular DAG-oriented systems in industrial environments is analyzed using the Tessera-DFE architecture as an example, which implements graph loading, module integration, event-driven execution, and independent processing contexts. It is noted that such solutions contribute to increased reliability, scalability, and adaptability of ICS software in the context of growing data volumes, increasing processing complexity, and the need for integration with external services.

Keywords: industrial Control Systems, technological data, directed acyclic graph, functional block diagrams, IEC 61131-3, modular architecture, dynamic configuration, distributed execution, plugin modules, event processing, scalability, reliability, Tessera-DFE

The article analyzes the main types of distributed denial-of-service attacks and explores classical and innovative methods of protecting web servers from threats, including packet filtering, intrusion detection and prevention systems, and load balancing architectures. Based on the research results, significant limitations of traditional approaches have been identified, such as low adaptability to new threats, high false positive rates, and inability to effectively counter modern multi-factor attacks. The paper highlights the potential of using artificial intelligence and neural networks to analyze network traffic and detect complex patterns of anomalies.

Keywords: web server protection, distributed attack, denial of service, traffic filtering, packet filtering, intrusion detection system

The paper presents an algorithm for calculating thermal processes in the chain section of rotary tubular kilns used for sintering nepheline concentrate with limestone. The model is based on heat and mass balances and accounts for interactions between the gas flow, the material bed, dust particles, and the refractory lining. The algorithm considers the influence of the geometry and packing density of the chain curtain on heat transfer and is integrated with submodels of axial material movement and dust carryover. The calculation is implemented as an iterative scheme that ensures consistency between gas and material temperatures. The algorithm makes it possible to determine temperature distributions, heat fluxes and losses, as well as to perform parametric optimization of kiln operating conditions and design.

Keywords: rotary tubular kiln, nepheline concentrate, mathematical model, chain section, heat transfer, material movement, dust carryover, algorithm