Currently, matters of dependability in the design of technical systems with moving boundaries require an increasingly complete consideration of underlying dynamic phenomena.

Aim. The aim of the study is to develop a mathematical model and an approximate analytical method for studying the transverse vibrations and resonant properties of a viscoelastic rope of variable length lying on an elastic foundation, taking into account energy dissipation. The relevance of the work is due to the widespread use of technical systems with moving boundaries (lifting mechanisms, flexible transmissions, railway contact networks, rail tracks, belt conveyors, drill strings, etc.), for which dynamic loads and resonance are dangerous. The existing methods do not allow for a complete consideration of a system of factors, i.e., changes in the object’s length, resistance of the medium, elastic properties of the foundation and internal friction.

Methods. To solve the problem, the Kantorovich–Galerkin method, effective for systems with moving boundaries, was applied. The original boundary value problem for a partial differential equation was reduced to a system of ordinary differential equations. The solution procedure included the transition to dimensionless variables, the selection of coordinate functions in the form of eigenmodes and the application of the Galerkin procedure. The small parameter method was used to analyze non–stationary processes. In the considered model, the drag force of the rope movement is assumed to be proportional to the velocity, and the bending rigidity of the structure is also taken into account.

Results. Calculation expressions are presented for the amplitude of oscillations corresponding to the n–th dynamic mode. Particular attention is paid to the study of the phenomena of steady–state resonance and passage through resonance. The solution covers the most common case in practice of the action of external disturbances on the moving boundary of the system. It is established that the amplitude significantly depends on the velocity of the boundary, dissipation parameters and the rigidity of the foundation. The conditions for steady–state resonance are determined for a certain ratio of the frequency of the external influence and the natural frequency of the system. The phenomenon of passage through resonance is studied. The resulting analytical expressions were verified by comparison with known special cases, confirming the method’s validity with an error of up to 5% for the fundamental modes.

Conclusions. The resulting analytical expressions for the oscillation amplitude, steady–state resonance conditions, and resonance passage parameters enable the formulation of a number of practical recommendations for design engineers aimed at increasing the dependability and durability of technical systems with moving boundaries and preventing resonant failures in variable–length systems. Key applied problems solved using this model include fatigue life assessment, residual life prediction, and emergency prevention. Consideration of dissipation and an elastic foundation is critical for assessing resonant properties. To prevent resonance, it is recommended to optimize the boundary velocity, use materials with increased friction or dampers, and increase the foundation rigidity. The results have practical significance for improving the dependability of systems with moving boundaries. Research prospects are related to taking into account nonlinear effects and non–harmonic influences.

For making well‑founded decisions on the elimination of failures and malfunctions occurring in railway infrastructure facilities, prompt access to information on previously identified faults and the dynamics of the irresolution is essential. Inspection logs such as DU‑46 contain valuable data on the condition of these facilities (tracks, turnouts, signals, power supply, contact lines, etc.); however, they are hardly used in practice when analyzing the causes of newly emerging failures.

Aim. To develop an algorithm for processing DU‑46 log records that allows operators, up on request, to obtain information on previous malfunctions or maintenance activities on specific infrastructure objects.

Methods. Text preprocessing, lemmatization using M. Korobov’s morphological analyzer, frequency‑based text analysis, TF–IDF, L2 normalization, cosine similarity calculation, and result sorting.

Result. A prototype application has been developed that enables search for relevant records and displays a similarity metric between the query and the retrieved fragments, which, among other things, may serve as a recommendatory function for determining the causes of failures.

Conclusion. The use of operation al inspection logs in combination with text mining methods can form the basis for building recommendation systems and decision support systems in the maintenance of railway infrastructure facilities.

The Aim of this study is to develop and validate a methodology for analyzing social media data to enhance the reliability of emergency response systems by enabling rapid identification, assessment of scale, location, and forecasting of crisis events based on regional specifics of public activity. To achieve this goal, a Python‑based program was developed to automate the collection, preprocessing, and comprehensive analysis of data from the VKontakte social network. The program was tested using data from two cities with different demographic characteristics and socio‑economic conditions, i.e., Arkhangelsk and Yekaterinburg.

Methods. The research employed methods of data collection from open VKontakte groups, followed by preprocessing and comprehensive analysis. The analysis included text analysis (sentiment analysis and word cloud generation), mathematical analysis (entropy and entropy derivative calculations to assess activity dynamics), and external factor analysis (the influence of meteorological conditions, holidays, and weekends).

Results. The study revealed significant regional differences in social activity levels across various categories of emergency situations. Activity levels in Arkhangelsk were at least twice as high as those in Yekaterinburg, despite the smaller population of the city. The nature of activity also differed significantly: sharp spikes in activity were observed in Yekaterinburg, while activity in Arkhangelsk was more evenly distributed. Seasonality manifested in increased activity during periods of technical work or extreme weather conditions. In the “Fire” category, both cities demonstrated high and sustained activity; however, sharper spikes were noted in Yekaterinburg, particularly at the end of March and beginning of April, potentially indicating major incidents. In the “Water Outage” category, two significant peaks in activity were recorded in Arkhangelsk, in April and onJuly 31 and August 1, possibly pointing to widespread water supply issues. In Yekaterinburg, activity in this category was lower but more frequent, likely reflecting minor disruptions or informational updates.

Conclusion. Social networks serve as a valuable source of data for analyzing public reactions to emergency situations. The identified regional characteristics of user behavior high light the need to create adaptive monitoring and forecasting systems that account for the specific features of each region. Using data from social networks enhances the reliability and efficiency of response systems by enabling rapid determination of incident scale, location, and consequences, as well as identifying seasonal and local threats. The findings confirm the necessity of implementing automated analytical tools capable of promptly assessing situations. Social networks can act as indicators of seasonal and local threats, allowing for proactive risk preparation. The observed differences in activity levels between regions underscore the importance of considering local conditions when developing strategies for communication and crisis management. Active use of social networks as a platform for civic participation demonstrates their potential to strengthen interaction between the public and authorities during crises.

The Russian Federation operates an extensive transport infrastructure network comprising over 100,000 facilities of various types: bridges, tunnels, overpasses, road and railway stations. Each of these facilities is of strategic importance for ensuring the country’s transport security and requires comprehensive protection against potential threats of various kinds. Providing complete physical protection for all transport infrastructure facilities (hereinafter referred to as TIF) against all possible threats is a practically unfeasible task, primarily due to economic inexpediency and colossal financial costs. Consequently, the primary task becomes identifying priority facilities requiring enhanced attention in terms of security. To effectively manage the security of critical facilities, the development of a specialized system is proposed. This system would enable: tracking the current security status of facilities; promptly responding to changes in security threats; adapting protective measures in accordance with the current situation; and planning security enhancement measures based on the analysis of new risks. Implementing such a system will create a flexible and adaptive transport infrastructure protection framework capable of promptly responding to emerging threats and ensuring the necessary level of security for the most significant facilities.

Purpose. Development of a comprehensive, multi‑level system for ensuring the security of TIF, capable of effectively countering modern threats and promptly adapting to changing conditions.

Methods. The research involved analyzing the existing TIF categorization system, followed by the development of a hierarchical facility security assessment system and the creation of a mathematical framework for calculating resilience indicators.

Results. The research yielded the following outcomes: a multi‑level hierarchical system for assessing facility security was developed; a comprehensive mathematical framework for calculating resilience indicators was created; a software solution for security management was proposed; and key performance indicators for the system were defined. Practical significance. The study’s practical significance lies in creating a system that enables: real‑time monitoring of facility security status; prompt identification of vulnerabilities and implementation of mitigation measures; generating a priority list of facilities for security system modernization; optimizing resource allocation for security provision; and establishing a unified security monitoring system. The developed system ensures an increased level of transport infrastructure security; reduced response time to threats; optimized security costs; improved coordination among various security services; capability for operational planning of measures; and transparency in security management.

Conclusion. Implementing the proposed system will create a dynamic transport security management framework capable of promptly adapting to new threats and ensuring the necessary level of protection for critical TIF. The research results can be used in modernizing existing security systems and creating new TIF protection systems, as well as serving as a tool for managerial decision‑makingin the face of natural and man‑made threats.

The theoretical and applied aspects of computer modeling are described, taking into account modern methods of constructing information, measurement and control systems (IIAs) of aircraft during their operation. The LA and IIiUS facilities are high‑tech and complex technical systems (CTC) that require combined approaches to their assessment. The ways of forming the main blocks of information models for obtaining the main indicators of aircraft reliability and safety are shown. Formulas are given for the use of adequate information technology processes and methods for assessing the technical level of created samples of both single‑level and multi‑level hierarchical systems in combination with known methods, operating algorithms and software, which are more complete in information content with probabilistic characteristics. The theoretical aspects of the work and the formulations are supported by a computational experiment, during which the aircraft cargo was delivered to a given area. The results of the work can be useful to developers of unmanned aircraft systems and specialists in the field of designing CTC facilities when predicting their technical condition with an assessment of functional safety and ensuring the desired efficiency.

The generally accepted methodology for calculating reliability indicators, proposed by Russian and foreign reliability assessment standards, does not take into account changes in ambient temperature over time. For the radio electronics that continuously operate for a year or several years in changing climatic conditions (for example, outdoors), this approach may be inaccurate. This is due to the fact that the temperature coefficient, which takes into account the thermal stress, depends on temperature non‑linearly. Therefore, for a more accurate assessment of reliability, temperature variations must be taken into account in the calculations.

Aim. To propose a method for assessing reliability during continuous outdoor operation, taking into account temperature changes over time.

Methods. The article uses methods of the statistical reliability theory, mathematical analysis and numerical methods.

Results. A method for assessing the reliability is proposed, taking into account temperature changes over time. A mathematical model is presented that describes changes in ambient temperature throughout the year, on the basis of which it is possible to calculate the failure rate of the radio electronics during continuous operation in outdoor conditions. Using the proposed method, the operational failure rate of a 16 Megabit ROM chip made using NMOS technology is calculated when operating continuously outdoors for a year. A quantitative assessment of the discrepancy between the results of calculating the failure rate when using a technique that does not take into account temperature variations has been carried out. The dependence of the discrepancy between the results and the values of the activation energy and the operating temperature is shown.

Conclusion. The approach proposed in the article makes it possible to calculate the reliability, taking into account temperature changes over time. Based on the proposed methodology, it is possible to more accurately calculate failure rate during continuous operation in outdoor conditions.

Aim. The aim of this work is to improve the quality of multi‑class classification for Intrusion Detection Systems (IDS) in the Internet of Things (IoT) environment. The goal of the research is to determine the impact of preliminary binary traffic filtering and the application of ensemble models on prediction accuracy, especially for minority attack classes, taking into account the computational constraints of IoT environments.

Methods. Three architectural approaches were studied: direct multi‑class classification, direct multi‑class classification (including the “normal” class), and a hierarchical architecture based on initial binary detection followed by classification by attack type. Eight machine learning algorithms, as well as three ensemble methods (Soft Voting Classifier (SVC), Hard Voting Classifier (HVC), and Stacking Classifier (SC)), were evaluated. Experiments were conducted on the UNSW‑NB15 dataset using metrics such as Precision, Recall, and F1‑score.

Results. The results show that direct classification provides better overall attack coverage (average F1‑score up to 63% for Gradient Boosting Classifier(GBC)), but may require longer training times (over2000 seconds for GBC). Hierarchical binary filtering significantly reduces computation time but can decrease performance for some rare classes. The GBC, Random Forest (RF), and Extra Trees (ET) algorithms stand out for their performance. Among the ensemble methods, the Stacking Classifier (SC) demonstrates the best results (F1‑score of 73.87%), surpassing individual classifiers, although it also requires substantial training time.

Conclusion. This research shows that implementing binary filtration is a relevant strategy for reducing computational costs, but a trade‑off must be found between performance, coverage, and efficiency. GBC remains the most effective meth‑ od for rare attacks but, due to its computational cost, is poorly suited for embedded systems. ET and RF represent an excellent compromise between accuracy and speed. SC, while the most effective, requires significant resources. The scientific novelty of the research lies in the systematic evaluation of hierarchical and ensemble approaches for IDS in IoT, paving the way for creating more robust architectures adapted to IoT cybersecurity tasks.

The Aim of the paper is to analyse the state of the art of artificial intelligence application in Russia as regards technological dependability, as well as to propose new promising areas of research and development.

Methods. The methods of contextual information search, system analysis, and dependability theory are used.

Results. A review of domestic publications in the area of interest was conducted and showed the applicability of various artificial intelligence methods, in particular machine learning, to improve the dependability of various technological items. Two main t asks are identified to be solved: identification of pre failures in order to prevent failures by conducting preventive maintenance or repair; rapid detection of failures that have already occurred and their localisation. Examples of existing similar solutions are provided. The possible ways to overcome the absence of initial learning data associated with rare failures, are analysed. For more accurate prediction of failures, it is proposed to collect and use not only the parameters that characterise an examined item itself, but also environmental parameters that can also affect the condition of the item. The paper shows the relevance of studies aimed at generalized and systematic results to serve as guidelines for preferred application of certain artificial intelligence methods. New promising areas of artificial intelligence application are indicated, i.e., identifying possible common causes in cases of multiple failures, which will help reduce recovery time, and analysing the root causes of failures in order to take measures to eliminate them or reduce their future impact.

Conclusion. The conducted analysis and the propose recommendations will contribute to the cross‑industry exchange of experience, the expansion and deepening of work on the use of artificial intelligence for dependability assurance and make them more practical.