The agricultural sector in the Russian Federation generates substantial volumes of organic waste, creating significant environmental challenges and presenting an opportunity for value-added products such as biochar. While biochar offers proven benefits for soil improvement, carbon sequestration, and waste valorization, no systematic, nationwide assessment of its market potential exists. This study aims to develop and apply a structured, multi-criteria approach to evaluate and rank the biochar sales potential of Russian regions. A fuzzy multiple-criteria decision-making model was constructed using six proxy indicators linked to key biochar applications: mineral fertilizer application rate (X1), fresh water usage for irrigation (X2), area of degraded land (X3), feed consumption per conventional head of cattle (X4), cattle population (X5), and sales volume of main agricultural products (X6). Weights were assigned based on each application's scale and quality requirements. The model was applied to statistical data from 78 Russian regions, and K-means and hierarchical clustering analysis were used to validate the results. The primary outcome of this study is the development of a replicable fuzzy multi-criteria approach for assessing the market potential of biochar. The application of the approach is demonstrated through the first comprehensive ranking of Russian regions by biochar sales potential. The Krasnodar Territory was identified as having the highest potential (indicator value = 0.186), followed by the Rostov Oblast (0.320) and the Saratov Oblast (0.335). In total, 15 regions were classified as having "High" or "Very High" potential, whereas more than 20 fell into the "Medium" category. Cluster analysis validated these rankings and revealed three distinct regional profiles based on their underlying socio-agro-economic characteristics. This study provides a novel, data-driven spatial framework for assessing the potential of the biochar market at a national scale. The resulting regional ranking offers a foundational tool for informing strategic decisions on the siting of biochar production facilities and for designing targeted, region-specific market development policies for Russia's emerging biochar economy.

Tourism development can both support and strain regional sustainability. Sustainable tourism matters especially in highly urbanized metropolitan areas, where resident mobility and tourist demand jointly use transport systems. This study evaluates transport infrastructure adequacy and quality under tourism pressure in regions containing Russia’s largest urban agglomerations. Because official tourist-flow statistics are available at the regional rather than agglomeration level, the analysis uses an exploratory regional proxy approach. The methods combine comparative analysis, correlation and regression analysis, index analysis, and sensitivity checks. Tourist flows show the strongest statistical associations with absolute indicators of bus infrastructure. Rail transport, especially commuter rail, also shows a stable positive association, which matters for large metropolitan areas and regions with intensive intermunicipal mobility. Overall, tourist flows in the studied regions correlate primarily with the scale of the existing passenger transport system. Therefore, the results represent diagnostic associations rather than causal estimates of tourist transport behavior. The study proposes a comparative index of tourism transport infrastructure adequacy that characterizes how well the selected territories’ transport systems can absorb tourist traffic under data limitations. The index reveals pronounced differentiation among the Moscow, Saint Petersburg, and Kaliningrad cases.

This paper proposes a contextual method for reliability-oriented decision support in complex technical systems when the primary evidence is contained in weakly structured incident narratives. The method fuses a lexical representation of each technical text with a contextual neighborhood constructed from semantically close reports, location-specific priors, and discriminative domain lexicons extracted from historical incidents. The empirical study uses 1,474 publicly available NRC event narratives, with a focused experiment on $\mathbf{1, 0 0 3}$ power reactor reports observed between 2002 and 2025. A chronological evaluation scheme is adopted: the model is trained on reports from 2002-2016 and tested on 2017-2025 incidents. Compared with a strong TF-IDF baseline, the proposed contextual fusion model increases the hold-out F1-score from 0.954 to 0.986, improves specificity from 0.859 to 0.958, reduces false alarms by $\mathbf{7 0. 0 \%}$ and lowers Brier loss by 79.39%. The resulting contextual clusters isolate severe weather, external coordination, and coolant leakage regimes with high emergency concentration. The study demonstrates that contextualized text processing can materially improve reliability management prioritization without requiring dense sensor telemetry.

Digital technologies are important in developing capital markets in modern economic realities. Capital markets also have an essential role and weight in financial systems and can stimulate economic growth, investment, and savings in the country. Therefore, this research aimed to evaluate the causal relationship between market capitalization and economic growth in 27 developing countries, focusing on the role of regional integration and digitalisation. The hypothesis is that developing countries should focus on improving capital markets through regional integration and digitalization to stimulate economic growth. Quantitative data was used and based on the research results policy recommendations were developed. The research adopted a mixed method, including statistical and econometric analysis. The statistical analysis was carried out using graphical representations, deduction, and logical assumptions, while the econometric analysis was based on a panel data regression framework. The results showed that the weak development of the capital market due to poor digitalization in developing countries with small open economies had a significant negative effect on the investment environment and economic growth. Furthermore, a 10% increase in market capitalisation leads to a 1.8% and 0.21% increase in GDP and a 0.21% increase in GDP and FDI inflow, respectively. Recommendations on a complex institutional reform of the sector have been developed to eliminate possible obstacles to financial integration in the following directions, including institutional, legislative, and technical issues related to digital technologies. Implementation of public-private partnerships was also recommended as the best solution for overcoming financial barriers implementing digital technologies and developing capital markets in resource-constrained countries.

The study examines an intelligent monitoring and route adaptation system for unmanned aerial vehicles (Drones) based on neural network risk analysis. The research considers autonomous navigation algorithms that enable environmental analysis and real-time trajectory correction in response to potential threats. The possibilities of applying computer vision, neural network algorithms, data preprocessing methods, object detection, semantic segmentation, trajectory planning algorithms, predictive control, and adaptive route optimization are assessed for identifying obstacles, moving objects, and restricted flight zones. The role of intelligent control systems for drones is analyzed, along with their impact on enhancing autonomy, resilience, and operational efficiency in dynamically changing environments. The proposed solutions are aimed at risk mitigation associated with emergency situations through the implementation of adaptive flight control strategies. The study employs methods of systems analysis, computer vision, and machine learning, including convolutional neural networks, image preprocessing algorithms, data filtering and segmentation, as well as sensor data analysis. The effectiveness assessment is carried out through trajectory modeling, testing of threat identification algorithms, and analysis of UAV route stability parameters. The scientific novelty lies in the development of an integrated system for intelligent UAV route correction based on neural network object classification methods and adaptive trajectory planning algorithms. Mechanisms for predictive risk analysis have been developed, ensuring automatic route adjustment upon detecting obstacles, adverse weather conditions, and restricted-access zones. The proposed control architecture integrates computer vision technologies, data stream analysis, and automated decision-making while using dynamic routing methods, real-time flight correction algorithms, and collision avoidance strategies. This approach enhances the level of UAV autonomy. The developed intelligent navigation algorithms can be implemented in modern autonomous UAV control systems, enabling adaptation to dynamic conditions and increasing the efficiency of task execution across various domains, including defense and industrial applications.

With the exponential growth of textual data, traditional topic modeling methods based on static analysis demonstrate limited effectiveness in tracking the dynamics of thematic content. This research aims to develop a method for quantifying the dynamics of topics within text corpora using a thematic signal (TS) function that accounts for temporal changes and semantic relationships. The proposed method combines associative tokens with original lexical units to reduce thematic entropy and information noise. Approaches employed include topic modeling (LDA), vector representations of texts (TF-IDF, Word2Vec), and time series analysis. The method was tested on a corpus of news texts (5000 documents). Results demonstrated robust identification of semantically meaningful thematic clusters. An inverse relationship was observed between the level of thematic significance and semantic diversity, confirming a reduction in entropy using the proposed method. This approach allows for quantifying topic dynamics, filtering noise, and determining the optimal number of clusters. Future applications include analyzing multilingual data and integration with neural network models. The method shows potential for monitoring information flows and predicting thematic trends.

This paper presents a novel methodology for modeling the distribution of substantive-content message properties in the information background. This study develops a toolkit to analyze and predict information dynamics by identifying key themes, evaluating their importance, and understanding their connections. The proposed approach is based on the concept of multimodality, where properties are characterized by peaks of varying intensity and frequency. Intensity and frequency components are modeled separately and combined into a unified probabilistic framework; model parameters (shape and internal-covariance coefficients) are searched within the range (0,1). The genetic search uses mutation of +/- 20% with probability 50% and normalization of the intensity scale (theta = 1). Model quality is assessed by the Mean Absolute Error between ranked histogram bins (discretization coefficient DC defines the number of bins). Intensity, reflecting the depth and saturation of the information signal, is modeled using the Gamma distribution, while frequency, reflecting the number of occurrences, is represented by the multivariate normal distribution. A genetic algorithm is employed to identify the optimal parameters for these distributions. The methodology offers a more comprehensive understanding of information dynamics by considering both intensity and frequency, and effectively handles complex interdependencies between properties. It can be applied to various domains, including social media analysis, political communication, and marketing, providing valuable insights for decision-making.

This article presents an automated method for analyzing aerial images from unmanned aerial vehicles (UAVs), aimed at improving the reliability of technical systems and tracking changes in natural and anthropogenic processes. The objective of this work is to develop an algorithm that ensures accurate detection of anomalies and prediction of potential failure threats based on image processing. The methodology involves the application of the Structural Similarity Index (SSIM) and Mean Squared Error (MSE) for assessing spatial variations between adjacent segments of the imagery. The proposed approach is characterized by high stability to changes in illumination, low computational costs, and the possibility of integration into autonomous UAV systems. This work is based on computer modeling and statistical analysis of anomaly detection accuracy. The algorithm was tested on various datasets of aerial images using machine vision techniques and mathematical statistics to evaluate the effectiveness of the proposed method. The results include the development and validation of the algorithm, the construction of SSIM and MSE heatmaps, as well as the evaluation of the accuracy and reliability of the method. The obtained data confirm its effectiveness in automated monitoring of infrastructure facilities and the assessment of environmental risks. The scope of application of the developed method encompasses automated surveillance of engineering structures, monitoring the condition of agricultural lands, analyzing the consequences of natural disasters, and environmental control. The method can be integrated into intelligent control systems for the reliability of technical objects. In conclusion, the developed algorithm significantly enhances the accuracy of anomaly detection, minimizes the influence of external factors, and automates the aerial image processing workflow. Its application contributes to improving the reliability of technical systems and reducing the probability of failures through the early identification of potential threats. Scientific Novelty: The scientific novelty lies in the development of a new method for assessing spatial variations based on a combination of the Structural Similarity Index (SSIM) and Mean Squared Error (MSE), which provides high accuracy in anomaly detection. In contrast to traditional image analysis methods, the proposed algorithm is characterized by robustness to changing imaging conditions, and its computational efficiency allows for real-time application. Furthermore, the method can be integrated into autonomous monitoring systems, expanding the capabilities of intelligent data analysis from UAVs. The obtained results and proposed solutions can be used to improve technologies for automated condition monitoring of objects and analysis of the dynamics of natural processes.

Innovation is an indispensable element in any sphere of social life, offering new vision on the primary challenges in global and Russian development, particularly at the regional level. Numerous studies acknowledge the significant role of the regional innovation system as a crucial point of development of regional potential. Therefore, this study aimed to estimate the core role of universities in fostering innovative regional systems and establishing the link between universities and regional innovation. The correlation was identified by building a model, using the Data Envelopment Analysis (DEA). The results showed that the regions with the most active universities-driven innovation include the Moscow region, the Arkhangelsk region, St. Petersburg, the Republic of Mordovia, the Republic of Tatarstan, the Perm region, the Amur region, and the Magadan region.

динамический анализ рынка жилой недвижимости представляет собой комплексный, многоаспектный методологический подход, целью которого является всестороннее понимание эволюционирующих процессов, формирующих рыночные условия и тенденции. Исследование базируется на синтезе количественных и качественных аналитических инструментов, направленных на детальную интерпретацию и прогнозирование ключевых экономических показателей, таких как спрос, предложение и ценообразование, с учетом временной и пространственной динамики. Ключевым аспектом динамического анализа выступает концепция времени как основного измерения, что позволяет отслеживать изменения на рынке и адаптировать стратегии соответственно. Исследование подчеркивает значимость интеграции многомерных данных для глубокого понимания влияния экономических, социальных и технологических изменений на рынок жилой недвижимости. Применение передовых методов анализа, включая машинное и глубокое обучение, обогатило понимание сложных зависимостей и улучшило прогностические способности. Исследование демонстрирует неоценимую роль динамического анализа для разработки стратегий различными участниками рынка, включая инвесторов, застройщиков и государственные органы, стремящихся к эффективному управлению и адаптации к изменяющейся рыночной среде. dynamic analysis of the residential real estate market is a comprehensive, multidimensional methodological approach, the purpose of which is a comprehensive understanding of the evolving processes shaping market conditions and trends. The study is based on a synthesis of quantitative and qualitative analytical tools aimed at detailed interpretation and forecasting of key economic indicators, such as demand, supply and pricing, taking into account temporal and spatial dynamics. A key aspect of dynamic analysis is the concept of time as the main dimension, which allows you to monitor changes in the market and adapt strategies accordingly. The study highlights the importance of integrating multidimensional data to deeply understand the impact of economic, social and technological changes on the residential real estate market. The use of advanced analysis techniques, including machine learning and deep learning, has enriched the understanding of complex relationships and improved predictive abilities. The study demonstrates the invaluable role of dynamic analysis in developing strategies by various market participants, including investors, developers and government agencies, seeking to effectively manage and adapt to a changing market environment.

This study examines the role of government subsidies and private sector lending in stimulating China's economic growth while assessing the prospects for expanding the social credit system. This study covers the period from 1990 to 2023 and is based on data from the National Bureau of Statistics of China, the World Bank and the International Monetary Fund. The paper applies theoretical analysis, as well as correlation and regression methods, to assess the long-term impact of various types of subsidies on macroeconomic indicators. The article examines theoretical models describing the relationship between social subsidies and economic growth and analyzes government subsidy programs. Special attention is given to the analysis of the social credit system, which evaluates the behavior of citizens and organizations, encouraging compliance with laws and regulations to enhance social stability and predictability in economic agents' behavior. The findings demonstrate significant correlations between the volume of social subsidies and economic growth, indicating that social subsidies encourage household spending. However, the long-term effectiveness of subsidies in increasing productivity and innovation remains limited. In conclusion, it is noted that there is a need to improve the mechanisms for distributing subsidies and integrating the social credit system in order to achieve more effective economic growth, taking into account the need to maintain social stability in Chinese society.

Subject. This article deals with the issues related to the stability of the region's electricity system. Objectives. The article aims to develop an original approach to monitoring the stability of the region's electric power system. Methods. For the study, we used a fuzzy logic approach. Results. The article proposes an algorithm for monitoring the stability of the region's electric power system based on socially accessible information, based on a fuzzy approach. The proposed forecasting research algorithm consists of five successive steps. The result of the forecasting was a polynomial function reflecting the change in the parameter of the load on the system over time. Conclusions and Relevance. The consumption indicator over time is unstable, prone to sharp changes both negatively and positively, which may be due to the specifics of the formation of demand for electricity, where the consumer's decision is of key importance. The results of the study can be used to develop strategies for regional electricity consumption systems, and can also be implemented in the practice of specific electric power enterprises as part of making forecasts for energy consumption.

In the context of the transition to a new type of economy, the digital economy, the question of systemic management of the entire environment is becoming increasingly relevant. The COVID-19 pandemic has caused all decision-making entities to switch to digital communication, resulting in the formation of a ubiquitous digital ecosystem. The social environment is the most significant in this case, but the models of social environment management currently in place are ineffective in the context of digital transformation. In this study, the authors set out to develop an effective model for managing the state of the social environment of the smart cities. To achieve this goal, they developed a model of interaction between indicative indicators of the basic components of the social environment of the city, which was transformed into a program-targeted graph model for managing the state of the social environment of the region. The findings of this study have significant implications for representatives of state and municipal management, as well as researchers in the field of social development. By using the program-targeted graph model for managing the state of the social environment of the cities, they can effectively manage the social environment in the context of digital transformation. Overall, this research provides valuable insights into the development of an effective model for managing the state of the social environment of the cities in the context of digital transformation. The findings of this study can inform the practical activities of decision-making entities and researchers in the field of social development.

The article investigates the multifaceted factors influencing the mental well-being of individuals within society. Through empirical analysis, this study reveals that material well-being and social institutions exhibit negligible effects on the central factors associated with mental health. Conversely, personal emotions emerge as a significant determinant, influenced by various social phenomena such as marriage, divorce, and bereavement. Furthermore, the research underscores the substantial impact of sleep duration on an individual's mental health. Extending sleep duration is found to elevate happiness levels, although individuals with inclinations towards criminal behavior and suicide tend to prefer even longer sleep. However, this specific phenomenon falls beyond the scope of this study, warranting further examination in relevant research endeavors. Moreover, education is identified as a positive influencer of mental well-being, as evidenced by its significant reduction in crime and suicide rates across examined countries. This finding underscores the imperative of enhancing the quality and accessibility of education on a global scale. It is crucial to acknowledge that due to the inherent complexities surrounding subjective concepts such as happiness and the ideal mental state, this study cannot be deemed conclusive. The presented work establishes general associations between the selected central factors and their influencing variables. To obtain more robust and reliable insights, it is recommended to conduct comprehensive research tailored to each country, accounting for their unique characteristics and incorporating a broader range of influential factors. In conclusion, this study contributes to understanding of the intricate interplay between socio-economic factors and mental health.

устойчивость региональных электроэнергетических систем является ключевым фактором в обеспечении надежного энергоснабжения и эффективного функционирования экономики страны. Актуальной задачей отрасли становится организация региональной электроэнергетической системы, устойчивой к изменениям внешних и внутренних факторов и способной обеспечить надежное, эффективное и экономически выгодное электропитание. Близлежащие регионы с ярко выраженным сходством экономической специфики образуют кластеры, внутри которого отношения строятся на принципах взаимодействия и взаимозависимости. Целью данной статьи является разработка кластерной модели на основе показателей региональной специфики для целей оптимизации процесса управления. Результатом статьи является система моделей, описывающая полученные кластеры и их соответствие специфике: экономической, социальной, природной и технологической. Основу исследования составили региональные статистические данные и научные работы отечественных авторов в сфере электроэнергетики и устойчивости системы. the sustainability of regional electric power systems is a key factor in ensuring reliable energy supply and efficient functioning of the country's economy. An urgent task for the industry is to organize a regional electric power system that is resistant to changes in external and internal factors and is capable of providing reliable, efficient and cost-effective power supply. Nearby regions with pronounced similarities in economic specificity form clusters, within which relations are built on the principles of interaction and interdependence. The purpose of this article is to develop a cluster model based on regional specific indicators for the purpose of optimizing the management process. The result of the article is a model system that describes the resulting clusters and their correspondence to the specifics: economic, social, natural and technological. The study was based on regional statistical data and scientific treatises by domestic authors in the field of electric power industry and system stability.

In this paper, the authors attempt to describe what factors influence the formation of the target consumer avatars in the Russian car market. An avatar or a characteristic representative of the target segment is a very significant category for automobile companies, since it is precisely by understanding its avatar, the brand competently develops the entire marketing mix. 30 brands most represented on the Russian market were considered in the research. Regression modeling made it possible to confirm the conceptual model and to establish what factors affect the color of a car and the type of transmission preferred by the most characteristic brand buyers, as well as the average age of a fan of a particular car brand and the proportion of women among them.

в статье рассматривается важность аналитических алгоритмов в оценке состояния и прогнозировании тенденций на рынке жилой недвижимости, с особым акцентом на применение гамма-распределения в качестве инструмента моделирования. Основное внимание уделяется адаптивности гамма-распределения к изменчивым данным рынка и его способности адекватно отражать колебания цен, что делает его ценным инструментом для анализа времени продажи домов и уровня колебаний цен. Исследование подчёркивает значимость разработки и настройки алгоритма анализа на основе гамма-распределения для повышения точности прогнозов и поддержки решений в сфере инвестиций и управления рынком недвижимости. Кроме того, статья освещает применение кластерного анализа на рынке вторичной жилой недвижимости в России, выявляя его потенциал для глубокого анализа рынка и формирования стратегических решений. Кластерный анализ позволяет выделить однородные группы объектов, что способствует более точному ценообразованию и определению направлений для инвестиций. В заключении подчёркивается необходимость комплексного подхода к анализу рынка недвижимости, сочетающего количественные и качественные методы исследования, и важность качества данных и выбора методологии для эффективности аналитических методов. the article discusses the importance of analytical algorithms in assessing the status and forecasting trends in the residential real estate market, with particular emphasis on the use of the gamma distribution as a modeling tool. The focus is on the gamma distribution's adaptability to volatile market data and its ability to adequately reflect price fluctuations, making it a valuable tool for analyzing the timing of home sales and the level of price fluctuations. The study highlights the importance of developing and tuning an analysis algorithm based on the gamma distribution to improve the accuracy of forecasts and support decisions in the field of investment and real estate market management. In addition, the article highlights the use of cluster analysis in the secondary residential real estate market in Russia, identifying its potential for in-depth market analysis and the formation of strategic decisions. Cluster analysis allows you to identify homogeneous groups of objects, which contributes to more accurate pricing and determination of areas for investment. The conclusion emphasizes the need for an integrated approach to analyzing the real estate market, combining quantitative and qualitative research methods, and the importance of data quality and choice of methodology for the effectiveness of analytical methods.

In the era of globalization, there is a high degree of interconnection between a country's economy and the state of its financial sector. Effective functioning and dynamic development of the financial sector become an urgent need for ensuring stable economic growth. However, quite often, many developing countries on their path to this development face a series of constraints. These restrictions can seriously affect their financial potential, hindering the development of financial systems. Given these factors, the importance of overcoming them and searching and developing the latest innovative methods for analyzing financial phenomena and processes comes to the fore and become a pressing task of the present. Following this trend, this paper presents the author's model of estimating the state of the financial market. The comparative basis for this assessment was the integral indicator of the state, formed based on partial estimates of financial depth, access to finance, financial stability, and financial efficiency. The foundation for it was the methodology of fuzzy-set modeling, the purpose of which, regarding the issues under investigation, is in-depth study of the influence of financial structures on economic growth and the classification of financial indicators. Applying this model in practice, the authors have collected and analyzed extensive arrays of data concerning integral indicators of access to finance, financial depth, stability, and efficiency for two countries, Russia and the USA, and conducted a comparative analysis of the financial markets' changes during the selected period. The obtained results and observations allow to conclude that, unlike the USA, where instability and negative dynamics are observed, the financial market of Russia remains relatively stable during the period under review. Thus, on the basis of applying this model, it is possible to develop a more effective financial and banking policy. The model provides significant opportunities for deep and comprehensive analysis of financial phenomena and processes, which contributes to a more accurate assessment of the state of the financial market and rational forecasting of its future development.

Today's volumes of information streams allow to mathematize a wide spectrum of processes and regularities, which qualitatively improves the possibilities of data analysis, prediction and planning. A mathematical model of an object of study, which has reached a satisfactory level of confidence in a particular situation, is a flexible tool in the hands of a knowledgeable analyst, providing, first and foremost, information about the relationships in the considered system. The understanding of situational relationships - within the framework of a limited model and system - is an effective means of control. Thus, a person possessing information can not only evaluate their expectations - making the most productive choices - but also transform influencing factors, exerting an appropriate influence on them, in order to achieve a targeted change in an specific index that requires adjustments. The algorithms for such modeling, descriptive analysis, and the process of forming conclusions are considered in this study on the example of the change in the volume of private capital in the music industry. The personal capital of popular musical artists is described in the context of its existence in the socio-economic environment of the music industry, which is mathematically estimated by the genre, quantitative composition of musical works, musical activity duration, number of streaming (estimated by a well-known entertainment-information edition that is relevant to the described environment), the number of Grammys awarded, and the number of nominations for this prestigious award, as well as the fact that the artist is included in the number of living or deceased.

The increasing energy consumption associated with scientific and technological progress has led to environmental concerns. The transition to renewable energy sources is a potential solution to mitigate the negative effects of energy consumption. This study’s objective is to determine the factors influencing the presence of renewable energy in countries’ energy systems and to describe the pattern of their influence. The validated regression model has a high coefficient of determination of 0.9034, indicating the model’s reliability in identifying factors influencing the presence of renewable energy in energy systems. The countries were divided into three groups based on their renewable energy usage level using cluster analysis, indicating the importance of the current usage for further development. The study found that the Human Development Index (HDI) is correlated negatively with the share of renewable energy in energy systems. An increase in the innovation index leads to the development of renewable energy. This study allows for an in-depth analysis of the individual countries in the sample and provides meaningful insights into the current state of renewable energy globally. Overall, this research helps to understand the factors influencing renewable energy usage, and the findings can be used to inform policy decisions regarding renewable energy development.