Vol 27, No 5 (2025)

The relevance of this study stems from the need to develop effective tools for managing innovative investment processes in a highly uncertain market environment. Traditional research approaches, such as econometric modeling or system dynamics, often encounter difficulties in describing the adaptive behavior of agents and unpredictable collective effects. Therefore, there is a need for tools that allow for more realistic simulation of the behavior of investment market participants in all its complexity.

Aim. The study is to develop and test a multi-agent model to evaluate the effectiveness of various innovative investment scenarios and identify optimal strategies for market participants.

Methods. This paper uses simulation and multi-agent modeling as the primary research methods.

Results. This article presents a multi-agent simulation model of an innovative investment system for analyzing interactions between investment market participants. Simulation experiments demonstrate that the developed model is able to replicate the dynamics of innovation system development, evaluate the effectiveness of various investment strategies, predict market participant behavior, and determine optimal parameters for interactions between agents.

Conclusions. Future studies propose expanding the model to include a more detailed classification of investors and projects, integration with real data, and additional learning and collective investment mechanisms. The developed model can serve as a basis for creating practical decision-making tools for innovative investment and contribute to improving the efficiency of investment activities.

Mudflows are some of the most destructive geological phenomena, and their prediction is challenging due to their complexity and the strong nonlinear relationships between the various factors that contribute to their formation. Traditional modeling methods have limitations in their ability to interpret and account for the complex interactions between different factors, and this lead to the need for the development of more advanced approaches.

Aim. The study aims to develop and test a sigma-pi neural network architecture for mudflow clustering based on morphometric and genetic characteristics as well as to identify the key factors and their combinations that contribute to the formation of different mudflow types.

Materials and methods. Cadastral data on mudflows in the southern European part of Russia is used as the initial data. A sigma-pi neural network capable of accounting for both linear features and their second-order interactions is employed for analysis. A silhouette coefficient is used to determine the number of clusters. The results are compared with those obtained using Kohonen's self-organizing maps (SOM).

Results. The model identified three stable clusters corresponding to mud, rock, and mud-rock types of mudflows. Analysis of the significance of features has revealed that the basin area, channel slope, and maximum sediment volume make the greatest contributions to cluster formation, as well as their various pairwise combinations. Comparison with the SOM (self-organizing map) confirmed the improved interpretability of the proposed model and its ability to identify hidden, nonlinear relationships.

Conclusions. The use of sigma-pi neural networks not only improves the accuracy of mudflow clustering, but also ensures the interpretability of the results by analyzing the significance of features and their combinations. This approach is promising for engineering geology and can be used in geoecological monitoring systems and forecasting of hazardous processes.

The relevance of this study lies in the fact that, despite the significant level of land contamination in Russia, there is no unified methodological approach for considering such areas as complex technical facilities. Most existing studies focus on individual aspects-bioremediation, physicochemical cleanup methods, monitoring, or legal regulation. These studies are fragmented and do not provide the holistic approach necessary for data integration and automated design. The novelty of this study lies in its treatment, for the first time, of contaminated lands as technical systems, incorporating physicochemical parameters, biological processes, and regulatory controls. Unlike existing studies, which focus on individual technologies or legal aspects, this article integrates systems analysis, pollutant categorization, and consideration of initial data uncertainty, forming a methodological basis for automated remediation design.

Aim. Is to substantiate the need to consider environmental land pollution as a complex technical object of systems analysis and formalize the reclamation project process based on the "inputs → transformations → states → outputs" model, taking into account the uncertainty of data and regulatory frameworks.

Results. This article substantiates the need to consider environmental land pollution as a complex technical object of systems analysis. It is shown that the traditional approach, based on the concept of "disturbed lands," only captures the fact of degradation and does not reflect the systemic characteristics of the object. The paper proposes a formalized description of contaminated areas using the "inputs → transformations → states → outputs" model, which allows for the identification of cause-and-effect relationships between pollutant types and the responses of soil-ecological systems.

Conclusions. The practical significance of the obtained results is indicated by the fact that they can be used by design organizations and regulatory authorities to reduce the time required to prepare design documentation, improve calculation accuracy, and reduce risks during project approval.

The development and implementation of neuroprosthetics is urgently needed to improve the functionality and effectiveness of technical rehabilitation tools for patients with lost or partially damaged organs, as well as to enhance their quality of life. The development of prosthetics, in its broadest sense, is linked to the need to address a range of challenges related to ensuring the structural and functional compatibility of complex artificial hardware and software devices with the tissues and systems of biological organisms.

Aim. The study is to develop and substantiate the concept of a system for autonomous collaborative design of neurocompatible prostheses.

Materials and methods. The object of this study is a methodology for creating an infrastructure for collaborative automated design and prototyping of neurocompatible prostheses. The subject of the study is the feasibility of developing a system for collaborative design and prototyping of neurocompatible prostheses based on intelligent software neurocognitive agents.

Results. A concept for autonomous collaborative design systems for neurocompatible prostheses has been developed and validated. Key requirements for intelligent control systems for neurocompatible prostheses and principles for their creation based on collaborative human-machine systems for autonomous design and prototyping have been developed. The feasibility of creating and developing an architecture for a collaborative autonomous design system for neurocompatible prostheses based on intelligent software neurocognitive agents has been substantiated.

The study presents a methodology for adaptive control of reversible thyristor electric drives in brewing lines. An integral-stability criterion is proposed, which simultaneously minimizes thermal losses and maintains the required system stability margin. Based on a posteriori analysis of the motor current signature, an adaptive PID controller was developed and implemented using an implicit difference scheme with a variable step. Simulation and full-scale tests confirmed a reduction in energy consumption by 9-10%, a decrease commutator temperature, and a more than fourfold reduction in unscheduled downtimes.

Aim. The study is to develop a control method for reversible electric drives that ensures both improved energy efficiency and operational reliability under high-frequency reversals and power supply fluctuations.

Methods. The proposed approach relies on an integral functional combining thermal loss minimization with stability assessment via the Lyapunov function. Controller parameters were adjusted using statistical characteristics of the motor current signature. The numerical implementation uses an implicit difference scheme involing an adaptive discretization algorithm. The effectiveness of the method was confirmed through simulation modeling and experimental testing on brewing equipment.

Results. The experiments demonstrate reducing specific energy consumption by 8-10%, reducing thermal stress and peak currents, and improving stability margin to at least 25%. The commutator temperature is reduced by 11-13 °C, extending insulation lifetime. The number of unscheduled shutdowns decreases more than fourfold compared with conventional PI control.

Conclusions. The integral-stability criterion has proven its effectiveness, enhancing simultaneously energy efficiency and reliability of electric drives. The developed controller is compatible with industrial PLCs and SCADA systems, which facilitates implementation. Economic evaluation confirmes the feasibility of the approach, with a payback period of less than 1.5 years, making the method promising for widespread application in brewing and related industries. The economic evaluation confirms the feasibility of this method, with a payback period of less than 1.5 years, which makes the approach promising for large-scale application in the brewing and related industries.

Bioengineered brain-computer interfaces (BBCIs) constitute a rapidly evolving interdisciplinary field at the intersection of neuroscience, bioengineering, materials science, and artificial intelligence. This introductory overview provides a concise synthesis of the current state of research across key domains: invasive, minimally invasive, and non-invasive platforms; emerging technologies (biohybrid interfaces, nanowire probes, in vitro neuromuscular models); clinical applications in neurorehabilitation and communication; and ethical-legal challenges - from neuroprivacy to cognitive rights. Special attention is given to regional development strategies, including the human-centered approach of the Russian scientific community. The review does not claim to offer a comprehensive analysis but aims to delineate conceptual boundaries and establish an informational foundation for forthcoming thematic publications focused on in-depth comparative assessments, regulatory modeling, and strategic priorities for clinical translation of BBCIs.

Training on contaminated data (outliers, heavy tails, label noise, preprocessing artifacts) makes arithmetic averaging in empirical risk unstable: multiple anomalies bias estimates, destabilize optimization steps, and degrade generalization ability. There is a need for a way to improve the robustness without changing the loss function or model architecture.

Aim. The paper aims to develop and demonstrate an alternative approach to batch averaging in ADAM, replacing it with a robust penalty-based averaging aggregation function, which mitigates the influence of outliers, while still maintaining the benefits of moment-based and coordinate-wise step adaptation.

Methods. Penalized, averaging aggregation means are used. The Huber dissimilarity function is used. Newton's method is used to find the optimal center and weights for batch elements. Performance is evaluated in a controlled experiment with synthetic outliers, by comparing it to the standard ADAM algorithm for training stability.

Results. Robust ADAM showed more robust training for synthetic linear regression, with the resulting model remaining stable even with up to 20% of outliers. The method keeps providing computational efficiency and compatibility by adding only a small number of iterations of the robust center search to each batch, while sustaining the same asymptotic behavior. With a quadratic penalty function, it degenerates into standard Adam, confirming the validity of the generalization.

Conclusion. A modification of the Adam optimization algorithm has been made using M-means. This method ensures the stability of linear regression, with outliers even up to 20%. The exact limitations are still to be determined. Computational overhead is associated with calculating the optimal value for each batch. However, due to the rapid convergence (approximately three iterations using Newton's method), the algorithm slowdown is not significant.

The article presents the results of the research work carried out by the laboratory of potato breeding and seed production at the Institute of Agriculture of the Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences for the period 2021-2023.

Aim. The study is to identify promising potato genotypes that exceed standard varieties in yield and resistance to major diseases (viral, late blight and alternaria) for further transfer of the best of them to state variety testing.

Research materials and methods. The research was conducted in the mid-mountain zone of the Kabardino-Balkarian Republic (900-1100 m above sea level), which is characterized by favorable climatic conditions for potato cultivation. The experimental portion of the study was conducted in accordance with state standards and proven methods. The breeding material was used in collaboration with the A.G. Lorkh Federal Research Center of Potatoes, using ecological variety testing.

Results. In the 2023 preliminary variety testing nursery, 28 new potato hybrids of various maturity groups (early, mid-early, mid-season, and mid-late) were identified. These hybrids yielded 26.6 to 47.2 t/ha, 1.8 to 18.7 t/ha higher than standard varieties, and exhibited good economic traits and resistance to key diseases.

Conclusions. In addition to high yields, the selected hybrids demonstrated good resistance to viral diseases, late blight, and Alternaria.

In the face of ongoing global challenges such as population growth, climate change, and soil degradation, the need to increase grain yields through digital transformation is becoming an urgent issue. Smart farming, through the widespread use of digital and intelligent technologies, robotics, and unmanned aerial vehicles, is crucial for increasing grain production and efficient resource management. One of the primary applications of unmanned aerial vehicles is multispectral imaging for effective agricultural monitoring, providing crop producers with detailed information on crop condition, which is particularly important for the prompt and timely implementation of management tasks related to grain crop breeding and seed production.

Aim. The paper aims to explore the monitoring and differentiated crop protection against common smut using digital and smart technologies.

Materials and methods. The study involves systematic monitoring for corn smut symptoms and subsequent spraying using various modern technologies, including traditional and unmanned aerial vehicles (UAVs). The research was conducted at an experimental site in the rural settlement of Opytnoye in the Tersky Municipal District, Kabardino-Balkarian Republic, during corn crops in 2022-2024. Operational monitoring of corn plots was conducted using a DJI Mavic 3M UAV, while the DJI AGRAS MG-1 and DJI Agras T10 were used for crop spraying in various years.

Results. This article presents the results of studies using different technical parameters of UAVs for multispectral monitoring and differentiated application of plant protection products to corn crops. Using a DJI Mavic 3M unmanned aerial vehicle (UAV) at an altitude of 150 m with automatic flight speed control from 5 to 9 m/s enables multispectral imaging, ensuring high-performance and high-quality phenotyping of grain crops. Variable-rate fungicide application using a DJI Agras T10 UAV at a working solution concentration of 75% of the recommended rate resulted in a 10.7-22.6% increase in yield in the experimental plots compared to the control plot.

Conclusion. The optimal fungicide application schedule in the experimental plots not only ensured uniform plant coverage and increased effectiveness in controlling common smut, but also reduced the cost of crop protection products, reduced chemical loads, and preserved the soil and environment.

The article analyzes the paradoxical changes in the standard of living of the Russian population between 2014 and 2024, characterized by serious macroeconomic shocks and government active interventions in the social sector.

Aim. The purpose of the study is to identify the causes of the discrepancy between the trajectories of stagnating real incomes of the population and the officially declining poverty rate.

Methodology. The study is based on a systematic analysis of data from the Federal State Statistics Service (Rosstat) on income, expenses, poverty and inequality, as well as on a review and synthesis of current scientific papers by Russian economists and sociologists.

Results. The analysis revealed that the decline in real disposable incomes in 2014-2020 was due to external shocks and structural features of the economy. It was revealed that the subsequent unprecedented decrease in the official poverty level was caused not by an organic increase in well-being, but rather by large-scale targeted social payments to families with children and specific economic factors in 2022-2024. At the same time, the high level of inequality (the Gini coefficient) continues to act as a structural barrier to improving the quality of life for the majority of citizens.

Conclusions. It is concluded that the existing model of social policy in Russia effectively copes with the task of statistical relief of extreme poverty, but does not solve the fundamental problems of the labor market, such as the phenomenon of the "working poor" and low labor costs. This masks the stagnation of real wealth and preserves structural imbalances in the economy.

Achieving food security and creating a balanced food system to meet the needs of the population is one of the most important socio-economic challenges of any country in the world, which determines the relevance of this study.

Aim. The study is to analyze modern concepts of food supply chain efficiency and sustainability based on a literature review of scientific publications.

Materials and methods. This review includes 44 scientific articles from journals indexed by both Scopus and Web of Science for the period from 2020 to 2024. This study used the PRISMA method to evaluate the identified publications in the field of food supply chain efficiency and sustainability.

Results. The study found that the number of scientific publications increased annually during the analyzed time period, confirming the high significance of this scientific area. Most of the analyzed studies focused on the sustainability of food supply chains and used quantitative research approaches. Continental Europe and Mediterranean Europe are the leading regions in terms of the number of scientific studies in the field of food supply chains.

Conclusion. The results of the presented literature review are important not only for describing the current state of research in the chosen field, but also for identifying the most promising areas for future research.

In recent years, Russian companies' financial strategies have been undergoing a dramatic transformation. This is due to the changes in the global monetary system, which have been caused by the politicalization of the US dollar. The use of the US dollar as a tool to restrict the access of countries and companies to the international payment system destroys the principles of fair competition and threatens the savings of both individuals and legal entities. The threat of dollar devaluation and changes in financial strategy of companies form the basis for the analysis and discussion among entrepreneurs from the BRICS member countries. In addition, the financial environment requires modeling the company strategies, considering the life cycle stages and relevant criteria.

Aim. The study aims to consider changes in the financial strategy of a Russian company in the context of the formation of the ecosystem of the BRICS countries, taking into account the stages of the technology life cycle.

Methods. Fama-French and APT models are used to assess losses incurred by companies due to the need for financial strategy redesign under sanctions pressure. To predict the financial implications of changes in strategy, the authors utilized the monographic method, the method of analogies, and prudential analysis.

Result. The authors identified a dataset of innovations to improve the effectiveness of the company's financial strategy.

Conclusions. The development of innovative financial strategies for BRICS companies plays a key role in strengthening their positions on the global stage.

The article addresses the issue of preserving and museumifying historical and cultural heritage (HCH) objects in Kabardino-Balkaria. According to the author, modern archeological and museological experience shows that museumification is the most effective way to preserve cultural heritage objects and an essential component of work on fostering patriotism among citizens of the country. The paper considers the basic principles and methods of museumification, as well as the objects in the region that could be museumified and used for educational tourism purposes. The author also provides examples of the destruction of federal monuments and HCH sites due to non-compliance with one or more principles of museumification.

This article is about the famous novel by Akhmedkhan Naloev "Riders of Dawn". The work explores the theme of heroism in a universal context. The author's research orbit includes the matrix of sacred themes of the writer: fate - homeland - desire for freedom - revolution - Russia - the Caucasus - the Russo-Japanese War.

Aim. The study is a literary analysis of Naloev's work, as well as an analysis of the originality and presentation of material in this work.

Results. The scientific novelty of this study lies in a thorough examination of the novel, which has not been previously subjected to a comprehensive literary analysis. Until now, there have been few serious works dedicated to this topic. The study addressed several objectives: revealing the key theme of heroism as a universal concept and incorporating other motifs observed in the novel. The author sets the task of analyzing the artistic structure of key episodes in the work, examining the system of characters and personalities, primarily the main character, Zalimgeriy Kerefov, moreover, the author considers the text stylistic and genre features. In addition, special attention is paid to how the author understands the historical events of the early 20th century - the Russo-Japanese War - through the prism of personal drama, social transformations and popular worldview. In addition, special attention is given to how the author interprets the historical events of the early 20th century - the Russo-Japanese War - through the lens of personal drama, social upheaval, and popular worldviews. As noted, despite the significance of the work of Akhmedkhan Naloev, the degree of scientific development of his heritage remains extremely low. Conclusions.

As a result of the study, the author comes to the conclusion that the novel "Riders of the Dawn" is a large-scale artistic canvas, which reflects the key events of the era: the Russo-Japanese War of 1904-1905, and the early stages of the revolutionary movement in Kabarda. The theme of the philosophy of heroism is revealed through the lives of ordinary mountain dwellers, particularly, those from the village of Khatu-Anzorovo, among whom is Zalimgeriy Kerefov, who personifies the spirit of heroism. This is a spiritually mature figure, whose internal transformation symbolizes the awakening of national self-awareness. Through the character of the hero, the author not only reveals the universal theme of heroism, but also explores the deep internal conflicts of the era including the clash between the traditional way of life and new ideas, as well as the search for moral guidance in a world that is falling apart.

The article for the first time explores the category of comedy and the features of the poetics in the short prose works of Z. Zokaev and M. Attoev. The relevance of the study is due to the insufficient coverage of the specific features of the comedy genre in Balkarian literary criticism. The focus is on identifying the originality of the ideological content (hypocrisy, greed, bribery, and reverence for rank) and the artistic features of the works.

Aim. This study aims to analyze the use of "words of humor" in the works of Z. Zokaev and M. Attoev. In this regard, the research provides an objective representation of the creative output of these authors and contributes to a more complete understanding of the development of the comedy genre in Karachay-Balkar literature.

Materials and methods. The study focuses on stories, comedies, interludes, and jokes by Z. Zokayev and M. Attoyev. Typological, structural-semantic, and comparative-historical methods are used.

Results. The specific functioning of the "comic word" in comedy is explored, revealing that it defines one of the most significant features of these writers' creative style. We analyze the role of various literary devices, such as puns, comic metaphors, sarcasm, wit, mockery, hyperbole, and dialectal inclusions, as well as the literal interpretation of phraseological units, successfully implemented in comedies, interludes, and jokes.