News of the Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences
ISSN (print): 1991-6639, ISSN (online): 2949-1940 Founder: Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences Editor-in-Chief: Ivanov Petr Matsovich, Doctor of Engineering Sciences, Professor Frequency / Access: 6 issues per year / Open Included in: Higher Attestation Commission List, RISC
Conceptual model of a multi-agent innovative investment system using neurocognitive architectures
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.
Copyright (c) 2025 Aigumov A.A., Pshenokova I.A. https://creativecommons.org/licenses/by/4.0
Traditional methods, such as systems of algebraic or differential equations, L-systems, or functional-structural models, are often unable to fully simulate the dynamic interactions of plants with their environment. Multi-agent systems allow the modeled object to be represented as a collective of autonomous agents representing individual functional parts, each of which follows local rules that ensure decision-making and interaction with the external environment. Aim. The study is to analyze modern approaches to multi-agent modeling in plant biology. An analysis of several publications revealed that multi-agent modeling reproduces orange tree growth, root system architecture, the morphological adaptation of black alder, and the behavioral plasticity of animals in plant ecosystems, enabling the implementation of digital twins of wheat. The reviewed studies place particular emphasis on the emergent properties of the proposed models, which manifest themselves without explicitly defining global rules. The results of the analysis demonstrate the high potential of the multi-agent approach as a tool for modeling the morphological and physiological processes of biological systems, as well as its potential for digital farming, breeding, and yield forecasting in a changing climate. This approach is capable of accounting for spatial heterogeneity of the environment and temporal changes in conditions. The presented review of research shows that the approach based on multi-agent systems is successfully applied to modeling tree growth, root systems, population dynamics, and digital twins of agricultural crops.
Copyright (c) 2025 Anchekov М.I., Kurashev Z.K. https://creativecommons.org/licenses/by/4.0
Sigma-pi neural network model for data clustering
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.
Copyright (c) 2025 Zhilov R.A. https://creativecommons.org/licenses/by/4.0
Features of power supply for autonomous objects in hard-to-reach areas
The development of the Arctic and other hard-to-reach territories is a strategic objective of the Russian Federation, ensuring national security and socio-economic development of the country. Sustainable operation of facilities located in hard-to-reach territories directly depends on the reliability and efficiency of their energy infrastructure. The relevance of the study is due to the growing number of autonomous facilities (weather stations, mining bases, telecommunication towers) in hard-to-reach regions of the Russian Federation (Arctic, Far East, Siberia), where connection to the unified energy system is technically impossible or economically impractical. Power supply of such facilities is associated with extreme climatic conditions, logistical difficulties and high reliability requirements. Aim. The purpose of the study is to develop a methodology for optimizing the composition of a hybrid energy system for autonomous facilities in hard-to-reach regions based on multi-criteria analysis, ensuring the minimization of energy costs under specified requirements for the reliability of power supply and environmental indicators. Methods. Methods of system analysis and mathematical modeling are used for a comprehensive assessment of the efficiency of hybrid energy systems (complexes) combining renewable energy sources with traditional diesel generators and energy storage systems. Results. The study developed a multi-criteria optimization model that allows determining the rational structure and parameters of hybrid energy systems according to the criteria of minimum life cycle cost, maximum reliability and minimum emissions. The conducted simulation modeling of the system operation under a random set of meteorological parameters and load confirmed the possibility of reducing diesel fuel consumption by 40-60% and CO2 emissions by 35-55% while maintaining a high level of energy supply reliability. Conclusions. The results of the work can be used to design and modernize energy supply systems for autonomous facilities operating in the harsh conditions of the Russian Arctic.
Copyright (c) 2025 Karelina M.Y., Klyuev R.V., Serdechnyy D.V. https://creativecommons.org/licenses/by/4.0
Environmental pollution of land as a complex technical object of systems analysis
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.
Copyright (c) 2025 Klimavicius J.E. https://creativecommons.org/licenses/by/4.0
Comparative analysis of class imbalance reduction methods in building machine learning models in the financial sector
Borrower default prediction is a pressing issue that underlies the financial stability of credit institutions. Aim. This study is to develop and evaluate an integrated borrower default prediction method. Materials and methods. The study was conducted by simulating the integrated borrower default prediction method, analyzing and comparing the results with the baseline AI model, and drawing conclusions. Results. Based on the analysis of dependencies, an integrated borrower default prediction methods developed and calculated. It demonstrated a significant improvement in quality metrics (an increase in average accuracy of 0.383, an increase in f1-score of 0.509, and an increase in accuracy of 0.792) relative to the baseline model. This article presents the results of experiments aimed at improving the quality metrics of machine learning models used to predict borrower default. Conclusion. The development of integrated borrower default prediction methods will improve the accuracy and reliability of forecast models, which is of great practical importance.
Copyright (c) 2025 Konstantinov A.F., Dyakonova L.P. https://creativecommons.org/licenses/by/4.0
Concept for collaborative system for automatic virtual prototyping of neuroprostheses based on epistemological algorithms for learning intelligent software agents
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.
Copyright (c) 2025 Nagoev Z.V., Nagoeva O.V. https://creativecommons.org/licenses/by/4.0
Adaptive control of reversible thyristor electric drives in brewing lines based on integral stability criterion
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.
Copyright (c) 2025 Artemyev V.S. https://creativecommons.org/licenses/by/4.0
Automated sorting control system development
This article discusses the development of a Dobot-based automated sorting system for products. Improving the speed and efficiency of sorting processes, as well as reducing manual labor costs in various industries and agriculture, is an urgent task. Aim. The study aims to advance the sorting process by developing and implementing an automated system to control it. Materials and methods. The main sorting criteria are the position, color, and temperature of the object. A robotic manipulator controlled by DobotStudio and Arduino IDE software are selected as the actuator of the control system. The sensor system consists of a diffusion photoelectric sensor, a color sensor and a temperature sensor. Results. A cyclic sort algorithm is presented, including the sensor-based sorting, a robotic arm that sorts objects based on their specified parameters. A three-dimensional (3D) model of the system has been developed, which helps to test the operability of the algorithm. Five series of experiments were conducted using two sorting methods: manual sorting and the developed control system. Conclusions. As a result of implementing the developed management system, we are able to increase productivity by 20% and improve the quality of sorting. The implementation of the developed system reduces the number of defective products and lead to an increase in the productive efficiency.
Copyright (c) 2025 Zakozhurnikov S.S., Zakozhurnikova G.S., Prikhodkov K.V., Gorshunova T.A., Pikhtilkova O.A., Pronina E.V., Lavrenov S.S. https://creativecommons.org/licenses/by/4.0
Bioengineered brain-computer interfaces: an introductory overview of technologies, clinical applications and ethical-legal challenges
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.
Copyright (c) 2025 Zammoev A.U., Abutalipov R.N. https://creativecommons.org/licenses/by/4.0
Cloud-based ecosystem of cognitive automation for integrated management of the CIP processes in brewing
The paper presents a cloud-edge cognitive architecture for managing brewery CIP processes. The system is based on a ResNet-CNN and Transformer ensemble operating within an active learning loop and integrated with multi-sensor monitoring ATP bioluminescence, IR fluorescence, and biofilm optical density. Edge nodes provide instant anomaly detection and local control, while the cloud level performs predictive optimization and model retraining. Pilot trials demonstrated reductions in reagent consumption by 29%, water usage by 22%, and energy use by 18%, along with a decrease in control latency to 140 ms and an increase in predictive accuracy to R2 = 0.92, accompanied by a 37% reduction in false alarms. The architecture ensures compliance with sanitary standards and enables a proactive paradigm for CIP cycle management. Aim. The aim of the study is to develop a cloud-edge ecosystem capable of reducing decision latency in CIP processes to less than 150 ms, cutting resource consumption, and enhancing sanitary reliability under the conditions of high variability in brewing recipes and technological parameters. Methods. The methodological foundation relied on the theory of distributed multi-agent systems and the principles of active learning. The dataset included 48,000 fouling profiles, incorporating ATP bioluminescence, IR fluorescence, and biofilm optical density. At the edge level, signal preprocessing is performed, an autoencoder generates compact embeddings, and a GRU-based classifier detects anomalies with a reaction time of less than 40 ms. At the cloud level, a hybrid ResNet-CNN and Transformer model predicts cleaning depth and optimizes CIP cycle parameters. SHAP values and Grad-CAM are used to ensure interpretability of decisions. System validation is conducted in accordance with ISO and GOST standards on metrology, cybersecurity, and sanitary compliance. Results. The experiments confirm stable real-time operation of the ecosystem and compliance with regulatory requirements. Average consumption of cleaning agents is reduced by 29%, water usage by 22%, and energy demand by 18%. Control latency decreased to 140 ms, while predictive accuracy reached R2 = 0.92. The system demonstrates a 37% reduction in false alarms and full fault tolerance under partial data loss. Economic analysis shows a 24.7% reduction in operating costs and a payback period of less than eight months. Conclusions. The developed cloud-edge cognitive architecture enables the transition of CIP processes from static operation to proactive control. The combination of fast edge modules and predictive cloud models ensures both resource efficiency and strict sanitary compliance.
Copyright (c) 2025 Maksimov A.S., Artemyev V.S., Mangusheva L.S., Meksheneva Z.V. https://creativecommons.org/licenses/by/4.0
Robust ADAM optimizer based on averaging aggregation functions
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.
Copyright (c) 2025 Kazakov M.A. https://creativecommons.org/licenses/by/4.0
Optimization of data transfer in urban information systems based on graph theory methods
The Moscow Urban Information Systems managed by the DIT represent a complex distributed ecosystem generating and processing huge volumes of heterogeneous data. Efficient transmission of this data, especially for critical services with strict requirements for latency and reliability, is a key factor in the functioning of the "smart city" and the quality of public services. Optimization of data transmission in the Moscow GIS based on graph theory is critical to improve QoS, reliability and efficiency. Aim. The research objective is to develop and verify methods for optimizing data transmission in urban information systems based on graph theory. The objectives include reducing delays, improving reliability, and enhancing the efficiency of network resources for critical services. Methods. The study was based on detailed modeling of the Moscow DIT infrastructure as a weighted graph, where vertices represent data processing/storage nodes, and edges represent communication channels with attributes of throughput, latency and reliability. Data flows for key services were specified with QoS requirements. Results. For optimization, specialized graph algorithms are used: modified A* with geographic heuristics for QoS routing, load balancing algorithms based on searching for the maximum flow/minimum cost, and methods for ensuring fault tolerance through searching for k-disjoint paths (k = 2). Using the A* algorithm allow us to reduce the average delay in video stream transmission for the Safe City system by 22-35 % compared to the basic approaches, while guaranteeing SLA compliance (<150 ms). The load balancing algorithms reduce the 95th percentile of transaction delays for making an appointment with a doctor from 65 ms to 42 ms by preventing overloads of key nodes. Using two disjoint backup paths reduce the recovery time for critical services after a channel failure from 500 ms to 50 ms. Conclusions. The obtained results convincingly prove the high practical value of applying graph theory to optimizing data transmission in large-scale urban systems. Taking into account the geographical specificity and hierarchical structure of the Moscow network proved to be a critical factor in success.
Copyright (c) 2025 Rybakov D.A. https://creativecommons.org/licenses/by/4.0
Potato breeding research in Kabardino-Balkaria
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.
Copyright (c) 2025 Abazov A.K., Abidova G.K., Likhova Z.K., Sarbasheva A.I., Batyrova O.A. https://creativecommons.org/licenses/by/4.0
Effectiveness of ammonium nitrate and CAS-32 fertilizer in cultivation of corn
Ammonium nitrate is a highly effective nitrogen fertilizer that can be used to feed the roots of corn. However, the use of this fertilizer is limited due to its high price. An alternative to ammonium nitrate could be the fertilizer KAS-32, which is a urea-ammonia mixture. the cost of which is lower and the application costs are also lower. Aim. The research aims to determine the most efficient application of ammonium nitrate and CAS-32 fertilizer for root-zone fertilization of corn plants. The experimental variants includes: 1) control (no fertilizers); 2) ammonium nitrate (Naa) at 100 kg/ha in physical weight applied during the 6-leaf stage + 8-leaf stage; 3) ammonium nitrate (Naa) at 100 kg/ha during the 6-leaf stage + CAS-32 (107 kg/ha) during the 8-leaf stage; 4) CAS-32 (107 kg/ha) applied during both the 6-leaf and 8-leaf stages. The effectiveness of nitrogen fertilization was studied on the corn hybrid Mashuk 390 MV (FAO 390). The study was conducted in the Stavropol Krai from 2022 to 2024. Results. After double fertilization with ammonium nitrate, the plant height during the flowering phase increased by an average of 10 cm over the 2022-2024 period. When ammonium nitrate and CAS-32 are combined, plants grow 12 cm taller. Fertilization with CAS-32 alone resulted in an increase in plant height of 9 cm. Root-zone fertilization with ammonium nitrate at a rate of 100 kg/ha during the 6-leaf and 8-leaf stages resulted in an average grain yield increase of 0.71 t/ha (11.8%) over three years. The combination of ammonium nitrate applied at the 6-leaf stage and CAS-32 at the 8-leaf stage increased grain yield by an average of 0.81 t/ha, or 13.4%. Double fertilization with liquid nitrogen fertilizer CAS-32 at the 6- and 8-leaf stages increases grain yield by an average of 0.71 t/ha (11.8%). The lowest costs (an average of 4,535.09 rubles/ha for 2022-2024) were observed when using liquid CAS-32 fertilizer at a rate of 107 kg/ha, while the highest costs (4973.96 rubles/ha) are associated with the application of ammonium nitrate. The highest return on investment (4754.52 rubles/ha) was achieved with ammonium nitrate applied during the 6-leaf stage and CAS-32 during the 8-leaf stage, yielding 1.04 rubles for every ruble spent. Conclusions. Due to its high efficiency and low cost, it is recommended to fertilize corn plants during the 6-leaf stage with ammonium nitrate (Naa100 kg/ha), followed by a second application of KAS-32 fertilizer during the 8-leaf stage with 107 kg/ha, or alternatively, KAS-32 can be used as the sole fertilizer for both applications.
Copyright (c) 2025 Bagrintseva V.N., Ivashenenko I.N. https://creativecommons.org/licenses/by/4.0
Optimizing corn production through digital and smart technologies
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.
Copyright (c) 2025 Shuganov V.M., Shogenov A.K., Kantiev Z.Y., Bizhoev R.V. https://creativecommons.org/licenses/by/4.0
Socio-economic development of Russia: analysis for impact of external shocks and structural constraints on living standards
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.
Copyright (c) 2025 Doholyan S.V. https://creativecommons.org/licenses/by/4.0
Assessing dynamics of number and salaries of municipal employees in Kabardino-Balkarian Republic
Local self-government is a vital area of public life. Its functioning is carried out by municipal employees - a specific type of professional employee directly and indirectly involved in local self-government and the pursuit of the interests of the population directly in their place of residence. Municipal employees are called upon to promptly and adequately realize the interests of the population of municipal entities. Aim. To develop and substantiate methodological recommendations for the development of a remuneration system for municipal employees in local government bodies of the Kabardino-Balkarian Republic based on an assessment of the dynamics of their salaries and numbers. Materials and methods. The following methods are used in preparing this article: comparative statistical analysis of the dynamics of the number and salaries of municipal employees, statistical analysis to study the relationship between the number of municipal employees and their salaries, taking into account various factors, and tabular and graphical visualization. The empirical basis of the study was formed by government statistics and research on the topic. Calculations were performed using Excel. Results. Based on the conducted assessment, the workload of municipal employees is increasing in the context of growing population needs and interests, improving living standards, and improving the quality of life. Therefore, appropriate employee motivation is necessary. According to estimates, the current level of municipal employee salaries is lower than the salaries of civil servants and employees of commercial enterprises. Furthermore, the number of municipal employees is also significantly lower, as the number of municipal employees is determined by a formal criterion - the population size of municipalities - and this dependence often proves irrational and ineffective. Conclusions. Based on an assessment of the dynamics of the number and salaries of municipal employees, the study identified key problems in the municipal employee remuneration system.
Copyright (c) 2025 Rakhaev K.M., Zhangorazova Z.S., Bakkuev E.S., Kunizheva L.K. https://creativecommons.org/licenses/by/4.0
Efficiency and sustainability in food supply chains: a systematic analysis of scientific literature
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.
Copyright (c) 2025 Fikire A.H., Korchagina E.V. https://creativecommons.org/licenses/by/4.0
Assessment of socio-economic prerequisites for the integration of ESG approaches into the strategic development of Russian regions
The relevance of the study is due to the need to adapt strategic planning of regional development to the goals of sustainability, transparency and social justice in accordance with the ESG agenda. The article considers the socio-economic characteristics of the Karachay-Cherkess Republic (KCR) as a region with high natural resource and demographic potential, but structural limitations in terms of employment, investment and technological infrastructure. Aim. The study is to identify critical limitations for the implementation of ESG-oriented strategies, as well as areas of potential growth. Methodology. The methodology of index assessment of regional conditions of ESG integration and sustainability parameters for components E, S and G are used. Results. We found out that the region demonstrates moderate growth rates of GRP and investment with high demographic sustainability and social vulnerability. A conclusion is made about the need to form a flexible regional ESG model with priority on the development of human capital, green infrastructure and management transparency. Conclusions. The presented results can be used for the purposes of regional strategizing, adjusting state programs and investment planning.
Copyright (c) 2025 Khamukova Z.P., Borov K.Y. https://creativecommons.org/licenses/by/4.0
BRICS financial horizons: innovative financial strategies for companies
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.
Copyright (c) 2025 Gryzunova N.V., Tramova A.M. https://creativecommons.org/licenses/by/4.0
Intellectual capital as basis of organization's strategic potential
This article analyzes the management of an organization's intellectual capital and its impact on its growth and development. In the context of the modern knowledge-based economy, intellectual capital is becoming a key factor in an organization's competitiveness, ensuring sustainable development and innovative potential. Effective intellectual capital management enables an organization to create supportable competitive advantages, foster an innovative culture, and ensure long-term success in a dynamic economic environment. Aim. The study is to identify the key components of intellectual capital, which are the basis of the strategic potential of the organization. Results. The study identifies aspects of the impact of key components of intellectual capital on an organization's strategic potential. It defines the role of intellectual capital in ensuring an organization's competitive advantage and long-term sustainability, as well as successful implementation of strategic initiatives. A model for managing intellectual capital has been developed to enhance the organization's strategic potential. The need for a systematic approach to managing intellectual capital and incorporating it into the organization's overall management and strategic planning process is emphasized. Conclusions. Intellectual capital and strategic potential are directly interconnected and complementary elements of an organization. Effective intellectual capital management allows organizations to build and advance their strategic potential, while ensuring a sustainable competitive benefits.
Copyright (c) 2025 Eremenko M.S., Kobzeva E.V. https://creativecommons.org/licenses/by/4.0
On issue of museumification of archaeological sites
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.
Copyright (c) 2025 Gukemukh I.K. https://creativecommons.org/licenses/by/4.0
Philosophy of heroism in Akhmedkhan Naloev's novel "Riders of the Dawn"
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.
Copyright (c) 2025 Alkhasova S.M. https://creativecommons.org/licenses/by/4.0
An experience of comparing Karachay-Balkar cuisine with Robert Burns' culinary ode
The article is a comparative-contrastive analysis of Karachay-Balkar and Scottish gastronomic traditions within the context of folklore and literature. The study is motivated by the need to draw parallels between two mountain cultures in order to identify both local and universal aspects of culinary art. The study aims to compare and contrast the typologically similar gastronomic traditions of the Karachay-Balkars and the Scots, as reflected in literary texts. The following works served as the basis for the study: "Zhalbaur algysh" ("Good wishes with zhalbaur") and "Boza algysh" ("Good wishes with buza"), as well as their Scottish counterparts - "To a Haggis" (Ode to the Scottish pudding "Haggis") and "Willie brew'd a peck o' maut" ("Our Willie brewed beer"). To solve the tasks set in the work, a set of complementary research methods was used: retrospective, comparative-historical and intertextual. To solve the tasks set in the paper, a range of complementary research methods were used: retrospective, comparative-historical, and intertextual analysis. The research allows to discover several similarities between the traditional dishes from the North Caucasus and Scotland - zhabaur and haggis, as well as buza and Scottish beer. The main sources of ethnocultural information were Karachay-Balkar folklore texts and the culinary odes of Robert Burns.
Copyright (c) 2025 Berberov B.A. https://creativecommons.org/licenses/by/4.0
Forms of comedy in the works of Z. Zokaev and M. Attoev
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.
Copyright (c) 2025 Kerimova R.A. https://creativecommons.org/licenses/by/4.0
Lyubov Vasilievna Maslienko is 75 years old
Copyright (c) 2025 Editorial T. https://creativecommons.org/licenses/by/4.0