At the present stage, the achievement of the set strategic goals of ensuring Russia's economic independence and technological leadership is associated with the development and implementation of domestic information and cognitive technologies. The country's agro-industrial complex, which is undergoing a complex process of digital transformation with the expansion of the use of robotic technology and intelligent systems, plays a special role in solving the basic tasks of maintaining state sovereignty. The development of platform solutions in agricultural production faces serious limitations and constraints on the effective application of the "digital twin" concept, due to unresolved issues regarding the conceptual and institutional justification for their construction for organizational systems. In this regard, the aim of this study is to substantiate proposals for defining the concept of a digital model of an agricultural enterprise and the formation of a possible option for describing the economic system and basic business processes for conducting full-cycle smart agriculture. The application of content and logical analysis methods, and reengineering technology, allowed us to appropriately define a reference digital model of an enterprise in the agricultural sector and present a possible design for a digital model of the economic system of a smart agricultural enterprise. Definitions of the concepts of "digital model" and "digital twin" for organizational systems are proposed, clarifying existing definitions in terms of reflecting the variability of the description of the organization's business model when displaying the entities of "business architecture" and "business processes" as separate structural elements and the contour of subjective perception of information when making decisions. The structure of a digital model of an agricultural enterprise's economic system in a networked precision farming environment is substantiated, taking into account changes in the composition and role of production factors in a data economy. We demonstrate the need to reflect in this model elements and relationships that address the requirements of ensuring environmental neutrality and social responsibility in full-cycle agricultural production. We recommend using the information image of a digital twin of an agricultural enterprise to design the structure and fill the model of the economic system with data based on regulated forms of planning and reporting documentation when building a digital platform to support management decision-making. The digital twin ontology description scheme expands our understanding of the theoretical foundations of the methodology and tools for designing and developing information models of objects and processes for business systems.
The rapid development of artificial intelligence (AI) is accompanied by increasing computational complexity and decreasing model transparency, which significantly limits its adoption in critical domains that require a high level of trust, interpretability, and justification of decisions. Under these conditions, the field of Explainable Artificial Intelligence (XAI) has gained particular importance as it focuses on approaches and technologies that enable understanding of AI system logic and interpretation of their outputs. This article examines the timely topic of implementing XAI in the context of Industry 5.0. Special attention is given to practical application scenarios: the authors present concrete industrial cases from IBM, Siemens, and other companies demonstrating how XAI contributes to enhancing the reliability, safety, efficiency, and trustworthiness of AI systems. The study includes a systematic search and analysis of the literature in this domain and proposes well-grounded key criteria for comparing existing XAI approaches. The article also outlines the advantages, current limitations, and promising directions for the development of XAI, highlighting the opportunities it opens for improving effectiveness, transparency, and trust in business.
Amid intensifying competition in the B2B e-commerce sector, particularly within the Do-It-Yourself (DIY) segment, traditional static search architectures increasingly suffer from limited adaptability and declining retrieval relevance. This study examines the limitations of rule-based ranking approaches and proposes a dynamic product ranking framework based on the Learning-to-Rank paradigm implemented with LightGBM. The primary objective of the research is to quantitatively evaluate the economic return on investment (ROI) associated with the deployment of personalized ranking algorithms. A simulation-based digital experiment was conducted using a synthetic user clickstream model to approximate real-world interaction behavior. The results indicate that the proposed dynamic ranking model yields significant improvements in search effectiveness, as measured by the metric, while simultaneously generating quantifiable gains in key business performance indicators. Specifically, the implementation resulted in a 2.1 percentage point increase in the conversion rate and a 14.5% uplift in incremental revenue. These observed effects achieved statistical significance. These findings provide empirical evidence supporting the economic viability of transitioning from static search systems to intelligent ranking architectures, highlighting their strategic importance for scalable and competitive B2B e-commerce platforms.
This paper presents a description of the current state and the results of an analysis of recent advances in the problem domain of automated task distribution among employees. The purpose of the study is to identify the main trends and patterns in the development of existing task allocation methods, to determine their strengths and limitations, and to justify the need for new approaches and algorithms that can improve the efficiency of task delegation to employees. Using a unified system of notations for the key concepts of the subject area, the article provides a concise descriptive review of ten universal task distribution algorithms published over the past twenty years. The comparative analysis was carried out according to a set of criteria reflecting both the technical and the organizational-behavioral aspects of how these algorithms function. The key evaluation criteria included: the degree to which performer competencies are taken into account; adaptability to changing external conditions and team composition; requirements for completeness and structure of the input data; robustness to incomplete or noisy data; transparency and explainability of decision-making; computational complexity; scalability with an increasing number of tasks and employees; implementation and maintenance costs; and orientation toward personnel development and competence enhancement. The comparative analysis we carried out made it possible to identify the advantages and shortcomings of each method and to formulate recommendations for their most effective practical application. The results showed that none of the examined algorithms can be considered a universal tool for delegation. Furthermore, it was found that comprehensive information about a performer's suitability for solving tasks requiring diverse competencies is either ignored or insufficiently utilized by many algorithms. This observation leaves open the problem of developing new approaches to task allocation and designing new algorithms based on them.
Modern challenges in organizational security, particularly within critical infrastructure sectors (energy, transportation, finance, IT), necessitate innovative solutions to mitigate risks associated with hiring unreliable personnel. This requires a shift from conducting fragmented checks to the creation and implementation of comprehensive systems for proactive risk assessment. The urgency of developing such systems is driven by the high frequency and catastrophic consequences of insider incidents, coupled with the inability of traditional methods to detect complex, multi-stage threats originating from employees. However, building intelligent systems that semantically integrate heterogeneous data (biographical, behavioral, financial, digital) presents new systemic challenges. The aim of this article is to analyze the key methodological, ethical-legal, and architectural requirements for designing such systems. The work sequentially examines: 1) ethical and legal dilemmas (fairness, privacy, the right to explanation) and the constraints imposed by personal data legislation; 2) specific cyber threats targeting the compromise of the knowledge base and system logic, along with architectural countermeasures based on Security by Design principles; 3) a comparative analysis of the technological components of a multi-level assessment system (documentary verification, psychometric testing, AI analysis), justifying the necessity for their integration. The scientific novelty lies in a synthetic approach that forms a holistic methodology, considering not only technological efficiency but also fundamental legal constraints and information security requirements. The practical significance of the work consists in formulating systemic requirements for the design of secure, lawful, and socially responsible intelligent decision support systems for personnel security.
