卷 15, 编号 4 (2025)

The development of a digital twin of an employee aims to reduce risks such as unpredictable or undesirable behavior, burnout, declining engagement in work processes, lower productivity, and the emergence of destructive conflicts. The paper outlines approaches to designing digital twins and proposes organizational and methodological support for their development as socio-economic systems characterized by goal-setting, dynamism, reflection, and bounded rationality. The prototype of an employee’s digital twin is based on a hybrid model that integrates mathematical models, which enable computer simulation of physical processes, with data-driven models incorporating data mining and reinforcement learning methods to identify patterns in data that are useful for decision-making. Such intelligently managed digital twin merges these two classes of models and is closely tied to the concept of individual human capital, understood as the sum of professional, intellectual, and social resources that define a worker’s productivity and income. The digital twin incorporates both a human capital assessment model and a management model that supports the creation of personalized professional development trajectories. The management model applies reinforcement learning algorithms to generate an optimal management regime, consisting of measures and decisions aimed at employee development while accounting for dynamic personal characteristics such as health, professional and other competencies, and motivation. The implementation of an employee’s digital twin has practical significance, as it helps to mitigate potential risks, enhance employee productivity, and improve the overall efficiency of the enterprise.

The use of project management systems based on artificial intelligence enhances the efficiency of collaborative work. This study aims to develop a concept for a system that supports collaborative design and prototyping grounded in a neurocognitive multi-agent approach. The structure of the proposed system is presented as a client-server application. System clients may include both human users and intelligent agents represented by standalone programs built on multi-agent neurocognitive architectures. The server stores a generalized behavioral model of the designed device under various conditions, as well as detailed information about all its components. The developed interaction scheme between the server and the system's clients enables the dynamic creation of an ontology that describes the structure of the designed device, thereby facilitating data processing and streamlining the design process. The presented system for designing robotic devices provides comprehensive project management capabilities, including the creation, editing, and deletion of projects, the integration of users and software agents, and efficient data exchange among all participants in the design process.

Systematic structuring of clinical data provides a comprehensive foundation for assessing the degree of functional impairment, activity level, and patient engagement in the rehabilitation process for traumatology and orthopedic cases. To standardize the representation of medical knowledge and establish a unified semantic environment for all participants in rehabilitation, an ontology-oriented approach to information system development has been adopted. The paper presents a set of interconnected, logically consistent ontological models that form the semantic core of an intelligent decision support system for medical rehabilitation. Models of medical diagnostics, rehabilitation procedure planning, and patient progress evaluation have been developed. The practical implementation of the proposed ontological framework is based on the IACPaaS cloud platform. The specialized resources and software tools derived from these ontological models constitute the foundation of a trauma surgeon’s decision support system.

The article explores the use of an ontological approach in designing industrial digital platforms within the framework of the Industry 4.0 paradigm. An approach to creating knowledge bases of technical system components using ontological models and fuzzy logic is proposed. The Industry 4.0 reference model is represented through ontologies, incorporating standards from smart manufacturing and ontology engineering applied to business model development. The study classifies the properties and interrelations of technical system elements that possess digital representations within industrial digital platforms. A multi-level ontology framework is introduced, comprising a meta-ontology, applied ontologies, and enterprise ontologies, which together enable data standardization and integration within digital platform ecosystems. The functions of an intelligent assistant for automated input of elements into ontological models in the context of Industry 4.0 are defined. A data structuring tool for corporate information systems in mechanical engineering and energy, based on the 1C: Enterprise platform, has been developed. The results demonstrate that applying an ontological approach to the management of industrial digital platforms and the creation of digital models of products and their manufacturing processes ensures a unified interpretation of data across all entities within production ecosystems, promotes interoperability and automation of knowledge processing throughout the product life cycle, facilitates the integration of new elements, and supports compliance with Industry 4.0 standards.

The article discusses a special type of integrity in databases – ancestral multiplicity integrity (AM-integrity), a concept introduced by the authors at the conceptual–ontological level within “entity–relationship” models. In this paper, this concept is extended to the relational model for the purpose of practical application in creating databases. Related notions are examined, including the lineage of ascending ancestry, the relation of ancestral multiplicity, and the corresponding integrity constraint. Transitioning to the relational model requires consideration of primary and foreign keys, referential integrity, and table triggers. As a universal means of ensuring integrity in the relational environment, the paper proposes an approach based on database triggers. These triggers detect and block insert and update operations that would lead to violations of integrity. The need for procedural programming of triggers is noted, along with the challenges of transferring data between different database management systems due to variations in procedural programming languages. An alternative approach is suggested, based on leveraging built-in mechanisms for maintaining referential integrity, in which ancestral multiplicity integrity is ensured as part of referential integrity. The specific solution depends on the use of simple or composite, natural or surrogate keys, as well as on the technical limitations of the database implementation environment. In some cases, redundant key components may need to be introduced to track ancestor instances along ancestral lineages. Test examples of relational models are provided for different relational systems (complete implementations in MySQL and MariaDB, and partial implementations in PostgreSQL, MS SQL Server, and Oracle Database).

This article describes methods for analyzing scientific activity and the tools developed on their basis to support informed decision-making at all levels of management in the scientific domain — from individual researchers to the heads of research organizations. A review of domestic and international studies in the field of scientometrics is provided, highlighting key trends and identifying existing gaps that the Institute of Control Sciences of the Russian Academy of Sciences (ICS RAS) project seeks to address through the creation of an information system for the analysis of scientific activity. The project involves the development of models, methods, and algorithms for analyzing the scientific activity of researchers, organizations, journals, and conferences in the field of control theory, using publication data. This problem is approached through an interdisciplinary framework that integrates network analysis, ontological design, big data processing, machine learning techniques, and optimization methods. The proposed system is built upon an ontology of scientific knowledge in control theory, the main principles of its construction and structure being discussed in this article. Combined with the system's technological capabilities, the presented ontology enables a level of analytical detail that surpasses existing scientometric systems (such as Web of Science, Scopus, Russian Science Citation Index, etc). It is demonstrated that the principles underlying the development and structure of the proposed ontology can be applied to other research domains. The paper also considers modern approaches to data collection and visualization, as well as methods of network and content analysis of scientific texts.The main concepts, methods, and models for the analysis of scientific activity are summarized, and their implementation within the developed information system is discussed.

This paper explores methods for topological optimization of structures and the particularities of their implementation. It describes lattice and other complex geometric structures with regularly repeating unit cells that provide non-solid internal filling of components, taking into account the feasibility of their fabrication using additive manufacturing technologies. A case study of topological optimization using an implicit geometric model representation was conducted for a component intended for production via selective laser melting of metal powder alloys. The optimization resulted in a 27% reduction in the component’s mass without compromising its mechanical strength, achieved through modification of the internal geometry. The study emphasizes the importance of verifying the results of topological optimization under operational loads while accounting for the specific characteristics of additive manufacturing technologies.