This article presents the results that relate to the development of an approach for constructing parametric fuzzy metrics based on additive generators of triangular norms. A family of continuous Archimedean triangular norms is chosen, represented by rational functions with the fractional linear function (LFAG) and the logarithm of the fractional linear function (lnLFAG) acting as generators. The restrictions on the parameters of generators are obtained, under which the corresponding triangular norm is strict. Fuzzy metrics that correspond to these generators are constructed, and their properties are investigated. The approbation of fuzzy metrics was carried out on the clustering problem, and the quality of the solution was evaluated according to various criteria for metric clustering algorithms.

In this study, the authors have developed a prototype of a three-dimensional convolutional neural network with an attention block that detects the probability of intracranial cerebral aneurysms in a single contrast computed tomography angiography study. DICOM contrast computed tomography angiography data with and without intracranial aneurysms were used to train the network. Metadata from the studies were not used. The data were divided into training and validation subsets in the proportion of 65 and 35%, respectively. Using Keras and TensorFlow libraries in the Python programming environment, a 192 × 192 × 128 three-dimensional convolutional neural network model with four convolutional layers, a kernel of dimension 3 and self-attention block was developed. The classification accuracy, precision, and recall on test samples reached 96, 99, and 93% respectively. This exceeded the performance of previously known neural networks.

The relevance of this research is determined by the development of digital technologies and the digital transformation of enterprises. The complexity is the modeling of business automation and innovation management, requiring methods of applied research management and the consideration of technology readiness. The purpose of this paper is to build an automation model in Orange analytical and simulation modeling environment. Descriptive, analytical and data discovery methods are used to achieve the research objective. The object of the study is the approaches to building a technology landscape. The subject of the study is the analysis of models and tools based on data mining. The result of the study is found in the developed model of automation based on intelligent data analysis, realized in the form of a conceptual scheme that can be used to build a technological landscape of innovative solutions at enterprises and conclusions are made about the feasibility of using this model.

Trust in artificial intelligence is a key factor in the widespread introduction of intelligent technologies into the economy and social sphere. The article discusses various aspects of this. These include trust in knowledge and data and in artificial intelligence and machine learning models; the risks and limits of applicability of the methods and technologies used; the explainability of decisions and human-oriented artificial intelligence; the primary validation and secondary validation (verification) of created systems; ChatGPT hallucinations and falsifications; and the ethical, legal, and organizational aspects of the use of artificial intelligence.

The paper examines the expressive and formal capabilities of the theory of system–object modeling for describing knowledge about the structure, functioning and states of a computer network. Methods for describing distributed information systems using systems calculus as functional objects are considered. The limitations of using the alphabet of elementary node objects are considered. A mechanism for describing the states of a computer network node is proposed.

This paper describes an intelligent recommendation system for the restoration and training of the human respiratory system using individually selected special exercises and increasing motivation when performing them. Personal recommendations for the composition of the exercises are formed on the basis of interactive intellectual analysis of video information about a person’s physical activity, taking into account his/her experience. Machine learning models and methods are used to select exercises and evaluate the effectiveness of their implementation. The results of testing the recommendation system are presented.

This article explores the potential of using open-access bibliographic databases (The Lens, Dimensions, OpenAlex) and the open-source data visualization program VOSViewer to create interdisciplinary thematic document collections and lists of the most productive and relevant scientific journals in selected priority areas of scientific and technological development. Incomplete metadata (abstracts and keywords) for many documents in these databases is noted. Experience in creating and statistically analyzing thematic article collections and journal lists on composite ceramics is presented. The experience of creating and statistically analyzing thematic collections of articles and journal lists on composite ceramics is presented. The scattering of specialized articles across journals is examined, and a quantitative assessment of the journal core zones identified in 2018 and 2024 is provided. A comparative analysis of the studied databases is provided based on several criteria.

This article discusses normative systems (formal systems built genetically) of system-structural and system-object analysis. An improved alphabet of connections and nodes of the normative system of system-object analysis based on the “unit–function–object approach is proposed. Variants of meaningful interpretation of alphabetic nodes as a means for modeling various processes are presented. For the process of manufacturing products from raw materials, the possibility of partially automating the procedure for constructing graphic-analytical models of processes using an improved alphabet is shown.

It is shown that information is a natural and legitimate phenomenon, a property inherent in matter. The conditions for its emergence are formulated. The sources of information uncertainty in the processes of perception/understanding are analyzed. Based on the principle of complementarity, the uncertainty ratio for documentary information is introduced. The typology of types of information and its forms related to the phases of existence is given. It is shown that data, information, and knowledge have a fundamentally common nature and a common “effective” part, differing only within the framework of the functional role approach, determined by the corresponding context. Because information is an entity of the second order of complexity, which presupposes a non-atomic mode of existence, three groups of information properties are defined: fundamental, attributive, and pragmatic.

This paper proposes a multidimensional framework for strategic talent ecosystem development that integrates attraction, retention, quality enhancement, and integration mechanisms into a cohesive theoretical structure. Unlike traditional approaches that examine talent management practices in isolation, this framework conceptualizes talent ecosystems as dynamically interconnected systems characterized by complex interactions and reinforcing cycles between dimensions. The paper identifies specific mechanisms within each dimension and explicates their functional characteristics, implementation requirements, and measurement approaches. Particular attention is devoted to analyzing cross-dimensional interactions that generate systemic properties beyond the sum of individual components, including reinforcement cycles, barrier patterns, and optimization strategies. By synthesizing previously fragmented research streams and addressing critical theoretical gaps, the framework provides both scholarly insight and practical guidance for organizations and regions seeking to develop sustainable competitive advantage through strategic talent concentration and enhancement. It concludes by outlining a future research agenda to further refine and validate the framework across diverse contexts and developmental trajectories.