The use of automatic text processing methods, including full-text description classification methods, allows achieving a significant reduction in labor costs when processing experimental data. This paper discusses the use of the automatic text classification method in the field of processing and classifying core elements and determining lithofacies. Lithofacies are coeval geological bodies (deposits) that differ in composition or structure from adjacent layers. When assessing the oil and gas potential of fields, it is necessary to construct maps and diagrams of lithofacies distribution. This requires classifying a large number of full-text descriptions of core sections prepared by specialists. The algorithm presented in the article allows, based on specified rules and dictionaries, to conduct classification taking into account the order and significance of keywords in sentences. The advantages of this approach are: the ability to distinguish between close lithofacies, the ability to use archival data, ease of adjustment to new classes, adaptation to Russian-language core descriptions and the possibility of local use without the need to transfer core descriptions to third-party applications.

This article focuses on developing forms for presenting the results of analyzing and comparing the characteristics of programming languages, systems, and paradigms. The proposed form is demonstrated through a comparison of the Lisp language, its most successful dialects (Scheme, Common Lisp, Racket, Clojure), and the functional programming paradigm across different levels of language and system definition. The form allows for a concise presentation of the inheritance of several features of the Lisp language and their evolution in its dialects, at the levels of concrete syntax, abstract semantics, and implementation pragmatics.

Scientific co-authorship is a direct reflection of scientific collaboration. Foreign studies based on Web of Science and Scopus data show that over the past decades there has been an increase in the number of co-authors of scientific publications in international journals in various disciplines. The paper compares the growth trends in the number of co-authors according to the RSCI and Scopus data. The study was conducted in five thematic areas (chemistry, history, mathematics, medicine, and physics) from 2000 to 2020. The article shows the identity of the trends in the growth of the number of co-authors in the cases of publications on history and mathematics, and a noticeable difference in other scientific fields.

The main purpose of this work is to explore new opportunities for organizing natural language queries in scientific local databases that are not relational. A brief review of recent research shows that there has been an active introduction of natural language queries into databases of various types, and the use of machine learning methods, such as neural algorithms, is noted. The widespread use of large language models in the last two years for query generation in various language settings and fields of expertise has been demonstrated. A study has been conducted to explore the potential of the AllegroGraph graph database in using large language models for natural language search. The functionality of the database has been examined using the example of a metadata system for thermophysical properties in the form of the "Thermal" domain ontology. Testing search queries in a bilingual (English and Russian) database environment has revealed some general problems that can be overcome, and it gives us good hope for the future application of new services using large language models.

Graph theory is increasingly turning from an academic discipline into a tool, mastery of which is becoming decisive for the successful use of computers in many applied areas. Despite the existence of extensive specialized literature on solving problems on graphs, the widespread use of the obtained mathematical results in programming practice is difficult due to the lack of a systematic description of them oriented towards programmers. Therefore, a significant class of practical problems, essentially reduced to a simple choice of a suitable solution method and to the construction of specific formulations of abstract algorithms, for many programmers still remains a field for intellectual activity in the “rediscovery” of known methods. The paper is devoted to the digital wiki dictionary WikiGRAPP on graph theory and its applications in computer science and programming and the digital wiki encyclopedia WEGA of graph-theoretical algorithms for solving computer science and programming problems, being developed at the A.P. Ershov Institute of Informatics Systems SB RAS.

The article presents an approach to the development of an intelligent multimodal monitoring service for the surveillance area using large neural network models. The proposed solution is capable of analyzing heterogeneous data – video streams, environmental sensor signals (temperature, humidity, etc.), and event logs – to obtain a complete picture of what is happening. The main tools used are large language and visual models (for example, LLaMA, MiniCPM‑V, etc.) deployed locally using the Ollama platform, which provides autonomous and secure information processing without the need to transfer data to the cloud. A prototype system has been developed that works offline and is capable of detecting critical situations, abnormal deviations from the norm and contextually significant events in the observed area. The method of forming test scenarios and conducting a qualitative assessment of the model's performance using the metrics F1-measure, Precision, Recall on a set of various situations is described. The experimental results confirm the applicability of multimodal models for monitoring tasks: the prototype successfully recognizes complex patterns of behavior and demonstrates the potential of large models in building adaptive and scalable surveillance systems.

This paper presents a brief overview of popular domestic OS distributions developed as part of the implementation of import substitution tasks in the field of software and telecommunications. One of the solutions for the transition from CentOS to RED OS is presented for a High Availability cluster based on Pacemaker and the DRBD distributed file system, which ensures the operation of the organization's website and MySQL database server.

In accordance with the requirement of the Higher Attestation Commission (HAC), journal issue data from the List of Peer-reviewed scientific publications in which the main scientific results of dissertations for the degree of Candidate of Sciences and for the degree of Doctor of Sciences (HAC List) have been regularly published in the Russian Science Citation Index (RSCI) in the bibliographic database eLibrary.ru for more than 20 years. In March 2023, the editorial offices of journals from the HAC List, in accordance with the recommendation of the HAC, have post data of 2022 year issues in the Russian Scientific Journals database (RSJ) created by the Russian Scientific Research Institute RIEPP. In April 2025, by order of the Ministry of Science and Higher Education of the Russian Federation, a new requirement was added — for a journal from the HAC List, along with registration in the RISC eLibrary.ru, registration in the Information System (IS) “Metaphora”, developed by the Russian Center for Scientific Information, is required. Journals from the HAC List are recommended to regularly transfer metadata of published issues of journals to the “Metaphora” through specially organized interfaces. What role do the RSJ and “Metaphora” databases play in the infrastructure of scientific publications?

In addition, according to commission of the Government of the Russian Federation, the Russian Center for Scientific Information performs the function of the operator of the “White List” of scientific journals. The “White List” in 2023 was formed by the Interdepartmental Working Group (IWG) of the Ministry of Education and Science of the Russian Federation. The "White List" is supposed to be used to monitor and evaluate the publication activity of Russian scientists. The "White List" currently includes about 29,000 English-language international journals and about 1,000 Russian-language journals from the Russian Science Citation Index (RSCI) database. In 2025, the Russian-language part of the "White List" significantly expanded due to the inclusion of journals from HAC List into the "White List". We would like to receive detailed information from the ideologists of the "White List" on how the levels (U1, U2, U3, U4) of the “White List” journals and the categories (K1, K2, K3) of journals on the HAC List will correspond?

The article is devoted to the development of a scientific and methodological apparatus for improving the effectiveness of protecting digital platforms from cyber threats by creating processing and detection algorithms that take into account the cognitive characteristics of users. A conceptual model of a three-stage protection system is proposed, integrating technical security mechanisms with cognitive decision-making models. A heuristic detection algorithm based on Random Forest machine learning with analysis of 47 features, including technical URL characteristics and cognitive-semantic content characteristics, has been developed. A methodology for dynamic integration of four threat data sources has been created, reducing response time from 12–14 hours to two hours. An algorithm for recursive analysis of redirection chains up to ten levels deep to detect masked threats is proposed. Experimental validation on an empirical base of approximately one million records confirmed detection accuracy of 87% when processing one hundred thousand records per hour. The developed solutions ensure compliance with the requirements of GOST R 57580.1-2017 and Russian legislation in the field of personal data protection.

Amidst the rapid growth of e-commerce and increasing demand for personalization, the Russian market for customized clothing faces a shortage of technological and widely accessible solutions. This paper presents the results of a research and implementation project focused on developing a multi-brand digital platform with an integrated 3D configurator, aimed at transforming the pre-order cycle. The solution enables customers to interactively create garment designs in a web environment, while allowing designers to optimize logistics and minimize overproduction.

The primary scientific and technical contribution of this work lies in its detailed description of the platform's target architecture and a scalable 3D model processing pipeline that ensures model optimization and correct browser-based rendering. An additional contribution is the developed methodology for preparing and optimizing 3D garment models for web visualization. Formalized as a set of technical requirements, this methodology achieves a balance between visual quality and performance.

As a result of this research, the authors have addressed the challenge of unifying 3D model formats from different designers within a multi-brand digital platform—a key distinction from existing single-brand solutions. Furthermore, the implemented technology enables the customization of 3D clothing models with interactive real-time visualization of all design modifications on a single screen.

The technological feasibility and effectiveness of the solution are substantiated by a comparative analysis of existing alternatives, a market analysis using the PAM-TAM-SAM-SOM model, and an assessment of functional requirements.

The article also outlines a practical strategy for implementing the digital platform, making it a valuable resource for researchers and practitioners working at the intersection of e-commerce, computer graphics, and the digital transformation of business processes.

The paper addresses the problem of constructing interpretable vector representations of scientific texts for intellectual academic genealogy. A typology of embeddings is proposed, comprising three classes: statistical, learned neural, and structured symbolic. The study argues for combining the strengths of neural embeddings (high semantic accuracy) with those of symbolic embeddings (interpretable dimensions). To operationalize this hybrid approach, an algorithm for learned symbolic embeddings is introduced, which utilizes a regression-based mapping from a model’s internal representation to an interpretable vector of scores.

The approach is evaluated on a corpus of fragments from dissertation abstracts in pedagogy. A compact transformer encoder with a regression head was trained to reproduce topic relevance scores produced by a state-of-the-art generative language model. A comparison of six training setups (three regression-head architectures and two encoder settings) shows that fine-tuning the upper encoder layers is the primary driver of quality improvements. The best configuration achieves R² = 0.57 and a Top-3 accuracy of 74% in identifying the most relevant concepts. These results suggest that, for tasks requiring formalized output representations, a compact encoder with a regression head can approximate a generative model’s behavior at substantially lower computational cost. More broadly, the further development of algorithms for constructing learned symbolic embeddings contributes to building a model of formal knowledge representation in which the convergence of neural and symbolic methods ensures both the scalability of scientific text processing and the interpretability of vector representations that encode their content.

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A previously developed pipeline for enriching news articles with structured data is summarized, and an updated configuration is presented in which GPT-3–OpenAI’s third-generation natural language processing model – is replaced with Qwen-Coder. As before, the updated enrichment pipeline uses a dataset of 400 pages selected from Google News, a free news aggregator by Google, remains compatible with the Google Rich Results Test (Google’s tool for validating eligible structured results), and demonstrates that GPT-3-comparable output quality can be achieved on a low-power desktop PC. We describe how this substitution reduces dependence on paid GPT services and report an evaluation comparing the similarity of outputs produced by Qwen-Coder against the GPT-based baseline. The results also show higher performance of the new algorithm compared with the GPT version. The proposed tools lower the barrier to adopting semantic markup practices and thereby broaden their application in digital journalism. Overall, the findings support Qwen-Coder as a cost-effective alternative to large proprietary models for metadata enrichment tasks.

This study examines the role of artificial intelligence (AI) in reshaping the ecosystem of digital scholarly communication, drawing on evidence from electronic libraries and large-scale knowledge aggregators. On the basis of an integrative review of recent international and Russian-language scholarship, AI is shown to be gradually evolving into a system-forming infrastructural mechanism across the life cycle of electronic collections, structuring processes of selection, digitisation, metadata creation, storage, and service-oriented resource discovery and access. In parallel, intelligent recommender systems are substantiated as an epistemic mediator influencing the configuration of scholarly reading, the distribution of research attention, and the visibility of peripheral forms of knowledge within the spatial–linguistic architecture of science. It is demonstrated that algorithmic personalisation cannot be reduced to improved search convenience; rather, it participates in constructing relevance norms, linguistic and regional hierarchies, and new regimes for interpreting collections. The effects identified make it possible to conceptualise algorithmic mediation at the intersection of the micro level of research identity and the macro level of the global distribution of scholarly knowledge, while also underscoring the need for reflexive governance of recommender loops in order to preserve epistemic diversity and enhance the transparency of libraries’ digital infrastructures.

Mathematical models for calculating the stress-strain state of rods with cracks under tension-compression and bending deformations are presented. A combination of the relations of the theory of elasticity and the theory of rods is used. The main provisions of the proposed modeling method are based on dividing the rod into fragments and finding deformations and stresses for each of the selected fragments according to the theory of rods or the theory of elasticity. Calculation algorithms are described, which are relatively simple to implement. Numerical data for solving problems are provided to illustrate the reliability and accuracy of calculations based on the models described in the article.

We propose a method for constructing scale-invariant representations of retail revenue time series based on three-bar Drummond Geometry (DG) computed over three adjacent periods, extended with a multi-timeframe context (day, partial calendar week, and a rolling 7-day window). Self-supervised pre-training on these “patches” is performed using a Joint-Embedding Predictive Architecture (JEPA) with spatiotemporal masking, followed by fine-tuning with output heads that quantify predictive uncertainty for next-day and next-week forecasts. The work analyzes the properties of affine invariance of the features and the identifiability of the weekly phase; empirical improvement over strong baseline models on real-world data is demonstrated.

The paper proposes an approach to multi-criteria decision support based on a cognitively oriented multi-agent information-analytical system. Cognitive modeling methods are developed, including a formal ontological representation of knowledge about production planning and a coalition–holonic agent architecture that ensures adaptability and transparency of computations. A hybrid evolutionary multi-criteria algorithm is introduced, in which agents generate alternative plans at the local level using a parallel genetic algorithm that optimizes a combination of several criteria. At the global level, a multi-stage selection of alternatives is implemented with filtering of resource overloads and similar solutions, followed by final aggregation using the PROMETHEE and ELECTRE multi-criteria decision-making methods.

An experimental study is carried out comparing manual planning with planning supported by the developed system, as well as analyzing the impact of dynamic adaptation of the genetic algorithm parameters. The results show that the use of the system makes it possible to reduce plan generation time by a factor of 20–30 while maintaining or improving solution quality. At the same time, resource overloads are completely eliminated, and early termination of evolutionary computations is ensured without loss of solution quality. The system and proposed algorithms are intended for use in planning project activities at manufacturing enterprises.