Russian Journal of Cybernetics

The Generation of Those Who Were Romancing Knowledge

2025-07-02T12:08:38+03:00

Spatial Heterogeneity of Carbonate Fractured-Porous Formations from Well Testing and Seismic Data

2025-07-04T18:51:10+03:00

we studied the spatial heterogeneity of carbonate fractured-porous reservoirs using a comprehensive interpretation of well-test data. Our goal was to evaluate the reservoir’s flow and storage properties and to identify high-permeability flow channels. We analyzed pressure build-up tests, pressure fall-off tests, and long-term pressure and rate histories. We interpreted the data by matching calculated and measured curves, and applying Burdine and Blasingame diagnostic plots. We also integrated the well test results with geophysical and seismic data, including duplex wave anomaly maps. The analysis provided quantitative estimates of permeability, diffusivity, skin factor, and distance to flow boundaries. We identified highly fractured zones, which correlated with anomalies on seismic attribute maps. The results show that combining dynamic and geophysical data improves interpretation reliability and helps delineate lateral barriers.

Dynamic Behavior of Impurities in the Flow Field of a Stratified Viscous Incompressible Fluid

2025-07-02T12:40:09+03:00

we studied the structure of the solution to the impurity transport equation under the influence of a vortex-type solution of the Navier–Stokes equations for incompressible fluid flow in planes orthogonal to a distinguished axis, referred to as the ”well”. We described the structure of these solutions in the vicinity of the well, which arises due to rotational motion in the invariant flow planes. Our analysis shows that the dynamics of impurities in such flows can generate a spatial structure resembling cavitation, observed in high-speed motion along the selected axis. This phenomenon results in the formation of a narrow region of impurity dilution. We also provided examples demonstrating the formation of such axially symmetric structures.

Mathematical Models for the Analysis and Forecasting of Oil Field Development: A Capacitive-Resistive Model Without Bottomhole Pressure and a Logarithmic Model of Well Water Breakthrough

2025-07-02T12:48:52+03:00

we developed a modified Capacitance-Resistive Model (CRM) that does not require bottomhole pressure data. This semi-analytical model applies to waterflooding and elastic-water-drive reservoir development. Unlike traditional CRM formulations, our approach relies on cumulative production and injection volumes and incorporates inter-well interactions along with the effects of aquifers.
We also introduced an empirical logarithmic model for water breakthrough, which relates water saturation to cumulative liquid production. The resulting equations enable the calculation of liquid flow rates and water breakthrough without bottomhole pressure, significantly broadening the model’s applicability in fields with limited monitoring data.
We tested the model on four production wells in a carbonate reservoir under elastic-water-drive conditions. Comparisons with the standard CRM model demonstrated acceptable accuracy in flow rate prediction without pressure input and high precision in modeling water breakthrough dynamics. This approach supports both development forecasting and real-time field analysis without relying on hard-toobtain parameters such as bottomhole pressure.

Constructing the Euler–Lagrange Equation Using the Poincaré Lemma

2025-07-03T21:39:01+03:00

we demonstrate that the Euler–Lagrange equation for a dynamical system with one degree of freedom can be derived using the Poincaré lemma. In the appendix, we prove the theorem on the existence and uniqueness of the skew-symmetry property of the simplest (basic) differential forms.

Classical and Quantum-Mechanical Corrections to the Solution of the Euler–Lagrange Equation

2025-07-02T12:58:18+03:00

using the Jacobi equation, we constructed a chain of closed systems of first-order ordinary differential equations that describe the evolution of moments, where the initial conditions can include Planck’s constant. We showed that the evolution of moments of different orders occurs independently of each other. We demonstrated that the source of dynamic chaos is, in particular, nonzero initial conditions for the system of equations governing second moments. This is because the mean square fluctuations of position and momentum, which measure the “divergence” of trajectories, grow exponentially fast in the region of negative Gaussian curvature of the potential. Since the solutions of the Jacobi equation must be small corrections to the solution of the Euler–Lagrange equation, the reverse influence of these corrections on the Euler–Lagrange equation can be accounted for using the Monte Carlo method for dynamic variables. This, in turn, leads to the emergence of nonzero initial conditions for higher-order moments.

Current Challenges and Approaches in Machine Translation

2025-07-04T00:41:30+03:00

we conducted a comprehensive analysis of the current challenges and recent advancements in machine translation (MT), which plays a vital role in enabling multilingual communication in today’s globalized world. We focused on the application of advanced technologies – neural networks and transformer architectures – to improve translation accuracy. We evaluated leading MT systems (DeepL, Yandex Translate, and Google Translate) using standard quality metrics: BLEU, METEOR, and POS-BLEU. Our evaluation emphasized lexical matching, semantic fidelity, and syntactic structure. We developed a Python-based tool that automatically assessed translation quality using these metrics. The program identified weaknesses in translations and generated detailed reports to guide optimization. Our results showed that DeepL and Yandex achieved higher BLEU scores, indicating better performance in lexical and syntactic accuracy. However, all three systems exhibited issues with syntax, vocabulary choices, and article usage, highlighting areas that require further development.
We emphasize the importance of multi-dimensional evaluation – combining automated metrics with expert assessment. We also examined MT performance in specialized domains, where high translation precision is critical. Our findings underscore the need for continued research to further improve MT systems and adapt them to diverse linguistic and cultural contexts.

Linear Difference Equations and Fibonacci Structures

2025-07-02T13:19:54+03:00

we studied the solution of linear difference equations of arbitrary order and demonstrated that they can be expressed using the Fibonacci structure, which we defined in the course of our presentation. This structure, which consists of number sequences related to the classical Fibonacci numbers, is of independent interest. Each sequence in the structure has an associated Phidias number. In this paper, we present several results on the properties of the Fibonacci structure and Phidias numbers.

FFF 3D Printing Technology Algorithms: Problems and Review

2025-07-02T13:26:21+03:00

we examined existing algorithms for preparing models for 3D printing and provided an overview of the key software tools used in this process. We also explored the potential of printing with unconventional materials, such as chocolate. Using one of the programs as a case study, we analysed model preparation algorithms and categorised them based on their functions. In addition, we reviewed common industry practices and discussed promising developments and solutions at various stages of the workflow. We proposed and described methods for testing printed models to evaluate the performance of new algorithms. Furthermore, we assessed the pace of algorithm development and identified key barriers to progress in 3D printing, including commercial restrictions and the lack of publicly available research due to closed-source development. Based on our review, we conclude that improving algorithms for model preparation can enhance the quality of printed products without significantly increasing production costs.

Sustainability of Highly Automated Critical Infrastructure

2025-07-02T13:30:43+03:00

we examined the need for new approaches to managing and protecting energy infrastructure in the context of rapid scientific and technological advancement and evolving geopolitical conditions. The technical solutions currently employed in this sector fail to ensure the required level of reliability and security. New systems and programs introduced across various economic sectors quickly become obsolete due to the continuous emergence of novel methods for bypassing protective mechanisms and the inherent difficulty in promptly detecting such threats.

Decentralized Technologies for Cadastral Systems

2025-07-02T13:33:39+03:00

we analyzed the limitations of modern real estate accounting systems in terms of security, transparency, and data processing speed. To address these challenges, we proposed an innovative blockchainbased model for cadastral accounting that ensures decentralized data storage, protection against unauthorized modifications, and automated transaction verification. We developed a mechanism for regulating transaction fees based on population size and for integrating the blockchain system with state registries. We also presented transaction models, system operation algorithms, and potential implementation scenarios.

Comparative Analysis of Keyword Extraction Strategies

2025-07-02T13:48:30+03:00

we conducted a comparative analysis of keyword extraction methods based on statistical and neural network approaches, including YAKE and transformer-based language models such as BERT. We evaluated their accuracy, performance, and suitability for different text types. Our primary goal was to give recommendations for optimizing these methods to enhance text processing efficiency as data volumes increase. We performed experiments with real scientific texts.

Sound Modeling in Virtual Environment Systems

2025-07-02T13:52:06+03:00

we presented technologies and methods for implementing a sound subsystem within virtual environment systems. We focused on integrating acoustic components into 3D scenes and developing sound management software. As a solution, we proposed a method for preparing Sound Source objects in the 3DS MAX three-dimensional modeling system, followed by export to the target platform. To enable real-time sound implementation, we developed software that dynamically calculates the positions of sound sources and the listener. We introduced a classification of sound sources into immovable (e.g. horn, siren, megaphone), dynamic (e.g. elevator operation, idling or loaded robot engine sounds), and ambient (e.g. varying rain or hail intensity), and outlined methods for determining the timing of sound accompaniment and playback of sound files. For stationary sources, we proposed a method for creating virtual control panels with functions such as sound activation, volume control, and pause. We tested the proposed methods and approaches within the virtual environment system VirSim. The results confirmed the adequacy of the proposed solutions and their applicability to sound modeling in virtual environments.

Assessment and Selection of an Optimal Model for Forecasting the End of the Frost Period

2025-07-04T01:04:04+03:00

one of the first steps in developing a mathematical forecasting model is to define the criteria for evaluating and selecting the optimal model from among several candidates. Since modelling objectives can vary, the choice of evaluation method must align with the specific goal of the study. The accuracy and usefulness of the final result depend heavily on how the model is assessed. In this study, we developed a procedure for evaluating and selecting a model to forecast the end of the frost period in the spring–summer season.

Automatic Extraction of Keywords and Summaries for Knowledge Base Population

2025-07-02T13:59:16+03:00

we studied a range of automatic term extraction methods, from well-established techniques such as TF-IDF, RAKE, and TextRank to recent transformer-based approaches, including KeyBERT and large language models (LLMs). Our findings show that hybrid approaches — combining statistical and neural methods – achieve superior performance by ensuring both formal relevance and semantic depth in term selection. We proposed a multi-stage pipeline: initial text preprocessing and annotation, followed by the parallel application of several extraction algorithms to minimize stochastic variation, and final refinement through neural network-based ranking. This integrated algorithm significantly improves the quality of term extraction and enhances both the accuracy and practical utility of the retrieved information. The proposed method enables scalable analysis of large unstructured text corpora without the need for manual annotation or domain-specific training, making it suitable for a wide range of research and applied digitalization tasks, including applications in medicine, education, and document management.

Functional Model of the Optimal Curriculum Decision Support System

2025-07-02T14:03:05+03:00

we presented a functional model of a decision support system (DSS) designed to generate an optimal university curriculum. We demonstrated the importance of the proposed DSS. We identified the types of mathematical models and algorithms for solving decision-making problems within the system. We built the model using the IDEF0 functional modeling standard. Based on the selected models and algorithms, we developed a context diagram that outlines the system as a whole, a first-level decomposition diagram that reflects its modular structure and potential aspects of curriculum optimization, and second-level decomposition diagrams for each system module that explain its internal functioning.