articles
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№ 4(118)
28 august 2025 year
Rubric: Researching of processes and systems Authors: Volkova V., Denisov M., Loginova A., Maksimov M. |
The paper analyzes the experience of development of automation of information provisioning of administrative bodies of territorial government. The analysis made it possible to understand that the information provision of administrative government is fundamentally different from the creation of information systems for industrial enterprises. The example of a specific district administration shows that the creation of a single automated information system in its original understanding in the theory of information systems is impossible. The implementation of the concept of “growing” the system of F. E. Temnikov based on the registration and analysis of incoming requests is presented. The idea was proposed by one of the authors of the article, the head of information and communication department of the Administration of the Kalininsky district of St. Petersburg). After the accumulation of a large volume of unordered decisions and acquired technical means, the concept and model of a multi-level information and control complex were proposed and applied taking into account the features of the administrative government body, providing a holistic view of the accumulated information support. The concept is based on the definition of the system definition article. Stratified model analysis methods have been proposed, helping to development and adjust the structure of the information-control complex. The prospects methods of creating an information system based on the application of service architecture for the field of administrative-government are considered. The relevance of the study is that it shows the usefulness of analyzing the history of information system development of a specific district administration of a city to development a theory of creating administrative management information systems. Continue... |
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| Modern trends in the development of Industry 4.0 and the digitalization of industry are leading to the creation of networks of various manufacturing enterprises that use shared digital platforms and cyber-physical systems, forming industrial digital ecosystems. This highlights the relevance of research aimed at standardizing and optimizing information exchange between stakeholders in production processes, which is crucial for enhancing competitiveness and reducing product life cycles. These challenges create a growing need for methods that ensure semantic coherence of digital models of products, processes, and enterprises within reference frameworks such as RAMI 4.0, particularly in the context of distributed manufacturing. The aim of this study is to develop a method for applying ontological models and knowledge bases in the design of cyber-physical systems aligned with the RAMI 4.0 standard, with an emphasis on achieving consistency, integrity, and dynamic interaction within digital platforms. The main results include the development of a system of hierarchically linked ontologies, a classification of asset properties and relationships within the RAMI 4.0 structure, a predicate framework for knowledge bases, and a practical implementation of the method using the 1C:Enterprise platform. The proposed approach ensures terminological unification, improves data exchange efficiency, and supports decision-making in the design and operation of cyber-physical systems. It contributes to the advancement of systems engineering by providing both a theoretical foundation and practical tools for industrial digitalization and the standardization of enterprise interactions within Industry 4.0 ecosystems, while also opening new prospects for research into intelligent manufacturing systems. Continue... | |
| Territorial inequality in access to healthcare remains a pressing issue for the healthcare system of the Russian Federation. Significant disparities in transport accessibility, staffing levels, and the spatial distribution of medical facilities complicate evidence-based decision-making, especially in regions with uneven population density and fragmented infrastructure. This creates the need for formalized and reproducible approaches to assessing healthcare accessibility that are adapted to regional specificities and suitable for digital implementation. The aim of this study is to develop a methodology for assessing the potential accessibility of medical facilities, based on a modified gravity model and implemented as an algorithm that accounts for travel time, facility capacity, and overlapping service areas. Unlike traditional models such as 2SFCA and classical gravity models, the proposed approach allows for parameter calibration based on empirical data and incorporates territorial competition for healthcare resources. The methodological foundation includes an exponential distance-decay function and dual normalization by total service supply. The novelty of the methodology lies in the integration of these components into a unified, computable index of potential spatial accessibility suitable for scalable digital implementation. The algorithm was developed in the R programming environment using the OSRM routing engine to calculate travel times over the road network. The model was tested using data from the municipalities of Sverdlovsk oblast. The results (R² = 0.252, mean absolute percentage error MAPE < 28%) confirmed the model’s interpretability and practical relevance. The proposed approach can be used for monitoring healthcare accessibility, identifying underserved areas, and informing spatial resource allocation. Moreover, the methodology can be adapted for other types of social infrastructure. Continue... | |
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№ 5(119)
24 october 2025 year
Rubric: Performance management Authors: Bulygina O. V., Anisimov A., Dli M. I., Vorotilova M. |
When managing complex projects related to the development and organization of production of innovative products, the decision-making process is influenced by many situational aspects. This complicates the assessment of the quality of decisions, which are usually multi-variant and require taking into account random influences. In such cases, a significant effect can be achieved by using bioinspired methods that allow one to find a solution acceptable for a specific situation, in which elements of fuzzy set theory are used to describe NON-factors. The article proposes a generalized approach to creating a model based on the specified methods, which is intended to support decision-making in managing an innovative project. This model is distinguished by the comprehensive use of fuzzy bioinspired methods for selecting and justifying options for action in strategic and operational planning and situational management of project activities, taking into account the general and specific characteristics of the project stages, as well as the dynamic nature of external and internal factors. The proposed approach forms the basis of the developed fuzzy method for selecting equipment for conducting experimental design work and organizing the production of innovative products using a model of the behavior of a pack of wolves during hunting. The method is distinguished by the use of a fuzzy Euclidean measure of proximity between the quality indicators of the options being evaluated and the three best ones selected at a given iteration (alpha, beta, and delta solutions) to determine the direction of the search for a rational set of equipment, a modification of the rules for searching for solutions (movement of individuals) based on the consideration of the “depth of matches” and the increment of the effect, including for finding a reasonable balance between directed and random search, and the use of a base of fuzzy production rules when choosing a method for forming the basis for an alpha solution at subsequent iterations. The method is implemented in Python 3.12.0. The effectiveness of the proposed approach is confirmed by data from a computational experiment. Continue... |
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№ 5(119)
24 october 2025 year
Rubric: Models and methods Authors: Minin V., Proleev G., Puchkov A., Trubin A. |
A method for forecasting a nonequidistant (irregular) time series with an irregular sampling interval is presented. Data presented as irregular time series are often encountered in various fields, such as healthcare, biomechanics, economics, climatology, and others. Forecasting irregular time series is in demand in these fields for early warning and proactive decision-making, but there is no universal method for taking into account the unevenness of sampling in the forecast, which determines the relevance of research in this area. The purpose of the study was to develop a method for forecasting a nonequidistant series based on deep neural networks, which allows for good forecast accuracy with a relatively lightweight network architecture. The novelty of the research results lies in the developed method for forecasting nonequidistant time series, the architecture of the deep neural network, and the algorithm that implements the proposed forecast method. The method uses a closed loop, in which the forecast results at the current step are used at the following steps. The original feature of the proposed forecasting method is the use of a multilayer perceptron to forecast the duration of the next irregular sampling interval. This interval is calculated taking into account the correlation time calculated based on the autocovariance function of the durations of irregular sampling intervals. A distinctive feature of the proposed architecture is the presence of a separate input channel of neural network data for analyzing the values of sampling intervals, which allows forecasting the next value of the series taking into account the duration of the forecasted sampling interval. The method is developed for a one-dimensional series, but it can be extended to multidimensional series if the synchronicity of the sampling of the components of the series is observed. The computational experiments showed that with low requirements for computing resources, the accuracy of the forecast based on the proposed method is comparable to modern forecast models within the correlation interval. Continue... |
| Underwater pipelines, being critical infrastructure for the transportation of hydrocarbons and other resources, require regular inspection of their condition, taking into account the economic and environmental nature of the consequences of possible accidents. Therefore, one of the key technological challenges today is the development of reliable methods for recognizing underwater pipelines for the purpose of their inspection using video information received by an autonomous unmanned underwater vehicle. A method is proposed for recognizing and tracking an underwater pipeline using optical images using an autonomous underwater vehicle, based on a multi-stage computational data processing scheme, including: vectorization of initial images on a contour basis, selection of visible boundaries of the pipeline in images and calculation of its spatial centerline. The method is based on the use of the author's modification of the Hough Transform algorithm with adaptive limitation of the analysis area and a new version of the author's method for constructing contours using the Otsu's method. The contours obtained using the method have minimal redundancy and sufficient accuracy to identify visible pipeline boundaries using a modified Hough algorithm. The method is characterized by low computational costs in comparison with analogues. The easy calculation of the centerline is carried out on the basis of the application of the local recognition algorithm previously developed by the authors. Computational experiments were conducted to obtain comparative estimates of reliability and computational performance in relation to the contour algorithms of Canny, K-means, Otsu and the boundary detection method (modification of the Hough method). Including comparison assessments with some analogues. The obtained assessments of the effectiveness of the proposed solutions confirmed their effectiveness. Continue... |
