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In this paper, we present an effective method for analyzing patterns in the Russia–Ukraine war based on the Lanchester model. Due to the limited availability of information on combat powers of engaging forces, we utilize the loss of armored equipment as the primary data source. To capture the intricate dynamics of modern warfare, we partition the combat loss data into disjoint subsets by examining their geometric properties. Separate systems of ordinary differential equations for these subsets are then identified using the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm under a generalized formulation of the historical Lanchester model. We provide simulations of our method to demonstrate its effectiveness and performance in analyzing contemporary warfare dynamics. [ABSTRACT FROM AUTHOR]

The ongoing Russia-Ukraine conflict has led to significant upheaval in the worldwide natural gas sector. Accurate natural gas price forecasting, as an essential tool for mitigating market uncertainty, plays a crucial role in commodity trading and regulatory decision-making. This study aims to develop a hybrid forecasting model, the FS-GA-SVR model, which integrates feature selection (FS), genetic algorithm (GA), and support vector regression (SVR) to investigate Henry Hub natural gas price prediction amidst the Russia-Ukraine conflict. The results show that: (1) The feature selection automates model input variable selection, decreasing the time required while improving the model's accuracy. (2) The use of genetic algorithm for selecting support vector regression hyperparameters significantly improves the accuracy of natural gas price predictions. The algorithm leads to a decrease of approximately 70% in measurement indicators. (3) During the Russia-Ukraine conflict, the FS-GA-SVR hybrid model demonstrates more consistent and accurate predictions for natural gas spot prices than the base SVR model. This study serves as a valuable theoretical reference for energy policymakers and natural gas market investors worldwide, supporting their ability to anticipate fluctuations in natural gas prices., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The global economy appears the trend of anti-globalization under the influence of COVID-19. Based on the input-output table of lead database from 2006 to 2020, this paper divides the factors that affect the development of financial industry in China, the United States and Russia into six aspects: price, intermediate input, household consumption, government consumption, export and import. ADGA-BP neural network model is proposed in this paper, which is based on six aspects of price, intermediate input, consumer, government consumption, export and import. The intermediate input is decomposed from the perspective of industrial structure to study the interrelationship between financial industry and other industries in the three countries. The results show that the intermediate input is the main factor in the development of financial industry in the three countries, but the source industries of the intermediate input are not the same; the two factors of household consumption and price are closely related to the development of financial industry in the three countries, and they all play a role in promoting China, while the relationship between household consumption and the United States and between price and Russia is reverse; Government consumption only has a significant impact on Russia; from the perspective of mutual influence, the mutual investment between the financial industry of China and the United States is relatively large, while the relationship between the Russian financial industry and the two countries is relatively weak. It shows that under the background of covid-19, the development of financial industry is affected. [ABSTRACT FROM AUTHOR]

In the present paper, we investigate how the general intelligence quotient (IQ) and its subtests changed for students from Russian University from 1991 to 2013. This study of the effect of such factors as gender, department, and year on the IQ response is carried out using the ANOVA model. Given the unevenness of the initial sample by years and departments, and consequently, heterogeneity of variances when divided by the original natural categories, we decided to aggregate the values of explanatory variables to build an adequate model. The paper proposes and investigates an algorithm for joint discretization and grouping, which uses the procedure of partial screening of solutions. It is an intermediate option between the greedy algorithm and exhaustive search. As a goodness function (an optimality criterion), we investigate 26 intermediate options between the AIC and BIC criteria. The BIC turned out to be the most informative and the most acceptable criterion for interpretation, which penalizes the complexity of the model, due to some decrease in accuracy. The resulting partition of the explanatory variables values into categories is used to interpret the modeling results and to arrive at the final conclusions of the data analysis. As a result, it is revealed that the observed features of the IQ dynamics are caused by changes in the education system and the socio-economic status of the family that occurred in Russia during the period of restructuring the society and intensive development of information technologies. [ABSTRACT FROM AUTHOR]

This research presents an algorithm for cluster identification based on input--output matrixes. Authors present an algorithm for downstream and upstream analysis of the symmetrical input--output matrix, which allows definition of the top input and output suppliers and consumers for each industry. As a result of the algorithm, related industries and clusters can be defined. The program, which implements the proposed algorithm, was written using Python. In this paper, the algorithm is applied to the analysis of «Construction» industry of Russia. We used the latest input-- output matrix available for Russia for 2016, which contained information on 98 industries. We defined clusters and industries that are the top suppliers and consumers of the «Construction» industry. Among the top suppliers for the «Construction» industry are «Metal manufacturing», «Automotive cluster», and «Chemical products cluster», which account for 15.01%, 9.63%, and 5.95% of overall consumption, respectively. Top consumers of the «Construction» industry are «Public administration and defense; compulsory social security», «Real estate activities», and «Human health and social work activities», which account for 23.3%, 12.19%, and 6.26%, respectively, in the volume of output. The proposed algorithm can be used for analyzing input-- output matrixes and cluster identification. Using the results of its application, the decision-makers can elaborate on policy for supporting the cluster-based development of the regions [ABSTRACT FROM AUTHOR]

Algorithmic statistics looks for models of observed data that are good in the following sense: a model is simple (i.e., has small Kolmogorov complexity) and captures all the algorithmically discoverable regularities in the data. However, this idea can not be used in practice as is because Kolmogorov complexity is not computable. In this paper we develop an algorithmic version of algorithmic statistics that uses space-bounded Kolmogorov complexity. We prove a space-bounded version of a basic result from "classical" algorithmic statistics, the connection between optimality and randomness deficiences. The main tool is the Nisan–Wigderson pseudo-random generator. An extended abstract of this paper was presented at the 12th International Computer Science Symposium in Russia (Milovanov 10). [ABSTRACT FROM AUTHOR]

The main goal of this paper is to develop a spell checker module for clinical text in Russian. The described approach combines string distance measure algorithms with technics of machine learning embedding methods. Our overall precision is 0.86, lexical precision - 0.975 and error precision is 0.74. We develop spell checker as a part of medical text mining tool regarding the problems of misspelling, negation, experiencer and temporality detection.

Rich-in-morphology language, such as Russian, present a challenge for extraction of professional medical information. In this paper, we report on our solution to identify adverse events (complications) in neurosurgery based on natural language processing and professional medical judgment. The algorithm we proposed is easily implemented and feasible in a broad spectrum of clinical studies.

Orienteering problem (OP) is a routing problem, where the aim is to generate a path through set of nodes, which would maximize total score and would not exceed the budget. In this paper, we present an extension of classic OP—Orienteering Problem with Functional Profits (OPFP), where the score of a specific point depends on its characteristics, position in the route, and other points in the route. For solving OPFP, we developed an open-source framework for solving orienteering problems, which utilizes four core components of OP in its modular architecture. Fully-written in Go programming language our framework can be extended for solving different types of tasks with different algorithms; this was demonstrated by implementation of two popular algorithms for OP solving—Ant Colony Optimization and Recursive Greedy Algorithm. Computational efficiency of the framework was shown through solving four well-known OP types: classic Orienteering Problem (OP), Orienteering Problem with Compulsory Vertices (OPCV), Orienteering Problem with Time Windows (OPTW), and Time Dependent Orienteering Problem (TDOP) along with OPFP. Experiments were conducted on a large multi-source dataset for Saint Petersburg, Russia, containing data from Instagram, TripAdvisor, Foursquare and official touristic website. Our framework is able to construct touristic paths for different OP types within few seconds using dataset with thousands of points of interest. [ABSTRACT FROM AUTHOR]

In this paper, we examine patterns of self-reported diagnosis of noncommunicable diseases (NCDs) and prevalences of algorithm/measured test-based, undiagnosed, and untreated NCDs in China, Ghana, India, Mexico, Russia, and South Africa. Nationally representative samples of older adults aged ≥50 years were analyzed from wave 1 of the World Health Organization's Study on Global Ageing and Adult Health (2007-2010; n = 34,149). Analyses focused on 6 conditions: angina, arthritis, asthma, chronic lung disease, depression, and hypertension. Outcomes for these NCDs were: 1) self-reported disease, 2) algorithm/measured test-based disease, 3) undiagnosed disease, and 4) untreated disease. Algorithm/measured test-based prevalence of NCDs was much higher than self-reported prevalence in all 6 countries, indicating underestimation of NCD prevalence in low- and middle-income countries. Undiagnosed prevalence of NCDs was highest for hypertension, ranging from 19.7% (95% confidence interval (CI): 18.1, 21.3) in India to 49.6% (95% CI: 46.2, 53.0) in South Africa. The proportion untreated among all diseases was highest for depression, ranging from 69.5% (95% CI: 57.1, 81.9) in South Africa to 93.2% (95% CI: 90.1, 95.7) in India. Higher levels of education and wealth significantly reduced the odds of an undiagnosed condition and untreated morbidity. A high prevalence of undiagnosed NCDs and an even higher proportion of untreated NCDs highlights the inadequacies in diagnosis and management of NCDs in local health-care systems. [ABSTRACT FROM AUTHOR]

Background: Over the past half century the global tendency for improvements in longevity has been uneven across countries. This has resulted in widening of inter-country disparities in life expectancy. Moreover, the pattern of divergence appears to be driven in part by processes at the level of country groupings defined in geopolitical terms. A systematic quantitative analysis of this phenomenon has not been possible using demographic decomposition approaches as these have not been suitably adapted for this purpose. In this paper we present an elaboration of conventional decomposition techniques to provide a toolkit for analysis of the inter-country variance, and illustrate its use by analyzing trends in life expectancy in developed countries over a 40-year period. Methods: We analyze trends in the population-weighted variance of life expectancy at birth across 36 developed countries and three country groups over the period 1970-2010. We have modified existing decomposition approaches using the stepwise replacement algorithm to compute age components of changes in the total variance as well as variance between and within groups of Established Market Economies (EME), Central and Eastern Europe (CEE), and the Former Soviet Union (FSU). The method is generally applicable to the decomposition of temporal changes in any aggregate index based on a set of populations. Results: The divergence in life expectancy between developed countries has generally increased over the study period. This tendency dominated from the beginning of 1970s to the early 2000s, and reversed only after 2005. From 1970 to 2010, the total standard deviation of life expectancy increased from 2.0 to 5.6 years among men and from 1.0 to 3.6 years among women. This was determined by the between-group effects due to polarization between the EME and the FSU. The latter contrast was largely fueled by the long-term health crisis in Russia. With respect to age, the increase in the overall divergence was attributable to between-country differences in mortality changes at ages 15-64 years compared to those aged 65 and older. The within-group variance increased, especially among women. This change was mostly produced by growing mortality differences at ages 65 and older. Conclusions: From the early 1970s to the mid-2000s, the strong divergence in life expectancy across developed countries was largely determined by the between-group variance and mortality polarization linked to the East--West geopolitical division. [ABSTRACT FROM AUTHOR]

The concept Web of Things (WoT) goes well beyond the emphasis on the Internet as a means of sharing data, instead of introducing all resources and connections involving computers, data, and people to the Web. It, therefore, focuses on a range of problems and opportunities, thus paving the way for several exciting industries applications. In cryptography a range of main characteristics of chaotic systems such as non-linearity, initial condition sensitivity, and mixing properties are available. These characteristics provide an essential connection between cryptography and chaos. GOST is the Russian norm of encryption. GOST block cipher is based on secret key secrecy. However, when the encryption process with the same key is used for plaintext, the same cipher text is created. Message replication can be easily detected by an adversary who is a bad link in every communication. In this paper, we propose to use a 5d chaotic system combined with GOST block cipher to create a new secure Web of Things (WoT) cryptography system. GOST is a symmetric block cipher. It is the basis of most secure information systems in Russia. The 5D chaotic system was used to generate chaotic random keys that used in the GOST algorithm to provide security as a higher strength to increases randomly. The National Institute of Standards and Technology (NIST) designed a set of fifteen statistical tests and modifies key schedule as security operations. [ABSTRACT FROM AUTHOR]

This article advances extant research that has audited search algorithms for misinformation in four respects. Firstly, this is the first misinformation audit not to implement a national but a cross-national research design. Secondly, it retrieves results not in response to the most popular query terms. Instead, it theorizes two semantic dimensions of search terms and illustrates how they impact the number of misinformative results returned. Furthermore, the analysis not only captures the mere presence of misinformative content but in addition whether the source websites are affiliated with a key misinformation actor (Russia's ruling elites) and whom the conspiracy narratives cast as the malicious plotters. Empirically, the audit compares Covid-19 conspiracy theories in Google search results across 5 key target countries of Russia's foreign communication (Belarus, Estonia, Germany, Ukraine, and the US) and Russia as of November 2020 (N = 5280 search results). It finds that, across all countries, primarily content published by mass media organizations rendered conspiracy theories visible in search results. Conspiratorial content published on websites affiliated with Russia's ruling elites was retrieved in the Belarusian, German and Russian contexts. Across all countries, the majority of conspiracy narratives suspected plotters from China. Malicious actors from the US were insinuated exclusively by sources affiliated with Russia's elites. Overall, conspiracy narratives did not primarily deepen divides within but between national communities, since – across all countries – only plotters from beyond the national borders were blamed. To conclude, the article discusses methodological advice and promising paths of research for future cross-national search engine audits. [ABSTRACT FROM AUTHOR]

In light of the growing concern about the potential for Russian state sponsored media to be used as a propaganda tool, this study uses the Yandex News algorithm to show the differences in Russian news generated for the United States, Estonia, and Russia. The authors apply and build on the traditional studies of news framing in order to (1) identify cases where headlines and articles are framed differently, (2) determine the relationship between the topic and frame used, (3) compare these findings across three countries, and (4) generate discussion surrounding the reasons for similarities and differences in each case. The use of the Yandex news algorithm, which has been a subject of speculation when it comes to Russian news media, sheds light on how the tool displays information in different country contexts and shows that there is a statistical difference in the issues that appear in the algorithm-generated headlines for each country. [ABSTRACT FROM AUTHOR]

The shock of Russia's war of aggression against Ukraine and the helplessness and disorientation we feel as contemporaries reminds us of Sigmund Freud's commentary on the outbreak of the First World War in Zeitgemäßes über Krieg und Tod (2015), which underlines the experience of disillusionment. But our perplexity is different now, as are our illusions, the collapse of which we must contend with today. We lived in an Automat Empire, behind a Silicon Curtain, and now have to realise that the military aggression against Ukraine has thrown us off the dream of a quasi-automatic civilisational progress. It is brute force that once again wants to rule our social, and increasingly our inner, lives. It is good that we have woken up from this dream. Because if psychoanalysis is right about one thing, it is that we should face reality. The reality that the process of humanisation cannot be ordered at the click of a button but must be done every day. [ABSTRACT FROM AUTHOR]

Growing amount of hydraulic fracturing (HF) jobs in the recent two decades resulted in a significant amount of measured data available for development of predictive models via machine learning (ML). In multistage fractured completions, post-fracturing production analysis (e.g., from production logging tools) reveals evidence that different stages produce very non-uniformly, and up to 30% may not be producing at all due to a combination of geomechanics and fracturing design factors. Hence, there is a significant room for improvement of current design practices. We propose a data-driven model for fracturing design optimization, where the workflow is essentially split into two stages. As a result of the first stage, the present paper summarizes the efforts in the creation of a digital database of field data from several thousands of multistage HF jobs on vertical, inclined and near-horizontal wells from circa 20 different oilfields in Western Siberia, Russia. In terms of the number of points (fracturing jobs), the present database is a rare case of a representative dataset of about 5000 data points, compared to typical databases available in the literature, comprising tens or hundreds of points at best. Each point in the data base contains the vector of 92 input variables (the reservoir, well and the frac design parameters) and the vector of production data, which is characterized by 16 parameters, including the target, cumulative oil production. The focus is made on data gathering from various sources, data preprocessing and development of the architecture of the database as well as solving the production forecast problem via ML. Data preparation has been done using various ML techniques: the problem of missing values in the database is solved with collaborative filtering for data imputation; outliers are removed using visualization of cluster data structure by t-SNE algorithm. The production forecast problem is solved via CatBoost algorithm. Prediction capability of the model is measured with the coefficient of determination (R 2) and reached 0.815. The inverse problem (selecting an optimum set of fracturing design parameters to maximize production) will be considered in the second part of the study to be published in another paper, along with a recommendation system for advising DESC and production stimulation engineers on an optimized fracturing design. • Digital database on fracturing jobs and production is built of ~6000 wells from ~20 oilfields in Western Siberia, Russia. • Detailed workflow is presented for database architecture, pre-processing, imputation of missing values with collaborative filtering and analysis. • Data is coming from various sources (reservoir geology, fracturing design, production). • Production forecast by a family of decision-tree methods (CatBoost outperforms with R2~80%); the issue of overfitting on small data is emphasized. [ABSTRACT FROM AUTHOR]

This paper aims to develop a principle for selecting the most informative samples for geological research from extensive collections of rock material. As a tool for this selection, we chose an original method of statistical comparison of X-ray powder diffraction (XRPD) and X-ray fluorescence (XRF) data using factor analysis (FA). A collection of carbonatites and aluminosilicate rocks from the Kontozero Devonian carbonatite paleovolcano complex (198 samples) is presented to test our technique. The factors extracted during FA were successfully mineralogically interpreted according to peak positions on the graphs of factor loadings. For the studied rock collection, this approach allowed us to identify more than 20 rock-forming minerals based only on XRPD data. We also found about ten mineral phases, the lines of which are low-intensity, and/or which overlap with more intense peaks of other minerals in the diffraction patterns. The mineralogical interpretation of the factors of such hidden minerals can be performed through electron probe microanalysis (EPMA) of the samples previously selected using FA. In this study, we report on an algorithm that facilitates the selection of the rock samples exhibiting the greatest contrast in mineral and chemical composition and which contain the entire set of mineral phases occurring in the geological object under study. From the collection of Kontozero rocks we examined, the 30 most representative samples were selected, amounting to about 15% of the initial sample set. [ABSTRACT FROM AUTHOR]

Research that audited search algorithms typically deployed queries in one language fielded from within only one country. In contrast, this study scrutinized 8,800 Google results retrieved in November 2020 from 5 countries (Russia, the United States, Germany, Ukraine, and Belarus) in response to queries on COVID-19 conspiracy theories in Russian and English. We found that the pandemic appeared similar to people who googled in Russian independent of their geolocation. The only exception was Ukraine, which had implemented rigorous media policies to limit the reach of websites affiliated with Russia within its national public sphere. Conspiracy narratives varied with input language. In response to Russian-language queries, 35.5% of the conspiratorial results suspected U.S. plotters to be behind the pandemic (English language: 5.8%). All source pages that blamed U.S. plotters showed connections with Russia's elites. These findings raise important theoretical questions for today's multilingual societies, where the practice of searching in nonlocal languages is increasing. [ABSTRACT FROM AUTHOR]

In January 2016, a new standard for symmetric block encryption was established in the Russian Federation. The standard contains two encryption algorithms: Magma and Kuznyechik. In this paper we propose to consider the possibility of applying the algebraic analysis method to these ciphers. To do this, we use the simplified algorithms Magma ⊕ and S-KN2. To solve sets of nonlinear Boolean equations, we choose two different approaches: a reduction and solving of the Boolean satisfiability problem (by using the CryptoMiniSat solver) and an extended linearization method (XL). In our research, we suggest using a security assessment approach that identifies the resistance of block ciphers to algebraic cryptanalysis. The algebraic analysis of an eight-round Magma (68 key bits were fixed) with the CryptoMiniSat solver demanded four known text pairs and took 3029.56 s to complete (the search took 416.31 s). The algebraic analysis of a five-round Magma cipher with weakened S-boxes required seven known text pairs and took 1135.61 s (the search took 3.36 s). The algebraic analysis of a five-round Magma cipher with disabled S-blocks (equivalent value substitution) led to getting only one solution for five known text pairs in 501.18 s (the search took 4.92 s). The complexity of the XL algebraic analysis of a four-round S-KN2 cipher with three text pairs was 236.33 s (took 1.191 Gb RAM). [ABSTRACT FROM AUTHOR]

The study is the first attempt to assess the role of climatic predictors in the rise of COVID-19 intensity in the Russian climatic region. The study used the Random Forest algorithm to understand the underlying associations and monthly scenarios. The results show that temperature seasonality (29.2 ± 0.9%) has the highest contribution for COVID-19 transmission in the humid continental region. In comparison, the diurnal temperature range (26.8 ± 0.4%) and temperature seasonality (14.6 ± 0.8%) had the highest impacts in the sub-arctic region. Our results also show that September and October have favorable climatic conditions for the COVID-19 spread in the sub-arctic and humid continental regions, respectively. From June to August, the high favorable zone for the spread of the disease will shift towards the sub-arctic region from the humid continental region. The study suggests that the government should implement strict measures for these months to prevent the second wave of COVID-19 outbreak in Russia. [ABSTRACT FROM AUTHOR]

The use of algorithmically tailored individual news feeds is increasingly viewed as an important strategy for accommodating consumers' information needs by legacy media. However, growing personalization of news distribution also raises normative concerns about the societal function of legacy media, in particular when dealing with personalization of traumatic and polarizing content. To extend the discussion of these concerns beyond the current focus on the role of news personalization in Western democracies, this article offers a conceptual assessment of perspectives for adopting personalization for conflict coverage in Ukraine and Russia, where media systems enjoy a lesser degree of press freedom. Using the coverage of the conflict in Eastern Ukraine as a case study, the article offers a conceptual framework for assessing the impact of personalization on the distribution of conflict-related news in a non-Western context. [ABSTRACT FROM AUTHOR]

The Baikal Gigaton Volume Detector (Baikal-GVD) is a km 3 -scale neutrino detector currently under construction in Lake Baikal, Russia. The detector consists of several thousand optical sensors arranged on vertical strings, with 36 sensors per string. The strings are grouped into clusters of 8 strings each. Each cluster can operate as a stand-alone neutrino detector. The detector layout is optimized for the measurement of astrophysical neutrinos with energies of ∼ 100 TeV and above. Events resulting from charged current interactions of muon (anti-)neutrinos will have a track-like topology in Baikal-GVD. A fast χ 2 -based reconstruction algorithm has been developed to reconstruct such track-like events. The algorithm has been applied to data collected in 2019 from the first five operational clusters of Baikal-GVD, resulting in observations of both downgoing atmospheric muons and upgoing atmospheric neutrinos. This serves as an important milestone towards experimental validation of the Baikal-GVD design. The analysis is limited to single-cluster data, favoring nearly-vertical tracks. [ABSTRACT FROM AUTHOR]

The article focuses on a study which examines large-scale observational evidence of cross-cultural differences in facial behavior using advanced in computer science. Topics discussed include the automated facial coding used, the reliability of the facial action unit detection algorithms across different testing subsets, multilevel regression estimates predicting each facial action's base rate percentage.

Benford's Law, a rule concerning first digits of an array of numbers, has frequently been used to test for the reporting quality of financial statements. When applied to the recent experience for Russian banks, one conclusion is that the 2004 regime shift in accounting standards produced higher quality financial statements. Prior to 2004, the Benford evidence suggests that Russian banks tended to round revenues up, expenses down and thus overstate net income. It also appears that banks may have presented stronger balance sheets than warranted. In the second part of the analysis, the practical use of Benford's Law to discern a looming bank failure appears limited. While there is, perhaps, some beneficial information to be drawn from testing for Benford distribution conformity, in isolation the tests for financial statement manipulation are inconclusive. Instead, Benford might be used with other early warning detection algorithms to recognize impending bank failures. [ABSTRACT FROM AUTHOR]

Forest fires have a negative impact on the economy in a number of regions, especially in Wildland Urban Interface (WUI) areas. An important link in the fight against fires in WUI areas is the development of information and computer systems for predicting the fire safety of infrastructural facilities of Russian Railways. In this work, a numerical study of heat transfer processes in the enclosing structure of a wooden building near the forest fire front was carried out using the technology of parallel computing. The novelty of the development is explained by the creation of its own program code, which is planned to be put into operation either in the Information System for Remote Monitoring of Forest Fires ISDM-Rosleskhoz, or in the information and computing system of JSC Russian Railways. In the Russian Federation, it is forbidden to use foreign systems in the security services of industrial facilities. The implementation of the deterministic model of heat transfer in the enclosing structure with the complexity of the algorithm O (2N2 + 2K) is presented. The program is implemented in Python 3.x using the NumPy and Concurrent libraries. Calculations were carried out on a multiprocessor cluster in the Sirius University of Science and Technology. The results of calculations and the acceleration coefficient for operating modes for 1, 2, 4, 8, 16, 32, 48 and 64 processes are presented. The developed algorithm can be applied to assess the fire safety of infrastructure facilities of Russian Railways. The main merit of the new development should be noted, which is explained by the ability to use large computational domains with a large number of computational grid nodes in space and time. The use of caching intermediate data in files made it possible to distribute a large number of computational nodes among the processors of a computing multiprocessor system. However, one should also note a drawback; namely, a decrease in the acceleration of computational operations with a large number of involved nodes of a multiprocessor computing system, which is explained by the write and read cycles in cache files. [ABSTRACT FROM AUTHOR]