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“Journal of Applied Informatics” is a peer-reviewed science journal with international representation of editorial board and authors, covering a significant part of Russian IT-area. The topics of the publications are connected to the aspects of theory and application of computer modeling and information technologies in various professional areas. The journal is indexed by Russian Science Citation Index on Web of Science platform.

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In accordance with the decision of the Higher Attestation Commission of the Ministry of Education and Science of Russian Federation, journal is included in the «List of Leading Peer-Reviewed Scientific Journals and Publications authorized to publish main dissertation results»

Algorithm for predicting the parameters of a system for processing waste apatite-nepheline ores

Within the framework of the concept of a circular economy, research in the field of creating technological systems for recycling waste from mining and processing plants occupies one of the key positions. This is connected, on the one hand, with significant volumes of such waste, reaching tens of millions of tons and posing a significant environmental hazard to air and water basins, human health, and, on the other hand, with their rich chemical and mineralogical composition, which makes it possible to call them accumulations of technogenic deposits. In this regard, the task of creating control systems for technological processes of processing such waste and their information support, including support for all stages of the passage of information processes, is urgent. The novelty of the presented research lies in the proposed structure of an intelligent control system for a complex chemical and energy technological system for processing apatite-nepheline ores, as well as in an algorithm for predicting technological parameters, which is part of the information support of the control system under consideration. The algorithm is based on the use of the apparatus of deep recurrent neural networks and Kalman filtering, which is used at the stage of data preprocessing to train the neural network. The paper describes the proposed algorithm for predicting multidimensional time series, adapted to the considered technological process, presents the software executed in the MatLab environment to demonstrate the efficiency of the specified combination of methods for processing technological parameters. In a model experiment, it has been shown that the use of filtering makes it possible to increase the accuracy of the forecast, which is especially noticeable at its large horizons. The practical significance of the research results is the proposed structure of an intelligent control system for the processing of apatite-nepheline ore waste and software for predicting its parameters, which can be used in various decision support systems.

Building and analyzing a machine learning model for short-term bitcoin market forecasting based on recurrent neural networks

In this article, the construction and analysis of machine learning models were performed for short-term forecasting in the cryptocurrency market on the example of bitcoin – one of the most popular cryptocurrencies in the world. The initial data for the study leads to the conclusion that over the long period of its existence, bitcoin has shown a high degree of volatility, especially evident in comparison with traditional financial instruments. The article substantiates that this market is influenced by a multitude of factors. No one can say for sure what makes up the value of a particular cryptocurrency, as it involves a range of reasons, which cannot be fully taken into account. To overcome this problem, we have considered the principle of recurrent neural network. It is described why networks with memory are better at making predictions on the time series than conventional autoregressive model and standard forward propagation networks. The initial data processing algorithm and transformation methods are defined. The sample was reduced in order to increase the speed of the network, by reducing the number of recalculations of weights. The algorithm of the family of recurrent neural networks was built and trained to test the hypothesis about their better adaptivity due to short-term and long-term memory. The model is evaluated on the test data representing the bitcoin exchange rate for 2021–2022, since this period is characterized by high volatility. It is concluded that it is reasonable to use a similar type of models for short-term forecasting of cryptocurrency rates.

Categorization of interconnected objects of critical information infrastructure

The problem of building an information infrastructure resistant to computer attacks is relevant for organizing the work of any enterprise. Therefore, the ability to assess the existing or developing information infrastructure is very important. In this regard, the article deals with the problem of categorizing objects of critical information infrastructure in the context of the need to assess their relationship. The current legislative acts, which are the information base for determining the objects of critical information infrastructure and determining their purpose, structure and composition, are considered, as well as the criteria for the significance of objects are determined. The article also defines the links between critical information infrastructure objects, their resistance to computer attacks, as well as possible damage due to disruption of their functioning or the performance of a critical process. The article provides a description of the criteria that are subject to assessment and a methodology for assessing the stability of critical information infrastructure objects to computer attacks and assessing possible damage due to disruption of the functioning or performance of critical processes by objects of critical information infrastructure. An augmented solution is proposed for assessing the stability of the functioning of critical information infrastructure objects with various options for their connection. The possibility of assessing the cumulative damage due to disruption of the functioning of interconnected objects of critical information infrastructure is considered.

Comparison of mathematical models of the dynamics of electrically charged gas suspensions for various concentrations of the dispersed component

The author: Tukmakov D.
This work is devoted to mathematical modeling of the dynamics of inhomogeneous electrically charged media. A dusty environment - solid particles suspended in a gas – was considered as an inhomogeneous medium. The mathematical model implemented a continuous approach to modeling the dynamics of inhomogeneous media. The complete hydrodynamic system of equations was solved for each component. The system of equations for the dynamics of each component included the equations of mass continuity, momentum components, and the energy conservation equation for the mixture component. Intercomponent interaction took into account momentum exchange and intercomponent heat transfer. The carrier medium was described as a viscous compressible heat-conducting gas. The flow was described as a flow with a two- dimensional geometry. The equations of the mathematical model were supplemented with initial and boundary conditions. The mathematical model took into account the wall viscosity in the channel. The system of equations of the mathematical model was integrated by McCormack's explicit finite-difference method. To obtain a monotonic grid function, a nonlinear scheme for correcting the numerical solution was used. The mathematical model was supplemented by the Poisson equation describing the electric field formed by charged dispersed particles. Poisson's equation was integrated by finite-difference methods on a gas-dynamic grid. Such a choice of the computational grid was necessary to calculate the concentration of particles required both for solving the electric field equation and for calculating the physical fields of the dynamics of inhomogeneous media. The reciprocal motion of a gas suspension caused by the movement of dispersed particles under the action of the Coulomb force was numerically investigated. The values of the surface and mass densities are determined, at which the models of the surface and mass densities of charges in the simulation of such a process are the same. It is revealed that the surface and mass models of charges are identical with respect to the volumetric content.

Determination of informative spectral ranges for the development of a transformer oil control system using deep learning neural networks

Optical spectral methods in the ultraviolet and visible regions can be used to develop transformer oil control technologies based on deep learning neural network models. The aim of the research is to identify informative spectral ranges of luminescent diagnostics for the automation system for monitoring the characteristics and parameters of transformer oil using deep learning neural networks. Measurements of the spectral characteristics of pure and spent transformer oil in the range of 180-700 nm were carried out on a diffraction spectrofluorimeter "Fluorat-02-Panorama". A qualitative and quantitative difference in the excitation spectra has been established: for waste oil, the spectra are shifted to the right and reduced by about four times to the maximum. The excitation maxima are located at wavelengths of 300, 322, 370 nm for pure and 388, 416 and 486 nm for waste oil. The photoluminescence spectra of pure oil at 300 nm excitation are a superposition of at least three curves, the largest of which has a maximum at 382 nm. For excitation of 370 nm, the spectrum is significantly wider and has maxima at wavelengths of 387, 405, 433-439 and 475-479 nm. The photoluminescence spectra of used oil are several times lower and have maxima at 446, 483 and 520-540 nm. The established excitation and luminescence ranges will be used when creating a methodology and installing quality control parameters of transformer oil during its operation. A deep learning neural network model based on the use of a self-organizing Kohonen map was also developed, which made it possible to predict the spectral characteristics of excitation based on the photoluminescence flow of transformer oil and, as a result, to determine the efficiency of the described method in industry through a decision-making system.

Development of cross-platform mobile applications – promising methods and standard practices

The study reveals the current methodological aspects of the development of cross-platform mobile applications. The theoretical significance of the research lies in the generalization and analysis of software products and IDE environments that provide cross- platform development, as well as code generation utilities. The results of a comparative analysis of cross-platform development approaches and the results of a comparative analysis of cross- platform mobile application development tools are presented. The factors that slow down the development of a cross-platform application are identified. Solutions for minimizing functional losses in the conditions of building a universal software product are indicated. Data on the use of Shell scripts was provided, which will increase the efficiency of using the code generation utility. The steps for integrating common logic into native projects are identified. The practical significance of the research results is represented by the methodology for developing cross- platform mobile applications using a combination of tools to remove the identified limitations. The study contains a set of sequential actions and conclusions that the researchers came to in the process of forming a methodology for the cross-platform development of mobile applications. The methodology will allow us to develop a cross-platform software product adapted for subsequent modifications and extensions. This material can be designed in the standard of the developer company in the format of an internal document (instructions) of the developer company.

Development of the blockchain architecture of the Industrial Internet of Things system of the enterprise

The development of the digital economy in the modern world requires solving the issue of security of Industrial Internet of Things (IIoT) applications. A large number of distributed, network-based, IIoT devices managed by intelligent programs (software agents) require protection. A successful attack on any IIoT device will lead to hacking of the IIoT application and to large financial losses, as well as to the termination of the IIoT application, therefore, the research topic is relevant. The purpose of this article is to radically solve the security problem of the IIoT application by developing a blockchain architecture of the application. The authors were tasked with investigating all aspects of the blockchain system that ensure the security of IIoT application devices. The peculiarity of the blockchain system is that its participants are software agents that control the application devices. As a result of the research, the concept of the blockchain architecture of the IIoT application is proposed. He mechanisms of consensus of intelligent programs of IIoT devices as equal active participants of the blockchain network are investigated. The consensus mechanism and the cryptographic system of the distributed registry of the blockchain network increase the information security of the IIoT application. The synergistic effect of the blockchain system and intelligent systems of software agents of IIoT application devices significantly increases the efficiency of the solution. Intelligent systems of software agents and IIoT applications are effectively trained on the blockchain platform, and as a result, we get a decentralized supercomputer in the form of a blockchain system.

Economic efficiency assessment of projects for the information systems creation

The author: Kultygin O.
The article is concerned with an approach to assess the economic efficiency of IT projects, which results are recommended to be used when choosing the informatization version for organizations of various types. This approach is based on the calculation of such indicators as capital costs for an IT project, the magnitude of the projected change in operating costs to ensure the functioning of the IS (information system), the growth rate in the efficiency of business processes as a result of informatization, as well as the costs to ensure the required level of information security. The analysis results of modern trends in the IS development for informatization of organization business processes are given, which made it possible to identify the main features of the solutions offered on the market. The analysis of direct and indirect costs for an IT project is shown to be rather difficult in a number of cases, which leads to inaccuracies in assessing its economic efficiency. In this case, the estimated payback period of the project (usually unreasonably short) can be exceeded many times in practice. At the same time, as a result of rapid progress in the field of information technology, the IT projects results are subjected to intense obsolescence, so long payback periods can lead to significant losses for the organization implementing the project. The consideration of the influence of the information security threats and the necessary additional funds for its provision on the forecast indicators for the economic efficiency of the proposed IT projects can significantly affect the decisions on choosing not only the configuration of the IS but the platform on which it operates as well. The article proposes an approach to determine the costs for ensuring information security, which should be considered when assessing the IT project economic efficiency. A mathematical model is described to choose an option for the tables rational placement on IS units (database servers) used by employees of various geographically detached divisions of an enterprise, which will allow reducing operating costs for this system operation.