The development of business analytics, decision-making and resource planning systems is one of the most important components of almost any enterprise. In these matters, enterprises and production facilities of the penitentiary system are no exception. The paper examines the problem of the relationship between existing databases and statistical reporting forms of the production, economic and labor sectors of the penitentiary system. It has been established that indirectly interrelated parameters are quite difficult to compare due to different data recording systems, as well as approved statistical forms. One of the first steps in solving this problem could be the introduction of a generalized data indexing system. The paper discusses data indexing systems, the construction of their hierarchical structures, as well as the possibility of practical application using SQL. Examples of implementation using ORM technology and the Python language are considered.

Keywords: databases, indexing, ORM, SQL, Python, manufacturing sector, economic indicators, penitentiary system

The article studies the possibility of using the continuous form of the maximum consistency method when constructing regression models to calculate the forecast values of the air transport passenger turnover indicator in the Russian Federation. The method under study is compared with classical methods of regression analysis - least squares and moduli. To assess the predictive properties of the methods, the average relative forecast error and the continuous form of the criterion for the consistency of behavior between the calculated and actual values of the dependent variable are used. As a result of the analysis, a conclusion was made about the possibility of using the method under study to solve forecast problems.

Keywords: least squares method, continuous form of the maximum consistency method, modeling, passenger turnover, air transport, adequacy criteria

Roads occupy an important place in the life of almost every person. The quality of the coating is the most significant characteristic of the roadway. To evaluate it, there are many systems, among which there are those that analyze the road surface using video information streams. In turn, the video is divided into frames, and the images are used to directly assess the road quality. Splitting video into frames in such systems works based on special software tools. To understand how effective a particular software is, a detailed analysis is needed. In this article, OpenCV, MoviePy and FFMpeg are selected as software tools for analysis. The research material is a two-minute video of the road surface with a frame rate 29.97 frames/s and mp4 format. The average time to get one frame from a video is used as an efficiency indicator. For each of the three software tools, 5 different experiments were conducted in which the frame size in pixels was consistently increased by 2 times: 40000, 80000, 160000, 320000, 640000. Each program has a linear dependence of O(n) average frame retrieving time on resolution, however, FFMpeg has the lowest absolute time indicators, as well as the lowest growth rate of the function, therefore it is the most effective tool compared to the others (OpenCV, MoviePy).

Keywords: comparison, analysis, effectiveness, software tool, library, program, video splitting, frame size, resolution, road surface

The article examines a new class of IT infrastructure monitoring systems that has been actively emerging in the last decade, the key feature of which is the widespread use of methods and techniques for working with big data. Depending on the market positioning, the systems under study are known under such names as AIOps, observability platform, all-in-one monitoring, umbrella monitoring. In their review of existing foreign and domestic commercial solutions, the authors focus on the use of big data methods in them. Based on the review, a classification of such products is proposed, which makes it possible to streamline the existing diversity and select the most suitable system for the tasks facing the organization in the field of monitoring an increasingly complex IT infrastructure. The relevance of the study is due to the lack of classification of the objects under study due to their relative novelty and pronounced practical nature.

Keywords: monitoring system, IT infrastructure, observability platform, AIOps, big data, machine learning

The work is aimed at developing and testing an algorithm for choosing the location of a new cargo storage warehouse, taking into account stochastic flows of cargo supplies to the warehouse and to consumers from the warehouse. When choosing a warehouse location, the costs that accompany the activities of a logistics company related to the organization of warehousing in the selected location, with the maintenance of the warehouse, storage of cargo, delivery of cargo from suppliers to the warehouse and from the warehouse to consumers are taken into account. The paper proposes an algorithm for solving the problem of choosing the location of a cargo storage warehouse, taking into account the forecast of the dynamics of cargo deliveries to the warehouse and to consumers from the warehouse. A mathematical toolkit is described that allows estimating the dynamics of costs for the organization and operation of a warehouse in conditions of non-stationary flows of incoming and outgoing cargo from a warehouse based on the application of the statistical modeling method. The approbation was carried out. The proposed toolkit has a novelty in terms of accounting for non-stationary flows of incoming and outgoing cargo to the warehouse and real transport routes when choosing the location of the warehouse.

Keywords: warehouse location, dynamics of warehouse costs, statistical modeling, mathematical model, logistics

In the modern world, it is increasingly necessary to process geographical information in a variety of forms. This paper discusses the concept of «tile», its purpose, features, as well as the process of retiling, which is a method of creating and updating tiles. This technology helps to increase the efficiency of modern cartographic services, reducing the loading time of maps. The main stages of the development of a microservice implementing the retiling logic are presented sequentially. The main data provider is the OpenStreetMap (OSM) open source project. The spatial data set is a core OSM product and contains up-to-date geographic data and information from around the world. The technology stack is based on the Python language, to which specialized modules for working with tiles are added, as well as a library for implementing a simple and high-quality API.

Keywords: Python, tile, retiling, OpenStreetMap, microservice, Flask-RESTX, mercantile

To date, the penitentiary system of the Russian Federation has collected quite extensive databases for the production sector. The collected data is a time series. However, when studying the mutual distributions of parameters, a number of problems arise, the main one of which is that a different data accounting system is maintained for different parameters: in some cases, data accounting is cumulative throughout the year, in other cases, actual values are taken into account (in other words, some time series are trending, while others are seasonal (cyclical)). Data accounting periods also differ: monthly, quarterly, or per year. Thus, at first glance, the related parameters are almost impossible to compare. The paper proposes a number of algorithms that would solve this problem. The aim of the work was to develop new algorithms that allow comparing trend and seasonal time series using the example of the industrial sector of the penitentiary system. The objectives of the study can be designated as: classification of parameters that are taken into account as seasonal and as trend time series; development of algorithms for their comparison; study of the applicability of the results obtained.

Keywords: algorithm, data processing, python, time series, penitentiary system, manufacturing sector.

The article discusses one of the possible ways to transfer (migrate) variable services from proprietary to an available free and popular solution, as well as ways to improve the structure and eliminate problem areas.

Keywords: variables, configuration, service, Octopus, Git, Vault, migration

The article presents the state of technology of websites for designing robots with artificial intelligence. The image of a modern technical site-book as a place for the development of artificial intelligence applications is considered, the possibility of executing algorithms from the page to ensure the connection of robots with real and virtual objects is shown.

Keywords: mathematical network, technical website-book, artificial intelligence, algorithms executed on the website-book, network development of robots

The article is devoted to the development of a mathematical model and a software package designed to automate scientific research in the field of financial industry news analysis. The authors propose an approach based on the use of graph theory methods to identify the most significant scientific hypotheses, the methods used, as well as the obtained qualitative and quantitative results of the scientific community in this field. The proposed model and software package make it possible to automate the process of scientific research, which contributes to a more effective analysis of it. The research results can be useful both for professional participants in financial markets and for the academic community, since the identification of the most cited and fundamental works serves as the starting point of any scientific work.

Keywords: software package, modeling, graph theory, news streams, Russian stock market, stocks, citation graph

The quality of training of incompletely connected neural networks based on decision's roots is discussed. Using the example of limited data on patients with clinically diagnosed Alzheimer's disease and conditionally healthy patients, a decision's root and the corresponding neural network structure are found by preprocessing the data. The results of training an incompletely connected artificial neural network of this type are demonstrated for the first time. The results of training of this type of neural network allowed us to find a neural network with an acceptable level of accuracy for the practical application of the obtained neural network to support medical decision making - in the considered example for the diagnosis of Alzheimer's disease.

Keywords: neural networks, complex assessment mechanisms; decision roots, criteria trees, convolution matrices, data preprocessing

The features of the (m,m) implementation scheme of visual cryptography are considered, which differs from the existing ones by the formation of shadow images (shares) of an image containing a secret. The proposed approach is based not on the decomposition of the secret image into shares, but on their step-by-step transformation by multiplication by orthogonal Hadamard matrices. The images obtained during each transformation of the stock are noise-resistant in the data transmission channel.

Keywords: image with a secret, image decomposition, image transformation, orthogonal Hadamard matrices, two-way matrix multiplication, noise-resistant image encoding

The paper presents a brief overview of publications describing the experience of using mathematical modeling methods to solve various problems. A multivariate piecewise linear regression model of a steel company was built using the continuous form of the maximum consistency method. To assess the adequacy of the model, the following criteria were used: average relative error of approximation, continuous criterion of consistency of behavior, sum of modules of approximation errors. It is concluded that the resulting model has sufficient accuracy and can be used for forecasting.

Keywords: mathematical modeling, piecewise linear regression, least modulus method, continuous form of maximum consistency method, steel company

Fifth-generation networks are of great interest for various studies. One of the most important and relevant technologies for efficient use of resources in fifth-generation networks is Network Slicing technology. The main purpose of the work is to simulate the probabilistic characteristics of blocking requests for access to wireless network radio resources. The main task is to analyze one of the options for implementing a two–service model of a wireless network radio access scheme with two slices and BG traffic. In the course of the work, the dependence of the probability of blocking a request depending on the intensity of receipt of applications of various types was considered. It turned out that the probability of blocking a type i application has the form of an exponential function. According to the results of the analysis, request blocking occurs predictably, taking into account the nature of incoming traffic. Previously, there are no significant drawbacks in the considered model. The developed model is of great interest for future, deeper and long-term research, for example, using simulation modeling, with the choice of optimal network parameters.

Keywords: queuing system, 5G, two - service queuing system, resource allocation, Network Slicing, elastic traffic, minimum guaranteed bitrate

One of the directions that makes it possible to increase the efficiency of low-orbit satellite Internet in conditions of destructive influences is the use of OFDM systems that support the frequency hopping mode. It is obvious that the effectiveness of countering interference generated by electronic warfare (EW) is largely determined by the algorithm for selecting operating frequencies. In this paper, it is proposed to implement a block of SSF based on the SPN cipher "Grasshopper", which provides high resistance to the selection of the operating frequency by the SREB. However, in the event of failures and failures in the operation of such a unit, the transmitter and receiver operating in the microwave mode will not be able to establish information transmission. To solve this problem, the article proposes to use polynomial modular residue class codes (PMCCS). However, the analysis of the well-known error correction algorithms in PMCS has shown that they cannot be used to increase the reliability of the SPN-based CCF unit.

Keywords: Keywords: OFDM systems supporting frequency hopping, pseudorandom number generation methods, Grasshopper SPN cipher, polynomial modular residue class codes, error correction algorithm

In the context of rapid urbanization of society, modeling the processes of sustainable urban development has attracted considerable attention from scientists. This paper presents a study of fuzzy cognitive maps (FCMs) as an interdisciplinary model for simulating urban development processes. This highlights the versatility of FCM in integrating expertise and quantifying the impact of indicators that shape urban space, from infrastructure and housing to environmental sustainability and community well-being. The study uses a synthesis of an extensive literature review and expert opinions to create and refine a cognitive map tailored for municipal development. The methodology outlined formulates a systematic approach to selecting concepts, assigning weights, and validating the model. Through collaboration with cross-disciplinary experts, the study confirms the value of FCM for identifying cascading effects in the decision-making process when shaping urban development strategies. Recognizing the limitations of expert methods and the fuzzy nature of data, the article argues for the effectiveness of FCM in not only identifying but also addressing emerging urbanization problems. Ultimately, this article contributes a nuanced perspective to strategic planning discourse by advocating for the use of NCC as a management decision support tool that can assist policymakers in achieving a sustainable and equitable urban future.

Keywords: fuzzy cognitive maps, urban development, urban planning, sustainable urbanization, expert systems, social well-being

Stepper motors are often used in automated laser cutting systems. The control circuit of a stepper motor requires a special electronic device - a driver, which receives logical signals as input and changes the current in the motor windings to provide motion parameters. This research study evaluated stepper motor drivers to determine the feasibility of their use - PLDS880, OSM-42RA, OSM-88RA. To control the system, software code was written, which was connected to the controller via a link board. With each driver, in different modes, optimal parameters were selected (initial speed, final speed and acceleration), that is, the movement of the carriage without stalling for ten passes with a minimum travel time. The results of the experiments are presented in the form of tables.

Keywords: laser, laser cutting, automation, technological process, stepper motor, performance, driver, controller, control circuit, optimal parameters

In this paper, we reviewed and analyzed various time series forecasting models using data collected from IoT mobile devices. The main attention is paid to models describing the behavior of traffic in telecommunication systems. Forecasting methods such as exponential smoothing, linear regression, autoregressive integrated moving average (ARIMA), and N-BEATS, which uses fully connected neural network layers to forecast univariate time series, are covered. The article briefly describes the features of each model, examines the process of their training, and conducts a comparative analysis of the quality of training. Based on data analysis, it was noted that for the UDP protocol, the ARIMA model has the best learning quality, for the TCP protocol - linear regression, and for the HTTPS protocol - ARIMA.

Keywords: telecommunication systems, traffic analysis, forecasting models, QoS, artificial intelligence, linear regression, ARIMA, Theta, N-BEATS

This article explores the introduction and implementation of neural network models in the field of agriculture, with an emphasis on their use in smart greenhouses. Smart greenhouses are innovative systems for controlling the microclimate and other factors affecting plant growth. Using neural networks trained on data on soil moisture, temperature, illumination and other parameters, it is possible to predict future indicators with high accuracy. The article discusses the stages of data collection and preparation, the learning process of neural networks, as well as the practical implementation of this approach. The results of the study highlight the prospects for the introduction of neural networks in the agricultural sector and their important role in optimizing plant growth processes and increasing the productivity of agricultural enterprises.

Keywords: neural network, predicting indicators, smart greenhouse, artificial intelligence, data modeling, microclimate

The article is dedicated to analyzing methods for processing experimental data in the field of improving manufacturing technologies, with a focus on the processing of titanium alloys. In the modern context of natural science experiments, characterized by large volumes of information, there is a need to apply mathematical methods and computational systems for effective data analysis. Research conducted at the Department of "High-Performance Processing Technologies" at MSTU "Stankin" aims to identify the optimal type of wear-resistant coating for cutting tools when processing titanium plates. Titanium, with its low thermal conductivity and high chemical reactivity, presents certain challenges during processing, necessitating a thorough investigation of the relationships between cutting conditions and process parameters. The article discusses existing problems related to the insufficient study of these relationships and proposes a solution for data storage and processing through the creation of a unified database. This will enhance the visualization and analysis of experimental results, thereby improving the efficiency of research and the quality of manufactured products. The results of this work may be beneficial for both scientific research and practical applications in industry.

Keywords: technological process research, cutting tool, cutting mode characteristics, systems analysis

The study is devoted to the development of models, algorithms and software for computer training complexes (CTC) for training developers of automated information systems (AIS). The process of automated control of students' knowledge and skills using CTC in studying the mathematical support of AIS (using fuzzy modeling as an example) is formalized based on IDEF0 diagrams, and the process of assessing exercise performance as one of the control components. The advantage of CTC is that the teacher does not need to develop individual exercise options, since CTC configures the structure and complexity of the exercise and then automatically generates a unique version of the exercise for each student undergoing knowledge testing on the topic being studied. The student's performance is checked automatically by comparing the mathematical models of the student's solution to the task and the reference solution generated in CTC based on the problem statement. Algorithms for assessing task performance in fuzzy modeling exercises have been developed. A prototype of CTC has been created in the form of a web system with personal accounts for the teacher and the student. The developed concept and algorithms for monitoring knowledge and skills in fuzzy modeling using the CTC can be adapted for various disciplines in the field of mathematical, software, information and other types of support for AIS.

Keywords: automated information systems, mathematical support, fuzzy modeling, computer training complex, e-learning, distance learning

The paper presents a refined regression model of water level dynamics in the Siberian river Iya, which includes six natural factors on the right side (the number of days with precipitation in the Sayan Mountains, average day and night temperatures for the month, the amount of precipitation, snow depth, average atmospheric pressure for the month ) taking into account the delay, as well as a specially generated seasonal variable. The high adequacy of the model is indicated by the values ​​of the criteria of multiple determination, Fisher, and the average relative error of approximation. The constructed model can be effectively used to solve a wide range of forecasting problems.

Keywords: regression model, river water level, lag time, seasonal variable, forecast

This paper examines and compares two neural networks, U-Net-Attention and SegGPT, which use different attention mechanisms to find relationships between different parts of the input and output data. The U-Net-Attention architecture is a dual-layer attention U-Net neural network, an efficient neural network for image segmentation. It has an encoder and decoder, combined connections between layers and connections that pass through hidden layers, which allows information about the local properties of feature maps to be conveyed. To improve the quality of segmentation, the original U-Net architecture includes an attention layer, which helps to enhance the search for the image features we need. The SegGPT model is based on the Visual Transformers architecture and also uses an attention mechanism. Both models focus attention on important aspects of a problem and can be effective in solving a variety of problems. In this work, we compared their work on segmenting cracks in road surface images to further classify the condition of the road surface as a whole. An analysis and conclusions are also made about the possibilities of using architectural transformers to solve a wide range of problems.

Keywords: machine learning, Transformer neural networks, U-Net-Attention, SegGPT, roadway condition analysis, computer vision

The article is devoted to the problems of managing the implementation of multi-scenario, multi-stage projects under conditions of uncertainty. The proposed approach is based on representing the project model in the form of a scenario network. The developed fuzzy linguistic model of a project stage is a set of linguistic variables corresponding to the stage indicators and external factors influencing the subsequent implementation of the project. The decisive rules for choosing the arc of transition to the next stage are constructed in the form of fuzzy products, the left parts of which are fuzzy statements regarding the preference of possible options. The constructed decision support procedure is based on the use of the Mamdani fuzzy inference algorithm, which has high interpretability. The proposed approach allows for multi-scenario planning and adaptability of management of the implementation of multi-stage projects.

Keywords: multi-scenario multi-stage projects, adaptive project management, scenario network, decision support, linguistic variable, fuzzy inference

The article is the result of an analytical study of the development of structures of medium and small businesses in the engineering implementation of the stages of survey, preparation in the production of building materials, semi-finished products, sections of projects, as well as participants in the commissioning of facilities for 2012-2022. During this period, the number of small and medium enterprises in the territory of the Russian Federation increased by 224 thousand units. In the Central Federal District (which includes the Tula Region), the increase was 31.8%. At the same time, their growth in construction amounted to 6.39%. However, the trend has changed from 2019 to 2022. the number of entrepreneurs significantly decreased by 457 thousand. In this regard, the authors in their studies solved the problem of analyzing the state, dynamics of changes in the number and content of the activities of structures of medium and small businesses in construction; developing proposals to improve development efficiency. The main attention is paid to specialization, the reasons for curbing the growth of business services and the economic results of their work.

Keywords: business planning, specialization, planning, project management, building complex