The journal Regional interuniversity compendium of scientific works "System technologies" was established in January 1997, is published 6 times per year. According to the order of the Ministry of Education and Science of Ukraine No. 1643 dated December 28, 2019, the regional intercollegiate collection of scientific works "System Technologies" is included in the List of scientific professional publications of Ukraine, category "Б". Knowledge area is technical sciences.
The National metallurgical academy of Ukraine
ADVANCED DAMAGE PLASTICITY MODELLING AND CALIBRATION STRATEGIES FOR ACCURATE FINITE ELEMENT ANALYSIS OF UNREINFORCED CONCRETE IN THIN WALLED STRUCTURES
Accurate prediction of unconventional, unreinforced concretes in three‑dimensional finite‑element analysis demands a synthesis of advanced constitutive theory, rigorous experimental calibration, and careful numerical implementation. This study consolidates recent progress and remaining challenges in modeling slag‑blended, recycled‑aggregate, fiber‑reinforced, and ultra‑thin formwork concretes within the ANSYS environment. A literature survey identifies three dominant strategies for plain concrete: the legacy smeared‑crack SOLID65 element, generalized Drucker–Prager plasticity with user‑defined damage, and detailed mesoscale representations that resolve aggregates, mortar, and interfaces. Comparative findings show that damage‑plasticity formulations, exemplified by the Concrete Damaged Plasticity (CDP) model, reproduce load–deflection responses and crack patterns within fifteen percent of experimental results when parameters are calibrated against comprehensive test sets that include compression, tension, fracture, and time‑dependent data. Calibration protocols remain inconsistent across studies, hindering re-producibility and cross‑comparison. The absence of an open benchmark database for non‑standard concretes is highlighted as a key barrier to consensus on default parameters. Thin‑walled elements expose additional difficulties: geometric nonlinearity couples with pro-gressive stiffness degradation, causing mesh‑dependent fracture energy dissipation and solver convergence issues. Remedies include refined through‑thickness meshes, nonlocal regulariza-tion, and robust arc‑length solution controls. Explicit crack‑tracking techniques such as phase‑field fracture and cohesive segments offer improved fidelity, especially for fiber‑rich mixes where residual tensile capacity governs serviceability, yet systematic validation of these methods remains sparse. Long‑term phenomena such as creep, shrinkage, and durability, along with high‑rate behaviors under impact and seismic loading, are underrepresented in current model verification, particularly for slag‑rich and recycled‑aggregate mixes. A practi-cal roadmap is proposed that integrates five core actions: creation of a public benchmark da-tabase with fully documented laboratory tests; development of unified modeling protocols that specify calibration sequences, error metrics, and reporting formats; targeted investment in explicit fracture models for thin and fiber‑reinforced members; expansion of long‑term and dynamic experimental programs; and adoption of machine‑learning tools to automate pa-rameter identification and flag anomalous model behavior. Complementary software ad-vances, including plug‑and‑play material subroutines and graphical calibration wizards, are recommended to lower the expertise threshold for practicing engineers. Collectively, these measures chart a pathway from current academic advances toward robust, industry‑ready simulations capable of guiding the design of sustainable, reinforcement‑free concrete struc-tures.
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Information technology for decision support in the field of video surveillance
The article explores the concept of developing a modern information technology system designed to provide effective decision support in the field of video surveillance. Given the in-creasing volume of video data generated by monitoring systems, especially in urban environ-ments, there is a growing need for high-performance solutions capable of operating in real time. The focus is placed on analyzing technological approaches to video stream processing using artificial intelligence, machine learning methods, edge computing, and cloud-based so-lutions. The study presents an architectural model of a multi-level information system that includes modules for data collection, preprocessing, analytics, and decision-making. Special attention is given to event detection algorithms and the classification of behavioral patterns, which helps reduce dependence on human intervention during surveillance analysis. The paper also examines aspects of integrating such systems with other information-analytical platforms, including biometric databases, geographic information systems, and ac-cess control systems. The advantages of edge architectures are substantiated, as they reduce video processing latency, enhance system autonomy, and lower the load on central infrastruc-ture. The research findings can be applied to the development of next-generation intelligent security systems, particularly within the framework of the Smart City concept. The proposed approach can be adapted to meet the needs of local authorities, law enforcement agencies, and private sector entities involved in the protection of critical infrastructure. The results demonstrate the practical value of implementing advanced IT solutions in the domains of se-curity and monitoring.
Optimization of hydrotransport system parameters taking into account reliability criteria
The article explores the problem of improving the reliability and efficiency of hydrotransport systems, which play a crucial role in many industrial processes involving the movement of large volumes of solid-liquid mixtures. These systems operate under complex conditions, being exposed to intensive hydroabrasive wear and variable loads. Recent studies highlight the need to move beyond traditional deterministic models that estimate average wear, as they fail to capture the stochastic nature of degradation under real operating conditions. Instead, probabilistic models based on the theory of stochastic processes and Markov chains have proven effective in predicting the reliability and remaining service life of pipelines and slurry pumps. The aim of the study is to enhance the operational reliability of hydrotransport systems by developing mathematical and optimization models that consider the randomness of wear and performance parameters. Particular attention is given to selecting rational configurations of pump stations and determining system layouts that minimize risks of failure. The methodology integrates monitoring of hydraulic parameters, statistical analysis of wear data, and modeling of component failures. The research results demonstrate that modeling hydroabrasive wear as a stochastic process with independent increments enables more accurate prediction of service life and failure probability. The paper presents analytical expressions for key reliability indicators and illustrates how different system configurations—single-stage, multi-stage, with or without redundancy—impact overall system availability. Optimization techniques, including nonlinear programming and simulation, are applied to determine ideal pump placement and sump volume. The proposed approach offers practical recommendations for maintenance scheduling, spare part management, and cost-effective system design. It is concluded that accounting for stochastic factors significantly enhances system resilience and economic performance in long-term operation.
Experimental studies of a combined impulse-vibration installation
The article presents the results of experimental studies on the compaction process of polystyrene concrete mixture using a specially developed combined impulse-vibration installation. The relevance of the research is due to the need to increase the density and compressive strength of lightweight concrete products by improving the compaction technology. The aim of the study is to determine the effective compaction parameters (gap between frames, mass of the loading device, and duration of action) and to evaluate the influence of horizontal vibrations on the strength of the products. The research methods included experimental design, physical modeling, and analysis of results using response surface methodology. It was found that the use of combined loading allows for an increase in compressive strength by 5–7% compared to traditional impulse compaction. The obtained results can be used to optimize technological parameters for the production of wall elements made of polystyrene concrete.
ANALYSIS OF SCIENTIFIC AND PRACTICAL ACHIEVEMENTS IN THE SPHERE OF REVERBERATION EFFECTS GENERATION
The study addresses the problem of creating realistic reverberation sound effects in computer games. The primary features of existing reverberation generation methods are outlined, including the complexity of manual sound parameter adjustments, the static nature of models, and limited adaptability to the geometry of the game environment. It was determined that the problem of creating realistic reverberation effects for computer games is achievable only when using modern approaches that reduce manual tuning and increase the adaptability of sound parameters. Based on the analysis, it was determined that there are significant obstacles that reduce the realism of acoustic accompaniment using available methods.
Optimal Mn/Si parameters for adjusting the mechanical properties of steel for railway axles
The work presented a comprehensive approach to finding the optimal steel composition within known grades based on the concept of directional chemical bonding. This approach made it possible to take into account the overall chemical composition and to analyze the na-ture of interactions between elements at the level of interatomic bonds, which is critical for predicting the properties of the final material. The concept of directed chemical bonding was based on the idea of the ability of individual elements to undergo electronic redistribution in the melt, which in turn affects the structure and stability of the system. The aim of the study was to determine the effect of variations in the content of the main matrix elements — carbon, silicon, and manganese - on the charge state of the system, represented by the generalized parameter ZY. This parameter reflects the degree of chemical balance in the alloy and serves as an indicator of the stability of the melt’s electronic structure. As a result, the sensitivity of the system to the Mn/Si ratio was investigated as an indicator defining electronic and struc-tural equilibrium. Thus, to enhance the ZY parameter, whose increase leads to improved hardness, it is necessary to prioritize manganese content after the Mn/Si ratio reaches 2.8–3.2, while below this interval — through an increase in silicon content. The manganese-to-silicon ratio (Mn/Si) should be within the range of 2.8–3.2. An increase in Mn/Si beyond 3.2 causes manganese to dominate, which may disrupt the balance. Therefore, controlling the content of alloying elements, especially silicon and manganese, is a key condition for produc-ing steels with predictable properties. The results of the modeling emphasize the feasibility of using computational approaches in materials science. Predictive modeling allows for the ef-fective adaptation of steel structure to the specified performance characteristics already at the stage of chemical composition design. This contributes to reducing experimental devel-opment costs and shortening the time to bring new alloys into production.
ADAPTATION OF A PREDICATE MODEL IN CONTROL PROBLEMS OF NONSTATIONARY STATIC OBJECTS
When controlling static objects at the optimization level, pattern recognition methods are used that allow partitioning the factor space into elementary subdomains in the form of n-dimensional hyperparallelepipeds. One of the main elements of the control structure for this approach to control is the adaptation algorithm, that makes it possible to refine the de-scription of a static object under nonstationarity conditions. Repeated use of the adaptation algorithm to refine the model leads to an unjustified complication of its logical structure and the accumulation of information that has lost its relevance. The paper proposes a method for minimizing the description of images of technological situations, that makes it possible to overcome the indicated disadvantages. The method is based on the property of invariance of the number of parameters defining the hyperparallelepiped to the size of the described area in the factor space. This made it possible to identify significant boundary sub-areas in the de-scription of the image of technological situations and, by their subsequent combination in the direction of the feature axes, to select a description with a minimum number of sub-areas. When performing these operations, “outdated” information is removed and the logical struc-ture of a static control object is simplified as much as possible. The paper shows the possibil-ity of implementing an algorithm for minimizing the description of images on the basis of α-algebra, that makes it possible to integrate its control structures using relational data models. The effectiveness of the proposed algorithm is confirmed by computational experiments in the control of the process of lump crushing for the conditions of a mining and processing plant.
method for minimizing the description of images
Analysis of automated mapping techniques for changes in water bodies
InInland water resources are vital components of the water system for every country, and their supply is strategic. Growing demand for water and climate change are exacerbating water management problems in arid and semi-arid regions, which experience water shortages due to low and irregular rainfall and high evapotranspiration. Monitoring inland waters is of strategic importance for a country's development. Assessing the ecological status, volume and area of water bodies is key to analysing and forecasting the water balance and spatial distri-bution among regions. Monitoring water bodies is particularly relevant in countries most af-fected by rapid climate change. As a result of climate change in Ukraine, there is a negative process of deterioration of natural moisture conditions, as a result of which areas of exces-sive moisture are disappearing in the Polissya zone, and the process of desertification has begun in the south. Ground-based measurements to assess changes in the area of water bod-ies are an expensive and resource-intensive approach, which is particularly difficult in re-mote areas. The article discusses the main methods of automated mapping of changes in wa-ter bodies using remote sensing data. The use of heterogeneous data in combination with a combination of methods based on spectral indices and machine learning demonstrates the best results. The article discusses automated mapping methods using remote monitoring and the applicability of these methods for analysing water resources in Ukraine. The main classi-cal methods are based on the use of spectral indices, machine learning and image threshold-ing. The use of heterogeneous data in combination with a combination of methods demon-strates the best results. It should be noted that such methods do not allow obtaining an accu-rate threshold value for relatively small water bodies. The method of mapping surface waters using radar images and the method of selecting the optimal threshold, the reference water mask, showed the best results.
Implementation and research of methods for automation human resource planning
The development of business process automation, along with the increasing complexity of human resource management, has led to the need to create effective planning systems. The article examines the process of automating personnel planning and budgeting, which allows managers to review and approve annual plans, and line managers to control their organiza-tion, identify staff shortages and plan staff recruitment. In addition, the system creates infor-mation panels for managers, which allows you to track the achievement of corporate goals. Project management is relevant both for increasing the efficiency of teams and for op-timizing the use of resources. In addition to many types of commercial use, project manage-ment systems are also of interest in scientific research and educational programs, where they help organize work on complex projects and ensure the achievement of set goals. Solving the problem of project management and human resource planning uses the achievements and stimulates the development of many branches of technology and manage-ment. The practical value of the study is that it helps to understand the key aspects of project management and human resource planning in IT companies, which contributes to increasing the efficiency and productivity of organizations. When analyzing human resource planning methods, the effectiveness of various human resource planning methods in IT companies was investigated, software was implemented to automate planning and resource allocation, the impact of resource planning on team produc-tivity was assessed, and human resource use was optimized. The implementation of the developed project management system and human resource planning methods leads to increased team efficiency and improved project results. It is rec-ommended to apply the developed methods and tools to increase the competitiveness of IT companies.
DETECTING FLAT ROOF DEFECTS WITH MACHINE LEARNING AND DEEP LEARNING TECHNIQUES
Deep learning has emerged as a transformative approach for detecting structural damage and deformations, particularly for flat roofs and large-scale infrastructure. This article synthesizes recent progress in applying convolutional neural networks (CNNs), segmentation models, object detectors (YOLO, Faster R-CNN), and autoencoders for unsupervised anomaly detection. Drones (UAVs), thermal imaging, and vibration sensing all contribute critical data. By training on images or signals indicative of healthy vs. damaged conditions, deep models can locate cracks, spalling, missing fasteners, or stiffness loss at high speed and with impressive accuracy - often above 85%. A review of more than 300 publications indicates that remote inspection with AI can drastically reduce manual labor and improve the consistency of damage identification, even in hazardous or inaccessible areas. A summary table compares deep learning effectiveness across beams, walls, decks, roofs, and other structural compo-nents. Real-world deployments on bridges, high-rise facades, and post-disaster zones confirm that deep learning, coupled with UAV-based inspections, can accelerate maintenance work-flows, detect subtle defects, and reduce safety risks. Ongoing challenges include data scarcity for rare failure modes, generalizing models to new environments, and the integration of physics-based reasoning. Recommendations for future research involve fusing multispectral data, automating calibration of deep models, and embedding AI in digital twins for continuous structural health monitoring.
Research of the cutting process of rolled section steel on rail-beam and large-size rolling mills
The aim of the work is to study the conditions of cutting the last bar of the measured length and the remaining rolled product under conditions of unstable rolling length, which are realized as a result of fluctuations in the sizes of the blanks and the temperature regime of rolling. The roll is cut into bars of the same measured length according to the order, but fluc-tuations in the length of the roll result in the last bar being either longer or shorter. If the bar is increased, then the amount of the remainder may be too large and, in order not to lose suitable rolled product and not to transfer the remainder to waste, the last bar and the remainder are cut into two bars of normal length. The work shows that the amount of the remainder, starting from which it is necessary to cut into two bars of normal length, is determined by economic feasibility, which is more ex-pensive – a bar of measured length and a remainder or two bars of normal length. An expression is given for obtaining the residual value depending on the size of the bar of the measured length, the cost coefficients of the bars of the normal length and waste. For the conditions of rolling the channel 24 on the mill 800, the remainder value is 0.86 m. It is shown that to ensure maximum profit depending on the remainder value, the last bar of the measured length can be cut in the following ways: into a bar of the measured length and the remainder, into two bars of the normal length or into a bar of the measured length and a bar of the normal length.
Development of an automated authorization center system
Software has been developed that performs the functions of forming and monitoring us-er authorization. Ensuring the reliability and convenience of the authorization monitoring process is implemented in the work due to the designed structure and architecture of the sys-tem, the use of API service, the implementation of functionality for registering new users, in-cluding the storage of their personal data and information for authorization; development of a mechanism for verifying the authenticity of users during authorization using secure proto-cols and data encryption. An effective model of storing and manipulating roles in the subsys-tems of the automated system of the authorization center based on the mechanisms of logging and monitoring user activity is proposed, which allows identifying potential security threats.
RESEARCH OF TIRE WEAR ON PORT CONTAINER SEMI-TRAILERS
Seaports play a crucial role in supporting global trade and logistics. They act as key hubs connecting various nations and regions. As integral components of the global supply chain, ports function as entry and exit points for goods, facilitating seamless transitions between different modes of transportation. In the rapidly evolving landscape of international trade, port semi-trailers hold a central position in facilitating the smooth flow of goods. These specialized vehicles are designed to efficiently transport containers between ports, terminals, and inland logistics centers. They combine a durable design, high load capacity, and advanced technology, ensuring reliability and safety in the most challenging operating conditions. The purpose of the study was to assess the degree of damage and to study the patterns of intensity and nature of tread wear on the tires of port semi-trailers carrying containers. 24 identical container semi-trailers were inspected, which are operated in 4 Black Sea ports. The mileage of the trailers varies slightly, and they are used to transport 40-pound containers. In the course of research, the residual depth of the tire tread was measured and compared with previous values, which allowed to establish the dynamics of wear under operating conditions. An analysis of vehicle inspections and logs has shown that the main causes of failure are tire tread wear, local destruction of tread pattern elements, damage to the breaker and frame due to external influences. A special tire depth gauge was used to measure tread wear. Tire wear is not proportional to mileage, but has a more complex nonlinear relationship. This often poses a direct threat to road safety. A detailed analysis showed that the wear on the trailer's tire treads was caused by the following factors: incorrect tire pressure, regular overloading, and improper wheel balancing. Optimizing the tire wear of a container truck trailer requires a comprehensive program that includes the following elements: selecting high-performance tires, performing regular maintenance, monitoring tire pressure, ensuring even load distribution, and improving driver training.
Testing the homogeneity of pseudorandom sample by the anderson and Bush-Wind criteria
The study of statistical homogeneity of samples is an important task in mathematical statistics, since it allows us to assess the reliability of experimental measurements and deter-mine whether they belong to the same general population. This article considers two ap-proaches to checking statistical homogeneity of samples: the two-sample Anderson criterion and the combined Bush-Wind criterion. Each of these criteria has its own characteristics, scope and effectiveness depending on the sample size and probability distribution being ana-lyzed. The purpose of the study: to assess the effectiveness of two approaches to checking sta-tistical homogeneity of samples: the two-sample Anderson criterion and the combined Bush-Wind criterion, as well as to determine the influence of sample size and statistical parameters of pseudo-random variables, and to identify the disadvantages and advantages of using these criteria. Research methods: the study considers the criteria for checking the homogeneity of pseudorandom variables: the two-sample Anderson criterion and the combined Bush-Wind criterion, which are used to determine the homogeneity of pseudorandom samples under con-ditions of unknown statistical parameters (probability distribution, mathematical expectation, variance. Scientific novelty of the study: it was determined that, if the samples under study are n≤100, then the Anderson criterion is more reliable for the analysis of pseudorandom vari-ables with unknown statistical parameters, since it demonstrates higher resistance to anoma-lies, compared to the combined Bush-Wind criterion. It was determined that when studying small samples (n=20÷40) both criteria show a high probability of making the correct decision when analyzing pseudorandom variables for homogeneity with different statistical parame-ters. Practical novelty of the study: for the analysis of random variables with unknown statis-tical parameters for short samples, it is proposed to use the two-sample Anderson criterion and the combined criterion Bush-Wind, but to increase the reliability of the results in the study, in conditions of contamination, it is advisable to combine statistical criteria with data cleaning methods or use robust analogues. Areas of application: the study can be used in ar-eas where it is necessary to assess the statistical homogeneity of samples, in experimental sci-ences, sociology, medicine and engineering, to assess the quality of measurements and iden-tify possible deviations in the data.
Numerical simulation of the interaction of a shock wave with a supersonic laminar boundary layer in the presence of heat and mass exchange with the surface
The results of parametric numerical experiments on the influence of heat and mass transfer on flow separation during the interaction of an oblique shock wave with a laminar boundary layer are presented. An implicit finite-volume algorithm for solving the Navier-Stokes equations for arbitrary coordinates, based on the Roe scheme for convective terms, is implemented. The second order of accuracy in space was provided by using the symmetric Jamecon flux limiter. The numerical algorithm was verified on the problems of transonic and supersonic laminar and turbulent flows when compared with experimental data. The study was carried out with the aim of comparative analysis of the possibilities of the flow separation control using heat and mass transfer with a streamlined surface under the same conditions of the shock wave/boundary layer interaction. Spatial pressure distributions, pressure and skin friction coefficient distributions along the plate, profiles of density, dynamic viscosity coefficient, longitudinal velocity components and momentum in the laminar boundary layer are presented. The main physical factors de-termining changes in the structure of the separation interaction under heat and mass transfer conditions were identified. The effect of heat transfer with the plate was manifested primarily in changes in the profiles of density and dynamic viscosity coefficient, which significantly de-pend on temperature. Removal of slowed particles from the boundary layer leads to a greater filling of the velocity profile by mass transfer with the practically constant value of the dy-namic viscosity coefficient on the surface. Based on the analysis of the results obtained, it was found that the main factor in pre-venting flow separation is the increase in the longitudinal component of the momentum dur-ing plate cooling by heat transfer and the removal of slowed particles by mass transfer. Therefore, despite the different physical nature of the impact, heat and mass transfer with the surface has a similar resulting effect on the size and structure of the supersonic separation zone. Using heat and mass transfer it is possible to prevent the occurrence of a separation zone in two-dimensional supersonic flows.
Numerical analysis of aerodynamic characteristicsof non-circular cones in supersonic flow
Numerical modeling of viscous gas flows around non-axisymmetric lifting bodies streamlined at an angle of attack is one of the urgent problems of modern aerodynamics. Such bodies have high aerodynamic quality and are promising in the creation of maneuverable aircraft. The paper considers the problem of supersonic flow of a viscous gas around a sharp cone with an arbitrary smooth cross-section. It was assumed that the effect of viscosity is con-centrated in a thin layer near the solid surface, and the entire flow region between the body surface and the shock wave can be divided into an inviscid flow and a boundary layer. The flow regimes considered were when the shock wave formed in front of the cone is attached to its top and the inviscid flow around it is conical. The integration of the system of differential equations of Euler was carried out using a stationary analogue of the Godunov method, using the principle of establishing a solution along the longitudinal coordinate. To solve the boundary layer problem, the Petukhov finite-difference method was used. As a result of solving the specified boundary value problems, the velocity and pressure fields in the boundary layer and in the inviscid flow region, as well as the integral aerodynamic coeffi-cients, were found. Based on the proposed methodology, calculations of aerodynamic coefficients for bi-elliptical cones were performed. The surface shape of these bodies is specified by three geo-metric parameters δ1, δ2, θk (θk is the half-angle of the cone in the plane of the major semi-axis, δ1 and δ2 are the ellipticity coefficients of the lower and upper semi-ellipses). Numerical analysis of aerodynamic characteristics showed that taking into account the influence of the boundary layer leads to a significant increase in drag and a decrease in aer-odynamic quality. The contribution of viscous components to the lift and longitudinal moment is insignificant. The shape of the cone cross-section has a significant effect on the value of its aerodynamic coefficients.
Optimization of sheet paper cutting for the needs of printing production
The article presents the concept, architecture and principles of implementation of the information module for decision support (IM PPR) for the technologist of a printing enter-prise. The module was developed to automate the processes of preparation for printing, selec-tion of optimal cutting parameters, assignment of materials and selection of a technological route. The proposed solution combines mathematical models, production data and heuristic rules from practice, which allows to reduce decision-making time and reduce the probability of errors.
Analysis of existing architectures for the development of an information system of air quality assessment
The paper focuses on the development of information technology for weather modeling and forecasting using modern deep learning methods. The relevance of the topic is driven by growing climate risks, an increase in the frequency of extreme weather events, and the need for highly accurate meteorological analytics for the agricultural sector, transportation, en-ergy, and civil defense systems. FourCastNet, a modern architecture based on deep neural networks and transformers that combines high performance with the ability to process large-scale historical meteorological data, was chosen as the main model for generating forecasts. FourCastNet was trained on data from global atmospheric analysis, which ensured forecast-ing accuracy at the same level with traditional numerical models at a much lower computa-tional load. To form the initial forecasting conditions, data from the GFS global numerical model in combination with the GDAS assimilation system were used. These sources provide weather data with a resolution of approximately 0.25° × 0.25° (~28 km), which made it possible to overcome the limitations of ERA5-type models (about 31 km) and make the system suitable for real-time use. In order to adapt the global forecast to a specific geographic location, a spatial interpolation approach was implemented using a modified UNet architecture. This model re-fines meteorological parameters based on data from local weather stations, thus improving the accuracy of forecasts at specific locations. The article describes in detail the architecture of the information system, which includes modules for processing input data, forecasting models, an interpolation system, and an end-user interface. The proposed solution makes it possible to obtain highly accurate forecasts in an online mode, which is especially important for decision-making in critical industries. Par-ticular attention is paid to the possibility of integrating the system into mobile applications and real-time notification platforms.
Application of artificial intelligence tools for data search and preliminary analysis in applied research of education systems
The article outlines the problems of using popular artificial intelligence tools GROK 3 and ChatCPT 4 for collecting data on educational systems available on the Internet, their pre-processing and analysis. A significant proportion of recent publications is devoted to the development of new artificial intelligence tools, the study of algorithms of its work, methodo-logical and ethical problems of its application in various fields, the impact of artificial intelli-gence in general or of specific tools on academic success and learning outcomes at different levels of education. At the same time, applied research has not paid enough attention to as-sessing the validity, reliability, and relevance of results obtained using artificial intelligence, in particular such tools as GROK 3 and ChatCPT 4. For this study, several problems of educational systems researches, which can be con-sidered as relevant also un broader contexts, were selected. The main attention was paid to the issues of collecting and preliminary statistical analysis of data on the number of students in higher education institutions, funding of higher education, and indicators of youth in-volvement in higher education. It has been shown that both tools significantly speed up and simplify the implementation of routine research stages, but GROK 3 allows to obtain more complete, reliable and high-quality data, which may be due to both the ability y to access current data on the Internet and differences in algorithms. ChatCPT 4 is usually limited to providing a small amount of the requested data present in its database, which is sometimes very outdated. The advantage of GROK 3 is also that it collects not only the requested numerical data from various types of sources, but is also able to independently calculate simple statistical characteristics of sam-ples, such as mean values, medians, standard deviations, minimum and maximum values, from the primary data without additional questions, including the ability to make plausible estimates from incomplete, highly asymmetric samples. Nevertheless, in this case too, the general methodology should provide for verification of the results of artificial intelligence, in particular by comparing them with other known data.
Non-parametric statistics of random variables with unknown probability distribution functions
The paper is devoted to the actual problem of data analysis with unknown distributions, where classical parametric methods are ineffective. On the example of two types of distribu-tions (logistic (symmetric) and exponential (asymmetric)), the application of a nonparametric approach, which consists of the stages of ranking and analysis of extreme values, is shown. Purpose: to conduct a qualitative analysis of the statistical homogeneity of time series by evaluating the main special criteria of samples - shifts and scales, to identify the advantages of such approaches for determining or further verifying the statistical homogeneity of time series samples. Research methods: The paper uses basic methods of nonparametric statistics: nonparametric criteria for statistical homogeneity are applied to simulation models of sam-ples. For samples with logistic and exponential distribution laws, their statistical parameters were found and analyzed and tested for statistical homogeneity. Practical novelty of the study: the approach to testing for statistical homogeneity presented in the paper expands the practical scope of applying nonparametric statistics methods for complex statistical analysis of time series with a distribution law other than normal. Applications: the results of the study can be used in areas requiring the analysis of statistical homogeneity of data, in particular in engineering to monitor the condition of technical objects and systems, in sociological re-search to identify significant differences between groups, in the medical field to control the quality of laboratory measurements.
MACHINE LEARNING METHODS FOR ANTIFRAUD SYSTEMS
Fraud in the financial sector, e-commerce, and online services is becoming increasingly frequent and sophisticated. Traditional rule-based systems, while still helpful in detecting known fraud patterns, struggle to keep up with new, evolving attack vectors, as static rules are quickly circumvented. In contrast, machine learning (ML) provides a dynamic and scalable approach that can process vast amounts of transactional and behavioral data to identify subtle anomalies and suspicious activity. This paper provides a comprehensive overview of current ML techniques used in fraud detec-tion, categorized into three main groups: classification models, anomaly detection methods, and deep learning architectures. It discusses real-world applications across various fraud scenarios, including credit card abuse, account takeovers, cybercrime, and scams in digital comerce. Emphasis is placed on the strengths and limitations of each approach, with attention to real-world considerations like scalability, model transparency, and the challenge of class imbal-ance. The paper also reviews recent advances, including graph-based representations of fi-nancial interactions, IP-based behavioral profiling, and the emergence of hybrid systems that integrate multiple ML techniques – such as combining autoencoders with boosting algorithms for improved accuracy, especially when labeled data is scarce. The findings aim to support the development of flexible, high-performance fraud detection solutions that leverage the most effective ML practices and capitalize on the synergy of hybrid model architectures.
Estimation and optimization of it project completion probability using the pert method
This article explores the application of the PERT (Program Evaluation and Review Technique) method for assessing and optimizing the probability of on-time completion of IT projects under uncertainty. In IT project management, dealing with ambiguous time estimates is a persistent challenge. The PERT method provides a probabilistic approach to modeling project durations by using three time estimates for each activity: optimistic, most likely, and pessimistic. This allows for the calculation of both the expected duration and the variance of each task, as well as for the entire project. Based on these parameters, it becomes possible to estimate the probability of completing the project within a specified deadline by using the properties of the normal distribution. The article offers a formal problem statement for minimizing the risk of project delay. The goal is to increase the likelihood of on-time completion by optimizing critical path activi-ties—either by reducing the expected duration or decreasing the variance (i.e., stabilizing the execution). A computational experiment is conducted using a sample network model of a pro-ject consisting of nine interrelated tasks. The critical path is identified, and the project's com-pletion probability is evaluated for a defined deadline. Subsequently, the parameters of two critical tasks were modified to simulate managerial interventions, such as standardizing processes or reducing complexity. As a result, the overall project duration decreased from 18.17 to 17.17 days, and the probability of meeting the dead-line increased significantly—from 15.78% to 43.34%. The visualization of the normal distri-bution before and after optimization clearly illustrates the improvement in project reliability. The findings confirm the effectiveness of integrating PERT with mathematical modeling and optimization techniques in IT project planning. Even minor adjustments to the task pa-rameters can lead to a substantial reduction in deadline risks. Future work may focus on de-veloping automated tools for critical path adjustment using machine learning or multi-objective optimization methods.