The article considers modern approaches to the organization and functioning of situational centers (SC). Particular attention is paid to the architecture and components of SC, data collection and processing technologies, as well as the use of machine learning and artificial intelligence (AI) in decision support systems. Data collection and processing technologies play a critical role in the operation of situational centers. The article considers modern approaches to data collection, including the use of satellite images and other sensors. Machine learning and AI are becoming an integral part of decision support systems in situational centers. Chatbots and virtual assistants are also becoming important tools in them, providing automation of interaction with users and decision support. Examples of their application for consultations, monitoring, and rapid response to incidents are described. The development of fundamentally new approaches to creating AI systems is one of the central topics of the article. Multi-connected, multidimensional, receptor-effector neural-like growing networks (mmren-GNs) are considered a promising technology superior to traditional machine learning methods. The article describes the unique capabilities of mmren-GN, including adaptability, self-organization, and efficiency of real-time information processing, and discusses the potential benefits of integrating and using mmren-GN in situational centers. The advantages and disadvantages of traditional machine learning and AI methods are analyzed in comparison with mmren-GN. The importance of these benefits for government and military SCs, especially in military confrontation conditions, is emphasized. Advantages and disadvantages of traditional machine learning and AI methods compared to mmren-GN are analyzed. The authors of the article believe that for the final verification of the effectiveness and potential of mmren-GN, it is necessary to create a prototype of an intelligent system similar to a biological brain. In conclusion, the authors emphasize that mmren-GN opens new horizons in the field of intelligent systems, providing powerful tools for processing, analyzing, and classifying information presented in multidimensional dimensions. Figs.: 3. Refs.: 17 titles.
The article analyses modern cybersecurity technologies and systemssuch as next-generation firewalls, micro-segmentation, network access control systems, zero-trust remote access, systems protecting against distributed denial-of-service attacks, systems protecting against portal attacks, secure access brokers to the cloud, malware protection systems, email attack protection system, mobile device management systems, user account and authorization management systems, privileged access management systems, physical access protection systems, data leak protection systems, multi-factor authentication and authorization systems, database protection systems, vulnerability analysis systems, attack simulation systems, cyber decoy systems, log and action collection, correlation, and analysis systems, as well as automation systems for actions during an investigation or incident response. The choice of necessary cybersecurity systems for designing a corporate information and cybersecurity system is determined, considering the corporate IT landscape. Critically important components of this system include network protection solutions such as NGFW, workstation and server protection against malicious software based on EDR/XDR, and email system protection. Highly recommended components also include network access management systems and remote access with zero trust, information leakage protection, and a complex system of authentication and authorization. For organizations with medium or high complexity of IT landscapes, cybersecurity analytics cluster systems are critically important, especially event collection and correlation systems and vulnerability analysis systems. Considering the above, a corporate architecture of information and cybersecurity is proposed, which will ensure a layered system of effective protection against cyber threats.Fig.: 1. Refs.: 8 titles.
The combination of edge computing technologies and the Internet of Things creates the edge Internet of Things.. Internet of Things edge computing represents the architecture of edge computing integrated into the Internet of Things system with the help of an edge (computing) gateway. This gateway, which integrates network and computing storage and application capabilities, is deployed at the network edge near devices or data sources to provide device management and Internet of Things system management services at network edge nodes. Edge artificial intelligence or «artificial intelligence at the edge» refers to the combination of edge computing and artificial intelligence to perform machine-learning tasks directly on interconnected edge devices. One of the trends in the evolution of software related to edge computing is the use of artificial intelligence methods and algorithms both directly for information processing and for adaptive software-controlled deployment of network infrastructure. The advantage of edge computing architecture is security. Many companies are reluctant to send sensitive company data to the cloud and prefer to keep it on-premises, thereby reducing cybersecurity risks. The convergence of information technology and operational technology increases the potential attack area. Therefore, it is important for enterprises to improve their edge computing nodes to protect their data. The rapid development and spread of artificial intelligence technologies is spreading towards edge computing, since artificial intelligence can potentially provide the means to achieve the properties of the «intelligent environment» of the edge-cloud continuum. The article analyzes the use of artificial intelligence in the frontier area of the Internet of Things. As a result of the analysis, the main advantages and problems related to the frontier area of the Internet of Things and approaches to their solution based on the use of artificial intelligence methods were determined. Edge computing and edge artificial intelligence have different applications, but the most important factor is improving the quality of service for Internet of Things devices.Figs.: 5. Refs.: 24 titles.
In December 2019, an outbreak of severe acute respiratory syndrome, now known as SARS-CoV-2, began in Wuhan, China. The virus soon spread around the world, becoming a pandemic. Since the early days of the pandemic, many mathematical models have been proposed to predict the spread of the disease. Since the outbreak, various measures have been introduced to contain and control the spread of the virus, and these measures have been largely based on the results of these models. All the applied models require model parameter refinement to improve forecast accuracy. SEIR-AGE allows for forecasting the spread of the COVID-19 virus infection, taking into account the age groups of the population and their spatial heterogeneity. SEIR-AGE is a system of ordinary differential equations. To numerically solve this system of ordinary differential equations, an explicit Runge-Kutta method of 8th order of accuracy with 5th order error estimation and control of the choice of integration step is used in the paper. The model parameters are refined by comparing the predicted results with the observed ones by solving a nonlinear least squares problem with constraints. The DQED procedure is used to solve the nonlinear least squares problem with constraints. The algorithm is based on the approximation of nonlinear functions using a quadratic tensor model. It uses a confidence region defined by a parallelepiped containing the current values of the unknowns. The objective function is allowed to increase at intermediate steps. This increase is allowed as long as the predictor indicates that a new set of the best values exists in the confidence region. If necessary, it is possible to return to the current best values. Numerical examples of refining the parameters of the SEIR-AGE model are presented in the paper. Таbl.:3. Figs.: 7. Refs.: 6 titles.
The paper presents the development of the topic of visitor safety in shopping malls, namely, describes the developed algorithm and software for calculating evacuation from a shopping mall (administrative building) in accordance with the algorithm standardized by DSTU. Despite the well-known algebraic algorithm, such software has been developed for the first time. The peculiarity of the work and the article is that it is based on a master’s thesis completed by a graduate of Taras Shevchenko National University of Kyiv in 2024. The article preserves the original text as much as possible because, at the thesis defense, the work was highly praised and recommended by the State Examination Commission for publication in a scientific journal. The software has been developed using the object-oriented programming paradigm, which allows for optimizing calculations by transforming individual program blocks into active objects. To model evacuation routes and times, graphs have been chosen because of their versatility and ability to effectively solve problems related to the analysis of the network of connections between nodes. The main advantage of graphs is their ability to represent complex systems of connections between objects in a simple and understandable way. Each building section is modeled as a vertex of the graph, and the connections between them represent possible evacuation routes. Python has been chosen as the programming language, and the development has been carried out in the PyCharm environment, which provides a wide range of functionality, simplifying the process of writing, debugging, and managing Python projects. Some classes of standard modules have been created based on the objects required in the context of the task, and the corresponding basic graph traversal algorithms DFS (Depth-First Search) and BFS (Breadth-First Search) have been selected, which allowed us to completely review all the routes and evacuation paths. The article continues the topic of modeling safety assurance processes in shopping malls, which has aroused great interest and multiple proposals for publications on the topic of developing IT for security, that is, the work on this subject is of public interest in many countries around the world. Figs.: 10. Refs.: 12 titles.
It is widely known that Ukrainian demographic statistics have significant shortcomings due to the Russian invasion (initially limited, and later full-scale) and mass emigration (initially labor-related, then due to hostilities). Consequently, such a widely recognized tool for studying the quality of life of the population as life expectancy (LE) has lost its accuracy. At the same time, even under such conditions, this tool is able to demonstrate largely comparable values. To determine the impact of COVID-19 on LE in 2022–2023 and to specify the impact in 2020–2021, the study modeled several scenarios without COVID-19, which were then compared with the actual ones. Graphs of LE at birth and upon reaching 15 and 65 years were constructed for each sex. The study found that the decrease in LE at birth for men due to excess mortality from COVID-19 decreased from 2.4 years in 2021 to 0.6 years in 2022 and 0.1 years in 2023. For women, in 2021, COVID-19 took away 3.3 years of LE, and 0.7 and 0.1 yearsin 2022 and 2023, respectively. At the same time, in terms of LE at the age of 65, the gender difference in LE losses was smaller: in 2021, men lost 1.9 years versus 2.1 years for women, in 2022 the losses were 0.6 years and 0.5 years, respectively, and in 2023, 0.11 and 0.08 years. Quite unexpectedly, despite taking into account 7 million migrants in the first half of 2022, in some cases, the actual calculated LE and, especially, LE for hypothetical scenarios, despite the full-scale war, reached significantly higher levels than before the COVID-19 pandemic or before the full-scale war. Such behavior can be questioned. However, as it follows from public reports, from the point of view of demographic dynamics, Ukrainian cities are still quite safe for life, and the contribution of Russian attacks on the civilian population to the decrease in LE for each sex may be close to 0.1 years. Figs.: 3. Refs.: 15 titles.
The importance of predicting the price of gold using artificial intelligence systems is considered. Proofs concerning the importance of accurate forecasting of price trends in the gold industry, which is a key factor for investors, financial institutions, and economic analysts, are provided. Using AI in this context can help to improve risk management strategies and decision-making in financial markets. Today, people use intelligent monitoring to obtain information about the properties of an object or process by creating and using a model knowledge base while processing the results of observations. When using intelligent monitoring to forecast financial indicators, there is a need to synthesize forecasting models based on limited information about the processhistory. Each future value of the forecasted indicator is determined by factors that took place in the past. The modeling of exchange bond pricing processes occurs under both structural and informational uncertainty. In order to reduce the uncertainty of the process of forecasting stock indicators, the paper presents the research results using a new method of machine learning as an additional structural element in combination with already existing algorithms for synthesizing models of a multilayer agent synthesizer of predictors. The use of a new element does not always improve the characteristics of the system as a whole. The hypothesis about the improvement of the characteristics of model agent synthesizers when using a new machine learning method as a structural element of the layer was tested. For example, the process of forecasting prices of gold on the stock exchange was studied. Simultaneously with the creation of new methods of machine learning, it is proposed to change the structure of the multilayer synthesizer of models. It was investigated how the new properties of the structural element of one of the layers change the structure of the agent synthesizer of models as a whole. The research results prove the effectiveness of the process of building a new method of machine learning as a structural element of a multi-layer agent synthesizer of models.Tabl.: 1. Fig.: 1. Refs.: 13 titles.
QUALITY, RELIABILITY, AND CERTIFICATION OFCOMPUTER TECHNIQUE AND SOFTWARE
The article is devoted to the issue of using the attribute model of dependability (AMD) to quantify the level of software dependability. Building an AMD of software is one of the most important stages of reliability assessment that allows for determining the need to use certain characteristics (attributes) of a software product. The completeness and adequacy of the used system of characteristics determine the reliability of the obtained reliability assessment. Two examples of a medical software are considered in the paper. From the laboratory information system (1-LIS), which automates the work of a medical laboratory, to the critical medical system (2-MS) for monitoring human life after resuscitation, which includes a comprehensive solution that allows for optimizing the patient’s treatment process. A comparison of the generalized characteristics of assurance was made. The attribute model of dependable software AMD was created and extended with special attributes and metrics in accordance with the requirements of DSTU ISO/IEC 25051:2016 Engineering of systems and software, requirements for the quality of systems and software and its evaluation (SQuaRE), and quality requirements for a ready-to-use software product (RUSP) and instructions for its testing (ISO/IEC 25051:2014, IDT). The article uses the mathematical representation of the AMD, which is intended to calculate the level of software dependability. The functional GАМD is used, the components of which are the expert normalized values of quantitative assessments of attributes and metrics with the corresponding weighting factors and influence factors. In order to evaluate the performance of the attribute model of software reliability, two variants of the model were analyzed, with and without weighting the metrics evaluation criteria. In addition, the attribute model of software reliability was tested on two types of software ― with low reliability requirements (the first software package) and high requirements (the second critical software package). Таbl.: 32. Refs.: 3 titles.
The assessment of software reliability in information systems is one of the most pressing issues in the modern IT industry. Considering the complexity and importance of tasks performed by information systems, especially systems of critical purpose, the necessity of ensuring their reliability is becoming more and more urgent. In this regard, studying and predicting the residual number of errors that may occur during the operation of the software is extremely important. This article is dedicated to forecasting the residual number of design errors in the software of information systems based on the results of the controlled operation. The paper describes a method for predicting the number of design errors, based on the hypothesis of a random Markov diffusion process with a DN-distribution for time between failures. Although this distribution has been traditionally used as a theoretical reliability model for components, devices, and computer systems, it is a very flexible function of a random argument. The authors of the article suggest that this distribution is worth testing as a model that describes the trend of eliminating accumulated design errors in software that lead to failures. As a result of analyzing thematic publications, a control example of the behavior of some software over time was formed. For this example, its actual failures over a long period of operation are known. The control data were subsequently compared with the obtained theoretical results. The model is described in Python, and calculations were carried out in the corresponding environment. As a result of the simulation, forecast data on the number of failures were obtained using the approach based on the DN-distribution for the time between failures. The evaluation of the results was assessed by the criterion of minimal sum of squared deviations.Таbl.: 4. Figs.: 2. Refs.: 5 titles.
The article is devoted to the analysis of the state of the problem of determining the residual resource of pneumatic electromagnetic valves (PEV). Such valves are used in many branches of industry, both for civil and critical purposes, and therefore, the calculation of the residual resource is of great importance for predicting their service life. The article analyzes the main areas of application and indicates a circle of scientists who deal with various issues related to pneumatic electromagnetic valves, including the problems of increasing the PEV resource. It has been established that the service life of PEV depends on the degree and number of loads, the duration of use, the presence of primary defects, and resistance to various external influences such as temperature, humidity, and corrosion. It has been investigated that the calculations of the residual life of PEV are based on conducting laboratory tests for destruction and durability, which allows for the development of recommendations for increasing the operational life of PEV. However, these approaches do not take into account the random nature of degradation processes. Therefore, a more effective method of calculating the residual life of pneumatic electromagnetic valves is the probabilistic physical method with DM-distribution of the degradation resource. According to one of the methods of DSTU 8646:2016, having data on the number of valve operation cycles in which there are no failures and data on the number of valve operation cycles in which critical destruction occurs, the calculation of the residual resource of PEV was carried out. Using for that the probabilistic-physical method gave results commensurate with real operating data. The quality of manufacturing, optimization of design features, and timely and high-quality statistics of PEV defects also significantly affect the forecasting and assessment of the remaining service life of pneumatic electromagnetic valves. Refs.: 3 titles.
