INNOVATIVE TECHNOLOGIES AND SCIENTIFIC SOLUTIONS FOR INDUSTRIES

Automation of the technological process of forming details with straight rifts

Denis Mospan — Sat, 30 Sep 2023 00:00:00 +0300

The subject matter of the article is technological line for the production of thick sheet parts with rifts, made of structural and special steels. The goal of the work is analysis and creation of methods of managing basic technological operations at all stages of the technological process of the automated production of such parts. The following tasks were solved in the article: formation of models of the main stages of forming parts with rectilinear rifts and control of dynamic properties of selected systems of their implementation. The following methods used are – methods of numerical integration and numerical methods of solving differential equations; analytical solution of differential equations based on the Dahlembert principle or by using the Lagrange equation; frequency analysis of transfer functions of individual links, presented in the form of fractional-rational functions. The following results were obtained – constructed in the first approximation the composition and sequence of the main operations of the generalized technological process of the automated production of thick-walled sheet parts with rifts; according to the results of the analysis of the process of elastic-plastic change of the shape of the region with many connections, the input and output parameters necessary for building a control model of the specified process were determined; it is determined that for its implementation it is necessary to ensure free deformation of at least 0.25 % of the rift surface area outside of contact with the forming tool, and the necessary technological methods for this are proposed; proposed variants of the technical implementation of the selected technological methods; the results of modeling schemes and modes of operation of the necessary technical equipment are presented. Conclusions: The application of the proposed structure, composition and sequence of main operations of the generalized technological process of the automated production of thick-walled sheet parts with cracks, based on its implementation by the selected methods on the proposed equipment, will allow to ensure the effective management of the quality of the production of the specified parts at each stage of the technological process.

Feasibility analysis of implant movement along arc trajectory under non-contact control in magnetic stereotaxic system

Jiao Hunkun, Oleg Avrunin — Sat, 30 Sep 2023 00:00:00 +0300

In this paper, the non-contact control of magnetic implants by changing the external magnetic field in the magnetic stereotaxic system is introduced, and the feasibility of making them move along the arc trajectory is analyzed. Through COMSOL software, the process of moving the miniature magnetic implant along the arc trajectory was simulated, the change of the micro-magnetic implant trajectory after the external magnetic field was changed, the relative position relationship between the large permanent magnets was determined, and the mechanical analysis of the miniature magnetic implant moving along the arc trajectory was carried out. In this experiment, we fix a large permanent magnet, only move the second permanent magnet, first, observe the process of small permanent magnets moving along a straight trajectory, determine the position of the large permanent magnet magnetic field when it contacts the small permanent magnet, and then, analyze the force of the small permanent magnet through the force calculation module, and determine the relative position relationship between the two large permanent magnets by comparing and , and when the small permanent magnet will start to move along the arc trajectory. Then, according to the previous data, we move two adjacent large permanent magnets at the same time at a certain interval, record the movement trajectory of the small magnet, Finally, with the force calculation module of the COMSOL software, force analysis of small permanent magnets moving along arc trajectories. The data from this experiment will be used to determine the relative position relationship between two large permanent magnets adjacent to each other during the actual experiment, and under what conditions the small permanent magnets will move along the arc trajectory. The purpose of this experiment is to provide theoretical and data support for the subsequent practical experiments of the magnetic stereotactic system, and all parameters in the COMSOL software are derived from the actual measurement data, so as to improve the reliability of the simulation results.

A model for forecasting the volume of the entertainment services market under non-determinative conditions

Nina Dovgopol, Olena Peresada, Inna Pribylnova — Sat, 30 Sep 2023 00:00:00 +0300

The subject matter of the article is the theoretical-methodical and applied principles of modelling and forecasting indicators of the market volume of entertainment services. The goal of the work is to find a mechanism that will allow determining the volume of subscribers, taking into account exogenous variables, especially during socially unstable situations, such as a pandemic, war, cataclysms, etc. The following tasks are solved in the article: formation of criteria for companies for which the created mechanism is planned to be applied; review of basic autoregression models; determination of factors that should serve as an external influence when predicting the number of subscribers; creation of a multi-criteria choice problem; conducting experiments according to the proposed methodology to test the proposed hypotheses and systematize the obtained results. The following methods are used: analytical and inductive methods for forming factors of external influence and description of target companies; expert evaluation method for determining the most influential external indicators; experimental method, statistical methods of processing time series and methods of multi-criteria evaluation to determine the most effective autoregression model. The following results were obtained: the factors of external influence were formed: As external variables, it was decided to choose: the dynamics of the incidence of coronavirus, the rate of change in the global gross domestic product, the change in the S&P500 index, and the news from the world's largest news agencies converted into numerical form; a set of criteria for comparing models was created, saving forecasting time, accuracy, the possibility of taking into account external influence and the specificity of taking it into account; it was determined that the most accurate model is autoregression of the moving average, which at the same time is the most effective model given the created problem of multi-criteria selection; the similarity of the obtained results of experiments with global and domestic research is established. Conclusions: the use of analytical and inductive methods in combination with an experimental approach made it possible to obtain an effective (with an accuracy of more than 95%) mechanism for forecasting the market volume of companies that operate in the film industry and have a signature pricing model. The obtained result will allow players with a smaller market volume not to lose their audience due to the instability of the external environment, and, accordingly, will stimulate the development of the industry in general.

Enhancing aspect-based financial sentiment analysis through contrastive learning

Viacheslav Ivanenko — Sat, 30 Sep 2023 00:00:00 +0300

The subject of research in the article explores the specialized application of Aspect-Based Financial Sentiment Analysis (ABFSA), focusing on the intricate and multifaceted emotional landscape of financial textual data. The study extends the current understanding of sentiment analysis by addressing its limitations and opportunities within a financial context. The purpose of the work is to advance the field of Aspect-Based Financial Sentiment Analysis by developing a more nuanced and effective methodology for analyzing sentiments in financial news. Additionally, the study aims to assess the efficacy of recent advancements in Natural Language Processing (NLP) and machine learning for enhancing ABFSA models. The article deals with the following tasks: Firstly, the study focuses on the rigorous pre-processing of the SEntFiN dataset to make it more amenable to advanced machine learning techniques, specifically contrastive learning methodologies. Secondly, it aims to architect a unified model that integrates state-of-the-art machine learning techniques, including DeBERTa v3, contrast learning, and LoRa fine-tuning. Lastly, the research critically evaluates the proposed model's performance metrics across the test dataset and compares them with existing methodologies. The following methods are used: Firstly, the study employs pre-processing techniques tailored for the SEntFiN dataset, which is explicitly designed for entity-sensitive sentiment analysis in financial news. Secondly, it utilizes advanced machine learning techniques such as DeBERTa v3 for language model pre-training, contrast learning for focusing on causal relationships, and LoRa for fine-tuning large language models. Lastly, performance evaluation methods are used to assess the efficacy of the proposed model, including comparisons with existing methodologies in the field.The following results were obtained: The study reveals that the proposed pre-processing framework successfully accommodates the variable number of entities present in financial news, thereby improving the granularity of sentiment classification. Furthermore, the integration of advanced NLP and machine learning techniques significantly enhances the accuracy and efficiency of ABFSA models. Conclusions: The paper concludes that specialized ABFSA methodologies, when augmented with advanced NLP techniques and a robust pre-processing framework, can offer a more nuanced and accurate representation of sentiment in financial narratives. The study lays the groundwork for future research in this nascent yet crucial interdisciplinary field, providing actionable insights for stakeholders ranging from investors to financial analysts.

Modern defining characteristics of innovative development of the ukrainian economy: theoretical and practical aspects

Olena Iastremska, Lyudmyla Malyarets, Olesia Iastremska, Igor Barannik — Sat, 30 Sep 2023 00:00:00 +0300

The subject of the article is theoretical substantiation, methodological support, and practical proposals for studying the innovative development of the economy in modern conditions. The aim of the study is to generalize and improve the theoretical foundations of innovative development, identify the main influencing factors and defining characteristics of its course and develop proposals to ensure innovative development of Ukraine's economy in the current conditions. The following tasks have been defined: to find out the attitude of specialists to the possibility and necessity of supporting innovative development in today's difficult military conditions; to summarize the theoretical basis of innovative development and improve its definition and group existing concepts; to substantiate the most influential factors that determine the intensification and/or containment of innovative development of the Ukrainian economy; to identify its defining characteristics, which are used to generate measures to ensure innovative development of the economy in wartime conditions. To solve the tasks, the following methods were used: theoretical generalization, analysis, synthesis, induction, deduction, structural and logical analysis, systemic and situational approaches, economic and statistical analysis, correlation analysis, multivariate regression analysis, graphical method. The following results have been obtained: the definition of the concept of "innovative development" has been improved; the concepts of innovative development have been analyzed; the main factors influencing the course of innovative development in Ukraine have been identified; multivariate regression models have been built according to the influential factors of innovative development and the most important of them have been substantiated; the current defining features of innovative development of the Ukrainian economy have been identified by significant factors and their specification has been carried out in accordance with the measures to ensure it in the conditions of military operations. Conclusions. It is proved that the improved concept of "innovative development" is that it characterizes the resolution of obvious and hidden contradictions on the basis of qualitative changes in the economic system through the accumulation of quantitative transformations in its components and/or factors of innovation processes, intellectual property, the latest technologies and products. In accordance with the above generalized concepts of innovation development, the author investigates the factors influencing this phenomenon based on the dynamics of the components of the global innovation index and statistical indicators of Ukraine's economy for the period 2015–2022. The most influential factors according to the outlined indicators are identified and multivariate regression models are built, which reveal the following modern leading features of innovation development: formation and maintenance of solvent demand of end users (customers) of innovations for the acquisition of intellectual property. With this in mind, the author proposes measures to ensure and intensify innovative development in the context of military operations in Ukraine.

Justifying the selection of a neural network linguistic classifier

Olessia Barkovska, Kseniia Voropaieva, Oleksandr Ruskikh — Sat, 30 Sep 2023 00:00:00 +0300

The subject matter of this article revolves around the exploration of neural network architectures to enhance the accuracy of text classification, particularly within the realm of natural language processing. The significance of text classification has grown notably in recent years due to its pivotal role in various applications like sentiment analysis, content filtering, and information categorization. Given the escalating demand for precision and efficiency in text classification methods, the evaluation and comparison of diverse neural network models become imperative to determine optimal strategies. The goal of this study is to address the challenges and opportunities inherent in text classification while shedding light on the comparative performance of two well-established neural network architectures: Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). To achieve the goal, the following tasks were solved: a comprehensive analysis of these neural network models was performed, considering several key aspects. These aspects include classification accuracy, training and prediction time, model size, data distribution, and overall ease of use. By systematically assessing these attributes, this study aims to provide valuable information about the strengths and weaknesses of each model and enable researchers and practitioners to make informed decisions when selecting a neural network classifier for text classification tasks. The following methods used are a comprehensive analysis of neural network models, assessment of classification accuracy, training and prediction time, model size, and data distribution. The following results were obtained: The LSTM model demonstrated superior classification accuracy across all three training sample sizes when compared to CNN. This highlights LSTM's ability to effectively adapt to diverse data types and consistently maintain high accuracy, even with substantial data volumes. Furthermore, the study revealed that computing power significantly influences model performance, emphasizing the need to consider available resources when selecting a model. Conclusions. Based on the study's findings, the Long Short-Term Memory (LSTM) model emerged as the preferred choice for text data classification. Its adeptness in handling sequential data, recognizing long-term dependencies, and consistently delivering high accuracy positions it as a robust solution for text analysis across various domains. The decision is supported by the model's swift training and prediction speed and its compact size, making it a suitable candidate for practical implementation.

The method of reuse of knowledge in the form of logical equations

Oleksandr Karataiev, Dmytro Sytnikov — Sat, 30 Sep 2023 00:00:00 +0300

The subject of research is the processes of building and replenishing knowledge bases, the processes of knowledge reuse and the creation of software systems based on knowledge bases, the interpretation of knowledge is one of the approaches to their repeated application, which consists in deriving new knowledge based on the available facts in the knowledge base. The purpose of the research is to develop a method of knowledge reuse by solving logical equations of finite predicates for a certain subject area. The tasks were performed: to investigate descriptive approaches to logical modeling of the subject area, which make it possible to reuse the knowledge given by the system of logical equations within the algebra of finite predicates, to develop a method of replenishing the knowledge base in the form of predicate equations by extracting variables from logical systems, where equations represent complex logical connections between discrete features of objects or processes. The methods were used to solve the listed problems: the algebra of finite predicates, quantifier operations with predicates for the interpretation of knowledge. The results were obtained: descriptive methods of knowledge reuse were considered; a method of replenishing the knowledge base in the form of logical equations is proposed simplify the further use of implicit connections between discrete features; an example of replenishment of the knowledge base of the medical field is considered, which makes it possible to reuse knowledge given implicitly. Conclusions: the proposed method makes it possible to replenish the knowledge base in the form of logic equations by adding predicate equations connecting separate sets of discrete variables that are of interest to a scientist or a practitioner. The method of replenishing the knowledge base is based on adding new simplified equations; simplified predicate equations model logical patterns implicitly contained in the main knowledge base; an experimental study was conducted

Method of planning data processing tasks in distributed systems with limited information about available resources

Andrii Kozyriev, Ihor Shubin — Sat, 30 Sep 2023 00:00:00 +0300

In today's digital landscape, distributed data processing systems (DDPs) are becoming increasingly critical to efficiently process, analyze, and manage large volumes of data. These systems are often used in commercial, scientific and social domains to process complex data in real-time or batch mode. One of the key components of such systems is task scheduling, which is an extremely complex process, particularly when information about resource requirements is not complete or accurate. The subject of research are algorithms, methods and approaches used for scheduling tasks between nodes in distributed systems. The purpose of the study is to create an optimized method of task planning in the RSOD with limited availability of information about available resources. The task of the research: to analyze the limitations of modern methods for scheduling tasks in distributed data processing systems (DDS); optimize the method of scheduling tasks based on metadata between RSOD nodes, based on the methodology of searching for nearest neighbors using the method of localized hashing and the algebra of finite predicates; develop the architecture of the software solution and its implementation based on the optimized method; test the algorithm on the example of a video decoding task. The following methods were used: statistical algorithms and techniques such as classification and cluster analysis were used to predict resource requirements, and visualization techniques assisted in the analysis and interpretation of results. As a result of the work: the limitations of modern methods for the distribution of tasks in distributed data processing systems (DDPs) were analyzed; an optimized method of task planning based on metadata in RSOD was created, based on the methodology of searching for nearest neighbors using the method of localized hashing and the algebra of finite predicates; the processes in the modified nearest neighbor search algorithm are detailed; the architecture of the software solution was developed, which integrates an optimized method of task planning based on metadata and resource allocation; validation of the software solution was carried out with the help of a practical scenario – the use of the created algorithm in the planning task for decoding video information. The conclusions of this study confirmed that the proposed method, based on the methodology of localized hashing and the use of finite redicate algebra, is effective even with insufficient or limited information about resource needs. This highlights the possibility of using dynamic scheduling strategies to adapt to changing load conditions and resource availability.

Methods of automation and interpretation of the quality educational tests in distributed virtual learning systems

Ihor Shubin, Volodymyr Liashyk — Sat, 30 Sep 2023 00:00:00 +0300

The subject matter of the article is the development of mathematical and algorithmic support for intellectual tools, which allows you to conduct continuous control of the knowledge of students (subjects of study) objectively and comprehensively. The goal of the work is to create methods for assessing the quality of educational tests and automating such processes. The following tasks were solved in the article: formation of a testing model in a distributed virtual learning environment and a validity assessment model based on the content of sets of test tasks. The following methods used are – algebra of finite predicates and operations, methods of mathematical statistics and methods of intellectual data analysis. The following results were obtained – the principles of intellectual analysis of the value of reliability coefficients, validity coefficients, discriminability coefficient, the difficulty index of the task of assessing the knowledge of subjects of training are formulated. Conclusions: the application of methods of formalization of test evaluation, analysis of software requirements, software development confirm the need to introduce quantitative methods of assessing students' knowledge into educational practice. The introduction of quantitative methods involves the correct setting of control goals, the selection of the measurement object and the selection of measurement tools. The use of pedagogical tests contributes to the effective implementation of all control functions and corresponds to its main principles for solving the problem of assessing the quality of tests, based on distributed virtual learning models and analysis methods. A test example of calculations was developed, with the help of which the efficiency of the proposed methods was researched.

Steganographic method of acoustic information protection in critical applications systems

Sat, 30 Sep 2023 00:00:00 +0300

The subject of the study is the process of protecting acoustic information in critical computer systems to ensure the required level of system security. The purpose of the article is to study the method of protecting acoustic information in critical computer systems by means of masking to ensure the impossibility of unauthorized access to the system. The paper outlines the following tasks: to analyze the software and hardware masking of speech; to study the masking of speech messages in order to introduce unrecognizability; to consider the features of speech message compression; to investigate methods of covert transmission of acoustic information. The results of the work, which were obtained using mathematical methods of information transformation in computer systems, are potentially possible methods of masking speech messages to ensure the impossibility of unauthorized access to the system. The analysis of the functioning of the proposed methods made it possible to formulate specific conclusions. The research has shown that the use of direct expansion of the spectrum of discrete signals for steganographic purposes helps to covertly embed information messages in still images. The task of extracting a message on the receiving side of a steganography system is equivalent to the task of detecting information from a mixture of a useful signal and an interference in a broadband communication system. The research revealed certain disadvantages of steganographic systems with an expanded spectrum of discrete signals: the probability of correct extraction of embedded data depends on the amount of distortion introduced, which depends on the provided bandwidth of the steganographic channel. Further research is desirable to analyze the possible use of methods for synthesizing large ensembles of quasi-orthogonal discrete signals with improved ensemble, structural, and correlation properties to ensure higher security of acoustic channels in computer systems for critical applications.

Designing the routing level in microservice architectures on the Spring platform

Svitlana Pereiaslavska, Olga Smahina — Sat, 30 Sep 2023 00:00:00 +0300

The subject matter of research is the routing of requests in the microservice architecture. The goal of the article is to develop a target design concept for the level of request routing in the microservice architecture using the Spring technology stack as an example. Tasks: to analyse modern approaches to the structure of microservice architecture; programming of the routing entity and establishment of processes that ensure the routing of requests; programming stacks of Spring technologies that implement routing; design the routing layer of the application on the Spring platform. The following methods are used: analysis and synthesis to study technologies of interaction between services; abstraction and generalization to determine the structure of the microservice architecture, routing level, generalization of technologies that ensure interaction between services; modelling for the purpose of building a model of microservice architecture, highlighting the level of routing and connections with other structural models. The following results were obtained: the structure of the microservice architecture was investigated, in particular the level of routing; the role of design patterns that provide routing is defined: Service discovery, API Gateway, Load Balancer, etc.; the types of interprocess interaction (synchronous, asynchronous, hybrid) were analysed and the advantages and expediency of the application were determined; models of system fault tolerance are presented; a stack of technologies on the Spring platform is defined for the implementation of the routing layer; a model of a multi-level microservice architecture project was developed using the Spring technology stack, which implements the most effective solutions in the context of request routing. Conclusions: it is advisable to consider microservice architecture as a multi-level structure built on functional levels and connections between them; the level of microservices routing should include all processes related to the establishment of interprocess interaction, service detection, load balancing and fault tolerance, and the creation of a single entry point; Spring is a popular microservice architecture development tool platform that provides requirements for implementing request routing; the developed project model is an example of effective solutions for designing a multi-level architecture using the Spring technology stack in the context of request routing.

The software platform for evaluation of the effectiveness of structural model aggregation of complex systems

Olha Ponomarenko, Valeriy Gorbachov — Sat, 30 Sep 2023 00:00:00 +0300

Complex systems have a large dimension, consist of a large number of elements and connections between them. Networks are used to represent complex systems. Due to the high dimension of modern systems, researchers evaluate proposed solutions using generated networks. The high dimension of the system leads to problems in modeling and control. To solve these problems, methods for reducing the dimension of complex systems are required. Aggregation of the structural model of the system consists in combining elements of the system into subsystems and as a result dimension of the system and computational complexity are reduced. The subject matter of research is a software platform for evaluation of the effectiveness of structural model aggregation of complex systems. The goal of the work is to develop the software platform for evaluation of the effectiveness of structural model aggregation of complex systems. The relevance of the work lies in the fact that using the software platform it is possible to make the structural model aggregation of systems with a large number of elements and it is also possible to evaluate the effectiveness of structural model aggregation of the system. The following tasks were solved in the work: development of the software platform which consists of a system generator module, a system aggregation module, a maximum flow searching module and a statistical data processing module; structural model aggregation of the system using the software platform; evaluation of the effectiveness of structural model aggregation of the system using the software platform. As a result of the work the software platform was created, the structural model aggregation of the system was made and the effectiveness of structural model aggregation of the system using the software platform was evaluated. The studies allow us to conclude: using the software platform it is possible to generate a system, make the structural model aggregation of the system and solve the problem of the maximum flow searching; the software platform also allows to evaluate the effectiveness of structural model aggregation of the system; the value of the maximum flow is the same at two levels of the system, so in this case the structural model aggregation of the system is made while preserving the correctness of its parameters.

Model of elemental data flow distribution in the internet of things supporting fog platform

Bohdan Rezanov, Heorhii Kuchuk — Sat, 30 Sep 2023 00:00:00 +0300

The subject of the research is models and methods for optimizing resource and task management in the fog computing environment of the Internet of Things (IoT). The increasing number of connected devices and the volumes of data collected in IoT networks make it essential to improve management systems that ensure the optimal distribution of tasks and resources. Fog computing addresses this challenge by distributing computational tasks closer to data sources and end-users. The goal of this work is to enhance the efficiency of fog computing technologies to achieve optimal task and resource allocation in IoT networks. The main tasks of this work are as follows. Firstly, considering the diverse requirements of computational resources and tasks in IoT, reviewing existing methods and developments in the field is necessary. Secondly, it is essential to investigate and compare clustering methods, particularly DBSCAN and C-Means, for effective resource management. The DBSCAN clustering method enables efficient task distribution based on their location, while the C-Means method allows grouping resources based on their characteristics. The final task involves developing a mathematical model that considers input parameters such as system response, cluster resource requirements, data proximity to processing, etc. This model will enable the analysis of potential scenarios and decision-making regarding the optimal distribution of tasks and resources in the IoT environment. Conclusion. This research aims to solve the urgent problem of managing resources and tasks in the fog IoT environment. A review of existing methods and developments in resource and task management in IoT is conducted. DBSCAN and C-Means clustering methods are compared to determine their effectiveness in resource management. A set-theoretic model is developed that considers various parameters for making optimal decisions on the distribution of tasks and resources. It is established that the use of clustering methods and the developed model help to improve system performance and ensure more efficient use of fog computing resources in the IoT environment.

Data structures for deductive simulation of HDL conditional operators

Oleksandr Shkil, Maryna Miroshnyk, Dariia Rakhlis, Oleh Trifanov — Sat, 30 Sep 2023 00:00:00 +0300

The subject of research in the article is qubit-vector models for combinational circuits’ description and procedures for faults deductive simulation based on these models. The object of research is the processes of diagnostic support creation for digital systems based on the usage of vector qubit data. The purpose of the work is to increase the speed and quality of diagnostic support creation for digital devices by creating optimal data structures and deductive fault simulation procedures based on structural-functional models of combinational circuits. The following tasks are solved in the article: analysis of concurrent and sequential conditional operators of hardware description languages and schematic structures into which they are synthesized; development of the procedure for the truth tables (Q-vectors) formation for schematic structures presented by HDL; development of an universal data structure for cubic and analytical deductive faults simulation; vector models improvement of qubit representation of structures and components of digital systems based on address coding of input signals to increase the manufacturability and speed of faults simulation; development of the procedure for obtaining Boolean derivatives by permuting the lines of the truth tables (Q-vectors) and using the XOR operation; development of a data structure for deductive fault simulation based on the cubic representation of digital circuit components. The following methods are used: deductive, cubic, deductive-parallel simulation of faults, faults simulation by deductive Q-vectors. The following results were obtained: the equivalence of concurrent and sequential conditional operators, as well as their schematic implementation in the form of multiplexers, was shown; method of obtaining truth tables of the synthesized circuit structure using TestBench (Xilinx ISE) was proposed; different technologies and data structures of deductive fault simulation for tabular, analytical and qubit methods of digital circuits description were considered; the software implementation of faults cubic deductive simulation is considered and the equivalence of the obtained results for the multiplexers circuits (MUX 2-in-1 and MUX 4-in-1) using the DCP software product was demonstrated. Conclusions: a new Q-method of interpretative faults simulation of digital circuit is proposed, which is characterized by the usage of compact Q-vectors instead of truth tables, which makes it possible to significantly increase the analysis speed due to the addressable formation of the functional primitives outputs and reduce the volume of data structures, which practically makes the method competitive with compilative simulation technologies.

Smart cities and the internet of things: the impact of it developments on the development of cities and improving the quality of life

Oleksandr Shpak, Pavlo Fedorka, Mykhailo Prygara — Sat, 30 Sep 2023 00:00:00 +0300

The subject of the article's research is the methods and capabilities of information technologies and electronic devices-carriers of the Internet of Things to develop a combined system with the aim of determining the characteristic features. The purpose of the work is to study and analyse the use of smart technologies for a smart city based on a combination of information technologies and Internet of Things devices, as well as to identify key criteria for the development of modern cities with an impact on improving the quality of life for the population. In accordance with the objective, the following tasks have been formulated: to consider the main possibilities of using information technologies in combination with electronic measuring and computing devices of the Internet of Things; to determine the features of building information technology models with the connection of Internet of Things devices to switching networks; analyse the architecture of fog and edge computing for Internet of Things devices; to identify the main risks and cybersecurity issues with encryption of Internet of Things device data using encryption algorithms; to determine the main criteria for each individual system of smart technologies for a smart city; to determine the feasibility of needs with the needs of the population based on the defined criteria. An urgent problem is to identify and study the main possibilities of using smart technologies in order to identify problems related to the feasibility of meeting the needs of the population. This paper considers and investigates the impact of a combined system with a set of smart technologies on improving the quality of life using eight branched systems, namely: "smart" infrastructure, "smart" economy, "smart" environment, "smart" governance, "smart" transport, "smart" services, "smart" people and "smart" life. The set of criteria of each system needs to be analysed and matched with the needs of the population so that the combined smart city system can provide the population with conditions that can improve the quality of life. The scientific novelty of this paper is the development and implementation of the concept of a combined smart city system to meet the needs of the population while improving the quality of life. Results achieved. Smart technologies are based on the use of information technology, data and innovation to achieve sustainable development of individual systems with the definition of criteria, which analyses the following: "smart" infrastructure contributes to the creation of a convenient, efficient and sustainable urban environment; "smart" transport improves traffic and mobility, reduces the negative impact on the environment, increases safety and convenience for citizens; "smart” governance makes a significant contribution to the development of various sectors, promoting the efficient use of resources and improving the quality of life with sustainable development for the population; "smart" environment contributes to the conservation of natural resources, reduces the negative impact on the environment and ensures an improved quality of life for the population; "smart" services ensure the sustainable development of healthcare, finance, tourism logistics, which helps to create a more convenient and efficient environment for consumers, contributing to the improvement of the quality and availability of various services; "smart" people make a significant contribution to the development and application of new opportunities for the development of information technologies in combination with the Internet of Things; "smart" life ensures sustainable development for citizens through the use of technology to improve aspects of everyday life; "smart" economy makes a significant contribution to the implementation of technological solutions and contributes to a more efficient functioning of the country's economy. Conclusions. The results of the study demonstrate the impact of the combined smart city system on improving the quality of life, which is explained by determining the compliance with the needs and conditions of the population. Thus, the combination of criteria of each smart technology allows transforming the city into a general combined system with digital services for the provision of services. The vector of further research will be aimed at developing the considered combined system using digital technologies in order to create a digital model of the "city of the future using appropriate software.