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Multiprocessing as a Way to Optimize Queries. Advances in Transdisciplinary Engineering
(2024) Khrystynets, Natalia; Melnyk, Kateryna; Lavrenchuk, Svitlana; Miskevych, Oksana; Kostiuchko, Serhii
Developing an effective web application involves the use of various methods and techniques to ensure fast and efficient processing of requests. Sometimes it is not possible to solve the problem of multiprocessing with a single tool, such as a programming language or framework. This work investigates the use of asynchronous methods of processing requests using queues. Job operation in background and non-background modes relative to the main web process is studied. Analytics are provided to analyze a web application with 13,000 requests to process daily. It is proposed to optimize the processing by using the Laravel framework and the Python server dual-tasking using the Supervisor tool on Linux, as well as using a task scheduler for each task. The paper presents positive findings about this algorithm, which contributes to the efficiency of web development and provides a great user experience on the website. Fast processing of web application requests can be a valuable competitive advantage for a business or organization. Research in this field helps to maintain their high competitiveness. In addition, the study of query processing speed is important in scientific research, as it contributes to the development of new algorithms, optimization methods and technologies.
Imitation of CNS-Control of Human Lower Limb: Joints Simulation
(2019-10) Bortnyk, Kateryna; Lavrenchuk, Svitlana; Lishchyna, Nataliia
In this paper, the imitation of central nervous system control of human lower limb is done and the controller simulation for ankle joints is presented. The CNS control is applied to multi-joints coordination relating to the problem of human postural balance. This control strategy was developed with aid of state-space model of the lower extremity of human in order to reconstruct natural human motion and to stabilize the human posture. Based on real model experiments, which simulates the human motion process in real time, we present the methodology of actual target values obtaining which correspond to position of human upright standing.
DIY Smart Auxiliary Power Supply for Emergency Use
(Springer Nature Switzerland, 2023) Zdolbitska, Nina; Delyavskyy, Mykhaylo; Lishchyna, Nataliia; Lishchyna, Valerii; Lavrenchuk, Svitlana; Sulim, Viktoriia
Implementation of Internet of Things technologies in various fields is one of the priority areas of modern development. Energy efficiency is one of the urgent problems in Ukraine today. As part of this study, the use of IoT will help monitor and control energy consumption data. Auxiliary power supplies are devices that provide supplemental power to an existing power source. They are often used in automotive, electrical, and industrial applications. DIY auxiliary power supplies are those that are built by the user instead of purchasing a ready-made product. Building your own power supply can be a cost-effective option, as well as an enjoyable DIY project. The purpose of the research is to develop a device based on the existing electrical engineering components, which allows you to completely or partially replace the functionality of industrial analogs of power supplies.
The system of dynamic optimization pricing by machine learning
(Greece, 2024) Konotopchyk, Artem; Melnyk, Kateryna; Lavrenchuk, Svitlana; Khrystynets, Nataliia; Melnyk, Pavlo; Melnyk, Pavlo; Bortnyk, Kateryna
Based on the real retail data collected over eight months, a number of models are developed to determine the most efficient machine learning algorithm. It is found that the KNeighbors Regressor model demonstrates the best performance for a small number of transactions, achieving a low MSE of 0.00091 and a high R2 of 0.72 in the validation set. For a large number of transactions, Random Forest Regressor and Decision Tree Regressor show the best ability to capture complex relationships and handle nonlinearties in the data, thanks to the ensemble learning technique. Whereas the Linear Regressor and Support Vector Regressor models demonstrated large deviations from the real price on the test data. The results of the study demonstrate the relevance of rtificial intelligence algorithms in dynamic pricing trategies, showing the ability to quickly process huge amounts of data, taking into account numerous factors and changes in market demand.

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