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Development of a system for predicting failures of bagging machines
(2025-12-20) Huliieva, Nataliia; Lishchyna, Nataliia
The reliability and effective operation of machines is a pressing problem for every enterprise, which requires labour intensive systematizationof production processes. The goal is to develop an algorithm and a system for predicting failures of packaging machines based on the analysisof operational indicators. The scientific novelty lies in the integration of statistical data to assess the efficiency of machine operation and predict possible failures, which allows significantly improving maintenance processes and reducing the risks of unforeseen breakdowns. The practical valueis the development of a forecasting system that collects the necessary statistical data and performs forecasting. Based on the collected data, an assessment of the efficiency of work and forecasting of possible failures is carried out. The forecasting system is demonstrated on the example of packaging machines LEMO INTERmat ST-SA 850 of "Tatrafan" LLC. Two research methods were used: calculation (mathematical) and forecasting system (least squares method). The forecasting system provides two ways of presenting data: tabular and graphical. Tabular presentation of data allows filtering information according to various criteria, while graphical display is implemented in the form of diagrams showing the operating time and downtime of machines.The main results are the determined range of probable failure of LEMO INTERmat ST-SA 850 packaging machines, which lies in the range from 9090.5to 12736.5 hours of operation and almost coincides with the manufacturer's warranty period. With timely maintenance, it is possible to increase the lower limit of this interval.
Architectural And Engineering Aspects Of Integrating The Novita Ai Api Into A Web Application For Image Generation
(Lutsk: LNTU, 2026-03-28) Povstiana, Yuliia; Lishchyna, Nataliia; Surynovych, Olena; Boiko, Lev
This paper investigates architectural and software engineering approaches to integrating the Novita AI image generation service into a web-based application that supports scalable and asynchronous request processing. The proposed solution is based on a clear separation of responsibilities between frontend and backend components, where the backend handles validation, orchestration of long-running tasks, interaction with external APIs, and data persistence. Communication is implemented using REST for synchronous operations and WebSocket-based notifications for asynchronous updates, while resource-intensive tasks are executed through a queue-based mechanism to prevent interface blocking. Integration with the Novita AI service is encapsulated within a dedicated service layer, ensuring modularity, maintainability, and extensibility. An experimental evaluation was conducted to measure execution times of key operations, including image generation, transformation, upscaling, and custom model training. The results confirm the effectiveness of asynchronous processing and demonstrate that custom models improve output consistency at the cost of a minor increase in generation time. The novelty of this study lies in the architectural justification of asynchronous API integration for AI-based image generation with support for custom model training, validated through experimental performance evaluation.
Architecture And Experimental Evaluation Of A Cross-Platform Mobile Application For Adaptive Learning Using Large Language Models
(Lutsk: LNTU, 2026-05-29) Povstiana, Yuliia; Samchuk, Lyudmila; Lishchyna, Nataliia; Boiko, Lev
The paper addresses the design and experimental evaluation of the architecture of a cross-platform mobile application for adaptive foreign language learning using large language models. An architectural approach based on a dedicated AI Integration Layer is proposed, enabling the separation of business logic and improving the reliability of interaction with external AI services. The system implements adaptive content generation considering individual user characteristics. Special attention is given to performance optimization through the implementation of a multi-level caching mechanism, which reduced AI service usage costs by 74 % and decreased response latency. An experimental evaluation of system performance demonstrated stable operation under a load of up to 100 concurrent users with an average response time of 280-500 ms, and identified a degradation threshold at 160-170 users. The obtained results confirm the effectiveness of the proposed approach and its applicability for developing modern mobile educational systems based on large language models.

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