Рубрика: Технические науки

Аннотация: (Українська) У роботі розглянуто інтеграцію підсистеми прогнозування попиту в розподілені інформаційні системи торговельних мереж. Запропоновано комбіновану архітектуру, що поєднує локальну обробку даних, федеративне узгодження параметрів і мікросервісну взаємодію. Визначено ключові компоненти та технології для забезпечення масштабованості, автономності локальних вузлів і зменшення передавання сирих даних.

Аннотация: (English) The article is devoted to the development of a technology for producing low-salt bread for children aged 3 to 10 years using beet kvass as a natural fermented starter. The aim of the work is to justify technological approaches to reducing the salt content in bread for preschool and primary school children and to create a recipe that meets the taste preferences of the children's audience and the requirements of children's nutrition. The work uses an analysis of scientific literature on sodium consumption norms for children, the peculiarities of children's taste perception, and technological methods of compensating for salt reduction, as well as practical experience in the development and production of bread at our own enterprise. The author's recipe for bread with a salt content of 1.0-1.2 g per 100 g of product, which is 40-50% lower than the standard level, is presented. The choice of beet kvass as a functional ingredient is justified, as it compensates for the reduction in saltiness through the formation of organic acids and aromatic compounds and gives the product a sweet and sour taste that is attractive to children. The technological challenges of working with reduced salt content are described: the effect on dough fermentation, gluten formation, porosity stability, and shelf life. Technological solutions to overcome these challenges are presented: the use of fermented sourdough, minimal yeast dosage, and the addition of honey to balance the taste. The nutritional value of the developed bread and its suitability for children's needs are analyzed. It is shown that the use of beet kvass increases the content of betaine, folic acid, and iron, which is important for children. The potential acceptability of the product for children is discussed based on scientific data on the sensory perception of low-salt bread by children. The practical value of the work lies in the creation of a technological card and recipe that can be used in children's nutrition establishments to reduce sodium consumption by children.

Аннотация: (English) The progressive retirement of legacy cellular technologies represents one of the most intricate operational challenges faced by contemporary mobile network operators. While the migration toward LTE and fifth-generation infrastructures has been widely documented, the methodological foundations governing the controlled sunset of earlier radio access technologies remain comparatively underexplored. In particular, the decommissioning of UMTS networks introduces a complex interplay between spectrum refarming, traffic redistribution, service continuity, and operational risk. If performed without analytical guidance, the shutdown of legacy radio layers may provoke severe congestion phenomena, localized capacity collapse, and degradation of user-perceived service quality. This study proposes a structured analytical framework for strategic UMTS sunset implementation within national-scale mobile networks. The research develops a mathematical model that conceptualizes technology retirement not as an isolated operational procedure but as a multi-stage optimization problem embedded within the broader dynamics of traffic migration and spectrum reallocation. The model integrates probabilistic risk estimation, predictive traffic forecasting, and performance-oriented optimization in order to determine the most balanced trajectory of legacy infrastructure decommissioning. To validate the analytical model, a dedicated experimental software environment was implemented in Python, enabling simulation of progressive UMTS shutdown scenarios and comparative evaluation of alternative decision strategies. The system incorporates modules for dataset preprocessing, predictive traffic modeling, iterative sunset simulation, and analytical visualization of network behavior. An openly available dataset describing cellular network performance indicators was used to construct a synthetic yet statistically plausible representation of sector-level radio conditions. Simulation results demonstrate that risk-aware sunset strategies significantly reduce the probability of network overload events while preserving higher LTE throughput levels during the transition process. In contrast, load-driven or stochastic shutdown strategies exhibit increased susceptibility to blackout conditions under heavy traffic migration scenarios. The obtained findings indicate that integrating predictive modeling and probabilistic risk assessment into sunset planning procedures enables a more stable and resource-efficient transformation of cellular infrastructures. The proposed methodological framework contributes to the emerging body of research on technological transitions in mobile networks by providing a coherent analytical approach to legacy technology retirement. Beyond its theoretical implications, the model may serve as a practical decision-support instrument for mobile operators planning large-scale spectrum refarming and infrastructure modernization initiatives.

Аннотация: (English) Artificial intelligence has grown capable enough to reopen a basic question: how should work be divided between humans and machines? Fully autonomous AI performs well on structured, data-rich tasks, yet collaboration between humans and AI consistently produces stronger results in complex, context-dependent, and creative settings. This article reviews how human-AI interaction models have evolved and proposes a three-level classification of collaboration: automation, augmentation, and identity-level partnership. Drawing on established complementarity frameworks and hands-on experience in AI-driven software development, the author introduces the Scalable Human Model (SHM) – a framework in which AI operates as an extension of one individual’s cognitive style, communication patterns, and decision-making logic. A case study of OPV Systems validates the model: one developer, supported by AI agents, reached productivity levels normally associated with a team of five to eight. The findings suggest that this mode of partnership differs qualitatively from augmentation – it lets professionals scale their expertise without sacrificing authenticity or strategic control. Implications for software engineering, digital legacy systems, and human-centric AI design are discussed.