Ovcharuk V.
PhD, Docent, Associate Professor of the Department of Informatics, National University of Food Technologies, Kyiv, Ukraine
Yuschuk I.
Senior Lecturer of the Department of Informatics, National University of Food Technologies, Kyiv, Ukraine
Sedyh O.
Senior Lecturer of the Department of Informatics, National University of Food Technologies, Kyiv, Ukraine

Abstract
The main idea of the systematic approach to analysis is to consider the market as a complex system where different elements interact with each other and influence the quality of products that meet international standards. This allows businesses to better understand market conditions and adapt their quality management strategy to ensure competitiveness and customer satisfaction.

Keywords: quality, market, profit, segment, standard

Fedorovich S.
Master of Science, Electrical engineer, Enter Engineering, Uzbekistan, Tashkent

Abstract
This article explores the delicate balance between engineering considerations and operational preferences in power system projects, specifically focusing on the decision-making process regarding the use of soft starters versus transformer upgrades. The conventional reliance on soft starters is challenged, particularly in greenfield projects, where alternative solutions during the design phase may eliminate the need for soft starters altogether. The study delves into the economic feasibility and advantages of opting for larger transformers as a means to address challenges, such as voltage drops during motor startups. The article employs the Electrical Transient Analysis Program (ETAP) to construct a comprehensive model, considering factors like motor power, transformer load, and the need for soft starters. The study concludes that a higher-capacity transformer can effectively address challenges related to voltage drops and evaluates the cost implications, comparing expenses associated with larger transformers against the installation of soft starters. The article advocates for a nuanced approach in optimizing power system configurations, considering both short-term and long-term implications.

Keywords: Soft Starters, Transformer Upgrades, Power System Optimization, Voltage Drops, ETAP, Motor Starting, Comparative Analysis, High Voltage (HV), Economic Feasibility, Engineering Considerations, Operational Preferences.

Bilyk O.
Cand. Tech. Sci., Professor, Professor of the Department of Bakery and Confectionery Technology National University of Food Technologies
Bogachov Iu.
Student of higher education educational degree Ph.D, Department of Technology of Bakery and Confectionery Products National University of Food Technologies
Dushchak O.
Cand. Tech. Sci, Associate Professor, Associate Professor  of Department of Canning technology of the National University of Food Technologies
Cand. Tech. Sci., Assoc. Prof.
Khomenko D.
Student of higher education educational degree Master’s of the Department of Bakery and Confectionery Technology National University of Food Technologies

Abstract
Bakery products are among the most common food products, but they are prone to mold, which leads to an unpleasant odor, accumulation of mycotoxins, and spoilage of the product. This article presents the results of research and a comparative analysis of calcium propionate of different brands. The presented results demonstrate the positive effect of using calcium propionate and its salts on the positive dynamics of suppressing the agents of microbiological spoilage of bakery products. It was found that the best effect on the suppression of mold fungi was exerted by calcium propionate of the trademark “PROBAKE” in an amount of 0.2% to the mass of flour.

Keywords: microbiological contamination, mold, calcium propionate, bakery products, safety

Momot A.
National Technical University of Ukraine
“Igor Sikorsky Kyiv Polytechnic Institute, PhD, Senior Lecturer
Zaboluieva M.
National Technical University of Ukraine
“Igor Sikorsky Kyiv Polytechnic Institute, Student
Galagan R.
National Technical University of Ukraine
“Igor Sikorsky Kyiv Polytechnic Institute, PhD, Assistant Professor

Abstract
The article deals with the method of automated semantic segmentation of ultrasound medical images using the Attention U-Net deep learning model. The advantages of using Attention blocks in neural network architectures for segmentation tasks are analyzed. To test the described algorithms, the Breast Ultrasound Images training dataset was chosen. The method described in the article allows for automating the process of detecting and preliminary classification of breast tumors based on the analysis of ultrasound images. As a result of training the Attention U-Net model, the Mean IOU value of 49.2% was obtained on the test set. The network can automatically classify the detected neoplasm as benign or malignant with an F1 Score of 0.87. The results indicate the prospects of using the Attention U-Net model in the tasks of analyzing ultrasound medical images. Ways to further improve the considered method are proposed.

Keywords: deep learning, segmentation, ultrasound, medical diagnostics, breast cancer.