The relationship between individual phytochemical constituents and overall antioxidant capacity or total phenolic content (TPC) is poorly understood in faba bean. This study used a range of linear and nonlinear regression techniques to investigate whether the antioxidant capacity and TPC of 60 faba bean samples (flour and methanolic extracts) could be predicted from 12 individual compounds (10 common polyphenols and 2 alkaloid glycosides) measured in the same samples. Nonlinear regression using machine learning with a Radial Basis Function showed the best performance for antioxidant and TPC prediction across all sample types, while multiple linear regression allowed moderately accurate predictions in most sample matrices. Improved performance metrics were seen for the methanolic extracts compared to the flour samples. The strongest predictors of antioxidant activity in the multiple linear regression models were protocatechuic acid, p-hydroxybenzoic acid, and ferulic acid, suggesting that these compounds are particularly important contributors to the high antioxidant activity of faba bean. Understanding the relationship between individual constituents and the antioxidant capacity may help food technologists and plant breeders develop faba bean products with maximal health benefits.

The article substantiates the need to integrate artificial intelligence with the Intranet solution of unified information and analytical space of the university. This study takes into account the development process of the digital economy of the Russian Federation to ensure the stated goals of studying artificial intelligence as a means to process information based on interpreted data processing. Approbation of the basic theoretical provisions of the proposed model and the practical implementation of the solution developed on its basis are taking place in the structural departments of the Humanities and Education Science Academy (branch) of V.I. Vernadsky Crimean Federal University in Yalta.

This study explores the application of computer vision for enhancing the selection of rootstock-graft combinations and detecting diseases in grape seedlings. Computer vision has various applications in viticulture, but publications and research have not reported the use of computer vision in rootstock-graft selection, which defines the novelty of this research. This paper presents elements of the technology for applying computer vision to rootstock-graft combinations and includes an analysis of grape seedling cuttings. This analysis allows for a more accurate determination of the compatibility between rootstock and graft, as well as the detection of potential seedling diseases. The utilization of computer vision to automate the grafting process of grape cuttings offers significant benefits in terms of increased efficiency, improved quality, and reduced costs. This technology can replace manual labor and ensure economic efficiency and reliability, among other advantages. It also facilitates monitoring the development of seedlings to determine the appropriate planting time. Image processing algorithms play a vital role in automatically determining seedling characteristics such as trunk diameter and the presence of any damage. Furthermore, computer vision can aid in the identification of diseases and defects in seedlings, which is crucial for assessing their overall quality. The automation of these processes offers several advantages, including increased efficiency, improved quality, and reduced costs through the reduction of manual labor and waste. To fulfill these objectives, a unique robotic assembly line is planned for the grafting of grape cuttings. This line will be equipped with two conveyor belts, a delta robot, and a computer vision system. The use of computer vision in automating the grafting process for grape cuttings offers significant benefits in terms of efficiency, quality improvement, and cost reduction. By incorporating image processing algorithms and advanced robotics, this technology has the potential to revolutionize the viticulture industry. Thanks to training a computer vision system to analyze data on rootstock and graft grape varieties, it is possible to reduce the number of defects by half. The implementation of a semi-automated computer vision system can improve crossbreeding efficiency by 90%. Reducing the time spent on pairing selection is also a significant advantage. While manual selection takes between 1 and 2 min, reducing the time to 30 s using the semi-automated system, and the prospect of further automation reducing the time to 10–15 s, will significantly increase the productivity and efficiency of the process. In addition to the aforementioned benefits, the integration of computer vision technology in grape grafting processes brings several other advantages. One notable advantage is the increased accuracy and precision in pairing selection. Computer vision algorithms can analyze a wide range of factors, including size, shape, color, and structural characteristics, to make more informed decisions when matching rootstock and graft varieties. This can lead to better compatibility and improved overall grafting success rates.

The article presents the results of development a model of an integrated information analysis environment of the university. The model includes conceptual model, user interaction algorithm and environment security policy. Approbation of the basic theoretical provisions of the proposed model and the practical implementation of the solution developed on its basis are taking place in the structural departments of the Humanities and Education Science Academy (branch) of V.I. Vernadsky Crimean Federal University in Yalta. Also, an integrated information environment of the university are used in the educational process for the training future specialists in Computer Science, for courses "Database Design and Development", "Information Systems Design", "Information Systems and Technologies", "Information Systems Design Methodology and Technology" for undergraduate program "Applied Computer Science".

The study aims to substantiate the importance of using a UIAS of the university for organizing remote education. The creation of a UIAS allows organizing accounting, storage and accessing the data collected in the course of the work of the structural divisions of the university online. It helps consolidating the date which is relevant and important for generating reports and analytical documentation of divisions when organizing remote education.

The article presents the results of developing an Intranet solution for managing a unified information and analytical space of a university. The technology works on the basis of GPAYALTA, a comprehensive unified information and analytical system for analyzing and monitoring the student and teacher contingent indicators of the educational institution of higher education, and provides management of students and teachers contingent in order to generate the necessary reports. Approbation of the basic theoretical provisions of the proposed model and the practical implementation of the solution developed on its basis are taking place in the structural departments of the Humanities and Education Science Academy (branch) of V.I. Vernadsky Crimean Federal University in Yalta.