Analysis of the rise in prices for consumer goods is a state’s priority task. The state assumes the obligation to regulate pricing in all spheres of consumption. First of all, the prices for essential commodities to which agricultural products belong are analyzed. The article shows the changes in prices for consumer goods of agricultural products (sugar) during a pandemic. The analysis of forecasting prices for sugar and its impact on the development of its production is carried out. The construction of the forecast model was based on extrapolation. The structure of a forecast model for price changes was based on the analysis of the time series of the Autoregressive Integrated Moving Average (ARIMA) class. This model consists of an autoregressive model and a moving average model. A forecast of the volume of domestic sugar transportation by rail has been completed. The algorithms implemented this model for searching for initial approximations and optimal parameters for the predictive model. The Hirotsugu Akaike Information Criterion (AIC) was used to select the best model. The algorithms were implemented in the Python programming language. The quality check of the description was performed with a predictive model of actual data. An economic interpretation of the rise in sugar prices and proof of the forecast’s truth obtained from a financial point of view were carried out.

Background and Aim: Enhancing the nutritional and biological value of meat obtained from young surplus replacement animals of dual-purpose breeds is a critical objective in the livestock industry. This study aimed to investigate the impact of thyroglobulin (TG5, c. −422C > T) and leptin (LEP, c. 239C > T) polymorphisms on the productivity, chemical composition, and fatty acid (FA) profile of meat from Simmental bulls. Materials and Methods: A total of 26 Simmental bulls were genotyped for TG5 (c. −422C > T) and LEP (c. 239C > T) polymorphisms and reared under the same fattening conditions. Controlled slaughter was conducted at 18 months of age. Subsequently, the experimental animals were evaluated to determine their slaughter traits and the chemical and FA composition of ground beef and the longissimus dorsi muscle. Results: The results showed that the TG5 (c. −422C > T) polymorphism significantly (p < 0.05) affected the differentiation of bulls in terms of the synthesis of stearic acid, linolenic acid, and total polyunsaturated FAs, as well as the fat and dry matter content in the longissimus dorsi muscle. Conversely, the presence of the T allele in the LEP (c. 239C > T) polymorphism was associated with increased dry matter and fat in ground beef, carcass weight, and internal fat weight. Conclusion: The analysis of slaughter traits and the chemical and FA composition of meat from the Simmental bulls genotyped for the TG5 and LEP genes revealed a genetic basis for the quantitative and qualitative characteristics of meat productivity. Thus, the genetic variability of bulls regarding the LEP and TG5 genes can be used to improve the quantitative and qualitative indicators of meat productivity in Simmental cattle through marker-assisted selection. Keywords: chemical composition, fatty acid, leptin, meat productivity, polymorphism, Simmental, thyroglobulin.

The quality of knowledge is the most important task of the learning system at any level and stage of education. The COVID-19 pandemic has made its own adjustments to the process of organizing education, requiring the transition from traditional to distance learning as soon as possible. In the new conditions, the use of adaptive knowledge control has become relevant, taking into account the individual level of knowledge of the trainees. The study is devoted to the analysis of the features of adaptive testing, the conditions of application and the possibilities of web technologies for its organization. The article presents the results of a study aimed at organizing and conducting adaptive knowledge control as one of the means of implementing an individual learning trajectory. The study presents algorithms for constructing an individual trajectory of adaptive testing for each user. The analysis of web technologies and learning management systems that are currently used in terms of their capabilities for the implementation of the adaptive learning module is carried out. An adaptive testing module has been developed for implementation into the LMS Moodle learning management system, built taking into account the modular organization of the system. The construction of the module is based on a Markov random process with discrete states, continuous and discrete time, which makes it possible to implement a condition for completing testing with the function of viewing detailed statistics of its passage. The problem of developing and implementing an adaptive testing module in an online learning system is considered. The results of experimental work confirming the effectiveness of the implementation of the adaptive testing module are presented. The study of technology by future teachers and its application in practice will contribute to an increasingly widespread implementation in practical activities.

Mathematical computer models of the permittivity of silicon-based nanostructures upon interaction with electromagnetic radiation in a wide frequency range have been developed. To implement computer models for studying the electrophysical properties of the structures under study, algorithms and a set of programs have been developed. The results of the study of materials will not only provide fundamental information about the physical effects occurring in composite nanostructures but will also be useful for solving problems related to calculations for given electrophysical problems. For a nanocomposite based on ceramics and semiconductor oxides of zinc grains, resonant bursts of permittivity are observed within a wavelength of 300–400 nm; it has been found that this is due to the presence of electronic polarization of the nanocomposite core. The paper presents the results of modeling the current-voltage characteristics of a nanocomposite based on ceramics and semiconductor grains of zinc oxide. The obtained results show that the geometrical parameters, such as the number of layers and sample width, affect the CVC of the nanocomposite, and the operating point of the CVC shifts. This may be of interest in the development of materials with desired electrical characteristics for the creation of varistors.

Climate change and increased anthropogenic impact on the environment, leading to environmental problems, pose a threat to all countries of the world and require the development of effective solutions in the field of organizing production processes, ensuring the use of resource-saving technologies, increasing production volumes through the use of progressive scientific achievements in the field of digitalization, ecology, chemistry, breeding, etc. As a result, the development of organic agriculture and an increase in its production volumes becomes relevant. The purpose of this paper is to develop a hybrid intelligent decision support system for assessing the effective production of organic crop production in regions, municipal districts, as well as on individual land plots, where data on natural and climatic characteristics, agrochemical soil analysis, monitoring of air parameters, etc. act as input parameters. Within the framework of the article, the authors focused on the consideration of the information, behavioral model and the model of the intellectual system components. Thanks to the implementation of these models of the system for improving the process of managing the manufacture of organic products, the user receives the most optimal proposed solutions on the issue of production, changing parameters and conditions characterizing the level of region development, climatic conditions, socio-economic situation, etc. at the entrance to the system.