The problem of the effectiveness of teaching can be successfully solved only if the high quality of lessons is supported by well-organized homework of students. The question of homework occupies one of the main places in educational activities since this question is directly related to the health of the child. A competent approach to minimizing the time for completing homework, taking into account the maximum efficiency obtained from the learning process, can preserve the health of students to some extent. The article describes a method for obtaining the most comfortable results of the process of completing homework, which are a Pareto set. This method is implemented using a genetic algorithm and queuing theory, and the selection of homework is carried out on the basis of intellectual analysis of the text of tasks and is a scale of a certain range. The proposed algorithm successfully obtains the solutions of the Pareto set and minimizes the efforts of school students while achieving the maximum efficiency of the educational process to preserve their health. Compared with other known algorithms, the results obtained show that the proposed algorithm demonstrates fairly accurate optimization characteristics presented in the form of a Pareto set. Furthermore, combining a genetic algorithm, queuing theory apparatus, and a neural network makes it possible to model the studied subject area more accurately.

This article deals with the multicriteria programming model to optimize the time of completing home assignments by school students in both in-class and online forms of teaching. To develop a solution, we defined 12 criteria influencing the school exercises’ effectiveness. In this amount, five criteria describe exercises themselves and seven others the conditions at which the exercises are completed. We used these criteria to design a neural network, which output influences target function and the search for optimal values with three optimization techniques: backtracking search optimization algorithm (BSA), particle swarm optimization algorithm (PSO), and genetic algorithm (GA). We propose to represent the findings for the optimal time to complete homework as a Pareto set.

The development of mathematical models and efficient technologies for the processing of protein-containing dairy and vegetable raw materials and the production of food and feed concentrates with controlled functional properties is one of the most promising areas within the agricultural industry. In this work, the suitability of the electroflotation coagulation method for the combined extraction of vegetable and milk proteins was established by changing the electrolysis parameters and directed regulation of the isoelectric state of proteins. The research methodology is based on modern achievements of leading domestic and foreign researchers in the field of electrolysis of solutions and the creation of reagentless technologies for extracting proteins, as well as on the use of guest methods of physicochemical analysis, pH-metry, potentiometric and organoleptic analysis, methods of cyclic chronovoltammetry and currentless chronopotentiometry. The paper presents technological schemes for the extraction of vegetable and milk proteins, based on the combination of electroflotation and electrocoagulation processes. We carried out technological tests, which made it possible to determine the optimal conditions that ensure the highest yield of the product and its quality indicators. Ready-made isolates and concentrates of chickpea proteins and curd whey were obtained.

Composite materials consisting of a dielectric matrix with conductive inclusions are promising in the field of micro- and optoelectronics. The properties of a nanocomposite material are strongly influenced by the characteristics of the substances included in its composition, as well as the shape and size of inclusions and the orientation of particles in the matrix. The use of nanocomposite material has significantly expanded and covers various systems. The anisotropic form of inclusions is the main reason for the appearance of optical anisotropy. In this article, models and methods describing the electrical conductivity of a layered nanocomposite of a self-similar structure are proposed. The method of modeling the electrical conductivity of individual blocks, layers, and composite as a whole is carried out similarly to the method of determining the dielectric constant. The advantage of the method proposed in this paper is the removal of restrictions imposed on the theory of generalized conductivity associated with the need to set the dielectric constant.

Currently, a significant group of industrial facilities can be classified as chemically hazardous facilities (CHFs). To predict the spread of harmful impurities in the programs being developed, Gaussian and Lagrangian models are actively used, on the basis of which the complexes used both in the EMERCOM of Russia and in research organizations are being implemented. These complexes require the introduction of a large amount of information, including the characteristics of the wind field in the distribution of an emergency chemically hazardous substance, which limits their use. In systems, the formation of which is influenced by a large number of different random factors, spatial scaling (similarity) is often found, and one or another parameter can be described using the methods of fractal geometry, which in the past few decades has been actively and successfully applied to the description of various physical objects. The purpose of this study is to analyze the possibility of using the random-addition method for early prediction of the distribution of harmful impurities in the surface air layer during the short-term release of a substance on the surface as a result of an emergency.