To solve a number of technological issues, it is advisable to use mathematical modeling, which will allow us to obtain the dependences of the influence of the technological parameters of chemical and thermal treatment processes on forming the depth of the diffusion layers of steels and alloys. The paper presents mathematical modeling of diffusion processes based on the existing chemical and thermal treatment of steel parts. Mathematical modeling is considered on the example of 38Cr2MoAl steel after gas nitriding. The gas nitriding technology was carried out at different temperatures for a duration of 20, 50, and 80 h in the SSHAM-12.12/7 electric furnace. When modeling the diffusion processes of surface hardening of parts in general, providing a specifically given distribution of nitrogen concentration over the diffusion layer’s depth from the product’s surface was solved. The model of the diffusion stage is used under the following assumptions: The diffusion coefficient of the saturating element primarily depends on temperature changes; the metal surface is instantly saturated to equilibrium concentrations with the saturating atmosphere; the surface layer and the entire product are heated unevenly, that is, the product temperature is a function of time and coordinates. Having satisfied the limit, initial, and boundary conditions, the temperature distribution equations over the diffusion layer’s depth were obtained. The final determination of the temperature was solved by an iterative method. Mathematical modeling allowed us to get functional dependencies for calculating the temperature distribution over the depth of the layer and studying the influence of various factors on the body’s temperature state of the body.

Within the states of the European Union, the traditional system of education in the concept of student-teacher-department dominates in the 21st century. Although technically oriented teaching is transferred to the digital world a bit more demanding compared to teaching in the humanities, there is a period of significant modernization and digitization in the field of education of technical subjects using a wide range of Learning management system approaches. This article is focused on the implementation of industry 4.0 using E-learning and M-learning approaches in technically-oriented education. The introductory part provides an overview of the issues of the Learning Management System by the concept of Industry 4.0. Based on research studies, the next part of the article provides a brief overview of the most well-known LMS systems with a subsequent description of the most suitable system. The main part of the article describes the implementation of Elearning and M-learning approaches on a specific example from the educational practice implemented at the Faculty of Manufacturing Technologies with a seat in Prešov.

This study was conducted with the aim of protecting groundwater, which plays a crucial role in ensuring food quality in the market, preserving public health, and safeguarding the ecosystem, as many regions rely on clean natural groundwater for their population’s survival. The objective of this study was to use the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) for groundwater at 12 stations in the Okhla Industrial Area, Nangloi, and Karol Bagh in the Delhi Region. CCME WQI is an effective tool for assessing groundwater quality and communicating water conditions to various users. The research methodology involved fieldwork from June to October 2020 for three different periods in the year: pre-monsoon, monsoon, and post-monsoon, to observe variations in water quality and differences in various physicochemical properties of water. The CCME WQI was applied using sixteen water quality parameters, fourteen of which were physicochemical parameters and two of which were microbiological parameters. Among the physicochemical parameters were color, odor, pH, turbidity, nitrate, total hardness, iron, chloride, fluoride, total dissolved solids, calcium, magnesium, sulfate, and alkalinity, while the microbiological parameters included the total coliform and Escherichia coli counts. Based on the results obtained from the water quality index, station A9 scored between 0 and 44, indicating the lowest water quality index due to wastewater discharges and industrial contamination. The water quality at other stations also requires attention to achieve excellent ratings. The study concludes that serious measures should be taken for proper management of the area to protect the population from hazardous diseases. The research results show that stations 1, 2, and 10 were rated as excellent, station 12 as good, stations 4, 5, and 8 as moderate, stations 3, 6, and 11 as marginal, and station 9 as the poorest in terms of water quality in the year 2020 during the pre-monsoon, monsoon, and post-monsoon periods. To improve the parameters and groundwater quality, it would be necessary to reduce the impact of industry, anthropogenic–geogenic activities, and domestic activities.