Higher psychological and physical well-being was observed in the experimental group as compared with the control group (P < 0.01) in 2 weeks after the first surgery and 2 months after scheduled corrective surgeries, which finished in denture installation.

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<p class="0abstract">Over the past few years, the teaching process has transformed radically under significant investments in information and communication technologies. In this context, mobile technologies emerge as an innovative educational tool. Mobile devices are being used by a vast number of so-called "digital generation" representatives in daily life and educational activities. It has been proven that the implementation of mobile technologies in education contributes to the increase of students' motivation, participation in the learning process, and faster acquisition of professional competencies. These technologies take the role of the "driving force" in training. However, their comprehensive understanding is essential to use them effectively. The objective of this study was to analyze the intensity of mobile technologies' use and investigate their evolution in higher education using the example of the I.M. Sechenov First Moscow State Medical University. The research sample was represented by 151 students (data collected for 2015/2016) and 274 students (data for 2019/2020). The average participants' age corresponded to 19.8. The study also involved three experts who were required to evaluate questionnaires completed in Google Forms. The scientific method of the study was based on the organized examination, strict control over the involved respondents, and quantitative research. The research outcomes were analyzed through Chi-Square goodness-of-fit test. The target questions of the survey were rated on a 5-point Likert scale. According to the study results, 95% of the respondents used mobile devices for educational purposes, of which 65% agreed with the convenience of having course materials on a mobile device. In the 2015/2016 academic year, the share of students using smartphones for learning comprised 10.4%, while in 2019/2020, their percentage increased to 61.5%. The study findings will be useful for university teachers and representatives of educational institutions' administration.</p>

This paper presents the results of modeling the distribution process of industrial emission components at specified distances from the emission source along the normal. The model uses a system of differential diffusion equations to compute the concentration profiles of aerosols, industrial gases, and fine particles in the atmosphere. In order to investigate the regularity of the emitter propagation into the atmosphere, a theory of impurity dispersion was developed. The model is constrained by the effect of particle interactions. The partial derivative equations are presented to calculate the concentrations of aerosols and fine particles under the turbulent airflow in the atmosphere, dispersion of inert impurities, and distribution of chemically active compounds. The adequacy of the mathematical model for a series of theoretical calculations was checked by contrasting the data of the atmospheric air monitoring for the cities of Almaty, Ust-Kamenogorsk, Pavlodar, Atyrau, Krasnodar, Chelyabinsk, Beijing, and Shanghai. Air monitoring data included PM10, SO2, and NO2 levels. The mathematical model solutions for the relative values of the emitter concentration in the direction along the normal of the pollution source at the surface were obtained. Graphical interpretation of the calculation results over the 0…200 m distance for time intervals ranging from 3 to 600 min was provided. According to the multiple factor cluster analysis, the critical values of SO2 concentrations in Atyrau exceeded MPC in 26.2% of cases. The level of NO2 for Shanghai was 15.6%, and those for PM10 concentrations in Almaty and Atyrau amounted to 16.4%. A comparison of theoretical values and results obtained from official sources showed arithmetic mean of 49.4 mg/m3 and maximum value of 823.0 mg/m3. Standard deviation comprised 48.9 mg/m3. Results were considered statistically significant at p≤0.005. The mathematical model developed in this study can be used to predict the status of atmospheric air.

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