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This paper presents a scientifically based approach to analyzing large volumes of data from digital traces of students on social networks, which allows you to effectively identify emerging and most discussed problems among students, as well as highlight pain points that provide opportunities for growth, development of universities and improvement of the characteristics of the educational process, support for students etc. The study is based on a thematic analysis of messages published in university communities on the VKontakte social network using big data tools. The study results showed that Russian university students still face a number of challenges, including weak technical infrastructure at universities, a digital divide in access to online education, and negative attitudes towards distance learning.The scientific problem of the study is the contradiction between the existing volume of unstructured data of students’ digital traces in social networks and the lack of a scientifically-based and proven methodological approach to the analysis and evaluation of this voluminous data, which creates obstacles to fundamental research into the relationship between students’ activity in social networks and their satisfaction quality of the educational process. The practical focus is determined in conducting data analysis using big data tools. The findings and evidence-based implications are useful for developing innovative strategies and tools for assessing and supporting students.The results show that the use of big data tools for tracking trends based on digital traces of students on social networks provides highly accurate analytical data and can become the basis for identifying problematic situations in individual universities and the industry as a whole, for data-driven decision-making and management .

Background. 1.5 years after the registration of the first vaccine against COVID-19 in Russia, national herd immunity reached only 49.7%. It is obvious that the success of vaccination measures depends on the readiness of the population for immunization and their attitude to the vaccine.The aim of the study was to research the attitude to vaccination against a new coronavirus infection among various socio-demographic population groups in Russia.Materials and methods. The study was conducted online by distributing via social networks a direct link to an electronic form with questions about the attitude to the COVID-19 pandemic and vaccination. A total of 2,786 people (of whom 66.9% were women) aged 16 to 77 years took part in the online survey.Results. It was shown that distrust of vaccination was more often expressed by women and younger people. A targeted approach to these population groups can improve the results of awareness-raising and preventive measures in the context of an ongoing pandemic.

Two models of machine learning are proposed for the automatic prediction of political views held by Russian users of the Vkontakte social network based on a microapproach to data analysis. The results are tested on various scientific and applied fields. One of them is monitoring of public opinion: based on testing a sample of 22 million digital fingerprints of adult user accounts, two estimates were made reflecting the political preferences of the users on the eve of the presidential election in 2018. When these estimates were used to develop a retrospective forecast of the elections, the average absolute errors were 12% and 19.4%, respectively; moreover, the first estimate was correct in ranking the first three candidates. In addition, an approach is presented to calibrate the parameters of mathematical models simulating the dynamics in opinions, namely, the quantities that determine the opinions held by users themselves. This approach is based on the estimates generated by the constructed algorithms. [ABSTRACT FROM AUTHOR]

On the basis of the special theory of relativity and a solution of the Schwarzschild equations of general relativity, a generalized equation of Friedmann-Einstein-de Sitter type has been obtained for the expanding Universe. As a result of our work on the boundary value problem for this equation, we have found the eigenvalue of the resulting eigenvalue problem, linking the density of the Universe in the comoving coordinates, the Hubble constant, and the gravitational constant. We have calculated a value for the density of matter in the Universe which is in agreement with the observational data. We have shown that the missing dark energy is the relativistic kinematic and potential energy of the expanding Universe. [ABSTRACT FROM AUTHOR]

The statement and solution of the problem of the expansion of the Universe in nonstationary spherically-symmetric coordinates in the Schwarzschild metric are considered without pressure taken into account. The observational data of astronomers investigating the rates of recession of distant stars are explained on the basis of the obtained solutions. [ABSTRACT FROM AUTHOR]

On the basis of the special relativity theory (SRT), the mean density of matter in the Universe is calculated with the relativistic kinetic energy of receding galaxies taken into account. Within the framework of the general relativity theory (GRT), we estimate the influence on the mean density of the effect of gravitation. A contradiction between the hypothesis of dark energy and conclusions following from SRT and GRT is noted. [ABSTRACT FROM AUTHOR]

The algorithm and results of numerical calculations of a detached turbulent gas flow by directly solving the Navier-Stokes equations are presented. It has been shown that the turbulent energy dissipated in a detached flow by heat conduction is of the same order of magnitude as that dissipated due to viscosity. It has been revealed that the actual turbulence dissipation begins in turbulent structures whose size is much greater than the internal (Kolmogorov) scale. [ABSTRACT FROM AUTHOR]