The article points out some of the challenges faced by students at the University of Ostrava, Comenius University in Bratislava and Al-Farabi Kazakh National University, who had almost no previous practice in online teaching during the COVID-19 pandemic situation. The students from the mentioned universities were interviewed to share their experiences with online teaching and learning during the COVID-19 lockdown. In this paper, qualitative pedagogical research in the local conditions of the three mentioned universities is used, with the processing of answers of selected students who were interviewed as an available sample. The students were also asked to describe their impressions of this situation from their social and personal points of view. The focus is on the positive and negative aspects, boundaries, and problems of online university teaching during the pandemic situation, as well as changes in the personal life of the students. The aim of this introductory small-scale study is to provide a basis for future research on the impact that the COVID-19 situation has had on the educational process at the above-mentioned universities, as well as to assist educational providers in foreseeing and eliminating the possible problems of lecturers when establishing an online educational environment. Some conclusions are drawn from the interviews that offer potential for further research in educational science, because many difficulties from the students’ point of view are related to their social status, lack of social contact, technical problems with Internet connections, the carrying out of lectures and the exams of students. The students’ answers are categorized, and each category is described.

The classification of variable stars is essential for understanding stellar evolution and dynamics. With the growing volume of light curve data from extensive surveys, there is a need for automated and accurate classification methods. Traditional methods often rely on manual feature extraction and selection, which can be time-consuming and less adaptable to large datasets. In this work, we present an approach using a convolutional neural network (CNN) to classify variable stars using only raw light curve data and their known periods, without the need for manual feature extraction or hand-selected data preprocessing. Our method utilizes phase-folding to organize the light curves and directly learns the variability patterns crucial for classification. Trained and tested on the Optical Gravitational Lensing Experiment (OGLE) dataset, our model demonstrates an average accuracy of 88% and an F1 score of 0.89 across five well-known classes of variable stars. We also compared our classification model with the Random Forest (RF) classifier and showed that our model gives better results across all of the classification metrics. By leveraging CNN, our approach does not need manual feature extraction and can handle diverse light curve shapes and sampling cadences. This automated, data-driven method offers a powerful tool for classifying variable stars, enabling efficient processing of large datasets from current and future sky surveys.

We present new spectroscopic orbits for the bright binaries Mizar B, 3 Pup, $\nu$ Gem, 2 Lac, and $\varphi$ Aql. Our analysis is based on medium-resolution ($R\approx$ 12,000) échelle spectra obtained with the 0.81-m telescope and fiber-fed eShel spectrograph of the Three College Observatory (Greensboro, NC, USA) between 2015 and 2024. Orbital elements were inferred with an affine-invariant Markov-chain Monte-Carlo sampler; convergence was verified through the integrated autocorrelation time and the Gelman–Rubin statistic. Errors quote the 16th–84th-percentile credible intervals. Compared with previously published orbital solutions for the studied stars, our method improves the root-mean-square residuals by 25–50% and bring the 1$\sigma$ uncertainties on the radial velocity (RV) semi-amplitudes down to 0.02–0.15 km $s^{-1}$. These gains translate into markedly tighter mass functions and systemic RVs, providing a robust dynamical baseline for future interferometric and photometric studies. A complete Python analysis pipeline is openly available in a GitHub repository, ensuring full reproducibility. The results demonstrate that a Bayesian RV analysis with well-motivated priors and rigorous convergence checks yields orbital parameters that are both more precise and more reproducible than previous determinations, while offering fully transparent uncertainty budgets.

Theoretical foundations are presented for the application of information–entropy methods from statistical physics to the determination of stellar evolutionary stages. A balance equation involving normalized conditional information and entropy is proposed. The conditional information is defined as the difference between the entropy of the phase space and the conditional probability entropy. A correspondence is demonstrated between theoretical predictions and observational data from stellar emission spectra with respect to their evolutionary classification. The proposed methodology is further applied to the analysis of complex FS CMa-type objects, which exhibit dusty and gaseous structures with components at different evolutionary stages. In this context, the conditional information derived from asymmetric spectral lines is shown to be consistent with the theoretical criteria for the evolutionary status of single, binary, and unclassified stars.