Observations of Sy2 galaxy NGC 3281 by XMM-Newton and INTEGRAL satellites

We present here the results of our analysis of X-ray properties of Seyfert 2 galaxy NGC 3281, based on the observational data obtained by XMM-Newton and INTEGRAL within the energy ranges 0.2-12 keV and 20-150 keV, respectively. The XMM-Newton spectrum of this object is presented for the first time. We show that fitting the X-ray spectrum of this galaxy with models based on the reflection from the disc with infinite column density yields non-physical results. More appropriate fit takes into account both transmitted and reflected emission, passed through a gas-dusty torus-like structure. Keeping this in mind, to model the inhomogeneous clumpy torus, we used the MYTorus model. Hence, we propose that the torus of NGC 3281 is not continuous structure, but it consists of separate clouds, which is in a good agreement with the results of near-IR observations. Using this assumption, we found that the torus inclination angle and the hydrogen column density are 66.98^{+2.63}_{-1.34} degrees and 2.08^{+0.35}_{-0.18}x10^{24} cm^{-2}, respectively. Also, the emission of the hot diffuse gas with temperature ~590 eV and warm absorption were detected.Comment: 8 pages, 5 figures, 2 tables, accepted for publication in Advances in Astronomy and Space Physic

Nonequilibrium transport equations and ab initio study of adsorption processes on carbon nanotubes

In a theoretical study of gas adsorption on carbon nanotubes (CNT) nonequilibrium processes of ionization, polarization, surface diffusion and desorption of atoms are considered self-consistently. The approach is based on Zubarev's method of nonequilibrium statistical operator and reaction-diffusion theory. The set of nonlinear transport equations are obtained for the chosen parameters of description: the average numbers of adsorbed atoms, ionized and polarized atoms in the electromagnetic field of CNT, and the average number of atoms desorbed from the CNT surface. Ab initio simulations are conducted for a "gas-single wall carbon nanotube" system for gases of particular practical interest: He and NO. The obtained values of adsorption energy reveal preferable localization sites of absorbed He atoms as well as their dependency on adsorption distances. A significant effect of NO adsorption on CNT electronic properties is demonstrated. The effect of presence of vacancies on adsorption nature is analyzed. It is shown that under the influence of vacancy formation the CNT structure undergoes reconstruction that enables chemisorption of NO molecules.Comment: 12 pages, 4 figure

Chronic Fatigue Syndrome in Medical Students

The term "burnout syndrome" was first used in 1974 by the American psychiatrist H.J. Freundenberger, who drew attention to this phenomenon in psychiatric workers and described it as "defeat, exhaustion or wear and tear that happens to a person as a result of sharply overestimated requirements for their own resources and forces" [5]. If a medical student during his studies does not feel his personal connection with professional activity, he does not see intrinsic value, can not really give himself to it, then inevitably there is internal devastation, because there is no dialogic exchange in which a person not only gives, but also receives. As a result, the disorder becomes depressive

Statistical description of electrodiffusion processes in the electron subsystem of a semibounded metal within the generalized jellium model

Based on the calculation of the quasiequilibrium statistical sum by means of the functional integration method, we obtained a nonequilibrium statistical operator for the electron subsystem of a semibounded metal in the framework of the generalized jellium model in the Gaussian and higher approximations with respect to the dynamic electron correlations. This approach allows one to go beyond the linear approximation with respect to the gradient of the electrochemical potential corresponding to weakly nonequilibrium processes and to obtain generalized transport equations that describe nonlinear processes.Comment: 13 page

Machine learning technique for morphological classification of galaxies at z<0.1 from the SDSS

Methods. We used different galaxy classification techniques: human labeling, multi-photometry diagrams, Naive Bayes, Logistic Regression, Support Vector Machine, Random Forest, k-Nearest Neighbors, and k-fold validation. Results. We present results of a binary automated morphological classification of galaxies conducted by human labeling, multiphotometry, and supervised Machine Learning methods. We applied its to the sample of galaxies from the SDSS DR9 with redshifts of 0.02 < z < 0.1 and absolute stellar magnitudes of 24m < Mr < 19.4m. To study the classifier, we used absolute magnitudes: Mu, Mg, Mr , Mi, Mz, Mu-Mr , Mg-Mi, Mu-Mg, Mr-Mz, and inverse concentration index to the center R50/R90. Using the Support vector machine classifier and the data on color indices, absolute magnitudes, inverse concentration index of galaxies with visual morphological types, we were able to classify 316 031 galaxies from the SDSS DR9 with unknown morphological types. Conclusions. The methods of Support Vector Machine and Random Forest with Scikit-learn machine learning in Python provide the highest accuracy for the binary galaxy morphological classification: 96.4% correctly classified (96.1% early E and 96.9% late L types) and 95.5% correctly classified (96.7% early E and 92.8% late L types), respectively. Applying the Support Vector Machine for the sample of 316 031 galaxies from the SDSS DR9 at z < 0.1, we found 141 211 E and 174 820 L types among them.Comment: 10 pages, 5 figures. The presentation of these results was given during the EWASS-2017, Symposium "Astroinformatics: From Big Data to Understanding the Universe at Large". It is vailable through \url{http://space.asu.cas.cz/~ewass17-soc/Presentations/S14/Dobrycheva_987.pdf