Astronomy in Ukraine

The current and prospective status of astronomical research in Ukraine is discussed. A brief history of astronomical research in Ukraine is presented and the system organizing scientific activity is described, including astronomy education, institutions and staff, awarding higher degrees/titles, government involvement, budgetary investments and international cooperation. Individuals contributing significantly to the field of astronomy and their accomplishments are mentioned. Major astronomical facilities, their capabilities, and their instrumentation are described. In terms of the number of institutions and personnel engaged in astronomy, and of past accomplishments, Ukraine ranks among major nations of Europe. Current difficulties associated with political, economic and technological changes are addressed and goals for future research activities presented.Comment: Paper to be published in ``Organizations and Strategies in Astronomy'' -- Vol. 7, Ed. A. Heck, 2006, Springer, Dordrecht; 25 pages, 2 figs, 2 table

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

P-tert-Butylthiacalix[4]arenes functionalized by N-(4'-nitrophenyl)acetamide and N,N-diethylacetamide fragments: Synthesis and binding of anionic guests

© 2017 Vavilova and Stoikov; Licensee Beilstein-Institut. New p-tert-butylthiacalix[4]arenes, which are mono-, 1,2-di- and tetrasubstituted at the lower rim containing N-(4'- nitrophenyl)acetamide and N,N-diethylacetamide groups in cone and partial cone conformations have been synthesized. Their complexation ability towards a number of tetrabutylammonium salts n-Bu 4 NX (X = F - , Cl - , Br - , I - , CH 3 CO 2 -, H 2 PO 4 - , NO 3 - ) was studied by UV spectroscopy. The effective receptor for the anions studied as well as selective receptors for F-, CH 3 CO 2 - and H 2 PO 4 - ions, which based on the synthesized thiacalix[4]arenes, have been obtained. It was shown that p-tertbutylthiacalix[ 4] arene tetrasubstituted at the lower rim by N-(4'-nitrophenyl)acetamide moieties bonded to the anions studied with association constants within the range of 3.55 × 10 3 -7.94 × 10 5 M -1 . Besides, the binding selectivity for F - , Cl - , CH 3 CO 2 - , and H 2 PO 4 - anions against other anions was in the range of 4.1-223.9. Substituting one or two fragments in the macrocycle with N,Ndiethylacetamide groups significantly reduces the complexation ability of the receptor. In contrast to the 1,3-disubstituted macrocycle containing two N-(4'-nitrophenyl)acetamide moieties, the 1,2-disubstituted thiacalix[4]arene, which contains only one such fragment and a N,N-diethylacetamide moiety, selectively binds F - anions