Первый директор БЕН РАН А. Г. Захаров. К 100-летию со дня рождения

The article is dedicated to the memory of the first director of the Library of Natural Sciences of the Russian Academy of Sciences (before 1991, the USSR Academy of Sciences). The Library for Natural Sciences was established in 1973 on the basis of the Sector for Special Libraries (in charge of collection development of Moscow research institutes and of their union catalog maintenance). The Library for Natural Sciences was conceived as an information library center focused on science and research information support based on modern technologies. Alexander Grigorievich Zakharov, newly-retired military professional, Lieutenant General, headed the library. In the shortest time possible, Alexander Zakharov built the centralized library system headed by the Library for Natural Sciences and meeting the demands of the time. For many years the Library’s Centralized Library System has been the largest and most developed network; the Library has been the leader in library automation based on the newest computer and network technologies. The authors have been working for the Library for over 40 years, and over 30 years under the leadership of Alexander G. Zakharov. They review the main stages of Zakharov’s career: the Great Patriotic War (he went through the war from beginning to end), his service at cosmodrome of Baikonur (launch operations, including support of Yury Gagarin’s flight), his work in the Library for Natural Sciences.Статья посвящена памяти первого директора Библиотеки по естественным наукам (БЕН) Российской академии наук (до 1991 г. – БЕН АН СССР). БЕН была образована в 1973 г. на базе Сектора сети специальных библиотек (занимался комплектованием библиотек академических институтов Москвы и ведением сводного каталога их фондов). БЕН создавалась как информационно-библиотечный центр, основная задача которого – информационное обеспечение учёных на основе современных технологий. Директором БЕН был назначен Александр Григорьевич Захаров – профессиональный военный, незадолго до назначения вышедший в отставку в звании генерал-лейтенанта. В кратчайшие сроки он создал централизованную библиотечную систему (ЦБС) во главе с БЕН, отвечающую самым строгим современным требованиям. На протяжении многих лет ЦБС БЕН оставалась наиболее крупной и развитой сетью библиотек страны, а БЕН – одной из ведущих библиотек в области автоматизации информационно-библиотечных процессов на основе новейших компьютерных и сетевых технологий. Авторы проработали в БЕН более 40 лет, из них – более 30 под руководством А. Г. Захарова. В статье рассмотрены основные этапы жизненного пути Александра Григорьевича: Великая Отечественная война (прошёл её от начала до конца), служба на космодроме Байконур (подготовка запуска космических кораблей, в том числе полёта Ю. А. Гагарина), работа в БЕН

Effective selection of informative SNPs and classification on the HapMap genotype data

<p>Abstract</p> <p>Background</p> <p>Since the single nucleotide polymorphisms (SNPs) are genetic variations which determine the difference between any two unrelated individuals, the SNPs can be used to identify the correct source population of an individual. For efficient population identification with the HapMap genotype data, as few informative SNPs as possible are required from the original 4 million SNPs. Recently, Park <it>et al.</it> (2006) adopted the nearest shrunken centroid method to classify the three populations, i.e., Utah residents with ancestry from Northern and Western Europe (CEU), Yoruba in Ibadan, Nigeria in West Africa (YRI), and Han Chinese in Beijing together with Japanese in Tokyo (CHB+JPT), from which 100,736 SNPs were obtained and the top 82 SNPs could completely classify the three populations.</p> <p>Results</p> <p>In this paper, we propose to first rank each feature (SNP) using a ranking measure, i.e., a modified t-test or F-statistics. Then from the ranking list, we form different feature subsets by sequentially choosing different numbers of features (e.g., 1, 2, 3, ..., 100.) with top ranking values, train and test them by a classifier, e.g., the support vector machine (SVM), thereby finding one subset which has the highest classification accuracy. Compared to the classification method of Park <it>et al.</it>, we obtain a better result, i.e., good classification of the 3 populations using on average 64 SNPs.</p> <p>Conclusion</p> <p>Experimental results show that the both of the modified t-test and F-statistics method are very effective in ranking SNPs about their classification capabilities. Combined with the SVM classifier, a desirable feature subset (with the minimum size and most informativeness) can be quickly found in the greedy manner after ranking all SNPs. Our method is able to identify a very small number of important SNPs that can determine the populations of individuals.</p

Mobile robot scheduling for cycle time optimization in flow-shop cells, a case study

The typical production system in carton box production companies is cell-production. These cells normally benefit from a mobile robot which serves the machines according to a given schedule. One of the main problems of such companies is finding the order of robot moves in a way that the time required for completing all jobs is minimized. In the studied case in this research, each cell contains three machines of which, two or three of them might be activated for production process depending on the product type. These machines are equipped with a one-capacitated input and output buffer. Considering the fact that the machines are capable of performing any operation, the assignments of the jobs to them may have several alternatives. The one-capacitated buffers make the robot scheduling more complex as they act as extra stations to be served by the robot (contribute to exponential increase in job assignments permutation). This study aims to deal with this complexity and provide a decision-making toolbox for business owners to determine and employ the best robot moving schedule according to the characteristics of the problem. The mentioned approach significantly contributes to decision-maker’s effective time management and results in adopting a better production scheme for each production cycle. In line with this prospect, this research proposes a sequential part production matrix (SPPM) to determine feasible robot move strategies through which the best scheduling scheme is introduced for different problem configurations. Additionally, a metaheuristic algorithm is proposed to determine the best robot move strategy for cases with more active machines in a cell as manual determination of the robot move strategies becomes exhaustive in such cases.</p