6,307 research outputs found

    The factors affecting pregnancy outcomes in the second trimester pregnant women

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    Adequate nutrient intake during pregnancy is important to fetal and maternal health. The purpose of this study was to investigate the factors affecting birth weight and gestational age and to provide basic data to promote more favorable pregnancy outcomes. Data were collected from 234 pregnant women at two hospitals in Seoul. Demographic characteristics, anthropometric measurements and health related habits were obtained using a questionnaire at the hospital visit during the second trimester. Dietary intakes were estimated by 24 hour recall at the hospital visit during the second trimester. Data on pregnancy outcomes, including birth weights and gestational ages, were obtained from hospital records after delivery. Birth weights were divided into a low birth weight group (birth weight<3.1 kg), a normal birth weight group (3.1-3.6 kg) and a high birth weight group (>3.6 kg). Gestational ages were divided into tertiles according to the gestational age of the subjects: group 1 (<38.53 weeks), group 2 (38.53-40.00 weeks) and group 3 (>40.00 weeks). The number of family members was significantly lower in the low birth weight group than in the normal birth weight group (p<0.05). In the low birth weight group, pregnancy weight was significantly lower than in the high birth weight group (p<0.05). Health related habits were not significantly different among any of the groups. Intakes of fiber, phosphorous, iron, vitamin B6 and folic acid were significantly higher in the high birth weight group than the low birth weight group (p<0.05). Gestational age was not significantly affected by nutrient intakes, but birth weight was affected by nutrient intake in the results of this study. Therefore, the adequacy of nutrient intake is important for the improvement of pregnancy outcomes

    Arctic Policy of the Republic of Korea

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    Korea has been aware of the humanitarian and national significance of the Arctic and Antarctic since before the millennium. On the one hand, Korea has strived in the field of scientific research by joining the Antarctic Treaty System. On the other hand, Korea has promoted various economic cooperation with the Arctic nations. Korea joined the Antarctic Treaty System in 1986, established the Antarctic King Sejong Station in 1988, established the Arctic Dasan Station in Ny-Ålesund, Norway in 2002, and joined the Svalbard Treaty in 2012. Furthermore, Korea has participated in summits with the Arctic nations since 2008. In 2012, President Lee, Myung-Bak visited Russia, Greenland, and Norway to promote cooperation over the Northern Sea Route, shipbuilding, and energy resources, among other things. Behind the government actions over Polar activities lie government policies and plans, such as the Basic Plan of Antarctic Research (2007-2011, 2012-2016), Measures for the Advancement of Polar Region Policy (2012), Comprehensive Arctic Plan (2013), and Korean Arctic Master Plan (2013). This article will focus on the background to Korea’s 2013 Basic Plan for Arctic Policy, and the remaining tasks that now lie before the Korean government

    Performance Comparison of CRUD Operations in IoT based Big Data Computing

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    Nowadays, due to the development of mobile devices, the kinds of data that are generated are becoming diverse, and the amount is becoming huge. The vast amount of data generated in this way is called big data. Big data must be processed in a different way than existing data processing methods. Representative methods of big data processing are RDBMS (Relational Database System) and NoSQL method. We compare NoSQL and RDBMS, which are representative database systems. In this paper, we use MySQL query and MongoDB query to compare RDBMS and NoSQL. We gradually compare the performance of CRUD operations in MySQL and MongoDB by increasing the amount of data. MongoDB sets index and compares it again.  Through result of these operations is to choose a database system that fits the situation.  This makes it possible to design and analyse big data more efficiently.
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