Analysis of Research Trends on Concrete-Polymer Composite (콘크리트-폴리머 복합체에 관한 연구동향 분석)
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- Proceedings of the Korea Concrete Institute Conference
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- 1992.10a
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- pp.63-69
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- 1992
Research trends of concrete-polymer composite were analyzed based on 549 papers that were presented at international Congresses on Polymer in Concrete that have been opened seven times all around world since 1975. The analysis revealed that relative quantities of the papers about PC, PCC, PIC and others were 51%, 24%, 13% and 12%, in the order. Basic research was the main stream for PCC, PC, PIC. In PC study, however, it was shown that many researches were actively performed for application of PC.
To establish the fundament for EBM of Traditional Korean Medicine, the papers on Samul-tang which was frequently used in medical institutions of Traditional Korean Medicine were analyzed through researching domestic and international literatures. The papers were classified by the registration of domestic or international journals, by the year of publishment, by experimental methods, by laboratory animals used in biological experiment and by the kinds of studies on biological efficacy. Of total 67 papers on Samul-tang, 58 volumes were registered in domestic journals and 9 volumes were in international journals of which 8 volumes were in SCI journals. Since 1978, publishments of papers have continuously increased. The papers on instrumental analyses were 6, biological studies were 58 volumes, clinical studies were 3. Instrumental analyses were preceeded with standard compounds(gallic acid, albiflorin, paeoniflorin, benzoic acid, ferulic acid, 5-HMF). And biological studies showed improvement of cardiovascular function and circulation, antianemia, brain protection, immunoregulation, antistress, radioprotection, antifatigue, antiinflammation and antiallergy, antioxidative effect. Through clinical studies, antifatigue, improvement of insomnia and osteoporosis were reported. Samul-tang could be used to tonify and activate blood. And further study on clinical field need to be conducted in accordance with biological study.
From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (