A review was given of the role of traditional morphometrics in plant morphological studies using 54 published studies in three major journals and others in Korea, such as Journal of Korean Forestry Society, Korean Journal of Plant Taxonomy, Korean Journal of Breeding, Korean Journal of Apiculture, Journal of Life Science, and Korean Journal of Plant Resources from 1997 to 2008. The two most commonly used techniques of data analysis, cluster analysis (CA) and principal components analysis (PCA) with other statistical tests were discussed. The common problem of PCA is the underlying assumptions of methods, like random sampling and multivariate normal distribution of data. The procedure was intended mainly for continuous data and was not efficient for data which were not well summarized by variances or covariances. Likewise CA was most appropriate for categorical rather than continuous data. Also, the CA produced clusters whether or not natural groupings existed, and the results depended on both the similarity measure chosen and the algorithm used for clustering. An additional problems of the PCA and the CA arised with both qualitative and quantitative data with a limited number of variables and/or too few numbers of samples. Some of these problems may be avoided if a certain number of variables (more than 20 at least) and sufficient samples (40-50 at least) are considered for morphometric analyses, but we do not think that the methods are all mighty tools for data analysts. Instead, we do believe that reasonable applications combined with focus on objectives and limitations of each procedure would be a step forward.
Proper packaging design can meet both the environmental and economic aspects of packaging materials by reducing the use of packaging materials, waste generation, material costs, and logistics costs. Finite element analysis(FEM) is used as a useful tool in various fields such as structural analysis, heat transfer, fluid motion, and electromagnetic field, but its application in the field of packaging is still insufficient. Therefore, the application of FEM to the field of packaging can save the cost and time in the future research because it is possible to design the package by computer simulation, and it is possible to reduce the packaging waste and logistics cost through proper packaging design. Therefore, this study investigated the FEM papers published in Korea for the purpose of helping research design using FEM program in the field of packaging in the future. In this paper, we analyzed the 29 papers that were directly related to the analysis of FEM papers published in domestic journals from 1991 to 2017. As a result, we analyzed the research topic, FEM program, and analysis method using each paper, and presented the direction that can be applied in future packaging field. When the FEM is applied to the packaging field, it is possible to change the structure and reduce the thickness through the stress and vibration analysis applied to the packaging material, thereby reducing the cost by improving the mechanical strength and reducing the amount of the packaging material. Therefore, in the field of packaging research in the future, if the FEM is performed together, economical and reasonable packaging design will be possible.
Media scholars take a lion stake in power circle. Not only do they take a part in media policies but seize prestigious positions like board members in Korea Communication Commission(KCC). Unfortunately, though, little has been known about who they are, what qualifications they have, and whether they meet public interests. This paper attempts to unveil the mechanism of those politicized intellectuals who are specialized on the media. Two categories divided into 'representative' and 'expertise' are employed for this purpose. On the one hand, the representative means the degree of committment into such public services as participation in conferences or non-profit organizations. On the other hand, the number of research articles, books and projects belong to the expertise. Evaluation levels consist of 'excellence, good and average' were allocated to those scholars who are(were) in 'Power Hole,' where decision makings come into being. Some interesting observations were made though this study. First of all, such criteria as representative and expertise vaguely suggested by the laws were hardly fit into those intellectuals, Rarely did they commit into public service let alone showing vigilance in academic activities. Secondly, both ideological loyalty and political activities in line with the government had much to do with taking such positions. Thirdly, not surprisingly, it showed that to graduate from Seoul National University and have Ph.D. degree from U.S.A. was one of the most essential factors. In final, most of them were very good at taking advantage of the press in way of boosting their publicity. To attend at policy making processes either in form of board members or advisers is inevitable for media experts. However, as shown in this study, such qualification of public service and academic eagerness shouldn't be underestimated. Academic integrity not selling intelligence solely for private interests needs to be protected as well. The authors hope this study to provide a valuable opportunity to establish a kind of ethical standards in participating into politics.
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 (