• Journal of Fisheries and Marine Sciences Education
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• v.26 no.1
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• pp.156-165
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• 2014

The purpose of this study was to find research trends of smart learning. For this, we identified the research's characteristics such as the subject or keyword of research, method, data collection, and statistical analysis method. The 2,865 articles published from 1995 to 2013 were gathered from five Korean academic journals related to smart learning. Among them, research keyword, areas, research method, data collection method, and statistical analysis method were analyzed on 596 papers. The findings of this study were as follows: (a) Smart learning papers such keyword likes u-learning, m-learning, and smart-learning were emerging after 2006. Smart learning papers with ICT related topics were highly increased after 2000, but they were decreased after 2006. Smart learning papers with e-learning related keywords were steadily increased after 2000 through 2013. (b) The research field of deign had the highest portion in smart learning research, but managing had the lowest portion. (c) Development was mainly used as a research method. Both questionnaire and experiment were mainly used for collecting data methods. T-test and frequency analysis were mainly used as statistical analysis methods.

• Korean Journal of Occupational Health Nursing
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• v.24 no.2
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• pp.76-85
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• 2015

Purpose: The purpose of this study was to identify knowledge structure of the Korean Journal of Occupational Health Nursing from 1991 to 2014. Methods: 400 articles between 1991 and 2014 were collected. 1,369 keywords as noun phrases were extracted from articles and standardized for analysis. Co-occurrence matrix was generated via a cosine similarity measure, then the network was analyzed and visualized using PFNet. Also NodeXL was applied to visualize intellectual interchanges among keywords. Results: According to the results of the content analysis and the cluster analysis of author keywords from the Korean Journal of Occupational Health Nursing articles, 7 most important research topics of the journal were 'Workers & Work-related Health Problem', 'Recognition & Preventive Health Behaviors', 'Health Promotion & Quality of Life', 'Occupational Health Nursing & Management', 'Clinical Nursing Environment', 'Caregivers and Social Support', and 'Job Satisfaction, Stress & Performance'. Newly emerging topics for 4-year period units were observed as research trends. Conclusion: Through this study, the knowledge structure of the Korean Journal of Occupational Health Nursing was identified. The network analysis of this study will be useful for identifying the knowledge structure as well as finding general view and current research trends. Furthermore, The results of this study could be utilized to seek the research direction in the Korean Journal of Occupational Health Nursing.

• Journal of the Korean Society of Propulsion Engineers
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• v.20 no.4
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• pp.104-111
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• 2016

The flame stabilization is one of the topics which have to be solved for the airbreathing propulsion systems, using the entering air which is supersonic velocity as an oxygen sources. Making a recirculation zone with an eddy flow, installed the reducing velocity devices such as the bluff body, is the typical method of the flame stabilization. Recently using a cavity flame stabilization at the wall is an emerging technique as an effective method which extends the stabilization zone, and the related research papers have been published on the flow separation and reattachment, pressures and oscillations including length/depth ratios in the cavities. Even though, still there are lots of topics to study more in the cavity flame stabilization field as the preceding techniques, as well as the research and the development of the airbreathing propulsion system itself.

• Journal of the Korean Society for information Management
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• v.40 no.4
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• pp.403-428
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• 2023

This study investigates the development path and intellectual structure of data literacy research, aiming to identify emerging topics in the field. A comprehensive search for data literacy-related articles on the Web of Science reveals that the field is primarily concentrated in Education & Educational Research and Information Science & Library Science, accounting for nearly 60% of the total. Citation network analysis, employing the PageRank algorithm, identifies key papers with high citation impact across various topics. To accurately trace the development path of data literacy research, an enhanced PageRank main path algorithm is developed, which overcomes the limitations of existing methods confined to the Education & Educational Research field. Keyword bibliographic coupling analysis is employed to unravel the intellectual structure of data literacy research. Utilizing the PNNC algorithm, the detailed structure and clusters of the derived keyword bibliographic coupling network are revealed, including two large clusters, one with two smaller clusters and the other with five smaller clusters. The growth index and mean publishing year of each keyword and cluster are measured to pinpoint emerging topics. The analysis highlights the emergence of critical data literacy for social justice in higher education amidst the ongoing pandemic and the rise of AI chatbots. The enhanced PageRank main path algorithm, developed in this study, demonstrates its effectiveness in identifying parallel research streams developing across different fields.

• Environmental Analysis Health and Toxicology
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• v.23 no.3
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• pp.143-164
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• 2008

Concern about perfluorinated compounds (PFCs) is growing nationally as well as globally. PFCs could be considered emerging POPs due to their environmentally persistent, bioaccumulative, and potentially harmful properties. Moreover. perfluoroalkylates (PFAs) such as PFOS and PFOA are reported to experience long-range transport (LRT) to the Arctic in spite of their low volatility and strong solubility. The possible pathways contributing to LRT have been proposed but are still in debate in combination with unclear source definition and uncertain physico-chemical properties. The environmental fate of PFCs is more complicated because of the presence of precursors that are degraded to PFAs and are extremely different from their daughters, PFAs. in physico-chemical properties. To what extent and through what pathways are human and wildlife exposed is determined by the environmental fate and distribution of PFCs. To define uncertainties in fate and distribution thus is critical to prevent erroneous policy and/or determination related with exposure and risk reduction. This article aimed to review controversy and/or uncertain issues for the environmental fate and distribution of PFCs and to prospect research topics necessary to dissolve uncertainties.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.65-82
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• 2015

Recently, there has been a surge of interest in finding core issues and analyzing emerging trends for the future. This represents efforts to devise national strategies and policies based on the selection of promising areas that can create economic and social added value. The existing studies, including those dedicated to the discovery of future promising fields, have mostly been dependent on qualitative research methods such as literature review and expert judgement. Deriving results from large amounts of information under this approach is both costly and time consuming. Efforts have been made to make up for the weaknesses of the conventional qualitative analysis approach designed to select key promising areas through discovery of future core issues and emerging trend analysis in various areas of academic research. There needs to be a paradigm shift in toward implementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The change is to ensure objective and practical emerging trend analysis results based on large amounts of data. However, even such studies have had shortcoming related to their dependence on simple keywords for analysis, which makes it difficult to derive meaning from data. Besides, no study has been carried out so far to develop core issues and analyze emerging trends in special domains like the aviation industry. The change used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and emerging trends from overall research papers pertaining to aviation through text mining, which is one of the big data analysis techniques. In this manner, the promising future areas for the air transport industry are selected based on objective data from aviation-related research papers. In order to compensate for the difficulties in grasping the meaning of single words in emerging trend analysis at keyword levels, this study will adopt topic analysis, which is a technique used to find out general themes latent in text document sets. The analysis will lead to the extraction of topics, which represent keyword sets, thereby discovering core issues and conducting emerging trend analysis. Based on the issues, it identified aviation-related research trends and selected the promising areas for the future. Research on core issue retrieval and emerging trend analysis for the aviation industry based on big data analysis is still in its incipient stages. So, the analysis targets for this study are restricted to data from aviation-related research papers. However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and presenting directions regarding the areas with good prospects for the future. In the future, the scope is slated to expand to cover relevant domestic or international news articles and bidding information as well, thus increasing the reliability of analysis results. On the basis of the topic analysis results, core issues for the aviation industry will be determined. Then, emerging trend analysis for the issues will be implemented by year in order to identify the changes they undergo in time series. Through these procedures, this study aims to prepare a system for developing key promising areas for the future aviation industry as well as for ensuring rapid response. Additionally, the promising areas selected based on the aforementioned results and the analysis of pertinent policy research reports will be compared with the areas in which the actual government investments are made. The results from this comparative analysis are expected to make useful reference materials for future policy development and budget establishment.

• Journal of the Korean Society of Visualization
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• v.1 no.2
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• pp.3-12
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• 2003

This paper describes the general aspects of various visualization techniques employed for marine engineering research including classical naval architecture, ocean engineering and other related topics. Visualization techniques performed mostly by authors' were introduced here, which range from old fashioned methods such as paint and tuft tests to the newly emerging PIV technique and Sonar in broad sense. Brief explanation of each technique was made for the instruction purposes. It is strongly recommended that the interdisciplinary project with experts in other research areas is necessary in order to develop more advanced and profitable techniques.

• IE interfaces
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• v.16 no.2
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• pp.117-124
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• 2003

This study focuses on the detailed explanation of the strategic allocation algorithm which can be used as an ATP(Available To Promise) from the perspective of customers, and as a sales plan for sales organizations. A strategic allocation algorithm includes three methods depending on FIXED RATIO, RANK and DEMAND BASIS. In addition, further topics would be discussed regarding the method of system implementation utilizing strategic allocation algorithms and information flow with an aim to integrate such a sales plan into the e-Biz. This study aims to provide a new solution in order to secure emerging competitive factors in today's enterprise world; that is, an achievement of faster business processes. It is suggested that this new solution be implemented in order to achieve an efficient business environment by systemizing the decision making process which in the past was manually conducted.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.7
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• pp.3521-3526
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• 2016

Digital breast tomosynthesis (DBT) as a breast cancer screening modality, through generation of three-dimensional images during standard mammographic compression, can reduce interference from breast tissue overlap, increasing conspicuity of invasive cancers while concomitantly reducing false-positive results. We here conducted a systematic review on previous studies to synthesize the evidence of DBT efficacy, eventually 18 articles being included in the analysis. The most commonly emerging topics were advantages of DBT screening tool in terms of recall rates, cancer detection rates and cost-effectiveness, preventing unnecessary burdens on women and the healthcare system. Further research is needed to evaluate the potential impact of DBT on longer-term outcomes, such as interval cancer rates and mortality, to better understand the broader clinical and economic implications of its adoption.