• Women's Health Nursing
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• v.29 no.1
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• pp.12-19
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• 2023

Purpose: The aim of this study was to conduct a scoping review of knowledge and information delivery modes related to preconception care (PCC) among adolescent girls and women. Methods: A scoping review was performed on studies selected from five electronic databases (Cochrane Library, PubMed, Science Direct, CINAHL/EBSCO, and ProQuest), published between 2012 and 2022, with predetermined keywords and criteria. We included English-language research articles available in full text and excluded irrelevant articles. Results: This study included eight articles, comprising seven quantitative studies and one qualitative study conducted among adolescent girls and women. Five were from low- and middle-income countries and three were from high-income countries. The synthesized themes generated from the data were PCC knowledge and PCC information delivery modes and effectiveness. In general, adolescent girls and women were found to have basic PCC knowledge, including risk prevention and management and a healthy lifestyle, although more extensive knowledge was found in higher-income countries than in lower-income countries. The delivery modes of PCC information have grown from individual face-to-face conventional methods, which are used predominantly in lower-income countries, to more effective digital mass media. Conclusion: Globally, many women still have insufficient knowledge regarding PCC, as not all of them receive access to PCC information and support. PCC promotion efforts should be initiated earlier by involving a wider group of reproductive-age women and combining individual, in-group, face-to-face, and electronic delivery modes.

• Journal of Korean Society of Archives and Records Management
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• v.6 no.1
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• pp.113-136
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• 2006

This study aims for the above mentioned. After all, through the analysis of Electronic Question/Answering Function to understand a user's demand under online circumstances, groping for the method to provide an appropriate Archival Information Service is the most important thing. For this, in this study, it researched the users interviews and the research related to users as a precedence study, and the studies having examined the state of demanding information by users through analyzing the e-mail actually. Additionally, by looking over the study of Library and Information Science that is activated in a field of Electronic Question/Answering Function rather than Archival Science, as a matter of fact, the study has come up with the standard for analyzing Electronic Question/Answering Function. And based on the precedence study, the instances for the National Archives from USA, England, Australia and Canada were analyzed, and the chance of activating Archival Information Service were tried to grope for in the study. This study might be one of methodologies in examining the users study that is not activated yet in Archival Science. Therefore, the users study can be carried out in various methods as well as Electronic Archives/Answering Service. This study might be the important information in providing far better Archival Information Services. It is desirable that based on this opportunity, the study related to the various users by examining not only Electronic Archives/Answering Function but also Question/Answering of the users and the Archivists in the filed to the larger extend will be activated for Archival Science.

• Journal of Plant Biotechnology
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• v.44 no.4
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• pp.372-378
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• 2017

For comparison of the transcription profiles in apple (Malus domestica L.) cultivars differing in flesh color expression, two cDNA libraries were constructed. Differences in gene expression between red flesh apple cultivar, 'Redfield' and white flesh apple cultivar, 'Granny Smith' were investigated by next-generation sequencing (NGS). Expressed sequence tag (EST) of clones from the red flesh apple cultivar and white flesh apple cultivar were selected for nucleotide sequence determination and homology searches. High resolution melting (HRM) technique measures temperature induced strand separation of short PCR amplicons, and is able to detect variation as small as one base difference between red flesh apple cultivars and white flesh apple cultivars. We applied high resolution melting (HRM) analysis to discover single nucleotide polymorphisms (SNP) based on the predicted SNP information derived from the apple EST database. All 103 pairs of SNPs were discriminated, and the HRM profiles of amplicons were established. Putative SNPs were screened from the apple EST contigs by HRM analysis displayed specific difference between 10 red flesh apple cultivars and 11 white flesh apple cultivars. In this study, we report an efficient method to develop SNP markers from an EST database with HRM analysis in apple. These SNP markers could be useful for apple marker assisted breeding and provide a good reference for relevant research on molecular mechanisms of color variation in apple cultivars.

• Journal of Information Management
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• v.39 no.4
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• pp.215-240
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• 2008

This paper presents a scientometric analysis of national patterns of research output and priorities in the sub-fields of Hepatitis covered in three bibliographic databases namely MEDLINE, CINAHL and IPA. The literature covered in three databases for the period 1984-2003 was considered. We have already discussed the Trends in the Growth of Literature on Hepatitis in our previous paper. Therefore in this paper only sub-fields analysis is presented. It has been found that the Hepatitis literature output has been grouped in 23 major sub-fields based on databases covered. It was found that there were high priorities for some of the sub-fields of Hepatitis research during 1984-1993. It was found that the research priority profile was more or less homogenous since majority of the sub-fields are showing either below or above average levels of priority profile. In the first phase of the research period covering from 1984-1993, there are high priorities in 10 sub-fields in USA followed by 9 in UK and 8 in Germany, 7 each in Canada, Russia and Netherlands. On the other hand, in the second phase (1994-2003) there are high priorities for 10 sub-fields in Germany, 9 in UK, 8 in USA, 7 each in Canada, Russia and Netherlands. In the productivity of Pediatrics sub-field, India is in the third position.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.65-75
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• 2014

The main focus of this paper is to analyze trend of event informations in a variety of mass media by graphical visualization in axis of the time and location. Especially, continuity analysis based on user-generated social media can reflect the social impact of a certain event according to change time and location and their directional changes. To reveal the characteristics of continuous events, we survey the data set collected from news articles and tweets during two years. Based on case studies on 'disease' and 'leisure', we verify the effectiveness and usefulness of our proposed method. Even though some events occurred during same period, we showed directional changes which have high-impact in social media referred user interest's, compared with fact-based continuous visualization results.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1029-1037
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• 2020

The machine learning algorithm has been widely used in water-related fields such as water resources, water management, hydrology, atmospheric science, water quality, water level prediction, weather forecasting, water discharge prediction, water quality forecasting, etc. However, water quality prediction studies based on the machine learning algorithm are limited compared to other water-related applications because of the limited water quality data. Most of the previous water quality prediction studies have predicted monthly water quality, which is useful information but not enough from a practical aspect. In this study, we predicted the dissolved oxygen (DO) using recurrent neural network with long short-term memory model recurrent neural network long-short term memory (RNN-LSTM) algorithms with hourly- and daily-datasets. Bugok Bridge in Oncheoncheon, located in Busan, where the data was collected in real time, was selected as the target for the DO prediction. The 10-month (temperature, wind speed, and relative humidity) data were used as time prediction inputs, and the 5-year (temperature, wind speed, relative humidity, and rainfall) data were used as the daily forecast inputs. Missing data were filled by linear interpolation. The prediction model was coded based on TensorFlow, an open-source library developed by Google. The performance of the RNN-LSTM algorithm for the hourly- or daily-based water quality prediction was tested and analyzed. Research results showed that the hourly data for the water quality is useful for machine learning, and the RNN-LSTM algorithm has potential to be used for hourly- or daily-based water quality forecasting.

• Journal of the Korean Society for Library and Information Science
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• v.31 no.4
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• pp.289-318
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• 1997

The purpose of this study is to identify users behavioral characteristics for using the online catalog opened in May 1996 at Duksung Women's University Library. 278 student users were surveyed from October 4th to 8th in 1996. Major findings are as follows. 1. Most users$(87.4\%)$ prefer the online catalog to the card catalog and regard the online catalog easy to use$(89.6\%)$ 2. $(65.8\%)$ of users are active users who frequently use the online catalog at least 10 times or more per semester. 3. $10.4\%$ of users feel the online catalog difficult because they do not know how to use it. 4. Most users prefer the menu search mode among menu, command and fill-in-blank search modes offered by DISCOVER. The most preferred access points are the title for known-item search and subject headings for subject search. 5. User's attitude toward the online catalog is very favorable$(83.5\%)$, however, the search success rate is rather low $(77.0\%)$ compared to that of the card catalog $(87.0\%)$ 6. The title and author are regarded easy to use among access points offered by DISCOVER. Classification numbers and call numbers are the least easy access points to use. 7. Since users show lack of knowledge of how to use the online catalog, education and training programs on the online catalog use for users are needed. 8. Users showed different search patterns for pursuing different search goals. The most preferred access points are the title for known-item search and subject headings for subject search. These search behaviors are different from those in using the card for both the known-item search and subject search was the title.

• Journal of Microbiology
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• v.45 no.6
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• pp.541-546
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• 2007

Red algae are distributed globally, and the group contains several commercially important species. Griffithsia okiensis is one of the most extensively studied red algal species. In this study, we conducted expressed sequence tag (ESTs) analysis and synonymous codon usage analysis using cultured G. okiensis samples. A total of 1,104 cDNA clones were sequenced using a cDNA library made from samples collected from Dolsan Island, on the southern coast of Korea. The clustering analysis of these sequences allowed for the identification of 1,048 unigene clusters consisting of 36 consensus and 1,012 singleton sequences. BLASTX searches generated 532 significant hits (E-value <$10^{-4}$) and via further Gene Ontology analysis, we constructed a functional classification of 434 unigenes. Our codon usage analysis showed that unigene clusters with more than three ESTs had higher GC contents (76.5%) at the third position of the codons than the singletons. Also, the majority of the optimal codons of G. okiensis and Chondrus crispus belonging to Bangiophycidae were G-ending, whereas those of Porphyra yezoensis belonging to Florideophycidae were G-ending. An orthologous gene search for the P. yezoensis EST database resulted in the identification of 39 unigenes commonly expressed in two rhodophytes, which have putative functions for structural proteins, protein degradation, signal transduction, stress response, and physiological processes. Although experiments have been conducted on a limited scale, this study provides a material basis for the development of microarrays useful for gene expression studies, as well as useful information for the comparative genomic analysis of red algae.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.4
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• pp.491-514
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• 2022

Public reforms based on New Public Management have made the public sector specialized, and accordingly the role of public administration has expanded as well as the demand on professional jobs has increased. On the other hand, with the rapid development of information and communication technology, the data produced by public sector organizations has also significantly increased. This environmental changes made data management and a data management job in the public sector critical. However, there have been very few studies of conceptualizations and systematic investigations on data management jobs. Moreover, specific definitions, types or qualifications of/for a data management job or a person who do this job are rarely reflected in relevant laws and regulations. Based on the systematic literature review, this study conceptualized professionalism, identified its multiple dimensions, and draw a conceptual research framework. Focusing on the professional control on personnel management which is one of the dimensions of professionalism, relevant laws, work guidelines and job descriptions included in job openings were analyzed with regard to a data management job in the public sector. The findings are as follows. First, an assigned role and responsibility associated with a data management job have vague boundaries. Second, work guidelines and manuals only focus on the post quality control stage rather than equally addressing all the eight stages of the data lifecycle. Third, neither a data management job in the public sector nor a person who take care of this job is not appropriately defined. Therefore, a role and responsibility of/for the job and a person in charge should be reflected in the relevant laws and guidelines in a tailored way. More importantly, job analyses and evaluations should be thoroughly conducted to enhance professionalism on data management jobs in the long term.