• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.1
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• pp.41-52
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• 2022

As a cultivar of Korean wheat, 'Keumgang' wheat variety has a fast growth period and can be grown stably. Hexaploid wheat (Triticum aestivum) has moderately high salt tolerance compared to tetraploid wheat (Triticum turgidum L.). However, the molecular mechanisms related to salt tolerance of hexaploid wheat have not been elucidated yet. In this study, the candidate genes related to salt tolerance were identified by investigating the genes that are differently expressed in Keumgang variety and examining salt tolerant mutation '2020-s1340.'. A total of 85,771,537 reads were obtained after quality filtering using NextSeq 500 Illumina sequencing technology. A total of 23,634,438 reads were aligned with the NCBI Campala Lr22a pseudomolecule v5 reference genome (Triticum aestivum). A total of 282 differentially expressed genes (DEGs) were identified in the two Triticum aestivum materials. These DEGs have functions, including salt tolerance related traits such as 'wall-associated receptor kinase-like 8', 'cytochrome P450', '6-phosphofructokinase 2'. In addition, the identified DEGs were classified into three categories, including biological process, molecular function, cellular component using gene ontology analysis. These DEGs were enriched significantly for terms such as the 'copper ion transport', 'oxidation-reduction process', 'alternative oxidase activity'. These results, which were obtained using RNA-seq analysis, will improve our understanding of salt tolerance of wheat. Moreover, this study will be a useful resource for breeding wheat varieties with improved salt tolerance using molecular breeding technology.

• Journal of Korea Entertainment Industry Association
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• v.13 no.4
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• pp.1-15
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• 2019

Psy's Gangnam style in 2012 showed K-pop's potential for global growth and BTS proved it by reaching three consecutive Billboard No.1. The success in the global music market brings tremendous economical and cultural power. This study is conducted for the continuous growth of K-pop music in the global music market by analyzing the musical factor of K-pop's global success. The top 20 most-viewed K-pop MV on Youtube is chosen as a research subject because Youtube is a worldwide platform that reflects global popularity. For the process of K-pop music creation, the role of the composer is expanded and many overseas producers participate in music creation. All 20 songs are created by the collective creation system and there is a consecutive collaboration between the main producers and certain artists. The top 20 most viewed K-pop songs have the musical characteristics of transnational genre convergence, hook songs, sophisticated sounds, frequent use of English lyrics, a reflection of the latest global trends, rhythm optimized for dance and clear concept. It makes the K-pop song easily remembered and familiar to overseas listeners. K-pop's healthy and fresh theme brings emotional empathy and reflects Korean sentiments. K-pop's global success is not a coincidence, but a result of continuous efforts to advance overseas. Some critics criticize K-pop's musical style is similar and it shows K-pop's limitation but K-pop progressed its musical evolution. By keeping the merits of K-pop's success factors and complementing its weak points, K-pop will continue its popularity and increase influence in the global music market.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.271-286
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• 2023

The global COVID-19 pandemic has transformed face masks from situational accessories to indispensable items in daily life, prompting a shift in public perception and behavior. While the relaxation of mandatory mask-wearing regulations is underway, a significant number of individuals continue to embrace face masks, turning them into a form of personal expression and identity. This phenomenon has given rise to the Fashion Mask industry, characterized by unique designs and colors, experiencing rapid growth in the market. However, existing research on masks is predominantly focused on their efficacy in preventing infection or exploring attitudes during the pandemic, leaving a gap in understanding consumer preferences for mask design. We address this gap by investigating consumer perceptions and preferences for two prevalent mask designs-horizontal fold flat masks and vertical fold flat masks. Through a comprehensive approach involving surveys and eye-tracking experiments, we aim to unravel the subtle differences in how consumers perceive these designs. Our research questions focus on determining which design is more appealing and exploring the reasons behind any observed differences. The study's findings reveal a clear preference for vertical fold flat masks, which are not only preferred but also perceived as unique, sophisticated, three-dimensional, and lively. The eye-tracking analysis provides insights into the visual attention patterns associated with mask designs, highlighting the pivotal role of the fold line in influencing these patterns. This research contributes to the evolving understanding of masks as a fashion statement and provides valuable insights for manufacturers and marketers in the Fashion Mask industry. The results have implications beyond the pandemic, emphasizing the importance of design elements in sustaining consumer interest in face masks.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.15 no.1
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• pp.8-15
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• 2010

An experiment was conducted to evaluate the biologically adverse effect of increased carbon dioxide in seawater on marine bacteria, Vibrio fischeri. We measured the bioluminescence and cell density at every 6 hours for 24 hours of the whole incubation period after exposing test microbes to a range of $CO_2$ concentration such as 380(Control), 1,000, 3,000, 10,000 and 30,000 ppm, respectively. Significant effect on relative luminescence(RLU) of V. fischeri was observed in treatments with $CO_2$ concentration higher than 3,000 ppm at t=12 h. However, the difference of RLU among treatments significantly decreased with the incubation time until t=24 h. Similar trend was observed for the variation of cell density, which was measured as optical density using spectrophotometer. The results showed that a significant relationship between $CO_2$ concentration and bioluminescence of test microbes was observed for the mean time. However, the inhibition of relative bioluminescence and also cell density could be recovered at the concentration levels higher than 3,000 ppm. The dissolved $CO_2$ can be absorbed directly by cell and it can decrease the intracellular pH. Our results implied that microbes might be adversely affected at the initial growing phase by increased $CO_2$. However, they could adapt by increasing ion transport including bicarbonate and then could make their pH back to normal level. Results of this study could be supported to understand the possible influence on marine bacteria by atmospheric increase of $CO_2$ in near future and also by released $CO_2$ during the marine $CO_2$ sequestration activity.

• The Mathematical Education
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• v.63 no.2
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• pp.255-272
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• 2024

This study aims to design mathematics-integrated classes that cultivate artificial intelligence (AI) thinking and to analyze students' AI thinking within these classes. To do this, four classes were designed through the integration of the AI4K12 Initiative's AI Big Ideas with the 2015 revised elementary mathematics curriculum. Implementation of three classes took place with 5th and 6th grade elementary school students. Leveraging the computational thinking taxonomy and the AI thinking components, a comprehensive framework for analyzing of AI thinking was established. Using this framework, analysis of students' AI thinking during these classes was conducted based on classroom discourse and supplementary worksheets. The results of the analysis were peer-reviewed by two researchers. The research findings affirm the potential of mathematics-integrated classes in nurturing students' AI thinking and underscore the viability of AI education for elementary school students. The classes, based on AI Big Ideas, facilitated elementary students' understanding of AI concepts and principles, enhanced their grasp of mathematical content elements, and reinforced mathematical process aspects. Furthermore, through activities that maintain structural consistency with previous problem-solving methods while applying them to new problems, the potential for the transfer of AI thinking was evidenced.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.

• 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 Intelligence and Information Systems
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• v.23 no.4
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• pp.169-196
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• 2017

The fourth industrial revolution represents a revolutionary change in the business environment and its ecosystem, which is a fusion of Information Technology (IT) and other industries. In line with these recent changes, the Ministry of Employment and Labor of South Korea announced 'the Fourth Industrial Revolution Leader Training Program,' which includes five key support areas such as (1) smart manufacturing, (2) Internet of Things (IoT), (3) big data including Artificial Intelligence (AI), (4) information security, and (5) bio innovation. Based on this program, we can get a glimpse of the South Korean government's efforts and willingness to emit leading human resource with advanced IT knowledge in various fusion technology-related and newly emerging industries. On the other hand, in order to nurture excellent IT manpower in preparation for the fourth industrial revolution, the role of educational institutions capable of providing high quality IT education services is most of importance. However, these days, most IT educational institutions have had difficulties in providing customized IT education services that meet the needs of consumers (i.e., learners), without breaking away from the traditional framework of providing supplier-oriented education services. From previous studies, it has been found that the provision of customized education services centered on learners leads to high satisfaction of learners, and that higher satisfaction increases not only task performance and the possibility of business application but also learners' recommendation intention. However, since research has not yet been conducted in a comprehensive way that consider both antecedent and consequent factors of the learner's satisfaction, more empirical research on this is highly desirable. With the advent of the fourth industrial revolution, a rising interest in various convergence technologies utilizing information technology (IT) has brought with the growing realization of the important role played by IT-related education services. However, research on the role of IT education service quality in the context of IT education is relatively scarce in spite of the fact that research on general education service quality and satisfaction has been actively conducted in various contexts. In this study, therefore, the five dimensions of IT education service quality (i.e., tangibles, reliability, responsiveness, assurance, and empathy) are derived from the context of IT education, based on the SERVPERF model and related previous studies. In addition, the effects of these detailed IT education service quality factors on learners' educational satisfaction and their work application/recommendation intentions are examined. Furthermore, the moderating roles of learner position (i.e., practitioner group vs. manager group) and participation motivation (i.e., voluntary participation vs. involuntary participation) in relationships between IT education service quality factors and learners' educational satisfaction, work application intention, and recommendation intention are also investigated. In an analysis using the structural equation model (SEM) technique based on a questionnaire given to 203 participants of IT education programs in an 'M' IT educational institution in Seoul, South Korea, tangibles, reliability, and assurance were found to have a significant effect on educational satisfaction. This educational satisfaction was found to have a significant effect on both work application intention and recommendation intention. Moreover, it was discovered that learner position and participation motivation have a partial moderating impact on the relationship between IT education service quality factors and educational satisfaction. This study holds academic implications in that it is one of the first studies to apply the SERVPERF model (rather than the SERVQUAL model, which has been widely adopted by prior studies) is to demonstrate the influence of IT education service quality on learners' educational satisfaction, work application intention, and recommendation intention in an IT education environment. The results of this study are expected to provide practical guidance for IT education service providers who wish to enhance learners' educational satisfaction and service management efficiency.

• Radiation Oncology Journal
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• v.25 no.4
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• pp.242-248
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• 2007

Purpose: To study the effect of recombinant human epidermal growth factor (rhEGF) on oral mucositis induced by cisplatin and radiotherapy in a mouse model. Materials and Methods: Twenty-four ICR mice were divided into three groups-the normal control group, the no rhEGF group (treatment with cisplatin and radiation) and the rhEGF group (treatment with cisplatin, radiation and rhEGF). A model of mucositis induced by cisplatin and radiotherapy was established by injecting mice with cisplatin (10 mg/kg) on day 1 and with radiation exposure (5 Gy/day) to the head and neck on days $1{\sim}5$. rhEGF was administered subcutaneously on days -1 to 0 (1 mg/kg/day) and on days 3 to 5 (1 mg/kg/day). Evaluation included body weight, oral intake, and histology. Results: For the comparison of the change of body weight between the rhEGF group and the no rhEGF group, a statistically significant difference was observed in the rhEGF group for the 5 days after day 3 of. the experiment. The rhEGF group and no rhEGF group had reduced food intake until day 5 of the experiment, and then the mice demonstrated increased food intake after day 13 of the of experiment. When the histological examination was conducted on day 7 after treatment with cisplatin and radiation, the rhEGF group showed a focal cellular reaction in the epidermal layer of the mucosa, while the no rhEGF group did not show inflammation of the oral mucosa. Conclusion: These findings suggest that rhEGF has a potential to reduce the oral mucositis burden in mice after treatment with cisplatin and radiation. The optimal dose, number and timing of the administration of rhEGF require further investigation.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.