• Title/Summary/Keyword: Big Five

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Selective Word Embedding for Sentence Classification by Considering Information Gain and Word Similarity (문장 분류를 위한 정보 이득 및 유사도에 따른 단어 제거와 선택적 단어 임베딩 방안)

  • Lee, Min Seok;Yang, Seok Woo;Lee, Hong Joo
    • Journal of Intelligence and Information Systems
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    • v.25 no.4
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    • pp.105-122
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    • 2019
  • Dimensionality reduction is one of the methods to handle big data in text mining. For dimensionality reduction, we should consider the density of data, which has a significant influence on the performance of sentence classification. It requires lots of computations for data of higher dimensions. Eventually, it can cause lots of computational cost and overfitting in the model. Thus, the dimension reduction process is necessary to improve the performance of the model. Diverse methods have been proposed from only lessening the noise of data like misspelling or informal text to including semantic and syntactic information. On top of it, the expression and selection of the text features have impacts on the performance of the classifier for sentence classification, which is one of the fields of Natural Language Processing. The common goal of dimension reduction is to find latent space that is representative of raw data from observation space. Existing methods utilize various algorithms for dimensionality reduction, such as feature extraction and feature selection. In addition to these algorithms, word embeddings, learning low-dimensional vector space representations of words, that can capture semantic and syntactic information from data are also utilized. For improving performance, recent studies have suggested methods that the word dictionary is modified according to the positive and negative score of pre-defined words. The basic idea of this study is that similar words have similar vector representations. Once the feature selection algorithm selects the words that are not important, we thought the words that are similar to the selected words also have no impacts on sentence classification. This study proposes two ways to achieve more accurate classification that conduct selective word elimination under specific regulations and construct word embedding based on Word2Vec embedding. To select words having low importance from the text, we use information gain algorithm to measure the importance and cosine similarity to search for similar words. First, we eliminate words that have comparatively low information gain values from the raw text and form word embedding. Second, we select words additionally that are similar to the words that have a low level of information gain values and make word embedding. In the end, these filtered text and word embedding apply to the deep learning models; Convolutional Neural Network and Attention-Based Bidirectional LSTM. This study uses customer reviews on Kindle in Amazon.com, IMDB, and Yelp as datasets, and classify each data using the deep learning models. The reviews got more than five helpful votes, and the ratio of helpful votes was over 70% classified as helpful reviews. Also, Yelp only shows the number of helpful votes. We extracted 100,000 reviews which got more than five helpful votes using a random sampling method among 750,000 reviews. The minimal preprocessing was executed to each dataset, such as removing numbers and special characters from text data. To evaluate the proposed methods, we compared the performances of Word2Vec and GloVe word embeddings, which used all the words. We showed that one of the proposed methods is better than the embeddings with all the words. By removing unimportant words, we can get better performance. However, if we removed too many words, it showed that the performance was lowered. For future research, it is required to consider diverse ways of preprocessing and the in-depth analysis for the co-occurrence of words to measure similarity values among words. Also, we only applied the proposed method with Word2Vec. Other embedding methods such as GloVe, fastText, ELMo can be applied with the proposed methods, and it is possible to identify the possible combinations between word embedding methods and elimination methods.

Sentiment analysis on movie review through building modified sentiment dictionary by movie genre (영역별 맞춤형 감성사전 구축을 통한 영화리뷰 감성분석)

  • Lee, Sang Hoon;Cui, Jing;Kim, Jong Woo
    • Journal of Intelligence and Information Systems
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    • v.22 no.2
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    • pp.97-113
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    • 2016
  • Due to the growth of internet data and the rapid development of internet technology, "big data" analysis is actively conducted to analyze enormous data for various purposes. Especially in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of existing structured data analysis. Various studies on sentiment analysis, the part of text mining techniques, are actively studied to score opinions based on the distribution of polarity of words in documents. Usually, the sentiment analysis uses sentiment dictionary contains positivity and negativity of vocabularies. As a part of such studies, this study tries to construct sentiment dictionary which is customized to specific data domain. Using a common sentiment dictionary for sentiment analysis without considering data domain characteristic cannot reflect contextual expression only used in the specific data domain. So, we can expect using a modified sentiment dictionary customized to data domain can lead the improvement of sentiment analysis efficiency. Therefore, this study aims to suggest a way to construct customized dictionary to reflect characteristics of data domain. Especially, in this study, movie review data are divided by genre and construct genre-customized dictionaries. The performance of customized dictionary in sentiment analysis is compared with a common sentiment dictionary. In this study, IMDb data are chosen as the subject of analysis, and movie reviews are categorized by genre. Six genres in IMDb, 'action', 'animation', 'comedy', 'drama', 'horror', and 'sci-fi' are selected. Five highest ranking movies and five lowest ranking movies per genre are selected as training data set and two years' movie data from 2012 September 2012 to June 2014 are collected as test data set. Using SO-PMI (Semantic Orientation from Point-wise Mutual Information) technique, we build customized sentiment dictionary per genre and compare prediction accuracy on review rating. As a result of the analysis, the prediction using customized dictionaries improves prediction accuracy. The performance improvement is 2.82% in overall and is statistical significant. Especially, the customized dictionary on 'sci-fi' leads the highest accuracy improvement among six genres. Even though this study shows the usefulness of customized dictionaries in sentiment analysis, further studies are required to generalize the results. In this study, we only consider adjectives as additional terms in customized sentiment dictionary. Other part of text such as verb and adverb can be considered to improve sentiment analysis performance. Also, we need to apply customized sentiment dictionary to other domain such as product reviews.

Public Sentiment Analysis of Korean Top-10 Companies: Big Data Approach Using Multi-categorical Sentiment Lexicon (국내 주요 10대 기업에 대한 국민 감성 분석: 다범주 감성사전을 활용한 빅 데이터 접근법)

  • Kim, Seo In;Kim, Dong Sung;Kim, Jong Woo
    • Journal of Intelligence and Information Systems
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    • v.22 no.3
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    • pp.45-69
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    • 2016
  • Recently, sentiment analysis using open Internet data is actively performed for various purposes. As online Internet communication channels become popular, companies try to capture public sentiment of them from online open information sources. This research is conducted for the purpose of analyzing pulbic sentiment of Korean Top-10 companies using a multi-categorical sentiment lexicon. Whereas existing researches related to public sentiment measurement based on big data approach classify sentiment into dimensions, this research classifies public sentiment into multiple categories. Dimensional sentiment structure has been commonly applied in sentiment analysis of various applications, because it is academically proven, and has a clear advantage of capturing degree of sentiment and interrelation of each dimension. However, the dimensional structure is not effective when measuring public sentiment because human sentiment is too complex to be divided into few dimensions. In addition, special training is needed for ordinary people to express their feeling into dimensional structure. People do not divide their sentiment into dimensions, nor do they need psychological training when they feel. People would not express their feeling in the way of dimensional structure like positive/negative or active/passive; rather they express theirs in the way of categorical sentiment like sadness, rage, happiness and so on. That is, categorial approach of sentiment analysis is more natural than dimensional approach. Accordingly, this research suggests multi-categorical sentiment structure as an alternative way to measure social sentiment from the point of the public. Multi-categorical sentiment structure classifies sentiments following the way that ordinary people do although there are possibility to contain some subjectiveness. In this research, nine categories: 'Sadness', 'Anger', 'Happiness', 'Disgust', 'Surprise', 'Fear', 'Interest', 'Boredom' and 'Pain' are used as multi-categorical sentiment structure. To capture public sentiment of Korean Top-10 companies, Internet news data of the companies are collected over the past 25 months from a representative Korean portal site. Based on the sentiment words extracted from previous researches, we have created a sentiment lexicon, and analyzed the frequency of the words coming up within the news data. The frequency of each sentiment category was calculated as a ratio out of the total sentiment words to make ranks of distributions. Sentiment comparison among top-4 companies, which are 'Samsung', 'Hyundai', 'SK', and 'LG', were separately visualized. As a next step, the research tested hypothesis to prove the usefulness of the multi-categorical sentiment lexicon. It tested how effective categorial sentiment can be used as relative comparison index in cross sectional and time series analysis. To test the effectiveness of the sentiment lexicon as cross sectional comparison index, pair-wise t-test and Duncan test were conducted. Two pairs of companies, 'Samsung' and 'Hanjin', 'SK' and 'Hanjin' were chosen to compare whether each categorical sentiment is significantly different in pair-wise t-test. Since category 'Sadness' has the largest vocabularies, it is chosen to figure out whether the subgroups of the companies are significantly different in Duncan test. It is proved that five sentiment categories of Samsung and Hanjin and four sentiment categories of SK and Hanjin are different significantly. In category 'Sadness', it has been figured out that there were six subgroups that are significantly different. To test the effectiveness of the sentiment lexicon as time series comparison index, 'nut rage' incident of Hanjin is selected as an example case. Term frequency of sentiment words of the month when the incident happened and term frequency of the one month before the event are compared. Sentiment categories was redivided into positive/negative sentiment, and it is tried to figure out whether the event actually has some negative impact on public sentiment of the company. The difference in each category was visualized, moreover the variation of word list of sentiment 'Rage' was shown to be more concrete. As a result, there was huge before-and-after difference of sentiment that ordinary people feel to the company. Both hypotheses have turned out to be statistically significant, and therefore sentiment analysis in business area using multi-categorical sentiment lexicons has persuasive power. This research implies that categorical sentiment analysis can be used as an alternative method to supplement dimensional sentiment analysis when figuring out public sentiment in business environment.

A Methodology of Customer Churn Prediction based on Two-Dimensional Loyalty Segmentation (이차원 고객충성도 세그먼트 기반의 고객이탈예측 방법론)

  • Kim, Hyung Su;Hong, Seung Woo
    • Journal of Intelligence and Information Systems
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    • v.26 no.4
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    • pp.111-126
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    • 2020
  • Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.

A Study on the Influence of IT Education Service Quality on Educational Satisfaction, Work Application Intention, and Recommendation Intention: Focusing on the Moderating Effects of Learner Position and Participation Motivation (IT교육 서비스품질이 교육만족도, 현업적용의도 및 추천의도에 미치는 영향에 관한 연구: 학습자 직위 및 참여동기의 조절효과를 중심으로)

  • Kang, Ryeo-Eun;Yang, Sung-Byung
    • 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.

The Landscape Configuration and Semantic Landscape of Hamheo-pavilion in Gokseong (곡성 함허정(涵虛亭)의 경관짜임과 의미경관)

  • Lee, Hyun-Woo;Sim, Woo-Kyung;Rho, Jae-Hyun;Shin, Sang-Sup
    • Journal of the Korean Institute of Traditional Landscape Architecture
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    • v.33 no.1
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    • pp.52-64
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    • 2015
  • This research traced the characteristics of the semantic landscape, construction intent, landscape composition, and geomantic conditions of the area subject to the research based on the research methods of 'field investigation, document studies, and interviews,' centering around the entire area of Gokseong Hamheo-pavilion (Jeonnam Tangible Cultural Assets No. 160). The result of the research, specifically revealing the forms and methods by which the reciprocal view of nature and landscape composition appearing in the landscape of the entire area of Hamheo-pavilion, as part of the analysis and interpretation over the view-based construction characteristics and position of the entire area of Gokseong Hamheo-pavilion, can be summarized as follows. First, Hamheo-pavilion is a pavilion built as a resting area and as a venue for educational activities in 1543 in the nearby areas after Gwang-hyeon Sim founded Gunjichon-jeongsa for educational activities and dwelling purposes at Gunchon at the 30th year of King Jungjong. Gunchon, where Hamheo-pavilion and Gunjichon-jeongsa is located, exhibits the typical form having water in the front, facing Sunja-river(present Seomjin-river), and a mountain in the back side. Dongak-mountain, which is a guardian mountain, is in a snail-type form where cows leisurely ruminate and lie on the riverside, and the Hamheo-pavilion area is said to be an area bordering on one's way of enjoying peace and richness as it is a place with plentiful grass bushes available for cows to ruminate and lie down while sheppards may leisurely play their flutes at the riverside. The back hill of Hamheo-pavilion is a blood vessel that enters the water into the underwater palace of the turtle, and the building sitting on the turtle's back is Hamheo-pavilion, and the Guam-jodae(龜巖釣臺) and lava on the southern side below the cliff can be interpreted to be the underwater fairly land wanted by the turtle.6) Second, Hamheo-pavilion is the scenery viewpoint of Sungang-Cheongpung (3rd Scenery) and Seolsan-Nakjo(雪山落照, 9th Scenery) among the eight sceneries of Gokseong, while also the scenery viewpoint of Hamheo-Sunja(2nd Scenery) and Cheonma-Gwiam(天馬歸岩, 3rd Scenery) among the eight sceneries of Ipmyeon. On the other hand, the pavilion is reproduced through the aesthetics of bends through sensible penetration and transcendental landscape viewed based on the Confucian-topos and ethics as the four bends among the five bends of Sunja-river arranged in the 'Santaegeuk(山太極) and Sutaeguek(水太極, formation of the yin-yang symbol by the mountain and water)' form, which is alike the connection of yin and yang. In particular, when based on the description over Mujinjeong (3rd Bend), Hoyeonjeong(4th Bend), andHapgangjeong(2nd Bend) among the five bends of Sunja-river in the records of Bibyeonsainbangan-jido(duringthe 18th century) and Okgwahyeonji(1788), the scenery of the five bends of Sunja-river allow to glimpse into its reputation as an attraction-type connected scenery in the latter period of the Joseon era, instead of only being perceived of its place identity embracing the fairyland world by crossing in and out of the world of this world and nirvana. Third, Hamheo-pavilion, which exhibits exquisite aesthetics of vacancy, is where the 'forest landscape composed of old big trees such as oak trees, oriental oak trees, and pine trees,' 'rock landscape such as Guam-jodae, lava, and layered rocks' and 'cultural landscape of Gunchon village' is spread close by. In the middle, it has a mountain scenery composed of Sunja-river, Masan-peak, and Gori-peak, and it is a place where the scenery by Gori-peak, Masan-peak, Mudeung-mountain, and Seol-mountain is spread and open in $180^{\circ}$ from the east to west. Mangseo-jae, the sarangchae (men's room)of Gunjichon-jeongsa, means a 'house observing Seoseok-mountain,' which has realized the diverse view-oriented intent, such as by allowing to look up Seol-mountain or Mudeung-mountain, which are back mountains behind the front mountain, through landscape configuration. Fourth, the private home, place for educational activities, pavilion, memorial room, and graveyard of Gunji-village, where the existence and ideal is connected, is a semantic connected scenery relating to the life cycle of the gentry linking 'formation - abundance - transcendence - regression.' In particular, based on the fact that the descriptions over reciprocal views of nature regarding an easy and comfortable life and appreciations for a picturesque scene of the areas nearby Sunja-river composes most of the poetic phrases relating to Hamheo-pavilion, it can be known that Hamheo-pavilion is expressed as the key to the idea of 'understanding how to be satisfied while maintaining one's positon with a comfortable mind' and 'returning to nature,' while also being expressed of its pedantic character as a place for reclusion for training one's mind and training others through metaphysical semantic scenery.

A Case Study about PET/CT Collaboration Operation (PET/CT 공동운영에 대한 사례 연구)

  • Kim, Chang-Ho;Pyo, Sung-Jae
    • The Korean Journal of Nuclear Medicine Technology
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    • v.14 no.2
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    • pp.87-92
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    • 2010
  • Purpose: In 2003, we decided to buy a PET/CT, at the time, it was the latest cancer diagnostic medical equipment. Equipment company was offered the marketing of collaboration operation because the highly cost of PET/CT. However, this hospital's choice was own purchase way. In this study we evaluated the collaboration operation way by post-mortem analysis to the current situation. Materials and Methods: From 2004 until 2008, five years, we investigate the revenue analysis the number of PET/CT cases about own purchase way and collaboration operation way according (ABC costing). Results: The year 2004, own purchase way is 4 billion 9 thousand 2 hundred million won in deficit, the collaboration operation way is 1 billion 1 thousand 7 hundred million won in deficit. The year 2005, own purchase way is 1 billion 5 hundred million won in deficit, collaboration operation way is 8 thousand 7 hundred million won in deficit. However, the year 2006, own purchase way is 5 billion 1thousand 3 hundred million won in surplus, collaboration operation way is 9 thousand 9 hundred million won in deficits. The year 2007 and 2008, revenue of own purchase way is more increased but the collaboration operation way is more decreased. From the year 2004 to 2008, subtotal of own purchase way is 10 billion 8 thousand 8 hundred won in surplus, sub-total of collaboration operation way is 6 billion 7 thousand million won in deficit. Conclusion: Own purchase way has been a big benefit occurs and to reflect the equipment price, the collaboration operation way became to deficit continues. In other words, the problem of collaboration operation way showed us. When you buy the high cost Equipment, consideration will be risk and economic analysis of variance, the appropriate of the initial investment cost, clinical diagnostic needs and etc.

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Effects of Paddy Soil Chemical Changes and Yield Components of Rice in Accordance with the Age and Usage of Organic Fertilizer and Chemical Fertilizers (유기질비료와 화학비료의 사용기간과 사용량에 따른 논토양 화학성 변화와 벼의 수량구성요소에 미치는 영향)

  • Oh, Tae-Seok;Kim, Chang-Ho;Kim, Seong-Min;Jang, Myoung-Jun;Park, Youn-Jin;Cho, Young-Koo
    • Korean Journal of Organic Agriculture
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    • v.24 no.4
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    • pp.969-980
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    • 2016
  • This experiment was conducted to assess changes to the chemical properties of soil and applicability in a case of rice cultivation with organic fertilizers. The investigator applied organic fertilizers to rice cultivation for five years to examine changes to the chemical properties of soil and found that the experiment group of organic fertilizers made an ongoing increase in pH, organism content, and available phosphate content annually compared with the control group with no big differences according to the amounts of organic fertilizers used. As for the yield components, there were no statistical differences in the number of spikelets and grain filling rate between the experiment group of organic fertilizers and the control group. The experiment group recorded a higher level in 1,000 seeds weight and yield than the control group. Experiment Group 4 recorded the highest level at 29.11 kg of 1,000 seeds weight. Experiment Groups 3 and 4, which used 222 kg and 267 kg per 10 a, respectively, recorded 576 kg and 572 kg of yield, respectively, which were 4.7% and 4.1% higher than 549 kg of control group, respectively. As for the quality of brown rice, there were no statistical differences in the head rice percentage between the control and experiment group, both of which were in the range of 83.2-85.7%. As for the protein content, Experiment Groups 3 and 4, both of which used a lot of organic fertilizers, were in the range of 6.9-7.1%, which was lower than 7.5% of control group. Those findings indicate that the long-term application of organic fertilizers can improve the chemical properties of soil and increase the yield more than the conventional method of fertilizer application. The findings also suggest that it will be effective to apply 222 kg of organic fertilizers or more per 10 a.

DRAG EFFECT OF KOMPSAT-1 DURING STRONG SOLAR AND GEOMAGNETIC ACTIVITY (강한 태양 및 지자기 활동 기간 중에 아리랑 위성 1호(KOMPSAT-1)의 궤도 변화)

  • Park, J.;Moon, Y.J.;Kim, K.H.;Cho, K.S.;Kim, H.D.;Kim, Y.H.;Park, Y.D.;Yi, Y.
    • Journal of Astronomy and Space Sciences
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    • v.24 no.2
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    • pp.125-134
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    • 2007
  • In this paper, we analyze the orbital variation of the Korea Multi-Purpose SATellite-1(KOMPSAT-1) in a strong space environment due to satellite drag by solar and geomagnetic activities. The satellite drag usually occurs slowly, but becomes serious satellite drag when the space environment suddenly changes via strong solar activity like a big flare eruption or coronal mass ejections(CMEs). Especially, KOMPSAT-1 as a low earth orbit satellite has a distinct increase of the drag acceleration by the variations of atmospheric friction. We consider factors of solar activity to have serious effects on the satellite drag from two points of view. One is an effect of high energy radiation when the flare occurs in the Sun. This radiation heats and expands the upper atmosphere of the Earth as the number of neutral particles is suddenly increased. The other is an effect of Joule and precipitating particle heating caused by current of plasma and precipitation of particles during geomagnetic storms by CMEs. It also affects the density of neutral particles by heating the upper atmo-sphere. We investigate the satellite drag acceleration associated with the two factors for five events selected based on solar and geomagnetic data from 2001 to 2002. The major results can be summarized as follows. First, the drag acceleration started to increase with solar EUV radiation with the best cross-correlation (r = 0.92) for 1 day delayed F10.7. Second, the drag acceleration and Dst index have similar patterns when the geomagnetic storm is dominant and the drag acceleration abruptly increases during the strong geomagnetic storm. Third, the background variation of the drag accelerations is governed by the solar radiation, while their short term (less than a day) variations is governed by geomagnetic storms.

Food habits, health behaviors related to the personality in Korean college students (대학생의 성격요인과 식습관 및 건강관련행태)

  • Kim, Nahyeon;Kim, Jinhee;Kye, Seunghee
    • Journal of Nutrition and Health
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    • v.53 no.1
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    • pp.13-26
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    • 2020
  • Purpose: This study identified the relationship between dietary habits and health-related behaviors depending on the Big Five personality factors (extraversion, openness, agreeableness, conscientiousness, neuroticism). Methods: The NEO-II test was administered to 337 male and female college students in Seongnam City, Gyeonggi Province, and their dietary habits and health-related behaviors were surveyed. Results: The male participants showed higher scores for extraversion, openness, agreeableness, and conscientiousness compared to that of their female counterparts, while the female participants showed higher scores for neuroticism. As for the results of multivariate logistic regression analysis, in the case of men, higher scores for extraversion were related to a lower intake of instant/fast foods and a higher intake of vegetables; higher agreeableness scores were related to a lower intake of fruit; and higher neuroticism scores were related to a heavy intake of high-cholesterol foods. It was found that higher openness scores were associated with a higher intake of burnt fish/meat and a lower intake of animal fat, while higher agreeableness scores were related to a lower intake of burnt fish/meat in women. Also, those subjects with higher openness and agreeableness scores were found to better consider the nutritional balance when having a meal. In the case of the male participants, higher openness scores were related to increased physical activity, while higher neuroticism scores were related to increased smoking and a lack of sleep. As for the women, those with higher extraversion scores smoked more, while those who recorded higher agreeableness scores were involved in more physical activities. Conclusion: Differences were observed in dietary habits and health-related behaviors between men and women depending on personality factors, and the analysis results of some dietary habits according to personality factors were inconsistent with those of the overseas studies. Therefore, to provide customized nutritional counseling when considering each individual's personality factors, more research results from domestic samples should be collected and accumulated.