• Journal of Life Science
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• v.24 no.9
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• pp.988-994
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• 2014

The effluents of chemical and petroleum industries often contain non-biodegradable aromatic compounds, with phenol being one of the major organic pollutants present among a wide variety of highly toxic organic chemicals. Phenol is toxic upon ingestion, contact, or inhalation, and it is lethal to fish even at concentrations as low as 0.005 ppm. Phenol biodegradation has been studied in detail using bacterial strains. However, these microorganisms suffer from substrate inhibition at high concentrations of phenol, whereby growth is inhibited. A phenol-degrading bacterium, P21, was isolated from oil-contaminated soil. The phenotypic characteristics and a phylogenetic analysis indicated the close relationship of strain P21 to Rhodococcus pyridinovorans. Phenol biodegradation by strain P21 was studied under shaking condition. The optimal conditions for phenol biodegradation by strain P21 were 0.09% $KNO_3$, 0.1% $K_2HPO_4$, 0.3% $NaH_2PO_4$, 0.015% $MgSO_4{\cdot}7H_2O$, 0.001% $FeSO_4{\cdot}7H_2O$, initial pH 9, and $20-30^{\circ}C$, respectively. When 1,000 ppm of phenol was added to the optimal medium, the strain P21 completely degraded it within two days. Rhodococcus pyridinovorans P21 could grow in up to 1,500 ppm of phenol as the sole carbon source in a batch culture, but it could not grow in a medium containing above 2,000 ppm. Moreover, strain P21 could utilize toxic compounds, such as toluene, xylene, and hexane, as a sole carbon source. However, no growth was detected on chloroform.

• Journal of Korean Home Economics Education Association
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• v.21 no.1
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• pp.51-69
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• 2009

This study is intended to provide fundamental information to improve the quality of student activities presented in the Clothing & Textiles How to Dress Appropriately' section of the current middle school 'Technology Home Economics' textbooks so that Home Economics may better reflect students' interests, making it applicable in real life. The survey was conducted to 154 male and 160 female students on their preferences regarding student activities. The results are as follows. First, students who like clothing & textiles section regard "Opportunities to take part in various kinds of practices and student activities" as the major reason for preference. And the single biggest reason why they dislike the unit was due to "Too much contents to be memorized." Among various contents regarding dress in the unit, "How to Wear Clothes That Look Good on Me, and the Right Ways to Wear Them" attracted the most attention, regardless of what contents they consider necessary, interesting, or helpful in real life. Second, as for the time of implementation of the activities, students preferred "End of each class". They also preferred small-group activities (group size), well-structured problems (type of problems) and tasks that require analysis based on theoretical principles through experiments and practices (methods of implementation). Third, the findings as to the actual experience of conducting the student activities indicated that, in most cases, student activities were conducted in accordance to what was suggested in the textbooks, but not to what the students preferred. Therefore, in order to make home economics more applicable to students, it is desirable to focus on their everyday lives as is favored by the students, and increase small-group activities. Also, suggesting various and comprehensive problematic situations such as non-structured, open-ended problems and encouraging diverse implementation would be helpful in improving students' critical and creative thinking abilities.

• Journal of Korean Home Economics Education Association
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• v.32 no.1
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• pp.15-34
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• 2020

The purpose of this study was to examine the secondary school technology and home economics teachers' perception of the school curriculum organization and high school credit system. For this purpose, the questionnaire data of 345 secondary technology and home economics teachers nationwide were analyzed through descriptive statistics, t-test, and F-test with SPSS 24. The research results were as follows. First, technology and home economics teachers recognized that current schools lacked the time to organize technology and home economics curriculum (61.1%) and the number of teachers (53%). Most of them have participated (62.0%) and were very willing to participate in the school curriculum organization (4.47, 89.9%). Second, technology and home economics teachers were aware of the high school credit system more than the average (3.34), and more negative (52.8%) than positive (37.7%). As a positive influence, students recognized career choices (3.88) and deepened professional content in their major fields (3.81). On the other hand, the negative impact was the decrease in choice due to non-entry subjects (3.90) and the difficulty in moving teachers to school (3.57). Third, in order to stably respond to the introduction of high school credit system, technology and home economics teachers recognized the importance of coordinating career elective courses (4.51), developing and disseminating teaching and learning materials for elective courses (4.46), separating technology and home economics (4.45), and providing training on evaluation methods and applications (4.44). This study would be useful to provide the basic information and data for the future development of technology and home economics curriculum at the national level based on high school credit system.

• Sleep Medicine and Psychophysiology
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• v.5 no.1
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• pp.45-53
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• 1998

Objectives : The change of sleep patterns commonly occurs in association with the pregnancy. This study was to investigate sleep habits during the course of normal pregnancy. Methods : Sleep habits questionnaire was administered to healthy women in their first trimester(TR1) of pregnancy and then the same questionnaire was repeatedly administered during their second(TR2) and third(TR3) trimesters. The following aspects were assessed : patterns of night sleep, daytime status, sleep posture, reasons for sleep alteration, and the experience of any particular parasomnias, as well as sleep problem-related treatment or medication. Data analysis was based on 26 women who maintaind good health throughout their pregnancy and completed the questionnaire three times. Results : In comparisons between each trimester and non-pregnant state, total night sleep time, daytime tiredness, and sleepiness were significantly increased in all trimesters. Sleep latency was significantly decreased in TR1 and TR2, but not in TR3. In addition, refreshed feeling on waking the following day was significantly decreased and the number of awakenings during night sleep was significantly increased in TR3, but not in TR1 and TR2. In comparisons between trimesters, there was a significant increase in sleep latency, daytime sleepiness and the number of awakenings during night sleep and a significant decrease in refreshed feeling on waking the following day in TR3 compared to TR1 and TR2. Over the course of pregnancy, the rate of lateral position during sleep was gradually increased and all the pregnant women took the lateral sleeping posture in TR3. The major reasons for sleep pattern alteration were nausea, vomiting and heartburn in TR1, urinary frequency, fetal movement and ache in hips in TR2, and urinary frequency, fetal movement, cramp in legs and backache in TR3. Conclusion : These findings are expected to be useful for educating pregnant women about sleep hygiene. In future studies, the underlying factors and mechanisms regarding sleep patterns during pregnancy will need to be clarified.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Journal of the Korean association of regional geographers
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• v.6 no.2
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• pp.21-40
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• 2000

The retail trades in many countries have changed recently according to the high quality, diversification, and marked individuality of consumer needs. Under the continually competing system of the WTO agreement, corporations based in the USA and the EU try to raise their market share in other countries so it is inevitable for Korean retail enterprises to compete with them. This paper is aimed at contributing to the efficient growth for Korean retail trade from the analysis of the development process in retail trade and its changing locations comparing Korea and the USA. Retailers in the USA have practiced diversified marketing strategies considerably in order to survive in a rapidly changing retailing environment. American retailing, which has the most advanced marketing system in the world, has been of growing concern to marketing strategies in Korea. The following is a brief summary of this study. 1. Speedy and higher quality consumption is needed in accordance with the great increase in the single-family household and the female labor force participation both in Korea and in the USA. Senior citizens have become a new consumer group due to the aging population. In the future the retail trade will switch over to diversified retail formats and internet shopping as countries are transformed into information and communication societies. 2. In Korea, the former retail system characterized by markets and department stores has been greatly changed since the late 1980s with emphasis on high quality and convenience in consumption behaviors, with large domestic enterprises and foreign distribution corporations participating in Korean retailing. In the USA, retailing mergers and takeovers by major retails, bankruptcies, and extra-large shopping centers have emerged since the late 1980s. Recently, the USA retailing formats have been changed from the lower price-oriented discount types to the large scale theme parks. Much emphasis was put on entertainment, resorts, and convention centers. On the other hand, non-store types, such as the internet shopping, the CATV shopping, as well as catalog and mail-order sales are drastically increasing, although the proportion of their sales is low up to now. 3. In Korea, most of the retail facilities are concentrated in Seoul and the Metropolitan Region, and the distribution ratio of facilities came to 52% in 1997. The periodic markets, traditional markets which open on a periodic basis, are located mainly in Chollanam-do and Kyungsangbuk-do. The large-sized discount stores have expanded their locations to the over-crowded apartment complexes in new towns, located in the Metropolitan Region, and the large provincial cities, unlike the suburban locations in the USA. Therefore we needed to give attention to the locational relations in retail facilities between Seoul & the Metropolitan Region and rural settlement areas. In the USA, urban areas grew quickly with the development of the automobile in the 1920s, and the location of stores changed from a dispersed style centering around rural areas to a centralized one in urban areas. There is an accelerated growth for suburban areas, which have grown rapidly since 1950. As the membership warehouse clubs were introduced in the 1970s, the decentralization of location was more intensified. On the other hand, inner cities were revitalized by rearranging existing facilities to cope with suburban areas. And the location-free virtual retailing & TV shopping are also growing every year. 4. In view of the above, the continuous and desirable development devices in Korean retail trade are summarized as follows: First, the countermeasures against economies of scale, increase in retailing sales, and rise of a employment percentage in retailing are in need. Second, a scheme of lowering the proportion of food retail sales, and increasing a ratio of durable goods sales need to be worked out. Third, the original ideas are needed to apply positively information, communication and technology to retailing, to graft the traditional types on modem ones based on the social culture. Fourth, strategies are needed to strengthen the competitiveness of our retail trade through cooperation and chains of smaller retailers, the large enterprises participating in the distribution industry. Fifth, in order to realize the above, the retail industry, the administration, and the academic world should support the retail segment with concern and a practical strategy plan.

• Journal of the Korean association of regional geographers
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• v.3 no.1
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• pp.63-80
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• 1997

This study attempts to establish the criteria of site selection for establishing solid waste disposal facility, to determine optimal solid waste disposal sites with the criteria, and to examine the suitability of the selected sites. The Multi-Criteria Evaluation(MCE) module in Idrisi is used to determine optimal sites for solid waste disposal. The MCE combines the information from several criteria in interval and/or ratio scale to form a single index of evaluation without leveling down the data scale into ordinal scale. The summary of this study is as follows: First, the considerable criteria are selected through reviewing the literature and the availability of data: namely, percent of slope, fault lines, bedrock characteristics, major residential areas, reservoirs of water supply, rivers, inundated area, roads, and tourist resorts. Second, the criteria maps of nine factors have been developed. Each factor map is standardized and multiplies by its weight, and then the results are summed. After all of the factors have been incorporated, the resulting suitability map is multiplied by each of the constraint in turn to "zero out" unsuitable area. The unsuitable areas are discovered in urban district and its adjacencies, and mountain region as well as river, roads, resort area and their adjacency districts. Third, the potential sites for establishing waste disposal facilities are twenty five districts in Youngyang-gun. Five districts are located in Subi-myun Sinam-ri, nine districts in Chunggi-myun Haehwa-ri and Moojin-ri, and eleven districts in Sukbo-myun Posan-ri. The first highest score of suitability for waste disposal sites is shown at number eleven district in Chunggi-myun Moojin-ri and the second highest one is discovered at number twenty one district in Sukbo-myun Posan-ri that is followed by number nine district in Chunggi-myun Haehwa-ri, number seventeen and twenty three in Sukbo-myun Posan-ri, and number two in Subi-myun Sinam-ri. The first lowest score is found in number six district in Chunggi-myun Haehwa-ri, and the second lowest one is number five district in Subi-myun Sinam-ri. Finally, the Geographic Information System (GIS) helps to select optimal sites with more objectively and to minimize conflict in the determination of waste disposal sites. It is important to present several potential sites with objective criteria for establishing waste disposal facilities and to discover characteristics of each potential site as a result of that final sites of waste disposal are determined through considering thought of residents. This study has a limitation of criteria as a result of the restriction of availability of data such as underground water, soil texture and mineralogy, and thought of residents. To improve selection of optimal sites for a waste disposal facility, more wide rage of spatial and non-spatial data base should be constructed.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.143-159
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• 2015

Predicting IT trends has been a long and important subject for information systems research. IT trend prediction makes it possible to acknowledge emerging eras of innovation and allocate budgets to prepare against rapidly changing technological trends. Towards the end of each year, various domestic and global organizations predict and announce IT trends for the following year. For example, Gartner Predicts 10 top IT trend during the next year, and these predictions affect IT and industry leaders and organization's basic assumptions about technology and the future of IT, but the accuracy of these reports are difficult to verify. Social media data can be useful tool to verify the accuracy. As social media services have gained in popularity, it is used in a variety of ways, from posting about personal daily life to keeping up to date with news and trends. In the recent years, rates of social media activity in Korea have reached unprecedented levels. Hundreds of millions of users now participate in online social networks and communicate with colleague and friends their opinions and thoughts. In particular, Twitter is currently the major micro blog service, it has an important function named 'tweets' which is to report their current thoughts and actions, comments on news and engage in discussions. For an analysis on IT trends, we chose Tweet data because not only it produces massive unstructured textual data in real time but also it serves as an influential channel for opinion leading on technology. Previous studies found that the tweet data provides useful information and detects the trend of society effectively, these studies also identifies that Twitter can track the issue faster than the other media, newspapers. Therefore, this study investigates how frequently the predicted IT trends for the following year announced by public organizations are mentioned on social network services like Twitter. IT trend predictions for 2013, announced near the end of 2012 from two domestic organizations, the National IT Industry Promotion Agency (NIPA) and the National Information Society Agency (NIA), were used as a basis for this research. The present study analyzes the Twitter data generated from Seoul (Korea) compared with the predictions of the two organizations to analyze the differences. Thus, Twitter data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. To overcome these challenges, we used SAS IRS (Information Retrieval Studio) developed by SAS to capture the trend in real-time processing big stream datasets of Twitter. The system offers a framework for crawling, normalizing, analyzing, indexing and searching tweet data. As a result, we have crawled the entire Twitter sphere in Seoul area and obtained 21,589 tweets in 2013 to review how frequently the IT trend topics announced by the two organizations were mentioned by the people in Seoul. The results shows that most IT trend predicted by NIPA and NIA were all frequently mentioned in Twitter except some topics such as 'new types of security threat', 'green IT', 'next generation semiconductor' since these topics non generalized compound words so they can be mentioned in Twitter with other words. To answer whether the IT trend tweets from Korea is related to the following year's IT trends in real world, we compared Twitter's trending topics with those in Nara Market, Korea's online e-Procurement system which is a nationwide web-based procurement system, dealing with whole procurement process of all public organizations in Korea. The correlation analysis show that Tweet frequencies on IT trending topics predicted by NIPA and NIA are significantly correlated with frequencies on IT topics mentioned in project announcements by Nara market in 2012 and 2013. The main contribution of our research can be found in the following aspects: i) the IT topic predictions announced by NIPA and NIA can provide an effective guideline to IT professionals and researchers in Korea who are looking for verified IT topic trends in the following topic, ii) researchers can use Twitter to get some useful ideas to detect and predict dynamic trends of technological and social issues.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Korean Journal of Weed Science
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• v.7 no.2
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• pp.144-155
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• 1987

Water extracts of polygonum hydropiper and Polygonum aviculare completely inhibited the germination of lettuce seeds. Methanol extracts from these two species also inhibited the seed germination of lettuce (Lactuca sativa) and Oenothera odorata. Fifteen phenolic acids in total were identified by GLC from P. hydropiper and eighteen from P. aviculare. The most common phenolic acids identified from P. hydropiper were sinapic, salicylic+vanillic and ferulic acid presented in all the fractions. In addition, salicylic+vanillic, tannic+gallic, sinapic, ferulic and p-coumaric acid seemed to be important phenolic compounds in terms of quantity. However, salicylic+vanillic acids were the unique phenolic acids occurred in all the fractions of P. aviculare. The others such as tannic+gallic, sinapic, ferulic, p-coumaric acid, p-cresol and catechol present in large amount appeared also the important phenolic substances influencing allelopathic effects of P. aviculare. Linolenic acid and oxalic acid were the major fatty and organic acids in both plant species, presented in 2.38mg/g and 20.588mg/g in P. hydropiper, 3.70mg/g and 14.288mg/g in P. aviculare, respectively, which seem to be exhibiting allelopathic effects of these plants. Total alkaloids were presented in low amount such as 0.20% in P. hydropiper arid 0.22% in P. aviculare which may not be important elements. Pet. ether extracts were 2.42% in P. hydropiper and 1.65% in P. aviculare, which exhibit another potential for allelopathic effects that need further investigation. Various authentic phenolic compounds at different concentrations inhibited the germination of lettuce seed, indicating that the phenolic substances identified here may be directly related to biologically active substance.