• Journal of Life Science
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• v.19 no.5
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• pp.593-597
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• 2009

The uterus plays a critical role in pregnancy and steroid hormones, and both estrogen (E2) and progesterone (P4) especially play important roles in the cross-talk between embryos and uterus to support the pregnancy. E2 stimulates uterine growth during early pregnancy to prepare for implantation of embryos. This cross-talk during the implantation period involves hormones (E2 and P4) and growth factors, including insulin-like growth factor-I (IGF-I). In the uterus of a pregnant pig, the action of E2 is mediated by estrogen receptor-${\beta}$ (ER-${\beta}$). The expression of ER-a was much higher in early pregnancy than in mid- and late- pregnancy, suggesting E2 secretion from embryos enhances transcription of ER-a during early pregnancy. In order to prove whether IGF-I is an E2 target gene, quantitative real-time PCR was performed on ovariectomized murine uterus with E2 and/or P4 treatment(s). Increased IGF-I mRNA expression was observed with E2 treatment, however, it was not significantly induced by P4 treatment, which clearly demonstrates that, in mice, E2 depends on the activation of uterine IGF-I gene expression. The expression of IGF-I in the uterus of pigs was much higher in early pregnancy than in mid- and late- pregnancy and these data exhibited the same expression pattern with the ER-${\beta}$ gene expression in the uterus. It suggests that a positive co-relationship between IGF-I and ER-${\beta}$ expression exists in the uterus, and that both gene expressions of IGF-I and ER-${\beta}$ are regulated by E2. It further suggests that uterine the IGF-I gene expression might be initiated by E2 secreted from embryos to increase ER-${\beta}$ gene expression, and that this increased ER-${\beta}$ further stimulates the expression of IGF-I in the uterus during early pregnancy.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.83-105
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• 2016

Measuring an individual's subjective wellbeing in an accurate, unobtrusive, and cost-effective manner is a core success factor of the wellbeing support system, which is a type of medical IT service. However, measurements with a self-report questionnaire and wearable sensors are cost-intensive and obtrusive when the wellbeing support system should be running in real-time, despite being very accurate. Recently, reasoning the state of subjective wellbeing with conventional sentiment analysis and unstructured data has been proposed as an alternative to resolve the drawbacks of the self-report questionnaire and wearable sensors. However, this approach does not consider contextual polarity, which results in lower measurement accuracy. Moreover, there is no sentimental word net or ontology for the subjective wellbeing area. Hence, this paper proposes a method to extract keywords and their contextual polarity representing the subjective wellbeing state from the unstructured text in online websites in order to improve the reasoning accuracy of the sentiment analysis. The proposed method is as follows. First, a set of general sentimental words is proposed. SentiWordNet was adopted; this is the most widely used dictionary and contains about 100,000 words such as nouns, verbs, adjectives, and adverbs with polarities from -1.0 (extremely negative) to 1.0 (extremely positive). Second, corpora on subjective wellbeing (SWB corpora) were obtained by crawling online text. A survey was conducted to prepare a learning dataset that includes an individual's opinion and the level of self-report wellness, such as stress and depression. The participants were asked to respond with their feelings about online news on two topics. Next, three data sources were extracted from the SWB corpora: demographic information, psychographic information, and the structural characteristics of the text (e.g., the number of words used in the text, simple statistics on the special characters used). These were considered to adjust the level of a specific SWB. Finally, a set of reasoning rules was generated for each wellbeing factor to estimate the SWB of an individual based on the text written by the individual. The experimental results suggested that using contextual polarity for each SWB factor (e.g., stress, depression) significantly improved the estimation accuracy compared to conventional sentiment analysis methods incorporating SentiWordNet. Even though literature is available on Korean sentiment analysis, such studies only used only a limited set of sentimental words. Due to the small number of words, many sentences are overlooked and ignored when estimating the level of sentiment. However, the proposed method can identify multiple sentiment-neutral words as sentiment words in the context of a specific SWB factor. The results also suggest that a specific type of senti-word dictionary containing contextual polarity needs to be constructed along with a dictionary based on common sense such as SenticNet. These efforts will enrich and enlarge the application area of sentic computing. The study is helpful to practitioners and managers of wellness services in that a couple of characteristics of unstructured text have been identified for improving SWB. Consistent with the literature, the results showed that the gender and age affect the SWB state when the individual is exposed to an identical queue from the online text. In addition, the length of the textual response and usage pattern of special characters were found to indicate the individual's SWB. These imply that better SWB measurement should involve collecting the textual structure and the individual's demographic conditions. In the future, the proposed method should be improved by automated identification of the contextual polarity in order to enlarge the vocabulary in a cost-effective manner.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.19-38
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• 2017

Collaborative filtering (CF) algorithm has been popularly used for implementing recommender systems. Until now, there have been many prior studies to improve the accuracy of CF. Among them, some recent studies adopt 'hybrid recommendation approach', which enhances the performance of conventional CF by using additional information. In this research, we propose a new hybrid recommender system which fuses CF and the results from the social network analysis on trust and distrust relationship networks among users to enhance prediction accuracy. The proposed algorithm of our study is based on memory-based CF. But, when calculating the similarity between users in CF, our proposed algorithm considers not only the correlation of the users' numeric rating patterns, but also the users' in-degree centrality values derived from trust and distrust relationship networks. In specific, it is designed to amplify the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the trust relationship network. Also, it attenuates the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the distrust relationship network. Our proposed algorithm considers four (4) types of user relationships - direct trust, indirect trust, direct distrust, and indirect distrust - in total. And, it uses four adjusting coefficients, which adjusts the level of amplification / attenuation for in-degree centrality values derived from direct / indirect trust and distrust relationship networks. To determine optimal adjusting coefficients, genetic algorithms (GA) has been adopted. Under this background, we named our proposed algorithm as SNACF-GA (Social Network Analysis - based CF using GA). To validate the performance of the SNACF-GA, we used a real-world data set which is called 'Extended Epinions dataset' provided by 'trustlet.org'. It is the data set contains user responses (rating scores and reviews) after purchasing specific items (e.g. car, movie, music, book) as well as trust / distrust relationship information indicating whom to trust or distrust between users. The experimental system was basically developed using Microsoft Visual Basic for Applications (VBA), but we also used UCINET 6 for calculating the in-degree centrality of trust / distrust relationship networks. In addition, we used Palisade Software's Evolver, which is a commercial software implements genetic algorithm. To examine the effectiveness of our proposed system more precisely, we adopted two comparison models. The first comparison model is conventional CF. It only uses users' explicit numeric ratings when calculating the similarities between users. That is, it does not consider trust / distrust relationship between users at all. The second comparison model is SNACF (Social Network Analysis - based CF). SNACF differs from the proposed algorithm SNACF-GA in that it considers only direct trust / distrust relationships. It also does not use GA optimization. The performances of the proposed algorithm and comparison models were evaluated by using average MAE (mean absolute error). Experimental result showed that the optimal adjusting coefficients for direct trust, indirect trust, direct distrust, indirect distrust were 0, 1.4287, 1.5, 0.4615 each. This implies that distrust relationships between users are more important than trust ones in recommender systems. From the perspective of recommendation accuracy, SNACF-GA (Avg. MAE = 0.111943), the proposed algorithm which reflects both direct and indirect trust / distrust relationships information, was found to greatly outperform a conventional CF (Avg. MAE = 0.112638). Also, the algorithm showed better recommendation accuracy than the SNACF (Avg. MAE = 0.112209). To confirm whether these differences are statistically significant or not, we applied paired samples t-test. The results from the paired samples t-test presented that the difference between SNACF-GA and conventional CF was statistical significant at the 1% significance level, and the difference between SNACF-GA and SNACF was statistical significant at the 5%. Our study found that the trust/distrust relationship can be important information for improving performance of recommendation algorithms. Especially, distrust relationship information was found to have a greater impact on the performance improvement of CF. This implies that we need to have more attention on distrust (negative) relationships rather than trust (positive) ones when tracking and managing social relationships between users.

• Korean Journal of Clinical Laboratory Science
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• v.49 no.2
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• pp.79-87
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• 2017

GenesWell$^{TM}$ BCT is a 12-gene test suggesting the prognostic risk score (BCT Score) for distant metastasis within the first 10 years in early breast cancer patients with hormone receptor-positive, HER2-negative, and pN0~1 tumors. In this study, we validated the analytical performance of GenesWell$^{TM}$ BCT. Gene expression values were measured by a one-step, real-time qPCR, using RNA extracted from FFPE specimens of early breast cancer patients. Limit of Blank, Limit of Detection, and dynamic range for each of the 12 genes were assessed by serially diluted RNA pools. The analytical precision and specificity were evaluated by three different RNA samples representing low risk group, high risk group, and near-cutoff group in accordance with their BCT Scores. GenesWell$^{TM}$ BCT could detect gene expression of each of the 12 genes from less than $1ng/{\mu}L$ of RNA. Repeatability and reproducibility across multiple testing sites resulted in 100% and 98.3% consistencies of risk classification, respectively. Moreover, it was confirmed that the potential interference substances does not affect the risk classification of the test. The findings demonstrate that GenesWell$^{TM}$ BCT have high analytical performance with over 95% consistency for risk classification.

• Survey Research
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• v.13 no.1
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• pp.135-158
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• 2012

Korean telephone surveys have been based on landline telephone directory or RDD(Random Digit Dialing) method. These days, however, there has been an increase of the households with no landline, or households with the line but not willing to register in the directory. Moreover, it is hard to contact young people or office workers who are usually staying out of home in the daytime. Due to these issues above, the predictability of election polls gets weaker. Especially, low accessibility to those who stay out of home when the poll's done, results in predictions with positive inclination toward conservatism. A solution to resolve this problem is to contact respondents by using both mobile and landline phones-via landline phone to those who are at home and via mobile phone to those who are out of home in the daytime(Mixed Mode Survey, hereafter MMS). To conduct MMS, 1) we need to obtain the sampling frames for the landline and mobile surveys, and 2) we need to decide the proportion of sample size of both. In this paper, we propose a heuristic method for conducting MMS. The method uses RDD for the landline phone survey, and the access panel list for the mobile phone survey. The proportion of sample sizes between landline and mobile phones are determined based on the 'Lifestyle and Time Use Study' conducted by Statistics Korea. As a case study, 4 election polls were conducted in the periods of the special election for the mayor of Seoul on Oct 26th, 2011. From the initial 3 polls, reactions and responses regarding the issues raised during the survey period were appropriately covered, and the final poll showed a very close prediction to the real election result.

• Journal of Korean Home Economics Education Association
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• v.26 no.1
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• pp.1-16
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• 2014

The purpose of this research is to develop a theme-based integrated teaching/learning plan in clothing, food, and housing in home economics and to apply the developed process in classes for evaluation in order to identify the suitability in schools. The theme-based integrated teaching/learning plan developed on the basis of textbooks consist four sub-themes; choosing($1^{st}$ and $2^{nd)$ lessons), using($3^{rd}$ lesson), processing ($4^{th}$ lesson), and alternatives($5^{th}$ and $6^{th}$ lessons) under the main theme of 'green family life'. The results from 20 individual and group activities showed that the students actively solved the problems when the presented cases were related to their own lives or experiences. The opportunity to implement green life through activities motivated students' willingness to proceed in real life. However, it is vital to assist integrated thinking through various examples before beginning due to students with difficulties connecting the issue from one area to the other during the problem-focused activity. The students' ability to solve the activity workbook had been improved as the lessons continued. From the survey questions on the theme-based integrated lessons, all items associated with integration of clothing, foods, and housing were positively responded. Also, questions regarding general understanding, suitability and satisfaction on the teaching/learning process were marked positive. The conclusion could be that the integrated theme related to clothing, food, and housing in our life would be appropriate for green family life. The theme-based integrated teaching/learning plan is effective in understanding the occurrence of green family life in relation with clothing, food, and housing, identifying the practical ideas implementing green life in those areas, and improving the integrated ability to solve the green life related problems. However, this research has its weakness in generalizing the results due to its limited survey respondents and post-evaluation being the only assessment conducted.

• Journal of Science Education
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• v.34 no.2
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• pp.405-420
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• 2010

The purpose of this study was to contrive the specific teaching plans based on the frame of 2007 revised science curriculum for applying open-inquiry lesson in real education situation and to research the effects of open-inquiry lesson on the student's science-related attitude, science process skill, and to investigate instructing teachers' cognition about open-inquiry. For this study, two fifth grade classes were chosen, one class was the experimental group, who were taught by open-inquiry based lesson, and another was the comparative group, who were taught by traditional method based lesson. The findings of this study were as follows: After open-inquiry lesson, the experimental group students came to enjoy open-inquiry learning and had the positive thought about it. After open-inquiry lesson, the experimental group marked higher mean score than the comparative group in science-related attitude's field but didn't showed the meaningful difference. On the other hand, in science process skill's field, the experimental group showed the significant higher improvement than the comparative one, especially in the subordinate area of basic science process skill. Finally, teachers who instructed students open-inquiry lesson thought open-inquiry lesson is the self-directed problem solving learning which raise the student's science process skill and interest. And the teachers thought the obstacles to instruct open-inquiry lesson are the lack of the student's cognition about open-inquiry and the insufficient circumstance for open-inquiry lesson. Therefore the teachers argued that the prerequisite for settling open-inquiry lesson successfully is to develope open-inquiry lesson curricula and teaching materials.

• Korean Journal of Psychosomatic Medicine
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• v.7 no.1
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• pp.124-133
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• 1999

Objective : Postpartum depression(PPD) was known to be caused by many factors including various psychosocial risk factors. This study was performed to identify the psychosocial risk factors for ppd, preliminarily in Korea. Methods : A group of 119 postpartum women, each of whom was at 6 to 8 weeks after delivery was identified at the time when they visited to the child health clinic or postnatal check-up clinic. The risk factors were surveyed by the self-reported questionnaire. The items of questionnaire were consisted of known risk factors in other studies and other possible stress-related factors. PPD was assessed by the Edinburgh Postnatal Depression Scale(EPDS) and the degree of postpartum depression was determined by its score. Results : 16 women(13.45%) in the high risk group were diagnosed as PPD among the 119 women. Risk factors including past experience of depressive symptoms and low level of marital satisfaction were founded more frequently in women in the high risk group than in the low risk group. The score of EPDS was significantly high in the group who experienced depressive symptoms in the past, anxiety or depression during pregnancy, stressful life event during the period of recent pregnancy and postpartum, and who had low level of marital satisfaction. There was a positive correlation between age and the score of EPDS. However, the postpartum depressive symptoms were not influenced by the level of education, job, retirement due to pregnancy and delivery, wanted or unwanted pregnancy, delivery method, feeding method, the hospitalization of infant, expected and real gender of infant. Conclusion : These results suggest that PPD is quite frequent at postpartum period. Various risk factors contribute to the development of PPD. If clinicians pay attention to the risk factors of PPD and give appropriate psychiatric intervention to the mothers during pregnancy and postpartum, it will be easy for the clinicians to recognize and treat PPD in the early stage.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.66-79
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• 2016

A discussion has recently emerged over the increase of approvals of K-REITs, which is concluded on the basis of how to raise funds for business activity, fulfill the expected rate of return and maximize the management of managing investment funds. In addition, corporations need to acknowledge the necessity of the capital structure reflected in the current economic environment and decision-making processes. This research analyzed the characteristics by investment types and influence factors about the debt ratio of K-REITs. The data were collected from general management about business state, investment, and finance from 2002 to 2015 in K-REITs (except for the GFC period of 2007~2009). The results of the research demonstrated the high ratios of the largest shareholder characteristics, which are corporation, pension funds, mutual funds, banks, securities, insurance, and, recently, the increasing ratio of the largest shareholder and major stockholder. The investment of K-REITs is increasing the role of institutional investors that take a leading development of K-REITs. The behaviors of simultaneous investment of institutional investors were analyzed to show that they received higher interest rates than other financial institutions and ran in parallel with attraction and compensation. The results of the multiple regressions analysis, utilizing variables about debt ratio were as follows. The debt ratio showed a negative (-) relation that profitability is increasing, which matches the pecking order theory and trade off theory. On the other hand, investment opportunities (growth potential) showed a negative (-) relation and assets scale that indicated a positive (+) relation. The research results are reflected as follows. K-REITs focused on private equity REITs more than public offering REITs, and in the case of financing the capital of others, loan capital is operated under the guarantee of tangible assets (most of real estate) more than financing of the stock market. Further, after the GFC, the capital of others was actively utilized in K-REITs business, and the debt ratio showed that the determinant factors by the ratio and characteristics of the largest shareholder and investment products.