• Korean Security Journal
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• no.21
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• pp.95-120
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• 2009

This study was designed to investigate the relationship between the stages of exercise behavior change and physical self-concept and self-efficacy of security employees in hotel casinos. The sampling was drawn from employees at 8 casinos which had more than 30 employees. Participants were selected by convenience sampling method and they completed questionnaires about Physical Self-Concept and Self- Efficacy by self-administration method under supervision of trained researchers SPSS 16.0 (Statistical Package for the Social Science) was used for data analysis in the present study. Reliability and validity were examined for the present study. The principle component factor analysis and varimax rotation were used for the present study. Eigen value 1.0 was the criterion for selecting factors. Chi-square (X) 2 test was utilized for measuring the difference in gender and types of job duties at the stages of exercise behavior change. One-way ANOVA was employed to examine the relationship between the stages of exercise behavior change as an independent variable and physical self-concept and self-efficacy as dependent variables. The Scheffe method was used to determine mean differences of groups as a follow-up test. Multiple regression analysis was utilized to test the difference of physical self-concept as dependent variable and self-efficacy as independent variable. To verify hypothesis for the study, a statistical significance level of $\alpha$=.05 was used. The results were as follow: first, there were differences found for gender and types of job responsibilities in the stages of exercise behavior change. Secondly, as security employees progressed through the stages of exercise behavior change, their physical self-concept and self-efficacy improved. Finally, physical activity and body fat had significant main effects on self-efficacy.

• Journal of Plant Biotechnology
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• v.43 no.3
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• pp.347-358
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• 2016

Brassinosteroid (BR), a plant steroid hormone, plays key roles in numerous growth and developmental processes as well as tolerance to both abiotic and biotic stress. To understand the biological networks involved in BR-mediated signaling pathways and stress tolerance, we performed comparative genome-wide transcriptome analysis of a constitutively activated BR bes1-D mutant with an Agilent Arabidopsis $4{\times}44K$ oligo chip. As a result, we newly identified 1,091 (562 up-regulated and 529 down-regulated) significant differentially expressed genes (DEGs). The combination of GO enrichment and protein network analysis revealed that stress-related processes, such as metabolism, development, abiotic/biotic stress, immunity, and defense, were critically linked to BR signaling pathways. Among the identified gene sets, we confirmed more than a 6-fold up-regulation of NB-ARC and FLS2 in bes1-D plants. However, some genes, including TIR1, TSA1 and OCP3, were down-regulated. Consistently, BR-activated plants showed higher tolerance to drought stress and pathogen infection compared to wild-type controls. In this study, we newly developed a useful, comprehensive method for large-scale identification of critical network and gene sets with global transcriptome analysis using a microarray. This study also showed that gain of function in the bes1-D gene can regulate the adaptive response of plants to various stressful conditions.

• Journal of Embryo Transfer
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• v.24 no.1
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• pp.33-37
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• 2009

Multiple ovulation and embryo transfer (MOET) has the potential to increase the rates of genetic improvement in cattle. Thus this study was performed to investigate several factors influencing in vivo embryo production in Holstein cattle under field conditions. The donors were superovulated with Folltropin-V and $PGF_2{\alpha}$ combination method. From Day 10 onward, donors were superovulated by i.m., twice daily, administration of 400mg Folltropin-V given in a series of decreasing doses over a 4-day period: on the first day, 3.5ml; on the second day, 3.0ml; on the third day, 2.0ml; and on the fourth day, 1.5ml (20ml in total, equivalent to 400mg of NIH-FSH-P1). Estrus was induced by i.m. administration of 25mg prostaglandin $F_2{\alpha}$ on the sixth and seventh of FSH treatment. Estrus detection was performed twice daily beginning 24h after the first prostaglandin $F_2{\alpha}$ injection. Donor cows were artificially inseminated 12 and 24 h after first standing estrus with semen from a proven Holstein sire. Embryos used in this study were recovered Day 7.5 of the cycle (Day 0: first standing estrus). From 195 superovulated dairy cows, 2,104 eggs were recovered, of which 1,172 were classified as transferable embryos based on morphological evaluation of quality. The results are summarized as follows: 1. The numbers of recovered and transferable embryos did not significantly differ among the capacity of milk production that were < 10,000kg/305days (group 1), $10,000{\sim}12,000\;kg$/305days (group 2) or > 12,000kg/305 days (group 3) (p>0.05, Table 1). 2. No differences in the numbers of recovered and transferable embryos were found among the donor's postparient days (p>0.05, Table 2). 3. Also, the numbers of recovered and transferable embryos of each superovulation seasons did not significantly differ among the four groups (p>0.05, Table 3).

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.53-77
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• 2015

Korea has witnessed an increasing number of domestic patent applications, but a majority of them are not utilized to their maximum potential but end up becoming obsolete. According to the 2012 National Congress' Inspection of Administration, about 73% of patents possessed by universities and public-funded research institutions failed to lead to creating social values, but remain latent. One of the main problem of this issue is that patent creators such as individual researcher, university, or research institution lack abilities to commercialize their patents into viable businesses with those enterprises that are in need of them. Also, for enterprises side, it is hard to find the appropriate patents by searching keywords on all such occasions. This system proposes a patent recommendation system that can identify and recommend intellectual rights appropriate to users' interested fields among a rapidly accumulating number of patent assets in a more easy and efficient manner. The proposed system extracts core contents and technology sectors from the existing pool of patents, and combines it with secondary social knowledge, which derives from tags information created by users, in order to find the best patents recommended for users. That is to say, in an early stage where there is no accumulated tag information, the recommendation is done by utilizing content characteristics, which are identified through an analysis of key words contained in such parameters as 'Title of Invention' and 'Claim' among the various patent attributes. In order to do this, the suggested system extracts only nouns from patents and assigns a weight to each noun according to the importance of it in all patents by performing TF-IDF analysis. After that, it finds patents which have similar weights with preferred patents by a user. In this paper, this similarity is called a "Domain Similarity". Next, the suggested system extract technology sector's characteristics from patent document by analyzing the international technology classification code (International Patent Classification, IPC). Every patents have more than one IPC, and each user can attach more than one tag to the patents they like. Thus, each user has a set of IPC codes included in tagged patents. The suggested system manages this IPC set to analyze technology preference of each user and find the well-fitted patents for them. In order to do this, the suggeted system calcuates a 'Technology_Similarity' between a set of IPC codes and IPC codes contained in all other patents. After that, when the tag information of multiple users are accumulated, the system expands the recommendations in consideration of other users' social tag information relating to the patent that is tagged by a concerned user. The similarity between tag information of perferred 'patents by user and other patents are called a 'Social Simialrity' in this paper. Lastly, a 'Total Similarity' are calculated by adding these three differenent similarites and patents having the highest 'Total Similarity' are recommended to each user. The suggested system are applied to a total of 1,638 korean patents obtained from the Korea Industrial Property Rights Information Service (KIPRIS) run by the Korea Intellectual Property Office. However, since this original dataset does not include tag information, we create virtual tag information and utilized this to construct the semi-virtual dataset. The proposed recommendation algorithm was implemented with JAVA, a computer programming language, and a prototype graphic user interface was also designed for this study. As the proposed system did not have dependent variables and uses virtual data, it is impossible to verify the recommendation system with a statistical method. Therefore, the study uses a scenario test method to verify the operational feasibility and recommendation effectiveness of the system. The results of this study are expected to improve the possibility of matching promising patents with the best suitable businesses. It is assumed that users' experiential knowledge can be accumulated, managed, and utilized in the As-Is patent system, which currently only manages standardized patent information.

• Pediatric Infection and Vaccine
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• v.9 no.1
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• pp.61-66
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• 2002

Purpose : To determine wether varicella can be prevented by administration of oral acyclovir(ACV) during the incubation period of the disease. Methods : Starting 9 days after exposure to the index case in their families, ACV(40 mg/kg/day in four divided doses) was given orally to 20 exposed children for 5 days. Their clinical features was compared with those of 20 control subjects. Antibody titers to VZV were measured in both group 1 week and 4 weeks after finishing the oral ACV administration. Results : The mean age of family members with varicella(51.4 months) were significantly high compared to that of ACV prophylaxis group(28.5 months) and control group(31 months) (P<0.05). Among the 12 children with ACV prophylaxis who completed follow up blood sampling, nine children were diagnosed as VZV infection on the serologic test(75%). Among them six children showed positive VZV IgM on the first blood sample and two children showed serocoversion to positive IgM on the second test after ACV prophylaxis. One child who was negative on both IgM and IgG, showed positive IgG on the second test. The incidence of fever and severity of skin rashes were significantly low in children received oral ACV than in the control group. No or reduced number of maculopapular eruption were observed in the oral ACV group compared to multiple vesicles of the control group. Conclusion : In the present study, we observed that oral ACV prophylaxis to the family contacts is effective in reducing severity of skin lesion. It is likely that oral ACV 9 days after contact prevents or reduces blood dissemination of VZV. Little is known about clinical effect and immunity to the virus in exposed children with no varicella symptom after treatment. We propose the checking up antibody to VZV some period after oral ACV, and considering vaccination to whom with no antibody. But further more studies are needed to practical application of oral ACV for the postexposure prophylaxis of varicella.

• Journal of Food Hygiene and Safety
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• v.30 no.3
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• pp.272-280
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• 2015

A simultaneous official method was developed for the determination of phorate and its metabolites (phorate sulfoxide, phorate sulfone, phorate oxon, phorate oxon sulfoxide, phorate oxon sulfone) in livestock samples. The analytes were quantified and confirmed via liquid chromatograph-tandem mass spectrometer (LC-MS/MS) in positive ion mode using multiple reaction monitoring (MRM). Phorate and its metabolites were extracted from beef and milk samples with acidified acetonitrile (containing 1% acetic acid) and partitioned with anhydrous magnesium sulfate. Then, the extract was purified through primary secondary amine (PSA) and C18 dispersive sorbent. Matrix matched calibration curves were linear over the calibration ranges (0.005-0.5 mg/L) for all the analytes into blank extract with $r^2$ > 0.996. For validation purposes, recovery studies were carried out at three different concentration levels (beef 0.004, 0.04 and 0.2 mg/kg; milk 0.008, 0.04 and 0.2 mg/kg, n = 5). The recoveries were within 79.2-113.9% with relative standard deviations (RSDs) less than 19.2% for all analytes. All values were consistent with the criteria ranges requested in the Codex guidelines. The limit of quantification was quite lower than the maximum residue limit (MRL) set by the Ministry of Food and Drug Safety (0.05 mg/kg). The proposed analytical method was accurate, effective and sensitive for phorate and its metabolites determination and it will be used to as an official analytical method in Korea.

• Korean Journal of Soil Science and Fertilizer
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• v.44 no.5
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• pp.878-886
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• 2011

The soybean is one of the oldest cultivated crops in the world. Microwave remote sensing is an important tool because it can penetrate into cloud independent of weather and it can acquire day or night time data. Especially a ground-based polarimetric scatterometer has advantages of monitoring crop conditions continuously with full polarization and different frequencies. In this study, soybean growth parameters and soil moisture were estimated using polarimetric discrimination ratio (PDR) by radar scatterometer. A ground-based polarimetric scatterometer operating at multiple frequencies was used to continuously monitor the soybean growth condition and soil moisture change. It was set up to obtain data automatically every 10 minutes. The temporal trend of the PDR for all bands agreed with the soybean growth data such as fresh weight, Leaf Area Index, Vegetation Water Content, plant height; i.e., increased until about DOY 271 and decreased afterward. Soil moisture lowly related with PDR in all bands during whole growth stage. In contrast, PDR is relative correlated with soil moisture during below LAI 2. We also analyzed the relationship between the PDR of each band and growth data. It was found that L-band PDR is the most correlated with fresh weight (r=0.96), LAI (r=0.91), vegetation water content (r=0.94) and soil moisture (r=0.86). In addition, the relationship between C-, X-band PDR and growth data were moderately correlated ($r{\geq}0.83$) with the exception of the soil moisture. Based on the analysis of the relation between the PDR at L, C, X-band and soybean growth parameters, we predicted the growth parameters and soil moisture using L-band PDR. Overall good agreement has been observed between retrieved growth data and observed growth data. Results from this study show that PDR appear effective to estimate soybean growth parameters and soil moisture.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.316-328
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• 2021

In this study, the leaf Na content prediction model for spring potato was established using 400-1000 nm hyperspectral sensor to develop the multispectral sensor for the salinity monitoring in reclaimed land. The irrigation conditions were standard, drought, and salinity (2, 4, 8 dS/m), and the irrigation amount was calculated based on the amount of evaporation. The leaves' Na contents were measured 1st and 2nd weeks after starting irrigation in the vegetative, tuber formative, and tuber growing periods, respectively. The reflectance of the leaves was converted from 5 nm to 10 nm, 25 nm, and 50 nm of FWHM (full width at half maximum) based on the 10 nm wavelength intervals. Using the variance importance in projections of partial least square regression(PLSR-VIP), ten band ratios were selected as the variables to predict salinity damage levels with Na content of spring potato leaves. The MLR(Multiple linear regression) models were estimated by removing the band ratios one by one in the order of the lowest weight among the ten band ratios. The performance of models was compared by not only R2, MAPE but also the number of band ratios, optimal FWHM to develop the compact multispectral sensor. It was an advantage to use 25 nm of FWHM to predict the amount of Na in leaves for spring potatoes during the 1st and 2nd weeks vegetative and tuber formative periods and 2 weeks tuber growing periods. The selected bandpass filters were 15 bands and mainly in red and red-edge regions such as 430/440, 490/500, 500/510, 550/560, 570/580, 590/600, 640/650, 650/660, 670/680, 680/690, 690/700, 700/710, 710/720, 720/730, 730/740 nm.

• Journal of Service Research and Studies
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• v.13 no.2
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• pp.181-203
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• 2023

YouTube exhibits a hybrid personality, incorporating traits of both over-the-top (OTT) and personal broadcasting platforms. However, limited research has investigated these hybrid characteristics, particularly in the context of paid YouTube channel memberships. Therefore, building upon consumption value theory and prior literature, this study examines the influence of consumption value factors associated with paid YouTube channel memberships on usage satisfaction and continuance intention. Specifically, the study identifies four perceived consumption value factors (functional, social, emotional, and epistemic values) within the paid YouTube channel membership context and assesses their impact on usage satisfaction and continuance intention. Additionally, the study explores the moderating role of conditional value (the experience of watching live streams on paid YouTube channels) in these relationships. Data was collected via an online survey from Korean adults who subscribed to multiple paid YouTube channel memberships, resulting in 274 responses. The proposed hypotheses were tested using structural equation modeling (SEM). The SEM results indicate that all four consumption value factors significantly influence usage satisfaction, with usage satisfaction in turn positively affecting continuance intention. Furthermore, the study reveals that conditional value moderates the relationships between functional/emotional values and usage satisfaction, as well as between usage satisfaction and continuance intention. This study is the first to focus on YouTube channel paid memberships, which encompass characteristics from both OTT and personal broadcasting platforms. It is anticipated that this research will offer insights to personal broadcasters and stakeholders regarding the motivational factors that impact user satisfaction and encourage subscriptions to channel memberships.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.