• Journal of Korean Medicine for Obesity Research
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• v.20 no.1
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• pp.40-51
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• 2020

Objectives: We aimed to compare the quality of sleep, tongue diagnosis, oral microbiology differences in insomnia of Liver qi stagnation (LQS) and Non-Liver qi stagnation (NLQS). Methods: 56 patients were classified as LQS or NLSQ type insomnia through the insomnia differentiation questionnaire. The depression scores between the groups were compared through beck depression inventory (BDI), and the sleep quality was compared through Pittsburgh sleep quality index (PSQI) and Insomnia Severity Index (ISI). We analyzed the sleep efficiency, total sleep time, total awake frequency, total and average awake time through actigraph. For the tongue diagnosis, the distribution of tongue coating in six areas were measured through Winkel tongue coating index (WTCI). Linear discriminant analysis was performed to observe the differences in composition of microbial strains between the groups. Results: The scores of BDI, ISI and PSQI were significantly higher in LQS group. The total sleep time in LQS group was significantly less than that of NLQS group. Among the areas of tongue, according to the WTCI, the amount of tongue coating in zones A and C was significantly small. In oral microbial analysis, there was no significant difference between the groups at the phylum level. At the genus level, Prevotella, Veillonella, and Streptococcus were predominant in LQS group, whereas Prevotella, Neisseria, and Streptococcus in NLQS group. Conclusions: It was meaningful that insomnia was more likely in LQS group than in NLQS group, and the composition of oral microorganisms was significantly different, which could lead to the diseases caused by stress.

• FLOWER RESEARCH JOURNAL
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• v.19 no.4
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• pp.219-224
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• 2011

Phalaenopsis (P.) has various species, and some of them have strong fragrance. There are fragrant species such as P. bellina, P. violacea, P. schilleriana and used in breeding program for fragrant Phalaenopsis. This study was performed for establishment of fragrance analysis system using GC-MS and electronic nose in eight P. resources. We analyzed fragrant compound using the tissue of sepal, petal, column, and lip of P. '3010'. The percentage of the major compound was high in the petal and lip tissues. The main compound emitted from P. bellina was linalool (21.21%). It was possible that fragrance pattern could be analyzed among the resources using the electronic nose. Discriminant function analysis (DFA) was more useful than the principal component analysis (PCA) in statistics program. We utilized GC-MS method for the major compounds of flower from our breeding materials. This study would be useful to the fragrant analysis system for the fragrant orchid breeding in the future.

• Journal of Animal Science and Technology
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• v.64 no.4
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• pp.800-811
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• 2022

The integration of metabolomics and transcriptomics may elucidate the correlation between the genotypic and phenotypic patterns in organisms. In equine physiology, various metabolite levels vary during exercise, which may be correlated with a modified gene expression pattern of related genes. Integrated metabolomic and transcriptomic studies in horses have not been conducted to date. The objective of this study was to detect the effect of moderate exercise on the metabolomic and transcriptomic levels in horses. In this study, using nuclear magnetic resonance (NMR) spectroscopy, we analyzed the concentrations of metabolites in muscle and plasma; we also determined the gene expression patterns of branched chain (alpha) keto acid dehydrogenase kinase complex (BCKDK), which encodes the key regulatory enzymes in branched-chain amino acid (BCAA) catabolism, in two breeds of horses, Thoroughbred and Jeju, at different time intervals. The concentrations of metabolites in muscle and plasma were measured by ¹H NMR (nuclear magnetic resonance) spectroscopy, and the relative metabolite levels before and after exercise in the two samples were compared. Subsequently, multivariate data analysis based on the metabolic profiles was performed using orthogonal partial least square discriminant analysis (OPLS-DA), and variable important plots and t-test were used for basic statistical analysis. The stress-induced expression patterns of BCKDK genes in horse muscle-derived cells were examined using quantitative reverse transcription polymerase chain reaction (qPCR) to gain insight into the role of transcript in response to exercise stress. In this study, we found higher concentrations of aspartate, leucine, isoleucine, and lysine in the skeletal muscle of Jeju horses than in Thoroughbred horses. In plasma, compared with Jeju horses, Thoroughbred horses had higher levels of alanine and methionine before exercise; whereas post-exercise, lysine levels were increased. Gene expression analysis revealed a decreased expression level of BCKDK in the post-exercise period in Thoroughbred horses.

• Journal of Global Scholars of Marketing Science
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• v.18 no.4
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• pp.217-244
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• 2008

Consumers' overall satisfaction on a specific library use is inferred to be primarily accrued from their performance perception and use satisfaction on the library information service system as recent information technology is being rapidly improved and more libraries are being equipped with advanced information technologies. However, prior research has been conducted only on general library service quality and visitors' satisfaction, leaving the important aspects of visitors' library use and information performance perception. Thus, the objectives of this research are to examine the effect of library use patterns such as general visit for book reading and more professional information search, coupled with service quality, on the library users' performance perception on the information system that in turn, affects library use satisfaction on the same information system. More specifically, this study examines whether library visitors perceive differenltly the information system performance according to their library use patterns such that professional library users may have less positive on information system service due to their higher expectation or more positive perception on it due to variety of information uses and positive judgment on advanced information system. Next, three dimensions of service quality, consisting of interaction, outcome, and physical evidence quality in visitors' library use situations, are hypothesized to affect performance perception on library information system. Thirdly, the performance perception on library information system is hypothesized to influence the system use satisfaction while these two constructs are to affect visitors' overall satisfaction. we develop the following research model in accordance with the above theoretical reasoning. All variables used in this study(General Use Patterns, Professional Use Patterns, Interaction Quality, Outcome Quality, Physical Evidence Quality, Information Performance Perception, Information Use Satisfaction, Overall Satisfaction) were defined operationally based on the underlying prior studies. A survey was conducted with prepared questionnaires to about 400 visitors of a specific university library. Among them, 353 proper questionnaires were finally used for the analyses. Two-step approach was used to test the hypotheses. First, confirmatory factor analysis was conducted to guarantee the validity and reliability of variables. The results showed that all variables had not only convergent and discriminant validity, but also reliability. Then, research model was examined with a structural equation using LISREL 8.30 version. The fitness of the research model was found to be within the acceptable level. The findings of this study are as follows. The professional library use pattern was found to affect the users' performance perception on the library information system while the general library use pattern was not. Second, three dimensions of service quality (interaction, outcome, physical evidence) were found to influence the information system performance respectively while none of them was not to information use satisfaction. Third, library users' performance perception on the information system operation was found to affect the information system use satisfaction, both of which also influence users' overall satisfaction of the library. The findings of this study suggest that contemporary libraries strengthen their advanced information system operation in a way of user orientation and more importantly maximize their visitors' utilization of information system, accompanying proper material and various program development. This study conceptualized the new constructs of library users' performance perception on the information system and information use satisfaction which could better explain library users' overall satisfaction. Thus, furture study related with library service could utilize the constructs of information system performance and satisfaction as well as the variety of library use patterns in the users' viewpoints.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.