• Journal of Ginseng Research
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• v.43 no.4
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• pp.676-683
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• 2019

Background: Korean Red Ginseng (KRG) is widely used for strengthening the immune system and fighting fatigue, especially in people with deficiency syndrome. However, there is concern that the long-term application or a high dose of KRG can cause "fireness" (上火 in Chinese) because of its "dryness" (燥性 in Chinese). The aim of this study was to assess the safety and efficacy of a 4-week treatment with KRG in participants with deficiency syndrome. Methods: This was a 4-week, randomized, double-blind, placebo-controlled clinical trial. A total of 180 Chinese participants were randomly allocated to three groups: placebo control group, participants were given a placebo, 3.6 g/d; KRG 1.8 g and 3.6 g groups. The primary outcomes were the changes in fireness and safety evaluation (adverse events, laboratory tests, and electrocardiogram). The secondary outcomes were the efficacy of KRG on fatigue, which include the following: traditional Chinese medicine (TCM) symptom scale and fatigue self-assessment scale. Results: Of the 180 patients, 174 completed the full study. After 4 weeks of KRG treatment, the Fire-heat symptoms score including Excess fire-heat score and Deficient fire-heat score showed no significant change as compared with placebo treatment, and no clinically significant changes in any safety parameter were observed. Based on the TCM syndrome score and fatigue self-assessment score, TCM symptoms and fatigue were greatly improved after treatment with KRG, which showed a dose- and time-dependent effect. The total effective rate was also significantly increased in the KRG groups. Conclusion: Our study revealed that KRG has a potent antifatigue effect without significant adverse effects in people with deficiency syndrome. Although a larger sample size and longer treatment may be required for a more definite conclusion, this clinical trial is the first to disprove the common conception of "fireness" related to KRG.

• Journal of Practical Engineering Education
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• v.14 no.1
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• pp.37-48
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• 2022

This study analyzed the effect of internal audit on the performance of the IATF 16949 automotive quality management system to understand the internal audit of companies and propose measures to activate the company's internal audit process. It was identified with the empirical analysis that 'The internal auditor competence, internal audit planning, internal audit implementation, infrastructure, culture/environment, and CEO support' to characterize IATF 16949 internal audit of automotive quality management system affects the internal performance and business performance of the company. In addition, I checked the size of the company and the period of certification period as moderating variables according to the sales based on the presented as factors that can improve the performance of the company, and how the moderating effects are seen in the relationship with the performance of the organization. I did analysis of technical statistics, exploratory factors, reliability, and multi-regression analysis with SPSS program. I summarized the results of the study, as a result of that, it was found that the internal audit planning, internal audit implementation, culture/ environment, and CEO support of independent variables affected the parameter and dependent variables (the internal performance and management performance of companies).

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.2
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• pp.59-66
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• 2008

In the construction site, to improve the man-dependent form work, non-stripping form has been studied but the developed non-stripping form was hard to applied with respect to the cost, form size and performance. This study is for evaluating the adaptability of the developed non-stripping form named as high performance permanent form (HPPF). To do this, the analytical approach and parametric study were performed based on the research for fundamental material characteristic of the HPPF. The target concrete structure is a wall structure because of its effectiveness of HPPF. To evaluate the structural efficiency of the HPPF applied wall structure, FEM analysis was performed to decide the maximum placing height at one time then it was applied to design the wall structure. In the result of the analysis, the HPPF applied wall structure showed the lots of advantages that it can reduce the cost resulted from reducing concrete and steel rebar even if it has same structural performance to the conventional concrete wall structure with same dimension. With this analysis result, it can be evaluated that the HPPF applied concrete structure can be a concrete structure with the long term durability in site.

• Asia pacific journal of information systems
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• v.19 no.4
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• pp.47-75
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• 2009

Recently, researches on Management Information Systems (MIS) have laid out theoretical foundation and academic paradigms by introducing diverse theories, themes, and methodologies. Especially, academic paradigms of MIS encourage a user-friendly approach by developing the technologies from the users' perspectives, which reflects the existence of strong causal relationships between information systems and user's behavior. As in other areas in social science the use of structural equation modeling (SEM) has rapidly increased in recent years especially in the MIS area. The SEM technique is important because it provides powerful ways to address key IS research problems. It also has a unique ability to simultaneously examine a series of casual relationships while analyzing multiple independent and dependent variables all at the same time. In spite of providing many benefits to the MIS researchers, there are some potential pitfalls with the analytical technique. The research objective of this study is to provide some guidelines for an appropriate use of SEM based on the assessment of current practice of using SEM in the MIS research. This study focuses on several statistical issues related to the use of SEM in the MIS research. Selected articles are assessed in three parts through the meta analysis. The first part is related to the initial specification of theoretical model of interest. The second is about data screening prior to model estimation and testing. And the last part concerns estimation and testing of theoretical models based on empirical data. This study reviewed the use of SEM in 164 empirical research articles published in four major MIS journals in Korea (APJIS, ISR, JIS and JITAM) from 1991 to 2007. APJIS, ISR, JIS and JITAM accounted for 73, 17, 58, and 16 of the total number of applications, respectively. The number of published applications has been increased over time. LISREL was the most frequently used SEM software among MIS researchers (97 studies (59.15%)), followed by AMOS (45 studies (27.44%)). In the first part, regarding issues related to the initial specification of theoretical model of interest, all of the studies have used cross-sectional data. The studies that use cross-sectional data may be able to better explain their structural model as a set of relationships. Most of SEM studies, meanwhile, have employed. confirmatory-type analysis (146 articles (89%)). For the model specification issue about model formulation, 159 (96.9%) of the studies were the full structural equation model. For only 5 researches, SEM was used for the measurement model with a set of observed variables. The average sample size for all models was 365.41, with some models retaining a sample as small as 50 and as large as 500. The second part of the issue is related to data screening prior to model estimation and testing. Data screening is important for researchers particularly in defining how they deal with missing values. Overall, discussion of data screening was reported in 118 (71.95%) of the studies while there was no study discussing evidence of multivariate normality for the models. On the third part, issues related to the estimation and testing of theoretical models on empirical data, assessing model fit is one of most important issues because it provides adequate statistical power for research models. There were multiple fit indices used in the SEM applications. The test was reported in the most of studies (146 (89%)), whereas normed-test was reported less frequently (65 studies (39.64%)). It is important that normed- of 3 or lower is required for adequate model fit. The most popular model fit indices were GFI (109 (66.46%)), AGFI (84 (51.22%)), NFI (44 (47.56%)), RMR (42 (25.61%)), CFI (59 (35.98%)), RMSEA (62 (37.80)), and NNFI (48 (29.27%)). Regarding the test of construct validity, convergent validity has been examined in 109 studies (66.46%) and discriminant validity in 98 (59.76%). 81 studies (49.39%) have reported the average variance extracted (AVE). However, there was little discussion of direct (47 (28.66%)), indirect, and total effect in the SEM models. Based on these findings, we suggest general guidelines for the use of SEM and propose some recommendations on concerning issues of latent variables models, raw data, sample size, data screening, reporting parameter estimated, model fit statistics, multivariate normality, confirmatory factor analysis, reliabilities and the decomposition of effects.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.