• Analytical Science and Technology
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• v.27 no.3
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• pp.172-180
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• 2014

Fenpyrazamine which is a pyrazole fungicide class for controlling gray mold, sclerotinia rot, and Monilinia in grapevines, stone fruit trees, and vegetables has been registered in republic of Korea in 2013 and the maximum residue limits of fenpyrazamine is set to grape, peach, and mandarin as 5.0, 2.0, and 2.0 mg/kg, respectively. Very reliable and sensitive analytical method for determination of fenpyrazamine residues is required for ensuring the food safety in agricultural products. Fenpyrazamine residues in samples were extracted with acetonitrile, partitioned with dichloromethane, and then purified with silica-SPE cartridge and eluted with hexane and acetone mixture. The purified samples were determined by HPLC-UVD and confirmed with LC-MS and quantified using external standard method. Linear range of fenpyrazamine was between $0.1{\sim}5.0{\mu}g/mL$ with the correlation coefficient (r) 0.999. The average recovery ranged from 71.8 to 102.7% at the spiked level of 0.05, 0.5, and 5.0 mg/kg, while the relative standard deviation was between 0.1 and 7.3%. In addition, limit of detection and limit of quantitation were 0.01 and 0.05 mg/L, respectively. The results revealed that the developed and validated analytical method is possible for fenpyrazamine determination in agricultural product samples and will be used as an official analytical method.

• Journal of Distribution Science
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• v.10 no.2
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• pp.43-52
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• 2012

As experiential education services are growing, the need for proper management is increasing. Considering that adequate measures are an essential factor for achieving success in managing something, it is important for managers to use a proper system of metrics to measure the performance of experiential education services. However, in spite of this need, little research has been done to develop a valid and reliable set of metrics for assessing the quality of experiential education services. The current study aims to develop a multi-item instrument for assessing the service quality of experiential education. The specific procedure is as follows. First, we generated a pool of possible metrics based on diverse literature on service quality. We elicited possiblemetric items not only from general service quality metrics such as SERVQUAL and SERVPERF but also from educational service quality metrics such as HEdPERF and PESPERF. Second, specialist teachers in the experiential education area screened the initial metrics to boost face validity. Third, we proceeded with multiple rounds of empirical validation of those metrics. Based on this processes, we refined the metrics to determine the final metrics to be used. Fourth, we examined predictive validity by checking the well-established positive relationship between each dimension of metrics and customer satisfaction. In sum, starting with the initial pool of scale items elicited from the previous literature and purifying them empirically through the surveying method, we developed a four-dimensional systemized scale to measure the superiority of experiential education and named it "Experiential Education PERFormance" (EEPERF). Our findings indicate that students (consumers) perceive the superiority of the experiential education (EE) service in the following four dimensions: EE-empathy, EE-reliability, EE-outcome, and EE-landscape. EE-empathy is a judgment in response to the question, "How empathetically does the experiential educational service provider interact with me?" Principal measures are "How well does the service provider understand my needs?," and "How well does the service provider listen to my voice?" Next, EE-reliability is a judgment in response to the question, "How reliably does the experiential educational service provider interact with me?" Major measures are "How reliable is the schedule here?," and "How credible is the service provider?" EE-outcome is a judgmentin response to the question, "What results could I get from this experiential educational service encounter?" Representative measures are "How good is the information that I will acquire form this service encounter?," and "How useful is this service encounter in helping me develop creativity?" Finally, EE-landscape is a judgment about the physical environment. Essential measures are "How convenient is the access to the service encounter?,"and "How well managed are the facilities?" We showed the reliability and validity of the system of metrics. All four dimensions influence customer satisfaction significantly. Practitioners may use the results in planning experiential educational service programs and evaluating each service encounter. The current study isexpected to act as a stepping-stone for future scale improvement. In this case, researchers may use the experience quality paradigm that has recently arisen.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• The Korean Journal of Nuclear Medicine Technology
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• v.17 no.1
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• pp.76-79
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• 2013

Purpose: It is very important to establish the appropriate reference range in the laboratory for preventing mistakes like false positive or false negative. Because the reference range in the laboratory is standard of patient test results interpretation. Proinsulin is precursor hormone of insulin, and the importance is increasing for diagnosing diabetes or insulinoma. Proinsulin reagent used in our laboratory is produced in the USA, and the reference range provided by manufacturer was adapted to our reference range after the validation test. But, it is generally recommend for the every laboratory to establish the their own reference range. So, we decided to re-evaluate the reference range with our patients' test results. Materials and Methods: Among 737 patients who had been to health promotion center in our hospital between Dec. $8^{th}$ 2011 and Dec. $21^{st}$ 2011, 563 patients are chosen with exception of diabetics patients and patients showing abnormal test results in Fasting Glucose, HbA1c, Insulin, and C-peptide. The 563 test results (275 males and 288 females) were classified with three groups(entire, male, female), and analysis of normal distribution was performed with aid of SPSS(version 19.0). Because Each group didn't show normal distribution, the reference range was set from the lowest limit of 2.5% to the highest limit of 97.5% with Percentile method used in non-normal distribution. Results: When evaluation values are sorted in ascending order, the entire range is 4.5~52.0 pM and 5.3~51.9 pM for male and 4.5~52.0 pM for female. The calculated reference range with percentile method shows 6.7~26.5 pM for entire group, 6.8~26.5 pM for male and 6.7~26.5 pM for female, respectively. Conclusion: The reference range provided by reagent manufacturer is 6.4~9.4 pM and the one established in this study is 6.7~26.5 pM. This difference might be caused by racial characteristics between Western people and Koreans. So an ideal reference range can be gotten with normal population visiting to every hospital. Our hospital has been using the newly re-establishing reference range under consultation with the department of endocrinology since Aug. $1^{st}$ 2012.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1319-1329
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• 2013

The purpose of this research was to develop and validate the coach-athlete relationship scale suitable to amateur golf players by applying the Rasch rating scale model. As the coach-athlete relationship scale, the Korean form of scale developed by Kim and Park (2008), which was revised based on the evidence on the basis of inspection contents, was used to conduct a survey on 217 amateur golf athletes. And the unidimensionality, which is the basic assumption of the Rasch model, was verified using the WINSTEPS program, and the appropriateness of the item category was established through the step calibration. The goodness of fit of each question was tested through the goodness-of-fit index and the differential item functioning (DIF) was estimated according to the golf career. When the goodness-of-fit index estimated for each question was 1.30 or more it was judged unfit and the significance level in the analysis was all set as.05. The results of the analysis showed that the measures variance explained by the Rasch measurement model was more (33.7%) than 20%, so the unidimensionality assumptions of the 11 questions (..hospitable posture when my coach is teaching) were satisfied. The result of analyzing the item category (7 scale) with step calibration was found to be unfit, but in the result of reanalyzing by rescoring into a 5-point scale, it was found to be fit. Particularly, in the result of estimating the goodness-of-fit using the systematized item category (5 scale), Question 10 (...my best when my coach is teaching) and Question 11 were found to be unfit, and as a result of estimating the differential functioning item according to golf career, Question 11 was found to be unevenly differentiated according to golf career. So the 5-point scale of Question 9 after eliminating the two questions which were unfit and differentiated was validated to be the coach-athlete relationship scale suitable to amateur golf athletes.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.19 no.1
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• pp.8-34
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• 2014

We reviewed foreign evaluation systems based on the macrobenthic and macroalgal communities and developed a system, composed of a set of ecological indices able to evaluate the functionality (FI, Functional Index; estimation of stability and productivity) and maturity (MI, Maturity Index; comparisons with biological parameters of natural reefs) of artificial reefs by comparing the status in the adjacent natural reefs in Korean coastal waters. The evaluation system was applied to natural and artificial reefs/reef-planned areas (natural reefs), established in the 5 marine ranching areas (Bangnyeong-Daechung, Yeonpyung, Taean, Seocheon and Buan) in the west coast of Korea. The FI ranged between 31.6 (Bangnyeong-Daechung) and 72.5% (Buan) and MI did between 53.1 (Seocheon) and 76.9% (Taean) in average. The evaluation of artificial reefs by the two indices, showed the most appropriate status in Taean. The FI between the adjacent artificial and natural reefs were in significant linear relationship ($r^2=0.83$, p=0.01). This indicated the local status of biological community may be critical in determining the functionality of the artificial reefs. We have suggested an integrative but preliminary evaluation system of artificial reefs in this study. The output from the evaluation system may be utilized as a tool for environment/resource managers or policy makers, responsible for effective use of funds and decision making. Given the importance, we need to use the options to enhance and improve the accuracy as follows: (1) continuous validation of the evaluation system and rescaling the criteria of indicators, (2) vigorous utilization of observation and experience through the application and data accumulation and (3) development and testing of brand-new indicators.

• Korean Journal of Environmental Agriculture
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• v.34 no.4
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• pp.274-281
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• 2015

BACKGROUND: A lasting release of low levels of persistence chemicals including pesticides and pharmaceuticals into river has a bad influence on aquatic ecosystems and humans. The present study monitored pesticide residues in the Yeongsan and Seomjin river basins and their tributaries as a fundamental study for water quality standard of pesticides.METHODS AND RESULTS: Nine pesticides(aldicarb, carbaryl, carbofuran, chlorpyrifos, 2,4-D, MCPA, methomyl, metolachlor, and molinate) were determined from water samples using SPE-Oasis HLB(pH 2) and LC/MS/MS. Validation of the method was conducted through matrix-matched internal calibration curve, method detection limit(MDL), limit of quantification(LOQ), accuracy, precision, and recovery. MDLs of all pesticides satisfied the GV/10 values. Linearity(r²) was 0.9965- 0.9999, and a percentage of accuracy, precision, and recovery was 89.4-113.6%, 3.1-14.0%, and 90.8-106.2%, respectively. All pesticides exclusive of aldicarb were determined in the river samples, and there was a connection between the positive monitoring results and agricultural use of the pesticides.CONCLUSION: Monitoring outcomes of the present study implied that pesticides were a possible non-point pollutant source in the Yeongsan and Seomjin river basins and tributaries. Therefore, it is required to produce and accumulate more monitoring results on pesticides in river waters to set water quality standards, finally to preserve aquatic ecosystems.

• Management & Information Systems Review
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• v.36 no.5
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• pp.27-44
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• 2017

The purpose of this study was to empirically verify the effect of social entrepreneurship on market orientation. total of 500 questionnaires were distributed to workers in social enterprise and preliminary social enterprise. 202 questionnaires were used for final validation of research model, The hypotheses set in this study were validated through SPSS18.0 and LISREL8.3 based on the research model. The results showed that all hypotheses were accepted, except for 5 hypotheses(Hypothesis 1-1, Hypothesis 1-2, Hypothesis 1-3, Hypothesis 1-6, Hypothesis 1-9). First, we examined the effect that empathy might have on market orientation in connection with social entrepreneurship. The results suggested that empathy did not have a statistically significant effect on customer-orientation, inter-department cooperation and coordination, and competitor orientation. Second, we examined the effect that innovativeness might have on market orientation in connection with social entrepreneurship. The results showed that innovativeness had a positive(+) effect on customer-orientation and inter-department cooperation and coordination but did not have a statistically significant effect on competitor-orientation. Third, we examined the effect that risk-taking might have on market orientation in connection with social entrepreneurship. The results implied that risk-taking had a positive(+) effect on customer-orientation and inter-department cooperation and coordination but did not have a statistically significant effect on competitor-orientation. Finally, the relationship among market orientation variables was like this: The inter-department cooperation and coordination had a positive(+) effect on both customer-orientation and competitor-orientation. The results of this study are expected to provide a useful basis for overall understanding about the effect of social entrepreneurship on market orientation and present important theoretical and practical implications.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.4
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• pp.353-365
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• 2019

A near-infrared reflectance spectroscopy (NIRS) prediction model was set to establish a rapid analysis system of wheat germplasm and provide statistical information on the characteristics of protein contents. The variability index value (VIV) of calibration resources was 0.80, the average protein content was 13.2%, and the content range was from 7.0% to 13.2%. After measuring the near-infrared spectra of calibration resources, the NIRS prediction model was developed through a regression analysis between protein content and spectra data, and then optimized by excluding outliers. The standard error of calibration, R², and the slope of the optimized model were 0.132, 0.997, and 1.000 respectively, and those of external validation results were 0.994, 0.191, and 1.013, respectively. Based on these results, a developed NIRS model could be applied to the rapid analysis of protein in wheat. The distribution of NIRS protein content of 6,794 resources were analyzed using a normal distribution analysis. The VIV was 0.79, the average protein was 12.1%, and the content range of resources accounting for 42.1% and 68% of the total accessions were 10-13% and 9.5-14.6%, respectively. The composition of total resources was classified into breeding line (3,128), landrace (2,705), and variety (961). The VIV in breeding line was 0.80, the protein average was 11.8%, and the contents of 68% of total resources ranged from 9.2% to 14.5%. The VIV in landrace was 0.76, the protein average was 12.1%, and the content range of resources of 68% of total accessions was 9.8-14.4%. The VIV in variety was 0.80, the protein average was 12.8%, and the accessions representing 68% of total resources ranged from 10.2% to 15.4%. These results should be helpful to the related experts of wheat breeding.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.