• Journal of Applied Biological Chemistry
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• v.66
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• pp.484-491
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• 2023

Maize is one of the major crops consumed in worldwide, which nutrients accounts for a large amount of starch, but also functional components, and phenolic acid is known to have a high content. Maize is divided into waxy maize, sweet maize, and normal maize with its shape and use, therefore there is also a difference in nutritional composition. This study was conducted to analyze the content of functional components according to the type of maize and to produce natural variation data in consideration of environmental factors. 3 shapes of maize (waxy maize, sweet maize, and normal maize) samples cultivated in 3 regions (Suwon, Daegu, and Hongcheon) were analyzed using HPLC and GC-TOF-MS. Comparing with type through ANOVA, multivariate statistical analysis, Pearson correlation analysis, 28 components, including carotenoids and tocopherols, showed significant differences among a total of 32 components (p <0.05), 15 of them showed very significant differences (p <0.001). When comparing with regions, 15 components showed significant differences and only vanillate, syringate, C23-ol of them showed most significant differences (p <0.001). As a result of principal component analysis, cluster classification was distinguished by shape than by region, with α-carotene, cholesterol for waxy maize, vanillate and stigmasterol for sweet maize, lutein and β-carotene for normal maize had a great effect on cluster formation. It suggests that the content of functional components is more affected by genetic factors than environmental factors.

• The Korean Journal of Applied Statistics
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• v.37 no.4
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• pp.485-497
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• 2024

With the development of modern society, the number of livestock is increasing, and the corresponding odor is recognized as a serious social problem. In particular, the consumption of poultry meat, such as chicken, duck, and turkey, is expected to rise steeply, making odor problems near poultry farms. To address the problem, it is important to understand the influence of odor components on the complex odor. In this study, the odor data obtained from poultry farms were used to predict the complex odor using machine learning models and analyze the influence of the components. Furthermore, we analyze the differences in the amount of the odor components at the site boundary, compost site, inside the farm, and outside the farm using analysis of variance. The analysis showed that ammonia, trimethylamine, dimethyldisulfide, and acetaldehyde have a high effect on the complex odor. In particular, ammonia, trimethylamine, and acetaldehyde have different amount of the occurence by the location.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• The Journal of Korean Medicine
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• v.17 no.2 s.32
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• pp.337-393
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• 1996

The purpose of this study is to evaluate and standardize the four scales of Questionnaire for the Sasang Constitution Classification 　II (QSCCII). QSCCII is newly prepared by statistical item analysis and is designed to examine its diagnostic discriminability. QSCCII is administered to 1366 random informants. From the survey, we could get the data for the standardization. The criteria of standardization are based on the data from 265 informants who are examined by professionals. Collectted data are analyzed by internal consistency, variation analysis(ANOVA), Duncan test and discrimination analysis of SPSS PC+ V4.0 program. The results are as follows reliability of four scales for QSCCII is relatively valid. The internal consistency of Tae-yang(太陽) (太陽) scale is Cronbach's a=0.5708. That of So-yang(少陽) scale is a=0.5708. That of Tae-eum(太陰) scale is a =0.5922. That of So-eum(少陰) scale is a=0.6319. 2. There is a significant difference between each group through variation analysis of four scales. 3. The process of standardization is based on the average value and standard deviation with respect to age and sex difference of each criteria 4. This study suggests a source of standardization of Sasang Constitution Classification by providing norms in which the differences of age, sex, and number of items are taken into deep consideration. QSCC Ⅱ, therefore, can be applied to every age(the 10's to the 60's) and sex groups. 5. The recalculation of the raw-score to standard value (T-score) shows that the diagnostic discriminability (Hit-ratio: 70.08%) of QSCC Ⅱ brings about 37% improvement than proportional chance criteria (33.33%). Especially, Hit-ratios of Tae-eum In(74.5%) and So-eum In(70.8%) are higher than that of So-yang In(60.0%). 6. QSCC has discriminability only to male informants. Compared with QSCC, however, QSCC II has relatively efficient discriminability both to male and female informants. 7. These results would be a demonstration of the fact that the QSCC II could be used as a tool for sasang constitution classification.

• Journal of dental hygiene science
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• v.5 no.1
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• pp.39-43
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• 2005

This study was carried out to provide fundamental data for an examination of a health promotion program by determining the influence of the Health Promoting Lifestyle Profile on the quality of life of female dental hygienists. The sample was selected from the population of 1,148 who were registered in the Association of Seoul Dental Hygienists. 800 subjects were randomly selected from 25 districts in Seoul. The data was collected by calling the dental hygienists to, explaining the contents and objective of our study, and sending them a questionnaire by post. The questionnaire consists of the total number of 97 questions: 62 questions on the Health Promoting Lifestyle Profile, 26 questions on the quality of life and 9 general characteristics questions. The data was collected from August 16 to October 15, 2004. Out of 800 subjects, 481(60.1%) completed the questionnaires. For statistical analysis, the frequency, percentage, arithmetic mean, ANOVA, and multiple regression analysis, were analyzed using the SAS 8.1 Analysis program. The significance level was set to 0.05. The results of this study were as follows: First, The average score of the subjects' quality of life was 3.1. For the sub-categories, it was shown that the degree of satisfaction on the condition of society was the highest at 3.2, and the degree of satisfaction on the condition of the individuals was the lowest at 3.1. The average score of the Health Promoting Lifestyle Profile variable was 2.5. For the sub-categories, it was shown that the degree of sanitary life was at 3.2, and degree of the professional health maintenance was the lowest at 1.7. Second, There were significant differences in the Quality of Life benefits of action with the general characteristics. There were significant differences in age, educational level, income, marital status, career, and Perceived Health Status. There were significant difference in Health Promoting Lifestyle Profile benefits of action with the general characteristics. There were significant differences in terms of age, educational level, income, marital status, career, and the Perceived Health Status. Finally, The stepwise multiple regression analysis revealed that the powerful predictors were Health Promoting Lifestyle Profile, income and the Perceived Health Status. These factors accounted for 37.6% of the variance in the Quality of Life patterns. As the subjects were limited to dental hygienists in Seoul, care should be taken when applying these results to all dental hygienists in Korea. In order to generalize the study, a large number of subjects selected from all regions in Korea will be needed.

• Journal of Sasang Constitutional Medicine
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• v.8 no.1
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• pp.187-246
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• 1996

The purpose of this study is to evaluate and standardize the four scales of Questionnaire for the Sasang Constitution ClassificationII (QSCCII). QSCCII is newly prepared by statistical item analysis and is designed to examine its diagnostic discriminability. QSCCII is administered to 1366 random informants. From the survey, we could get the data for the standardization. The criteria of standardization are based on the data from 265 informants who are examined by professionals. Collected data are analyzed by internal consistency, variation analysis(ANOVA), Duncan test and discrimination analysis of SPSS PC+ V4.0 program. The results are as follows 1) The reliability of four scales for QSCCII is relatively valid. The internal consistency of Tae-yang(太陽) scale is Cronbach's ${\alpha}=0.5708$. That of So-yang(少陽) scale is ${\alpha}=0.5708$. That of Tae-eum(太陰) scale is ${\alpha}=0.5922$. That of So-eum(少陰) scale is ${\alpha}=0.6319$. 2) There is a significant difference between each group through variation analysis of four scales. 3) The process of standardization is based on the average value and standard deviation with respect to age and sex difference of each criteria. 4) This study suggests a source of standardization of Sasang Constitution Classification by providing norms in which the differences of age, sex, and number of items are taken into deep consideration. QSCCII, therefore, can be applied to every age(the 10's to the 60's) and sex groups. 5) The recalculation of the raw-score to standard value (T-score) shows that the diagnostic discriminability (Hit-ratio : 70.08%) of QSCCII brings about 37% improvement than proportional chance criteria(33.33%). Especially, Hit-ratios of Tae-eum In(74.5%) and So-eum In(70.8%) are higher than that of So-yang In(60.0%). 6) QSCC has discriminability only to male informants. Compared with QSCC, however, QSCCII has relatively efficient discriminability both to male and female informants. 7) These results would be a demonstration of the fact that the QSCCII could be used as a tool for sasang constitution classification.

• 한국사회정책
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• v.25 no.2
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• pp.125-163
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• 2018

Social and health problems related to drinking are serious. Drinking behavior is affected not only by personal factors but also by environment factors. The purpose of this study is to find out how the alcoholic beverage stores in community influence the drinking behaviors of individuals after adjusting the individual level variables and provide it as basic data for alcohol related regulatory policies. In order to identify the factors affecting drinking behavior, we conducted a multilevel logistic regression analysis with high-risk drinking and current drinking as dependent variables. Individual-level data provided by 2015 community health survey from respondents of urban residents, and regional level data provided by the National Statistical office. The variables such as age, education level, and income level were used as individual level variables and the number of basic living allowances, divorce rate, and the number of pubs were used as community level variables. According to the research results, after controlling all variables, the number of bar, retail per $1km^2$ in residential area effect on current drinking. But, they are not effect on high risk drinking. In the high risk drinking, only the divorce rate effect on drinking behavior. As a result of the stratified analysis, there was no difference in the current drinking. But, it shows that the higher the number of retail stores and the total alcohol availability, the higher risk drinking behavior in the 60s. The results of this study suggest that policies aimed not only on individuals but also on the local environment are necessary.

• The Journal of Korean Academy of Prosthodontics
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• v.57 no.2
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• pp.102-109
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• 2019

Purpose: The purpose of this study was to evaluate the accuracy of three types of intraoral scanners and the accuracy of the single abutment and bridge abutment model. Materials and methods: In this study, a single abutment, and a bridge abutment with missing first molar was fabricated and set as the reference model. The reference model was scanned with an industrial three-dimensional scanner and set as reference scan data. The reference model was scanned five times using the three intraoral scanners (CS3600, CS3500, and EZIS PO). This was set as the evaluation scan data. In the three-dimensional analysis (Geomagic control X), the divided abutment region was selected and analyzed to verify the scan accuracy of the abutment. Statistical analysis was performed using SPSS software (${\alpha}=.05$). The accuracy of intraoral scanners was compared using the Kruskal-Wallis test and post-test was performed using the Pairwise test. The accuracy difference between the single abutment model and the bridge abutment model was analyzed by the Mann-Whitney U test. Results: The accuracy according to the intraoral scanner was significantly different (P < .05). The trueness of the single abutment model and the bridge abutment model showed a statistically significant difference and showed better trueness in the single abutment (P < .05). There was no significant difference in the precision (P = .616). Conclusion: As a result of comparing the accuracy of single and bridge abutments, the error of abutment scan increased with increasing scan area, and the accuracy of bridge abutment model was clinically acceptable in three types of intraoral scanners.

• Journal of Dental Rehabilitation and Applied Science
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• v.39 no.3
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• pp.105-118
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• 2023

Purpose: This study aimed to gain better understanding of the importance of dental prosthesis order platform services and to identify the essential elements for their enhancement and wider adoption among dental professionals. Materials and Methods: A survey was conducted to assess the perspectives of dentists, dental technicians, dental hygienists, and dental industry professionals toward dental prosthesis ordering and associated platform services (a total of 53 respondents). The questionnaire was devised after an expert review and assessed for reliability using Cronbach's alpha coefficient. Factor analysis revealed that 57 factors across five categories accounted for 88.417% of the total variance. The survey was administered through an online questionnaire platform, and data analysis was conducted using a statistical software, employing one-way analysis of variance and Tukey's honestly significant difference test (α = 0.05). Results: The essential elements identified were accurate information input, effective communication, delivery of distortion-free impressions, convenience in data transmission and storage, development of stable and affordable platform services (P < 0.05). Furthermore, significant differences were observed in the importance of these items based on age, dental profession, and career experience (P < 0.05). Conclusion: The dental prosthesis ordering platform services, the requirements of dental personnel were stability, economic efficiency, and ease of transmitting and storing prosthesis data. The findings can serve as important indicators for the development and improvement of dental prosthesis order platform services.

• The KIPS Transactions:PartA
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• v.11A no.4
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• pp.243-250
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• 2004

Monitoring is used to see if a real-time system provides a service on time. Generally, monitoring for real-time focuses on investigating the current status of a real-time system. To support a stable performance of a real-time system, it should have not only a function to see the current status of real-time process but also a function to predict executions of real-time processes, however. The legacy prediction model has some limitation to apply it to a real-time monitoring. First, it performs a static prediction after a real-time process finished. Second, it needs a statistical pre-analysis before a prediction. Third, transition probability and data about clustering is not based on the current data. We propose the execution prediction model based on learning algorithm to solve these problems and apply it to real-time monitoring. This model gets rid of unnecessary pre-processing and supports a precise prediction based on current data. In addition, this supports multi-level prediction by a trend analysis of past execution data. Most of all, We designed the model to support dynamic prediction which is performed within a real-time process' execution. The results from some experiments show that the judgment accuracy is greater than 80% if the size of a training set is set to over 10, and, in the case of the multi-level prediction, that the prediction difference of the multi-level prediction is minimized if the number of execution is bigger than the size of a training set. The execution prediction model proposed in this model has some limitation that the model used the most simplest learning algorithm and that it didn't consider the multi-regional space model managing CPU, memory and I/O data. The execution prediction model based on a learning algorithm proposed in this paper is used in some areas related to real-time monitoring and control.