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Key Food Selection for Assessement of Oral Health Related Quality of Life among Some Korean Elderly (일부 한국 노인 구강건강 관련 삶의 질 평가를 위한 핵심 음식 선택)

  • Hwang, Soo-Jeong
    • Journal of dental hygiene science
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    • v.16 no.5
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    • pp.361-369
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    • 2016
  • Oral health can influence on diverse food intake, and food intake affect oral health related quality of life. The aim of this study was to select key foods to be able to represent oral health related quality of life in Korea. We used the data of 503 Korean older persons to participate in the oral health promotion programme in 2009. The low consumption or low intake foods with criteria in 2012 National Nutrition Statistics were eliminated among 30 foods of food intake ability (FIA) at first. Decision tree model, correlation analysis, factor analysis, and internal reliablity test were used for oral health related quailty of life (OHRQoL) key food selection. We selected 13 foods-hard persimmon, dried peanut, pickled radish, caramel, rib of pork, glutinous rice cake, cabbage kimchi, apple, yellow melon, boiled chicken meat, boiled fish, mandarin, noodles as OHRQoL Key Foods 13. Thirty foods of FIA and OHRQoL Key Foods 13 displayed the same pattern of variation among sociodemographic groups. In a regression model, both of 30 foods of FIA and OHRQoL Key Foods 13 influenced on oral health impact profile-14. The findings suggest that OHRQoL Key Foods 13 have good reliability and validity and be able to use in oral health survey.

A Study on the Analysis Effect Factors of Illegal Parking Using Data Mining Techniques (데이터마이닝 기법을 활용한 불법주차 영향요인 분석)

  • Lee, Chang-Hee;Kim, Myung-Soo;Seo, So-Min
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.13 no.4
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    • pp.63-72
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    • 2014
  • With the rapid development in the economy and other fields as well, the standard of living in South Korea has been improved, and consequently, the demand of automobiles has quickly increased. It leads to various traffic issues such as traffic congestion, traffic accident, and parking problem. In particular, this illegal parking caused by the increase in the number of automobiles has been considered one of the main reasons to bring about traffic congestion as intensifying any dispute between neighbors in relation to a parking space, which has been also coming to the fore as a social issue. Therefore, this study looked into Daejeon Metropolitan City, the city that is understood to have the highest automobile sharing rate in South Korea but with relatively few cases of illegal parking crackdowns. In order to investigate the theoretical problems of the illegal parking, this study conducted a decision-making tree model-based Exhaustive CHAID analysis to figure out not only what makes drivers park illegally when they try to park vehicles but also those factors that would tempt the drivers into the illegal parking. The study, then, comes up with solutions to the problem. According to the analysis, in terms of the influential factors that encourage the drivers to park at some illegal areas, it was learned that these factors, the distance, a driver's experience of getting caught, the occupation and the use time in order, have an effect on the drivers' deciding to park illegally. After working on the prediction model, four nodes were finally extracted. Given the analysis result, as a solution to the illegal parking, it is necessary to establish public parking lots additionally and first secure the parking space for the vehicles used for living and working, and to activate the campaign for enhancing illegal parking crackdown and encouraging civic consciousness.

Research on Change of Heart Rate Variability and Psychological Scale by Sasang Constitution according to before and after of the Meditation Programs (α version) (명상프로그램(α version) 시행 전후의 사상체질별 심리척도 및 HRV 변화 연구)

  • Kim, Geun-Woo;Bae, Hyo-Sang;Son, Han-Bum;Lee, Pil-Won;Kim, Byoung-Soo;Park, Seong-Sik
    • Journal of Oriental Neuropsychiatry
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    • v.25 no.1
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    • pp.1-12
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    • 2014
  • Objectives: In this study, the meditation programs (${\alpha}$ version), which are properly coordinated according to the motion, breathing, and relaxation, are evaluated and researched upon to have positive effects on stress and in the area of psychology. Methods: Approved by the Clinical Trials Deliberation Committee in Oriental Medicine, Dongguk University, Ilsan Hospital, this study collected data according to the applicant's consents, demographic information and anthropometry for the Sasang Constitutional diagnosis. Sasang Constitutional diagnosis measured the beta tools by Institute of Oriental Medicine and a decision tree was made for the Sasang Constitutional questionnaires. The STAI, STAXI, BDI, and HRV were measured before and after the meditation in order to compare the effects of meditation according to Sasang Constitution. The HRV was used as a ProComP KM Tech (co). Results: 1) The positive changes available in the Time-domain analysis of heart rate variability assessment showed that the peace of mind is increased. By analyzing the Sasang constitution, So-eum In's peace of mind included a physical stability of the autonomic nervous system. 2) According to the psychological scale evaluation, each depression scale, trait anger, anger-in, state anxiety and trait anxiety index proved significantly positive effects. By analyzing the Sasang constitution, Eun-In which involved So-eum In and Tae-eum In, had positive effects. 3) The psychological scale changed the group of diagnosed depression or anxiety, it did not mean that the psychological scale changes in the depression group, but the index of the anxiety group had been significantly reduced. This program had clinical effects for anxious patients and Eum-In which involved Tae-eum In and So-eum In according to the analysis of Sasang constitution. 4) Correlations between the gender of each psychological scale showed that women have overall low correlations, but, there were no significant changes. Conclusions: The meditation program developed by adequately mixing Action, relaxation and breathing shows that it is effective for overall Eum-in physical and mental relaxation and concentration. In the future, It will have to be developed Meditation program to show the same effect for all people.

Affected Model of Indoor Radon Concentrations Based on Lifestyle, Greenery Ratio, and Radon Levels in Groundwater (생활 습관, 주거지 주변 녹지 비율 및 지하수 내 라돈 농도 따른 실내 라돈 농도 영향 모델)

  • Lee, Hyun Young;Park, Ji Hyun;Lee, Cheol-Min;Kang, Dae Ryong
    • Journal of health informatics and statistics
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    • pp.309-316
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    • 2017
  • Objectives: Radon and its progeny pose environmental risks as a carcinogen, especially to the lungs. Investigating factors affecting indoor radon concentrations and models thereof are needed to prevent exposure to radon and to reduce indoor radon concentrations. The purpose of this study was to identify factors affecting indoor radon concentration and to construct a comprehensive model thereof. Methods: Questionnaires were administered to obtain data on residential environments, including building materials and life style. Decision tree and structural equation modeling were applied to predict residences at risk for higher radon concentrations and to develop the comprehensive model. Results: Greenery ratio, impermeable layer ratio, residence at ground level, daily ventilation, long-term heating, crack around the measuring device, and bedroom were significantly shown to be predictive factors of higher indoor radon concentrations. Daily ventilation reduced the probability of homes having indoor radon concentrations ${\geq}200Bq/m^3$ by 11.6%. Meanwhile, a greenery ratio ${\geq}65%$ without daily ventilation increased this probability by 15.3% compared to daily ventilation. The constructed model indicated greenery ratio and ventilation rate directly affecting indoor radon concentrations. Conclusions: Our model highlights the combined influences of geographical properties, groundwater, and lifestyle factors of an individual resident on indoor radon concentrations in Korea.

Cost-Utility Analysis of Pegfilgrastim and Pegteograstim in Patients with Breast Cancer using Doxorubicin and Cyclophosphamide (Doxorubicin과 Cyclophosphamide를 투여받는 유방암 환자에서 Pegfilgrastim과 Pegteograstim의 비용-효용 분석)

  • Kwon, Su Ji;Geum, Min Jung;Kim, Jae Song;Son, Eun Sun;Kwon, Kyeng Hee
    • 병원약사회지
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    • v.35 no.4
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    • pp.409-417
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    • 2018
  • Background : Febrile neutropenia (FN) is one of the side effects in the patients treated with chemotherapy, and the patients who have FN generally need immediate treatment with extended-spectrum antibiotics and hospitalization. Pegfilgrastim and pegteograstim, which are used for the prevention of FN as a granulocyte-colony stimulating factor (G-CSF), have been granted insurance coverage in the Republic of Korea for certain breast cancer patients using doxorubicin and cyclophosphamide (AC) from September 2016. Methods : The data of the patients with breast cancer using AC regimen and G-CSF were collected retrospectively. This study involves cost-utility analysis of pegfilgrastim and pegteograstim. In this study, we constructed a simple decision tree model for short-term observation and calculated quality-adjusted life year (QALY) and the direct medical costs from the medical provider's perspective. Results : From September 2016 to May 2017, 15 patients were treated with pegfilgrastim and 15 patients were treated with pegteograstim. As a result of dividing the average cost by QALY for each treatment group, it was observed that pegfilgrastim and pegteograstim were consumed 24,923,384 won and 22,808,336 won per 1QALY, respectively. Consequently, incremental cost effectiveness ratio (ICER) showed 2,115,048 won more per pegfilgrastim than pegteograstim per 1QALY, and the cost per 1QALY of both the drugs was lower than 30,500,000 won; the Koreans were willing to pay this amount. Conclusions : This study suggests that pegfilgrastim and pegteograstim can be used to improve the quality of life of breast cancer patients undergoing AC therapy. Among the two drugs, pegteograstim seems to be more cost-effective. However, since this study was conducted as a retrospective observation method on a small scale, it is associated with many limitations. Therefore, a long-term prospective cohort study is needed to supplement the present findings.

Fundamental Economic Feasibility Analysis on the Transition of Production Structure for a Forest Village in LAO PDR (라오스 산촌마을의 생산구조전환을 위한 투자 경제성 기초 분석)

  • Lee, Bohwe;Kim, Sebin;Lee, Joon-Woo;Rhee, Hakjun;Lee, Sangjin;Lee, Joong-goo;Baek, Woongi;Park, Bum-Jin;Koo, Seungmo
    • The Journal of the Korean Institute of Forest Recreation
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    • v.22 no.4
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    • pp.11-22
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    • 2018
  • This study analyzes the economic feasibility on the transition of production structure to increase income for a local forest village in Laos PDR. The study area was the Nongboua village in Sangthong district where the primary product is rice from rice paddy. Possible strategies were considered to increase the villagers' revenue, and Noni (Morinda citrifolia) was production in the short-term. We assumed that the project period was for 20 years for the analysis, and a total of 1,100 Noni tree was planted in 1 ha by $3m{\times}3m$ spacing. This study classified basic scenario one, scenario two, scenario three by the survival rate and purchase pirce of Noni. Generally Noni grows well. However, the seedlings' average survival rate (= production volume) was set up conservatively in this study to consider potential risks such as no production experience of Noni and tree disease. The scenario one assumed that the survival rate of Noni seedlings was 50% for 0-1 years, 60% for 0-2 years, and 70% for 3-20 years; the scenario two, 10% less, i.e., 40%, 50%, and 60%; and the scenario three, 10% less, i.e., 40%, 50%, 60% and purchase price 10% less, i.e., $0.29 to $0.26, respectively. Our analysis showed that all 3 scenarios resulted in economically-feasible IRR (internal rate of return) of 24.81%, 19.02%, and 16.30% of with a discounting rate of 10%. The B/C (benefit/cost) ratio for a unit area (1ha) was also analyzed for the three scenarios with a discounting rate of 10%, resutling in the B/C ratio of 1.71, 1.47, and 1.31. The study results showed that the Nongboua village would have a good opportunity to improve its low-income structure through planting and managing alternative crops such as Noni. Also the results can be used as useful decision-making information at a preliminary analysis level for planning other government and public investment projects for the Nonboua village.

VKOSPI Forecasting and Option Trading Application Using SVM (SVM을 이용한 VKOSPI 일 중 변화 예측과 실제 옵션 매매에의 적용)

  • Ra, Yun Seon;Choi, Heung Sik;Kim, Sun Woong
    • Journal of Intelligence and Information Systems
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    • v.22 no.4
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    • pp.177-192
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    • 2016
  • Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

Steel Plate Faults Diagnosis with S-MTS (S-MTS를 이용한 강판의 표면 결함 진단)

  • Kim, Joon-Young;Cha, Jae-Min;Shin, Junguk;Yeom, Choongsub
    • Journal of Intelligence and Information Systems
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    • v.23 no.1
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    • pp.47-67
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    • 2017
  • Steel plate faults is one of important factors to affect the quality and price of the steel plates. So far many steelmakers generally have used visual inspection method that could be based on an inspector's intuition or experience. Specifically, the inspector checks the steel plate faults by looking the surface of the steel plates. However, the accuracy of this method is critically low that it can cause errors above 30% in judgment. Therefore, accurate steel plate faults diagnosis system has been continuously required in the industry. In order to meet the needs, this study proposed a new steel plate faults diagnosis system using Simultaneous MTS (S-MTS), which is an advanced Mahalanobis Taguchi System (MTS) algorithm, to classify various surface defects of the steel plates. MTS has generally been used to solve binary classification problems in various fields, but MTS was not used for multiclass classification due to its low accuracy. The reason is that only one mahalanobis space is established in the MTS. In contrast, S-MTS is suitable for multi-class classification. That is, S-MTS establishes individual mahalanobis space for each class. 'Simultaneous' implies comparing mahalanobis distances at the same time. The proposed steel plate faults diagnosis system was developed in four main stages. In the first stage, after various reference groups and related variables are defined, data of the steel plate faults is collected and used to establish the individual mahalanobis space per the reference groups and construct the full measurement scale. In the second stage, the mahalanobis distances of test groups is calculated based on the established mahalanobis spaces of the reference groups. Then, appropriateness of the spaces is verified by examining the separability of the mahalanobis diatances. In the third stage, orthogonal arrays and Signal-to-Noise (SN) ratio of dynamic type are applied for variable optimization. Also, Overall SN ratio gain is derived from the SN ratio and SN ratio gain. If the derived overall SN ratio gain is negative, it means that the variable should be removed. However, the variable with the positive gain may be considered as worth keeping. Finally, in the fourth stage, the measurement scale that is composed of selected useful variables is reconstructed. Next, an experimental test should be implemented to verify the ability of multi-class classification and thus the accuracy of the classification is acquired. If the accuracy is acceptable, this diagnosis system can be used for future applications. Also, this study compared the accuracy of the proposed steel plate faults diagnosis system with that of other popular classification algorithms including Decision Tree, Multi Perception Neural Network (MLPNN), Logistic Regression (LR), Support Vector Machine (SVM), Tree Bagger Random Forest, Grid Search (GS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The steel plates faults dataset used in the study is taken from the University of California at Irvine (UCI) machine learning repository. As a result, the proposed steel plate faults diagnosis system based on S-MTS shows 90.79% of classification accuracy. The accuracy of the proposed diagnosis system is 6-27% higher than MLPNN, LR, GS, GA and PSO. Based on the fact that the accuracy of commercial systems is only about 75-80%, it means that the proposed system has enough classification performance to be applied in the industry. In addition, the proposed system can reduce the number of measurement sensors that are installed in the fields because of variable optimization process. These results show that the proposed system not only can have a good ability on the steel plate faults diagnosis but also reduce operation and maintenance cost. For our future work, it will be applied in the fields to validate actual effectiveness of the proposed system and plan to improve the accuracy based on the results.

Strategy for Store Management Using SOM Based on RFM (RFM 기반 SOM을 이용한 매장관리 전략 도출)

  • Jeong, Yoon Jeong;Choi, Il Young;Kim, Jae Kyeong;Choi, Ju Choel
    • Journal of Intelligence and Information Systems
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    • v.21 no.2
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    • pp.93-112
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    • 2015
  • Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.

HACCP Model for Quality Control of Sushi Production in the Eine Japanese Restaurants in Korea (일본전문식당의 급식품질 개선을 위한 HACCP 시스템 적용 연구)

  • 김혜경;이복희;김인호;조경동
    • Journal of the East Asian Society of Dietary Life
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    • v.13 no.1
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    • pp.25-38
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    • 2003
  • This study was conducted to establish the microbiological quality standards applying the HACCP system on sushi items of Japanese restaurant in Korea. The study evaluated hygienic conditions of kitchen and workers, pH time-temperature relationship, and microbial assessments during whole process of sushi making in 2001. Overall hygienic conditions were normal for both kitchen and for workers by 3 point scale, but hygienic controls against the cross-contamination were still needed. Each process of sushi making was performed under the risk of microbial contamination, since pH value of most of ingredients was over pH 4.6 and also production time(3.5~6 hrs) were long enough to cause problems. Microorganisms were high enough to cause foodborne illness ranged 8.0$\times$10$^2$~3.3$\times$10$^{6}$ CFU/g of TPC and 1.0$\times$10$^1$~1.6$\times$10$^3$CFU/g of coliforms, although TPC, coliforms and Staphylcoccus aureus were within the standard limits (TPC 10$^2$~10$^{6}$ CFU/g, coliforms 10$^3$CFU/g). However, Salmonella and Vibrio parahaemolyticus were not detected. High populations TPC and coliforms were also found in the cooks' hands and cooking utensils(TPC 10$^2$~10$^{6}$ CFU/100cm$^2$and Coliforms 10$^1$~10$^3$CFU/100cm$^2$). Based on the CCP decision tree analysis, the CCPs were the holding steps far six sushi production line except the tuna and the thawing step for tuna sushi. In conclusion, overall state of sushi production was fairly good but much improvement was still needed.

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