• Korean Journal of Community Nutrition
• /
• v.3 no.4
• /
• pp.655-667
• /
• 1998

The Computer-assisted Hazard Analysis and Critical Control Point(HACCP) program has been developed for a systematic implementation of HACCP principles in identifying, assessing and controlling hazards in institutional foodservics operations. The HACCP-based sanitation evaluation tool has been developed, based on the results of the computerized assisted HACCP program in 4 service sites of C contracted foodservice company, including 2 general hospitals with 650-beds, one office operation of 400 meals per day, and one factory foodservice of 1,000 meals per day. All database files and processing programs were created by using Unify Vision tool with Windows 95 of user environments. The results of this study can be summarized as follows : 1. This program consists of the pre-stage for HACCP study and the implementation stage of the HACCP system. 1) The pre-stage for HACCP study includes the selection of menu items, the development of the HACCP recipe, the construction of product flow diagrams, and printing the HACCP recipes and product flow diagrams. 2) The implementation of the HACCP system includes the identification of microbiological hazards, the determination of critical control points based on the decision tree base files. 3) The HACCP-based sanitation evaluation tool consisted of 3 dimensions of time-temperature relationship, personal hygiene, and equipment-facility sanitation. The Cronbach's alphas calculation indicated that the tool was reliable. The results showed that the focus groups rated the mean of importance in time-temperature relationship, personal hygiene, and equipment-facility sanitation as 4.57, 4.59 and 4.55 respectively. Based on the results, this HACCP-based sanitation evaluation tool was considered as an effective tool for assuring product quality. This program will assist foodservice managers to encourage a standardized approach in the HACCP study and to maintain a systematic approach for ensuring that the HACCP principles are applied correctly.

• Gut and Liver
• /
• v.12 no.6
• /
• pp.674-681
• /
• 2018

Background/Aims: The cumulative surgery rate and postoperative relapse of intestinal Behçet's disease (BD) have been reported to be high. This study aimed to establish a scoring system based on follow-up endoscopic findings that can predict intestinal BD recurrence after surgery. Methods: Fifty-four patients with intestinal BD who underwent surgery due to bowel complications and underwent follow-up colonoscopy were retrospectively investigated. Their clinical data, including colonoscopic findings, were retrieved. Classification and regression tree analysis was used to develop an appropriate endoscopic classification model that can explain the postsurgical recurrence of intestinal BD most accurately based on the following classification: e0, no lesions; e1, solitary ulcer <20 mm in size; e2, solitary ulcer ${\geq}20mm$ in size; and e3, multiple ulcers regardless of size. Results: Clinical relapse occurred in 37 patients (68.5%). Among 38 patients with colonoscopic recurrence, only 29 patients had clinically relapsed. Multivariate analysis identified higher disease activity index for intestinal BD at colonoscopy (hazard ratio [HR], 1.013; 95% confidence interval [CI], 1.005 to 1.021; p=0.002) and colonoscopic recurrence (HR, 2.829; 95% CI, 1.223 to 6.545; p=0.015) as independent risk factors for clinical relapse of intestinal BD. Endoscopic findings were classified into four groups, and multivariate analysis showed that the endoscopic score was an independent risk factor of clinical relapse (p=0.012). The risk of clinical relapse was higher in the e3 group compared to the e0 group (HR, 6.284; 95% CI, 2.036 to 19.391; p=0.001). Conclusions: This new endoscopic scoring system could predict clinical relapse in patients after surgical resection of intestinal BD.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.37 no.1
• /
• pp.57-65
• /
• 2024

In a fire-resistant structure, uncertainties arise in factors such as ventilation, material elasticity modulus, yield strength, coefficient of thermal expansion, external forces, and fire location. The ventilation uncertainty affects thefactor contributes to uncertainties in fire temperature, subsequently impacting the structural temperature. These temperatures, combined with material properties, give rise to uncertain structural responses. Given the nonlinear behavior of structures under fire conditions, calculating fire fragility traditionally involves time-consuming Monte Carlo simulations. To address this, recent studies have explored leveraging machine learning algorithms to predict fire fragility, aiming to enhance efficiency while maintaining accuracy. This study focuses on predicting the fire fragility of a steel moment frame building, accounting for uncertainties in fire size, location, and structural material properties. The fragility curve, derived from nonlinear structural behavior under fire, follows a log-normal distribution. The results demonstrate that the proposed method accurately and efficiently predicts fire fragility, showcasing its effectiveness in streamlining the analysis process.

• Journal of the Korean Geosynthetics Society
• /
• v.16 no.3
• /
• pp.51-62
• /
• 2017

For the purpose of the study, of the 76 areas subject to preliminary concentrated management on sediment disaster in the downtown area, 9 areas were selected as research areas. They were classified into three stratified rock areas (Gyeongsan City, Goheung-gun and Daegu Metropolitan City), three igneous rock areas (Daejeon City, Sejong Special Self-Governing City and Wonju City) and three metamorphic rock areas (Namyangju City, Uiwang City and Inje District) according to the characteristics of the bedrock in the research areas. As for the 9 areas, analyses were conducted based on tests required to calculate soil characteristics, a predictive model for root adhesive power, loading of trees and on-the-spot research. As for a rainfall scenario (rainfall intensity), the probability of rainfall was applied as offered by APEC Climate Center (APCC) in Busan. As for the prediction of landslide risks in the 9 areas, TRIGRS and LSMAP were applied. As a result of TRIGRIS prediction, the risk rate was recorded 30.45% in stratified rock areas, 41.03% in igneous rock areas and 45.04% in metamorphic rock areas on average. As a result of LSMAP prediction based on root cohesion and the weight of trees according to crown density, it turned out to a 1.34% risk rate in the stratified rock areas, 2.76% in the igneous rock areas and 1.64% in the metamorphic rock areas. Analysis through LSMAP was considered to be relatively local predictive rather than analysis using TRIGRS.

• Journal of Digital Convergence
• /
• v.15 no.11
• /
• pp.245-250
• /
• 2017

Flash floods is defined as the flooding of intense rainfall over a relatively small area that flows through river and valley rapidly in short time with no advance warning. So that it can cause damage property and casuality. This study is to establish the flash-flood warning system using 38 accident data, reported from the National Disaster Information Center and Land Surface Model(TOPLATS) between 2009 and 2012. Three variables were used in the Land Surface Model: precipitation, soil moisture, and surface runoff. The three variables of 6 hours preceding flash flood were reduced to 3 factors through factor analysis. Decision tree, random forest, Naive Bayes, Support Vector Machine, and logistic regression model are considered as big data methods. The prediction performance was evaluated by comparison of Accuracy, Kappa, TP Rate, FP Rate and F-Measure. The best method was suggested based on reproducibility evaluation at the each points of flash flood occurrence and predicted count versus actual count using 4 years data.

• Journal of the Korean Home Economics Association
• /
• v.41 no.10 s.188
• /
• pp.101-116
• /
• 2003

The study was conducted to establish hygienic standards of salad items for pizza restaurant located in Seoul by applying HACCP system during the summer of 2000. The study measured temperature, time, pH, Aw and microbial assessments. The hygienic conditions of the kitchen and workers were on the average(1.21, 1.0 out of 3 pts.), but some improvement should be made: separate use of trash can and leftover disposal, separate use of knives and cutting boards, habits for hand washing and wearing hygienic gloves. For salad production, all procedures were peformed under food safety danger zone ($5{\~}60^{\circ}C$). The ingredients were mostly above pH 5.0 and high in Aw($0.94{\~}0.99$). Microbial assessments for salad production revealed that TPC($1.8{\times}10^3{\~}1.0{\times}10^{10}CFU/g$) and coliforms($1.5{\times}10{\~}5.2{\times}10^5 CFU/g$) exceeded the standards by Solberg et al.(TPC: $10^6CFU/g$, coliforms: $10^3CFU/g$). S. aureus was not detected but Salmonella was found in three food items(egg, macaroni and macaroni salad). Moreover, the workers' hands contained 3.1 104 CFU/g of TPC and 4.2 102 CFU/g of S. aureus requiring further remedy since it exceeded the safety standards suggested by Harrigan and McCance (500 CFU/g of TPC per $100cm^2$ and 10 CFU/g of coliforms per $100cm^2$). According to the critical control point(CCP) decision tree analysis, vegetable receiving, vegetable holding, mixing, display on coleslaw, macaroni draining, display on macaroni salad, egg peeling & cutting, apple cutting, and display on salad bar were determined as CCPs. From the findings it would be suggested that purchase of Quality materials, short holding and display time, storing food at right temperature, using sanitary cooking utensils, and improvement of workers' food handing practices are needed to ensure the safe salad production in this specific pizza restaurant.

• Journal of Korea Water Resources Association
• /
• v.56 no.5
• /
• pp.311-323
• /
• 2023

In order to reduce disaster damage by localized heavy rains, floods, and urban inundation, it is important to know in advance whether natural disasters occur. Currently, heavy rain watch and heavy rain warning by the criteria of the Korea Meteorological Administration are being issued in Korea. However, since this one criterion is applied to the whole country, we can not clearly recognize heavy rain damage for a specific region in advance. Therefore, in this paper, we tried to reset the current criteria for a special weather report which considers the regional characteristics and to predict the damage caused by rainfall after 1 hour. The study area was selected as Gyeonggi-province, where has more frequent heavy rain damage than other regions. Then, the rainfall inducing disaster or hazard-triggering rainfall was set by utilizing hourly rainfall and heavy rain damage data, considering the local characteristics. The heavy rain damage prediction model was developed by a decision tree model and a random forest model, which are machine learning technique and by rainfall inducing disaster and rainfall data. In addition, long short-term memory and deep neural network models were used for predicting rainfall after 1 hour. The predicted rainfall by a developed prediction model was applied to the trained classification model and we predicted whether the rain damage after 1 hour will be occurred or not and we called this as 1ST-Model. The 1ST-Model can be used for preventing and preparing heavy rain disaster and it is judged to be of great contribution in reducing damage caused by heavy rain.

• Economic and Environmental Geology
• /
• v.47 no.4
• /
• pp.405-420
• /
• 2014

To investigate geochemical characteristics of soil and atmospheric environments by anthropogenic source, we have analyzed and determined heavy metal concentrations of the surface soils beneath roadside trees and leaves of Platanus occidentalis from 52 points in Seoul during autumn 2001. For comparison of the contents of heavy metal for the soil and leaf, we have analyzed heavy metal contents of the surface soils beneath roadside trees and leaves from 2 points in rural area of Yesan during the same time period. The composition of heavy metals of soils are relatively high for Cd, Co, Cr and Ni in industrial area (IA, Industrial Area) and high for Cu, Pb and Zn in heavy traffic area (HTA, Heavy Traffic Area). The heavy metal contents of rural area in Seoul are higher than those in Yesan. The differences of chemical compositions between the washed and unwashed leaves are high for Cd, Cu, Pb and Zn in the HTA. The element couples of Cd-Co, Cr-Ni and Pb-Zn for the soils had shown a good correlation and their contamination sources could be similar. The relationship for Pb-Cu and Cu-Zn showed good correlation in Platanus leaves. The relationship between soils and unwashed leaves show a good correlation for Cr, Cu, Pb and Zn but low correlation for Cd, Co, Fe, Mn and Ni. It is thought that the Cr, Cu, Pb and Zn were derived from contaminants of soils, whereas Cd, Co, Fe, Mn and Ni were originated from atmospheric source. From the spatial variations of elements for soils and leaves, Ni and Cr were dominant in the soils of IA and Cd, Cu and Zn were dominant in those of HTA. The Contamination by Cd-Pb and Cu-Zn in unwashed leaves were analyzed to show similar patterns. Using the enrichment factors (EF) of heavy metals in unwashed leaves, the EF sequences were to be Cu, Zn, Pb, Mn, Co, Ni, Cd and Cr. We identified that Cu, Zn, Pb and Mn were most problematic of environmental hazard in Seoul.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.51 no.2
• /
• pp.1-11
• /
• 2023

This study intended to develop a technique for quantitatively and 3-dimensionally predicting the potential failure zone and impulse that may occur when trees are fall down. The main outcomes of this study are as follows. First, this study established the potential failure zone and impulse calculation formula in order to quantitatively calculate the risks generated when trees are fallen down. When estimating the potential failure zone, the calculation was performed by magnifying the height of trees by 1.5 times, reflecting the likelihood of trees falling down and slipping. With regard to the slope of a tree, the range of 360° centered on the root collar was set in the case of trees that grow upright and the range of 180° from the inclined direction was set in the case of trees that grow inclined. The angular momentum was calculated by reflecting the rotational motion from the root collar when the trees fell down, and the impulse was calculated by converting it into the linear momentum. Second, the program to calculate a potential failure zone and impulse was developed using Rhino3D and Grasshopper. This study created the 3-dimensional models of the shapes for topography, buildings, and trees using the Rhino3D, thereby connecting them to Grasshopper to construct the spatial information. The algorithm was programmed using the calculation formula in the stage of risk calculation. This calculation considered the information on the trees' growth such as the height, inclination, and weight of trees and the surrounding environment including adjacent trees, damage targets, and analysis ranges. In the stage of risk inquiry, the calculation results were visualized into a three-dimensional model by summarizing them. For instance, the risk degrees were classified into various colors to efficiently determine the dangerous trees and dangerous areas.