• Journal of Food Hygiene and Safety
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• v.29 no.4
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• pp.305-311
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• 2014

Minimum infective dose (MID) data has been recognized as an important and absolutely needed in quantitative microbiological assessment (QMRA). In this study, we performed a comprehensive literature review and meta-analysis to better quantify this association. The meta-analysis applied a final selection of 82 published papers for total 12 species foodborne disease pathogens (bacteria 9, virus 2, and parasite 1 species) which were identified and classified based on the dose-response models related to QMRA studies from PubMed, ScienceDirect database and internet websites during 1980-2012. The main search keywords used the combination "food", "foodborne disease pathogen", "minimum infective dose", and "quantitative microbiological risk assessment". The appropriate minimum infective dose for B. cereus, C. jejuni, Cl. perfringens, Pathogenic E. coli (EHEC, ETEC, EPEC, EIEC), L. monocytogenes, Salmonella spp., Shigella spp., S. aureus, V. parahaemolyticus, Hepatitis A virus, Noro virus, and C. pavum were $10^5cells/g$ (fi = 0.32), 500 cells/g (fi = 0.57), $10^7cells/g$ (fi = 0.56), 10 cells/g (fi = 0.47) / $10^8cells/g$ (fi = 0.71) / $10^6cells/g$ (fi = 0.70) / $10^6cells/g$ (fi = 0.60), $10^2{\sim}10^3cells/g$ (fi = 0.23), 10 cells/g (fi = 0.30), 100 cells/g (fi = 0.32), $10^5cells/g$ (fi = 0.45), $10^6cells/g$ (fi = 0.64), $10{\sim}10^2particles/g$ (fi = 0.33), 10 particles/g (fi = 0.71), and $10{\sim}10^2oocyst/g$ (fi = 0.33), respectively. Therefore, these results provide the preliminary data necessary for the development of foodborne pathogens QMRA.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.17-35
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• 2015

Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase for a certain period of time. Developing precise forecasting models are considered important since corporates can make strategic decisions on new markets based on future demand estimated by the models. Many studies have developed market growth curve models, such as Bass, Logistic, Gompertz models, which estimate future demand when a market is in its early stage. Among the models, Bass model, which explains the demand from two types of adopters, innovators and imitators, has been widely used in forecasting. Such models require sufficient demand observations to ensure qualified results. In the beginning of a new market, however, observations are not sufficient for the models to precisely estimate the market's future demand. For this reason, as an alternative, demands guessed from those of most adjacent markets are often used as references in such cases. Reference markets can be those whose products are developed with the same categorical technologies. A market's demand may be expected to have the similar pattern with that of a reference market in case the adoption pattern of a product in the market is determined mainly by the technology related to the product. However, such processes may not always ensure pleasing results because the similarity between markets depends on intuition and/or experience. There are two major drawbacks that human experts cannot effectively handle in this approach. One is the abundance of candidate reference markets to consider, and the other is the difficulty in calculating the similarity between markets. First, there can be too many markets to consider in selecting reference markets. Mostly, markets in the same category in an industrial hierarchy can be reference markets because they are usually based on the similar technologies. However, markets can be classified into different categories even if they are based on the same generic technologies. Therefore, markets in other categories also need to be considered as potential candidates. Next, even domain experts cannot consistently calculate the similarity between markets with their own qualitative standards. The inconsistency implies missing adjacent reference markets, which may lead to the imprecise estimation of future demand. Even though there are no missing reference markets, the new market's parameters can be hardly estimated from the reference markets without quantitative standards. For this reason, this study proposes a case-based expert system that helps experts overcome the drawbacks in discovering referential markets. First, this study proposes the use of Euclidean distance measure to calculate the similarity between markets. Based on their similarities, markets are grouped into clusters. Then, missing markets with the characteristics of the cluster are searched for. Potential candidate reference markets are extracted and recommended to users. After the iteration of these steps, definite reference markets are determined according to the user's selection among those candidates. Then, finally, the new market's parameters are estimated from the reference markets. For this procedure, two techniques are used in the model. One is clustering data mining technique, and the other content-based filtering of recommender systems. The proposed system implemented with those techniques can determine the most adjacent markets based on whether a user accepts candidate markets. Experiments were conducted to validate the usefulness of the system with five ICT experts involved. In the experiments, the experts were given the list of 16 ICT markets whose parameters to be estimated. For each of the markets, the experts estimated its parameters of growth curve models with intuition at first, and then with the system. The comparison of the experiments results show that the estimated parameters are closer when they use the system in comparison with the results when they guessed them without the system.

• The Korean Journal of Pesticide Science
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• v.9 no.1
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• pp.26-34
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• 2005

3D-QSAR studies for the fungicidal activities against resistance phytophthora blight (RPC; 95CC7303) and sensitive phytophthora blight (Phytopthora capsici) (SPC; 95CC7105) by a series of new 2-alkoxyphenyl-3-phenylthioisoindoline-1-one derivatives (A & B) were studieded using comparative molecular similarity indices analyses (CoMSIA) methodology. From the based on the results, the two CoMSIA models, R5 and S1: as the best models were derivated. The statistical results of the models showed the best predictability and fitness for the fungicidal activities based on the cross- validated value ($q^2=0.714{\sim}0.823$) and non cross-validated, value ($r^2_{ncv.}=0.918{\sim}0.954$), respectively. The model R5 for fungicidal activity of RPC generated from the field fit alignment and combination of electrostatic field, H-bond acceptor field and LUMO molecular orbital field. The model S1 (or S5) for fungicidal activity of SPC generated from the atom based fit alignment and combination of steric field and HOMO molecular orbital field. The models also shows that inclusion of H-bond acceptor field (A) improved the statistical significance of the models. From the based graphical analyses of CoMSIA contribution maps, it was revealed that the novel selective character for fungicidal activities between the two fungi by modify of X-sub-stituent on the N-phenyl group and R-substituent on the S-phenyl group will be able to achivement.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.163-179
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• 2010

Recentlythe concept of fast fashion is drawing attention as customer needs are diversified and supply lead time is getting shorter in fashion industry. It is emphasized as one of the critical success factors in the fashion industry how quickly and efficiently to satisfy the customer needs as the competition has intensified. Because the fast fashion is inherently susceptible to trend, it is very important for fashion retailers to make quick decisions regarding items to launch, quantity based on demand prediction, and the time to respond. Also the planning decisions must be executed through the business processes of procurement, production, and logistics in real time. In order to adapt to this trend, the fashion industry urgently needs supports from intelligent quick response(QR) system. However, the traditional functions of QR systems have not been able to completely satisfy such demands of the fast fashion industry. This paper proposes an intelligent quick response system for the fast fashion(IQRS-FF). Presented are models for QR process, QR principles and execution, and QR quantity and timing computation. IQRS-FF models support the decision makers by providing useful information with automated and rule-based algorithms. If the predefined conditions of a rule are satisfied, the actions defined in the rule are automatically taken or informed to the decision makers. In IQRS-FF, QRdecisions are made in two stages: pre-season and in-season. In pre-season, firstly master demand prediction is performed based on the macro level analysis such as local and global economy, fashion trends and competitors. The prediction proceeds to the master production and procurement planning. Checking availability and delivery of materials for production, decision makers must make reservations or request procurements. For the outsourcing materials, they must check the availability and capacity of partners. By the master plans, the performance of the QR during the in-season is greatly enhanced and the decision to select the QR items is made fully considering the availability of materials in warehouse as well as partners' capacity. During in-season, the decision makers must find the right time to QR as the actual sales occur in stores. Then they are to decide items to QRbased not only on the qualitative criteria such as opinions from sales persons but also on the quantitative criteria such as sales volume, the recent sales trend, inventory level, the remaining period, the forecast for the remaining period, and competitors' performance. To calculate QR quantity in IQRS-FF, two calculation methods are designed: QR Index based calculation and attribute similarity based calculation using demographic cluster. In the early period of a new season, the attribute similarity based QR amount calculation is better used because there are not enough historical sales data. By analyzing sales trends of the categories or items that have similar attributes, QR quantity can be computed. On the other hand, in case of having enough information to analyze the sales trends or forecasting, the QR Index based calculation method can be used. Having defined the models for decision making for QR, we design KPIs(Key Performance Indicators) to test the reliability of the models in critical decision makings: the difference of sales volumebetween QR items and non-QR items; the accuracy rate of QR the lead-time spent on QR decision-making. To verify the effectiveness and practicality of the proposed models, a case study has been performed for a representative fashion company which recently developed and launched the IQRS-FF. The case study shows that the average sales rateof QR items increased by 15%, the differences in sales rate between QR items and non-QR items increased by 10%, the QR accuracy was 70%, the lead time for QR dramatically decreased from 120 hours to 8 hours.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.3
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• pp.327-334
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• 2018

Objective: The objective of the present study was to validate genes and genomic regions associated with carcass weight using a low-density single nucleotide polymorphism (SNP) Chip in Hanwoo cattle breed. Methods: Commercial Hanwoo steers (n = 220) were genotyped with 20K GeneSeek genomic profiler BeadChip. After applying the quality control of criteria of a call rate ${\geq}90%$ and minor allele frequency (MAF) ${\geq}0.01$, a total of 15,235 autosomal SNPs were left for genome-wide association (GWA) analysis. The GWA tests were performed using single-locus mixed linear model. Age at slaughter was fitted as fixed effect and sire included as a covariate. The level of genome-wide significance was set at $3.28{\times}10^{-6}$ (0.05/15,235), corresponding to Bonferroni correction for 15,235 multiple independent tests. Results: By employing EMMAX approach which is based on a mixed linear model and accounts for population stratification and relatedness, we identified 17 and 16 loci significantly (p<0.001) associated with carcass weight for the additive and dominant models, respectively. The second most significant (p = 0.000049) SNP (ARS-BFGL-NGS-28234) on bovine chromosome 4 (BTA4) at 21 Mb had an allele substitution effect of 43.45 kg. Some of the identified regions on BTA2, 6, 14, 22, and 24 were previously reported to be associated with quantitative trait loci for carcass weight in several beef cattle breeds. Conclusion: This is the first genome-wide association study using SNP chips on commercial Hanwoo steers, and some of the loci newly identified in this study may help to better DNA markers that determine increased beef production in commercial Hanwoo cattle. Further studies using a larger sample size will allow confirmation of the candidates identified in this study.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.8
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• pp.1188-1196
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• 2016

This study evaluated the risk of Clostridium perfringens (C. perfringens) foodborne illness from natural and processed cheeses. Microbial risk assessment in this study was conducted according to four steps: hazard identification, hazard characterization, exposure assessment, and risk characterization. The hazard identification of C. perfringens on cheese was identified through literature, and dose response models were utilized for hazard characterization of the pathogen. For exposure assessment, the prevalence of C. perfringens, storage temperatures, storage time, and annual amounts of cheese consumption were surveyed. Eventually, a simulation model was developed using the collected data and the simulation result was used to estimate the probability of C. perfringens foodborne illness by cheese consumption with @RISK. C. perfringens was determined to be low risk on cheese based on hazard identification, and the exponential model ($r=1.82{\times}10^{-11}$) was deemed appropriate for hazard characterization. Annual amounts of natural and processed cheese consumption were $12.40{\pm}19.43g$ and $19.46{\pm}14.39g$, respectively. Since the contamination levels of C. perfringens on natural (0.30 Log CFU/g) and processed cheeses (0.45 Log CFU/g) were below the detection limit, the initial contamination levels of natural and processed cheeses were estimated by beta distribution (${\alpha}1=1$, ${\alpha}2=91$; ${\alpha}1=1$, ${\alpha}2=309$)${\times}$uniform distribution (a = 0, b = 2; a = 0, b = 2.8) to be -2.35 and -2.73 Log CFU/g, respectively. Moreover, no growth of C. perfringens was observed for exposure assessment to simulated conditions of distribution and storage. These data were used for risk characterization by a simulation model, and the mean values of the probability of C. perfringens foodborne illness by cheese consumption per person per day for natural and processed cheeses were $9.57{\times}10^{-14}$ and $3.58{\times}10^{-14}$, respectively. These results indicate that probability of C. perfringens foodborne illness by consumption cheese is low, and it can be used to establish microbial criteria for C. perfringens on natural and processed cheeses.

• Journal of the Korean Society for Nondestructive Testing
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• v.16 no.2
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• pp.95-108
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• 1996

In ultrasonic testing, flaw signal from which quantitative information on flaws is determined is influenced by 3 factors : (1) the incident wavefield.produced by the transducer, (2) the scattered waves produced by flaws, and (3) the reception of the scattered waves back at the transducer. So even small changes in transducer performance due to aging or unexpected damages can produce the changes in the characteristics of flaw signal and finally the changes in the quantitative information on flaws. Thus a reliable calibration method of transducer performance is desired. Recently, theoretical models for ultrasonic testing have been employed as reference standards for the calibration of transducers which are considered as circular planar piston sources in the most of cases. But this simplification cannot be applied to partially damaged transducer which has lost their symmetry in performance, even not in appearance. Unfortunately there has been no reliable practical model which can be used for the calibration of partially damaged transducers. Here a pulse-echo testing model for partially damaged ultrasonic transducers was developed with experimental verification. The experimental responses agree very well with the theoretical prediction. So we expect that this model can be served as a theoretical reference standards for transducer calibration.

• Journal of Korea Technology Innovation Society
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• v.19 no.2
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• pp.254-279
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• 2016

Recently, with the conversion towards knowledge-based economy era, the importance of the evaluation for patent valuation has been growing rapidly because technology transactions are increasing with the purpose of practically utilizing R&D outcomes such as technology commercialization and technology transfer. Nevertheless, there is a lack of research on determinants of patent valuation by analyzing technology transactions due to the difficulty of collecting data in practice. Hence, to suggest quantitative determinants for the patent valuation which could be applied to scoring methods, 15 patent valuation models domestically and overseas are analysed in order to assure the objectiveness for subjective results from qualitative methods such as expert surveys, comparison assessment, etc. Through this analysis, the important 6 common determinants are drawn and patent information is matched which can be used as proxy variables of individual determinant factors by advanced researches. In addition, to validate whether the model proposed has a statistically meaningful effect, total 517 technology transactions are collected from both public and private technology transaction offices and analysed by multiple regression analysis, which led to significant patent determinant factors in deciding its value. As a result, it is herein presented that patent connectivity(number of literature cited) and commercialization stage in market influence significantly on patent valuation. The meaning of this study is in that it suggests the significant quantitative determinants of patent valuation based on the technology transactions data in practice, and if research results by industry are systematically verified through seamless collection of transaction data and their monitoring, we would propose the customized patent valuation model by industry which is applicable for both strategic planning of patent registration and achievement assessment of research projects (with representative patents).

• Journal of the Korea institute for structural maintenance and inspection
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• v.21 no.6
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• pp.185-195
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• 2017

In recent years, unexpected bridge fire accidents have increased because of augmenting the number of traffic volumes and hazardous materials by the increment in traffics and distribution business. Furthermore, in accordance with the effort of using the under space of bridges, the ratio of occupied by combustible materials like oil tanker or lorry has been increased. As a result, the occurrence of bridge fire has been growing drastically. In order to mitigate the accident of bridge fire, risk assessment of bridge fire has been studied, however, practical risk models considering safety from users' viewpoints were scarce. This study represented quantitative risk assessment model applicable to national road bridges in Korea. The primary factors with significant impacts on bridge fire accidents was chosen such as clearance height, materials of bridges, arrival time of fire truck and fire intensity. The selected factors were used for Fire Dynamics Simulation (FDS) and the peak temperature calculated by FDS in accordance with the fire duration and fire intensity. The risk assessment model in bridge fire reflected the FDS analysis results, the fire damage criteria, and the grade of fire truck arrival time was established. Response plans for bridge fire accidents according to the risk assessment output has been discussed. Lastly, distances between bridges and fire stations were calculated by GIS network analysis. Based on the suggested assessment model and methodology, sample bridges were selected and graded for the risk assessment.