• Journal of Food Hygiene and Safety
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• v.25 no.2
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• pp.153-161
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• 2010

The purpose of this study was to examine the appearance of norovirus in the water for food in food service institutions and the influence of physicochemical and microbial factors of norovirus in order to work out basic data to predict the detection of norovirus. Among 82 samples of water for food in food service institutions, norovirus appeared in 7 samples and the rate of appearance was 8.5%. As for the type of norovirus, one samples contained GI type (genotype GI-6) and six samples contained GII type (genotype GII-2, GII-4, GII-12). In the regression model of prediction of norovirus, the rate of appearance was correlated with $NH_3$-N, total solids and the consumption of $KMnO_4$, out of such variables as $NH_3$-N, total solids, the consumption of $KMnO_4$, depth, chloride and total colony counts, and its contribution rate for effectiveness was 78.60%. In order to examine the influential factor of environment upon the detection of norovirus, Pearson's correlation analysis was carried out. The predictable regression formula for appearance rate of norovirus was expressed as -1.818 + 42.677 [$NH_3$-N] + 0.023 [total solids] + 0.762 [consumption of $KMnO_4$] -0.009 [depth] -0.146 [chloride] + 0.007 [total colony counts] (R = 0.904, $R^2$ = 0.818, adjusted $R^2$ = 0.786, p < 0.05). The most influential factors upon the detection of norovirus were $NH_3$-N, total solids and the consumption of $KMnO_4$. In other words, when the measured values of $NH_3$-N, total solids and the consumption of $KMnO_4$ were higher, the possibility of appearance of norovirus increased.

• Tuberculosis and Respiratory Diseases
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• v.47 no.2
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• pp.218-230
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• 1999

Background: Craniofacial anatomic abnormalities related to structural narrowing of the upper airway have been reported in patients with obstructive sleep apnea syndrome. In this study, we evaluated the craniofacial anatomic characteristics of Korean patients with obstructive sleep apnea syndrome, and the role of cephalometric analysis in the prediction of abnormal breathing during sleep. Methods: Thirty-nine patients with obstructive sleep apnea syndrome(OSAS), 39 simple snorers(simple snorers) and 20 controls(control) had cephalometric analysis using the technique of Riley et al, and underwent standardized polysomnographic recordings. Different variables, including sex, body mass index, cephalometric and polysomnographic data, were statistically analyzed. Results: Pm-UPW and V-LPW distances were significantly shorter in OSAS when compared with simple snorers or control. PAS in simple snorers was shorter than in control. ANS-Gn distance in OSAS was significantly longer than in control. PNS-P distance in OSAS or simple snorers was significantly longer than in control. MP-H distance in OSAS was significantly longer than in simple snorers or control and MP-H distance in simple snorers was also longer than in control. NL/Pm-P angle in OSAS was lesser than in control. MP-H distance in OSAS or in the combined groups of OSAS and simple snorers was significantly correlated with apneahypopnea index(AHI). PNS-P distance in the combined groups of OSAS and simple snorers was correlated with AHI. In male of all subjects, body mass index was significantly correlated with PNS-P or MP-H distance. Conclusion: Cephalometric analysis can be useful tool in determining the craniofacial anatomic abnormalities in patients with obstructive sleep apnea syndrome. Cephalometric parameters, especially MP-H distance, can be useful for predicting frequency of narrowing or obstruction of upper airway during sleep.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.218-233
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• 2022

The long-term (1986~2020) predictability of the number of days of heat and cold damages for each growth stage of soybean is evaluated using the daily maximum and minimum temperature (Tmax and Tmin) data produced by Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF). The Predictability evaluation methods for the number of days of damages are Normalized Standard Deviations (NSD), Root Mean Square Error (RMSE), Hit Rate (HR), and Heidke Skill Score (HSS). First, we verified the simulation performance of the Tmax and Tmin, which are the variables that define the heat and cold damages of soybean. As a result, although there are some differences depending on the month starting with initial conditions from January (01RUN) to May (05RUN), the result after a systematic bias correction by the Variance Scaling method is similar to the observation compared to the bias-uncorrected one. The simulation performance for correction Tmax and Tmin from March to October is overall high in the results (ENS) averaged by applying the Simple Composite Method (SCM) from 01RUN to 05RUN. In addition, the model well simulates the regional patterns and characteristics of the number of days of heat and cold damages by according to the growth stages of soybean, compared with observations. In ENS, HR and HSS for heat damage (cold damage) of soybean have ranged from 0.45~0.75, 0.02~0.10 (0.49~0.76, -0.04~0.11) during each growth stage. In conclusion, 01RUN~05RUN and ENS of PNU CGCM-WRF Chain have the reasonable performance to predict heat and cold damages for each growth stage of soybean in South Korea.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.29 no.2
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• pp.109-120
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• 2017

A discounted cost model for preventive maintenance of armor units of rubble-mound breakwaters is mathematically derived by combining the deterioration model based on a discrete-time stochastic process of shock occurrence with the cost model of renewal process together. The discounted cost model of condition-based maintenance proposed in this paper can take into account the nonlinearity of cumulative damage process as well as the discounting effect of cost. By comparing the present results with the previous other results, the verification is carried out satisfactorily. In addition, it is known from the sensitivity analysis on variables related to the model that the more often preventive maintenance should be implemented, the more crucial the level of importance of system is. However, the tendency is shown in reverse as the interest rate is increased. Meanwhile, the present model has been applied to the armor units of rubble-mound breakwaters. The parameters of damage intensity function have been estimated through the time-dependent prediction of the expected cumulative damage level obtained from the sample path method. In particular, it is confirmed that the shock occurrences can be considered to be a discrete-time stochastic process by investigating the effects of uncertainty of the shock occurrences on the expected cumulative damage level with homogeneous Poisson process and doubly stochastic Poisson process that are the continuous-time stochastic processes. It can be also seen that the stochastic process of cumulative damage would depend directly on the design conditions, thus the preventive maintenance would be varied due to those. Finally, the optimal periods and scale for the preventive maintenance of armor units of rubble-mound breakwaters can be quantitatively determined with the failure limits, the levels of importance of structure, and the interest rates.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.761-774
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• 2022

Accurate hydrologic prediction is essential to analyze the effects of drought, flood, and climate change on flow rates, water quality, and ecosystems. Disentangling the uncertainty of the hydrological model is one of the important issues in hydrology and water resources research. Hydrologic data assimilation (DA), a technique that updates the status or parameters of a hydrological model to produce the most likely estimates of the initial conditions of the model, is one of the ways to minimize uncertainty in hydrological simulations and improve predictive accuracy. In this study, the two ensemble-based sequential DA techniques, ensemble Kalman filter, and particle filter are comparatively analyzed for the daily discharge simulation at the Yongdam catchment using airGRdatassim. The results showed that the values of Kling-Gupta efficiency (KGE) were improved from 0.799 in the open loop simulation to 0.826 in the ensemble Kalman filter and to 0.933 in the particle filter. In addition, we analyzed the effects of hyper-parameters related to the data assimilation methods such as precipitation and potential evaporation forcing error parameters and selection of perturbed and updated states. For the case of forcing error conditions, the particle filter was superior to the ensemble in terms of the KGE index. The size of the optimal forcing noise was relatively smaller in the particle filter compared to the ensemble Kalman filter. In addition, with more state variables included in the updating step, performance of data assimilation improved, implicating that adequate selection of updating states can be considered as a hyper-parameter. The simulation experiments in this study implied that DA hyper-parameters needed to be carefully optimized to exploit the potential of DA methods.

• Geotechnical Engineering
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• v.14 no.3
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• pp.25-46
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• 1998

This paper shows that there are the results of a series of model tests on the behavior of single pipe pile which is subjected to lateral load in, Nak-dong River sand. The purpose of the present paper is to estimate the effect of Non-homogeneity. constraint condition of pile head, lateral load velocity, relative density, and embedded length of pile on the behavior of single pile. These effects can be quantified only by the results of model tests. Also, these are compared with the results of the numerical methods (p-y method, modified Vlasov method; new ${\gamma}$ parameter, Characteristic Load Method'CLM). In this study, a new ${\gamma}$ parameter equation based on the Vlasov method was developed to calculate the modulus of subgrade reaction (E. : nhz.) proportional to the depth. The p-y method of analysis is characterized by nonlinear behavior. and is an effective method of designing deep foundations subjected to lateral loads. The new method, which is called the characteristic load method (CLM). is simpler than p-y analysis. but its results closely approximates p-y analysis results. The method uses dimensional analysis to characterize the nonlinear behavior of laterally loaded piles with respect to be relationships among dimensionless variables. The modulus of subgrade reaction used in p-y analysis and modified Vlasov method obtained from back analysis using direct shear test (DST) results. The coefficients obtained from DST and the modified ones used for the prediction of lateral behavior of ultimate soil reaction range from 0.014 to 0.05. and from 0.2 to 0.4 respectively. It is shown that the predicted numerical results by the new method (CLM), p-y analysis, and modified Vlasov method (new parameter) agree well with measured results as the relative density increases. Also, the characteristic load method established applicability on the Q-Mnu. relationship below y/D=0.2.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• Korean journal of applied entomology
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• v.25 no.4 s.69
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• pp.221-228
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• 1986

An empirical model to predict initial disease occurrence and subsequent progress of Alternaria leaf spot was constructed based on the modified degree day temperature and frequency of rainfall in three years field experiments. Climatic factors were analized 10-day bases, beginning April 20 to the end of August, and were used as variables for model construction. Cumulative degree portion (CDP) that is over $10^{\circ}C$ in the daily average temperature was used as a parameter to determine the relationship between temperature and initial disease occurrence. Around one hundred and sixty of CDP was needed to initiate disease incidence. This value was considered as temperature threshhold. After reaching 160 CDP, time of initial occurrence was determined by frequency of rainfall. At least four times of rainfall were necessary to be accumulated for initial occurrence of the disease after passing temperature threshhold. Disease progress after initial incidence generally followed the pattern of frequency of rainfall accumulated in those periods. Apparent infection rate (r) in the general differential equation dx/dt=xr(1-x) for individual epidemics when x is disease proportion and t is time, was a linear function of accumulation rate of rainfall frequency (Rc) and was able to be directly estimated based on the equation r=1.06Rc-0.11($R^2=0.993$). Disease severity (x) after t time could be predicted using exponential equation $[x/(1-x)]=[x_0/(1-x)]e^{(b_0+b_1R_c)t}$ derived from the differential equation, when $x_0$ is initial disease, $b_0\;and\;b_1$ are constants. There was a significant linear relationship between disease progress and cumulative number of air-borne conidia of Alternaria mali. When the cumulative number of air-borne conidia was used as an independent variable to predict disease severity, accuracy of prediction was poor with $R^2=0.3328$.

• Journal of the Korean Society of Radiology
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• v.81 no.6
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• pp.1436-1447
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• 2020

Purpose To investigate the clinical and CT features at admission to predict the progression to necrotizing pancreatitis (NP) in patients initially diagnosed with interstitial edematous pancreatitis (IEP). Materials and Methods Patients with IEP who underwent contrast-enhanced CT at admission and follow-up CT (< 14 days) were included (n = 178). Two radiologists performed a consensus review of follow-up CT scans and diagnosed the type of acute pancreatitis as IEP or NP. Laboratory findings at admission were recorded. Clinical, CT, and laboratory findings were compared between the IEP-IEP group and IEP-NP group using the chi-square test and the t-test. Multivariate analysis was also performed. Results There were 112 and 66 patients in the IEP-IEP and the IEP-NP groups, respectively. The proportion of patients with alcohol etiology was significantly larger in the IEP-NP group. Among the CT findings, the presence of peripancreatic fluid and heterogeneous parenchymal enhancement were more frequently observed in the IEP-NP group. Among the laboratory variables, serum C-reactive protein levels and white blood cell counts were significantly higher in the IEP-NP group. Multivariate analysis revealed that the presence of peripancreatic fluid and heterogeneous parenchymal enhancement were significant findings distinguishing the two groups. Conclusion CT findings, such as the presence of peripancreatic fluid and heterogeneous pancreatic parenchymal enhancement, may be helpful in predicting the progression to NP in patients initially diagnosed with IEP.

• Korean Journal of Culture and Social Issue
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• v.29 no.4
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• pp.455-476
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• 2023

This study focused on the characteristics of motiveless crimes that mainly originated from interpersonal problems and were acts of revenge against innocent third parties. This study confirmed the relationship between the experience of social exclusion and displaced aggression and examined the relationship between the two variables. We sought to confirm the role of related factors such as stress and social support. For this purpose, we established and tested hypotheses about the mediatingon effect of stress and the moderated mediatingon effect of social support on the effect of social exclusion experience on displaced aggression among 353 adult males aged between 19 and 49 years. The main results are that, first, social exclusion had a positive effect on displaced aggression. Second, stress was found to partially mediate the relationship between social exclusion and displaced aggression. Third, the hypothesis that social support would moderate the mediating effect of stress was not provedvaild, but the conditional direct effect of social support was confirmed in the mediation model. In other words, social support did not affect the indirect effect mediated by stress, but appeared to moderate the direct effect between social exclusion and displaced aggression. Social exclusion's prediction of displaced aggression was significant only in the average social support group (mean) and the high group (M+1SD), and appeared to increase as the group increased. This means that in groups with high social support, displaced aggression is used as a stress control strategy, which is a different result from previous studies that found that social support plays a role in lowerings aggression. People with low levels of social support showed unexpected results in that they used displaced aggression less frequently despite their experiencinge of social exclusion. In the discussion, the social implications of these results were interpreted, and additional research ideas were proposed to specify the relationship between social exclusion and displaced aggression.