• Journal of Chest Surgery
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• v.36 no.3
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• pp.131-135
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• 2003

Bovine pericardium fixed in glutaraldehyde solution (GA) has been one of the most popular surgical bioprosthesis, however, late calcific degeneration after implantation remains to be solved. To mitigate calcific degeneration, we posttreated the bovine pericardium with amino acids after GA fixation. Material and Method: 40 small pieces of bovine pericardia were fixed in 0.625% GA solution with 4 g/L $MgCl_26H_2O$as a control group (group 1). 40 pieces fixed in the same GA solution were posttreated with 2% chitosan solution (group 2) and the other 40 pieces posttreated with 8% glutamate (group 3). These were implanted into the belly of forty Fisher 344 rats subdermally and extracted at f month, 2 months, 3 months and 4 months after implantation. Result: With atomic absorption spectrophotometry we measured the deposited calcium amount and the results were as follows; 2.01 $\pm$0.13 mg/g in group 1, 2.34$\pm$0.73 mg/g in group 2, 2.49$\pm$0.15 mg/g in group 3 at 1 month after implantation, and 3.57$\pm$0.15 mg/g in group 1, 3.52$\pm$0.92 mg/g in group 2, 3.46$\pm$0.12 mg/g in group 3 at the second month. But 5.45$\pm$0.42 mg/g in group 1, 3.22 $\pm$1.31 mg/g in group 2 and 4.20$\pm$0.55 mg/g in group 3 at the 3rd month, which have statistical significance in group 2 (p<0.05). Finally at 4th month, 6.01$\pm$1.21 mg/g in group 1, 3.78$\pm$1.82 mg/g in group 2, 3.92$\pm$0.92 mg/g in group 3, which also have statistical significance (p < 0.05). Conclusion: This means posttreatment with 2% chitosan shows meaningful calcium mitigation effects after 3rd month on subcutaneously implanted bovine pericardium in the rat models but 8% glutamate shows mitigation effect after 4months in this experiment.

• Journal of Nutrition and Health
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• v.48 no.3
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• pp.258-268
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• 2015

Purpose: The aim of this study was to explore the relationship of ready-to-eat cereal (RTEC) consumption with nutrition and health status. Examination of health status for this project included obesity, abdominal obesity, hypertension, hypertriglyceridemia, hypercholesterolemia, low-HDL-cholesterolemia, diabetes, anemia, and metabolic syndrome. Methods: Two groups, RTEC consumers and those who did not consume RTEC, were identified using 24-hour dietary recall data from the 2012 Korea National Health and Nutrition Examination Survey (KNHANES). Nutritional intakes and risk factors of the two groups were compared using covariates-adjusted statistical procedures. Statistical analyses were performed using SAS survey procedures, and strata, cluster, and weight were considered. Subjects of analysis of nutritional intake were between the ages of 1 and 75, and those considered in the risk factor analysis were between the ages of 19 and 75. Results: Results showed that 3.8% of the Korean population was RTEC consumers. Compared to the subjects who did not intake RTEC, RTEC consumers exhibited significantly higher intakes of calcium, thiamin, riboflavin, and vitamin C. It was also discovered that the percentage of people whose intakes were less than EAR decreased with RTEC consumption. RTEC consumption showed significant association with decreased systolic blood pressure, diastolic blood pressure, serum triglyceride, and serum total cholesterol. Consequently, prevalence of hypertension among RTEC consumers was significantly lower than that among non-consumers, and the odds ratio for hypertension was 0.19 after adjusting the models for covariates. Conclusion: Results of this study clearly suggest an association of RTEC consumption with improved nutritional status and cardiometabolic risk profile in Korean adults. Conduct of additional studies will be necessary in order to determine the nature of these relationships.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Journal of Korea Water Resources Association
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• v.34 no.6
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• pp.659-672
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• 2001

Generally, design flood for a hydraulic structure is estimated using statistical analysis of runoff data. However, due to the lack of runoff data, it is difficult that the statistical method is applied for estimation of design flood. In this case, the synthetic unit hydrograph method is used generally and the models such as NYMO method, Snyder method, SCS method, and HYMO method have been widely used in Korea. In this study, these methods and KICT method, which is developed in year 2000, are compared and analyzed in 10 study areas. Firstly, peak flow and peak time of representative unit hydrograph and synthetic unit hydrograph in study area are compared, and secondly, the shape of unit hydrograph is compared using a root mean square error(RMSE). In Nakayasu method developed in Japan, synthetic unit hydrograph is very different from peak flow, peak time, and the shape of representative unit hydrograph, and KICT method(2000) is superior to others. Also, KICT method(2000) is superior to others in the aspects of using hydrologic and topographical data. Therefore, Nakayasu method is not a proper in hydrological practice. Moreover, it is considered that KICT model is a better method for the estimation of design flood. However, if other model, i.e. SCS method, Nakayasu method, and HYMO method, is used, parameters or regression equations must be adjusted by analysis of real data in Korea.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.136-152
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• 2017

The thermodynamic models, PC-SAFT (Perturbed-Chain Statistical Associated Fluid Theory) state equation and the Two-model approach liquid activity coefficient model NRTL (Non Random Two Liquid) + Henry + Peng-Robinson, for modeling the Rectisol process using methanol aqueous solution as the $CO_2$ removal solvent were compared. In addition, to determine the new binary interaction parameters of the PC-SAFT state equations and the Henry's constant of the two-model approach, absorption equilibrium experiments between carbon dioxide and methanol at 273.25K and 262.35K were carried out and regression analysis was performed. The accuracy of the newly determined parameters was verified through the regression results of the experimental data. These model equations and validated parameters were used to model the carbon dioxide removal process. In the case of using the two-model approach, the methanol solvent flow rate required to remove 99.00% of $CO_2$ was estimated to be approximately 43.72% higher, the cooling water consumption in the distillation tower was 39.22% higher, and the steam consumption was 43.09% higher than that using PC-SAFT EOS. In conclusion, the Rectisol process operating under high pressure was designed to be larger than that using the PC-SAFT state equation when modeled using the liquid activity coefficient model equation with Henry's relation. For this reason, if the quantity of low-solubility gas components dissolved in a liquid at a constant temperature is proportional to the partial pressure of the gas phase, the carbon dioxide with high solubility in methanol does not predict the absorption characteristics between methanol and carbon dioxide.

• Journal of the Korean Society of Groundwater Environment
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• v.5 no.4
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• pp.177-191
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• 1998

For various kinds of waters including surface water, shallow groundwater (＜70 m deep) and deep groundwater (500∼810 m deep) from the Pungki area, an integrated study based on hydrochemical, multivariate statistical, thermodynamic, environmental isotopic (tritium, oxygen-hydrogen, carbon and sulfur), and mass-balance approaches was attempted to elucidate the hydrogeochemical and hydrologic characteristics of the groundwater system in the gneiss area. Shallow groundwaters are typified as the 'Ca-HCO$_3$'type with higher concentrations of Ca, Mg, SO$_4$and NO$_3$, whereas deep groundwaters are the 'Na-HCO$_3$'type with elevated concentrations of Na, Ba, Li, H$_2$S, F and Cl and are supersaturated with respect to calcite. The waters in the area are largely classified into two groups: 1) surface waters and most of shallow groundwaters, and 2) deep groundwaters and one sample of shallow groundwater. Seasonal compositional variations are recognized for the former. Multivariate statistical analysis indicates that three factors may explain about 86% of the compositional variations observed in deep groundwaters. These are: 1) plagioclase dissolution and calcite precipitation, 2) sulfate reduction, and 3) acid hydrolysis of hydroxyl-bearing minerals(mainly mica). By combining with results of thermodynamic calculation, four appropriate models of water/ rock interaction, each showing the dissolution of plagioclase, kaolinite and micas and the precipitation of calcite, illite, laumontite, chlorite and smectite, are proposed by mass balance modelling in order to explain the water quality of deep groundwaters. Oxygen-hydrogen isotope data indicate that deep groundwaters were originated from a local meteoric water recharged from distant, topograpically high mountainous region and underwent larger degrees of water/rock interaction during the regional deep circulation, whereas the shallow groundwaters were recharged from nearby, topograpically low region. Tritium data show that the recharge time was the pre-thermonuclear age for deep groundwaters (＜0.2 TU) but the post-thermonuclear age for shallow groundwaters (5.66∼7.79 TU). The $\delta$$\^$34/S values of dissolved sulfate indicate that high amounts of dissolved H$_2$S (up to 3.9 mg/1), a characteristic of deep groundwaters in this area, might be derived from the reduction of sulfate. The $\delta$$\^$13/C values of dissolved carbonates are controlled by not only the dissolution of carbonate minerals by dissolved soil CO$_2$(for shallow groundwaters) but also the reprecipitation of calcite (for deep groundwaters). An integrated model of the origin, flow and chemical evolution for the groundwaters in this area is proposed in this study.

• Journal of The Korean Society of Grassland and Forage Science
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• v.38 no.1
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• pp.53-60
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• 2018

Yield prediction model for mixed pasture was developed with a shortage that the relationship between dry matter yield (DMY) and days of summer depression (DSD) was not properly reflected in the model in the previous research. Therefore, this study was designed to eliminate the data of the regions with distinctly different climatic conditions and then investigate their relationships DMY and DSD using the data in each region separately of regions with distinct climatic characteristics and classify the data based on regions for further analysis based on the previous mixed pasture prediction model. The data set used in the research kept 582 data points from 11 regions and 41 mixed pasture types. The relationship between DMY and DSD in each region were analyzed through scatter plot, correlation analysis and multiple regression analysis in each region separately. In the statistical analysis, DMY was taken as the response variable and 5 climatic variables including DSD were taken as explanatory variables. The results of scatter plot showed that negative correlations between DMY and DSD were observed in 7 out of 9 regions. Therefore, it was confirmed that analyzing the relationship between DMY and DSD based on each region is necessary and 5 regions were selected (Hwaseong, Suwon, Daejeon, Siheung and Gwangju) since the data size in these regions is large enough to perform the further statistical analysis based on large sample approximation theory. Correlation analysis showed that negative correlations were found between DMY and DSD in 3 (Hwaseong, Suwon and Siheung) out of the 5 regions, meanwhile the negative relationship in Hwaseong was confirmed through multiple regression analysis. Therefore, it was concluded that the interpretability of the yield prediction model for mixed pasture could be improved based on constructing the models using the data from each region separately instead of using the pooled data from different regions.

• The Journal of Korean Academy of Prosthodontics
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• v.48 no.1
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• pp.48-54
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• 2010

Purpose: The objective of this study was to evaluate and compare the accuracy of impression body taking by the closed and the open tray impression technique with 3 types of impression tray. Individual tray, metal stock tray and polycarbonate tray were used. Materials and methods: Nine closed tray impressions were taken by individual tray, metal stock tray and polycarbonate stock tray, respectively with polyether impression material. 9 open tray impressions were also acquired by same manner. Precision analysis on the master models was performed by attaching the reference frameworks with alternate single screws and measuring the vertical fit discrepancy of respective analogues in working cast with a stereo microscope. Data were analyzed by 1 way ANOVA and independent t-test. Results: The average fit accuracy of impression bodies was calculated. With the closed tray impression technique, there were significant statistical differences in vertical fit discrepancy according to the types of tray. The individual tray group showed the lowest value and the polycarbonate stock tray group represented the highest. With the open tray impression technique, there was no significant difference in vertical fit discrepancy. Significant statistical difference in vertical fit discrepancy was found between the open and the closed impression technique with the polycarbonate stock tray. Conclusion: From the results above, more precise impressions could be acquired by the rigid individual tray compared with the polycarbonate stock tray. It was hard to get consistent accuracy impressions by the closed tray impression technique with polycarbonate stock trays.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1243-1254
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• 2021

For water resources operation or agricultural water management, it is important to accurately predict evapotranspiration for a long-term future over a seasonal or monthly basis. In this study, reference evapotranspiration forecast (up to 12 months in advance) was performed using statistically predicted monthly temperatures and temperature-based Hamon method for the Han River basin. First, the daily maximum and minimum temperature data for 15 meterological stations in the basin were derived by spatial-temporal downscaling the monthly temperature forecasts. The results of goodness-of-fit test for the downscaled temperature data at each site showed that the percent bias (PBIAS) ranged from 1.3 to 6.9%, the ratio of the root mean square error to the standard deviation of the observations (RSR) ranged from 0.22 to 0.27, the Nash-Sutcliffe efficiency (NSE) ranged from 0.93 to 0.95, and the Pearson correlation coefficient (r) ranged from 0.97 to 0.98 for the monthly average daily maximum temperature. And for the monthly average daily minimum temperature, PBIAS was 7.8 to 44.7%, RSR was 0.21 to 0.25, NSE was 0.94 to 0.96, and r was 0.98 to 0.99. The difference by site was not large, and the downscaled results were similar to the observations. In the results of comparing the forecasted reference evapotranspiration calculated using the downscaled data with the observed values for the entire region, PBIAS was 2.2 to 5.4%, RSR was 0.21 to 0.28, NSE was 0.92 to 0.96, and r was 0.96 to 0.98, indicating a very high fit. Due to the characteristics of the statistical models and uncertainty in the downscaling process, the predicted reference evapotranspiration may slightly deviate from the observed value in some periods when temperatures completely different from the past are observed. However, considering that it is a forecast result for the future period, it will be sufficiently useful as information for the evaluation or operation of water resources in the future.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.1-18
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• 2019

This research measured and visualized the spatial dependency and the spatial heterogeneity of the small business in Cheonan-si, Asan-si with $100m{\times}100m$ grids based on global and local spatial autocorrelation. First, we confirmed positive spatial autocorrelation of small business in the research area using Moran's I Index, which is ESDA(Exploratory Spatial Data Analysis). And then, through Getis-Ord $GI{\ast}$, one kind of LISA(Local Indicators of Spatial Association), local patterns of spatial autocorrelation were visualized. These verified that Spatial Regression Model is valid for the location factor analysis on small business commercial buildings. Next, GWR(Geographically Weighted Regression) was used to analyze the spatial relations between the distribution of small business, hourly mobile traffic-based floating population, land use attributes index, residence, commercial building, road networks, and the node of traffic networks. Final six variables were applied and the accessibility to bus stops, afternoon time floating population, and evening time floating population were excluded due to multicollinearity. By this, we demonstrated that GWR is statistically improved compared to OLS. We visualized the spatial influence of the individual variables using the regression coefficients and local coefficients of determinant of the six variables. This research applied the measured population information in a practical way. Reflecting the dynamic information of the urban people using the commercial area. It is different from other studies that performed commercial analysis. Finally, this research has a differentiated advantage over the existing commercial area analysis in that it employed hourly changing commercial service population data and it applied spatial statistical models to micro spatial units. This research proposed new framework for the commercial analysis area analysis.