• Proceedings of the Korean Environmental Health Society Conference
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• 2004.06a
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• pp.193-196
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• 2004

To evaluate the acute effects of fine particles on pulmonary function, a longitudinal study was conducted. This study was carried out for the schoolchildren (3rd and 6th grades) living in Beijing, China. Children were asked to record their daily levels of peak expiratory flow rate using portable peak flow meter (mini-Wright) for 40 days. The relationship between daily PEFR and fine particle levels was analyzed using a mixed linear regression models including gender, height, the presence of respiratory symptoms, and daily average temperature and relative humidity as extraneous variables. The total number of students participating in this longitudinal study was 87. Daily measured PEFR was in the range of $253{\sim}501L/min$. On the daily basis, a PEFR measured in the morning was shown to be lower than that measured in the evening (or afternoon). The daily mean concentrations of $PM_{10}$ and $PM_{2.5}$ over the study period were $180.2\;{\mu}g/m^3$ and $103.2\;{\mu}g/m^3$, respectively. The IQR (inter-quartile range) of $PM_{10}$ and $PM_{2.5}$ were $91.8\;{\mu}g/m^3$ and $58.0\;{\mu}g/m^3$. Daily mean PEFR was regressed with the 24-hour average $PM_{10}$ (or $PM_{2.5}$) levels, weather information such as air temperature and relative humidity, and individual characteristics including gender, height, and respiratory symptoms. The analysis showed that the increase of fine particle concentrations was negatively associated with the variability in PEFR. The IQR increments of $PM_{10}$ or $PM_{2.5}$ (at 1-day time lag) were also shown to be related with 1.54L/min (95% Confidence intervals -2.14, -0.94) and 1.56L/min (95% CI -2.16, -0.95) decline in PEFR.

• Korea Journal of Hospital Management
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• v.10 no.3
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• pp.45-66
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• 2005

The purpose of this study was to analyze related factors affecting profitability on general hospitals(300-499 beds). The data were derived from survey by the Korean Hospital Association on 33 hospitals during 10 years (from 1993 to 2002). Profitability was measured by 3 ratios - net profit to total assets, normal profit to total assets and operating margin to gross revenue - as dependent variables. Independent variables were classified by general factors (ownership, number of bed, period of establishment, region), financial factors (total asset turnover, current ratio, liabilities to total assets, personnel costs per operation profit, material costs per operation profits), productivity index(number of daily patient per nurse), the score of quality assurance activity and the time lag score. Multiple regression model was used in this study. First, Number of bed, region was not statistically significant for profitability. But ownership was affect positively to normal profit to total assets and operating margin to gross revenue. Private hospitals had higher profitability than that of public hospitals Second, the score of quality assurance activity was not statistically significant to profitability. Third, Those hospitals having more daily patient per nurse had significantly higher profitability than the others. Fourth, Those hospitals having higher proportion in total asset turnover had significantly higher profitability than other hospitals. But liabilities to total assets and liquidity ratio had no difference to the profitability. Those hospitals having higher proportion in personnel costs and material costs per operation profits had significantly lower hospital profitability than others.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.53 no.5
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• pp.699-706
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• 2020

Predictive models in food microbiology are used for predicting microbial growth or death rates using mathematical and statistical tools considering the intrinsic and extrinsic factors of food. This study developed predictive growth models for Bacillus cereus on dried laver Pyropia pseudolinearis stored at different temperatures (5, 10, 15, 20, and 25℃). Primary models developed for specific growth rate (SGR), lag time (LT), and maximum population density (MPD) indicated a good fit (R²≥0.98) with the Gompertz equation. The SGR values were 0.03, 0.08, and 0.12, and the LT values were 12.64, 4.01, and 2.17 h, at the storage temperatures of 15, 20, and 25℃, respectively. Secondary models for the same parameters were determined via nonlinear regression as follows: SGR=0.0228-0.0069*T₁+0.0005*T₁²; LT=113.0685-9.6256*T₁+0.2079*T₁²; MPD=1.6630+0.4284*T₁-0.0080*T₁² (where T₁ is the storage temperature). The appropriateness of the secondary models was validated using statistical indices, such as mean squared error (MSE<0.01), bias factor (0.99≤B_f≤1.07), and accuracy factor (1.01≤A_f≤1.14). External validation was performed at three random temperatures, and the results were consistent with each other. Thus, these models may be useful for predicting the growth of B. cereus on dried laver.

• Food Science and Biotechnology
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• v.16 no.2
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• pp.238-242
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• 2007

Square root models were developed for predicting the kinetics of growth of Listeria monocytogenes in sesame leaves as a function of temperature (4, 10, or $25^{\circ}C$). At these storage temperatures, the primary growth curves fit well ($R^2=0.898$ to 0.980) to a Gompertz equation to obtain lag time (LT) and specific growth rate (SGR). The square root models for natural logarithm transformations of the LT and SGR as a function of temperature were obtained by SAS's regression analysis. As storage temperature ($4-25^{\circ}C$) decreased, LT increased and SGR decreased, respectively. Square root models were identified as appropriate secondary models for LT and SGR on the basis of most statistical indices such as coefficient determination ($R^2=0.961$ for LT, 0.988 for SGR), mean square error (MSE=0.l97 for LT, 0.005 for SGR), and accuracy factor ($A_f=1.356$ for LT, 1.251 for SGR) although the model for LT was partially not appropriate as a secondary model due to the high value of bias factor ($B_f=1.572$). In general, our secondary model supported predictions of the effects of temperature on both LT and SGR for L. monocytogenes in sesame leaves.

• International Area Studies Review
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• v.15 no.3
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• pp.393-405
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• 2011

Three different models have been consecutively employed with the U.S. yield curve and the Korean composite stock price index, firstly to see the coupling between the economies of the U.S. and Korea, secondly to find out the time consumed completing the coupling, and lastly to figure out the impact of the recent U.S. financial crisis on this coupling. This study has, first of all, produced an empirical research outcome which proved the existence of coupling between two countries' economies. The direction of this coupling was consistent with the general expectation that when the yield spread between the U.S. 10-year Treasury Note and the U.S. 3-month Treasury Bill increased which often occurred with better prospects of U.S. economy, the asset price of emerging economies including Korea also rose reflecting the accompanying change in investment atmosphere in favor of risk. It has also found out that the degree of the coupling was maximized with a lag of one week. And finally the recent US financial crisis has been revealed to reduce the degree of the coupling by as much as half in a regression model with a dummy variable.

• Environmental and Resource Economics Review
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• v.23 no.2
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• pp.187-224
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• 2014

We present key issues to consider in estimating damages from price-fixing cases and then apply the procedure addressing those issues to a transportation fuel market. Among the five methods of overcharge calculation, the regression analysis incorporating the yardstick method is the best. If the price equation relates the domestic price to the foreign price and the exchange rate as in the transportation fuel market, the functional form satisfying both logical consistency and modeling flexibility is the log-log functional form. If the data under analysis is of time series in nature, then the ARDL model should be the base model for each market and the regression analysis incorporating the yardstick method combines these ARDL equations to account for inter-market correlation and arrange constant terms and collusion-period dummies across component equations appropriately so as to identify the overcharge parameter. We propose a two-step test for the benchmarked market: (a) conduct market-by-market Spearman or Kendall test for randomness of the individual market price series first and (b) then conduct across-market Friedman test for homogeneity of the market price series. Statistical significance is the minimal requirement to establish the alleged proposition in the world of uncertainty. Between the sensitivity analysis and the model selection process for the best fitting model, the latter is far more important in the economic analysis of damage in price-fixing litigation. We applied our framework to a transportation fuel market and could not reject the null hypothesis of no overcharge.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Journal of the Korean earth science society
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• v.36 no.3
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• pp.199-209
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• 2015

The Atlantic meridional overturning circulation (AMOC), driven by high density water sinking around Greenland serves as a global climate regulator, because it transports heat and materials in the climate system. We analyzed the mechanism of AMOC on a decadal time scale simulated with the HadGEM2-AO model. The lead-lag regression analysis with AMOC index shows that the decadal variability of the thermohaline circulation in the Atlantic Ocean can be considered as a self-sustained variability. This means that the long-term change of AMOC is related to the instability which is originated from the phase difference between the meridional temperature gradient and the ocean circulation. When the overturning circulation becomes stronger, the heat moves northward and decreases the horizontal temperature-dominated density gradients. Subsequently, this leads to weakening of the circulation, which in turn generates the anomalous cooling at high latitudes and, thereby strengthening the AMOC. In this mechanism, the density anomalies at high latitudes are controlled by the thermal advection from low latitudes, meaning that the variation of the AMOC is thermally driven and not salinity driven.

• Journal of Korea Technology Innovation Society
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• v.19 no.4
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• pp.711-735
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• 2016

Drug Development is very important for promoting public health and pharmaceutical industry. There has been many studies on the efficiency of drug development, but there are few studies on the drug development R&D performed by government. Since CCR model assumes unidirectional influence of input and output, it is not appropriate to analyze the efficiency of R&D due to the time-lag and spill-over effect. Also, BBC model which assumes variable returns to scale has difficulty in deriving priorities between decision making units. Recently, Range Adjusted Measure (RAM) model has been suggested in R&D efficiency analysis. RAM model measures the efficincy by eliminating inefficiencies under variable returns to scale assumption, and its strong monotonicity enables to provide clear priorities between decision making units. In this study, we analyzed the efficiency of national R&D programs for drug development using the two-step approach, including RAM model and Tobit regression analysis, and discussed major policy implications.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.12
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• pp.1691-1698
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• 2000

An in situ digestion trial (Experiment 1) and a lactation trial (Experiment 2) were conducted to determine the effects of replacing corn and wheat bran with soyhulls (SH) in lactating dairy cow diets on the extent and kinetics of digestion of DM and NDF, and lactation performance. In experiment 1, five mixed feeds consisting of mixed concentrate and roughages (50:50 on a DM basis) were formulated on isonitrogenous and isoenergetic bases to produce five levels (0, 25, 50, 75 and 100%) of SH replacement for corn and wheat bran. SH had high in situ digestion (92 and 89% for potentially digestible DM and NDF) and fairly fast digestion rate (7.2 and 6.3 %/h for DM and NDF). Increasing level of SH replacement resulted in increased NDF digestibility (linear, p=0.001-0.04) and similar DM digestibility (beyond 12 h incubation, p=0.10-0.41). As level of SH replacement increased, percentage of slowly digestible fraction (b) of DM increased (linear, p=0.03), percentage of rapidly digestible fraction (a) of DM tended to decrease (linear, p=0.14), and DM digestion lag time tended to be longer (linear, p=0.13). Percentage of potentially digestible fraction (a+b) and digestion rate (c) of slowly digestible fraction of dietary DM remained unaltered (p=0.36-0.90) with increasing SH in the diet. Increasing level of SH for replacing corn and wheat bran in the diet resulted in increases in percentages of b (quadratic, p<0.001), a (linear, p=0.08), a+b (quadratic, p=0.001) and a tendency to increase in c for NDF (linear, p<0.19). It was also observed that there was a satisfactory fit of a non-linear regression model to NDF digestion data ($R^2=0.986-0.998$), but a relatively poor fit of the model to DM digestion data ($R^2=0.915-0.968$). In experiment 2, 42 lactating Holstein cows were used in a randomized complete block design. SH replaced corn and wheat bran in mixed concentrates at 0, 25, and 50%, respectively. These mixed concentrates were mixed with roughages and fed ad libitum as complete diets. Replacing corn and wheat bran with SH at 0, 25 and 50% levels did not influence (p=0.56-0.95) DM intakes (18.4, 18.6, and 18.5 kg/d), milk yields (27.7, 28.4 and 27.6 kg/d), 4% fat-corrected-milk (FCM) yields (26.2, 27.6, and 27.3 kg/d) and percentages of milk protein (3.12, 3.17 and 3.18%), milk lactose (4.69, 4.76 and 4.68%) and SNF (8.50, 8.64, and 8.54%). On the other hand, milk fat percentges linearly increased (3.63, 3.85 and 3.90% for SH replacement rates of 0, 25 and 50% in the diet, p=0.08), while feed costs per kg FCM production were reduced.