• Journal of the Korean Geophysical Society
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• v.9 no.3
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• pp.171-179
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• 2006

Predictive equations of ground motions are one of the most important factors in the seismic hazard analysis. Unfortunately, studies on predictive equations of ground motions in Korea had been hampered due to the lack of seismic data. To overcome the lack of data, seismologists adopted the stochastic method based on the seismological model. Korean predictive equations developed by the stochastic method show large differences in their predictions. It was turned out through the analysis of the existing studies that the main sources of the differences are the uncertainties in the (Brune) stress drop and spectral decay rate . Therefore, it is necessary to focus the future research on the reduction of the uncertainties in the two parameters.

• Korean Journal of Community Nutrition
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• v.14 no.4
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• pp.462-473
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• 2009

The purpose of this study is to analyze the accuracy of predictive equations for resting metabolic rate (RMR) in Korean college students. Subjects were 60 healthy Korean college students (30 males, 30 females) aged 18-25 years. RMR was measured by indirect calorimetry. Predicted RMRs were calculated using the Harris-Benedict, Schofield (W)/(WH), FAO/ WHO/UNU(W)/(WH), Owen, Mifflin, Cunningham, Liu, IMNA and Henry (W)/(WH) equations. The accuracy of the equations was evaluated on basis of accurate prediction (the percentage of subjects whose RMR was predicted within 90% to 110% of the RMR measured), mean difference, RMSPE, mean % difference, limits of agreement of Bland- Altman method between predicted and measured RMR. Measured RMR of male and female students were $1833.4{\pm}307.4kcal/day$ and $1454.3{\pm}208.0kcal/day$, respectively. All predictive equations underestimated measured RMR. Of the predictive equations tested, the Harris-Benedict equation (mean difference: -80.4 kcal/day, RMSPE: 236 kcal/day, mean % difference: -3.1%) was the most accurate and precise, but accurate prediction of the equation was only 42%. Thus, this study suggests that the ethnicity-specific predictive equation from Korean people should be developed to improve the accuracy of predicted RMR for Koreans. (Korean J Community Nutrition 14(4) : 462${\sim}$473, 2009)

• Korean Journal of Community Nutrition
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• v.19 no.6
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• pp.568-580
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• 2014

Objectives: The purpose of this study was to measure the resting metabolic rate (RMR) and to assess the accuracy of RMR predictive equations for Korean farmers. Methods: Subjects were 161 healthy Korean farmers (50 males, 111 females) in Gangwon-area. The RMR was measured by indirect calorimetry for 20 minutes following a 12-hour overnight fasting. Selected predictive equations were Harris-Benedict, Mifflin, Liu, KDRI, Cunningham (1980, 1991), Owen-W, F, FAO/WHO/UNU-W, WH, Schofield-W, WH, Henry-W, WH. The accuracy of the equations was evaluated on the basis of bias, RMSPE, accurate prediction and Bland-Altman plot. Further, new RMR predictive equations for the subjects were developed by multiple regression analysis using the variables highly related to RMR. Results: The mean of the measured RMR was 1703 kcal/day in males and 1343 kcal/day in females. The Cunningham (1980) equation was the closest to measured RMR than others in males and in females (males Bias -0.47%, RMSPE 110 kcal/day, accurate prediction 80%, females Bias 1.4%, RMSPE 63 kcal/day, accurate prediction 81%). Body weight, BMI, circumferences of waist and hip, fat mass and FFM were significantly correlated with measured RMR. Thus, derived prediction equation as follow : males RMR = 447.5 + 17.4 Wt, females RMR = 684.5 - 3.5 Ht + 11.8 Wt + 12.4 FFM. Conclusions: This study showed that Cunningham (1980) equation was the most accurate to predict RMR of the subjects. Thus, Cunningham (1980) equation could be used to predict RMR of Korean farmers studied in this study. Future studies including larger subjects should be carried out to develop RMR predictive equations for Korean farmers.

• Nutrition Research and Practice
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• v.6 no.1
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• pp.51-60
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• 2012

Weight-controlling can be supported by a proper prescription of energy intake. The individual energy requirement is usually determined through resting energy expenditure (REE) and physical activity. Because REE contributes to 60-70% of daily energy expenditure, the assessment of REE is very important. REE is often predicted using various equations, which are usually based on the body weight, height, age, gender, and so on. The aim of this study is to validate the published predictive equations for resting energy expenditure in 76 normal weight and 52 obese Korean children and adolescents in the 7-18 years old age group. The open-circuit indirect calorimetry using a ventilated hood system was used to measure REE. Sixteen REE predictive equations were included, which were based on weight and/or height of children and adolescents, or which were commonly used in clinical settings despite its use based on adults. The accuracy of the equations was evaluated on bias, RMSPE, and percentage of accurate prediction. The means of age and height were not significantly different among the groups. Weight and BMI were significantly higher in obese group (64.0 kg, $25.9kg/m^2$) than in the non-obese group (44.8 kg, $19.0kg/m^2$). For the obese group, the Molnar, Mifflin, Liu, and Harris-Benedict equations provided the accurate predictions of > 70% (87%, 79% 77%, and 73%, respectively). On the other hand, for non-obese group, only the Molnar equation had a high level of accuracy (bias of 0.6%, RMSPE of 90.4 kcal/d, and accurate prediction of 72%). The accurate prediction of the Schofield (W/WH), WHO (W/WH), and Henry (W/WH) equations was less than 60% for all groups. Our results showed that the Molnar equation appears to be the most accurate and precise for both the non-obese and the obese groups. This equation might be useful for clinical professionals when calculating energy needs in Korean children and adolescents.

• Nutrition Research and Practice
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• v.12 no.4
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• pp.283-290
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• 2018

BACKGROUND/OBJECTIVES: Indirect calorimetry is the gold-standard method for the measurement of resting energy expenditure. However, this method is time consuming, expensive, and requires highly trained personnel. To overcome these limitations, various predictive equations have been developed. The objective of this study was to assess the validity of predictive equations for resting energy expenditure (REE) in Korean non-obese adults. SUBJECTS/METHODS: The present study involved 109 participants (54 men and 55 women) aged between 20 and 64 years. The REE was measured by indirect calorimetry. Nineteen REE equations were evaluated for validity, by comparing predicted and measured REE results. Predictive equation accuracy was assessed by determining percent bias, root mean squared prediction error (RMSE), and percentage of accurate predictions. RESULTS: The measured REE was significantly higher in men than in women (P < 0.001), but the difference was not significant after adjusting for body weight (P > 0.05). The equation developed in this study had an accuracy rate of 71%, a bias of 0%, and an RMSE of 155 kcal/day. Among published equations, the $FAO_{weight}$ equation gave the highest accuracy rate (70%), along with a bias of -4.4% and an RMSE of 184 kcal/day. CONCLUSIONS: The newly developed equation provided the best accuracy in predicting REE for Korean non-obese adults. Among the previously published equations, the $FAO_{weight}$ equation showed the highest overall accuracy. Regardless, at an individual level, the equations could lead to inaccuracies in a considerable number of subjects.

• The Journal of Korean Medicine
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• v.40 no.4
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• pp.49-60
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• 2019

Objectives: To develop a standardized diagnostic pattern identification equation for stroke patients, our group conducted a study to derive the predictive logistic equations. However, the sample size was relatively small. In the current study, we aimed to derive new predictive logistic equations for each diagnostic pattern using an expanded number of subjects. Methods: This study was a hospital-based multi-center trial recruited stroke patients within 30 days of symptom onset. Patients' general information, and the variables related to diagnostic pattern identification were measured. The diagnostic pattern of each patient was identified independently by two Korean Medicine Doctors. To derive a predictive model for pattern identification, binary logistic regression analysis was applied. Results: Among the 1,251 patients, 385 patients (30.8%) had the Fire Heat Pattern, 460 patients (36.8%) the Phlegm Dampness Pattern, 212 patients (16.9%) the Qi Deficiency Pattern, and 194 patients (15.5%) the Yin Deficiency Pattern. After the regression analysis, the predictive logistic equations for each pattern were determined. Conclusion: The predictive equations for Fire Heat, Phlegm Dampness, Qi Deficiency, and Yin Deficiency would be useful to determine individual stroke patients' pattern identification in the clinical setting. However, further studies using objective measurements are necessary to validate these data.

• Nutrition Research and Practice
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• v.13 no.3
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• pp.256-262
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• 2019

BACKGROUND/OBJECTIVES: The objectives of this study were to evaluate the accuracy of the Dietary Reference Intakes (DRI) for estimating the energy requirements of older adults, and to develop and validate new equations for predicting the energy requirements of this population group. MATERIALS/METHODS: The study subjects were 25 men and 23 women with a mean age of $72.2{\pm}3.9\;years$ and $70.0{\pm}3.3\;years$, and mean BMI of $24.0{\pm}2.1$ and $23.9{\pm}2.7$, respectively. The total energy expenditure (TEE) was measured by using the doubly labeled water (DLW) method, and used to validate the DRI predictive equations for estimated energy requirements (EER) and to develop new EER predictive equations. These developed equations were cross-validated by using the leave-one-out technique. RESULTS: In men, the DRI equation had a -7.2% bias and accurately predicted the EER (meaning EER values within ${\pm}10%$ of the measured TEE) for 64% of the subjects, whereas our developed equation had a bias of -0.1% and an accuracy rate of 84%. In women, the bias was -6.6% for the DRI equation and 0.2% for our developed equation, and the accuracy rate was 74% and 83%, respectively. The predicted EER was strongly correlated with the measured TEE, for both the DRI equations and our developed equations (Pearson's r = 0.915 and 0.908, respectively). CONCLUSIONS: The DRI equations provided an acceptable prediction of EER in older adults and these study results therefore support the use of these equations in this population group. Our developed equations had a better predictive accuracy than the DRI equations, but more studies need to be performed to assess the performance of these new equations when applied to an independent sample of older adults.

• Magazine of the Korean Society of Agricultural Engineers
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• v.41 no.4
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• pp.57-65
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• 1999

A modified gas tracer method was used to obtain reaeration coefficient from an artificial channel and a reach of Bokha stream, Ichon city. Propane was used as the tracer gas and Rhodamine-B dye as a dispersion and dulution tracer. Concentrations of propane in water sample were measured using a gas chromatograph and concentrationsof dye using UV-Spectrophotometer. To compare measured values with predicted values,commonly used 14 equations were selected . Results of this study suggested that the modified gas tracer method is a potentially useful procedure for th edetermination of reaeration cofficients. However, estimated reaeration coefficients from predictive equations were significantly different from that of this study. Therefore, when using predictive equations, careful selection of equation with consideration for hydraulic characteristics such as flow depth and average velocity, or use of newly derived predictive equation which is adequate for questioned stream would be needed.

• Journal of Trauma and Injury
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• v.36 no.4
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• pp.337-342
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• 2023

Purpose: This study aimed to compare the resting energy expenditure (REE) measured using indirect calorimetry with that estimated using predictive equations in severe trauma patients to determine the appropriate caloric requirements. Methods: Patients admitted to the surgical intensive care unit between January 2020 and March 2023 were included in this study. Indirect calorimetry was used to measure the patients' REE values. These values were subsequently compared with those estimated using predictive equations: the weight-based equation (rule of thumb, 25 kcal/kg/day), Harris-Benedict, Ireton-Jones, and the Penn State 2003 equations. Results: A total of 27 severe trauma patients were included in this study, and 47 indirect calorimetric measurements were conducted. The weight-based equation (mean difference [MD], -28.96±303.58 kcal) and the Penn State 2003 equation (MD, - 3.56±270.39 kcal) showed the closest results to REE measured by indirect calorimetry. However, the REE values estimated using the Harris-Benedict equation (MD, 156.64±276.54 kcal) and Ireton-Jones equation (MD, 250.87±332.54 kcal) displayed significant differences from those measured using indirect calorimetry. The concordance rate, which the predictive REE differs from the measured REE value within 10%, was up to 36.2%. Conclusions: The REE values estimated using predictive equations exhibited substantial differences from those measured via indirect calorimetry. Therefore, it is necessary to measure the REE value through indirect calorimetry in severe trauma patients.

• Fire Science and Engineering
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• v.17 no.2
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• pp.50-55
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• 2003

The lower flash points for the binary system, n-butanol+n-decane, were measured by Pensky-Martens closed cup tester. The experimental results showed the minimum in the flash point versus composition curve. The experimental data were compared with the values calculated by the reduced model under an ideal solution assumption and the flash point-prediction models based on the Van Laar and Wilson equations. The predictive curve based upon the reduced model deviated form the experimental data for this system. The experimental results were in good agreement with the predictive curves, which use the Van Laar and Wilson equations to estimate activity coefficients. However, the predictive curve of the flash point prediction model based on the Willson equation described the experimentally-derived data more effectively than that of the flash point prediction model based on the Van Laar equation.