• Nutrition Research and Practice
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• v.9 no.4
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• pp.370-378
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• 2015

BACKGROUND/OBJECTIVES: Athletes generally desire changes in body composition in order to enhance their athletic performance. Often, athletes will practice chronic energy restrictions to attain body composition changes, altering their energy needs. Prediction of resting metabolic rates (RMR) is important in helping to determine an athlete's energy expenditure. This study compared measured RMR of athletic and non-athletic adolescents with predicted RMR from commonly used prediction equations to identify the most accurate equation applicable for adolescent athletes. SUBJECTS/METHODS: A total of 50 athletes (mean age of $16.6{\pm}1.0years$, 30 males and 20 females) and 50 non-athletes (mean age of $16.5{\pm}0.5years$, 30 males and 20 females) were enrolled in the study. The RMR of subjects was measured using indirect calorimetry. The accuracy of 11 RMR prediction equations was evaluated for bias, Pearson's correlation coefficient, and Bland-Altman analysis. RESULTS: Until more accurate prediction equations are developed, our findings recommend using the formulas by Cunningham (-29.8 kcal/day, limits of agreement -318.7 and +259.1 kcal/day) and Park (-0.842 kcal/day, limits of agreement -198.9 and +196.9 kcal/day) for prediction of RMR when studying male adolescent athletes. Among the new prediction formulas reviewed, the formula included in the fat-free mass as a variable [$RMR=730.4+15{\times}fat-free\;mass$] is paramount when examining athletes. CONCLUSIONS: The RMR prediction equation developed in this study is better in assessing the resting metabolic rate of Korean athletic adolescents.

• Journal of the Society of Disaster Information
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• v.16 no.1
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• pp.133-143
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• 2020

Purpose: This study aims to derive a predictive empirical equation for PGV prediction from P-wave using earthquake records in Korea and to verify the reliability of Onsite EEW. Method: The noise of P wave is removed from the observations of 627 seismic events in Korea to derive an empirical equation with PGV on the base rock, and reliability of Onsite alarms is verified from comparing PGV's predictions and observations through simulation using the empirical equation. Result: P-waves were extracted using the Filter Picker from earthquake observation records that eliminated noises, a linear regression with PGV was used to derive a predictive empirical equation for Onsite EEW. Through the on-site warning simulation we could get a success rate of 80% within the MMI±1 error range above MMI IV or higher. Conclusion: Through this study, the design feasibility and performance of Onsite EEWS using domestic earthquake records were verified. In order to increase validity, additional medium-sized seismic observations from abroad are required, the mis-detection of P waves is controlled, and the effect of seismic amplification on the surface is required.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.384-395
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• 2020

Breaking waves generated by wave shoaling in coastal areas have a close relationship with various physical phenomena in coastal regions, such as sediment transport, longshore currents, and shock wave pressure. Therefore, it is crucial to accurately predict breaker index such as breaking wave height and breaking depth, when designing coastal structures. Numerous scientific efforts have been made in the past by many researchers to identify and predict the breaking phenomenon. Representative studies on wave breaking provide many empirical formulas for the prediction of breaking index, mainly through hydraulic model experiments. However, the existing empirical formulas for breaking index determine the coefficients of the assumed equation through statistical analysis of data under the assumption of a specific equation. In this paper, we applied a representative linear-based supervised machine learning algorithms that show high predictive performance in various research fields related to regression or classification problems. Based on the used machine learning methods, a model for prediction of the breaking index is developed from previously published experimental data on the breaking wave, and a new linear equation for prediction of breaker index is presented from the trained model. The newly proposed breaker index formula showed similar predictive performance compared to the existing empirical formula, although it was a simple linear equation.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2020.08a
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• pp.100-100
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• 2020

Wheat is mainly composed of carbohydrate but it contains a moderate amount of protein, which gives a very useful characteristics to flour food such as the unique elasticity and stickiness of the dough. We developed a calibration equation for analyzing crude protein content using Near Infrared Spectroscopy to quick analyze the crude protein content of wheat germplasm stored in the National Agrobiodiversity Center, RDA, Korea. The 1,798 wheat germplasms were used to draw up the calibration formula. The crude protein's interval distribution of 1,798 wheat germplasms used for the calibration was 7.04-20.84%, the average content was 13.2%, and standard deviation was 2.6%. The germplasms distribution was composed of a suitable group for the preparation of the calibration formula because the content distribution was a normal, excluding the 13.0-15.5% content section. In order to verify the applicability of the NIRS prediction model, we measured the crude protein content of the 300 wheat germplasms that were not used for the calibration using both Kjeldahl analysis and NIR spectrum. The analysis value calculated using each method were statistically processed, and the test results and statistical indicators of the predictive model were compared. As a result, The R² value of the optimized NIRS prediction model was 0.997, and the Standard error of Calibration value(SEC) was 0.132, and slope value was 1.000. With prediction model selection, compared to Kjeldahl method, R² values were 0.994(Kjeldahl), 0.998(NIRS), and the SEC value were 0.191 and 0.132, respectively, comparing the statistical indices of the forecast model. And slope value were 1.013, 1.000, respectively. The analysis of crude protein content by the NIRS predictive model developed by each statistical index showing similar figures is judged to show a high degree of correlation with the Kjeldahl analysis. The proven calibration equation will be used to measure the crude protein content of wheat germplasms held by the National Agrobiodiversity Center, and by dividing the wheat germplasms by their use according to the crude protein content, it will provide useful information to relevant researchers.

• Journal of Korean Academy of Nursing
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• v.44 no.5
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• pp.525-533
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• 2014

Purpose: This study was done to propose and test a predictive model that would explain and predict fall prevention behaviors in postmenopausal women. The health belief model was the theoretical basis to aid development of a nursing intervention fall prevention program. Methods: Data for 421 postmenopausal women were selected from an original data set using a survey design. The structural equation model was tested for 3 constructs: modifying factors, expectation factors, and threat factors. Expectation factors were measured as relative perceived benefit (perceived benefit minus perceived barrier), self-efficacy, and health motivation; threat factors, as perceived susceptibility (fear of falling) and perceived severity (avoiding activity for fear of falling); and modifying factors: level of education and knowledge about fall prevention. Data were analyzed using SPSS Windows and AMOS program. Results: Mean age was 55.7 years (range 45-64), and 19.7% had experienced a fall within the past year. Fall prevention behaviors were explained by expectation and threat factors indicating significant direct effects. Mediating effect of health beliefs was significant in the relationship between modifying factors and fall prevention behaviors. The proposed model explained 33% of the variance. Conclusion: Results indicate that fall prevention education should include knowledge, expectation, and threat factors based on health belief model.

• Journal of Welding and Joining
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• v.33 no.1
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• pp.80-86
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• 2015

The purpose of this study is to establish the predictive method of the angular distortion caused by the line heating process with high frequency induction heating. In order to do it, the heat input model for the high frequency induction heating system was established through comparing the temperature evaluation results obtained by both FEA and experiment. The critical heating conditions to prevent the degradation of the work piece with various thicknesses were identified by FEA and microstructure test results. Under the critical heating conditions, the extensive line heating tests were performed. According to the test results, it was found that the angular distortion behavior of the heated plates could be defined as the function of heat intensity and the rigidity of heated plate. In addition, it was clarified that the angular distortion strongly depended on the size of test specimen such as the length and the width of the heated plate. Based on these results, the predictive equation for the angular distortion was established with the function of heat intensity, bending rigidity and size of heated plate.

• Journal of Dental Rehabilitation and Applied Science
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• v.16 no.1
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• pp.61-67
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• 2000

It was the aim of present study to grope the relationship of the maxillary first molar width to the various transverse skeletal measurements in frontal headfilm, and to formulate the predictive equations of the maxillary intermolar width (U6-U6) from each of the variables. Frontal cephalograms of 17 males from 18 to 26 YO and 13 females from 17 to 25 YO who manifested balanced skeletal profiles, normal occlusion, and no history of orthodontic and prosthodontic treatment were employed as subjects. Nine transverse measurements were scrutinized with Pearson's correlation analysis, simple and stepwise multiple regression analysis in specific regards to the intermolar width of maxillary first molar. Statistical output demonstrated that there were intimate relationships within the various transverse skeletal measurements each other, and among the others, high correlation was found between facial width and maxillary first intermolar width. Regression analyses provided the reliable and clinically applicable predictive equations to set the ideal maxillary first intermolar width(U6-U6) from the given skeletal framework.

• Journal of the Korean Institute of Gas
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• v.12 no.3
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• pp.50-55
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• 2008

The flash points for the systems, ethlybenzene+n-butanol and ethlybenzene+n-hexanol, were measured by using Tag open-cup tester (ASTM D1310-86). These binary mixtures exhibited MFPB (minimum flash point behavior), which leads to the minimum on the flash point vs composition curve. The experimental data were compared with the values calculated by the Raoult's law, the UNIQUAC equation and the NRTL equation. The calculated values based on the UNIQUAC and NRTL equations were found to be better than those based on the Raoult's law. It was concluded that the UNIQUAC and NRTL equations were more effective than the Raoult' law at describing the activity coefficients for nonideal solution such as the ethlybenzene+n-butanol and ethlybenzene+n-hexanol systems. And the predictive curve of the flash point prediction model based on the NRTL equation described the experimentally-derived data more effectively than was the case when the prediction model was based upon the UNIQUAC equation.

• Korean Journal of Agricultural Science
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• v.49 no.1
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• pp.61-75
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• 2022

The first purpose of this study is to evaluate the usefulness of pork traceability data, which is monthly time-series data, and to draw implications with regard to its usefulness. The second purpose is to construct a dynamic ecological equation model (DEEM) that reflects the biological characteristics at each growth stage, such as pregnancy, birth and growth, and the slaughter of pigs, using traceability data. With the monthly pig model devised in this study, it is expected that the number of slaughtered animals (supply) that can be shipped in the future is predictable and that policy simulations are possible. However, this study was limited to traceability data and focused only on building a supply-side model. As a result of verifying the traceability data, it was found that approximately 6% of farms produce by mixing great grand parent (GGP), grand parent (GP), parent stock (PS), and artificial insemination (AI), meaning that it is necessary to separate them by business type. However, the analysis also showed that the coefficient values estimated by constructing an equation for each growth stage were consistent with the pig growth outcomes. Also, the model predictive power test was excellent. For this reason, it is judged that the model design and traceability data constructed with the cohort and the dynamic ecological equation model system considering biological growth and shipment times are excellent. Finally, the model constructed in this study is expected to be used as basic data to inform producers in their decision-making activities and to help with governmental policy directions with regard to supply and demand. Research on the demand side is left for future researchers.

• Food Science of Animal Resources
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• v.30 no.5
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• pp.730-738
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• 2010

We developed predictive growth models for Staphylococcus aureus and Bacillus cereus on various food matrices consisting primarily of ready-to-eat (RTE) foods. A cocktail of three S. aureus strains, producing enterotoxins A, C, and D, or a B. cereus strain, were inoculated on sliced bread, cooked rice, boiled Chinese noodles, boiled bean sprouts, tofu, baked fish, smoked chicken, and baked hamburger patties at an initial concentration of 3 log CFU/g and stored at 8, 10, 13, 17, 24, and $30^{\circ}C$. Growth kinetic parameters were determined by the Gompertz equation. The square-root and Davey models were used to determine specific growth rate and lag time values, respectively, as a function of temperature. Model performance was evaluated based on bias and accuracy factors. S. aureus and B. cereus growth were most delayed on sliced bread. Overall, S. aureus growth was significantly (p<0.05) more rapid on animal protein foods than carbohydrate-based foods and vegetable protein foods. The fastest growth of S. aureus was observed on smoked chicken. B. cereus growth was not observed at 8 and $10^{\circ}C$. B. cereus growth was significantly (p<0.05) more rapid on vegetable protein foods than on carbohydrate-based foods. The secondary models developed in this study showed suitable performance for predicting the growth of S. aureus and B. cereus on various food matrices consisting of RTE foods.