• Corrosion Science and Technology
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• v.18 no.2
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• pp.61-71
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• 2019

Flow-Accelerated Corrosion (FAC) is a phenomenon in which a protective coating on a metal surface is dissolved by a flow of fluid in a metal pipe, leading to continuous wall-thinning. Recently, many countries have developed computer codes to manage FAC in power plants, and the FAC prediction model in these computer codes plays an important role in predictive performance. Herein, the FAC prediction model was developed by applying a machine learning method and the conventional nonlinear regression method. The random forest, a widely used machine learning technique in predictive modeling led to easy calculation of FAC tendency for five input variables: flow rate, temperature, pH, Cr content, and dissolved oxygen concentration. However, the model showed significant errors in some input conditions, and it was difficult to obtain proper regression results without using additional data points. In contrast, nonlinear regression analysis predicted robust estimation even with relatively insufficient data by assuming an empirical equation and the model showed better predictive power when the interaction between DO and pH was considered. The comparative analysis of this study is believed to provide important insights for developing a more sophisticated FAC prediction model.

• Journal of Korean Society on Water Environment
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• v.39 no.6
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• pp.465-474
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• 2023

The chemical composition and molecular weight characteristics of dissolved organic matter (DOM) exert a profound influence on the efficiency of organic matter removal in water treatment systems, acting as efficiency predictive indicators. This research evaluated the primary chemical and molecular weight properties of DOM derived from diverse sources, including rivers, lakes, and biomasses, and assessed their relationship with the efficiency of coagulation/flocculation treatments. Dissolved organic carbon (DOC) removal efficiency through coagulation/flocculation exhibited significant correlations with DOM's hydrophobic distribution, the ratio of humic-like to protein-like fluorescence, and the molecular weight associated with humic substances (HS). These findings suggest that the DOC removal rate in coagulation/flocculation processes is enhanced by a higher presence of HS in DOM, an increased influence of externally sourced DOM, and more presence of high molecular weight compounds. The results of this study further posit that the efficacy of water treatment processes can be more accurately predicted when considering multiple DOM characteristics rather than relying on a singular trait. Based on major results from this study, a predictive model for DOC removal efficiency by coagulation/flocculation was formulated as: 24.3 - 7.83 × (fluorescence index) + 0.089 × (hydrophilic distribution) + 0.102 × (HS molecular weight). This proposed model, coupled with supplementary monitoring of influent organic matter, has the potential to enhance the design and predictive accuracy for coagulation/flocculation treatments targeting DOC removal in future applications.

• 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.

• Computers and Concrete
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• v.19 no.5
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• pp.457-465
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• 2017

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.

• Geophysics and Geophysical Exploration
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• v.12 no.4
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• pp.289-298
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• 2009

Optimal data processing parameters were designed to attenuate multiples in seismic data acquired in the south-eastern area of the East Sea, in 2008. Bunch of multiples caused by shallow sea water depth were perceived periodically up to two way travel time of 1,750 ms at every 250 ms over seismic traces. We abbreviated sea bottom multiple as SBM, Peg-leg multiple as PLM, and free-surface multiple as FSM. To attenuate these multiples, seismic data processing flow was constructed including NMO, stack, minimum phase predictive deconvolution filter and wave equation multiple rejections (WEMR). Prevalent multiples were suppressed by predictive deconvolution and remaining multiples were attenuated by WEMR. We concluded that combining deconvolution with WEMR was effective to a seismic data of study area. Derived parameter can be applied to the seismic data processing on adjacent survey area.

• Journal of the Korean Society of Safety
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• v.20 no.2 s.70
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• pp.162-168
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• 2005

The lower flash points for the n-propanol+n-decane flammable mixture were measured by the Tag open-cup apparatus(ASTM D 1310). The experimental results of mixture exhibited the lower flash point than those of pure component in the flash point versus composition curve. The experimental value of the minimum flash point is $27^{\circ}C$ at a mole fraction of n-propanol of 0.71, and the flash point of n-propanol was $28^{\circ}C$. The experimentally obtained data were compared with the values that had been calculated by use of the prediction model, which assumes an ideal solution, and the flash point prediction models based on the van Laar equation were used to estimate the activity coefficients. The predictive curve based on an ideal solution deviated from the experimental data for this system. The experimental results demonstrate a close agreement with the predicted curves, which used the van Laar equation. The average absolute deviation(A.A.D.) from using the van Lau equation is $0.83^{\circ}C$. The methodology proposed here in this paper can thus be applied to incorporate an inherently safer design for chemical processes, such as determining safe storage and handling conditions for flammable solutions.

• Women's Health Nursing
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• v.4 no.2
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• pp.231-244
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• 1998

The purpose of this study was to provide a basis for nursing intervention to enhance quality if life in women having hysterectomies. Data was collected using a self-report questionnaire from 205 women having hysterectomies at the outpatient clinics of four general hospitals and a mail survey in Pusan City. Reliability of eight instrument's was tested with Cronbach's alpha which ranged from .601-.901. The data were analyzed by percentage, mean, SD, Pearson's Correlation and Stepwise Multiple Regression by using the SPSS 7.5 WIN Program. The results are as follows: 1) The average score for the quality of life was 74.33(score range 23-92). 2) There was a significant correlation between the predictive variables on quality of life. The most significant correlation was sexual identity(r=.516, p=.000). 3) When quality if life score was entered into the equation as the dependent variable, 7variables explaining 54.5% of the variation in quality if life score. Sexual identity was the main predictor of quality of life and accounted for 24.6% of the variance in quality of life. 4) When physical domain score was entered into equation as the dependent variable, 5variables explaining 29.2% of the variation in physical domain score. 5) When psychological domain score was entered into the equation as the dependent variable, 5variables explaining 46.0% of the variation in psychological domain score. 6) When sexual life domain score was entered into the equation as the dependent variable, 6variables explaining 39.4% of the variation in sexual life domain score. In conclusion, sexual identity, pre-operational symptom, sense of loss, spouse's support, age, professional support, coping behavior were identified as important variables in the quality of life in women having hysterectomies.

• Journal of Trauma and Injury
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• v.34 no.1
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• pp.13-20
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• 2021

Purpose: Nutritional therapy in the intensive care unit is an essential factor for patient progress. The purpose of this study was to compare resting energy expenditure (REE) calculated by prediction equations (PEs) to the REE measured by indirect calorimetry (IC) in trauma patients. Methods: Patients admitted to the trauma intensive care unit who received mechanical ventilation between January and December 2015 were enrolled. REE was measured by IC (CCM Express, MGC Diagnostics) and calculated by the following PEs: Harris-Benedict, Fleisch, Robertson and Reid, Ireton-Jones, and the maximum value (25 kcal/kg/day) of the European Society for Clinical Nutrition and Metabolism (ESPEN). All patients were ventilated at a fraction of inspired oxygen (FiO2) below 60%. Results: Of the 31 patients included in this study, 24 (77.4%) were men and seven (22.6%) were women. The mean age of the patients was 49.7±13.2 years, their mean weight was 68.1±9.6 kg, and their mean Injury Severity Score was 26.1±11.3. The mean respiratory quotient on IC was 0.93±0.19, and their mean FiO2 was 38.72%±6.97%. The mean REE measured by IC was 2,146±444.36 kcal/day, and the mean REE values calculated by the PEs were 1,509.39±205.34 kcal/day by the Harris and Benedict equation, 1,509.39±154.33 kcal/day by the Fleisch equation, and 1,443.39±159.61 kcal/day by the Robertson and Reid equation. The Ireton-Jones equation yielded a higher value (2,278.90±202.35 kcal/day), which was not significantly different from the value measured using IC (p=0.53). The ESPEN maximum value (1,704.03±449.36 kcal/day) was lower, but this difference was likewise not significant (p=0.127). Conclusions: The REE measured by IC was somewhat higher than that calculated using PEs. Further studies are needed to determine the proper nutritional support for trauma patients.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.2
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• pp.69-76
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• 2024

The rheological properties of fresh concrete significantly influence its manufacturing and performance. However, the diversification of newly developed mixtures and manufacturing techniques has made it challenging to accurately predict these properties using traditional empirical methods. This study introduces a multiscale rheological property prediction model designed to quantitatively anticipate the rheological characteristics from nano-scale interparticle interactions, such as those among cement particles, to micro-scale behaviors, such as those involving fine aggregates. The Yield Stress Model (YODEL), the Chateau-Ovarlez-Trung equation, and the Krieger-Dougherty equation were utilized to predict the yield stress for cement paste and mortar, as well as the plastic viscosity. Initially, predictions were made for the paste scale, using the water-cement ratio (W/C) of the cement paste. These predictions then served as a basis for further forecasting of the rheological properties at the mortar scale, incorporating the same W/C and adding the cement-sand volume ratio (C/S). Lastly, the practicality of the predictive model was assessed by comparing the forecasted outcomes to experimental results obtained from rotational rheometer.