• Textile Coloration and Finishing
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• v.19 no.2
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• pp.14-23
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• 2007

This research article explores the use of phthalocyanine reactive dye on nylon substrate. The effect of factors such as pH, temperature, liquor ratio and alkali addition on level of dye exhaustion, fixation and total fixation efficiency. Low pH, high temperature and low liquor ratio were found to be suitable conditions for maximum % exhaustion values. The effect of sulphatoethylsulphone(SES) and vinylsulphone(VS) form of the dyes on level of dye fixation was also discussed. The optimized exhaustion (%E), fixation(%F) and total fixation efficiency were determined. Modification of the dyeing process with alkali addition displayed that dye fixation(%) increased by alkali addition. Vinylsulphone(VS) moiety of the dye was found to be superior to. maximum fixation (%F). Appropriate predictable empirical models, relatively a new approach in dyeing processes, were developed incorporating interactions effects of temperature, pH and liquor ratio for predicting % exhaustion, fixation and total fixation efficiency. The significance of the mathematical model developed was ascertained using microsoft excel regression(solver) analysis module. High correlation coefficient was obtained (R2=0.9895 for % exhaustion, R2=0.9932 for fixation, R2=0.9965 for total fixation efficiency) for the model which shows prominent prediction capacity of the model for my conditions. The predictable polynomial equations developed from tile experimental results were thoroughly analyzed by ANOVA (Analysis of Variance) statistical concepts.

• Journal of Korean Society of Environmental Engineers
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• v.39 no.2
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• pp.66-73
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• 2017

This study was to evaluate the influential parameters such as intial Cs concentration, reaction temperature, contact time and pH variation of solution on Cs adsorption. Using the experimental data, adsorption kinetics, isotherms and thermodynamic properties were analyzed. The Cs ion adsorption of the zeolite X was effective in the range from pH 5 to 10 and reached equilibrium after 60 minutes. The adsorption kinetics and isotherms of Cs ion with the zeolite X was described well by the pseudo-second-order kinetic and Langmuir isotherm model. The maximum adsorption capacities of Cs ion calculated from Langmuir isotherm model at 293~333 K were from 303.03 mg/g to 333.33 mg/g. It was found that thermodynamic property of Cs ion absorption on the zeolite X was spontaneous and endothermic reaction. The experimental data were fitted a second-order polynomial equation by the multiple regression analysis. The values of the dependent variable calculated by this best fitted model equation were in very good agreement with the experimentally obtained values.

• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.148-159
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• 2018

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.

• Journal of Food Hygiene and Safety
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• v.26 no.1
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• pp.25-30
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• 2011

In this study, predictive mathematical models were developed to predict the kinetics of Listeria monocytogenes growth in the mixed fresh-cut vegetables, which is the most popular ready-to-eat food in the world, as a function of temperature (4, 10, 20 and $30^{\circ}C$). At the specified storage temperatures, the primary growth curve fit well ($r^2$=0.916~0.981) with a Gompertz and Baranyi equation to determine the specific growth rate (SGR). The Polynomial model for natural logarithm transformation of the SGR as a function of temperature was obtained by nonlinear regression (Prism, version 4.0, GraphPad Software). As the storage temperature decreased from $30^{\circ}C$ to $4^{\circ}C$, the SGR decreased, respectively. Polynomial model was identified as appropriate secondary model for SGR on the basis of most statistical indices such as mean square error (MSE=0.002718 by Gompertz, 0.055186 by Baranyi), bias factor (Bf=1.050084 by Gompertz, 1.931472 by Baranyi) and accuracy factor (Af=1.160767 by Gompertz, 2.137181 by Baranyi). Results indicate L. monocytogenes growth was affected by temperature mainly, and equation was developed by Gompertz model (-0.1606+$0.0574^*Temp$+$0.0009^*Temp^*Temp$) was more effective than equation was developed by Baranyi model (0.3502-$0.0496^*Temp$+$0.0022^*Temp^*Temp$) for specific growth rate prediction of L.monocytogenes in the mixed fresh-cut vegetables.

• Food Science and Preservation
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• v.10 no.3
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• pp.320-325
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• 2003

This study was conducted to optimize sugaring process of yam for development of new snack product and enhancement acceptability. Three variables by five level central composite design and response surface methodology were used to determine optimum conditions for sugaring time, temperature and concentration. Optimization of the process was conducted using the combination of the moisture content, solid content, color and rehydration ratio. The regression polynomial model was suitable (P>0.05) model by Lack-of-Fit analysis with highly significant. To optimize the process, based on surface response and contour plots, superimposing the individual contour plots for the response variables. The optimum conditions for this process were 5.5 hours and 58％ at 40$^{\circ}C$ under the optimum of restricted variables as moisture content was 66 to 70, solid content was 25 to 30％, L value was above 75, a value was -2.1 to -2.4, b value was above 5 and rehydration ratio was 200 to 250.

• Journal of Korean Society of Forest Science
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• v.101 no.1
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• pp.53-61
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• 2012

This study developed a growth model for Larix leptolepis based on the WLS model to examine the effects of climate change on them. The site index was chosen as the dependent variable and location, weather, and edaphic factor were chosen as independent variables. Simulations were performed under three A1B climate change scenarios with the temperature ranging from $-3.3^{\circ}C$ to $+3.3^{\circ}C$. The simulation results showed that the site index decreased with peak at $-0.8^{\circ}C$. The decrease level of the site index by region was also analyzed. Each scenario, site index has decreased mostly but some region was increased. When the temperature increased up to $3^{\circ}C$, site index was decreased to everywhere.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.2
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• pp.9-17
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• 2022

To take into account of the increasing speed of next generation high-speed trains, a new design code for the traffic safety of railway bridges is required. To solve dynamic responses of the bridge, this research offers a numerical analyses of PSC (Pre-stressed Concrete) box girder bridge, which is most representative of all the bridges on Gyungbu high-speed train line. This model takes into account of the inertial mass forces by the 38-degree-of-freedom and interaction forces as well as track irregularities. Our numerical analyses analyze the maximum vertical deflection and DAF (Dynamic Amplification Factor) between simple span and two-span continuous bridges to show the dynamic stability of the bridge. The third-order polynomial regression equations we use predict the maximum vertical deflections depending on varying running speeds of the train. We also compare the vertical deflections at several cross-sectional positions to check the influence of running speeds and the maximum irregularity at a longitudinal level. Moreover, our model analyzes the influence lines of vertical deflection accelerations of the bridge to evaluate traffic safety.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.36 no.2
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• pp.37-49
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• 2024

To date, numerous empirical formulas have been proposed through hydraulic model experiments to predict the wave breaker index, including wave height and depth of wave breaking, due to the inherent complexity of generation mechanisms. Unfortunately, research on the characteristics of wave breaking and the prediction of the wave breaker index for gravel beaches has been limited. This study aims to forecast the wave breaker index for gravel beaches using representative linear-based machine learning techniques known for their high predictive performance in regression or classification problems across various research fields. Initially, the applicability of existing empirical formulas for wave breaker indices to gravel seabeds was assessed. Various linear-based machine learning algorithms were then employed to build prediction models, aiming to overcome the limitations of existing empirical formulas in predicting wave breaker indices for gravel seabeds. Among the developed machine learning models, a new calculation formula for easily computable wave breaker indices based on the model was proposed, demonstrating high predictive performance for wave height and depth of wave breaking on gravel beaches. The study validated the predictive capabilities of the proposed wave breaker indices through hydraulic model experiments and compared them with existing empirical formulas. Despite its simplicity as a polynomial, the newly proposed empirical formula for wave breaking indices in this study exhibited exceptional predictive performance for gravel beaches.

• Korean Journal of Food Science and Technology
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• v.22 no.5
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• pp.497-502
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• 1990

The effect of sugar concentration, immersion time and temperature on water loss, solid gain or loss, and sugar molality of potatoes during osmotic concentration was analyzed by a response surface methodology (RSM), and those values were predicted by using a second degree polynomial regression model. Effect of osmotic concentration and blanching on vitamin C retention of air dried potatoes (6% MC: wet basis) was also evaluated. The most significant factor was sugar concentration for water loss, solid gain or loss, sugar molality, rate parameter and retention of vitamin C. Second and third factors were immersion time and temperature respectively. Water loss and solid gain were rapid in the first 10 min and then levelled off. A 44.6% of water loss was observed during osmotic concentration using a sugar solution $(60\;Brix,\;80^{\circ}C$) with 20 min of immersion time. Dried potatoes after osmotic concentration had higher vitamin C content than dried potatoes after blanching. Optimum regions for osmotic concentration process of potatoes were $60-70^{\circ}C$ of immersion temperature, 60 Brix of sugar solution and 16-20 min of immersion time based on above 30% of water loss and 50% of vitamin C retention.

• Food Science and Preservation
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• v.10 no.3
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• pp.314-319
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• 2003

This study was conducted to the optimize salting process of eggplant for development new product and enhancement quality for export. Three variables by five level central composite design and response surface methodology were used to determine optimum conditions for salting time, temperature and salt concentration. Optimization of the process was conducted using the combination of the moisture content, salinity and color of surface and inside of salted eggplant. The regression polynomial model was suitable (P>0.05) by Lack-of-Fit analysis with highly significant. To optimize the process, based on surface response and contour plots, the individual contour plots of the response variables were superimposed. The optimum conditions for this process were 6 days and 15$^{\circ}C$ at 30％ concentration under the optimum of restricted variables as moisture content was below 84％, salinity was below 14％, L and b value of surface were 10 to 20 and below 0, L value and b value of inside were 70 to 75 and 16 to 18.