• Journal of The Korean Society of Grassland and Forage Science
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• v.34 no.4
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• pp.277-282
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• 2014

Nutritive value analysis of feed is very important for the growth of livestock, and ensures the efficiency of feeds as well as economic status. However, general laboratory analyses require considerable time and high cost. Near-infrared reflectance spectroscopy (NIRS) is a spectroscopic technique used to analyze the nutritive values of seeds. It is very effective and less costly than the conventional method. The sample used in this study was a corn kernel and the partial least square regression method was used for evaluating nutrient composition, digestibility, and energy value based on the calibration equation. The evaluation methods employed were the coefficient of determination ($R^2$) and the root mean squared error of prediction (RMSEP). The results showed the moisture content ($R^2_{val}=0.97$, RMSEP=0.109), crude protein content ($R^2_{val}=0.94$, RMSEP=0.212), neutral detergent fiber content ($R^2_{val}=0.96$, RMSEP=0.763), acid detergent fiber content ($R^2_{val}=0.96$, RMSEP=0.142), gross energy ($R^2_{val}=0.82$, RMSEP=23.249), in vitro dry matter digestibility ($R^2_{val}=0.68$, RMSEP=1.69), and metabolizable energy (approximately $R^2_{val}$ >0.80). This study confirmed that the nutritive components of corn kernels can be predicted using near-infrared reflectance spectroscopy.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.spc
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• pp.108-114
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• 2008

Near infrared spectroscopy (NIRS) is a rapid and accurate analytical method for determining the composition of agricultural products and feeds. The applicability of near infrared reflectance spectroscopic method was tested to determine the color value (L, a, b) of green tea. A total of 162 green tea calibration samples and 82 validation samples were used for NIRS equation development and validation, respectively. In the developed NIRS equation for analysis of the color value (L, a, b), the most accurate equation for L value was obtained at 2, 8, 6, 1 (2nd derivative, 8 nm gap, 6 points smoothing, and 1pointsecond smoothing), and for a, and b value were obtained at 1, 4, 4, 1 (1st derivative, 4 nm gap, 4points smoothing, and 1 point second smoothing) math treatment condition with SNVD (Standard Normal Variate and Detrend) scatter correction method and entire spectrum ($400{\sim}2,500\;nm$) by using MPLS (Modified Partial Least Squares) regression. Validation results of these NIRS equations showed very low bias (L: 0.005%, a: 0.003%, b: -0.013%) and standard error of prediction (SEP, L: 0.361%, a: 0.141%, b: 0.306%) as well as high coefficient of determination ($R^2$, L: 0.905, a: 0.986, b: 0.931). Therefore, these NIRS equations can be applicable and reliable for determination of color value (L, a, b) of green tea, and NIRS method could be used as a mass screening technique for breeding programs and quality control in the green tea industry.

• Computers and Concrete
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• v.34 no.4
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• pp.477-487
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• 2024

The in-situ stresses of concrete are an essential index for assessing the safety performance of concrete structures. Conventional methods for pore pressure release often face challenges in selecting drilling ring parameters, uncontrollable stress release, and unstable detection accuracy. In this paper, the parameters affecting the results of the concrete ring hole stress release method are cross-combined, and finite elements are used to simulate the combined parameters and extract the stress release values to establish a training set. The GridSearchCV function is utilized to determine the optimal hyperparameters. The mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) are used as evaluation indexes to train the gradient boosting decision tree (GBDT) algorithm, and the other three common algorithms are compared. The RMSE of the GBDT algorithm for the test set is 4.499, and the R² of the GBDT algorithm for the test set is 0.962, which is 9.66% higher than the R² of the best-performing comparison algorithm. The model generated by the GBDT algorithm can accurately calculate the concrete in-situ stresses based on the drilling ring parameters and the corresponding stress release values and has a high accuracy and generalization ability.

• The Korean Journal of Emergency Medical Services
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• v.19 no.2
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• pp.51-69
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• 2015

Purpose: This study aimed to identify the effects of job insecurity and job engagement on turnover intention of paramedics who work in emergency medical institutions. Methods: From October 14 to 28, 2014, data were collected by structured questionnaires from 171 paramedics who were working in emergency medical institutions. The data were analyzed by using SPSS/WIN 21.0. Results: Of the 171 subjects, 57.3% were temporary employees, of whom 87.5% were working in regional emergency medical centers. The mean scores were 3.19 for job insecurity, 4.58 for job engagement, and 3.28 for turnover intention. The correlation between the variables showed that the higher the job insecurity of the participants, the higher their turnover intention (r = .397, p <.001). Moreover, the higher their job engagement, the lower their turnover intention (r = -.354, p <.001). The variable that most significantly affected turnover intention was job insecurity. The coefficient of determination ($R^2$) of job insecurity and job engagement was 24.3%. Conclusion: A law should be enacted to involve paramedics as required personnel for emergency medical institutions in order to enhance the quality of emergency medical services and provide prompt and professional emergency medical services to emergency patients.

• Journal of Environmental Science International
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• v.18 no.4
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• pp.355-367
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• 2009

Comparison study on the land surface temperatures, which are calculated from four different algorithms for MODIS data, was carried out and the characteristics of each algorithm on land surface temperature estimation were also analysed in this study. Algorithms, which are well used for various satellite data analysis, in the comparisons are proposed by Price, Becker and Li, Ulivieri et al., and Wan. Verification of estimated land surface temperature from each algorithm is also performed using observation based regression data. The coefficient of determination ($R^2$) for daytime land surface temperature estimated from Wan's algorithm is higher than that of another algorithms at all seasons and the value of $R^2$ reach on 0.92 at spring. Although $R^2$ for Ulivieri's algorithm is slightly lower than that for Wan's algorithm, the variation pattern of land surface temperature for two algorithms are similar. However, the difference of estimated values among four algorithms become small at the region of high land surface temperature.

• Food Science and Biotechnology
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• v.18 no.6
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• pp.1430-1434
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• 2009

The present study used the liquid extraction pretreatment method and developed a liquid chromatographyultraviolet detector (LC-UV) for the simultaneous determination of 14 sulfonamides (SAs) residues in porcine and chicken muscle. Linearity within a range of $50-150\;{\mu}g/kg$ was obtained with the correlation coefficient ($r^2$) of 0.9673-0.9997. The mean recovery of SAs was 55.9-109.7% (relative standard deviations; RSDs 1.7-17.3%) in porcine muscle and 52.8-112.4% (RSDs 2.3-16.9%) in chicken muscle. The limits of detection (LODs) and limits of quantification (LOQs) were 2-32 and $7-96\;{\mu}g/kg$ in porcine muscle, and 4-32 and $13-97\;{\mu}g/kg$ in chicken muscle, respectively. These values were lower than the maximum residue limits (MRLs) established by the European Union. The sum of all SAs residues present should be less than $100\;{\mu}g/kg$.

• Magazine of the Korean Society of Agricultural Engineers
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• v.41 no.5
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• pp.86-92
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• 1999

Effluent concentration estimation equations for wetland system were developed throught statistical analysis of treatment wetland experimental data. Existin g empirical equations were reviewed for thier accuracy with experimental data, and compared with the estimatin equations. About 70 experimental data sets were used for multiple regression, and variables include influent concentration, hydraulic loading rate, average daily air temperature , and plant coverage. The estimatin equations developed for BOD5 , SS ,T-P, and T-N predicted effluent concentrations moderately well, and coefficient fo determination ($R^2$) for them was 0.74 , 0.60, 0.59 and 0.58 respectively. The equations obtained from same data but excluding plant coverage showed relatively lower $R^2$ than the former case, and it was 0.66, 0.52, 0.41 and 0.57 respectively. The EPA, WPCF , and Kadlec and Knight equations worked poorly and $R^2$ for them was significantly lower than the estimation equation developed in the study. The reason might be that the existing equations were oversimplified that they did ot include important parameters such as air temperature and plant coverage. Therefore, developing reasonable estimation equations from experiment under realistic condition is highly recommended rather than using exiting estimation equations.

• Journal of the Korean GEO-environmental Society
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• v.9 no.3
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• pp.17-28
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• 2008

In this study, the residual strength prediction models were proposed by measuring various residual strength according to pit characteristics for metallic water pipes such as cast iron pipe (CIP), ductile iron pipe (DIP), and steel pipe (SP). The exponential prediction model was better fitted to measured residual tensile strength for CIP. In case of DIP and SP, the prediction model using loss of strength was more exactly predicted compared with other model types. The fracture toughness were averagely $40.46kgf/mm^2{\sqrt{mm}}$ for CIP, $85.27kgf/mm^2{\sqrt{mm}}$ for DIP, and $92.27kgf/mm^2{\sqrt{mm}}$ for SP, the determination coefficient ($R^2$) of between measured residual tensile strength and predicted values for residual strength prediction models using fracture toughness was estimated from 0.44 to 0.86. Especially, the proposed residual tensile strength prediction models were applied for the verification and reliability to CIPs and DIPs at 14 sites. The determination coefficient ($R^2$) between measured residual tensile strength and predicted values was estimated from 0.76 to 0.78. Therefore it was thought that the proposed residual tensile strength models could help to support resonable and economical decision of rehabilitation/replacement.

• KSBB Journal
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• v.28 no.2
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• pp.65-73
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• 2013

This study aimed to investigate the enzymatic hydrolysis of mannitol using Viscozyme$^{(R)}$ L, Celluclast$^{(R)}$ 1.5 L, Saczyme$^{(R)}$, Novozym$^{(R)}$, Fungamyl$^{(R)}$ 800 L, Driselase$^{(R)}$ Basidiomycetes sp., and Alginate Lyase, and to optimize of reaction conditions for production of reducing sugar. Response surface methodology (RSM) based on central composite rotatable design was used to study effects of the independent variables such as enzyme (1-9% v/w), reaction time (10-30 h), pH (3.0-7.0) and reaction temperature ($30-70^{\circ}C$) on production of reducing sugar from mannitol. The coefficient of determination ($R^2$) of $Y_1$ (yield of reducing sugar by Viscozyme$^{(R)}$ L) and $Y_3$ (yield of reducing sugar by Saczyme$^{(R)}$) for the dependent variable regression equation was analyzed as 0.985 and 0.814. And the p-value of $Y_1$ and $Y_3$ showing 0.000 and 0.001 within 1% (p < 0.01), respectively, was very significant. The optimum conditions for production of reducing sugar with Viscozyme$^{(R)}$ L were 9.0 % (v/w) amount of enzyme, 30.0 hours of reaction time, pH 4.5 and $30.0^{\circ}C$ of reaction temperature, and those with Saczyme$^{(R)}$ were 9.0% (v/w) of amount of enzyme dosage, 30.0 h of reaction time, pH 7.0 and $30.0^{\circ}C$ of reaction temperature, consequently, the predicted reducing sugar yields were 22.5 and 27.9 mg/g-mannitol, respectively.

• Advances in nano research
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• v.12 no.5
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• pp.515-527
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• 2022

The application of fibers in concrete obviously enhances the properties of concrete, also the application of natural fibers in concrete is raising due to the availability, low cost and environmentally friendly. Besides, predicting the mechanical properties of concrete in general and shear strength in particular is highly significant in concrete mixture with fiber nanocomposite reinforced concrete (FRC) in construction projects. Despite numerous studies in shear strength, determining this strength still needs more investigations. In this research, Adaptive Neuro-Fuzzy Inference System (ANFIS) have been employed to determine the strength of reinforced concrete with fiber. 180 empirical data were gathered from reliable literature to develop the methods. Models were developed, validated and their statistical results were compared through the root mean squared error (RMSE), determination coefficient (R²), mean absolute error (MAE) and Pearson correlation coefficient (r). Comparing the RMSE of PSO (0.8859) and ANFIS (0.6047) have emphasized the significant role of structural parameters on the shear strength of concrete, also effective depth, web width, and a clear depth rate are essential parameters in modeling the shear capacity of FRC. Considering the accuracy of our models in determining the shear strength of FRC, the outcomes have shown that the R² values of PSO (0.7487) was better than ANFIS (2.4048). Thus, in this research, PSO has demonstrated better performance than ANFIS in predicting the shear strength of FRC in case of accuracy and the least error ratio. Thus, PSO could be applied as a proper tool to maximum accuracy predict the shear strength of FRC.