• Journal of Gifted/Talented Education
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• v.23 no.5
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• pp.671-695
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• 2013

This study provides an illustrative example of using the multivariate generalizability theory. Specifically, it investigates relative effects of each error source, and finds optimal measurement conditions for the number of items within each content domain that maximizes the reliability-like coefficients, such as a generalizability coefficient and an index of dependability. The method is based on teacher recommendation letters and self-introduction letters, using an analytic scoring method in the context of selection of mathematically gifted students by observation and nomination. This study analyzed data from the 2011 academic year in the science education institute for the gifted, which is attached to the university located in the Seoul metropolitan area. It should be noted that the optimal scoring structures of this study are not generalizable to other selection instruments. However, the methodology applied in this study can be utilized to find optimal measurement conditions for the number of raters, the number of content domains, and the number of items in other selection instruments self-developed by many institutions including: the education institutes for the gifted at provincial offices of education, gifted classes, and the science education institutes for the gifted attached to universities in general. In addition, the methodology will provide bases for making informed decisions in selection instruments of the gifted based on measurement traits.

• Geophysics and Geophysical Exploration
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• v.19 no.2
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• pp.76-83
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• 2016

Migration image can be distorted due to reflected waves in the source and receiver wavefields when discontinuities of input velocity model exist in seismic imaging. To remove reflected waves coming from layer interfaces, it is a common practice to smooth the velocity model for migration. If the velocity model is smoothed, however, the subsurface image can be distorted because the velocity changes around interfaces. In this paper, we attempt to minimize the distortion by reducing reflection energy in the source and receiver wavefields through acoustic impedance homogenization. To make acoustic impedance constant, we define fake density model and use it for migration. When the acoustic impedance is constant over all layers, the reflection coefficient at normal incidence becomes zero and the minimized reflection energy results in the improvement of migration result. To verify our algorithm, we implement the reverse-time migration using cell-based finite-difference method. Through numerical examples, we can note that the migration image is improved at the layer interfaces with high velocity contrast, and it shows the marked improvement particularly in the shallow part.

• Polymer(Korea)
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• v.36 no.2
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• pp.177-181
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• 2012

This study presents the preparation of double emulsions in a poly(dimethylsiloxane) (PDMS)-based microfluidic device. To improve the wettability of hydrophilic continuous phase onto a hydrophobic PDMS microchannel, the surface was modified with 3-(trimethoxysilyl) propyl methacrylate (TPM) and then sequentially reacted with acrylic acid monomer solution, which produced selective covalent bonding between acrylic acids and methacrylate groups. For the proof of selective surface modification, tolonium chloride solution was used to identify the modified region and we confirmed that the approach was successfully performed. When water containing 0.5% w/w sodium dodecyl sulfate and 1% w/w Span80 with hexadecane were loaded into the selectively modified microfluidic channels, we can produce stable double emulsion. Based on the spreading coefficients, we predict the morphology of double emulsions. Our proposed method efficiently produces monodisperse double emulsions having 48.5 ${\mu}m$(CV:1.6%) core and 65.1 ${\mu}m$ (CV:1.6%) shell. Furthermore, the multiple emulsions having different numbers of core were easily prepared by simple control of flow rates.

• Korean Journal of Materials Research
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• v.18 no.10
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• pp.547-551
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• 2008

Crack-free joining of $Si_3N_4\;and\;Al_2O_3$ using 15 layers has been achieved by a unique approach introducing Sialon polytypoids as a functionally graded materials (FGMs) bonding layer. In the past, hot press sintering of multilayered FGMs with 20 layers of thickness $500{\mu}m$ each has been fabricated successfully. In this study, the number of layers for FGM was reduced to 15 layers from 20 layers for optimization. For fabrication, model was hot pressed at 38 MPa while heating up to $1700^{\circ}$, and it was cooled at $2^{\circ}$/min to minimize residual stress during sintering. Initially, FGM with 15 layers had cracks near 90 wt.% 12H / 10 wt.% $Al_2O_3$ and 90 wt.% 12H/10 wt.% $Si_3N_4$ layers. To solve this problem, FEM (finite element method) program based on the maximum tensile stress theory was applied to design optimized FGM layers of crack free joint. The sample is 3-dimensional cylindrical shape where this has been transformed to 2-dimensional axisymmetric mode. Based on the simulation, crack-free FGM sample was obtained by designing axial, hoop and radial stresses less than tensile strength values across all the layers of FGM. Therefore, we were able to predict and prevent the damage by calculating its thermal stress using its elastic modulus and coefficient of thermal expansion. Such analyses are especially useful for FGM samples where the residual stresses are very difficult to measure experimentally.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.6
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• pp.1044-1053
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• 2022

Time domain reflectometry (TDR) is a diagnostic technique to evaluate the physical integrity of cable and finds application in leak detection and localization of piping system. In this study, a cable-shaped leak detection sensor was proposed using the TDR technique for monitoring leakage detection of ship's engine room seawater piping system. The cable sensor was developed using a twisted pair arrangement and wound by an absorbent material. The availability and performance of the sensor for leak detection and localization were evaluated on a lab-scale pipeline set up. The developed sensor was installed onto the pipes and flanges of the lab-scale set up and various TDR waveforms were acquired and analyzed according to the dif erent variables including the number of twists and sheath thickness. The result indicated that the twisted cable sensor was able to produce clear and smooth signal as compared to the TDR sensor with a parallel arrangement. The optimal number of twist was determined to be above 10 per the unit length. The optimal diameter of sheath thickness that results in the desired sensitivity was determined to be ranging from 80% up to 120% of the diameter of the conductor. The linear regression analysis for estimation of leak localization was carried out to estimate the location of the leakage, and the result was a determination coefficient of 0.9998, indicating a positive relationship with the actual leakage point. The proposed TDR based leak detection method appears to be an effective method for monitoring leakage of ship's seawater piping system.

• Journal of Radiation Protection and Research
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• v.47 no.3
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• pp.158-166
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• 2022

Background: The effects of radiation on the health of radiation workers who are constantly susceptible to occupational exposure must be assessed based on an accurate and reliable reconstruction of organ-absorbed doses that can be calculated using personal dosimeter readings measured as H_p(10) and dose conversion coefficients. However, the data used in the dose reconstruction contain significant biases arising from the lack of reality and could result in an inaccurate measure of organ-absorbed doses. Therefore, this study quantified the biases involved in organ dose reconstruction and calculated the bias-corrected H_p(10)-to-organ-absorbed dose coefficients for the use in epidemiological studies of Korean radiation workers. Materials and Methods: Two major biases were considered: (a) the bias in H_p(10) arising from the difference between the dosimeter calibration geometry and the actual exposure geometry, and (b) the bias in air kerma-to-H_p(10) conversion coefficients resulting from geometric differences between the human body and slab phantom. The biases were quantified by implementing personal dosimeters on the slab and human phantoms coupled with a Monte Carlo method and considered to calculate the bias-corrected H_p(10)-to-organ-absorbed dose conversion coefficients. Results and Discussion: The bias in H_p(10) was significant for large incident angles and low energies (e.g., 0.32 for right lateral at 218 keV), whereas the bias in dose coefficients was significant for the posteroanterior (PA) geometry only (e.g., 0.79 at 218 keV). The bias-corrected H_p(10)-to-organ-absorbed dose conversion coefficients derived in this study were up to 3.09- fold greater than those from the International Commission on Radiological Protection publications without considering the biases. Conclusion: The obtained results will aid future studies in assessing the health effects of occupational exposure of Korean radiation workers. The bias-corrected dose coefficients of this study can be used to calculate organ doses for Korean radiation workers based on personal dose records.

• Steel and Composite Structures
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• v.47 no.1
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• pp.91-102
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• 2023

To study the evaluation standard and control limit of mortar filling layer void length, in this paper, the train sub-model was developed by MATLAB and the track-bridge sub-model considering the mortar filling layer void was established by ANSYS. The two sub-models were assembled into a train-track-bridge coupling dynamic model through the wheel-rail contact relationship, and the validity was corroborated by the coupling dynamic model with the literature model. Considering the randomness of fastening stiffness, mortar elastic modulus, length of mortar filling layer void, and pier settlement, the test points were designed by the Box-Behnken method based on Design-Expert software. The coupled dynamic model was calculated, and the support vector regression (SVR) nonlinear mapping model of the wheel-rail system was established. The learning, prediction, and verification were carried out. Finally, the reliable probability of the amplification coefficient distribution of the response index of the train and structure in different ranges was obtained based on the SVR nonlinear mapping model and Latin hypercube sampling method. The limit of the length of the mortar filling layer void was, thus, obtained. The results show that the SVR nonlinear mapping model developed in this paper has a high fitting accuracy of 0.993, and the computational efficiency is significantly improved by 99.86%. It can be used to calculate the dynamic response of the wheel-rail system. The length of the mortar filling layer void significantly affects the wheel-rail vertical force, wheel weight load reduction ratio, rail vertical displacement, and track plate vertical displacement. The dynamic response of the track structure has a more significant effect on the limit value of the length of the mortar filling layer void than the dynamic response of the vehicle, and the rail vertical displacement is the most obvious. At 250 km/h - 350 km/h train running speed, the limit values of grade I, II, and III of the lengths of the mortar filling layer void are 3.932 m, 4.337 m, and 4.766 m, respectively. The results can provide some reference for the long-term service performance reliability of the ballastless track-bridge system of HRS.

• Korean Chemical Engineering Research
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• v.59 no.2
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• pp.209-218
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• 2021

First principle-based modeling studies have been performed to improve the heat exchange efficiency of ORV and optimize operation, but the heat transfer coefficient of ORV is an irregular system according to time and location, and it undergoes a complex modeling process. In this study, FNN, LSTM, and AutoML-based modeling were performed to confirm the effectiveness of data-based modeling for complex systems. The prediction accuracy indicated high performance in the order of LSTM > AutoML > FNN in MSE. The performance of AutoML, an automatic design method for machine learning models, was superior to developed FNN, and the total time required for model development was 1/15 compared to LSTM, showing the possibility of using AutoML. The prediction of NG and seawater discharged temperatures using LSTM and AutoML showed an error of less than 0.5K. Using the predictive model, real-time optimization of the amount of LNG vaporized that can be processed using ORV in winter is performed, confirming that up to 23.5% of LNG can be additionally processed, and an ORV optimal operation guideline based on the developed dynamic prediction model was presented.

• Journal of Applied Biological Chemistry
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• v.64 no.2
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• pp.177-184
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• 2021

While veterinary antibiotics are used only in a part of the dose administered, the rest are excreted as urine or feces. Residual antibiotics enter the adjacent agricultural environments by spraying manure-based composts on farmlands and lead to secondary pollution. Therefore, it is necessary to develop the technique for post management such as regulatory levels of antibiotics in the agricultural environments. This study was conducted to compare by different matrices the amount of residual antibiotics such as tetracyclines and sulfonamides, which are known to be frequently used in Korea and to practice risk assessment by different antibiotics in soils before and after application of composts. Pre-treatment with modified typical method using buffer and solid phase extraction showed the recovery of composts and soils was more than 70% at ppb level and the limits of detection were 0.13-0.46 and 0.05-0.25 ㎍/kg, respectively. Analysis of manure-based composts revealed concentrations from 5.38 to 196.0 ㎍/kg for tetracyclines, from below the detection of limit (BDL) to 259.0 ㎍/kg for sulfonamides. In case of agricultural soils, residual concentrations of selected veterinary antibiotics were ranged 0.30-53.3 ㎍/kg, BDL-4.16 ㎍/kg respectively and the concentration level of tetracyclines, which had higher soil distribution coefficient (Kd) values, was higher than that of sulfonamides. There was a difference in human risk assessment by different antibiotics in soil before and after application of composts. But, it was indicated that detection values of all of 5 antibiotics were very safe on the basis that Hazard Quotient was safe below 1.

• Korean Chemical Engineering Research
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• v.54 no.5
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• pp.621-629
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• 2016

Recently, the researches on quantitative structure activity relationship (QSAR) for describing toxicities or activities of chemicals based on chemical structural characteristics have been widely carried out in order to estimate the toxicity of chemicals in multiuse facilities. Because the toxicity of chemicals are explained by various kinds of molecular descriptors, an important step for QSAR model development is how to select significant molecular descriptors. This research proposes a statistical selection of significant molecular descriptors and a new QSAR model based on partial least square (PLS). The proposed QSAR model is applied to estimate the logarithm of partition coefficients (log P) of 130 polychlorinated biphenyls (PCBs) and lethal concentration ($LC_{50}$) of 14 PCBs, where the prediction accuracies of the proposed QSAR model are compared to a conventional QSAR model provided by OECD QSAR toolbox. For the selection of significant molecular descriptors that have high correlation with molecular descriptors and activity information of the chemicals of interest, correlation coefficient (r) and variable importance of projection (VIP) are applied and then PLS model of the selected molecular descriptors and activity information is used to predict toxicities and activity information of chemicals. In the prediction results of coefficient of regression ($R^2$) and prediction residual error sum of square (PRESS), the proposed QSAR model showed improved prediction performances of log P and $LC_{50}$ by 26% and 91% than the conventional QSAR model, respectively. The proposed QSAR method based on computational toxicology can improve the prediction performance of the toxicities and the activity information of chemicals, which can contribute to the health and environmental risk assessment of toxic chemicals.