• Computers and Concrete
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• v.33 no.2
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• pp.147-162
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• 2024

This study presents a random-aggregate mesoscale model integrating the random distribution of the coarse aggerates and the damage mechanics of the mortar and interfacial transition zone (ITZ). This mesoscale model can generate the random distribution of the coarse aggregates according to the prescribed particle size distribution which enables the automation of the current methodology with different coarse aggregates' distribution. The main innovation of this work is to propose the "correction factor" to eliminate the dimensionally dependent mesh sensitivity of the concrete damaged plasticity (CDP) model. After implementing the correction factor through the user-defined subroutine in the randomly meshed mesoscale model, the predicted fracture resistance is in good agreement with the average experimental results of a series of geometrically similar single-edge-notched beams (SENB) concrete specimens. The simulated cracking pattern is also more realistic than the conventional concrete material models. The proposed random-aggregate mesoscale model hence demonstrates its validity in the application of concrete fracture failure and statistical size effect analysis.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.3
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• pp.303-310
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• 2015

The study was conducted to analyze the genetic parameters of somatic cell score (SCS) of Holstein cows, which is an important indicator to udder health. Test-day records of somatic cell counts (SCC) of 305-day lactation design from first to fifth lactations were collected on Holsteins in Korea during 2000 to 2012. Records of animals within 18 to 42 months, 30 to 54 months, 42 to 66 months, 54 to 78 months, and 66 to 90 months of age at the first, second, third, fourth and fifth parities were analyzed, respectively. Somatic cell scores were calculated, and adjusted for lactation production stages by Wilmink's function. Lactation averages of SCS ($LSCS_1$ through $LSCS_5$) were derived by further adjustments of each test-day SCS for five age groups in particular lactations. Two datasets were prepared through restrictions on number of sires/herd and dams/herd, progenies/sire, and number of parities/cow to reduce data size and attain better relationships among animals. All LSCS traits were treated as individual trait and, analyzed through multiple-trait sire models and single trait animal models via VCE 6.0 software package. Herd-year was fitted as a random effect. Age at calving was regressed as a fixed covariate. The mean LSCS of five lactations were between 3.507 and 4.322 that corresponded to a SCC range between 71,000 and 125,000 cells/mL; with coefficient of variation from 28.2% to 29.9%. Heritability estimates from sire models were within the range of 0.10 to 0.16 for all LSCS. Heritability was the highest at lactation 2 from both datasets (0.14/0.16) and lowest at lactation 5 (0.11/0.10) using sire model. Heritabilities from single trait animal model analyses were slightly higher than sire models. Genetic correlations between LSCS traits were strong (0.62 to 0.99). Very strong associations (0.96 to 0.99) were present between successive records of later lactations. Phenotypic correlations were relatively weaker (<0.55). All correlations became weaker at distant lactations. The estimated breeding values (EBVs) of LSCS traits were somewhat similar over the years for a particular lactation, but increased with lactation number increment. The lowest EBV in first lactation indicated that selection for SCS (mastitis resistance) might be better with later lactation records. It is expected that results obtained from these multi-trait lactation model analyses, being the first large scale SCS data analysis in Korea, would create a good starting step for application of advanced statistical tools for future genomic studies focusing on selection for mastitis resistance in Holsteins of Korea.

• Journal of the Korean Geotechnical Society
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• v.35 no.6
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• pp.17-26
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• 2019

In this study, the statistical characteristics of the resistance bias factors were analyzed using a high-quality field load test database, and the total resistance bias factors were estimated considering the soil uncertainty and construction errors for the application of the limit state design of aggregate pier foundation. The MLR model by Bong and Kim (2017), which has a higher prediction performance than the previous models was used for estimating the resistance bias factors, and its suitability was evaluated. The chi-square goodness of fit test was performed to estimate the probability distribution of the resistance bias factors, and the normal distribution was found to be most suitable. The total variability in the nominal resistance was estimated including the uncertainty of undrained shear strength and construction errors that can occur during the aggregate pier construction. Finally, the probability distribution of the total resistance bias factors is shown to follow a log-normal distribution. The parameters of the probability distribution according to the coefficient of variation of total resistance bias factors were estimated by Monte Carlo simulation, and their regression equations were proposed for simple application.

• The Korean Journal of Pesticide Science
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• v.9 no.1
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• pp.26-34
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• 2005

3D-QSAR studies for the fungicidal activities against resistance phytophthora blight (RPC; 95CC7303) and sensitive phytophthora blight (Phytopthora capsici) (SPC; 95CC7105) by a series of new 2-alkoxyphenyl-3-phenylthioisoindoline-1-one derivatives (A & B) were studieded using comparative molecular similarity indices analyses (CoMSIA) methodology. From the based on the results, the two CoMSIA models, R5 and S1: as the best models were derivated. The statistical results of the models showed the best predictability and fitness for the fungicidal activities based on the cross- validated value ($q^2=0.714{\sim}0.823$) and non cross-validated, value ($r^2_{ncv.}=0.918{\sim}0.954$), respectively. The model R5 for fungicidal activity of RPC generated from the field fit alignment and combination of electrostatic field, H-bond acceptor field and LUMO molecular orbital field. The model S1 (or S5) for fungicidal activity of SPC generated from the atom based fit alignment and combination of steric field and HOMO molecular orbital field. The models also shows that inclusion of H-bond acceptor field (A) improved the statistical significance of the models. From the based graphical analyses of CoMSIA contribution maps, it was revealed that the novel selective character for fungicidal activities between the two fungi by modify of X-sub-stituent on the N-phenyl group and R-substituent on the S-phenyl group will be able to achivement.

• Structural Engineering and Mechanics
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• v.47 no.5
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• pp.679-700
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• 2013

Two new predictive design methods are presented in this study. The first is a hybrid method, called neuro-fuzzy, based on neural networks with fuzzy learning. A total of 280 experimental datasets obtained from the literature concerning concentric punching shear tests of reinforced concrete slab-column connections without shear reinforcement were used to test the model (194 for experimentation and 86 for validation) and were endorsed by statistical validation criteria. The punching shear strength predicted by the neuro-fuzzy model was compared with those predicted by current models of punching shear, widely used in the design practice, such as ACI 318-08, SIA262 and CBA93. The neuro-fuzzy model showed high predictive accuracy of resistance to punching according to all of the relevant codes. A second, more user-friendly design method is presented based on a predictive linear regression model that supports all the geometric and material parameters involved in predicting punching shear. Despite its simplicity, this formulation showed accuracy equivalent to that of the neuro-fuzzy model.

• Journal of Information Technology Applications and Management
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• v.26 no.1
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• pp.39-51
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• 2019

As data grows rapidly, the provision of appropriate information needed by individuals has become an area of new services, and customized services which is enabling the analysis of optimal services through collecting, storing, and analyzing personal data are emerging in many fields. However, due to the characteristics of customized services based on various information collected by customers during the use of the service, the problem of privacy infringement is raised at the same time, and many studies are being actively conducted to solve this problem. This study seeks to explore how the customer's in-depth and customized services has an impact on their customers, which has not been derived from quantitative research using the grounded theory methodology. Through this, 84 concepts, 33 subcategories, 13 Categories and paradigm models were derived. In addition, 'Understanding and acceptance of online behavioral advertising (OBA)' was derived as a core category, and finally, acceptance types of OBA were classified into 'positive acceptance type', 'indifferent type', 'calculating type', and 'active resistance type' based on the key categories. This study divides the acceptance types of online behavioral advertising through the emotions and behaviors of the consumers throughout the procedure of online behavioral advertising experiences. In addition to the statistical and quantitative information currently used for providing behavioral advertising, it provides new criteria to reflect the refinement of behavioral advertising and personal tendencies or characteristics.

• Journal of the Society of Naval Architects of Korea
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• v.58 no.4
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• pp.234-242
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• 2021

In this study, we introduce the prediction of brake power for low-speed full ships and container carriers using the linear regression and a machine learning approach. The residual resistance coefficient, wake fraction coefficient, and thrust deduction factor are predicted by regression models using the main dimensions of ship and propeller. The brake power of a ship can be calculated by these coefficients according to the 1978 ITTC performance prediction method. The mean absolute error of the predicted power was under 7%. As a result of several validation cases, it was confirmed that the machine learning model showed slightly better results than linear regression.

• Computers and Concrete
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• v.30 no.6
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• pp.393-407
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• 2022

Due to the enormous cement content, pozzolanic materials, and the use of different aggregates, sustainable controlled low-strength material (CLSM) has a higher material cost than conventional concrete and sustainable construction issues. However, by selecting appropriate materials and formulations, as well as cement and aggregate content, whitethorn costs can be reduced while having a positive environmental impact. This research explores the desire to optimize plastic properties and 28-day unconfined compressive strength (UCS) of CLSM containing powder content from unprocessed-fly ash (u-FA) and recycled fine aggregate (RFA). The mixtures' input parameters consist of water-to-cementitious material ratio (W/CM), fly ash-to-cementitious materials (FA/CM), and paste volume percentage (PV%), while flowability, bleeding, segregation index, and 28-day UCS were the desired responses. The central composite design (CCD) notion was used to produce twenty CLSM mixes and was experimentally validated using MATLAB by an Artificial Neural Network (ANN). Variance analysis (ANOVA) was used for the determination of statistical models. Results revealed that the plastic properties of CLSM improve with the FA/CM rise when the strength declines for 28 days-with an increase in FA/CM, the diameter of the flowability and bleeding decreased. Meanwhile, the u-FA's rise strengthens the CLSM's segregation resistance and raises its strength over 28 days. Using calcareous powder as a substitute for cement has a detrimental effect on bleeding, and 28-day UCS increases segregation resistance. The response surface method (RSM) can establish high correlations between responses and the constituent materials of sustainable CLSM, and the optimal values of variables can be measured to achieve the desired response properties.

• Computers and Concrete
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• v.19 no.1
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• pp.59-68
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• 2017

This study focused on modeling the behavior of the compressive stress using the average strain and ultrasonic test results in concrete. Feed-forward backpropagation artificial neural network (ANN) models were used to compare four types of concrete mixtures with varying water cement ratio (WC), ordinary concrete (ORC) and concrete with short steel fiber-reinforcement (FRC). Sixteen (16) $150mm{\times}150mm{\times}150mm$ concrete cubes were used; each contained eighteen (18) data sets. Ultrasonic test with pitch-catch configuration was conducted at each loading state to record linear and nonlinear test response with multiple step loads. Statistical Spearman's rank correlation was used to reduce the input parameters. Different types of concrete produced similar top five input parameters that had high correlation to compressive stress: average strain (${\varepsilon}$), fundamental harmonic amplitude (A1), $2^{nd}$ harmonic amplitude (A2), $3^{rd}$ harmonic amplitude (A3), and peak to peak amplitude (PPA). Twenty-eight ANN models were trained, validated and tested. A model was chosen for each WC with the highest Pearson correlation coefficient (R) in testing, and the soundness of the behavior for the input parameters in relation to the compressive stress. The ANN model showed increasing WC produced delayed response to stress at initial stages, abruptly responding after 40%. This was due to the presence of more voids for high water cement ratio that activated Contact Acoustic Nonlinearity (CAN) at the latter stage of the loading path. FRC showed slow response to stress than ORC, indicating the resistance of short steel fiber that delayed stress increase against the loading path.

• Geotechnical Engineering
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• v.7 no.2
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• pp.51-66
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• 1991

In the stability analysis of hillside slopes, the roots of vegetation have been considered to act as a soil reinforcement. In order to predict the amount of increase in soil shear resistance, produced by tensile strength of roots that intersect a potential slip surface in hillside slopes, new soil -root interaction models are proposed in this paper. For this purpose, firstly, laboratary teats and in-situ tests wert performed on soil-root systems, and experimental results were compared with a couple of soil-root interaction models which had been proposed by Gray, Waldron, and Wu etc. Based on this comparison, a new soil-root interaction model is proposed. Secondly, a probabilistic soil-root model is proposed based on statistical analysis considering random nature of root distribution, root characteristics, and soil-root interactions. Finally, to examine the effect of this root reinforcement system on stability of hillside slopes, a simple three-dimensional stability analysis was performed, and it was shown that root reinforcement had a significant stabilizing influence on shallow slips rather than deep slips in hillside slopes.