• Structural Engineering and Mechanics
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• v.84 no.2
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• pp.269-283
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• 2022

In this paper, a particular type of all-steel HSS brace members with a locally reduced cross-sectional area was experimentally and numerically investigated. The brace member was strengthened against local buckling with inner and outer boxes in the reduced area. Four single-span braced frames were tested under cyclic lateral loadings. Specimens included a simple steel frame with a conventional box-shaped brace and three other all-steel reduced section buckling-restrained braces. After conducting the experimental program, numerical models of the proposed brace were developed and verified with experimental results. Then the length of the proposed fuse was increased and its effect on the cyclic behavior of the brace was investigated numerically. Eventually, the brace was detailed with a fuse-to-brace length of 30%, as well as the cross-sectional area of the fuse-to-brace of 30%, and the cyclic behavior of the system was studied numerically. The study showed that the proposed brace is stable up to a 2% drift ratio, and the plastic cumulative deformation requirement of AISC (2016) is easily achieved. The proposed brace has sufficient ductility and stability and is lighter, as well as easier to be fabricated, compared to the conventional mortar-filled BRB and all-steel BRB.

• Journal of the Korean Institute of Landscape Architecture
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• v.14 no.1
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• pp.1-13
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• 1986

The purpose of this study is to investigate the effect of temperature control by a shade tree and the lawn area. In this investigation, we find out that artificial-lawn, concerte, and exposed soil are more higher temperature than covered with plant materials. The results of the measurement may to summerized as follows; 1) Low-temperature effects of zoysia japonica is more controlled by condition of growth than leaf length of grass. Surface temperature make 0.7$^{\circ}C$ difference between long grass (15cm), and short grass (5cm), but make 5$^{\circ}C$ difference between good growth grass (230/10$\textrm{cm}^2$) and bad growth grass (80/10$\textrm{cm}^2$). 2) The surface temperature of the lawn area is 40.5$^{\circ}C$ lower on a maxinum than that of the artificial lawn (July 28, 1985). During the day of summer, shade area under the shade tree is 0.9$^{\circ}C$ lower then lawn area surface temperature, 6.9$^{\circ}C$ lower than bad growth lawn, 10.3$^{\circ}C$ lower than exposed soil, and 18$^{\circ}C$ lower than concrete surface temperature. 3) Natural irrigation effect on the surface temperature fluctuation. But this effect is changed by compositions of ground materials and time-lapse. 4) Sunny day is more effective than cloud day. 5) In summer season, surface temperature make a difference compare to temperature of 0.5-1.5m height from ground : Surface temperature is 3.4$^{\circ}C$ lower at the lawn area (11 a.m.), 4.2$^{\circ}C$ lower at the shade area the shade tree, 12.7$^{\circ}C$ higher at the concrete area (3p.m.), 38.8$^{\circ}C$ higher at the artificial lawn (2p.m.) 6) According to compositions of ground materials and season have specific vertical temperature distribution curve. 7) In summer season, temperature distribution of 0.5-1.5m hight at the shade tree is 4.8-5.7$^{\circ}C$ lower than concrete area (noon-3p.m.)

• Journal of Magnetics
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• v.11 no.2
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• pp.66-69
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• 2006

Local magnetization reversal in the exchange-biased NiFe/FeMn and $[Pd/Co]_5/FeMn$ multilayers with in-plane and out-of-plane magnetic anisotropy was achieved by using laser annealing. The local annealed NiFe/FeMn film under the opposite magnetic field shows a magnetoresistance (MR) curve having two symmetric peaks at the positive and negative exchange biasing field (${\pm}H_{ex}$). The intensity of the nucleated MR peak rises as the exposed area extends during the laser annealing process, and the peak disappears under the reverse magnetic field. In the case of [Pd/Co]/FeMn films, the local magnetization reversal increased gradually as the laser power increases. The locally reversed magnetization was restored under the opposite magnetic field.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.1
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• pp.145-150
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• 2008

One of the unresolved questions in articular cartilage biomechanics is the magnitude of the dynamic modulus and tissue compressive strains under physiological loading conditions. The objective of this study was to characterize the dynamic modulus and compressive strain magnitudes of bovine articular cartilage at physiological compressive stress level and loading frequency. Four bovine calf shoulder joints (ages 2-4 months) were loaded in Instron testing system under load control, with a load amplitude up to 800 N and loading frequency of 1 Hz, resulting in peak engineering stress amplitude of ${\sim}5.8\;MPa$. The corresponding peak deformation of the articular layer reached ${\sim}27%$ of its thickness. The effective dynamic modulus determined from the slope of stress versus strain curve was ${\sim}23\;MPa$, and the phase angle difference between the applied stress and measured strain which is equivalent to the area of the hystresis loop in the stress-strain response was ${\sim}8.3^{\circ}$. These results are representative of the functional properties of articular cartilage in a physiological loading environment. This study provides novel experimental findings on the physiological strain magnitudes and dynamic modulus achieved in intact articular layers under cyclical loading conditions.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.305-332
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• 2022

Purpose: As the public interest in entrepreneurship has been highlighted and entrepreneurship policies have been generated, this study is to construct Entrepreneurship Ecosystem (EE) models which have a significant relationship to national entrepreneurship with quantitative analysis. It aims to provide implications to EE policymakers that which national components are effective in cultivating innovative entrepreneurship and validate its EE quality based on quantitative performance goals. Methods: This study utilizes secondary data, categorized under the PESTLE factor from credible international organizations (WB, UNDP, GEM, GEDI, and OECD) to determine significant factors in the quality of the entrepreneurial ecosystem. This paper uses the Multiple Linear Regression (MLR) analysis to select the significant variables contributing to entrepreneurship performance. Using the AUC-ROC performance evaluation method for machine learning MLR results, this paper evaluates the performance of EE models so that it can allow approving EE quality by predicting potential performance. Results: Among nine hypothesis models, MLR analysis examines that the number of the Unicorn company, Unicorn companies' economic value, and entrepreneurship measured as GEI can be reasonable dependent variables to indicate the performance derived from EE quality. Rather than government policies and regulations, the social, finance, technology, and economic variables are significant factors of EE quality determining its performance. By having high Area Under Curve values under AUC-ROC analysis, accepted MLR models are regarded as having high prediction accuracy. Conclusion: Superior EE contributes to the outstanding Unicorn companies, and improvement in macro-environmental components can enhance EE quality.

• Environmental Mutagens and Carcinogens
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• v.21 no.2
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• pp.122-127
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• 2001

4-tert-octylphenol (OP) is a surfactant additive widely used in the manufacture of a variety of detergents and plastic products. Also, OP is known to have estrogenic activity by interacting with development and functions of endocrine system. This study was carried out to obtain toxicokinetic parameters of OP in male Sprague-Dawley rats. Male rats were administered OP, by either single oral (gavage) applications of 50, 100 or 200 mg/kg body weight. or a single intravenous injections of 1, 5 or 10 mg/kg body weight. Blood samples taken at several time intervals after administration were obtained from the femoral artery. Analysis of blood samples for OP was performed by gas chromatography mass spectrometry (GC/MS). The detection limit of OP was 1.9 ng/$m\ell$ at SIM (selected ion monitoring) mode of GC/MS. Calibration curve for analysis of the concentrations of OP in plasma was (OP/butylphenol peak area ratio) = 0.0294 $\times$ (plasma cone.) + 0.028 ($r^2$= 0.9991). The OP plasma concentration was 3921 ng/$m\ell$ immediately after single intravenous application, decreased rapidly within 45 min, and was detectable at low concentration up to 6 hr after application. When administered orally in rats (50, 100 and 200 mg/kg), OP was detected in the blood early after gavage administration, indicating the rapid initial uptake from gastrointestinal tract, with Tmax obtained from 0.67~0.83 hr. Using the AUC (area under the curve) of plasma concentration vs. time, low oral bioavailabilities of 1.2, 5.0 and 5.3% were calculated for the 50, 100 and 200 mg/kg groups, respectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.3
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• pp.1611-1619
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• 2015

This study examined the toughness improvement of a track shoe used as the undercarriage of excavator and bulldozer parts. The excavator is operated under poor conditions, such as the build-up field and quarry. Therefore, the track shoe requires high strength and impact toughness to endure immense shock while at work. The track shoe was made of heat treated boron steel. The sufficient possibility of hardenability with the theoretical Jominy curve for boron steel was confirmed while quenching. The Taguchi orthogonal array experiment method was used to optimize the process variables, such as area reduction ratio and heat treatment conditions (tempering temperature and holding time), to achieve toughness improvement. The toughness of the track shoe increased with increasing area reduction, and a tempering temperature of $210^{\circ}C$ and a tempering time of 80 min are beneficial for improving the toughness of the track shoe.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.4
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• pp.1447-1452
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• 2012

Aim: To evaluate the differential diagnostic potential of lesion stiffness assessed by the sonoelastographic strain index ratio (SR) and elastographic color scoring system (UE) for breast lesions. Materials and Methods: Three hundred and forty two breast masses (158 benign and 184 malignant) from 325 consecutive patients (mean age 44.2 years; range 16-81)who had been scheduled for a sonographically guided core biopsy were examined proposed by Itoh et al, with scoring 1-3=benign and 4-5=malignant. Strain and area ratios of each lesion were calculated within the same machine. Histological diagnosis was used as the reference standard. The area under the curve (AUC) and cut-off point were obtained by receiver operating curve and the cross table Fischer Test was carried out for assessing diagnostic value. Sensitivity, specificity, PPV, NPV, accuracy and false-discovery rates were compared. Results: The mean strain ratios for benign and malignant lesions were 1.87 and 7.9 respectively. (P<0.0001). When a cutoff point of 3.54 was used, SR had a sensitivity of 94.6%, a specificity 94.3%, a PPV of 95.1%, an NPV of 93.7% and an accuracy of 94.4%. The AUC values were 0.90 for the 5 point scoring system (UE) and 0.96 for the strain index ratio. The overall diagnostic performance was SR method was better (P<0.05). Conclusions: Strain ratio measurement could be another effective predictor in elastography imaging besides 5 the point scoring system for differential diagnosis of breast lesions.

• Imaging Science in Dentistry
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• v.48 no.2
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• pp.87-96
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• 2018

Purpose: The present study aimed to evaluate which of the following imaging methods best assessed misfit at the tooth-restoration interface: (1) bitewing radiographs, both conventional and digital, performed using a photostimulable phosphor plate (PSP) and a charge-coupled device (CCD) system; (2) panoramic radiographs, both conventional and digital; and (3) cone-beam computed tomography (CBCT). Materials and Methods: Forty healthy human molars with class I cavities were selected and divided into 4 groups according to the restoration that was applied: composite resin, composite resin with liner material to simulate misfit, dental amalgam, and dental amalgam with liner material to simulate misfit. Radiography and tomography were performed using the various imaging methods, and the resulting images were analyzed by 2 calibrated radiologists. The true presence or absence of misfit corresponding to an area of radiolucency in regions subjacent to the esthetic and metal restorations was validated with microscopy. The data were analyzed using a receiver operating characteristic (ROC) curve, and the scores were compared using the Cohen kappa coefficient. Results: For bitewing images, the digital systems (CCD and PSP) showed a higher area under the ROC curve (AUROC) for the evaluation of resin restorations, while the conventional images exhibited a larger AUROC for the evaluation of amalgam restorations. Conventional and digital panoramic radiographs did not yield good results for the evaluation of resin and amalgam restorations (P<.05). CBCT images exhibited good results for resin restorations(P>.05), but showed no discriminatory ability for amalgam restorations(P<.05). Conclusion: Bitewing radiographs (conventional or digital) should be the method of choice when assessing dental restoration misfit.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.