• Preventive Nutrition and Food Science
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• v.10 no.2
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• pp.111-117
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• 2005

Radical scavenging activity of water and methanol extracts of mulberry juice and cake prepared from mulberry fruit (Morus spp.) was evaluated using three in vitro assay systems. Mulberry fruits were homogenized with $0.5\%$ trifluoroacetic acid (TFA) in distilled water, filtered with cheeze-cloth and centrifuged to yield mulberry juice and cake. Mulberry juice was evaporated and solubilized in $0.5\%$ TFA in distilled water or $0.5\%$ TFA in $80\%$ aqueous methanol, followed by filtration and evaporation to obtain water (WMJ) and methanol (MMJ) extracts of mulberry juice. Mulberrry cake also was extracted with the above same solvents, and thereby finally obtaining water (WMC) and methanol (MMC) extracts of mulberry cake. Among four extracts, the MMC showed the most potent radical scavenging activity against DPPH radical $(IC_{50}=167.45\;{\mu}g/mL)$, and superoxide $(IC_{50}=36.18\;{\mu}g/mL)$ and hydroxyl radicals $(IC_{50}=467.08\;{\mu}g/mL)$. The WMC also exhibited stronger radical scavenging activity than those of two other mulberry juice extract, WMJ and MMJ. Meanwhile, the MMJ exerted stronger three radical scavenging activity than the WMJ. Total phenolic content of the water and MeOH extracts from mulberry cake was higher than that of the water and MeOH extracts from mulberry juice. Thus, these results suggest that the extracts of mulberry cake with high dietary phenolics may be useful potential source of natural antioxidant as radical scavenger.

• Journal of KIISE:Software and Applications
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• v.32 no.9
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• pp.847-852
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• 2005

Over the last decade, numerous complexity measurement techniques for Object-Oriented (OO) software system have been proposed for managing the effects of OO codes. These techniques may be based on source code analysis such as WMC (Weighted Methods per Class) and LCOM (Lack of Cohesion in Methods). The techniques are limited to count the number of functions (C++). However. we suggested a new weighted method that checks the number of parameters, the return value and its data type. Then we addressed an effective complexity measurement technique based on the weight of class interfaces to provide guidelines for measuring the class complexity of OO codes in reverse engineering. The results of this research show that the proposed complexity measurement technique ECC(Enhanced Class Complexity) is consistent and accurate in C++ environment.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.73-78
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• 2002

In this paper we develop a method for constructing a Bayesian HPD (highest probability density) interval of a ratio of two multivariate normal generalized variances. The method gives a way of comparing two multivariate populations in terms of their dispersion or spread, because the generalized variance is a scalar measure of the overall multivariate scatter. Fully parametric frequentist approaches for the interval is intractable and thus a Bayesian HPD(highest probability densith) interval is pursued using a variant of weighted Monte Carlo (WMC) sampling based approach introduced by Chen and Shao(1999). Necessary theory involved in the method and computation is provided.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10b
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• pp.520-522
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• 1998

기존의 소프트웨어에서 함수나 클래스와 같은 부분을 추출해 재사용하는 방법은 비용면에서 상당히 효율적이다. 그러나 이러한 방법을 사용한 기존의 연구들은 추출해 낸 부분이 표준 컴포넌트의 형태를 갖추지 못해서 일반적인 개발환경에서 활용되지 못하고 있다. 본 연구에서는 기존의 객체지향 소프트웨어에서 재사용성 높은 부분을 추출하여 이를 표준 컴포넌트로 변환하는 재사용 컴포넌트 생성 시스템을 설계 및 구현하였다. 구현환경 Solaris에서 자바를 이용하였고 대상 컴포넌트 모델은 자바빈즈이다. 본 연구에서는 추출되는 컴포넌트의 높은 재사용성을 보장하기 위하여 객체지향 재사용 메트릭스 WMC, LCOM과 독립성을 적용한다. 특히, 생성된 컴포넌트는 자신의 서브컴포넌트 추출을 지원함으로써 높은 재사용성과 유사성을 보장하고, 개발환경에서 적절히 활용될 수 있다.

• Research in Mathematical Education
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• v.17 no.3
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• pp.199-220
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• 2013

The main objective of this study is to explore the relationship between psychological factors (i.e. math anxiety, attention, attitude, Working Memory Capacity (WMC), and Field dependency) and students' mathematics problem solving based on Revised Bloom Taxonomy. A sample of 169 K11 school girls were tested on (1) The Witkin's cognitive style (Group Embedded Figure Test). (2) Digit Span Backwards Test. (3) Mathematics Anxiety Rating Scale (MARS). (4) Modified Fennema-Sherman Attitude Scales. (5) Mathematics Attention Test (MAT), and (6) Mathematics questions based on Revised Bloom Taxonomy (RBT). Results obtained indicate that the effect of these items on students mathematical problem solving is different in each cognitive process and level of knowledge dimension.

• Research in Mathematical Education
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• v.13 no.3
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• pp.181-195
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• 2009

Students' approaches to mathematical problem solving vary greatly with each other. The main objective of the current study was to compare students' performance with different thinking styles (divergent vs. convergent) and working memory capacity upon mathematical problem solving. A sample of 150 high school girls, ages 15 to 16, was studied based on Hudson's test and Digit Span Backwards test as well as a math exam. The results indicated that the effect of thinking styles and working memory on students' performance in problem solving was significant. Moreover, students with divergent thinking style and high working memory capacity showed higher performance than ones with convergent thinking style. The implications of these results on math teaching and problem solving emphasizes that cognitive predictor variable (Convergent/Divergent) and working memory, in particular could be challenging and a rather distinctive factor for students.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.4
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• pp.182-193
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• 2011

In order to compare the spatio-temporal variation pattern of sea surface temperature (SST) in Korea's Southern areas of the sea according to a regional scale, this study has selected the winter and summer seasons for 31 years (1980~2010) in a period aspect and selected three areas of the sea such as the Western areas of the sea (region B) and Eastern areas of the sea (region C) around Jeju Island in addition to overall Southern areas of the sea (region A) in regional aspect. The regression analysis was applied to find out a temporal variation pattern of SST, and the weighted mean center (WMC) of SST as well as analysis of a standard deviational ellipse (SDE) was respectively applied. As a result of regression analysis of SST, it showed a rising long-term trend for all two seasons in three regions. However, though the average SST for 31 years was all similar in three regions in the summer season, the region C appeared more highly than region B in the winter season. The spatial variation pattern of SST for two seasons showed that it is respectively different from each other in three regions. The spatial variation pattern of SST appeared as E-W direction in region A, SE-NW direction in region B and SW-NE direction in region C. In addition, the relationship between the location of the WMC of SST and the average SST showed correlation in regions A and B in the winter season, whereas it appeared that there is no correlation in region C. Accordingly, it can be known that the regional scale should be considered in case of analysis of spatio-temporal variation patterns of SST.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.4
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• pp.91-100
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• 2004

In this paper, we propose the method that analyzes the depth of object using 2 images in the disparity algorithm. It also presents the design and implementation of the proposed method for a real time processing. The proposed system uses the vertical strip structure for calculating similar pixel numbers for the processing and converts the depth of object into gray scale images in order to be displayed on various display devices. The hardware using the proposed method is operating with 30 frames/sec and verified by using the Altera APEX 20K1000EBC652-3. The proposed method is also Implemented into It by using the Hynix 0.35${\mu}{\textrm}{m}$ CB35 ASIC library and 256PQFP package.

• The Journal of the Korean Society for Microbiology
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• v.21 no.1
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• pp.63-71
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• 1986

The antifungal activities of amphotericin B, 5-fluorocytosine, and ketoconazole in combination of amphotericin B/ketoconazole and 5-fluorocytosine/ketoconazole were determined against 42 strains of Candida spp. isolated from oral cavity. Among 42 strains of Candida species, 36 strains of Candida albicans, 2 strains of Candida parapsilosis and Candida tropicalis 1 strain of Candida krusei and Candida stellatoidea were identified. The minimum inhibitory concentrations(MICs) of amphotericin B, 5-fluorocytosine and ketoconazole for these strains were ranged from 0.05-1.56 mcg/ml, 12.5->100.0 mcg/ml and 0.2-50.0 mcg/ml. In all of the experimental strains, amphotericin B had the greatest antifungal activity on a dilution basis. When a microtiter checkerboard technique was used 5-fluorocytosine acted synergistically with ketoconazole against all strains, whereas amphotericin B has a reduced effect. The killing curve experiments with on strain of Candida albicans WMC-85024 demonstrated that the combination of amphotericin B/ketoconazole and 5-fluorocytosine/ketoconazole produced a decrease in number of colony forming unit of >3 logs in 72 hours.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.751-770
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• 2018

Web applications are indispensable in the software industry and continuously evolve either meeting a newer criteria and/or including new functionalities. However, despite assuring quality via testing, what hinders a straightforward development is the presence of defects. Several factors contribute to defects and are often minimized at high expense in terms of man-hours. Thus, detection of fault proneness in early phases of software development is important. Therefore, a fault prediction model for identifying fault-prone classes in a web application is highly desired. In this work, we compare 14 machine learning techniques to analyse the relationship between object oriented metrics and fault prediction in web applications. The study is carried out using various releases of Apache Click and Apache Rave datasets. En-route to the predictive analysis, the input basis set for each release is first optimized using filter based correlation feature selection (CFS) method. It is found that the LCOM3, WMC, NPM and DAM metrics are the most significant predictors. The statistical analysis of these metrics also finds good conformity with the CFS evaluation and affirms the role of these metrics in the defect prediction of web applications. The overall predictive ability of different fault prediction models is first ranked using Friedman technique and then statistically compared using Nemenyi post-hoc analysis. The results not only upholds the predictive capability of machine learning models for faulty classes using web applications, but also finds that ensemble algorithms are most appropriate for defect prediction in Apache datasets. Further, we also derive a consensus between the metrics selected by the CFS technique and the statistical analysis of the datasets.