• Journal of Life Science
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• v.28 no.6
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• pp.708-717
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• 2018

The antioxidant activity and protective effects of a hot water extract from the Stauntonia hexaphylla fruit (WESHF) were investigated in vitro and in vivo. The total polyphenol and flavonoid contents of WESHF were $16.13{\pm}0.27mg$ gallic acid equivalent/g and $4.7{\pm}0.80mg$ catechin equivalent/g, respectively. In addition, the DPPH radical-scavenging activity ($SC_{50}$) and the Oxygen Radical Absorbance capacity of WESHF were $63.62{\pm}4.10{\mu}g/ml$ and $90.63{\pm}5.29{\mu}M$ trolox equivalent/g, respectively. The hepatoprotective effect of WESHF against hydrogen peroxide-induced oxidative damage was investigated. $H_2O_2$-induced liver damage on HepG2 cells was prevented by $200{\mu}g/ml$ of WESHF. Furthermore, to investigate the protection mechanism of WESHF on hydrogen peroxide-induced cytotoxicity in HepG2 cells, pre-treatment with $200{\mu}g/ml$ of WESHF significantly attenuated a decrease in the activities of CAT, SOD, GR, and GPx. The hepatoprotective activity of WESHF was evaluated in an experimental model of hepatic damage induced by acetaminophen (APAP). The levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were significantly decreased in the livers of mice treated with 200 mg/kg of WESHF compared to the APAP-treated group. The lipid peroxidation level, which increased after APAP administration, was significantly reduced in the WESHF group. In addition, histological examinations of the liver showed the same protective effect of WESHF treatment. Based on these findings, it is suggested that WESHF has potent hepatoprotective effects, and the mechanism that causes this type of protection could be related to antioxidant pathways.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1211-1224
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• 2023

Clouds cause many difficult problems in observing land surface phenomena using optical satellites, such as national land observation, disaster response, and change detection. In addition, the presence of clouds affects not only the image processing stage but also the final data quality, so it is necessary to identify and remove them. Therefore, in this study, we developed a new cloud detection technique that automatically performs a series of processes to search and extract the pixels closest to the spectral pattern of clouds in satellite images, select the optimal threshold, and produce a cloud mask based on the threshold. The cloud detection technique largely consists of three steps. In the first step, the process of converting the Digital Number (DN) unit image into top-of-atmosphere reflectance units was performed. In the second step, preprocessing such as Hue-Value-Saturation (HSV) transformation, triangle thresholding, and maximum likelihood classification was applied using the top of the atmosphere reflectance image, and the threshold for generating the initial cloud mask was determined for each image. In the third post-processing step, the noise included in the initial cloud mask created was removed and the cloud boundaries and interior were improved. As experimental data for cloud detection, CAS500-1 L2G images acquired in the Korean Peninsula from April to November, which show the diversity of spatial and seasonal distribution of clouds, were used. To verify the performance of the proposed method, the results generated by a simple thresholding method were compared. As a result of the experiment, compared to the existing method, the proposed method was able to detect clouds more accurately by considering the radiometric characteristics of each image through the preprocessing process. In addition, the results showed that the influence of bright objects (panel roofs, concrete roads, sand, etc.) other than cloud objects was minimized. The proposed method showed more than 30% improved results(F1-score) compared to the existing method but showed limitations in certain images containing snow.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.1-17
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• 2017

The deep learning framework is software designed to help develop deep learning models. Some of its important functions include "automatic differentiation" and "utilization of GPU". The list of popular deep learning framework includes Caffe (BVLC) and Theano (University of Montreal). And recently, Microsoft's deep learning framework, Microsoft Cognitive Toolkit, was released as open-source license, following Google's Tensorflow a year earlier. The early deep learning frameworks have been developed mainly for research at universities. Beginning with the inception of Tensorflow, however, it seems that companies such as Microsoft and Facebook have started to join the competition of framework development. Given the trend, Google and other companies are expected to continue investing in the deep learning framework to bring forward the initiative in the artificial intelligence business. From this point of view, we think it is a good time to compare some of deep learning frameworks. So we compare three deep learning frameworks which can be used as a Python library. Those are Google's Tensorflow, Microsoft's CNTK, and Theano which is sort of a predecessor of the preceding two. The most common and important function of deep learning frameworks is the ability to perform automatic differentiation. Basically all the mathematical expressions of deep learning models can be represented as computational graphs, which consist of nodes and edges. Partial derivatives on each edge of a computational graph can then be obtained. With the partial derivatives, we can let software compute differentiation of any node with respect to any variable by utilizing chain rule of Calculus. First of all, the convenience of coding is in the order of CNTK, Tensorflow, and Theano. The criterion is simply based on the lengths of the codes and the learning curve and the ease of coding are not the main concern. According to the criteria, Theano was the most difficult to implement with, and CNTK and Tensorflow were somewhat easier. With Tensorflow, we need to define weight variables and biases explicitly. The reason that CNTK and Tensorflow are easier to implement with is that those frameworks provide us with more abstraction than Theano. We, however, need to mention that low-level coding is not always bad. It gives us flexibility of coding. With the low-level coding such as in Theano, we can implement and test any new deep learning models or any new search methods that we can think of. The assessment of the execution speed of each framework is that there is not meaningful difference. According to the experiment, execution speeds of Theano and Tensorflow are very similar, although the experiment was limited to a CNN model. In the case of CNTK, the experimental environment was not maintained as the same. The code written in CNTK has to be run in PC environment without GPU where codes execute as much as 50 times slower than with GPU. But we concluded that the difference of execution speed was within the range of variation caused by the different hardware setup. In this study, we compared three types of deep learning framework: Theano, Tensorflow, and CNTK. According to Wikipedia, there are 12 available deep learning frameworks. And 15 different attributes differentiate each framework. Some of the important attributes would include interface language (Python, C ++, Java, etc.) and the availability of libraries on various deep learning models such as CNN, RNN, DBN, and etc. And if a user implements a large scale deep learning model, it will also be important to support multiple GPU or multiple servers. Also, if you are learning the deep learning model, it would also be important if there are enough examples and references.

• Journal of The Korean Association For Science Education
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• v.33 no.4
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• pp.826-839
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• 2013

The purpose of this study is to examine science education researchers' views on what and how much science educational theories would be needed for pre-service science teachers, and to investigate the relationship between their views and the Examination for Appointing Secondary School Science Teachers(EASST). For this study, the views of science education professors on science education theories have been analyzed in terms of their priorities for contributing to the improvement of science teacher competency and literacy. Their views have been compared with proportions of questions related to science education theories of the EASST in terms of what kinds of science education theories have been used for solving each item. As results of this study show, they have perceived that more essential things are needed for the improvement of science teacher competency and literacy including science inquiry process, methods of experimental equipments and tools, laboratory safety, misconception of students, discussion, writing, evaluation of scientific knowledges, and evaluation of scientific inquiry ability other than science philosophy, changes of science curricula, science curricula of foreign countries, Bruner's instructional theory, Karplus's Learning Cycle model, generative learning model, discovery learning model, and Klopfer's taxonomy of educational objectives. There is a higher proportion of questions related to science curriculum and Ausubel's learning theory in the EASST. They are hardly correlated with science education professors' selections of science educational theories for EASST questions. This study advocates the needs of exploring a new method of narrowing down the gap between science educators' opinions and questions of ESSAT in terms of science educaiton theories.

• Journal of Radiation Protection and Research
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• v.40 no.1
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• pp.10-16
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• 2015

Bone changes are common sequela of irradiation in growing animal. The purpose of this study was to establish an experimental model of radiation-induced bone loss in growing mice using micro-computed tomography (${\mu}CT$). The extent of changes following 2 Gy gamma irradiation ($2Gy{\cdot}min^{-1}$) was studied at 4, 8 or 12 weeks after exposure. Mice that received 0.5, 1.0, 2.0 or 4.0 Gy of gamma-rays were examined 8 weeks after irradiation. Tibiae were analyzed using ${\mu}CT$. Serum alkaline phosphatase (ALP) and biomechanical properties were measured and the osteoclast surface was examined. A significant loss of trabecular bone in tibiae was evident 8 weeks after exposure. Measurements performed after irradiation showed a dose-related decrease in trabecular bone volume fraction (BV/TV) and bone mineral density (BMD), respectively. The best-fitting dose-response curves were linear-quadratic. Taking the controls into accounts, the lines of best fit were as follows: BV/TV (%) = $0.9584D^2-6.0168D+20.377$ ($r^2$ = 0.946, D = dose in Gy) and BMD ($mg{\cdot}cm^{-3}$) = $8.8115D^2-56.197D+194.41$ ($r^2$ = 0.999, D = dose in Gy). Body weight did not differ among the groups. No dose-dependent differences were apparent among the groups with regard to mechanical and anatomical properties of tibia, serum ALP and osteoclast activity. The findings provide the basis required for better understanding of the results that will be obtained in any further studies of radiation-induced bone responses.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.167-183
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• 2023

In the recent field of recommendation systems, various studies have been conducted to model sparse data effectively. Among these, GLocal-K(Global and Local Kernels for Recommender Systems) is a research endeavor combining global and local kernels to provide personalized recommendations by considering global data patterns and individual user characteristics. However, due to its utilization of kernel tricks, GLocal-K exhibits diminished performance on highly sparse data and struggles to offer recommendations for new users or items due to the absence of side information. In this paper, to address these limitations of GLocal-K, we propose the GEase-K (Global and EASE kernels for Recommender Systems) model, incorporating the EASE(Embarrassingly Shallow Autoencoders for Sparse Data) model and leveraging side information. Initially, we substitute EASE for the local kernel in GLocal-K to enhance recommendation performance on highly sparse data. EASE, functioning as a simple linear operational structure, is an autoencoder that performs highly on extremely sparse data through regularization and learning item similarity. Additionally, we utilize side information to alleviate the cold-start problem. We enhance the understanding of user-item similarities by employing a conditional autoencoder structure during the training process to incorporate side information. In conclusion, GEase-K demonstrates resilience in highly sparse data and cold-start situations by combining linear and nonlinear structures and utilizing side information. Experimental results show that GEase-K outperforms GLocal-K based on the RMSE and MAE metrics on the highly sparse GoodReads and ModCloth datasets. Furthermore, in cold-start experiments divided into four groups using the GoodReads and ModCloth datasets, GEase-K denotes superior performance compared to GLocal-K.

• Clean Technology
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• v.23 no.3
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• pp.286-293
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• 2017

The goal of this paper was to improve the performance of the adsorbent to remove $H_2S$. Pellet type adsorbents were prepared by using four kinds of materials ($Fe_2O_3$, $Ca(OH)_2$, Activated carbon, $Al(OH)_2)$ for use as a basic carrier. As the results of $H_2S$ adsorption tests, $Fe_2O_3$ and Activated Carbon improved the adsorption performance of $H_2S$ by 1.5 ~ 2 times, and $Ca(OH)_2$ and $Al(OH)_2$ showed no effect on $H_2S$ adsorption performance. Four basic materials were as carriers, and 5 wt% of KI, KOH and $K_2CO_3$ were added on the carriers, respectively. As the results of $H_2S$ adsorption tests, adsorbent containing $K_2CO_3$ showed the best performance. As a result of $H_2S$ adsorption test with varying $K_2CO_3$ content from 5 to 30 wt%, it was confirmed that adsorption performance was increased up to 20 wt% of $K_2CO_3$ and adsorption performance decreased to 30 wt%. The $H_2S$ adsorption performance was modeled by using Thomas model with varying $K_2CO_3$ contents and the results were used for the adsorption tower design. It was shown that the experimental values and the simulated values were in good agreement with the contents range of $K_2CO_3$ up to 20 wt%. Based on these results, it is expected that not only the adsorption performance of $H_2S$ adsorbent is improved but also life time of the adsorbent is increased.

• Clinical and Experimental Pediatrics
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• v.51 no.8
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• pp.874-878
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• 2008

Purpose : This study was undertaken to develop an animal model of periventricular leukomalacia (PVL) induced by in utero clamping of pregnant rat aorta in fetal rats. Methods : A timed pregnanct Sprague-Dawley rat on embryonic day 21 just prior to delivery was sedated and anesthetized, and a Harvard ventilator for small animals was applied. Following laparotomy, the maternal aorta was clamped reversibly for 40 minutes using a surgical clip. The fetal rats were then delivered by Cesarean section, resuscitated if necessary, and reared by a surrogate mother rat until postnatal day 21 to obtain the brain specimen. After systemic perfusion and fixation, $10{\mu}m$ thick serial brain sections were obtained and stained for pathologic examination and assessment of ventriculomegaly. Ventriculomegaly was assessed by the measured ventricle to total brain volume ratio. Results : Eight out of eleven fetal rats (73%) survived in the ischemia group after induction of in utero ischemia by clamping maternal rat aorta, and all ten survived in the control group. Body and brain weights measured at postnatal day 21 were significantly lower in the ischemia group compared to the control group. In pathologic findings, significant ventriculomagaly ($3.67{\pm}1.21%$ vs. $0.23{\pm}0.06%$) was observed in the ischemia group compared to the control group; although cystic lesion was not observed, mild (n=6) and moderate (n=2) rerefaction of the brain tissue was observed. Conclusion : A fetal rat model of PVL induced by in utero clamping of pregnant rat aorta was developed.

• Journal of the Korean Society of Food Science and Nutrition
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• v.40 no.3
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• pp.401-408
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• 2011

Type 2 diabetes mellitus (NIDDM) is a metabolic disorder that is characterized by high blood glucose in the context of insulin resistance and relative insulin deficiency. In order to control the type 2 diabetes mellitus, anti-hyperglycemic effect of Triticum aestivum L. water extracts (TAWE) was investigated in 7 week old male diabetic C57BL6/J-ob/ob mice. For the experiments, the diabetic animal model ob/ob mice and non-diabetic animal model lean mice were divided into 3 groups: non-treatment control group (Control), and two experimental groups orally treated with 25 or 100 mg/kg/day dose of TAWE (TAWE-25 and TAWE-100, respectively). The lean mice were used as the non-diabetic normal control. TAWE was orally administrated for 6 weeks and the diabetic clinical markers, including blood glucose level, body weight, organs weight and insulin level were determined. The oral administration of TAWE-100 in ob/ob diabetic mice significantly decreased blood glucose level (78.4%) and body weight (11.9%) compared with diabetic control group. The weights of organs, including spleen, liver, kidneys, heart and lung were not different among groups, while the treatments of TAWE-100 in ob/ob diabetic mice significantly reduced blood total cholesterol (24.35%) and triglyceride (23.97%) levels compared with the diabetic control group. The levels of serum insulin and glucose tolerance were improved after TAWE-100 treatment in ob/ob diabetic mice. Moreover, the immunohistochemical staining for insulin detection in pancreatic islet $\beta$-cells expressed high level of insulin in TAWE-100 treated ob/ob mice. From the above results, the intake of TAWE may be effective in anti-hyperglycemia by the attenuation of glucose and lipid levels. TAWE-containing diets or drugs may be beneficial for controlling diabetes mellitus type 2 in human.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.