• Journal of Environmental Health Sciences
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• v.45 no.5
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• pp.443-456
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• 2019

Objectives: Children are exposed to various environmental pollutants through contact with children's products. We investigated the KC mark, certification number, and contained substances labeled on children's products through market research and collected the toxicological data on these substances. Methods: The environmentally hazardous substances labeled on children's products (n=6576), including toys (n=2812), personal care products (n=2212), stationary/books (n=1333), and playground equipment (n=219) were examined. For the components that could be identified by CAS number, toxicological data on oral, inhalation, and dermal routes, cancer slope factor, and reference dose were collected. Results: Among the investigated products, KC marks or certification numbers were found for 4557 products (69.3%). Except for cosmetics and cleansers, the material information was labeled on most of the products. The frequency of labeling substance information in toys and stationary/books was low since this information could be omitted if KC certification was obtained. In the target products, 617 substances were identified by CAS number, and polypropylene, acrylonitrile butadiene styrene, and polyester were the most frequently displayed. Chronic toxicity data was found for only 32.4% of individual components, and information on toxicity through the dermal route was also highly limited. Conclusion: Our study suggested that labeling guidelines should be required to identify the environmentally hazardous substances contained in children's products. In addition, the toxicological data on many ingredients in children's products were insufficient. The data gap for toxicity data should be filled for future risk assessment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.9
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• pp.1336-1339
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• 2012

In this paper, we focus on solving the classification problem by using semisupervised learning strategy. Traditional classifiers are constructed based on labeled data in supervised learning. Labeled data, however, are often difficult, expensive or time consuming to obtain, as they require the efforts of experienced human annotators. Unlabeled data are significantly easier to obtain without human efforts. Thus, we use AdaBoost algorithm with SVM-KNN classifier to apply semisupervised learning problem and improve the classifier performance. Experimental results on both artificial and UCI data sets show that the proposed methodology can reduce the error rate.

• The KIPS Transactions:PartD
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• v.19D no.2
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• pp.147-150
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• 2012

While LDA is a supervised dimension reduction method which finds projective directions to maximize separability between classes, the performance of LDA is severely degraded when the number of labeled data is small. Recently semi-supervised dimension reduction methods have been proposed which utilize abundant unlabeled data and overcome the shortage of labeled data. However, matrix computation usually used in statistical dimension reduction methods becomes hindrance to make the utilization of a large number of unlabeled data difficult, and moreover too much information from unlabeled data may not so helpful compared to the increase of its processing time. In order to solve these problems, we propose an ensemble approach for semi-supervised dimension reduction. Extensive experimental results in text classification demonstrates the effectiveness of the proposed method.

• Journal of Nutrition and Health
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• v.32 no.4
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• pp.369-375
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• 1999

Hyperthroidism in known to alter the activity of a number of enzymes involved in the catabolism of histidine to CO2. 10-Formyltetrahydrofolate dehydrogenase(EC 1.5, 1.6, 10-formyl-THE dehydrogenase) catalyzes the NADP-dependent conversion of 10-formyltetrahydrofolate to tetrahydrofolate and CO2. In previous studies, 10-formyl-THF dehydrogenase purified from rat and pig liver was coidentified with the cytosolic folate-binding protein. In this study, we investigated the effects of feeding thyroid powder (TP) and thiouracil(TU) on the folate-binding properties of 10-formyl-THE dehydrogenase, the uptake of an injected dose of [3H] folate, and the metabolism of labeled folate to pteroylopoly-${\gamma}$-glutamate in rat liver. The initial hepatic uptake(24hr) of the labeled folate dose was higher in TU-rats and slightly higher in TP-rats in controls. With longer time periods, decreased hepatic uptake of labeled folate was observed in TP-animals compared to euthroid animals, and high levels of hepatic uptake of labeled folate were maintained in TU-animals. This data shows that high levels of thyroid hormone decreased the retention of folate in rat liver. Folate polygutamate chain length was shorter in TU-rats than controls, which suggests that thyroid states do not affect the ability to synthesize pteroylpolyglutamates and that folate polyglutamate might be modulated by altered folate pool size. The ability of 10-formyl-THE dehydrogenase to bind folate in rat liver was similar in both TP-and TU-rats although dehydrogenase activity was changed by thyroid sates.

• Nutrition Research and Practice
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• v.8 no.3
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• pp.241-248
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• 2014

The accuracy and noninvasive nature of the doubly labeled water (DLW) method makes it ideal for the study of human energy metabolism in free-living conditions. However, the DLW method is not always practical in many developing and Asian countries because of the high costs of isotopes and equipment for isotope analysis as well as the expertise required for analysis. This review provides information about the theoretical background and practical aspects of the DLW method, including optimal dose, basic protocols of two-and multiple-point approaches, experimental procedures, and isotopic analysis. We also introduce applications of DLW data, such as determining the equations of estimated energy requirement and validation studies of energy intake.

• The Korean Journal of Physiology and Pharmacology
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• v.5 no.1
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• pp.9-17
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• 2001

It has been demonstrated that multipotent neuronal progenitor cells can be isolated from the developing or adult CNS and proliferated in vitro in response to epidermal growth factor. The present study was undertaken to investigate the differentiation of neuronal progenitor cells after transplantation into the neonatal rat forebrain striatum. Primary cultured progenitor cells were labeled with 3,3'-dioctadecycloxacarbonyl- amine perchlorate (DiO). DiO labeled progenitor cells were implanted into neonatal rat striatum. Implanted DiO labeled progenitor cells were differentiated into astrocytes and GABAergic neurons. These results suggest that implanted progenitor cells can be differentiated into neurons in host forebrain striatum. In addition, our data show that DiO labeling is a useful technique for tracing implanted progenitor cells.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2801-2816
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• 2016

Owing to their low scalability, weak support on big data, insufficient data collaborative analysis and inadequate situational awareness, the traditional methods fail to meet the needs of the security data analysis. This paper proposes visualization methods to fuse the multi-source security data and grasp the network situation. Firstly, data sources are classified at their collection positions, with the objects of security data taken from three different layers. Secondly, the Heatmap is adopted to show host status; the Treemap is used to visualize Netflow logs; and the radial Node-link diagram is employed to express IPS logs. Finally, the Labeled Treemap is invented to make a fusion at data-level and the Time-series features are extracted to fuse data at feature-level. The comparative analyses with the prize-winning works prove this method enjoying substantial advantages for network analysts to facilitate data feature fusion, better understand network security situation with a unified, convenient and accurate mode.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.2
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• pp.69-85
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• 2006

Transductive Support Vector Machine(TSVM) is one of semi-supervised learning algorithms which exploit the domain structure of the whole data by considering labeled and unlabeled data together. Although it was proposed several years ago, there has been no efficient algorithm which can handle problems with more than hundreds of training examples. In this paper, we propose an efficient branch-and-bound algorithm which can solve large-scale TSVM problems with thousands of training examples. The proposed algorithm uses two bounding techniques: min-cut bound and reduced SVM bound. The min-cut bound is derived from a capacitated graph whose cuts represent a lower bound to the optimal objective function value of the dual problem. The reduced SVM bound is obtained by constructing the SVM problem with only labeled data. Experimental results show that the accuracy rate of TSVM can be significantly improved by learning from the optimal solution of TSVM, rather than an approximated solution.

• Journal of Korean Academy of Nursing Administration
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• v.7 no.3
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• pp.473-485
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• 2001

This study is intended to develop a reliable and appropriate instrument of the clinical nursing education. This research consisted of 4 steps. First step is construction of the content validity by 1 Korean literature professor and 1 teaching professor in Ga Chon Gil College, the pilot study for the content validity by 14 professors and survey with four points Likert Scale, which includes from the point 'strongly valid' to the point of 'strongly non-valid', by 113 head nurses who guide and evaluate the students in clinical practice. The third step is the test of validity and reliability of the preliminary evaluation tool. The fourth step is the test of validity and reliability of the developmental evaluation tool. The data were collected from Sep. 10th, 2001 to Sep. 28th, 2001. This study was analyzed by SPSS PC+ for descriptive statistics, factor analysis and Cronbach's Co-efficient Alpha of the collected data. The results of these analysis are like as follows. 1. Evaluation tool of Clinical practice consists of 16 items including four categories : factor 1 was labeled 'desirable attitude'(5 items), factor 2 was labeled 'correctly judgement and nursing problem solving'(4 items), factor 3 was labeled 'adaptive ability of nursing knowledge and skill'(4 items), factor 4 was labeled 'desirable human relationship'(3 items) and these contributed 71.992% of the variance in the total score. 2. Cronbach's Co-efficient Alpha for internal consistency was .9128 for the total 16 items. For further research, it need to develop a variable and reliable instrument of the student self-evaluation and instrument that based on community.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6B
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• pp.331-337
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• 2005

The optical Internet is considered as a feasible solution for transporting huge amount of traffic volume in the future Internet. Among optical switching technology for the optical Internet, OBS becomes one of the most promoting solution. Recently, a lebeled OBS(LOBS) architecture is considered for an efficient control on OBS network. Given that a data burst may contain few thousands of IP packets, a single loss of data burst results in a serious throughput degradation in LOBS network. In this paper, we improve the performance of LOBS network by introducing the burst congestion control mechanism. More specifically, the OBS router at the network core detects the network congestion by measuring the loss probability of burst control packet. The OBS router at the network edge reduces the burst generation according to the network condition repored by the OBS router at the network core. Through the simulations, it is shown that the proposed congestion control mechanism can reduce the burst loss probability and improve the LOBS network throughput.