• Korean Journal of Plant Taxonomy
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• v.49 no.3
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• pp.224-239
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• 2019

Species identification for the Korean tribe Desmodieae was conducted using the DNA barcoding genes rbcL, matK (from chloroplast DNA) and ITS (from nuclear ribosomal DNA). A total of 25 taxa (n = 75) in five genera were sequenced, and neighbor-joining trees were constructed using different combinations of DNA barcodes. When comparing these phylogenetic trees, a tree with all loci combined (rbcL + matK + ITS) showed the highest rate of identification success (72%). On this tree, two subtribes and five genera within the tribe were supported as monophyletic. In the Desmodiinae clade, Desmodium and Hylodesmum were more closely related to each other than to Ohwia. In the Hylodesmum clade, H. oldhamii was found to be a sister to H. podocarpum complex, and all taxa within the complex were identified successfully. Subsp. fallax, regarded as a variety of subsp. oxyphyllum, is closely clustered with subsp. podocarpum. Although var. mandshuricum has been regarded as a synonym of var. oxyphyllum, this taxon is supported as a distinct variety. For the Lespedezinae clade, all species of Kummerowia were monophyletic, while nine of 16 Lespedeza taxa were identified successfully. In particular, the resolution of Macrolespedeza (28.5%) was lower than that of Junceae (77.8%). Among the Lespedeza taxa, L. cuneata was distinguishable from L. lichiyuniae, despite morphological similarities. It has been suggested that both L. maritima and L. inschanica are hybrids. The former is thought to be an independent species. While it is difficult to determine whether the latter originated via hybridization, this study showed that it is closely related to L. juncea.

• Journal of Life Science
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• v.29 no.9
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• pp.1002-1009
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• 2019

Early life stress (ELS) increases the risk of depression. ELS may be involved in the susceptibility to subsequent stress exposure during adulthood. We investigated whether epigenetic mechanisms of p11 promoter affect the vulnerability to chronic unpredictable stress (CUS) induced by the maternal separation (MS). Mice pups were separated from their dams (3 hr/day from P1-P21). When the pups reached adulthood, we applied CUS (daily for 3 weeks). The levels of hippocampal p11 expression were analyzed by quantitative real-time PCR. The levels of acetylated and methylated histone H3 at p11 promoter were measured by chromatin immunoprecipitation. Depression-like behavior was measured by the forced swimming test (FST). The MS and CUS group exhibited significant decreases in p11 mRNA level and the MS plus CUS group had a greater reduction in this level than the CUS group. The MS plus CUS group also resulted in greater reduction in H3 acetylation than the CUS group. This reduction was associated with an upregulation of histone deacetylase 5. Additionally, the MS plus CUS group showed a greater decrease in H3K4met3 level and a greater increase in H3K27 met3 level than the CUS group. Consistent with the reduction of p11 expression, the MS plus CUS group displayed longer immobility times in the FST compared to the control group. Mice exposed to MS followed by CUS had much greater epigenetic alterations in the hippocampus compared to adult mice that only experienced CUS. ELS can exacerbate the effect of stress exposure during adulthood through histone modification of p11 gene.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.5
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• pp.572-580
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• 2019

In view of the numerous discussions on global environmental issues, policies have been implemented to limit emissions in the field of marine transport, which accounts for a major part of international trade. In this study, a ship's emissions were calculated by applying the engine load factor to determine the total quantity of emissions based on the ship's speed reduction. For ships entering and leaving the Busan North Port from 1 January to 31 December 2017, emissions were calculated and analyzed based on the ship's type and its speed in the reduced speed zone (RSZ), which was set to 20 nautical miles. The comparison of the total amount of emissions under all situations, such as cruising, maneuvering, and hotelling modes revealed that the vessels that generated the most emissions were container ships at 76.1 %, general cargo ships at 7.2 %, and passenger ships at 6.8 %. In the cruising and maneuvering modes, general cargo ships discharged a lesser amount of emission in comparison with passenger ships; however, in the hotelling mode, the general cargo ships discharged a larger amount of emission than passenger ships. The total emissions of nitrogen oxides (NOx), sulphur oxides (SOx), particulate matter (PM), and volatile organic compounds (VOC), were 49.4 %, 45 %, 4 %, and 1.6 %, respectively. Furthermore, the amounts of emission were compared when ships navigated at their average service speed, 12, 10, and 8 knots in the RSZ, respectively. At 12 knots, the reduction in emissions was more than that of the ships navigating at their average service speed by 39 % in NOx, 40 % in VOC, 42 % in PM, and 38 % in Sox. At 10 knots, the emission reductions were 52 %, 54 %, 56 %, and 50 % in NOx, VOC, PM, and Sox, respectively. At 8 knots, the emission reductions were 62 %, 64 %, 67 %, and 59 % in NOx, VOC, PM, and Sox, respectively. As a result, the emissions were ef ectively reduced when there was a reduction in the ship's speed. Therefore, it is necessary to consider limiting the speed of ships entering and leaving the port to decrease the total quantity of emissions.

• Korean Journal of Heritage: History & Science
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• v.52 no.3
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• pp.38-55
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• 2019

The purpose of this study is to clarify the historical flow of baekdong alloy's usage according to the alloying materials mentioned in document records. For this purpose, we first overviewed the use of copper as a base material for white copper alloys and other types of copper alloys. Baekdong is an alloy of copper and other metals and is currently defined as an alloy of copper and nickel. However, depending on the research subjects and time of the scholars, baekdong may be defined as a metal with over a certain percentage of tin added to copper, or as an alloy of tin, zinc, and lead with copper. There is disagreement regarding the interpretation of this term. Baekdong, which started to appear in the literature of the Three Kingdoms Period, has been steadily seen through the Goryeo and Chosun Dynasties to the modern period. It has been used in various ways, according to each age and culture, from the symbol of the office to trading goods, daily life goods, and money. In the literature, baekdong's alloying material is not only copper and nickel, which are currently defined as alloys, but it is the same in that copper is used as the base metal of the alloy, although it varies slightly from generation to generation. In addition to copper, tin, zeolite, and emerald, zinc and lead also appeared. It was found that baekdong, which means alloy, and baekdong, which means white metal, were mixed. Nickel, which is the alloy material of baekdong as it is currently defined, is a metal with a relatively high discovery time and is widely used as a material for modern industrial fields. Nickel was introduced into Korea at the end of the Joseon Dynasty, but its use is not known in detail. In this study, we examined the acceptance and use of nickel-based baekdong in articles of modern newspapers and in statistical data. Based on the experience of craftsmen, we estimated the period when nickel-based alloys were used in crafts. Material is a direct factor in the development and deterioration of technology, and the development of technology is the basis for the changing of civilizations and cultures. In this context, this study was to investigate baekdong with the material of alloys as a starting point.

• Korean Journal of Poultry Science
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• v.46 no.3
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• pp.161-171
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• 2019

Four-week-old male Korean native chicks (KNC) were assigned to 3 groups with 6 replicates (8 birds/replicate) in each group: a basal diet (CON, 100 ppm of Zn), basal diet fortified with 50 ppm of Zn with zinc oxide (ZnO), or basal diet fortified with 50 ppm of Zn with Zn-methionine (ZnM). Immediately after a 4-week-feeding trial, 6 birds per group were used to evaluate the effects of zinc supplements on antioxidant indicators and the mRNA expression of zinc transport genes. The nitrogen components, lipid peroxidation, and total antioxidant status in blood were not influenced by Zn fortified diets. However, the ZnM group showed a significant (P<0.05) increase in uric acid levels than those in the ZnO group. In the small intestine, superoxide dismutase (SOD) and glutathione peroxidase (GPX) activities, and malondialdehyde (MDA) level were unaffected by zinc supplements. The activity of glutathione S-transferase (GST) was significantly (P<0.05) enhanced by Zn-methionine supplementation. In the liver, the activity of GST was significantly (P<0.05) increased by Zn-methionine supplement without affecting SOD, GPX, and MDA levels. With respect to the mRNA expression of zinc transport genes, the ZnM group displayed a strong tendency for increases in intestinal ZnT-1 (P=0.09) and ZnT-5 (P=0.06) levels, compared to those in the CON group. Moreover, the ZnM group showed a tendency (P=0.10) for up-regulation of hepatic metallothionein mRNA as compared with the CON group. In conclusion, the Zn-fortified diet with 50 ppm of Zn-methionine helped to improve GST activity and Zn transport gene expression in the small intestine or liver of KNC.

• The Journal of the Convergence on Culture Technology
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• v.5 no.3
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• pp.271-282
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• 2019

This study was conducted to evaluate physiological activity of Robinia pseudo-acacia leaf and its skin penetration using polymer micelles and skin penetrating peptide. After extraction with Robinia pseudo-acacia using the ethanol and distilled water, various physiological activities were examined. The total concentration of polyphenol compounds was determined to be 47.42 mg/g (ethanol extract), 56.88 mg/g (hydrothermal extract) and DPPH radical scavenging ability at $1,000{\mu}g/mL$ was 44.24% in ethanol extract and it is higher than value(41.50%) in hydrothermal extract. The elastase inhibitory assay showed concentration dependence and elastase inhibition of Robinia pseudo acacia leaf ethanol extract was 54.09%, which was the highest at $500{\mu}g/mL$. In the SOD-like experiments, the concentration-dependent results were showed and the SOD-like activity of the Robinia pseudo-acacia leaf ethanol extract was higher than that of the Robinia pseudo acacia leaf hydrothermal extract at all concentrations. At a concentration of $500{\mu}g/mL$, Robinia pseudo acacia leaf ethanol extract showed the highest SOD-like activity of 76.41%. The tyrosinase inhibition at $20{\mu}g/mL$ was determined to be 56.47% (ethanol extract), 23.05% (hydrothermal extract). In the antimicrobial experiments, the hydrothermal extract had no effect, but ethanol extract represented maximum clear zone of 11.00 mm in Propionbacterium acnes strain and maximum clear zone of 10.50 mm. in Bacillus subtilis strain. To solve the problem of insolubility and to improve skin penetration, PCL-PEG polymer micelles containing Robinia pseudo-acacia leaf ethanol extracts and 1.0% cell permeable peptide, hexa-D-arginine (R6) were successfully prepared with particle size of 108.23 and 126.47 nm and excellent skin permeation effects could be showed.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.82-98
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• 2019

The importance of spatial information is rapidly rising. In particular, 3D spatial information construction and modeling for Real World Objects, such as smart cities and digital twins, has become an important core technology. The constructed 3D spatial information is used in various fields such as land management, landscape analysis, environment and welfare service. Three-dimensional modeling with image has the hig visibility and reality of objects by generating texturing. However, some texturing might have occlusion area inevitably generated due to physical deposits such as roadside trees, adjacent objects, vehicles, banners, etc. at the time of acquiring image Such occlusion area is a major cause of the deterioration of reality and accuracy of the constructed 3D modeling. Various studies have been conducted to solve the occlusion area. Recently the researches of deep learning algorithm have been conducted for detecting and resolving the occlusion area. For deep learning algorithm, sufficient training data is required, and the collected training data quality directly affects the performance and the result of the deep learning. Therefore, this study analyzed the ability of detecting the occlusion area of the image using various image quality to verify the performance and the result of deep learning according to the quality of the learning data. An image containing an object that causes occlusion is generated for each artificial and quantified image quality and applied to the implemented deep learning algorithm. The study found that the image quality for adjusting brightness was lower at 0.56 detection ratio for brighter images and that the image quality for pixel size and artificial noise control decreased rapidly from images adjusted from the main image to the middle level. In the F-measure performance evaluation method, the change in noise-controlled image resolution was the highest at 0.53 points. The ability to detect occlusion zones by image quality will be used as a valuable criterion for actual application of deep learning in the future. In the acquiring image, it is expected to contribute a lot to the practical application of deep learning by providing a certain level of image acquisition.

• Korean Journal of Poultry Science
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• v.46 no.3
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• pp.193-204
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• 2019

This study aimed to investigate the changes in production performance of five strains of Korean native chickens (KNCs), which have been collected and established at the National Institute of Animal Science, Korea, since 1992. A total of 38,026 KNCs were tested and survival rate, body weight, age at first egg-laying, hen-housed egg production, and egg weight was analyzed. The mean survival rates of KNCs were $87.9{\pm}0.8%$, but no significant difference in survival rate between strains and in the annual survival rates of KNCs was observed. The average body weight of KNCs was $1,609.7{\pm}21.3g$ at 150 d. The average body weight of KNC-Black was the highest, whereas KNC-White had the lowest weight. A gradual increase in the annual weight change has been observed in almost all strains after 2004. The average age at first egg-laying was $146.9{\pm}1.1d$ in KNCs where KNC-White was the earliest and KNC-Black was the latest. The age at first egg-laying has increased after 2003, but has gradually decreased after 2008. The average hen-housed egg production at 270 d was $77.3{\pm}1.7$ in KNCs, wherein KNC-Yellowish brown was the highest and KNC-Black was the lowest. The average egg weight at 270 d was $51.2{\pm}0.3g$ in KNCs, indicating that KNC-Black was the heaviest, whereas KNC-White was the lightest. A significant increase in annual egg weight has been observed since 2008. To conclude, the appearance and feather colors of KNCs have improved uniformly, and the body weight has also improved slightly since 2002.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.19-41
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• 2019

According to the rapidly increasing demand for text data analysis, research and investment in text mining are being actively conducted not only in academia but also in various industries. Text mining is generally conducted in two steps. In the first step, the text of the collected document is tokenized and structured to convert the original document into a computer-readable form. In the second step, tasks such as document classification, clustering, and topic modeling are conducted according to the purpose of analysis. Until recently, text mining-related studies have been focused on the application of the second steps, such as document classification, clustering, and topic modeling. However, with the discovery that the text structuring process substantially influences the quality of the analysis results, various embedding methods have actively been studied to improve the quality of analysis results by preserving the meaning of words and documents in the process of representing text data as vectors. Unlike structured data, which can be directly applied to a variety of operations and traditional analysis techniques, Unstructured text should be preceded by a structuring task that transforms the original document into a form that the computer can understand before analysis. It is called "Embedding" that arbitrary objects are mapped to a specific dimension space while maintaining algebraic properties for structuring the text data. Recently, attempts have been made to embed not only words but also sentences, paragraphs, and entire documents in various aspects. Particularly, with the demand for analysis of document embedding increases rapidly, many algorithms have been developed to support it. Among them, doc2Vec which extends word2Vec and embeds each document into one vector is most widely used. However, the traditional document embedding method represented by doc2Vec generates a vector for each document using the whole corpus included in the document. This causes a limit that the document vector is affected by not only core words but also miscellaneous words. Additionally, the traditional document embedding schemes usually map each document into a single corresponding vector. Therefore, it is difficult to represent a complex document with multiple subjects into a single vector accurately using the traditional approach. In this paper, we propose a new multi-vector document embedding method to overcome these limitations of the traditional document embedding methods. This study targets documents that explicitly separate body content and keywords. In the case of a document without keywords, this method can be applied after extract keywords through various analysis methods. However, since this is not the core subject of the proposed method, we introduce the process of applying the proposed method to documents that predefine keywords in the text. The proposed method consists of (1) Parsing, (2) Word Embedding, (3) Keyword Vector Extraction, (4) Keyword Clustering, and (5) Multiple-Vector Generation. The specific process is as follows. all text in a document is tokenized and each token is represented as a vector having N-dimensional real value through word embedding. After that, to overcome the limitations of the traditional document embedding method that is affected by not only the core word but also the miscellaneous words, vectors corresponding to the keywords of each document are extracted and make up sets of keyword vector for each document. Next, clustering is conducted on a set of keywords for each document to identify multiple subjects included in the document. Finally, a Multi-vector is generated from vectors of keywords constituting each cluster. The experiments for 3.147 academic papers revealed that the single vector-based traditional approach cannot properly map complex documents because of interference among subjects in each vector. With the proposed multi-vector based method, we ascertained that complex documents can be vectorized more accurately by eliminating the interference among subjects.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.201-220
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• 2019

Fake news has emerged as a significant issue over the last few years, igniting discussions and research on how to solve this problem. In particular, studies on automated fact-checking and fake news detection using artificial intelligence and text analysis techniques have drawn attention. Fake news detection research entails a form of document classification; thus, document classification techniques have been widely used in this type of research. However, document summarization techniques have been inconspicuous in this field. At the same time, automatic news summarization services have become popular, and a recent study found that the use of news summarized through abstractive summarization has strengthened the predictive performance of fake news detection models. Therefore, the need to study the integration of document summarization technology in the domestic news data environment has become evident. In order to examine the effect of extractive summarization on the fake news detection model, we first summarized news articles through extractive summarization. Second, we created a summarized news-based detection model. Finally, we compared our model with the full-text-based detection model. The study found that BPN(Back Propagation Neural Network) and SVM(Support Vector Machine) did not exhibit a large difference in performance; however, for DT(Decision Tree), the full-text-based model demonstrated a somewhat better performance. In the case of LR(Logistic Regression), our model exhibited the superior performance. Nonetheless, the results did not show a statistically significant difference between our model and the full-text-based model. Therefore, when the summary is applied, at least the core information of the fake news is preserved, and the LR-based model can confirm the possibility of performance improvement. This study features an experimental application of extractive summarization in fake news detection research by employing various machine-learning algorithms. The study's limitations are, essentially, the relatively small amount of data and the lack of comparison between various summarization technologies. Therefore, an in-depth analysis that applies various analytical techniques to a larger data volume would be helpful in the future.