• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.143-150
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• 2019

This study aimed to analyze the keyword related to Health Disparity in Korea through the method of keyword network analysis and to establish a basic database for suggesting ideas for prospective studies in physical education. To achieve the goal, this study crawled co-occured keyword with 'health' and 'disparity' from news casted in 20 different channels. The duration of the news was 3 months, from September 11th, 2018 to December 11th. The results are as follows. First, among the news during recent 3 months, there were 1,383 keyword related to health disparity and this study selected 173 keyword which had co-occured over 3 times. Second, the inclusiveness of the network was 97.674% and the density was .038. Third, analyzing news related to health disparity, 'mortality' was the most co-occured keyword and 'disparity', 'reinforcement', 'the most', 'health', '6 times', 'Seoul', 'half', 'medicine', and 'local' were shown similarly. And common keyword in 4 centrality were 13 keyword. Lastly, by analyzing eigenvector centrality, significantly different result has shown. 'Disparity' was the most co-occured keyword. Based on this result, this study showed the necessity for reinforcing the public physical education in public education system in Korea. In order to achieve it, the field of physical education must look beyond present elite-focused physical education to public physical activity.

• Mycobiology
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• v.46 no.4
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• pp.361-369
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• 2018

The rice blast fungus, Magnaporthe oryzae, is an important pathogen of rice plants. It is well known that genes encoded in the genome have different evolutionary histories that are related to their functions. Phylostratigraphy is a method that correlates the evolutionary origin of genes with evolutionary transitions. Here we applied phylostratigraphy to partition total gene content of M. oryzae into distinct classes (phylostrata), which we designated PS1 to PS7, based on estimation of their emergence time. Genes in individual phylostrata did not show significant biases in their global distribution among seven chromosomes, but at the local level, clustering of genes belonging to the same phylostratum was observed. Our phylostrata-wide analysis of genes revealed that genes in the same phylostratum tend to be similar in many physical and functional characteristics such as gene length and structure, GC contents, codon adaptation index, and level of transcription, which correlates with biological functions in evolutionary context. We also found that a significant proportion of genes in the genome are orphans, for which no orthologs can be detected in the database. Among them, we narrowed down to seven orphan genes having transcriptional and translational evidences, and showed that one of them is implicated in asexual reproduction and virulence, suggesting ongoing evolution in this fungus through lineage-specific genes. Our results provide genomic basis for linking functions of pathogenicity factors and gene emergence time.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.127-136
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• 2021

Real-time human recognition is a challenging task, as the images are captured in an unconstrained environment with different poses, makeups, and styles. This limitation is addressed by generating several facial images with poses, makeup, and styles with a single reference image of a person using Generative Adversarial Networks (GAN). In this paper, we propose deep learning-based human recognition using integration of GAN and Spatial Domain Techniques. A novel concept of human recognition based on face depiction approach by generating several dissimilar face images from single reference face image using Domain Transfer Generative Adversarial Networks (DT-GAN) combined with feature extraction techniques such as Local Binary Pattern (LBP) and Histogram is deliberated. The Euclidean Distance (ED) is used in the matching section for comparison of features to test the performance of the method. A database of millions of people with a single reference face image per person, instead of multiple reference face images, is created and saved on the centralized server, which helps to reduce memory load on the centralized server. It is noticed that the recognition accuracy is 100% for smaller size datasets and a little less accuracy for larger size datasets and also, results are compared with present methods to show the superiority of proposed method.

• Asia-Pacific Journal of Business
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• v.11 no.3
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• pp.1-20
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• 2020

Purpose - This paper shows an unexplored area related to involuntary delisting. Specifically, this research investigates the effect of target firm information asymmetry on the likelihood that the acquirer or newly merged firm will be forcibly delisted post-merger. Design/methodology/approach - The research uses a sample gathered on local US mergers and acquisitions from the Thomson Reuters Securities Data Company (SDC) Platinum Mergers and Acquisitions database. It applies the logistic regression with industry and year effects and corrects the error term using clustering at the industry level. The research also matches the forced delisted firms to control firms based on industry, acquisition completion year, and firm size and then employs a matched sample analysis. Findings - Findings show that M&As between firms where the target firm is opaque and burdened with high information asymmetry issues are likely to be paid for using majority stock and that M&As involving such opaque targets also have a higher likelihood of getting delisted post-merger. Research implications or Originality - Our results are relevant given the very nature of M&As which involve two players: the acquirer and target who both may have different incentives. Acquirers especially have the tendency to suffer losses and even get delisted if they over-pay for or get merged to a poor target which conceals its poor performance evidenced by higher accruals quality.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.5
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• pp.53-58
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• 2020

The purpose of this study is to build a database of Spatial information Bigdata of cities using satellite images and spatial information, and to examine the correlations with the surface temperature. Using architectural structure and usage in building information, DEM and Slope topographical information for constructed with 300 × 300 mesh grids for Busan. The satellite image is used to prepare the Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Bare Soil Index (BI), and Land Surface Temperature (LST). In addition, the building area in the grid was calculated and the building ratio was constructed to build the urban environment DB. In architectural structure, positive correlation was found in masonry and concrete structures. On the terrain, negative correlations were observed between DEM and slope. NDBI and BI were positively correlated, and NDVI was negatively correlated. The higher the Building ratio, the higher the surface temperature. It was found that the urban environment DB could be used as a basic data for urban environment analysis, and it was possible to quantitatively grasp the impact on the architecture and urban environment by adding local meteorological factors. This result is expected to be used as basic data for future urban environment planning and disaster prevention data construction.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.5 no.1
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• pp.51-64
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• 1995

In distributed environments, the DB secure models have been being studied to include the multi-level mechanism which is effective to control access according to the level of the data value. These mechanisms have the problems. The first, it is impossible to maintain the global data which is protected in the multi-level mechanism. The second, the access and the relation of the data is not clear due to the access revocation between the local data and the global's. In this paper, we proposed the mechanism using shema. The mechanism doesn't have the access revocation, and provides the protection of the data and the control to the global data.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.8
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• pp.109-121
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• 2022

The study empirically examines the horizontal spillover effects of foreign direct investment (FDI) on the productivity of Indian pharmaceutical firms. Robust least squares and the Generalized Method of Moments estimators are applied for the firm-level panel data of Indian pharmaceutical companies whose shares were traded on the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE). The information was collected from the Centre for Monitoring Indian Economy (CMIE) Prowess database from 2015 to 2019. Based on the regularity in data availability, the sample firms are limited to 112 companies, 100 of which are domestic firms and 12 international firms. Firms with more than 10 percent foreign equity are classified as FDI firms, while those with less than that are classified as domestic firms. Estimation results show that foreign ownership does not contribute to the productivity of domestic firms. Due to increased competition, the Indian pharmaceutical companies with foreign equity participation are not more productive than local ones. Moreover, the findings reveal a negative and insignificant horizontal spillover effect from FDI on the productivity of domestic enterprises. The absence of horizontal spillovers may be attributable to foreign enterprises' ability to prevent technological outflow to competitors in the same industry.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3962-3973
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• 2022

Several critical heat flux (CHF) correlations including the look-up table in the MARS code have been assessed for the prediction of CHF in a downward-flow narrow rectangular channel. For the assessment, we built an experiment database that covers pressures between 1.01 and 39.0 bar, gap sizes between 1.09 and 6.53 mm, mass fluxes up to 25,772 kg/m²s, and under one-sided and two-sided heating conditions. The results of the assessment showed that the Kaminaga correlation has the best overall prediction compared to others. However, because the correlation uses global variables, such as inlet and outlet subcooling and total heat transfer area, it is difficult to use in a system code. A new CHF correlation is then proposed by replacing the global variables in the Kaminaga correlation with local ones and adding correction factors to consider the effect of gap size, mass flux, and the number of heating walls. Additional correction factor is added to consider the effect of inlet subcooling. It is shown that the new one is better than the Kaminaga correlation and it is easy to implement to any system code.

• Asian Journal of Innovation and Policy
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• v.11 no.2
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• pp.258-276
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• 2022

The historical aspects, policies, institutions, awards and measurement results of scientific literacy and scientific culture development in Indonesia have currently attracted further exploration. This paper utilizes secondary data research, further analyzed by employing the Supplementary Analysis technique. The results revealed that the tradition of writing and publishing scientific journals in Indonesia has existed ever since the Dutch East Indies with the journal's publication entitled 'Natuurkundig tijdschrift voor Nederlandsch Indië' in 1850. To date, Indonesia has owned 5,990 nationally accredited journals. Policy support has been provided at the national and regional levels, despite limitations in cultivating literacy and reading habit. From the institutional perspective, Indonesia provides a wide array of public support, including the effort of the Ministry of Education and Culture for advocating the national literacy movement and the availability of a reference database and scientific access established by the National Library; the Indonesian Institute of Sciences, and the Ministry of Research and Technology. Similarly, in the award-related perspective, the Indonesia government has granted awards to individuals or groups and local governments engaging in the cultivation of scientific literacy and scientific culture. However, among the global measurements for literacy development in Indonesia (in 2020) recorded that three indicators scored less than those in 2019.

• Journal of information and communication convergence engineering
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• v.20 no.2
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• pp.103-112
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• 2022

The goal of pattern mining is to identify novel patterns in a database. High utility itemset mining (HUIM) is a research direction for pattern mining. This is different from frequent itemset mining (FIM), which additionally considers the quantity and profit of the commodity. Several algorithms have been used to mine high utility itemsets (HUIs). The original BPSO algorithm lacks local search capabilities in the subsequent stage, resulting in insufficient HUIs to be mined. Compared to the transfer function used in the original PSO algorithm, the V-shaped transfer function more sufficiently reflects the probability between the velocity and position change of the particles. Considering the influence of the acceleration factor on the particle motion mode and trajectory, a nonlinear acceleration strategy was used to enhance the search ability of the particles. Experiments show that the number of mined HUIs is 73% higher than that of the original BPSO algorithm, which indicates better performance of the proposed algorithm.