• Title/Summary/Keyword: cluster method

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Development of a nanoparticle multi-generator for assessment of inhalation hazard

  • Lee, Sung-Bae;Han, Jeong-Hee;Kim, Tae-Hyun;Cha, Hyo-Geun;Lim, Cheal-Hong
    • Analytical Science and Technology
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    • v.34 no.2
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    • pp.87-98
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    • 2021
  • In this study, we developed the nanoparticle multi-generator by 3D printer fusion deposition modeling (FDM) method that can reliably generate and deliver nanoparticles at a constant concentration for inhalation risk assessment. A white ABS filament was used as the test material, and SMPS was used for concentration analysis such as particle size and particle distribution. In the case of particle size, the particle size was divided by 100 nm or less and 100 to 1,000 nm, and the number of particles concentration, mass concentration, median diameter of particles, geometric average particle diameter, etc were measured. The occurrence conditions were the extruder temperature, the extruding speed of the nozzle, and the air flow rate, and experiments were conducted according to the change of conditions including the manufacturer's standard conditions. In addition, the utility of inhalation risk assessment was reviewed through a stability maintenance experiment for 6 h. As a result of the experiment, the size of the nanoparticles increased as the discharger temperature increased, as the discharge speed of the nozzle increased, and as the air flow rate decreased. Also, a constant pattern was shown according to the conditions. Even when particles were generated for a long time (6 h), the concentration was kept constant without significant deviation. The distribution of the particles was approximately 80 % for particles of 60 nm to 260 nm, 1.7 % for 1 ㎛ or larger, 0.908 mg/㎥ for the mass concentration, 111 nm for MMAD and 2.10 for GSD. Most of the ABS particles were circular with a size of less than 10 nm, and these circular particles were aggregated to form a cluster of grape with a size of several tens to several hundred nm.

ABO Incompatible Living Donor Liver Transplantation: A Single Center Experience

  • Lee, Seung Hoon;Choi, Ho Joong;You, Young Kyoung;Kim, Dong Goo;Na, Gun Hyung
    • Korean Journal of Transplantation
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    • v.32 no.4
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    • pp.84-91
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    • 2018
  • Background: This study examined the outcomes of ABO incompatible living donor liver transplantation (LDLT). The changes in the immunologic factors that might help predict the long term outcomes were also studied. Methods: Twenty-three patients, who underwent ABO incompatible LDLT from 2010 to 2015, were reviewed retrospectively. The protocol was the same as for ABO compatible LDLT except for the administration of rituximab and plasma exchange. The clinical outcomes and immunologic factors, such as isoagglutinin titer and cluster of differentiation 20+ (CD20+) lymphocyte levels were reviewed. Results: The center showed a 3-year survival of 64% with no case of antibody-mediated rejection. When transplantation-unrelated mortalities (for example, traffic accidents and myocardial infarction) were removed from statistical analysis, the 3-year survival was 77.8%. Although isoagglutinin titers continued to remain at low levels, the CD20+ lymphocyte levels recovered to the pre-Rituximab levels at postoperative one year. Conclusions: As donor shortages continue, ABO incompatible liver transplantation is a feasible method to expand the donor pool. On the other hand, caution is still needed until more long-term outcomes are reported. Because CD20+ lymphocytes are recovered with time, more immunologic studies will be needed in the future.

Analysis of Genetic Diversity in Cymbidium Varieties Using SRAP (SRAP을 이용한 국내육성 심비디움 품종의 유전적 다양성 분석)

  • Park, Pue Hee;Kim, Mi Seon;Lee, Young Ran;Park, Pil Man;Lee, Dong Soo;Yae, Byeong Woo
    • Korean Journal of Breeding Science
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    • v.43 no.5
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    • pp.399-404
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    • 2011
  • Genetic diversity among 28 Cymbidium varieties was evaluated by using a sequence-related amplified polymorphism (SRAP) marker system. The SRAP marker which was based on the open reading frames (ORFs) regions was developed primarily for Brassica species, but has been applied to various crops. A total of 30 SRAP primer combinations were initially screened. Twenty-eight SRAP primer combinations showed high polymorphism among the 28 Cymbidium varieties, which were consisted of breeding varieties and their parents in National Institute of Horticultural & Herbal Science (NIHHS). The amplified DNA fragments were separated by denaturing acrylamide gels and detected silver staining method. One hundred ninety six polymorphic bands (7 per primer) were generated and ranged from 0.3 to 1.0 kb in size. Polymorphic fragments were scored for calculating simple matching coefficient of genetic similarity and cluster analysis with multi-variate statistical package (MVSP) 3.1. The mean genetic similarity coefficient value was 0.588. The results showed that the correlation between $F_1$ varieties and their parents was high. These studied SRAP markers will be useful tools for genotype identification, germplasm conservation, genetic relationships in Cymbidium.

Analysis of Massive Scholarly Keywords using Inverted-Index based Bottom-up Clustering (역인덱스 기반 상향식 군집화 기법을 이용한 대규모 학술 핵심어 분석)

  • Oh, Heung-Seon;Jung, Yuchul
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.19 no.11
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    • pp.758-764
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    • 2018
  • Digital documents such as patents, scholarly papers and research reports have author keywords which summarize the topics of documents. Different documents are likely to describe the same topic if they share the same keywords. Document clustering aims at clustering documents to similar topics with an unsupervised learning method. However, it is difficult to apply to a large amount of documents event though the document clustering is utilized to in various data analysis due to computational complexity. In this case, we can cluster and connect massive documents using keywords efficiently. Existing bottom-up hierarchical clustering requires huge computation and time complexity for clustering a large number of keywords. This paper proposes an inverted index based bottom-up clustering for keywords and analyzes the results of clustering with massive keywords extracted from scholarly papers and research reports.

A study on internet shopping behaviors for clothing according to shopping orientation of chinese female consumers in their 20s~30s (중국 20~30대 여성 소비자의 쇼핑성향에 따른 의류제품의 인터넷 쇼핑행동 연구)

  • Wang, Fengjiao;Lee, Mi-Sook
    • Journal of the Korea Fashion and Costume Design Association
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    • v.21 no.3
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    • pp.37-53
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    • 2019
  • The purposes of this study were to investigate Chinese female consumers' shopping orientation and clothing shopping behaviors on the internet and to find the differences in internet shopping behaviors of consumer groups segmented by clothing shopping orientation. The subjects were 417 women in their 20s and 30s from the Gillim Province, China. The research method was a survey, and the questionnaire consisted of a clothing shopping orientation subscale, clothing, their shopping behaviors via the internet, and the subjects' demographic characteristics. For data analysis, a frequency analysis, a cross-tab analysis, a factor analysis, a cluster analysis, ANOVA, and Duncan's multiple range test were performed. The results of this study were as follows. The clothing shopping orientation was derived using five factors (trend pursuit, pleasure pursuit, brand pursuit, economic pursuit, and convenience pursuit). Chinese female consumers were classified into three groups (hedonic group, ambivalent group, and practical group) by clothing shopping orientation. These three groups showed many significant differences in their clothing shopping behaviors on the internet. The hedonic group preferred the specialty and cross-border shopping malls, and considered product quality and trend as their main purchase motives. The ambivalent group considered the convenience of the purchase and trend as important motives as compared to the other groups, and they use more various product selection criteria. The practical group considered low price and convenience and the search simplicity of various products as major purchase motives. In addition, the hedonic and ambivalent groups had a higher purchase satisfaction and purchase intention from internet shopping than the practical group. This study suggested that clothing shopping orientation is one of the useful segmentation variables and fashion marketers needed to establish differentiated marketing strategies for each consumer group that is segmented by clothing shopping orientation.

Identification of Strategic Fields for Developing Smart City in Busan Using Text Mining (텍스트 마이닝을 이용한 스마트 도시계획 수립을 위한 전략분야 도출연구: 부산 사례를 바탕으로)

  • Chae, Yoonsik;Lee, Sanghoon
    • Journal of Digital Convergence
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    • v.16 no.11
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    • pp.1-15
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    • 2018
  • The purpose of this study is to analyze bibliographic information of Busan and other cities' reports for urban development initiative and identify the strategic fields for future smart city plan. Text mining method is used in this study to extract keywords and identify the characteristics and patterns of information in urban development reports. As a result, in earlier stage, Busan city focused on service creation for industrial development but there are lack of discussions on the linkage of information systems with ICT technology. However, recent urban planning in Busan contained various contents related to integrated connections of infrastructure, ICT system, and operation management of city in the specific fields of traffic, tourism, welfare, port/logistics, culture/MICE. This results of study is expected to provide policy implications for planning the future urban initiatives of smart city development.

A Study on Spatial Characteristics of the Medical Device Industry in Korea (한국 의료기기산업의 공간적 특성에 관한 연구)

  • Hwang, Inkyun
    • Journal of the Economic Geographical Society of Korea
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    • v.22 no.1
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    • pp.1-17
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    • 2019
  • The purpose of this paper is to examine the spatial characteristics of the medical device industry in accordance with the promotion of regional industrial policy. For this aim, I suggest a research methodology that can examine the productivity of production activity and the profitability of sales activity by the government. As the government's large-scale support is localized by the 'selective support' method, the spatial characteristics of the productivity and profitability of the industry are different. In this context, the results of the review of the government's regional industrial policy in three aspects are as follows. First, the government's support did not come down to increased productivity and profitability. Second, high productivity at the regional level did not necessarily lead to high profitability. Finally, the high market share in terms of profitability did not guarantee high profits. Therefore, it is necessary for the government to reconsider the direction of regional industrial policy so that the link between productivity and profitability can be secured in the examination of the achievement of industrial specificity and policy support.

Non-linearity Mitigation Method of Particulate Matter using Machine Learning Clustering Algorithms (기계학습 군집 알고리즘을 이용한 미세먼지 비선형성 완화방안)

  • Lee, Sang-gwon;Cho, Kyoung-woo;Oh, Chang-heon
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2019.05a
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    • pp.341-343
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    • 2019
  • As the generation of high concentration particulate matter increases, much attention is focused on the prediction of particulate matter. Particulate matter refers to particulate matter less than $10{\mu}m$ diameter in the atmosphere and is affected by weather changes such as temperature, relative humidity and wind speed. Therefore, various studies have been conducted to analyze the correlation with weather information for particulate matter prediction. However, the nonlinear time series distribution of particulate matter increases the complexity of the prediction model and can lead to inaccurate predictions. In this paper, we try to mitigate the nonlinear characteristics of particulate matter by using cluster algorithm and classification algorithm of machine learning. The machine learning algorithms used are agglomerative clustering, density-based spatial clustering of applications with noise(DBSCAN).

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Parallelization of Genome Sequence Data Pre-Processing on Big Data and HPC Framework (빅데이터 및 고성능컴퓨팅 프레임워크를 활용한 유전체 데이터 전처리 과정의 병렬화)

  • Byun, Eun-Kyu;Kwak, Jae-Hyuck;Mun, Jihyeob
    • KIPS Transactions on Computer and Communication Systems
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    • v.8 no.10
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    • pp.231-238
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    • 2019
  • Analyzing next-generation genome sequencing data in a conventional way using single server may take several tens of hours depending on the data size. However, in order to cope with emergency situations where the results need to be known within a few hours, it is required to improve the performance of a single genome analysis. In this paper, we propose a parallelized method for pre-processing genome sequence data which can reduce the analysis time by utilizing the big data technology and the highperformance computing cluster which is connected to the high-speed network and shares the parallel file system. For the reliability of analytical data, we have chosen a strategy to parallelize the existing analytical tools and algorithms to the new environment. Parallelized processing, data distribution, and parallel merging techniques have been developed and performance improvements have been confirmed through experiments.

Genetic diversity analysis of fourteen geese breeds based on microsatellite genotyping technique

  • Moniem, Hebatallah Abdel;Zong, Yang Yao;Abdallah, Alwasella;Chen, Guo-hong
    • Asian-Australasian Journal of Animal Sciences
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    • v.32 no.11
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    • pp.1664-1672
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    • 2019
  • Objective: This study aimed to measure genetic diversity and to determine the relationships among fourteen goose breeds. Methods: Microsatellite markers were isolated from the genomic DNA of geese based on previous literature. The DNA segments, including short tandem repeats, were tested for their diversity among fourteen populations of geese. The diversity was tested on both breeds and loci level and by mean of unweighted pair group method with arithmetic mean and structure program, phylogenetic tree and population structure were tested. Results: A total of 108 distinct alleles (1%) were observed across the fourteen breeds, with 36 out of the 108 alleles (33.2%) being unique to only one breed. Genetic parameters were measured per the 14 breeds and the 9 loci. Medium to high heterozygosity was reported with high effective numbers of alleles (Ne). Polymorphic information contents (PIC) of the screened loci was found to be highly polymorphic for eleven breeds; while 3 breeds were reported moderately polymorphic. Breeding coefficient ($F_{IS}$) ranged from -0.033 to 0.358, and the pair wise genetic differentiation ($F_{ST}$) ranged from 0.01 to 0.36 across the fourteen breeds; for the 9 loci observed and expected heterozygosity, and Ne were same as the breeds parameters, PIC of the screened loci reported 6 loci highly polymorphic and 3 loci to be medium polymorphic, and $F_{IS}$ ranged from -0.113 to 0.368. In addition, genetic distance estimate revealed a close genetic distance between Canada goose and Hortobagy goose breeds by 0.04, and the highest distance was between Taihu goose and Graylag goose (anser anser) breed by 0.54. Conclusion: Cluster analyses were made, and they revealed that goose breeds had hybridized frequently, resulting in a loss of genetic distinctiveness for some breeds.