• Asian Pacific Journal of Cancer Prevention
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• v.14 no.7
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• pp.4283-4287
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• 2013

Background: In this meta-analysis we review evidence suggesting that exposure to cadmium is a cause of breast cancer. Materials and Methods: We conducted Medline/PubMed and Scopus searches using selected MeSH keywords to identify papers published from January 1, 1980 through January 1, 2013. Data were merged and summary mean differences were estimated using either a random-effects model or a fixed-effects model. Results: There were 13 studies including 978 exposed cases and 1,279 controls. There was no statistically significant difference in the frequencies of breast cancer between cadmium-exposed and control groups, and the summary estimate of mean difference was 0.71 (95%CI: 0.33-1.08). However, stratification showed that there were statistically significant differences in the frequencies of breast cancer between cadmium-exposed and control groups among Asian compared with Caucasian population, and the summary estimates of mean difference were 1.45 (95%CI: 0.62-2.28) vs. 0.25 (95%CI: -0.09-0.6), respectively. There was a difference in the frequencies of breast cancer between cadmium-exposed and control groups in peripheral venous blood sampling methods, and the summary estimate of mean difference was 1.41 (95%CI: 0.46-2.37). Conclusions: Data indicate that the frequencies of breast cancer might be an indicator of early genetic effects for cadmium-exposed populations. However, our meta-analysis was performed on population-based studies; meta-analysis based on individual data might provide more precise and reliable results. Therefore, it is necessary to construct an international database on genetic damage among populations exposed to cadmium that may contain all raw data of studies examining genetic toxicity.

• Journal of Internet Computing and Services
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• v.15 no.5
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• pp.63-72
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• 2014

Twitter has been a popular social media platform where people post short messages of 140 characters or less via the web. A hashtag is a word or acronym created by Twitter users to open a discussion about certain topics and issues that have a very high percentage of trending. Since the hashtag posts are sorted by time, not relevancy, people who firstly use Twitter have had difficulty understanding their context. In this paper, we propose a HBase-based automatic summary system in order to reduce the difficulty of understanding. The proposed system combines an automatic summary method with a fuzzy system after storing the streaming data provided by Twitter API to the HBase. Throughout this procedure, we have eliminated the duplicate of contents in the hashtag posts and have computed scores between posts so that the users can access to the trending topics with relevancy.

• Journal of KIISE:Databases
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• v.29 no.2
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• pp.99-109
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• 2002

ROLAP(Relational Online Analytical Processing) is a process and methodology for a multidimensional data analysis that is essential to extract desired data and to derive value-added information from an enterprise data warehouse. In order to speed up query processing, most ROLAP systems pre-compute summary tables. This process is called 'cube generation' and it mostly involves intensive table sorting stages. (1) showed that it is much faster to generate ROLAP summary tables indirectly using a MOLAP(multidimensional OLAP) cube generation algorithm. In this paper, we present such an indirect ROLAP cube generation algorithm that is fast and scalable. High memory utilization is achieved by slicing the input fact table along one or more dimensions before generating summary tables. High speed is achieved by producing summary tables from their smallest parents. We showed the efficiency of our algorithm through experiments.

• The Journal of Korean Association of Computer Education
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• v.22 no.3
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• pp.79-87
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• 2019

This study examined the effect of e-learning contents with different dual-coding, media-richness, and cognitive-load degree on learning. To do so, after dividing summary and explanation presentation methods in e-learning contents according to information's quantity and kind, the effects on teaching presence and academic achievement were examined. The summary presentation method was produced as text type and text+illustration type and the explanation presentation method as audio type and audio+video type. The results of this study are as follows. First, in the summary method, the text+illustration type had significantly higher teaching presence than text type. Second, in the explanation method, the audio type was found to be significantly higher than the audio+video type. Third, the interaction between the summary method and explanation method was found to be significant in teaching presence and academic achievement.

• ALGAE
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• v.28 no.1
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• pp.31-43
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• 2013

This review provides a comprehensive summary of the PCR primers and profiles currently in use in our laboratory for red algal DNA barcoding and phylogenetic research. While work focuses on florideophyte taxa, many of the markers have been applied successfully to the Bangiales, as well as other lineages previously assigned to the Bangiophyceae sensu lato. All of the primers currently in use with their respective amplification profiles and strategies are provided, which can include full fragment, overlapping fragments and what might best be called "informed overlapping fragments", i.e., a fragment for a marker is amplified and sequenced for a taxon and those sequence data are then used to identify the best primers to amplify the remaining fragment(s) for that marker. We extend this strategy for the more variable markers with sequence from the external PCR primers used to "inform" the selection of internal sequencing primers. This summary will hopefully serve as a useful resource to systematists in the red algal community.

• Korean Journal of Adult Nursing
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• v.21 no.2
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• pp.235-244
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• 2009

Purpose: This research was carried out to evaluate the validity and reliability of the Korean version of the Summary of Diabetes Self-Care Activities Questionnaire (SDSCA) for Korean older adults with type 2 diabetes. Methods: Translation and back-translation were performed to develop the Korean version of the SDSCA. Then the Korean version SDSCA was applied to a sample of 112 older adults who had participated in diabetes self management education in Seoul. The internal consistency and the test-retest reliability were examined to test the reliability. Factor analysis was used to examine the construct validity. Results: The internal consistency measured with Cronbach's alpha was .77 and the total test-retest reliability was .68 with items ranging from .21 to 1.00. As the result of the factor analysis, six factors -foot care, diet, exercise, blood sugar test, medication, and smoking- were revealed as the original instrument subcategories. These six factors explained 81.17% of total variance. Conclusion: The reliability and validity of the Korean version SDSCA Questionnaire was supported for use in older patients with type 2 diabetes in Korea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5888-5903
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• 2019

The Relational ranking method applies authority-based ranking in relational dataset that can be modeled as graphs considering also their tuples' values. Authority directions from tuples that contain the given keywords and transfer to their corresponding neighboring nodes in accordance with their values and semantic connections. From our previous work, ObjectRank extends to ValueRank that also takes into account the value of tuples in authority transfer flows. In a maked difference from ObjectRank, which only considers authority flows through relationships, it is only valid in the bibliographic databases e.g. DBLP dataset, ValueRank facilitates the estimation of importance for any databases, e.g. trading databases, etc. A relational keyword search paradigm Object Summary (denote as OS) is proposed recently, given a set of keywords, a group of Object Summaries as its query result. An OS is a multilevel-tree data structure, in which node (namely the tuple with keywords) is OS's root node, and the surrounding nodes are the summary of all data on the graph. But, some of these trees have a very large in total number of tuples, size-l OSs are the OS snippets, have also been investigated using ValueRank.We evaluated the real bibliographical dataset and Microsoft business databases to verify of our proposed approach.

• Journal of the Korea Society of Computer and Information
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• v.5 no.2
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• pp.14-21
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• 2000

In the position of the users, it suggests the technology information space as a now paradigm, which supplement the function of science information DB. ICPIS which inputs described papers with keywords, offers the itemized summary of these contents, the visual indication and comparison of similar thesis. and it also supplises the abundant summary information, survey information, more than ten volumes of info communication thesis with starting the casual relation extraction for the users, playing a significant role in ICPIS is called KP, and it is package of domain knowledge that unifies the extraction and structure narration of the technology information. ICPIS extracts the technology information among the thesis that are deserved by the natual language treatment in the itemized KP described , and form the prescribed summary structure in KP.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.755-764
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• 2011

In many companies field failure data is used to predict the future number of failures, especially when an unexpected failure mode happens to be a problem. It is because they want to predict the number of spare parts needed and the future quality warranty cost associated with the part based on the predictions of the future number of failures. In this paper field summary data is used to predict the future number of failures based on an appropriate distribution. Other types of data are also investigated to identify the appropriate distribution.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.488-497
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• 2004

This paper describes the hybrid document summarization using the indicative summarization and the query-based summarization. The learning models are built from teaming documents in order to extract topic phrases. We use Naive Bayesian, Decision Tree and Supported Vector Machine as the machine learning algorithm. The system extracts topic phrases automatically from new document based on these models and outputs the summary of the document using query-based summarization which considers the extracted topic phrases as queries and calculates the locality-based similarity of each topic phrase. We examine how the topic phrases affect the summarization and how many phrases are proper to summarization. Then, we evaluate the extracted summary by comparing with manual summary, and we also compare our summarization system with summarization mettled from MS-Word.