• Genomics & Informatics
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• v.9 no.2
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• pp.52-58
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• 2011

Osteoporotic fracture (OF), along with bone mineral density (BMD), is an important diagnostic parameter and a clinical predictive risk factor in the assessment of osteoporosis in the elderly population. However, a genome-wide association study (GWAS) on OF has not yet been clarified sufficiently. To identify OF-associated genetic variants and candidate genes, we conducted a GWAS in a population-based cohort (Korean Association Resource [KARE], n=1,427 [case: 288 and control: 1139]) and performed a de novo replication study in hospital-based individuals (Asan and Catholic Medical Center [ACMC], n=1,082 [case: 272 and control: 810]). In a combined meta-analysis, a newly identified genetic locus in an intergenic region at 10p11.2 (near genes FZD8 and ANKRD30A ) showed the most significant association (odd ratio [OR] = 2.00, 95% confidence interval [CI] = 1.47~2.74, p=$1.27{\times}10^{-6}$) in the same direction. We provide the first evidence for a common genetic variant influencing OF and genetic information for further investigation in bone metabolism.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.

• Journal of Korean Academy of Nursing
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• v.25 no.1
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• pp.30-44
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• 1995

This study aimed at analyzing the trend of re-search on pain in Korea, suggesting direction future pain research, and contributing to the use of pain interventions in nursing practice. Research studies on pain were selected from journals of medical and nursing schools, the Korean Nurse, the Korean Nurses' Academic Society Journal, the Central Journal of Medicine, the New Medical Journal, and from theses and dissertations, which were conducted between 1970 and 1994. The total number of the studies was 93. These studies were analyzed for 1) time of publication or presentation, 2) thesis for a degree or nondegree, 3) research design, 4) characteristics of subjects used in each study, 5) measurement tool, 6) types of correlated variables, 7) Korean terms for pain 8) types of nursing interventions, and 9) results of studies. The findings of the analysis can be summerized as follows : 1) The number of studies related to pain has increased rapidly since the early 1980's. The number of experimental research studies related to pain has increased chronologically, but the number of survey research studies related to pain was highest from 1981 to 1985, after that it decreased slowly. 2) The subjects in 19 studies were healthy people and, in 73 studies, patients with various illnesses. Thirty two studies were conducted with surgical patients. 3) Sixty one pain research studies were done for a thesis for a degree and 32 were nondegree research studies. 4) As measurement tools for pain, self- report pain scales were used in 54 studies and more than two tools were used in 28 studies. In the experimental studies, the trend was to use more than two tools. And in the nonexperirnental studies, the trend was to use self-report pain scales only. 5) There were 11 correlational studies. In these studies, the trend was to study anxiety, depression and variables such as intravenous infusion as related to pain.6) In the thirty six experimental studies, the effects of 16 types of nursing interventions weretested. Teaching and information, and relaxation technique were the most popular interventions for pain. 7) In eighteen methodological studies, the majority were studies testing the validity and re-liability of Dr. Lee's Korean Pain Rating Questionnaire. The following suggestions are made based on the above findings : 1) The patterns of these studies related to pain in Korea need to be compared with trends in other countries. 2) Meta - analysis should be done to analyze and integrate the results of various studies. 3) This analysis of pain research is needed to identify the present trend of pain research and to suggest the direction of future pain research, so these patterns of studies should be done in 5 to 10 year intervals. 4) More replicated pain research is needed to prove the effect of nursing interventions and more qualitative research on pain is needed to identify indepth the meaning of pain. 5) Pain researchers should make an effort to apply research result in various clinical settings and try to carry out team research with clinical nurses or with other multidiscipinary researchers.

• Journal of KIISE:Databases
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• v.37 no.5
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• pp.258-266
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• 2010

In this paper, we introduce a novel method to predict next update of blogs. The number of RSS feeds registered on meta-blogs is on the order of several million. Checking for updates is very time consuming and imposes a heavy burden on network resources. Since blog search engine has limited resources, there is a fix number of blogs that it can visit on a day. Nevertheless we need to maximize chances of getting new data, and the proposed method which predicts update probability on blogs could bring better chances for it. Also this work is important to avoid distributed denial-of-service attack for the owners of blogs. Furthermore, for the internet as whole this work is important, too, because our approach could minimize traffic. In this study, we assumed that there is a specific pattern to when a blogger is actively posting, in terms of days of the week and, more specifically, hours of the day. We analyzed 15,119 blogs to determine a blogger's posting preference. This paper proposes a method to predict the update probability based on a blogger's posting history and preferred days of the week. We applied proposed method to 12,115 blogs to check the precision of our predictions. The evaluation shows that the model has a precision of 0.5 for over 93.06% of the blogs examined.

• Journal of KIISE
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• v.41 no.10
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• pp.762-773
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• 2014

As the web of data is increasingly producing large RDFS datasets, it becomes essential in building scalable reasoning engines over large triples. There have been many researches used expensive distributed framework, such as Hadoop, to reason over large RDFS triples. However, in many cases we are required to handle millions of triples. In such cases, it is not necessary to deploy expensive distributed systems because logic program based reasoners in a single machine can produce similar reasoning performances with that of distributed reasoner using Hadoop. In this paper, we propose a scalable RDFS reasoner using logical programming methods in a single machine and compare our empirical results with that of distributed systems. We show that our logic programming based reasoner using a single machine performs as similar as expensive distributed reasoner does up to 200 million RDFS triples. In addition, we designed a meta data structure by decomposing the ontology triples into separate sectors. Instead of loading all the triples into a single model, we selected an appropriate subset of the triples for each ontology reasoning rule. Unification makes it easy to handle conjunctive queries for RDFS schema reasoning, therefore, we have designed and implemented RDFS axioms using logic programming unifications and efficient conjunctive query handling mechanisms. The throughputs of our approach reached to 166K Triples/sec over LUBM1500 with 200 million triples. It is comparable to that of WebPIE, distributed reasoner using Hadoop and Map Reduce, which performs 185K Triples/sec. We show that it is unnecessary to use the distributed system up to 200 million triples and the performance of logic programming based reasoner in a single machine becomes comparable with that of expensive distributed reasoner which employs Hadoop framework.

• Journal of Sensor Science and Technology
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• v.27 no.5
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• pp.317-327
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• 2018

Terahertz (THz) time-domain spectroscopy(TDS), imaging techniques, and related systems have become mature technologies, widely used in many universities and research laboratories. However, the development of creative technologies still requires improved THz application systems. A few key points are discussed, including the innovative advances of mode-locking energy-emitting semiconductor lasers and better photoconductive semiconductor quantum structures. To realize a compact, low cost, and high performance THz system, it is essential that THz spectroscopy and imaging technologies are better characterized by semiconductor and nano-devices, both static and time-resolved. We introduce the THz spectroscopy and imaging systems, the OSCAT(Optical Sampling by laser CAvity Tuning) system and the ASOPS(ASynchronous Optical Sampling) system, are constructed by our research team. We report on the THz images obtained from their use.

• Journal of Internet Computing and Services
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• v.12 no.3
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• pp.27-36
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• 2011

The sensor nodes of sensor network system are capable of wireless communication with sink nodes. They also acquire and transmit sensor data in broad region where people cannot access easily. However, the transmission of redundant data from sensor nodes reduces the lifetime of the entire system and substantial amount of resulted data needs to be resorted before implementing them to the specific applications. In this paper, the mobile agent middleware to eliminate the redundant sensor data is designed and implemented. In the proposed system, the mobile agent visits the destination sensor nodes according to the migration list offered by the meta table in the name space of the naming agent, eliminates the redundant sensor data corresponding to user condition, and acquires and transmits sensor data according to the purpose and needs. Thus, the excess transmission of the sensor data is avoided and the lifetime of the entire system can be extended. Moreover, the experiments using the mobile agent middleware with the conditions and limitations that are possible in real situation ore done to verify the successful elimination of the redundant sensor data and the efficiency of the data acquisition. Also, we show the potential applicability of the mobile agent middleware in various active sensor networks through the active rule based mobile agent middleware or the interaction with the active rule system.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.2
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• pp.163-176
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• 2010

The representative method to efficiently manage the organization's data is to avoid data duplication through the promotion of sharing and reusing existing data. The systematic structuring of existing data and efficient searching should be supported in order to promote the sharing and reusing of data. Without regard for these points, the data for the system development would be duplicated, which would deteriorate the quality of the data. Data taxonomy provides some methods that can enable the needed data elements to be searched quickly with a systematic order of managing data. This paper proposes that the Origin data taxonomy method can best maximize data sharing, reusing, and consolidation, and it can be used for Meta Data Registry (MDR) and Semantic Web efficiently. The Origin data taxonomy method constructs the data taxonomy structure built upon the intrinsic nature of data, so it can classify the data with independence from business classification. Also, it shows a deployment method for data elements used in various areas according to the Origin data taxonomy structure with a data taxonomic procedure that supports the proposed taxonomy. Based on this case study, the proposed data taxonomy and taxonomic procedure can be applied to real world data efficiently.

• Journal of KIISE:Software and Applications
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• v.35 no.12
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• pp.716-730
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• 2008

In Service-Oriented Architecture (SOA), service providers develop and deploy reusable services on the repositories, and service consumers utilize blackbox form of services through their interfaces. Services are also highly evolvable and often heterogeneous. Due to these characteristics of the service, it is hard to manage the faults if faults occur on the services. Autonomic Computing (AC) is a way of designing systems which can manage themselves without direct human intervention. Applying the key disciplines of AC to service management is appealing since key technical issues for service management can be effectively resolved by AC. In this paper, we present a theoretical model, Symptom-Cause-Actuator (SCA), to enable autonomous service fault management in SOA. We derive SCA model from our rigorous observation on how physicians treat patients. In this paper, we first define a five-phase computing model and meta-model of SCA. And, we define a schema of SCA profile, which contains instances of symptoms, causes, actuators and their dependency values in a machine readable form. Then, we present detailed algorithms for the five phases that are used to manage faults the services. To show the applicability of our approach, we demonstrate the result of our case study for the domain of 'Flight Ticket Management Services'.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.10
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• pp.4265-4269
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• 2014

Purpose: This study aimed at summarizing epidemiological evidence of the association between gestational diabetes mellitus (GDM) and subsequent risk of cancer. Materials and Methods: We searched Medline, Embase, Cancer Lit and CINAHL for epidemiological studies published by February 1, 2014 examining the risk of cancer in patients with history of GDM using highly inclusive algorithms. Information about first author, year of publication, country of study, study design, cancer sites, sample sizes, attained age of subjects and methods used for determining GDM status were extracted by two researchers and Stata version 11.0 was used to perform the meta-analysis and estimate the pooled effects. Results: A total of 9 articles documented 5 cohort and 4 case-control studies containing 10,630 cancer cases and 14,608 women with a history of GDM were included in this review. Taken together, the pooled odds ratio (OR) between GDM and breast cancer risk was 1.01 (0.87-1.17); yet the same pooled ORs of case-control and cohort studies were 0.87 (0.71-1.06) and 1.25 (1.00-1.56) respectively. There are indications that GDM is strongly associated with higher risk of pancreatic cancer (HR=8.68) and hematologic malignancies (HR=4.53), but no relationships were detected between GDM and other types of cancer. Conclusions: Although GDM increases the risk of certain types of cancer, these results should be interpreted with caution becuase of some methodological flaws. The issue merits added investigation and coordinated efforts between researchers, antenatal clinics and cancer treatment and registration agencies to help attain better understanding.