• Health Policy and Management
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• v.29 no.3
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• pp.277-287
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• 2019

Background: Diabetes is known as one of the most important ambulatory care sensitive conditions. This study purposed to assess the status of continuity of care (COC) and diabetes-related avoidable hospitalizations (DRAHs) of a group of middle- and old-aged patients and to observe the relationship of the two elements by the two age groups. Methods: This study utilized the National Health Insurance Service's National Sample Cohort data and the subjects are diabetes patients of 45 and over, classified into two groups of 'middle-aged'(45-64 years) and 'old-aged'(${\geq}65years$) patients. The dependent variable was DRAHs, which was defined in accordance with the definition of the Organization for Economic Cooperation and Development "Health Care Quality Indicators" project. COC, as an independent variable, is measured by the COC index in this study. Two-part model (multi-variate and multi-level analyses) was utilized. Results: Factors associated with the status and the number of DRAHs differed by each age group. Meanwhile, the two-part model showed that higher COC was associated with a lower risk of preventable hospitalizations in both middle- and old-aged groups. Conclusion: Study findings can provide health policy insights and implications in order to strengthen the primary care system for further improvement of diabetes management, especially for middle- and old-aged groups.

• Journal of Veterinary Science
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• v.22 no.2
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• pp.23.1-23.10
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• 2021

Background: Pseudorabies (PR), caused by the pseudorabies virus (PRV), is an endemic disease in some regions of China. Although there are many reports on epidemiological investigations into pseudorabies, information on PRV gI antibody dynamics in one pig farm is sparse. Objectives: To diagnose PR and analyze the course of PR eradication in one pig farm. Methods: Ten brains and 1,513 serum samples from different groups of pigs in a pig farm were collected to detect PRV gE gene and PRV gI antibody presence using real-time polymerase chain reaction and enzyme-linked immunosorbent assay, respectively. Results: The July 2015 results indicated that almost all brain samples were PRV gE gene positive, but PRV gI antibody results in the serum samples of the same piglets were all negative. In the boar herd, from October 2015 to July 2018 three positive individuals were culled in October 2015, and the negative status of the remaining boars was maintained in the following tests. In the sow herd, the PRV gI antibody positive rate was always more than 70% from October 2015 to October 2017; however, it decreased to 27% in January 2018 but increased to 40% and 52% in April and July 2018, respectively. The PRV gI antibody positive rate in 100-day pigs markedly decreased in October 2016 and was maintained at less than 30% in the following tests. For 150-day pigs, the PRV gI antibody positive rate decreased notably to 10% in April 2017 and maintained a negative status from July 2017. The positive trend of PRV gI antibody with an increase in pig age remarkably decreased in three tests in 2018. Conclusions: The results indicate that serological testing is not sensitive in the early stage of a PRV infection and that gilt introduction is a risk factor for a PRV-negative pig farm. The data on PRV gI antibody dynamics can provide reference information for pig farms wanting to eradicate PR.

• Journal of Digital Convergence
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• v.17 no.7
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• pp.285-292
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• 2019

With the popularization of PC, SNS and IoT, a lot of data is generated and the amount is increasing exponentially. Artificial neural network learning is a topic that attracts attention in many fields in recent years by using huge amounts of data. Artificial neural network learning has shown tremendous potential in speech recognition and image recognition, and is widely applied to a variety of complex areas such as medical diagnosis, artificial intelligence games, and face recognition. The results of artificial neural networks are accurate enough to surpass real human beings. Despite these many advantages, privacy problems still exist in artificial neural network learning. Learning data for artificial neural network learning includes various information including personal sensitive information, so that privacy can be exposed due to malicious attackers. There is a privacy risk that occurs when an attacker interferes with learning and degrades learning or attacks a model that has completed learning. In this paper, we analyze the attack method of the recently proposed neural network model and its privacy protection method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.69-85
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• 2019

BeiDou navigation satellite system (BDS) is one of the four main types of global navigation satellite systems. The current system has been widely used by the military and by the aerospace, transportation, and marine fields, among others. However, challenges still remain in the BeiDou system, which requires rapid responses for delay-sensitive devices. A differential positioning algorithm called the data center-based differential positioning (DCDP) method is widely used to avoid the influence of errors. In this method, the positioning information of multiple base stations is uploaded to the data center, and the positioning errors are calculated uniformly by the data center based on the minimum variance or a weighted average algorithm. However, the DCDP method has high delay and overload risk. To solve these problems, this paper introduces edge computing to relieve pressure on the data center. Instead of transmitting the positioning information to the data center, a novel method called edge computing-based differential positioning (ECDP) chooses the nearest reference station to perform edge computing and transmits the difference value to the mobile receiver directly. Simulation results and experiments demonstrate that the performance of the ECDP outperforms that of the DCDP method. The delay of the ECDP method is about 500ms less than that of the DCDP method. Moreover, in the range of allowable burst error, the median of the positioning accuracy of the ECDP method is 0.7923m while that of the DCDP method is 0.8028m.

• Journal of Climate Change Research
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• v.9 no.3
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• pp.235-243
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• 2018

Loss of favorable habitats for species due to temperature increase is one of the main concerns of climate change on the ecosystem, and endangered species might be much more sensitive to such unfavorable changes. This study aimed to analyze the impact of future climate change on endangered wild animals in South Korea by investigating thermal sensitivity and vulnerability to temperature increase. We determined thermal sensitivity by testing normality in species distribution according to temperature. Then, we defined the vulnerability when the future temperature range of South Korea completely deviate from the current temperature range of species distribution. We identified 13 species with higher thermal sensitivity. Based on IPCC future scenarios RCP 4.5 and RCP 8.5, the number of species vulnerable to future warming doubled from 3 under RCP4.5 to 7 under the RCP8.5 scenario. The species anticipated to be at risk under RCP 8.5 are flying squirrel (Pteromys volans aluco), ural owl (Pteromys volans aluco), black woodpecker (Dryocopus martius), tawny owl (Strix aluco), watercock (Gallicrex cinerea), schrenck?s bittern (Ixobrychus eurhythmus), and fairy pitta (Pitta nympha). The other 10 species showing very narrow temperature ranges even without normal distributions and out of the future temperature range may also need to be treated as vulnerable species, considering the inevitable observation scarcity of such endangered species.

• Journal of Korean Society of Disaster and Security
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• v.11 no.2
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• pp.83-89
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• 2018

The significant sedimentation at the estuary in the eastern coast of Korea frequently causes river mouth occlusion where disconnection between the river and sea is observed. River mouth occlusion causing watershed retention raises the environmental risk of the area as it impairs water quality and threatens the area's safety in the event of floods. This study proposes a plan to maintain stability of river channel and flow of flood discharge at the estuary with loss of its function for disaster prevention. To this end, the study tries to change the location and width of stream path, focusing on the center line of stream near the sand bar of river mouth. This allows to identify a shape of stream path that leads the most stable flow. To review the result, this study uses the SRH-2D, a model for two-dimensional hydraulic analysis, and conduct numeric simulation. The simulation result showed that the most effective plan for maintaining the stable flow of running water without having the area sensitive to changes in hydraulic characteristics is to lower the overall river bed height of the sand bar near the center line of stream to a equal level.

• Ecology and Resilient Infrastructure
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• v.7 no.4
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• pp.336-344
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• 2020

The prediction and evaluation of driftwood accumulation around river-crossing structures are essential because driftwood accumulation increases during flood disasters. In this study, the driftwood accumulation and behavior around bridge piers were evaluated via a numerical model that could be employed to analyze three-dimensional turbulent flow and driftwood motion. The moving particle semi-implicit-based model for driftwood motion was sensitive to the number of spheres. The numerical results showed that the approach velocity and the ratio of driftwood length to pier width were the key factors influencing driftwood accumulation, whereas the driftwood density had only a minor influence. Overall, it is expected that this study will contribute to the development of improved risk evaluation indexes for assessing driftwood accumulation around river-crossing structures.

• International Area Studies Review
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• v.13 no.2
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• pp.255-276
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• 2009

This article has examined the international transmission of returns among S&P500, Nikkei225 and SENSEX stock index cash markets using the daily closing prices covered from January 4, 2002 to February 6, 2009. For this purpose we employed dynamic time series models such as the Granger causality analysis and variance decomposition analysis based on VAR model. The main empirical results are as follows; First, according to Granger causality tests we find that S&P500 stock index has a significant prediction power on the changes of SENSEX and Nikkei225 stock index market and vice versa. However, US stock market's influence is dominant to the other stock markets at a significant level statistically. Second, according to variance decomposition, SENSEX stock index is more sensitive to the movement of S&P500 than that of Nikkei225 stock index. These kinds of empirical results shows that the three stock markets are integrated over times and these results will be informative for the international investors to build the world-wide investment portfolio and risk management strategies, etc.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.4
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• pp.115-124
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• 2011

There has been significant research in security technology such as e-passport standards, as e-passports have been introduced internationally. E-passports combine the latest security technologies such as smart card, public key infrastructure, and biometric recognition, so that these technologies can prevent unauthorized copies and counterfeits. Biometric information stored in e-passports is the most sensitive personal information, and it is expected to bring the highest risk of damages in case of its forgery or duplication. The present e-passport standards cannot handle security features that verify whether its biometric information is copied or not. In this paper, we propose an e-passport security technology in which biometric watermarking is used to prevent the copy of biometric information in the e-passport. The proposed method, biometric watermarking, embeds the invisible date of acquisition into the original data during the e-passport issuing process so that the human visual system cannot perceive its invisibly watermarked information. Then the biometric sample, having its unauthorized copy, is retrieved at the moment of reading the e-passport from the issuing database. The previous e-passport security technology placed an emphasis on both access control readers and anti-cloning chip features, and it is expected that the proposed feature, copy protection of biometric information, will be demanded as the cases of biometric recognition to verify personal identity information has increased.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.270-281
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• 2021

With the development of information technology, the size of insurance fraud is increasing rapidly every year, and the method is being organized and advanced in conspiracy. Although various forms of prediction models are being studied to predict and detect this, insurance-related information is highly sensitive, which poses a high risk of sharing and access and has many legal or technical constraints. In this paper, we propose a machine learning insurance fraud prediction model based on blockchain, one of the most popular technologies with the recent advent of the Fourth Industrial Revolution. We utilize blockchain technology to realize a safe and trusted insurance information sharing system, apply the theory of social relationship analysis for more efficient and accurate fraud prediction, and propose machine learning fraud prediction patterns in four stages. Claims with high probability of fraud have the effect of being detected at a higher prediction rate at an earlier stage, and claims with low probability are applied differentially for post-reference management. The core mechanism of the proposed model has been verified by constructing an Ethereum local network, requiring more sophisticated performance evaluations in the future.