• The Journal of KAIRB
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• v.5 no.2
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• pp.51-58
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• 2023

Purpose: To validate the effectiveness of obtaining consent education on errors in the consent process and to develop the education program for researchers. Methods: From February 2019 to February 2022, a 30-minute, 1:1 face-to-face consent education developed using the ADDIE model was conducted on 78 nurses as principal investigators. An informed consent audit tool, which includes 6 items developed by Asan Medical Center Human Research Protection Center, was used to analyze errors in obtaining informed consent process. Data analysis was performed using the SPSS ver. 25.0, and the Mann-Whitney U-test and χ²-test were utilized to verify the difference in errors between the experimental and control groups. Results: The participants consisted of 42 in the experimental group and 36 in the control group, with no statistically significant difference between the 2 groups. Both 2 groups showed the highest frequency of documentation errors, followed by format errors, errors related to a suitability of investigator, participant, or participant's legally acceptable representative, witness and confidentiality issues. After education, there was a significant decrease in both format errors (p=0.002) and documentation errors (p<0.001) in the experimental group. The proportion of participants without any errors in all items was higher in the experimental group (35.7%) compared to the control group (5.6%), and this difference was statistically significant (p=0.001). Conclusion: The obtaining consent education program was found to be effective in reducing informed consent errors. This study emphasizes the importance of education, suggesting the need for its expansion and accessibility, as well as the necessity for all researchers conducting clinical studies to receive the obtaining consent education.

• Information Systems Review
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• v.21 no.2
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• pp.1-27
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• 2019

It is possible to provide Smart Tourism Service through the development of information technology. It is necessary for the tourism industry to understand and utilize Big Data that has tourists' consumption patterns and service usage patterns in order to continuously create a new business model by converging with other industries. This study suggests to activate Jeju Smart Tourism by analyzing Big Data based on credit card usage records and location of tourists in Jeju. The results of the study show that First, the percentage of Chinese tourists visiting Jeju has decreased because of the effect of THAAD. Second, Consumption pattern of Chinese tourists is mostly occurring in the northern areas where airports and duty-free shops are located, while one in other regions is very low. The regional economy of Jeju City and Seogwipo City shows a overall stagnation, without changes in policy, existing consumption trends and growth rates will continue in line with regional characteristics. Third, we need a policy that young people flow into by building Jeju Multi-complex Mall where they can eat, drink, and go shopping at once because the number of young tourists and the price they spend are increasing. Furthermore, it is necessary to provide services for life-support related to weather, shopping, traffic, and facilities etc. through analyzing Wi-Fi usage location. Based on the results, we suggests the marketing strategies and public policies for understanding Jeju tourists' patterns and stimulating Jeju tourism industry.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.109-122
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• 2016

Recently, big data analysis has developed into a field of interest to individuals and non-experts as well as companies and professionals. Accordingly, it is utilized for marketing and social problem solving by analyzing the data currently opened or collected directly. In Korea, various companies and individuals are challenging big data analysis, but it is difficult from the initial stage of analysis due to limitation of big data disclosure and collection difficulties. Nowadays, the system improvement for big data activation and big data disclosure services are variously carried out in Korea and abroad, and services for opening public data such as domestic government 3.0 (data.go.kr) are mainly implemented. In addition to the efforts made by the government, services that share data held by corporations or individuals are running, but it is difficult to find useful data because of the lack of shared data. In addition, big data traffic problems can occur because it is necessary to download and examine the entire data in order to grasp the attributes and simple information about the shared data. Therefore, We need for a new system for big data processing and utilization. First, big data pre-analysis technology is needed as a way to solve big data sharing problem. Pre-analysis is a concept proposed in this paper in order to solve the problem of sharing big data, and it means to provide users with the results generated by pre-analyzing the data in advance. Through preliminary analysis, it is possible to improve the usability of big data by providing information that can grasp the properties and characteristics of big data when the data user searches for big data. In addition, by sharing the summary data or sample data generated through the pre-analysis, it is possible to solve the security problem that may occur when the original data is disclosed, thereby enabling the big data sharing between the data provider and the data user. Second, it is necessary to quickly generate appropriate preprocessing results according to the level of disclosure or network status of raw data and to provide the results to users through big data distribution processing using spark. Third, in order to solve the problem of big traffic, the system monitors the traffic of the network in real time. When preprocessing the data requested by the user, preprocessing to a size available in the current network and transmitting it to the user is required so that no big traffic occurs. In this paper, we present various data sizes according to the level of disclosure through pre - analysis. This method is expected to show a low traffic volume when compared with the conventional method of sharing only raw data in a large number of systems. In this paper, we describe how to solve problems that occur when big data is released and used, and to help facilitate sharing and analysis. The client-server model uses SPARK for fast analysis and processing of user requests. Server Agent and a Client Agent, each of which is deployed on the Server and Client side. The Server Agent is a necessary agent for the data provider and performs preliminary analysis of big data to generate Data Descriptor with information of Sample Data, Summary Data, and Raw Data. In addition, it performs fast and efficient big data preprocessing through big data distribution processing and continuously monitors network traffic. The Client Agent is an agent placed on the data user side. It can search the big data through the Data Descriptor which is the result of the pre-analysis and can quickly search the data. The desired data can be requested from the server to download the big data according to the level of disclosure. It separates the Server Agent and the client agent when the data provider publishes the data for data to be used by the user. In particular, we focus on the Big Data Sharing, Distributed Big Data Processing, Big Traffic problem, and construct the detailed module of the client - server model and present the design method of each module. The system designed on the basis of the proposed model, the user who acquires the data analyzes the data in the desired direction or preprocesses the new data. By analyzing the newly processed data through the server agent, the data user changes its role as the data provider. The data provider can also obtain useful statistical information from the Data Descriptor of the data it discloses and become a data user to perform new analysis using the sample data. In this way, raw data is processed and processed big data is utilized by the user, thereby forming a natural shared environment. The role of data provider and data user is not distinguished, and provides an ideal shared service that enables everyone to be a provider and a user. The client-server model solves the problem of sharing big data and provides a free sharing environment to securely big data disclosure and provides an ideal shared service to easily find big data.

• Journal of Life Science
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• v.23 no.6
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• pp.812-824
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• 2013

Reserpine, an anti-hypertensive drug, is able to positively modulate several phenotypes associated with $A{\beta}$ toxicity in a Caenorhabditis elegans model of Alzheimer's disease (AD). We investigated into the therapeutic effects of reserpine on mammalian neurodegenerative disorders, and found that significant alteration of the key factors influencing AD was detected in Tg2576 mice after reserpine treatment for 30 days. The aggressive behavior of Tg2576 mice was significantly improved upon reserpine treatment, whereas their social contact was consistently maintained. Furthermore, the levels of $A{\beta}$-42 peptide in the hippocampus of the brain and blood serum were lower in the reserpine-treated group than in the vehicle-treated group. Among g-secretase components, the expression levels of PS-2, Pen-2, and APH-1 were slightly lower in reserpine-treated Tg2576 mice, although a significant change in nicastrin (NCT) expression was not detected. Furthermore, the serum level of nerve growth factor (NGF) increased in reserpine-treated Tg2576 mice compared with vehicle-treated mice. Among down-stream effectors of the NGF receptor TrkA signaling pathway, reserpine treatment induced elevation of TrkA phosphorylation and reduction of ERK phosphorylation. In addition, in the NGF receptor $p75^{NTR}$ signaling pathway, the expression levels of $p75^{NTR}$ and Bcl-2 were enhanced in reserpine-treated Tg2576 mice compared with vehicle-treated mice, whereas the expression level of RhoA declined. Overall, these results suggest that reserpine can help relieve AD pathogenesis in Tg2576 mice through downregulation of $A{\beta}$-42 deposition, alteration of ${\gamma}$-secretase components, and regulation of NGF metabolism.

• Korean Journal of Social Welfare
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• v.60 no.1
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• pp.187-209
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• 2008

The authors developed 28 needs assessment tools for integrated assessment centered on needs, which is the core element in care management for the elderly in home. Also, the authors collected the assessment data of 676 elderly persons in home from 120 centers under the Korea Association of Senior Welfare Centers by using the needs assessment tools, and finally developed needs extraction algorithm through decision tree analysis in data mining to identify their actual needs and provide social welfare service suitable for such needs. The needs extraction algorithm for 28 needs of the elderly in home are summarized in