• Information Systems Review
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• v.23 no.3
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• pp.97-125
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• 2021

Blockchain is a core technology to solve personal information leakage and data management issues, which are limitations of existing Genomic Sequencing services. Due to continuous cost reduction and deregulation, the market size of Genomic Sequencing has been increasing, also the potential of services is expected to increase when Blockchain's security and connectivity are combined. We created our research model by combining the Technology Acceptance Model (TAM) and the Innovation Resistance Theory also analyzed the factors affecting the acceptance intention and innovation resistance of the Blockchain Based Genomic Sequencing Information Platform. A survey was conducted on 150 potential users of Blockchain and Genomic Sequencing services. The analysis was conducted by setting the four Blockchain variables: Security, transparency, availability, and diversity). Also, we set the Perceived Usefulness, Perceived risk, and Perceived Complexity for Technology Acceptance and Innovation Resistance variables and analyzed the effect of the characteristics of the Blockchain on acceptance intention and innovation resistance through these variables. Through this analysis, key variables that need to be considered important to reduce resistance and increase acceptance intention could be identified. This study presents innovation factors that should be considered in companies preparing a new Blockchain Based Genomic Sequencing Information Platform.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.71-82
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• 2017

Most banks in Myanmar have begun to provide their services via mobile phones. However, few studies investigated the factors that may help to set mobile services from a customer perspective. So, this study aims to propose and test a conceptual research model to predict the user's intention to use and actual use level of mobile banking service by combining UTAUT and DeLone-Mclean IS model. Data were collected from 206 citizens who had experienced mobile banking in various regions of Myanmar. The study found that performance expectancy, effort expectancy, information quality and service quality influence the user 's intention to adopt mobile banking services which directly affects the user's actual use of them. However, social influence, facilitating condition and system quality don't influence the user's intention. The study results contribute to meeting customer's needs and reducing customer risk in Myanmar's mobile banking industry, suggesting to seamlessly provide the necessary resources like technology improvements, organizational infrastructure and service centers. Another future study are required to include service's security and trust factors so that the service providers could gain their customers' reliability and trust.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.13
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• pp.5365-5370
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• 2015

Background: Combining risk factors for prostate cancer into a predictive tool may improve the detection of prostate cancer while decreasing the number of benign biopsies. We compare one such tool, age multiplied by prostate volume divided by total serum PSA (PSA-AV) with PSA density and detection of primary malignant circulating prostate cells (CPCs) in a Chilean prostate cancer screening program. The objectives were not only to determine the predictive values of each, but to determine the number of clinically significant cancers that would have been detected or missed. Materials and Methods: A prospective study was conducted of all men undergoing 12 core ultrasound guided prostate biopsy for suspicion of cancer attending the Hospital DIPRECA and Hospital de Carabineros de Chile. Total serum PSA was registered, prostate volumecalculated at the moment of biopsy, and an 8ml blood simple taken immediately before the biopsy procedure. Mononuclear cells were obtained from the blood simple using differential gel centrifugation and CPCs identified using immunocytchemistry with anti-PSA and anti-P504S. Biopsy results were classed as positive or negative for cancer and if positive the Gleason score, number of positive cores and percent infiltration recorded. Results: A total of 664 men participated, of whom 234 (35.2%) had cancer detected. They were older, had higher mean PSA, PSA density and lower PSA-AV. Detection of CPCs had high predictive score, sensitivity, sensibility and positive and negative predictive values, PSA-AV was not significantly different from PSA density in this population. The use of CPC detection avoided more biopsies and missed fewer significant cancers.Conclusions: In this screening population the use of CPC detection predicted the presence of clinically significant prostate cancer better than the other parameters. The high negative predictive value would allow men CPC negative to avoid biopsy but remain in follow up. The formula PSA-AV did not add to the predictive performance using PSA density.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.257-268
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• 2021

As the variety and scale of facilities increase due to industrialization and urbanization, blind spots for facility safety management have occurred, resulting in numerous casualties.To meet safety and security needs of citizens living in smart cities, we present a procedure for establishing an integrated platform for facility safety management by combining 5G and digital twin technologies. It can be used to perform inspection according to risk factors and aging of facilities. In this paper, the current status of facility management and application directions of new 5G digital twin-based smart city technologies are reviewed and digital twin implementation procedures are presented. Five cities were selected as target areas: Osan, Gwangmyeong, Guri, Uijeongbu, and Anyang. Old and emergency facilities of each local government were selected. A total of 33 digital twin facilities reflecting policy directions of each city were selected. Focusing on facilities determined by each city, the purpose of this study was to define information technology infrastructure elements for the application of the 5G digital twin facility safety management integrated platform, define categories of implementation services, and suggest a concrete integrated platform configuration plan.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Radiation Oncology Journal
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• v.22 no.3
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• pp.177-183
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• 2004

Purpose: Authors would report the results of sequential CHOP chemotherapy (cyclophosphamide, adriamycin, vincristine, and prednisone) and involved field radiotherapy (IFRT) for early stage nasal natural killer/T-cell Iymphoma (NKTCL). Materials and Methods: Fourteen among 17 patients, who were registered at the Samsung Medical Center tumor registry with stage I and II nasal NKTCL from March 1995 to December 1999 received this treatment protocol. Three to four cycles of CHOP chemotherapy were given at 3 weeks' interval, which was followed by local IFRT including the known tumor extent and the adjacent draining lymphatics. Results: Favorable responses after chemotherapy (before IFRT) were achievable only in seven patients (5 CR's+2 PR's: 50%), while seven patients showed disease progression. There were six patients with local failures, two with distant relapses, and none with regional lymphatic failure. The actuarial overall survival and progression-free survival at 3 years were 50.0% and 42.9%. All the failures and deaths occurred within 13 months of the treatment start. The factors that correlated with the improved survival were the absence of 'B' symptoms, the favorable response to chemotherapy and overall treatment, and the low risk by international prognostic index on univariate analyses. Conclusion: Compared with the historic treatment results by IFRT either alone or followed by chemotherapy, the current trial failed to demonstrate advantages with respect to the failure pattern and survival. Development of new treatment strategy in combining IFRT and chemotherapy is required for improving outcomes.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.107-119
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• 2020

This study analyzed past drought characteristics based on the observed rainfall data and performed a long-term outlook for future extreme droughts using Representative Concentration Pathways 8.5 (RCP 8.5) climate change scenarios. Standardized Precipitation Index (SPI) used duration of 1, 3, 6, 9 and 12 months, a meteorological drought index, was applied for quantitative drought analysis. A single long-term time series was constructed by combining daily rainfall observation data and RCP scenario. The constructed data was used as SPI input factors for each different duration. For the analysis of meteorological drought observed relatively long-term since 1954 in Korea, 12 rainfall stations were selected and applied 10 general circulation models (GCM) at the same point. In order to analyze drought characteristics according to climate change, trend analysis and clustering were performed. For non-stationary frequency analysis using sampling technique, we adopted the technique DEMC that combines Bayesian-based differential evolution ("DE") and Markov chain Monte Carlo ("MCMC"). A non-stationary drought frequency analysis was used to derive Severity-Duration-Frequency (SDF) curves for the 12 locations. A quantitative outlook for future droughts was carried out by deriving SDF curves with long-term hydrologic data assuming non-stationarity, and by quantitatively identifying potential drought risks. As a result of performing cluster analysis to identify the spatial characteristics, it was analyzed that there is a high risk of drought in the future in Jeonju, Gwangju, Yeosun, Mokpo, and Chupyeongryeong except Jeju corresponding to Zone 1-2, 2, and 3-2. They could be efficiently utilized in future drought management policies.