• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.5
• /
• pp.2140-2149
• /
• 2013

Managing uncertainty or discontinuity in an innovation is still a challenge to most companies. For sustainable corporate survival over the long term, one of the problems caused by discontinuous innovation is the innovator's dilemma. In specific, the dynamics between discontinuous innovation and incumbents inspires the interestof researchers and managers. This paper employs catastrophe theory as a theoretical basis to explain the driving force of new discontinuous change. In other words, we extract the control variables overcoming innovation dilemma by interpreting the dynamics of corporate strategy for discontinuous innovation from the perspective of catastrophe theory. First, we define four types of discontinuity such as technology discontinuity, product discontinuity, business discontinuity, and consumer preference discontinuity. Second, we analyze the dynamics of the competition between companies by interpreting the cases of discontinuous innovation. This analyzing process enables us to identify the control variable which can, in advance, respond to the discontinuous situation.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.9
• /
• pp.3923-3926
• /
• 2015

Background: Lung cancer is a fatal malignancy with high mortality and short survival time. The aim of this study was to estimate survival rates of Iranian patients with lung cancer and its associate predictive factors. Materials and Methods: The study was conducted on 355 patients admitted to hospitals of West Azerbaijan in the year 2007. The patients were followed up by phone calls until the end of June 2014. The survival rate was estimated using the Kaplan-Meier method and log-rank test for comparison. The Cox's proportional hazard model was used to investigate the effect of various variables on patient survival time, including age, sex, Eastern Cooperative Oncology Group (ECOG) performance, smoking status, tumor type, tumor stage, treatment, metastasis, and blood hemoglobin concentration. Results: Of the 355 patients under study, 240 died and 115 were censored. The mean and median survival time of patients was 13 and 4.8 months, respectively. According to the results of Kaplan-Meier method, 1, 2, and 3 years survival rates were 39%, 18%, and 0.07%, respectively. Based on Cox regression analysis, the risk of death was associated with ECOG group V (1.83, 95% CI: 1 Conclusions: The survival time of the patients with lung cancer is very short. While early diagnosis may improve the life expectancy effective treatment is not available.

• Pediatric Gastroenterology, Hepatology & Nutrition
• /
• v.20 no.1
• /
• pp.22-26
• /
• 2017

Purpose: The diagnosis of cow's milk (CM) allergy is a challenge. The Cow's Milk-related-Symptom-Score ($CoMiSS^{TM}$) was developed to offer primary health care providers a reliable diagnostic tool for CM related symptoms. The predictive prospective value of the $CoMiSS^{TM}$ was evaluated in three clinical trials. Methods: Pooled analyses of the three studies were conducted based on regressing the results of the month-1 challenge test on the month-1 $CoMiSS^{TM}$, adjusting for baseline $CoMiSS^{TM}$ using a logistic regression model. In addition a logistic regression model was also fitted to the month-1 challenge test result with the change in $CoMiSS^{TM}$ from baseline as a predictor. Results: Results suggest that infants having a low $CoMiSS^{TM}$ (median, 5) after 1 month dietary treatment free from intact CM protein have a significant risk of having a positive challenge test (odds ratio, 0.83; 95% confidence interval, 0.75-0.93; p=0.002). Pooled data suggest that the change in $CoMiSS^{TM}$ from baseline to month-1 can predict CM related symptoms as a confirmed diagnosis according to the challenge test at month-1. However, in order to validate such a tool, infants without CM related symptoms would also need to be enrolled in a validation trial. A concern is that it may not be ethical to expose healthy infants to a therapeutic formula and a challenge test. Conclusion: Pooled data analysis emphasizes that the $CoMiSS^{TM}$ has the potential to be of interest in infants suspected to have CM-related-symptoms. A prospective validation trial is needed.

• Health Policy and Management
• /
• v.22 no.2
• /
• pp.263-274
• /
• 2012

This study describes the hypertensive patients characteristics associated with the health incentive point program, and develops and analyzes a simple predictive model of participation in the program. Based on the Incheon Chronic Disease Management System(iCDMS), a model program of community partnership for hypertensive or diabetic patients detection and follow-up since 2005 in Incheon metropolitan city, a cross-sectional design was used in this study. An effective 10.844 adults sample was divided into groups according to participation in the health incentive point program and continuing treatment, and individual and health characteristics among groups were compared. Furthermore, the predictors associated with participation in the program were identified by the logistic regression analysis. After the health incentive point program in iCDMS was introduced, the number of hypertensive patients participation in the program increased 23.9 times which is vastly high given the various programs were provided. There were statistically significant differences among the groups: age(p=0.000), treatment compliance(p=0.000), and blood pressure control at the last measurement(p=0.000), in particular, between participation group(GroupI, n=246) and non-participation group(GroupIII, n=10,408). Furthermore, age over 60 years(OR: 0.33), treatment compliance(OR: 3.49~3.78) and blood pressure controls(OR: 2.13~2.30) were statistically significant predictors associated with participation in the program, based on the logistic regression analysis with GroupI and GroupIII. To increase participation in the health incentive point program, variables such as age, treatment compliance and blood pressure controls are more concerned. And, high-risk patients and family members need targeted health incentive programs.

• The Korean Journal of Applied Statistics
• /
• v.32 no.1
• /
• pp.83-97
• /
• 2019

As society develops, the dissemination of microdata has increased to respond to diverse analytical needs of users. Analysis of microdata for policy making, academic purposes, etc. is highly desirable in terms of value creation. However, the provision of microdata, whose usefulness is guaranteed, has a risk of exposure of personal information. Several methods have been considered to ensure the protection of personal information while ensuring the usefulness of the data. One of these methods has been studied to generate and utilize synthetic data. This paper aims to understand the synthetic data by exploring methodologies and precautions related to synthetic data. To this end, we first explain muptiple imputation, Bayesian predictive model, and Bayesian bootstrap, which are basic foundations for synthetic data. And then, we link these concepts to the construction of fully/partially synthetic data. To understand the creation of synthetic data, we review a real longitudinal synthetic data example which is based on sequential regression multivariate imputation.

• The Journal of Engineering Geology
• /
• v.34 no.1
• /
• pp.107-115
• /
• 2024

This study assesses forest fire susceptibility in Gangwon-do, South Korea, which hosts the largest forested area in the nation and constitutes ~21% of the country's forested land. With 81% of its terrain forested, Gangwon-do is particularly susceptible to wildfires, as evidenced by the fact that seven out of the ten most extensive wildfires in Korea have occurred in this region, with significant ecological and economic implications. Here, we analyze 480 historical wildfire occurrences in Gangwon-do between 2003 and 2019 using 17 predictor variables of wildfire occurrence. We utilized three machine learning algorithms—random forest, logistic regression, and support vector machine—to construct wildfire susceptibility prediction models and identify the best-performing model for Gangwon-do. Forest and soil map data were integrated as important indicators of wildfire susceptibility and enhanced the precision of the three models in identifying areas at high risk of wildfires. Of the three models examined, the random forest model showed the best predictive performance, with an area-under-the-curve value of 0.936. The findings of this study, especially the maps generated by the models, are expected to offer important guidance to local governments in formulating effective management and conservation strategies. These strategies aim to ensure the sustainable preservation of forest resources and to enhance the well-being of communities situated in areas adjacent to forests. Furthermore, the outcomes of this study are anticipated to contribute to the safeguarding of forest resources and biodiversity and to the development of comprehensive plans for forest resource protection, biodiversity conservation, and environmental management.

• Journal of Intelligence and Information Systems
• /
• v.17 no.4
• /
• pp.157-173
• /
• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• Geotechnical Engineering
• /
• v.8 no.1
• /
• pp.81-102
• /
• 1992

Landslides on hillside slopes with shallow soil cover over a sloping bedrock are frequently caused by increases in porewater pressures following of heavy rainfall and it is one of the most important factors of assessing the risk of landslide to predict the groundwater level fluctuations in hillslopes. This paper presents the comparative study of three unsaturated flow models developed by Sloan et al., Reddi, L.N., and Thomas, H.A., Jr., respectively, which are used to predict the increase of groundwater levels in hillside slopes. The parametric study for each of models is also presented. The Kinematic Storage Model(KSM) developed by Sloan et at. is utilized to predict the saturated groundwater flow. They are applied to the two sites in Korea so as to examine the possibility of use in the groundwater flow model. The results show that two unsaturated models developed by Sloan et al. and Reddi, L. N. are largely affected by the uncertain parameters like saturated permeability and saturated water content : the abed model has the potential of use in unsaturated flow model with the optimal estimates of model parameters utilizing available optimization techniques. And it is also found that the KSM must be modified to account for the time delay effect in the saturated zone. The results of this paper are able to be utilized in developing the predictive model of groan dwater level fluctuations in a hillslope.

• Journal of the Korean Society for Marine Environment & Energy
• /
• v.15 no.4
• /
• pp.281-291
• /
• 2012

In this study, WET (whole effluent toxicity) test with Skeletonema costatum, Tigriopus japonicus and Paralichthys olivaceus and ERA (environmental risk assessment) were conducted to assess the unacceptable effect on marine ecosystem by emitting the treated discharge water from 'ARA Plasma BWTS' BWMS (ballast water management system) using filtration, Plasma and MPUV module. 34 psu treated discharge water from ARA Plasma BWTS shown slight chronic toxicity effect on the P. olivaceus ($7d-LC_{50}{\Rightarrow}100.00%$ treated discharge water, $7d-LC_{25}{\Rightarrow}85.15%$ treated discharge water). Bromobenzene, chlorobenzene and 4-chlorotoluene in 34 psu treated discharge water from ARA Plasma BWTS were higher than in the background original content of seawater. The PECs (predictive environmental concentrations) of bromobenzene, chlorobenzene and 4-chlorotoluene calculated by MAMPEC (marine antifoulant model to predict environmental concentrations) program (ver. 3.0) were 3.34E-03, 2.10E-03 and 1.73E-03 ${\mu}g\;L^{-1}$, respectively and PNECs (predicted no effect concentrations) of them were 1.6, 0.5 and 1.9 ${\mu}g\;L^{-1}$. The PEC/PNEC ratio of bromobenzene, chlorobenzene and 4-chlorotoluene did not exceed one and 3 substances did not consider as persistence, bioaccumulative and toxic. Therefore, it was suggested that treated discharge water from ARA Plasma BWTS did not pose unacceptable effect on marine ecosystem.

• Korean Journal of Radiology
• /
• v.23 no.1
• /
• pp.89-100
• /
• 2022

Objective: To improve the N biomarker in the amyloid/tau/neurodegeneration system by radiomics and study its value for predicting cognitive progression in individuals with mild cognitive impairment (MCI). Materials and Methods: A group of 147 healthy controls (HCs) (72 male; mean age ± standard deviation, 73.7 ± 6.3 years), 197 patients with MCI (114 male; 72.2 ± 7.1 years), and 128 patients with Alzheimer's disease (AD) (74 male; 73.7 ± 8.4 years) were included. Optimal A, T, and N biomarkers for discriminating HC and AD were selected using receiver operating characteristic (ROC) curve analysis. A radiomics model containing comprehensive information of the whole cerebral cortex and deep nuclei was established to create a new N biomarker. Cerebrospinal fluid (CSF) biomarkers were evaluated to determine the optimal A or T biomarkers. All MCI patients were followed up until AD conversion or for at least 60 months. The predictive value of A, T, and the radiomics-based N biomarker for cognitive progression of MCI to AD were analyzed using Kaplan-Meier estimates and the log-rank test. Results: The radiomics-based N biomarker showed an ROC curve area of 0.998 for discriminating between AD and HC. CSF Aβ42 and p-tau proteins were identified as the optimal A and T biomarkers, respectively. For MCI patients on the Alzheimer's continuum, isolated A+ was an indicator of cognitive stability, while abnormalities of T and N, separately or simultaneously, indicated a high risk of progression. For MCI patients with suspected non-Alzheimer's disease pathophysiology, isolated T+ indicated cognitive stability, while the appearance of the radiomics-based N+ indicated a high risk of progression to AD. Conclusion: We proposed a new radiomics-based improved N biomarker that could help identify patients with MCI who are at a higher risk for cognitive progression. In addition, we clarified the value of a single A/T/N biomarker for predicting the cognitive progression of MCI.