• Journal of Gastric Cancer
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• v.19 no.1
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• pp.121-131
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• 2019

Purpose: The significance of neuroendocrine differentiation (NED) in gastric carcinoma (GC) is controversial, leading to ambiguous concepts in traditional classifications. This study aimed to determine the prognostic threshold of meaningful NED in GC and clarify its unclear features in existing classifications. Materials and Methods: Immunohistochemical staining for synaptophysin, chromogranin A, and neural cell adhesion molecule was performed for 945 GC specimens. Survival analysis was performed using the log-rank test and univariate/multivariate models with percentages of NED ($P_{NED}$) and demographic and clinicopathological parameters. Results: In total, 275 (29.1%) cases were immunoreactive to at least 1 neuroendocrine (NE) marker. GC-NED was more common in the upper third of the stomach. $P_{NED}$, and Borrmann's classification and tumor, lymph node, metastasis stages were independent prognostic factors. The cutoff $P_{NED}$ was 10%, beyond which patients had significantly worse outcomes, although the risk did not increase with higher $P_{NED}$. Tumors with ${\geq}10%$ NED tended to manifest as Borrmann type III lesion with mixed/diffuse morphology and poorer histological differentiation; the NE components in this population mainly grew in insulae/nests, which differed from the predominant growth pattern (glandular/acinar) in GC with <10% NED. Conclusions: GC with ${\geq}10%$ NED should be classified as a distinct subtype because of its worse prognosis, and more attention should be paid to the necessity of additional therapeutics for NE components.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.126-136
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• 2018

The purpose of this study was to develop a severity-adjustment model for predicting mortality in acute stroke patients using machine learning. Using the Korean National Hospital Discharge In-depth Injury Survey from 2006 to 2015, the study population with disease code I60-I63 (KCD 7) were extracted for further analysis. Three tools were used for the severity-adjustment of comorbidity: the Charlson Comorbidity Index (CCI), the Elixhauser comorbidity index (ECI), and the Clinical Classification Software (CCS). The severity-adjustment models for mortality prediction in patients with acute stroke were developed using logistic regression, decision tree, neural network, and support vector machine methods. The most common comorbid disease in stroke patients were hypertension, uncomplicated (43.8%) in the ECI, and essential hypertension (43.9%) in the CCS. Among the CCI, ECI, and CCS, CCS had the highest AUC value. CCS was confirmed as the best severity correction tool. In addition, the AUC values for variables of CCS including main diagnosis, gender, age, hospitalization route, and existence of surgery were 0.808 for the logistic regression analysis, 0.785 for the decision tree, 0.809 for the neural network and 0.830 for the support vector machine. Therefore, the best predictive power was achieved by the support vector machine technique. The results of this study can be used in the establishment of health policy in the future.

• ALGAE
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• v.34 no.3
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• pp.237-251
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• 2019

The dinoflagellate genus Alexandrium is known to often form harmful algal blooms causing human illness and large-scale mortality of marine organisms. Therefore, the population dynamics of Alexandrium species are of primary concern to scientists and aquaculture farmers. The growth rate of the Alexandrium species is the most important parameter in prediction models and nutrient conditions are critical parameters affecting the growth of phototrophic species. In Korean coastal waters, Alexandrium affine and Alexandrium fraterculus, of similar sizes, often form red-tide patches together. Thus, to understand bloom dynamics of A. affine and A. fraterculus, growth rates and nitrate uptake of each species as a function of nitrate ($NO_3$) concentration at $100{\mu}mol\;photons\;m^{-2}s^{-1}$ under 14-h light : 10-h dark and continuous light conditions were determined using a nutrient repletion method. With increasing $NO_3$ concentration, growth rates and $NO_3$ uptake of A. affine or A. fraterculus increased, but became saturated. Under light : dark conditions, the maximum growth rates of A. affine and A. fraterculus were 0.45 and $0.42d^{-1}$, respectively. However, under continuous light conditions, the maximum growth rate of A. affine slightly increased to $0.46d^{-1}$, but that of A. fraterculus largely decreased. Furthermore, the maximum nitrate uptake of A. affine and A. fraterculus under light : dark conditions were 12.9 and $30.1pM\;cell^{-1}d^{-1}$, respectively. The maximum nitrate uptake of A. affine under continuous light conditions was $16.4pM\;cell^{-1}d^{-1}$. Thus, A. affine and A. fraterculus have similar maximum growth rates at the given $NO_3$ concentration ranges, but they have different maximum nitrate uptake rates. A. affine may have a higher conversion rate of $NO_3$ to body nitrogen than A. fraterculus. Moreover, a longer exposure time to the light may confer an advantage to A. affine over A. fraterculus.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.17 no.1
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• pp.1-13
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• 2019

The accident at the Fukushima Dai-ichi Nuclear Power Plant (FDNPP) resulted in the deposition of large quantities of radionuclides over parts of eastern Japan. Radioactive contaminants have been observed over a large area including forests, cities, rivers and lakes. Due to the strong adsorption of radioactive cesium by soil particles, radioactive cesium migrates with the eroded soil, follows the surface flow paths, and is delivered downstream of population-rich regions and eventually to coastal areas. In this study, we developed a model to simulate the transport of contaminated sediment in a watershed hydrological system and this model was compared with observation data from eroded soil observation instruments located at the Korea Atomic Energy Research Institute. Two methods were applied to analyze the soil particle size distribution of the collected soil samples, including standardized sieve analysis and image analysis methods. Numerical models were developed to simulate the movement of soil along with actual rainfall considering initial saturation, rainfall infiltration, multilayer and rain splash. In the 2019 study, a numerical model will be used to add rainfall shield effect by trees, evaporation effect and shield effects of surface water. An eroded soil observation instrument has been installed near the Wolsong nuclear power plant since 2018 and observation data are being continuously collected. Based on these observations data, we will develop the numerical model to analyze long-term behavior of radionuclides on land as they move from land to rivers, lakes and coastal areas.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2895-2905
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• 2018

We present a mixed multiplicative quantitative randomized response model which added a unrelated quantitative attribute and forced answer to the multiplicative model suggested by Bar-Lev et al. (2004). We also try to set up theoretical grounds for estimating sensitive quantitative attribute according to circumstances whether or not the information for unrelated quantitative attribute is known. We also extend it into the stratified mixed multiplicative quantitative randomized response model for stratified population along with two allocation methods, proportional and optimum allocation. We can see that the various quantitative randomized response models such as Eichhorn-Hayre's model (1983), Bar-Lev et al.'s model (2004), Gjestvang-Singh's model (2007) and Lee's model (2016a), are one of the special occasions of the suggested model. Finally, We compare the efficiency of our suggested model with Bar-Lev et al.'s (2004) and see that the bigger the value of $C_z$, the more the efficiency of the suggested model is obtained.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.67-75
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• 2021

The structure of universities needs to be adjusted and reformed to cope with the decrease in admission resources and the quality of education due to the low birth rate and aging population. Such a policy is receiving much attention. To analyze the relative efficiency of private universities in Korea from the perspective of resource and performance, this study evaluated the efficiency of private university operation by applying a DEA(Data Envelopment Analysis) technique. The DEA measurements were compared with the diagnosis results of the department of education (Government) in 2018. The input and output variables used in the research analysis were utilized by the university's notification materials (public disclosure information). An analysis of the operational efficiency showed that 48% (12 universities) of the 25 DMUs (Decision Making Unit) were efficient for DEA-BCC models and that some of the capacity-building universities were operating efficiently. In addition, the DEA analysis found ways to improve inefficient groups through DEA-Additive results. This paper can be meaningful because it confirmed the relative efficiency of private universities and suggested improvement directions through the DEA method, which is characterized by the simultaneous consideration of various input and output factors. This will help apply the limited resources related to the input and output elements of each university.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.239-247
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• 2022

Cities are becoming more complex due to rapid industrialization and population growth in modern times. In particular, urban areas are rapidly changing due to housing site development, reconstruction, and demolition. Thus accurate road information is necessary for various purposes, such as High Definition Map for autonomous car driving. In the case of the Republic of Korea, accurate spatial information can be generated by making a map through the existing map production process. However, targeting a large area is limited due to time and money. Road, one of the map elements, is a hub and essential means of transportation that provides many different resources for human civilization. Therefore, it is essential to update road information accurately and quickly. This study uses Semantic Segmentation algorithms Such as LinkNet, D-LinkNet, and NL-LinkNet to extract roads from drone images and then apply hyperparameter optimization to models with the highest performance. As a result, the LinkNet model using pre-trained ResNet-34 as the encoder achieved 85.125 mIoU. Subsequent studies should focus on comparing the results of this study with those of studies using state-of-the-art object detection algorithms or semi-supervised learning-based Semantic Segmentation techniques. The results of this study can be applied to improve the speed of the existing map update process.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1633-1641
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• 2022

Urban population concentration and indiscriminate development are causing various environmental problems such as air pollution and heat island phenomena, and causing human resources to deteriorate the damage caused by natural disasters. Urban trees have been proposed as a solution to these urban problems, and actually play an important role, such as providing environmental improvement functions. Accordingly, quantitative measurement and analysis of individual trees in urban trees are required to understand the effect of trees on the urban environment. However, the complexity and diversity of urban trees have a problem of lowering the accuracy of single tree detection. Therefore, we conducted a study to effectively detect trees in Dongjak-gu using high-resolution aerial images that enable effective detection of tree objects and You Only Look Once Version 5 (YOLOv5), which showed excellent performance in object detection. Labeling guidelines for the construction of tree AI learning datasets were generated, and box annotation was performed on Dongjak-gu trees based on this. We tested various scale YOLOv5 models from the constructed dataset and adopted the optimal model to perform more efficient urban tree detection, resulting in significant results of mean Average Precision (mAP) 0.663.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.457-468
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• 2022

In aged society, it is important to prevent older people from being disability needing long-term care. The purpose of this study is to develop a prediction model to discover high-risk groups who are likely to be beneficiaries of Long-Term Care Insurance. This study is a retrospective study using database of National Health Insurance Service (NHIS) collected in the past of the study subjects. The study subjects are 7,724,101, the population over 65 years of age registered for medical insurance. To develop the prediction model, we used logistic regression, decision tree, random forest, and multi-layer perceptron neural network. Finally, random forest was selected as the prediction model based on the performances of models obtained through internal and external validation. Random forest could predict about 90% of the older people in need of long-term care using DB without any information from the assessment of eligibility for long-term care. The findings might be useful in evidencebased health management for prevention services and can contribute to preemptively discovering those who need preventive services in older people.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.619-629
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• 2022

The five major violent crimes including murder, robbery, rape·forced indecent act, theft, and violence are representative crimes that threaten the safety of members of society and occur frequently in real life. These crimes have negative effects such as lowering the quality of citizens' life. In the case of Seoul, the capital of Korea, the risk for the five major violent crimes is increasing because the population density of Seoul is increasing as a large number of people in the provinces move to Seoul. In this study, to reduce this risk, the relative risk for the occurrence of the five major violent crimes in Seoul is modeled using three spatio-temporal models. In addition, various risk factors are included to identify factors that significantly affect the relative risk of the five major violent crimes. The best model is selected in terms of the deviance information criterion, and the analysis results including various visualizations for the best model are provided. This study will help to establish efficient strategies to sustain people's safe everyday living by analyzing important risk factors affecting the risk of the five major violent crimes and the relative risk of each region.