• Journal of Korean Neurosurgical Society
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• v.67 no.1
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• pp.31-41
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• 2024

Objective : Collateral circulation is associated with the differential treatment effect of endovascular thrombectomy (EVT) in acute ischemic stroke. We aimed to verify the ability of the collateral map to predict futile EVT in patients with acute anterior circulation ischemic stroke. Methods : This secondary analysis of a prospective observational study included data from participants underwent EVT for acute ischemic stroke due to occlusion of the internal carotid artery and/or the middle cerebral artery within 8 hours of symptom onset. Multiple logistic regression analyses were conducted to identify independent predictors of futile recanalization (modified Rankin scale score at 90 days of 4-6 despite of successful reperfusion). Results : In a total of 214 participants, older age (odds ratio [OR], 2.40; 95% confidence interval [CI], 1.56 to 3.67; p<0.001), higher baseline National Institutes of Health Stroke Scale (NIHSS) scores (OR, 1.12; 95% CI, 1.04 to 1.21; p=0.004), very poor collateral perfusion grade (OR, 35.09; 95% CI, 3.50 to 351.33; p=0.002), longer door-to-puncture time (OR, 1.08; 95% CI, 1.02 to 1.14; p=0.009), and failed reperfusion (OR, 3.73; 95% CI, 1.30 to 10.76; p=0.015) were associated with unfavorable functional outcomes. In 184 participants who achieved successful reperfusion, older age (OR, 2.30; 95% CI, 1.44 to 3.67; p<0.001), higher baseline NIHSS scores (OR, 1.12; 95% CI, 1.03 to 1.22; p=0.006), very poor collateral perfusion grade (OR, 4.96; 95% CI, 1.42 to 17.37; p=0.012), and longer door-to-reperfusion time (OR, 1.09; 95% CI, 1.03 to 1.15; p=0.003) were associated with unfavorable functional outcomes. Conclusion : The assessment of collateral perfusion status using the collateral map can predict futile EVT, which may help select ineligible patients for EVT, thereby potentially reducing the rate of futile EVT.

• Spatial Information Research
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• v.18 no.5
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• pp.119-131
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• 2010

Researchers require repetitive works such as data format analysis, reformatting and map reprojection in order to use geospatial data. To solve above problems, they are building web-based simulation systems with web developers. But the web-based systems are not efficiently developed because there is not the appropriate simulation framework for a web-based system using geospatial data. In this study, the geospatial simulation framework that can be effectively applied to the web-based system was designed and proposed. Also, the framework was composed of 7 modules; web mapping service, GIS mapping, statistics, model, processing,graphics, and geospatial datasets. In order to evaluate the effectiveness of the framework, a case study of urban growth has been verified. Experts who are not specialized in geospatial information disciplines expect to build easily a web-based system using geospatial data.

• Journal of Advanced Navigation Technology
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• v.19 no.6
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• pp.484-491
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• 2015

Aerial scene is greatly increased by the introduction and supply of the image due to the growth of digital optical imaging technology and development of the UAV. It has been used as the extraction of ground properties, classification, change detection, image fusion and mapping based on the aerial image. In particular, in the image analysis and utilization of deep learning algorithm it has shown a new paradigm to overcome the limitation of the field of pattern recognition. This paper presents the possibility to apply a more wide range and various fields through the segmentation and classification of aerial scene based on the Deep learning(ConvNet). We build 4-classes image database consists of Road, Building, Yard, Forest total 3000. Each of the classes has a certain pattern, the results with feature vector map come out differently. Our system consists of feature extraction, classification and training. Feature extraction is built up of two layers based on ConvNet. And then, it is classified by using the Multilayer perceptron and Logistic regression, the algorithm as a classification process.

• Environmental Analysis Health and Toxicology
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• v.28
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• pp.10.1-10.4
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• 2013

Objectives Children are one of the most vulnerable populations to the impact of disasters. We aimed to examine children's mental health in the area affected by the Hebei Spirit oil spill accident on December 7, 2007. Methods A cross-sectional questionnaire survey was conducted using the Korean versions of the Children's Depression Inventory and State Anxiety Inventory for Children on 1,362 children attending elementary schools in the affected area. The information on distances between the nearest contaminated coastline to the child's residential house or attending school were obtained using a web-based map by inputting two address points. The symptom risks of depression and state anxiety were estimated by multiple logistic regression analyses adjusted for age, gender, and other covariates. Results Children with the closest distance (in the fourth quartile) to the school from the contaminated coastline showed a significantly higher symptom risk of depression compared to those with the farthest distance (first quartile)(odds ratio, 2.73; 95% confidence interval, 1.40-5.33), while there was no significant association between anxiety symptoms and distance. Conclusions Children, a vulnerable population for mental health impact by the oil spill accident, should be included in mental health programs in the community along with their family as victims of the disaster.

• The Korean Journal of Applied Statistics
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• v.26 no.4
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• pp.623-634
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• 2013

In this study, we provide empirical evidence of whether a spatial correlation among mode choices at the TAZ(Traffic Analysis Zone) level exists based on transit smart card data observed in Seoul, Korea. The results show that the areas with a higher probability that passengers choose to take a bus are clustered and that those regions have fewer metro stations than bus stations. We also found that the spatial correlation turned out to be statistically meaningful and provided an opportunity for the potential use of the spatial correlation in modeling mode choices. A reliable spatial interaction would constitute valuable information for transportation agencies in terms of their route planning and scheduling based on the transit smart card data.

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1077-1094
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• 2016

Apache Spark is a fast and general-purpose cluster computing package. It provides a new abstraction named resilient distributed dataset, which is capable of support for fault tolerance while keeping data in memory. This type of abstraction results in a significant speedup compared to legacy large-scale data framework, MapReduce. In particular, Spark framework is suitable for iterative machine learning applications such as logistic regression and K-means clustering, and interactive data querying. Spark also supports high level libraries for various applications such as machine learning, streaming data processing, database querying and graph data mining thanks to its versatility. In this work, we introduce the concept and programming model of Spark as well as show some implementations of simple statistical computing applications. We also review the machine learning package MLlib, and the R language interface SparkR.

• Journal of dental hygiene science
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• v.19 no.1
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• pp.48-59
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• 2019

Background: The purpose of this study is to provide the data for discussions related to oral health promotion policies for single-person households by analyzing the status of unmet dental needs and related factors in single-person households in Korea, based on the Anderson model. Methods: The data, obtained from 544 single-person households of those over 20 years old who were targeted for the 6th Korea National Health and Nutrition Examination Survey, were analyzed through a complex sample frequency analysis, complex sample cross analysis (Rao-Scott chi-square test), and complex sample binary logistic regression analysis on a complex sampling design. Results: The most frequently given reason for an unmet dental need among single-person households was economic (52.4%). Factors related to the unmet dental needs of single-person households are smoking, which is a predisposing factor; personal income levels, which are an enabling factor; chewing discomfort; and limited daily activities, which are need factors. Smokers, the high-income group, the chewing-discomfort group, and the limited activity group showed high unmet dental care experience. Smokers had a 2.75 times higher rate of unmet dental care than non-smokers, and the high-income group had a 5.29 times higher rate of unmet dental needs than the median group. The rate of unmet dental needs for the chewing discomfort group was 3.27 times higher than the non-chewing discomfort group, and the limited activity group had a 7.87 times higher rate of unmet dental needs than the non-limited activity group. Conclusion: It is necessary to map out policies designed to help maintain and promote met dental needs considered to be internally heterogeneous to single-person householders, based on the Anderson model.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.179-193
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• 2019

Landslides are one of the most damaging geological hazards worldwide, threating both humans and property. Hence, there have been many efforts to prevent landslides and mitigate the damage that they cause. Among such efforts, there have been many studies on mapping landslide susceptibility. Geographic information system (GIS)-based techniques have been developed and applied widely, and are now the main tools used to map landslide susceptibility. We reviewed the status of landslide susceptibility mapping using GIS by number of papers, year, study area, number of landslides, cause, and models applied, based on 776 articles over the last 20 years (1999-2018). The number of studies published annually increased rapidly over time. The total study area spanned 65 countries, and 47.7% of study areas were in China, India, South Korea, and Iran, where more than 500 landslides, 27.3% of all landslides, have occurred. Slope (97.6% of total articles) and geology (82.7% of total articles) were most often implicated as causes, and logistic regression (26.9% of total articles) and frequency ratio (24.7% of total article) models were the most widely used models. We analyzed trends in the causes of and models used to simulate landslides. The main causes were similar each year, but machine learning models have increased in popularity over time. In the future, more study areas should be investigated to improve the generalizability and accuracy of the results. Furthermore, more causes, especially those related to topography and soil, should be considered and more machine learning models should be applied. Finally, landslide hazard and risk maps should be studied in addition to landslide susceptibility maps.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.99-112
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• 2021

This study was performed to predict spatial change of future forestland area in South Korea at regional level for supporting forest-related plans established by local governments. In the study, land use was classified to three types which are forestland, agricultural land, and urban and other lands. A logistic regression model was developed using transitional interaction between each land use type and topographical factors, land use restriction factors, socioeconomic indices, and development infrastructures. In this model, change probability from a target land use type to other land use types was estimated using raster dataset(30m×30m) for each variable. With priority order map based on the probability of land use change, the total annual amount of land use change was allocated to the cells in the order of the highest transition potential for the spatial analysis. In results, it was found that slope degree and slope standard value by the local government were the main factors affecting the probability of change from forestland to urban and other land. Also, forestland was more likely to change to urban and other land in the conditions of a more gentle slope, lower slope criterion allowed to developed, and higher land price and population density. Consequently, it was predicted that forestland area would decrease by 2027 due to the change from forestland to urban and others, especially in metropolitan and major cities, and that forestland area would increase between 2028 and 2050 in the most local provincial cities except Seoul, Gyeonggi-do, and Jeju Island due to locality extinction with decline in population. Thus, local government is required to set an adequate forestland use criterion for balanced development, reasonable use and conservation, and to establish the regional forest strategies and policies considering the future land use change trends.

• Journal of Yeungnam Medical Science
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• v.16 no.2
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• pp.333-341
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• 1999

Background and Methods: In order to evaluate characteristics and modulatory factors of blood pressure in peritoneal dialysis(PD), studies were conducted on the 69 patients who had underwent peritoneal equilibration test(PET). Results: The results were as follows; 1) All patients received an antihypertensive drug before PD, but, 15 of 69 patients successfully quit taking the antihypertensive drug after peritoneal dialysis. 2) During peritoneal dialysis, mean arterial pressure(MAP) was significantly decreased for the first 3 months, and this lasted for 1 year, and antihypertensive drug requirements were significantly decreased continuously up to 9 months(p<0.05). 3) After changing the modality from hemodialysis to peritoneal dialysis, MAP(mmHg, from $107.0{\pm}4.5$ to $98.6{\pm}8.8$, p<0.05), antihypertensive drug requirements(from $5.6{\pm}2.6$, to $2.0{\pm}2.5$, p<0.01) and erythropoietin dosages(Uint/week, from $4600{\pm}2660$ to $2000{\pm}1630$, p<0.05) were decreased. 4) Multiple logistic regression analysis showed that MAP(p<0.01) and daily ultrafiltration volume(p<0.05) can contribute to the determination of antihypertensive drug requirements. However the relationship between antihypertensive drug requirements and PET results or dialysis adequacy indices(weekly Kt/V, weekly creatinine clearance) was not revealed. Conclusion: In conclusion, the prescription of antihypertensive drugs should be considered according to daily ultrafiltration volume, especially during first year after initiating PD, and follow-ups for over a year may be needed.