• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.370-370
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• 2017

최근 기후변화와 이상기후의 영향으로 인하여 홍수재해의 시 공간적 패턴은 보다 복잡해지고, 예측이 어려워지고 있다. 이러한 기상이변에 따른 홍수피해를 예방하기 위한 비구조적 대책으로 홍수위험등급 및 범람범위 등의 정보를 포함하고 있는 홍수위험지도의 작성이 필요하다. 실제로 고정밀도 홍수위험지도를 작성하기 위해서는 지형, 지질, 기상 등의 디지털 정보 및 사회 경제와 관련된 다양한 DB를 필요로 하며, 강우-유출-범람해석 모델링을 통해 범람면적 및 침수깊이 등의 정보를 획득하게 된다. 하지만 일부지역, 특히 개발도상국에서는 이러한 계측 홍수 데이터가 부족하거나 획득할 수가 없어 홍수위험지도 제작이 불가능하거나 그 정확도가 매우 낮은 실정이다. 따라서 본 연구에서는 ASTER 또는 SRTM과 같은 범용 DEM 등 지형자료만을 기반으로 한 선형이진분류법(Liner binary classifiers)과 ROC분석(Receiver Operation Characteristics)을 이용하여 미계측 유역 (DB부재 또는 부족으로 강우-유출-범람해석 모델링이 불가능한 북한지역)의 홍수위험지역을 탐지하고, 적용성을 평가하고자 한다. 5개의 단일 지형학적 지수와 6개의 복합 지형학적 지수를 이용하여 Area Under the Curve (AUC)를 계산하고, Sensitivity (민감도)와 Specificity (특이도)가 가장 높은 지수를 선별하여 홍수위험지도를 작성하고, 실제 홍수범람 영상(2007년 북한 함경남도지역 용흥강 홍수)과 비교 분석하였다. 본 연구에서 제시하는 선형이진분류법과 ROC분석 방법은 홍수범람해석을 위한 다양한 기초정보를 필요로 하지 않고, 지형정보만을 사용하기 때문에 관측 데이터가 없거나 부족한 지역에 대해서 우선적으로 홍수위험지역을 탐지하고, 선별하는데 유용할 것으로 판단된다.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.5
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• pp.39-47
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• 2021

With rising concerns about pesticide spray drifts, this study analyzed the drift patterns of two typically-used nozzles, XR nozzle and AI nozzle, concerning their working pressures and wind speeds by wind tunnel experiments. AI nozzle showed low drift potential with larger droplet sizes compared to XR nozzle. Airborne and deposition drifts of XR nozzle were two times higher than those of AI nozzle under high wind speeds (≥2 m s^-1). In all cases, higher working pressures decreased the droplet sizes, thereby increasing the airborne and deposition drifts. Higher wind speeds also resulted in more airborne drifts, while ground deposition was increased under lower wind speeds. These effects of working pressures and wind speeds on the airborne and deposition drifts were observed at leeward distances less than 4 m from the nozzles. However, the airborne and deposition drifts were barely affected by the working pressures and wind speeds at leeward distances more than 11 m. The measurements were fitted to regression models of the drift curve with acceptable R² values greater than 0.8, demonstrating that further studies will be useful to settle domestic issues of spray drifts.

• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.323-330
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• 2018

The growth of the older population is expected to further increase social problems associated with population aging, such as isolation, poverty, and depression. The emerging issues associated with the older population are also expected to provide further momentum on studies about the dwelling environment as factors that ensure the health of older people as well as improve their quality of life. Therefore, approaches for explaining the issues of the older age group should be diversified using a variety of factors and appropriate analytic tools. Studies on measuring depression have principally focused on assessing an objective self-report questionnaire, usually in a highly structured, textual form which may not reflect the cognitive impairment of older adults. The aim of this study was to define and measure dwelling depression among older adults in Korea. There are two specific hypotheses in this study as follows: (a) there will be statistically significant relationships with dwelling dissatisfaction and depression, and (b) dwelling depression tools containing text and images will be, respectively, assessment tools that have a good construct with content validity and reliability. In the first experiment, to define and measure dwelling depression, 301 people over 65 years old living in single and two-person households were surveyed using a text-based dwelling depression questionnaires from September 1-30, 2017. In the second experiment, to examine whether the projective image questionnaire could serve as a suitable replacement for the text-based questionnaires, the same participants were surveyed from January 22 to February 2, 2018. The results show that depression has a close correlation with dwelling dissatisfaction. In addition, the geriatric dwelling depression index (GDDI) based on the projective image was refined. Additionally, the projective image questionnaire has a close correlation with the text-based questionnaire. Finally, through ROC curve analysis, it was found that the projective image questionnaire can accurately predict a depression group. To this end, this preliminary study examined the validity of the projective image questionnaire in older adults to make this instrument feasible for older populations and to contribute to a profound understanding of geriatric depression due to the living environment. We hope they will provide a basis for further research on psychological diagnoses using projective images.

• Nutrition Research and Practice
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• v.13 no.1
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• pp.11-16
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• 2019

BACKGROUND/OBJECTIVES: Fasting and postprandial hyperglycemia should be controlled to avoid complications of diabetes mellitus. This study investigated the effects of autumn olive (Elaeagnus umbellata Thunb.) berry (AOB) on fasting and postprandial hyperglycemia in mice. MATERIALS/METHODS: In vitro ${\alpha}$-glucosidase inhibitory effect of AOB was determined. Maltose solution (2 g/kg) with and without AOB extract at 500 mg/kg or acarbose at 50 mg/kg was orally administered to normal mice after overnight fasting and glucose levels were measured. To study the effects of chronic consumption of AOB, db/db mice received the basal diet or a diet containing AOB extract at 0.4% or 0.8%, or acarbose at 0.04% for 7 weeks. Blood glycated hemoglobin and serum glucose and insulin levels were measured. Expression of adiponectin protein in epididymal white adipose tissue was determined by Western blotting. RESULTS: In vitro inhibitory effect of AOB extract on ${\alpha}$-glucosidase was 92% as strong as that of acarbose. The AOB extract (500 mg/kg) or acarbose (50 mg/kg) significantly suppressed the postprandial rise of blood glucose after maltose challenge and the area under the glycemic response curve in normal mice. The AOB extract at 0.4% or 0.8% of diet or acarbose at 0.04% of diet significantly lowered levels of serum glucose and blood glycated hemoglobin and homeostasis model assessment for insulin resistance values in db/db mice. The expression of adiponectin protein in adipose tissue was significantly elevated by the consumption of AOB at 0.8% of diet. CONCLUSIONS: Autumn olive (E. umbellata Thunb.) berry may reduce postprandial hyperglycemia by inhibiting ${\alpha}$-glucosidase in normal mice. Chronic consumption of AOB may alleviate fasting hyperglycemia in db/db mice partly by inhibiting ${\alpha}$-glucosidase and upregulating adiponectin expression.

• Biomedical Science Letters
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• v.24 no.4
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• pp.418-425
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• 2018

Amyloid brain positron emission tomography (PET) images are visually and subjectively analyzed by the physician with a lot of time and effort to determine the ${\beta}$-Amyloid ($A{\beta}$) deposition. We designed a convolutional neural network (CNN) model that predicts the $A{\beta}$-positive and $A{\beta}$-negative status. We performed 18F-florbetaben (FBB) brain PET on controls and patients (n=176) with mild cognitive impairment and Alzheimer's Disease (AD). We classified brain PET images visually as per the on the brain amyloid plaque load score. We designed the visual geometry group (VGG16) model for the visual assessment of slice-based samples. To evaluate only the gray matter and not the white matter, gray matter masking (GMM) was applied to the slice-based standard samples. All the performance metrics were higher with GMM than without GMM (accuracy 92.39 vs. 89.60, sensitivity 87.93 vs. 85.76, and specificity 98.94 vs. 95.32). For the patient-based standard, all the performance metrics were almost the same (accuracy 89.78 vs. 89.21), lower (sensitivity 93.97 vs. 99.14), and higher (specificity 81.67 vs. 70.00). The area under curve with the VGG16 model that observed the gray matter region only was slightly higher than the model that observed the whole brain for both slice-based and patient-based decision processes. Amyloid brain PET images can be appropriately analyzed using the CNN model for predicting the $A{\beta}$-positive and $A{\beta}$-negative status.

• Mass Spectrometry Letters
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• v.10 no.2
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• pp.43-49
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• 2019

The aim of this study was conducted to develop an analytical method to determine the concentration of ceftiofur residue in eel, flatfish, and shrimp. For derivatization and extraction, the sample was hydrolyzed with dithioerythritol to produce desfuroylceftiofur, which was then derivatized by iodoacetamide to obtain desfuroylceftiofur acetamide. For purification, the process of solid phase extraction (Oasis HLB) was used. The target analytes were confirmed and quantified in $C_{18}$ column using liquid chromatography-tandem mass spectrometry with 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B) as the mobile phase. The linearity of the standard calibration curve was confirmed by a correlation coefficient, $r^2>0.99$. The limit of quantification for ceftiofur was 0.002 mg/kg; the accuracy (expressed as the average recoveries) was 80.6-105%; the precision (expressed as the coefficient of variation) was below 6.3% at 0.015, 0.03, and 0.06 mg/kg. The validated method demonstrated high accuracy and acceptable sensitivity to meet the Codex guideline requirements. The developed method was tested using market samples. As a results, ceftiofur was detected in one sample. Therefore, it can be applied to the analysis of ceftiofur residues in fishery products.

• Journal of Yeungnam Medical Science
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• v.36 no.3
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• pp.231-240
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• 2019

Background: We sought to determine the value of combining diffusion-weighted (DW) and perfusion-weighted (PW) sequences with a conventional magnetic resonance (MR) sequence to assess solid components of borderline ovarian tumors (BOTs) and stage I carcinomas. Methods: Conventional, DW, and PW sequences in the tumor imaging studies of 70 patients (BOTs, n=38; stage I carcinomas, n=32) who underwent surgery with pathologic correlation were assessed. Two independent radiologists calculated the parameters apparent diffusion coefficient (ADC), $K^{trans}$ (vessel permeability), and $V_e$ (cell density) for the solid components. The distribution on conventional MR sequence and mean, standard deviation, and 95% confidence interval of each DW and PW parameter were calculated. The inter-observer agreement among the two radiologists was assessed. Area under the receiver operating characteristic curve (AUC) and multivariate logistic regression were performed to compare the effectiveness of DW and PW sequences for average values and to characterize the diagnostic performance of combined DW and PW sequences. Results: There were excellent agreements for DW and PW parameters between radiologists. The distributions of ADC, $K^{trans}$, and $V_e$ values were significantly different between BOTs and stage I carcinomas, yielding AUCs of 0.58 and 0.68, 0.78 and 0.82, and 0.70 and 0.72, respectively, with ADC yielding the lowest diagnostic performance. The AUCs of the DW, PW, and combined PW and DW sequences were $0.71{\pm}0.05$, $0.80{\pm}0.05$, and $0.85{\pm}0.05$, respectively. Conclusion: Combining PW and DW sequences to a conventional sequence potentially improves the diagnostic accuracy in the differentiation of BOTs and stage I carcinomas.

• Journal of Gastric Cancer
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• v.19 no.3
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• pp.278-289
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• 2019

Purpose: Phase angle obtained using bioelectrical impedance analysis (BIA) provides a relatively precise assessment of the nutritional status of elderly patients. This study aimed to evaluate the significance of phase angle as a risk factor for complications after gastrectomy in elderly patients. Materials and Methods: We evaluated 210 elderly patients (aged ${\geq}65years$) who had undergone gastrectomy for gastric cancer between August 2016 and August 2017. The phase angle cutoff value was calculated using receiver operating characteristic curve analysis according to sex. A retrospective analysis regarding the correlation between early postoperative complications and well-known risk factors, including the phase angle, was performed. Results: Multivariate analysis revealed that the presence of two or more comorbidities (odds ratio [OR], 3.675) and hypoalbuminemia (OR, 4.059) were independent risk factors for overall complications, and female sex (OR, 2.993) was independent risk factor for severe complications. A low phase angle (OR, 2.901 and 4.348, respectively) and total gastrectomy (OR, 4.718 and 3.473, respectively) were independent risk factors for both overall and severe complications. Conclusions: Our findings show that preoperative low phase angle predicts the risk of overall and severe complications. Our findings suggest that BIA should be performed to assess the risk of postoperative complications in elderly patients with gastric cancer.

• Acute and Critical Care
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• v.33 no.4
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• pp.222-229
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• 2018

Background: The diagnosis of pediatric acute respiratory distress syndrome (PARDS) is a pragmatic decision based on the degree of hypoxia at the time of onset. We aimed to determine whether reclassification using oxygenation metrics 24 hours after diagnosis could provide prognostic ability for outcomes in PARDS. Methods: Two hundred and eighty-eight pediatric patients admitted between January 1, 2010 and January 30, 2017, who met the inclusion criteria for PARDS were retrospectively analyzed. Reclassification based on data measured 24 hours after diagnosis was compared with the initial classification, and changes in pressure parameters and oxygenation were investigated for their prognostic value with respect to mortality. Results: PARDS severity varied widely in the first 24 hours; 52.4% of patients showed an improvement, 35.4% showed no change, and 12.2% either showed progression of PARDS or died. Multivariate analysis revealed that mortality risk significantly increased for the severe group, based on classification using metrics collected 24 hours after diagnosis (adjusted odds ratio, 26.84; 95% confidence interval [CI], 3.43 to 209.89; P=0.002). Compared to changes in pressure variables (peak inspiratory pressure and driving pressure), changes in oxygenation (arterial partial pressure of oxygen to fraction of inspired oxygen) over the first 24 hours showed statistically better discriminative power for mortality (area under the receiver operating characteristic curve, 0.701; 95% CI, 0.636 to 0.766; P<0.001). Conclusions: Implementation of reclassification based on oxygenation metrics 24 hours after diagnosis effectively stratified outcomes in PARDS. Progress within the first 24 hours was significantly associated with outcomes in PARDS, and oxygenation response was the most discernable surrogate metric for mortality.

• Journal of The Korean Society of Emergency Medicine
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• v.29 no.5
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• pp.455-464
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• 2018

Objective: Machine learning is not yet widely used in the medical field. Therefore, this study was conducted to compare the performance of preexisting severity prediction models and machine learning based models (random forest [RF], gradient boosting [GB]) for mortality prediction in pneumonia patients. Methods: We retrospectively collected data from patients who visited the emergency department of a tertiary training hospital in Seoul, Korea from January to March of 2015. The Pneumonia Severity Index (PSI) and Sequential Organ Failure Assessment (SOFA) scores were calculated for both groups and the area under the curve (AUC) for mortality prediction was computed. For the RF and GB models, data were divided into a test set and a validation set by the random split method. The training set was learned in RF and GB models and the AUC was obtained from the validation set. The mean AUC was compared with the other two AUCs. Results: Of the 536 investigated patients, 395 were enrolled and 41 of them died. The AUC values of PSI and SOFA scores were 0.799 (0.737-0.862) and 0.865 (0.811-0.918), respectively. The mean AUC values obtained by the RF and GB models were 0.928 (0.899-0.957) and 0.919 (0.886-0.952), respectively. There were significant differences between preexisting severity prediction models and machine learning based models (P<0.001). Conclusion: Classification through machine learning may help predict the mortality of pneumonia patients visiting the emergency department.