• 대한간호학회지
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• 제41권6호
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• pp.758-767
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• 2011

Purpose: This study was done to examine the threshold value of estimated height loss at which the risk of osteoporosis increases and to verify its discriminative ability in the detection of osteoporosis. Methods: It was conducted based on epidemiological descriptive methods on 732 Korean women at a public healthcare center in Seoul between July and November 2010. ANOVA, Pearson correlation, logistic regression analysis and receiver operating characteristics (ROC) curve were used for data analysis. Results: There was an age-related correlation between bone mineral density (lumbar spine: F=37.88, p<.001; femur: F=54.27, p<.001) and estimated height loss (F=27.68, p<.001). Estimated height loss increased significantly with decreasing bone mineral density (lumbar spine: r=-.23, p<.001; femur: r=-.34, p<.001). The odds ratio for the point at which the estimated height loss affects the occurrence of osteoporosis was found to increase at a cut-off value of 2 cm and the area under ROC curve was .71 and .82 in lumbar spine and femur, respectively. Conclusion: The optimal cut-off value of the estimated height loss for detection of osteoporosis was 2 cm. Height loss is therefore a useful indicator for the self-assessment and prognosis of osteoporosis.

• 한국식물병리학회지
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• 제13권1호
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• pp.37-45
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• 1997

Disease incidence (DI), pre-emergence damping-off (PDO), days until the first symptom appeared (DUS), disease progress curve (DPC), and area under disease progress curve (AUDPC) were investigated in vivo after sowing ginseng seeds in each of 37 ginseng-cultivated soils which were sampled from 4 regions in Korea. Non linear fitting parameters, A, B, K and M, were estimated from the Richards' function, one of the disease progress models, by using the DI at each day from the bioassay. Inter- and intra-relationships between disease variables and stand-missing rate (SMR) in fields were investigated by using the simple correlation analysis. Disease variables of the root rot were divided into two groups: variables related to disease incidence, e.g., DI, AUDPC and A parameter, and variables related to disease progress, e.g., B, K and M parameters. DI, AUDPC, and DUS had significant correlations with SMR in ginseng fields, and then it showed that the disease development in vivo corresponded with that in fields. Soil samples could be separated into 3 and 4 groups, respectively, on the basis of the principal component 1 (PC1) and the principal component 2 (PC2), which were derived from the principal component analysis (PCA) of Richards' parameters, A, B, K and M. PC1 accounted for B, K and M parameters, and PC2 accounted for A parameter.

• Asian-Australasian Journal of Animal Sciences
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• 제18권6호
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• pp.873-878
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• 2005

Emissions of nitrous oxide (N$_2$O) and methane (CH$_4$) from poultry enteric fermentation were investigated using a respiration chamber. Birds were placed in a respiration chamber for certain intervals during their growing period or for the whole life cycle. The accumulated gas inside the chamber was sampled and analyzed for N$_2$O and CH$_4$ production. A curve for gas production during a life cycle was fitted. The calculated area under the curve estimated the emission factor of poultry enteric fermentation on a life cycle basis (mg bird$^{-1}$ life cycle$^{-1}$). This method can be used to estimate CH$_4$ or N$_2$O emissions from different types of avian species taking into account factors such as diet, season or thermal effects. The CH$_4$/N$_2$O emission factors estimated for commercial broiler chickens, Taiwan country chickens and White Roman Geese were 15.87/0.03, 84.8/16.4 and 1,500/49 (mg bird$^{-1}$ life cycle$^{-1}$), respectively, while the calculated CH$_4$/N$_2$O emission from enteric fermentations were 3.03/0.006, 14.73/2.84 and 9.5/0.31 (Mg year$^{-1}$), respectively in Taiwan in the year of 2000. The described method is applicable to most poultry species and the reported emission factors were applicable to meat type poultry only.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2005년도 전기학술대회논문집
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• pp.395-400
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• 2005

The ship hull part is always exposed to severe corrosive environments. Therefore, it should be protected in appropriate ways to reduce corrosion problems. So there are two effective methods in order to protect the corrosion of ship hull. One is the paint coating as a barrier between steel and electrolyte (seawater) and the other is the cathodic protection(CP) supplying protection current. In the conventional design process of the cathodic protection system the required current densities of protected materials have been used. However, the anode position of field or laboratory experiment for obtaining the required current density for CP is significantly different from anode position for real structures. Therefore, the recent CP design must consider the optimum anode position for potential distribution equally over the ship hull. The CP design companies in the advanced countries can obtain the potential distribution results on the cathodic materials by using the computer analysis module. This study would show how to approach the potential analysis in the field of corrosion engineering. The computer program can predict the under protection area on the structure when the boundary condition and analysis procedure are reasonable. In this analysis the polarization curve is converted to the boundary condition in material data.

• 태양에너지
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• 제20권1호
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• pp.21-29
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• 2000

본 연구에서는 하루중의 각 시간별로 서로 다른 경사각도별로 수광면에 입사하는 태양에너지의 강도를 실측을 통하여 정량적인 검토와 분석이 가능하도록 측정된 데이터를 기술자료화하고, 각각의 날별로 일사량 강도가 유사한 날들을 그룹화하기 위하여 시간별 일사량 변화에 따른 다항회귀모형을 일별로 산출하고, 각 날별로 서로 비교하기 위해 일별로 산출된 다항회귀모형의 두 그래프사이의 면적을 계산하여 면적의 차가 거의 없는 날들을 일사량 강도가 유사한 날들로 그룹화하는 기법을 개발하여, 월별 또는 전년을 통하여 이들 그룹을 모형별로 제시한다. 또한 다시 이들 모형별로 태양에너지 이용시스템의 효율을 최대화 할 수 있는 시스템 최적경사각도를 제시하여 해당 지역에 적합한 시스템의 최적 설계기준을 제시하고자 한다.

• 대한간호학회지
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• 제50권6호
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• pp.787-798
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• 2020

Purpose: This study aimed to assign weights for subscales and items of the Post-Intensive Care Syndrome questionnaire and suggest optimal cut-off values for screening unplanned hospital readmissions of critical care survivors. Methods: Seventeen experts participated in an analytic hierarchy process for weight assignment. Participants for cut-off analysis were 240 survivors who had been admitted to intensive care units for more than 48 hours in three cities in Korea. We assessed participants using the 18-item Post-Intensive Care Syndrome questionnaire, generated receiver operating characteristic curves, and analysed cut-off values for unplanned readmission based on sensitivity, specificity, and positive likelihood ratios. Results: Cognitive, physical, and mental subscale weights were 1.13, 0.95, and 0.92, respectively. Incidence of unplanned readmission was 25.4%. Optimal cut-off values were 23.00 for raw scores and 23.73 for weighted scores (total score 54.00), with an area of under the curve (AUC) of .933 and .929, respectively. There was no significant difference in accuracy for original and weighted scores. Conclusion: The optimal cut-off value accuracy is excellent for screening of unplanned readmissions. We recommend that nurses use the Post-Intensive Care Syndrome Questionnaire to screen for readmission risk or evaluating relevant interventions for critical care survivors.

• Journal of Veterinary Science
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• 제19권6호
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• pp.840-845
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• 2018

The purpose of this study was to propose a standard for differentiation between normal dogs and patients with hyperadrenocorticism (HAC) by measuring skin thickness via ultrasonography in small breed dogs. Significant changes in skin thickness of patients treated with prednisolone (PDS) or patients with HAC treated with trilostane were evaluated. Skin thickness was retrospectively measured on three abdominal digital images obtained from small breed dogs weighing < 15 kg that underwent abdominal ultrasonography. Mean skin thickness of normal dogs was $1.03{\pm}0.25mm$ (mean ${\pm}$ SD). Both the HAC and PDS groups showed significantly thinner skin than that in the normal group. Seven of the 10 HAC patients treated with trilostane had increased skin thickness. The area under the curve value of 0.807 was based on the receiver operating characteristics (ROC) curve for differentiating normal dogs from HAC patients. Sensitivity was 76% and specificity was 73% when skin thickness was less than the 0.83 mm cutoff value. In conclusion, measurement of skin thickness in small breed dogs by using ultrasonography is likely to provide clinical information useful in differentiating HAC patients from normal dogs. However, exposure to PDS, trilostane, and other conditions may have a significant effect on skin thickness.

• Clinical and Experimental Reproductive Medicine
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• 제48권2호
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• pp.156-162
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• 2021

Objective: The aim of the present study was to evaluate the predictive capability of fasting-state measurements of glucose and insulin levels alone for abnormal glucose tolerance in women with polycystic ovary syndrome (PCOS). Methods: In total, 153 Korean women with PCOS were included in this study. The correlations between the 2-hour postload glucose (2-hr PG) level during the 75-g oral glucose tolerance test (OGTT) and other parameters were evaluated using Pearson correlation coefficients and linear regression analysis. The predictive accuracy of fasting glucose and insulin levels and other fasting-state indices for assessing insulin sensitivity derived from glucose and insulin levels for abnormal glucose tolerance was evaluated using receiver operating characteristic (ROC) curve analysis. Results: Significant correlations were observed between the 2-hr PG level and most fasting-state parameters in women with PCOS. However, the area under the ROC curve values for each fasting-state parameter for predicting abnormal glucose tolerance were all between 0.5 and 0.7 in the study participants, which falls into the "less accurate" category for prediction. Conclusion: Fasting-state measurements of glucose and insulin alone are not enough to predict abnormal glucose tolerance in women with PCOS. A standard OGTT is needed to screen for impaired glucose tolerance and type 2 diabetes mellitus in women with PCOS.

• Imaging Science in Dentistry
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• 제52권2호
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• pp.187-195
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• 2022

Purpose: This study developed a convolutional neural network (CNN) model to diagnose maxillary sinusitis on panoramic radiographs(PRs) and cone-beam computed tomographic (CBCT) images and evaluated its performance. Materials and Methods: A CNN model, which is an artificial intelligence method, was utilized. The model was trained and tested by applying 5-fold cross-validation to a dataset of 148 healthy and 148 inflamed sinus images. The CNN model was implemented using the PyTorch library of the Python programming language. A receiver operating characteristic curve was plotted, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive values for both imaging techniques were calculated to evaluate the model. Results: The average accuracy, sensitivity, and specificity of the model in diagnosing sinusitis from PRs were 75.7%, 75.7%, and 75.7%, respectively. The accuracy, sensitivity, and specificity of the deep-learning system in diagnosing sinusitis from CBCT images were 99.7%, 100%, and 99.3%, respectively. Conclusion: The diagnostic performance of the CNN for maxillary sinusitis from PRs was moderately high, whereas it was clearly higher with CBCT images. Three-dimensional images are accepted as the "gold standard" for diagnosis; therefore, this was not an unexpected result. Based on these results, deep-learning systems could be used as an effective guide in assisting with diagnoses, especially for less experienced practitioners.

• 자원환경지질
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• 제56권1호
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• pp.65-73
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• 2023

As one of the most vulnerable countries to floods, there should be an increased necessity for accurate and reliable flood forecasting in Indonesia. Therefore, a new prediction model using a machine learning algorithm is proposed to provide daily flood prediction in Indonesia. Data crawling was conducted to obtain daily rainfall, streamflow, land cover, and flood data from 2008 to 2021. The model was built using a Random Forest (RF) algorithm for classification to predict future floods by inputting three days of rainfall rate, forest ratio, and stream flow. The accuracy, specificity, precision, recall, and F1-score on the test dataset using the RF algorithm are approximately 94.93%, 68.24%, 94.34%, 99.97%, and 97.08%, respectively. Moreover, the AUC (Area Under the Curve) of the ROC (Receiver Operating Characteristics) curve results in 71%. The objective of this research is providing a model that predicts flood events accurately in Indonesian regions 3 months prior the day of flood. As a trial, we used the month of June 2022 and the model predicted the flood events accurately. The result of prediction is then published to the website as a warning system as a form of flood mitigation.