• Wind and Structures
• /
• v.22 no.5
• /
• pp.573-594
• /
• 2016

Simultaneous pressure measurements on 2D square prisms with various corner modifications were performed in uniform flow with low turbulence level, and the testing Reynolds numbers varied from $1.0{\times}10^5$ to $4.8{\times}10^5$. Experimental models were a square prism, three chamfered-corner square prisms (B/D=5%, 10%, and 15%, where B is the chamfered corner dimension and D is the cross-sectional dimension), and six rounded-corner square prisms (R/D =5%, 10%, 15%, 20%, 30%, and 40%, where R is the corner radius). Experimental results of drag coefficients, wind pressure distributions, power spectra of aerodynamic force coefficients, and Strouhal numbers are presented. Ten models are divided into various categories according to the variations of mean drag coefficients with Reynolds number. The mean drag coefficients of models with $B/D{\leq}15%$ and $R/D{\leq}15%$ are unaffected by the Reynolds number. On the contrary, the mean drag coefficients of models with R/D=20%, 30%, and 40% are obviously dependent on Reynolds number. Wind pressure distributions around each model are analyzed according to the categorized results.The influence mechanisms of corner modifications on the aerodynamic characteristics of the square prism are revealed from the perspective of flow around the model, which can be obtained by analyzing the local pressures acting on the model surface.

• Physical Therapy Korea
• /
• v.28 no.3
• /
• pp.177-185
• /
• 2021

Background: Muscle undergoes change continuously with aging. Sarcopenia, in which muscle mass decrease with aging, is associated with various diseases, the risk of falling, and the deterioration of quality of life. Obesity and sarcopenia also have a synergy effect on the disease of the older adults. Objects: This study examined the risk factors for sarcopenia, sarcopenic obesity, and sarcopenia without obesity and developed prediction models. Methods: This machine-learning study used the 2008-2011 Korea National Health and Nutrition Examination Surveys in the analysis. After data curation, 5,563 older participants were selected, of whom 1,169 had sarcopenia, 538 had sarcopenic obesity, and 631 had sarcopenia without obesity; the remaining 4,394 were normal. Decision tree and random forest models were used to identify risk factors. Results: The risk factors for sarcopenia chosen by both methods were body mass index (BMI) and duration of moderate physical activity; those for sarcopenic obesity were sex, BMI, and duration of moderate physical activity; and those for sarcopenia without obesity were BMI and sex. The areas under the receiver operating characteristic curves of all prediction models exceeded 0.75. BMI could predict sarcopenia-related disease. Conclusion: Risk factors for sarcopenia-related diseases should be identified and programs for sarcopenia-related disease prevention should be developed. Data-mining research using population data should be conducted to enhance the effectiveness of early treatment for people with sarcopenia-related diseases through predictive models.

• International Journal of Computer Science & Network Security
• /
• v.24 no.7
• /
• pp.11-23
• /
• 2024

Triage is a practice of accurately prioritizing patients in emergency department (ED) based on their medical condition to provide them with proper treatment service. The variation in triage assessment among medical staff can cause mis-triage which affect the patients negatively. Developing ED triage system based on machine learning (ML) techniques can lead to accurate and efficient triage outcomes. This study aspires to develop a triage system using machine learning techniques to predict ED triage levels using patients' information. We conducted a retrospective study using Security Forces Hospital ED data, from 2021 through 2023 during Hajj period in Saudia Arabi. Using demographics, vital signs, and chief complaints as predictors, two machine learning models were investigated, naming gradient boosted decision tree (XGB) and deep neural network (DNN). The models were trained to predict ED triage levels and their predictive performance was evaluated using area under the receiver operating characteristic curve (AUC) and confusion matrix. A total of 11,584 ED visits were collected and used in this study. XGB and DNN models exhibit high abilities in the predicting performance with AUC-ROC scores 0.85 and 0.82, respectively. Compared to the traditional approach, our proposed system demonstrated better performance and can be implemented in real-world clinical settings. Utilizing ML applications can power the triage decision-making, clinical care, and resource utilization.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.11 no.3
• /
• pp.28-38
• /
• 2008

The purpose of this study are to compare existing presence-absence predictive models and to predict suitable habitat for Goral (Nemorhaedus caudatus raddeanus) that is an endangered and protected species in Seoraksan national park using the best model among existing predictive models. The methods of this study are as follows. First, 375 location data and 9 environmental data layers were implemented to build a model. Secondly, 4 existing presence-absence models : Generalized Linear Model (GLM), Generalized Addictive Model (GAM), Classification and Regression Tree (CART), and Artificial Neural Network (ANN) were tested to predict the Goal habitat. Thirdly, ROC (Receiver Operating Characteristic) and Kappa statistics were used to calculate a model performance. Lastly, we verified models and created habitat suitability maps. The ROC AUC (Area Under the Curve) and Kappa values were 0.697/0.266 (GLM), 0.729/0.313 (GAM), 0.776/0.453 (CART), and 0.858/0.559 (ANN). Therefore, ANN was selected as the best model among 4 models. The models showed that elevation, slope, and distance to stream were the significant factors for Goal habitat. The ratio of predicted area of ANN using a threshold was 31.29%, but the area decreased when human effect was considered. We need to investigate the difference of various models to build a suitable wildlife habitat model under a given condition.

• Journal of Korean Society of Water and Wastewater
• /
• v.34 no.6
• /
• pp.503-512
• /
• 2020

Identifying the water circulation status is one of the indispensable processes for watershed management in an urban area. Recently, various water circulation models have been developed to simulate the water circulation, but it takes a lot of time and cost to make a water circulation model that could adapt the characteristics of the watershed. This paper aims to develop a water circulation state estimation model that could easily calculate the status of water circulation in an urban watershed by using multiple linear regression analysis. The study watershed is a watershed in Seoul that applied the impermeable area ratio in 1962 and 2000. And, It was divided into 73 watersheds in order to consider changes in water circulation status according to the urban characteristic factors. The input data of the SHER(Similar Hydrologic Element Response) model, a water circulation model, were used as data for the urban characteristic factors of each watershed. A total of seven factors were considered as urban characteristic factors. Those factors included annual precipitation, watershed area, average land-surface slope, impervious surface ratio, coefficient of saturated permeability, hydraulic gradient of groundwater surface, and length of contact line with downstream block. With significance probabilities (or p-values) of 0.05 and below, all five models showed significant results in estimating the water circulation status such as the surface runoff rate and the evapotranspiration rate. The model that was applied all seven urban characteristics factors, can calculate the most similar results such as the existing water circulation model. The water circulation estimation model developed in this study is not only useful to simply estimate the water circulation status of ungauged watersheds but can also provide data for parameter calibration and validation.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
• /
• v.17 no.3
• /
• pp.329-338
• /
• 2019

Suction of unsaturated bentonite buffers is a very important input parameter for hydro-mechanical performance assessment and design of an engineered barrier system. This study analyzed suction measurement techniques and constitutive models of unsaturated porous media reported in the literature, and suggested suction measurement techniques and constitutive models suitable for bentonite buffer in an HLW repository. The literature review showed the suction of bentonite buffer to be much higher than that of soil, as measured by total suction including matric suction and osmotic suction. The measurement methods (RH-Cell, RH-Cell/Sensor) using a relative humidity sensor were suitable for suction measurement of the bentonite buffer; the RH-Cell /Sensor method was more preferred in consideration of the temperature change due to radioactive decay heat and measurement time. Various water retention models of bentonite buffers have been proposed through experiments, but the van Genuchten model is mainly used as a constitutive model of hydro-mechanical performance assessment of unsaturated buffers. The water characteristic curve of bentonite buffers showed different tendencies according to bentonite type, dry density, temperature, salinity, sample state and hysteresis. Selection of water retention models and determination of model input parameters should consider the effects of these controlling factors so as to improve overall reliability.

• Genomics & Informatics
• /
• v.17 no.4
• /
• pp.47.1-47.12
• /
• 2019

The achievements of genome-wide association studies have suggested ways to predict diseases, such as type 2 diabetes (T2D), using single-nucleotide polymorphisms (SNPs). Most T2D risk prediction models have used SNPs in combination with demographic variables. However, it is difficult to evaluate the pure additive contribution of genetic variants to classically used demographic models. Since prediction models include some heritable traits, such as body mass index, the contribution of SNPs using unmatched case-control samples may be underestimated. In this article, we propose a method that uses propensity score matching to avoid underestimation by matching case and control samples, thereby determining the pure additive contribution of SNPs. To illustrate the proposed propensity score matching method, we used SNP data from the Korea Association Resources project and reported SNPs from the genome-wide association study catalog. We selected various SNP sets via stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and the elastic-net (EN) algorithm. Using these SNP sets, we made predictions using SLR, LASSO, and EN as logistic regression modeling techniques. The accuracy of the predictions was compared in terms of area under the receiver operating characteristic curve (AUC). The contribution of SNPs to T2D was evaluated by the difference in the AUC between models using only demographic variables and models that included the SNPs. The largest difference among our models showed that the AUC of the model using genetic variants with demographic variables could be 0.107 higher than that of the corresponding model using only demographic variables.

• Earthquakes and Structures
• /
• v.12 no.1
• /
• pp.67-77
• /
• 2017

In this paper, it is aimed to present a comparative study about the structural behavior of tall buildings consisting of different type of materials such as concrete, steel or timber using finite element analyses and experimental measurements on shaking table. For this purpose, two 1/60 scaled 28 and 30-stories wooden building models with $40{\times}40cm$ and $35{\times}35cm$ ground/floor area and 1.45 m-1.55 m total height are built in laboratory condition. Considering the frequency range, mode shapes, maximum displacements and relative story drifts for structural models as well as acceleration, displacement and weight limits for shaking table, to obtain the typical building response as soon as possible, balsa is selected as a material property, and additional masses are bonded to some floors. Finite element models of the building models are constituted in SAP2000 program. According to the main purposes of earthquake resistant design, three different earthquake records are used to simulate the weak, medium and strong ground motions. The displacement and acceleration time-histories are obtained for all earthquake records at the top of building models. To validate the numerical results, shaking table tests are performed. The selected earthquake records are applied to first mode (lateral) direction, and the responses are recorded by sensitive accelerometers. Comparisons between the numerical and experimental results show that shaking table tests are enough to identify the structural response of wooden buildings. Considering 20%, 10% and 5% damping rations, differences are obtained within the range 4.03-26.16%, 3.91-65.51% and 6.31-66.49% for acceleration, velocity and displacements in Model-1, respectively. Also, these differences are obtained as 0.49-31.15%, 6.03-6.66% and 16.97-66.41% for Model-2, respectively. It is thought that these differences are caused by anisotropic structural characteristic of the material due to changes in directions parallel and perpendicular to fibers, and should be minimized using the model updating procedure.

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.6
• /
• pp.1207-1216
• /
• 2015

This study aims to compare the model power in developing corporate credit rating models and to suggest a good way to build models based on the characteristic of data. Among many measurement methods, AR is used to measure the model power under various conditions. SAS/MACRO is in use for similar repetitions to reduce time to build models under several combination of conditions. A corporate credit rating model is composed of two sub-models; a credit scoring model and a default prediction model. We verify that the latter performs better than the former under various conditions. From the result of size comparisons, models of large size corporate are more powerful and more meaningful in financial viewpoint than those of small size corporate. As a corporate size gets smaller, the gap between sub-models becomes huge and the effect of outliers becomes serious.

• Interdisciplinary Bio Central
• /
• v.3 no.4
• /
• pp.15.1-15.11
• /
• 2011

Background: Histone deacetylase (HDAC) 8 is one of its family members catalyzes the removal of acetyl groups from N-terminal lysine residues of histone proteins thereby restricts transcription factors from being expressed. Inhibition of HDAC8 has become an emerging and effective anti-cancer therapy for various cancers. Application computational methodologies may result in identifying the key components that can be used in developing future potent HDAC8 inhibitors. Results: Facilitating the discovery of novel and potential chemical scaffolds as starting points in the future HDAC8 inhibitor design, quantitative structure-activity relationship models were generated with 30 training set compounds using genetic function approximation (GFA) and Bayesian algorithms. Six GFA models were selected based on the significant statistical parameters calculated during model development. A Bayesian model using fingerprints was developed with a receiver operating characteristic curve cross-validation value of 0.902. An external test set of 54 diverse compounds was used in validating the models. Conclusions: Finally two out of six models based on their predictive ability over the test set compounds were selected as final GFA models. The Bayesian model has displayed a high classifying ability with the same test set compounds and the positively and negatively contributing molecular fingerprints were also unveiled by the model. The effectively contributing physicochemical properties and molecular fingerprints from a set of known HDAC8 inhibitors were identified and can be used in designing future HDAC8 inhibitors.