• International journal of steel structures
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• v.18 no.4
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• pp.1177-1190
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• 2018

This study presents an analytical framework for estimating the thermo-mechanical behavior of a composite beam exposed to fire. The framework involves: a fire simulation from which the evolution of temperature on the structure surface is obtained; data transfer by an interface model, whereby the surface temperature is assigned to the finite element model of the structure for thermo-mechanical analysis; and nonlinear thermo-mechanical analysis for predicting the structural response under high temperatures. We use a plastic-damage model for calculating the response of concrete slabs, and propose a method to determine the stiffness degradation parameter of the plastic-damage model by a nonlinear regression of concrete cylinder test data. To validate simulation results, structural fire experiments have been performed on a real-scale steel-concrete composite beam using the fire load prescribed by ASTM E119 standard fire curve. The calculated evolution of deflection at the center of the beam shows good agreement with experimental results. The local test results as well as the effective plastic strain distribution and section rotation of the composite beam at elevated temperatures are also investigated.

• Korean Journal of Metals and Materials
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• v.49 no.10
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• pp.774-779
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• 2011

S-N fatigue behavior of cast 304 stainless steel was studied at 25, -50 and $-196^{\circ}C$ and at a stress ratio of -1 in uniaxial and bending loading condition. It was found that the resistance to S-N fatigue was greatly improved with decreasing testing temperature. The normalized S-N fatigue curves by tensile strength at three different testing temperatures matched each other, suggesting that tensile strength determines the S-N fatigue resistance of cast 304 stainless steel at low temperatures. The effects of different loading on the resistance to S-N fatigue of cast 304 stainless steel were quantified. The S-N fatigue curves at 25, -50 and $-196^{\circ}C$ were described by using Basquin's law the relationship between the S-N fatigue curve and the testing temperature was obtained by using a simple regression method.

• Journal of Korea Planning Association
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• v.54 no.3
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• pp.15-26
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• 2019

The development of science and transportation technology leads to the increase of inter - city networks that play an important role in urban growth. Overall, numerous studies based on network theory pay attention to positive effects of urban network on urban growth. However, some studies have pointed out the negative effects of inter-city interactions such as straw effects. This implies that the network between cities may not be positively correlated with urban growth, and that the direction of the influence may vary from a certain threshold, such as the marginal utility curve. In this context, the purpose of this study is to measure the impacts of network with central city on urban growth in the capital region and examine the relationship between urban network and growth. Two multiple regression models are employed with changes in population and employment as dependent variables. The urban network index and other control variables are used as independent variables. Especially, the urban network indexes are used in quadratic forms to examine non linear relations with urban growth such U-shape or an inverted U-shape. The results show that the relationships between networks with the central city and urban growth are not a simple linear, and the influence can be changed from the critical point.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.2
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• pp.71-83
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• 2022

In this study, an indicator and assessment system for evaluating the monthly hydrological cycle was prepared using simple factors such as the landuse status of the watershed and topographic characteristics to the dynamic water balance model (DWBM) based on the Budyko framework. The parameters a1 of DWBM are introduced as hydrologic cycle indicators. An indicator estimation regression model was developed using watershed characteristics data for the introduced indicator, and an assessment system was prepared through K-means cluster analysis. The hydrological cycle assessment system developed in this study can assess the hydrological cycle with simple data such as land use, CN, and watershed slope, so it can quickly assess changes in hydrological cycle factors in the past and present. Because of this advantage is expected that the developed assessment system can predict changes in the hydrological cycle and use an auxiliary tool for policymaking.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.17 no.4
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• pp.15-27
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• 2021

In this study, the effect of design factors in a pump station on the pressure variations which are the main cause of water hammering has been investigated by numerical simulations. As design factors, the flow rate, Young's modulus, diameter, thickness, roughness coefficient of pipeline are considered. The relationships between the pressure variations and the design factors are analyzed. The results show that the pressure variation increases sensitively with the flow rate and Young's modulus, and increases gradually with the thickness and roughness coefficient of pipe, whereas it decreases with the pipe diameter. The wavelength of the pressure wave becomes longer for a smaller Young's modulus, a smaller pipe thickness and a bigger pipe diameter. These relationships are nondimensionalized, and logarithmic curve-fitted functions are proposed by regression analysis. Most effective factors on the nondimensional pressure variation is Young's modulus. Flow rate, roughness coefficient, relative thickness and pipe diameters are the next impact factors.

• Structural Engineering and Mechanics
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• v.85 no.3
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• pp.315-335
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• 2023

Limited studies are available on the mathematical estimates of the compressive strength (CS) of glass fiber-embedded polymer (glass-FRP) compressive elements. The present study has endeavored to estimate the CS of glass-FRP normal strength concrete (NSTC) compression elements (glass-FRP-NSTC) employing two various methodologies; mathematical modeling and artificial neural networks (ANNs). The dataset of 288 glass-FRP-NSTC compression elements was constructed from the various testing investigations available in the literature. Diverse equations for CS of glass-FRP-NSTC compression elements suggested in the previous research studies were evaluated employing the constructed dataset to examine their correctness. A new mathematical equation for the CS of glass-FRP-NSTC compression elements was put forwarded employing the procedures of curve-fitting and general regression in MATLAB. The newly suggested ANN equation was calibrated for various hidden layers and neurons to secure the optimized estimates. The suggested equations reported a good correlation among themselves and presented precise estimates compared with the estimates of the equations available in the literature with R²= 0.769, and R² =0.9702 for the mathematical and ANN equations, respectively. The statistical comparison of diverse factors for the estimates of the projected equations also authenticated their high correctness for apprehending the CS of glass-FRP-NSTC compression elements. A broad parametric examination employing the projected ANN equation was also performed to examine the effect of diverse factors of the glass-FRP-NSTC compression elements.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.305-332
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• 2022

Purpose: As the public interest in entrepreneurship has been highlighted and entrepreneurship policies have been generated, this study is to construct Entrepreneurship Ecosystem (EE) models which have a significant relationship to national entrepreneurship with quantitative analysis. It aims to provide implications to EE policymakers that which national components are effective in cultivating innovative entrepreneurship and validate its EE quality based on quantitative performance goals. Methods: This study utilizes secondary data, categorized under the PESTLE factor from credible international organizations (WB, UNDP, GEM, GEDI, and OECD) to determine significant factors in the quality of the entrepreneurial ecosystem. This paper uses the Multiple Linear Regression (MLR) analysis to select the significant variables contributing to entrepreneurship performance. Using the AUC-ROC performance evaluation method for machine learning MLR results, this paper evaluates the performance of EE models so that it can allow approving EE quality by predicting potential performance. Results: Among nine hypothesis models, MLR analysis examines that the number of the Unicorn company, Unicorn companies' economic value, and entrepreneurship measured as GEI can be reasonable dependent variables to indicate the performance derived from EE quality. Rather than government policies and regulations, the social, finance, technology, and economic variables are significant factors of EE quality determining its performance. By having high Area Under Curve values under AUC-ROC analysis, accepted MLR models are regarded as having high prediction accuracy. Conclusion: Superior EE contributes to the outstanding Unicorn companies, and improvement in macro-environmental components can enhance EE quality.

• Asia-Pacific Journal of Business
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• v.13 no.2
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• pp.107-122
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• 2022

Purpose - This study aims to develop machine learning models to predict administrative issue designation in KOSDAQ Market using financial data. Design/methodology/approach - Employing four classification techniques including logistic regression, support vector machine, random forest, and gradient boosting to a matched sample of five hundred and thirty-six firms over an eight-year period, the authors develop prediction models and explore the practicality of the models. Findings - The resulting four binary selection models reveal overall satisfactory classification performance in terms of various measures including AUC (area under the receiver operating characteristic curve), accuracy, F1-score, and top quartile lift, while the ensemble models (random forest and gradienct boosting) outperform the others in terms of most measures. Research implications or Originality - Although the assessment of administrative issue potential of firms is critical information to investors and financial institutions, detailed empirical investigation has lagged behind. The current research fills this gap in the literature by proposing parsimonious prediction models based on a few financial variables and validating the applicability of the models.

• Journal of Trauma and Injury
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• v.35 no.4
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• pp.268-276
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• 2022

Purpose: Rib fractures are common injuries that can lead to morbidity and mortality. Methods: Data on all patients with rib fractures admitted to a single trauma center between January 1, 2008 and December 31, 2018 were reviewed. Results: A total of 1,671 admissions for rib fracture were examined. Patients' median age was 57 years, the median Injury Severity Score (ISS) was 14, and the median number of fractured ribs was three. The in-hospital mortality rate was 4%. Age, the number of rib fractures, and Charlson Comorbidity Index scores were poor predictors of mortality, while the ISS was a slightly better predictor, with area under the receiver operating characteristic curve values of 0.60, 0.55, 0.58, and 0.74, respectively. Multivariate regression showed that age, ISS, and Charlson Comorbidity Index score, but not the number of rib fractures, were associated with significantly elevated adjusted odds ratios for mortality (1.03, 1.14, and 1.28, respectively). Conclusions: Age, ISS, and comorbidities were independently associated with the risk of mortality; however, they were not accurate predictors of death. The factors associated with rib fracture mortality are complex and cannot be explained by a single variable. Interventions to improve outcomes must be multifaceted.

• The Journal of Information Systems
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• v.32 no.2
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• pp.87-108
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• 2023

Purpose The main purpose of this study is to improve fake news detection performance by using video information to overcome the limitations of extant text- and image-oriented studies that do not reflect the latest news consumption trend. Design/methodology/approach This study collected video clips and related information including news scripts, speakers' facial expression, and video metadata from YouTube to develop fake news detection model. Based on the collected data, seven combinations of related information (i.e. scripts, video metadata, facial expression, scripts and video metadata, scripts and facial expression, and scripts, video metadata, and facial expression) were used as an input for taining and evaluation. The input data was analyzed using six models such as support vector machine and deep neural network. The area under the curve(AUC) was used to evaluate the performance of classification model. Findings The results showed that the ACU and accuracy values of three features combination (scripts, video metadata, and facial expression) were the highest in logistic regression, naïve bayes, and deep neural network models. This result implied that the fake news detection could be improved by using video information(video metadata and facial expression). Sample size of this study was relatively small. The generalizablity of the results would be enhanced with a larger sample size.