• Structural Engineering and Mechanics
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• v.70 no.1
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• pp.13-31
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• 2019

A nonlinear finite element model (FEM) using ATENA-3D software to simulate the axially compressive behavior of circular steel tube confined concrete (CSTCC) columns infilled with ultra high performance concrete (UHPC) was presented in this paper. Some modifications to the material type "CC3DNonlinCementitious2User" of UHPC without and with the incorporation of steel fibers (UHPFRC) in compression and tension were adopted in FEM. The predictions of utimate strength and axial load versus axial strain curves obtained from FEM were in a good agreement with the test results of eighteen tested columns. Based on the results of FEM, the load distribution on the steel tube and the concrete core was derived for each modeled column. Furthermore, the effect of bonding between the steel tube and the concrete core was clarified by the change of friction coefficient in the material type "CC3DInterface" in FEM. The numerical results revealed that the increase in the friction coefficient leads to a greater contribution from the steel tube, a decrease in the ultimate load and an increase in the magnitude of the loss of load capacity. By comparing the results of FEM with experimental results, the appropriate friction coefficient between the steel tube and the concrete core was defined as 0.3 to 0.6. In addition to the numerical evaluation, eighteen analytical models for confined concrete in the literature were used to predict the peak confined strength to assess their suitability. To cope with CSTCC stub and intermediate columns, the equations for estimating the lateral confining stress and the equations for considering the slenderness in the selected models were proposed. It was found that all selected models except for EC2 (2004) gave a very good prediction. Among them, the model of Bing et al. (2001) was the best predictor.

• Human Ecology Research
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• v.57 no.2
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• pp.225-241
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• 2019

This study develops and validates an objective scale to measure newspaper use at home by elementary school students and parents. We developed a preliminary scale of 59 items through the review of literature on newspaper use and mediation as well as the examination of content validity by education experts. Collected data were analyzed using SPSS 21.0 and AMOS 21.0 programs. A total of 42 items were supported by 703 parents with students in grades 1-6 using exploratory factor analysis. The model included 3 categories and 9 sub-factors: instruction (modeling, restriction, and text instruction), activities (play activity, conversation, online mediation, and scrap activity), and belief (academic achievement and information acquisition). Confirmatory factor analysis confirmed and validated the model fit; in addition, convergent validity, and discriminant validity, and cross validity was confirmed through correlation analysis by gender comparison and grade comparison. We also verified the validity of this scale through correlation analysis based on Yu and Jung (2012)'s newspaper mediation variables and scale in regards to children's motivation for using newspapers. Finally, internal consistency reliability and half reliability were also confirmed. In conclusion, the suitability and stability of home newspaper utilization scale for elementary students (HNUS-E) were confirmed. This scale provides parents and educators with ideas for the development of the children's literacy, cognitive, and affective domains that can be effectively used in research on newspaper use for school-aged children.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.27-33
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• 2021

The whole world now is dealing with Coronavirus, and it has turned to be one of the most widespread and long-lived pandemics of our times. Reports reveal that the infectious disease has taken toll of the almost 80% of the world's population. Amidst a lot of research going on with regards to the prediction on growth and transmission through Symptomatic carriers of the virus, it can't be ignored that pre-symptomatic and asymptomatic carriers also play a crucial role in spreading the reach of the virus. Classification Algorithm has been widely used to classify different types of COVID-19 carriers ranging from simple feature-based classification to Convolutional Neural Networks (CNNs). This research paper aims to present a novel technique using a Random Forest Machine learning algorithm with hyper-parameter tuning to classify different types COVID-19-carriers such that these carriers can be accurately characterized and hence dealt timely to contain the spread of the virus. The main idea for selecting Random Forest is that it works on the powerful concept of "the wisdom of crowd" which produces ensemble prediction. The results are quite convincing and the model records an accuracy score of 99.72 %. The results have been compared with the same dataset being subjected to K-Nearest Neighbour, logistic regression, support vector machine (SVM), and Decision Tree algorithms where the accuracy score has been recorded as 78.58%, 70.11%, 70.385,99% respectively, thus establishing the concreteness and suitability of our approach.

• Journal of the Korea Safety Management & Science
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• v.24 no.1
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• pp.49-60
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• 2022

The purpose of this study is to verify the structural relationship between job crafting and job enthusiasm, informal learning, social support, and positive psychological capital, and to investigate the effect of informal learning, social support, and positive psychological capital on job crafting through job enthusiasm. A survey was conducted on 451 safety workers at large domestic companies, and the collected data were analyzed for model suitability, influence relations between variables, and mediating effects with AMOS 23.0 using SPSS 23.0. Through research, we found five important results. First, the structural model of job crafting, job enthusiasm, informal learning, social support, and positive psychological capital properly explained the empirical data. Second, social support and positive psychological capital had a positive effect on job enthusiasm, but informal learning did not significantly affect job enthusiasm. Third, informal learning and positive psychological capital had a positive effect on job crafting, while social support did not significantly affect job crafting. Fourth, job enthusiasm had a positive effect on job crafting. Finally, job enthusiasm was found to mediate the relationship between social support and positive psychological capital and job crafting. These suggest that continuous environmental efforts and systematic management measures are needed to promote job crafting of safety workers so that informal learning, social support, positive psychological capital, and job enthusiasm can be expressed. Therefore, the necessity of developing various sub-factors of informal learning that can promote job crafting of safety workers was suggested as a follow-up study.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.51-58
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• 2023

The purpose of this study is to identify various variables that affect the intention to use the metaverse, which has recently attracted attention. In particular, while previous studies have focused only on the variables that have a positive effect on the spread of the metaverse, this study tried to examine both the use and resistance perspectives by examining the psychological variables of consumers who reject specific changes, such as innovation resistance. By constructing consumer characteristics and innovation characteristic variables that affect innovation diffusion, the relationship between innovation resistance, attitude toward the metaverse, and intention to use the metaverse was examined. As a result of the study, it was found that innovation propensity, social image, and conformity had a negative effect on resistance to the metaverse. In addition, innovation propensity, social image, suitability, relative advantage, complexity, and observability mediate innovation resistance and attitudes toward the metaverse, and were finally revealed as variables that have positive or negative influences on the intention to use the metaverse.

• Journal of Biomedical Engineering Research
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• v.43 no.6
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• pp.434-440
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• 2022

Liver transplantation is a critical used treatment method for patients with end-stage liver disease. The number of cases of living donor liver transplantation is increasing due to the imbalance in needs and supplies for brain-dead organ donation. As a result, the importance of the accuracy of the donor's suitability evaluation is also increasing rapidly. To measure the donor's liver volume accurately is the most important, that is absolutely necessary for the recipient's postoperative progress and the donor's safety. Therefore, we propose liver segmentation in abdominal CT images from pre-operation, POD 7, and POD 63 with a two-dimensional U-Net. In addition, we introduce an algorithm to measure the volume of the segmented liver and measure the hepatectomy rate and regeneration rate of pre-operation, POD 7, and POD 63. The performance for the learning model shows the best results in the images from pre-operation. Each dataset from pre-operation, POD 7, and POD 63 has the DSC of 94.55 ± 9.24%, 88.40 ± 18.01%, and 90.64 ± 14.35%. The mean of the measured liver volumes by trained model are 1423.44 ± 270.17 ml in pre-operation, 842.99 ± 190.95 ml in POD 7, and 1048.32 ± 201.02 ml in POD 63. The donor's hepatectomy rate is an average of 39.68 ± 13.06%, and the regeneration rate in POD 63 is an average of 14.78 ± 14.07%.

• Journal of the Korean Geotechnical Society
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• v.38 no.12
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• pp.29-43
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• 2022

Various underground facilities are being constructed to improve the urban environment. Therefore, it is more necessary than ever to reasonably evaluate the seismic response of underground utility tunnels, playing a significant part in urban infrastructure. In this study, the major features and differences of two types of existing pseudo-static analysis methods are reviewed. Each method uses a simplified 2D frame model to represent the seismic behavior of underground structures. Applying each method to a one-barrel rectangular utility tunnel in Korea, the suitability in predicting seismic responses, especially the racking deformation of the tunnel, is examined. In addition, several precautions and suggestions are provided in this study against the inattentive application of the methods to seismic evaluation of underground structures.

• Journal of Convergence for Information Technology
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• v.12 no.5
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• pp.15-20
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• 2022

This research studied the evaluation of reliability among the software quality characteristics: suitability, reliability, usability, portability, maintainability, performance efficiency, security, and compatibility. It proposes a quantitative evaluation of reliability in the measurement of software quality. This study introduces a method for measuring the failure rate included in maturity during reliability evaluation, which is one of the characteristics of software quality, and is a study with experimental data on how the failure rate changes depending on the form of failure data. Focusing on software testing, the failure rate was measured and compared according to the type of failure data by applying it to the software reliability growth model, focusing on the number of failures per day. The failure rate was measured around the failure time found through the 6-day test, and the failure rate was compared with the failure rate proposed by the international standard ISO/IEC 25023 using the measurement results, and the application was reviewed according to the data type.

• Journal of Forest and Environmental Science
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• v.39 no.2
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• pp.105-117
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• 2023

Climate change and invasive alien plant species (IAPs) are having a significant impact on mountain ecosystems. The combination of climate change and socio-economic development is exacerbating the invasion of IAPs, which are a major threat to biodiversity loss and ecosystem functioning. Species distribution modelling has become an important tool in predicting the invasion or suitability probability under climate change based on occurrence data and environmental variables. MaxEnt modelling was applied to predict the current suitable distribution of most noxious weed A. adenophora (Spreng) R. King and H. Robinson and analysed the changes in distribution with the use of current (year 2000) environmental variables and future (year 2050) climatic scenarios consisting of 3 representative concentration pathways (RCP 2.6, RCP 4.5 and RCP 8.5) in Bhutan. Species occurrence data was collected from the region of interest along the road side using GPS handset. The model performance of both current and future climatic scenario was moderate in performance with mean temperature of wettest quarter being the most important variable that contributed in model fit. The study shows that current climatic condition favours the A. adenophora for its invasion and RCP 2.6 climatic scenario would promote aggression of invasion as compared to RCP 4.5 and RCP 8.5 climatic scenarios. This can lead to characterization of the species as preferring moderate change in climatic conditions to be invasive, while extreme conditions can inhibit its invasiveness. This study can serve as reference point for the conservation and management strategies in control of this species and further research.

• Geomechanics and Engineering
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• v.31 no.4
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• pp.339-352
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• 2022

Studying slope stability is an important branch of civil engineering. In this way, engineers have employed machine learning models, due to their high efficiency in complex calculations. This paper examines the robustness of various novel optimization schemes, namely equilibrium optimizer (EO), Harris hawks optimization (HHO), water cycle algorithm (WCA), biogeography-based optimization (BBO), dragonfly algorithm (DA), grey wolf optimization (GWO), and teaching learning-based optimization (TLBO) for enhancing the performance of adaptive neuro-fuzzy inference system (ANFIS) in slope stability prediction. The hybrid models estimate the factor of safety (F_S) of a cohesive soil-footing system. The role of these algorithms lies in finding the optimal parameters of the membership function in the fuzzy system. By examining the convergence proceeding of the proposed hybrids, the best population sizes are selected, and the corresponding results are compared to the typical ANFIS. Accuracy assessments via root mean square error, mean absolute error, mean absolute percentage error, and Pearson correlation coefficient showed that all models can reliably understand and reproduce the F_S behavior. Moreover, applying the WCA, EO, GWO, and TLBO resulted in reducing both learning and prediction error of the ANFIS. Also, an efficiency comparison demonstrated the WCA-ANFIS as the most accurate hybrid, while the GWO-ANFIS was the fastest promising model. Overall, the findings of this research professed the suitability of improved intelligent models for practical slope stability evaluations.