• Journal of Science Education
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• v.47 no.1
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• pp.37-51
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• 2023

This study explored the factors influencing major satisfaction and academic satisfaction of science education major students at the College of Education using machine learning models, random forest, gradient boosting model, and SHAP. Analysis results showed that the performance of the gradient boosting model was better than that of the random forest, but the difference was not large. Factors influencing major satisfaction include 'satisfaction with science teachers in high school corresponding to the subject of one's major', 'motivation for teaching job', and 'age'. Through the SHAP value, the influence of variables was identified, and the results were derived for the group as a whole and for individual analysis. The comprehensive and individual results could be complementary with each other. Based on the research results, implications for ways to support pre-service science teachers' major and academic satisfaction were proposed.

• Structural Engineering and Mechanics
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• v.85 no.4
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• pp.531-543
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• 2023

The existing concrete bridges are time-varying working systems, where the maintenance strategy should be planned according to the time-varying performance of the bridge. This work proposes a time-dependent residual capacity assessment procedure, which considers the non-stationary bridge load effects under growing traffic and non-stationary structural deterioration owing to material degradations. Lifetime bridge load effects under traffic growth are predicated by the non-stationary peaks-over-threshold (POT) method using time-dependent generalized Pareto distribution (GPD) models. The non-stationary structural resistance owing to material degradation is modeled by incorporating the Gamma deterioration process and field inspection data. A three-span continuous box-girder bridge is illustrated as an example to demonstrate the application of the proposed procedure, and the time-varying reliability indexes of the bridge girder are calculated. The accuracy of the proposed non-stationary POT method is verified through numerical examples, where the shape parameter of the time-varying GPD model is constant but the threshold and scale parameters are polynomial functions increasing with time. The case study illustrates that the residual flexural capacities show a degradation trend from a slow decrease to an accelerated decrease under traffic growth and material degradation. The reliability index for the mid-span cross-section reduces from 4.91 to 4.55 after being in service for 100 years, and the value is from 4.96 to 4.75 for the mid-support cross-section. The studied bridge shows no safety risk under traffic growth and structural deterioration owing to its high design safety reserve. However, applying the proposed numerical approach to analyze the degradation of residual bearing capacity for bridge structures with low safety reserves is of great significance for management and maintenance.

• Journal of the Korea Fashion and Costume Design Association
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• v.25 no.3
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• pp.53-67
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• 2023

Blood circulation is one of the most important life support functions of our body. It is essential to maintain healthy blood circulation as problems with blood circulation can lead to numerous diseases and serious complications. This study developed women's leggings with gradual compression and soft surface heating functions to improve blood circulation, and evaluated their performance and wearability. A silicon print pattern was developed to provide gradual compression, and a flexible heating surface coated with MWCNT (multi-walled carbon nanotube) conductive ink was fabricated for comfort and thermal effect. For the design, incision lines and materials were applied in consideration of aesthetic aspects, and design lines and colors were altered using a 3D program. The developed leggings showed that blood circulation can be improved when gradual compression and heating functions are simultaneously applied. Results were confirmed through measurements of clothing pressure, blood flow, and surface temperature. In the subjective wearability evaluation, it was confirmed that wearers felt gradual pressure, and they showed high satisfaction with wearability and design.

• Journal of Industrial Convergence
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• v.21 no.9
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• pp.133-142
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• 2023

The use of digital information technology has become important in order to effectively respond to changes in production conditions in Korean agriculture, which are continuously worsening due to a decrease in the rural population, deepening aging, and climate change. Accordingly, this study analyzed the factors affecting farmers' adoption of information devices use and the effect of information devices use on agricultural product sales using the propensity score matching method. As a result of the analysis, it was found that low-age farmers, high-education farmers, and leading farmers are highly likely to adopt use of information devices. For farms with similar characteristics such as age, management size, and farming type, it has been confirmed that farms that have adopted information devices use in agricultural management have higher sales of agricultural products. Therefore, increasing farmers' access to information and the ability to use information devices provides implications that farm income can be improved. The government's informatization support project in the agricultural and rural sectors is important so that farmers can have the ability to distribute informatization devices and utilize agricultural information, and active investment should also be made in information infrastructure.

• Journal of the Korean institute of surface engineering
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• v.56 no.6
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• pp.437-442
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• 2023

This research aims to enhance the efficiency of Pt/C catalysts due to the limited availability and high cost of platinum in contemporary fuel cell catalysts. Nano-sized platinum particles were distributed onto a carbon-based support via the polyol process, utilizing the metal precursor H₂PtCl₆·6H₂O. Key parameters such as pH, temperature, and RPM were carefully regulated. The findings revealed variations in the particle size, distribution, and dispersion of nano-sized Pt particles, influenced by temperature and pH. Following sodium hydroxide treatment, heat treatment procedures were systematically executed at diverse temperatures, specifically 120, 140, and 160 ℃. Notably, the thermal treatment at 140 ℃ facilitated the production of Pt/C catalysts characterized by the smallest platinum particle size, measuring at 1.49 nm. Comparative evaluations between the commercially available Pt/C catalysts and those synthesized in this study were meticulously conducted through cyclic voltammetry, X-ray diffraction (XRD), and field-emission scanning electron microscopy-energy dispersive X-ray spectroscopy (FE-SEM EDS) methodologies. The catalyst synthesized at 160 ℃ demonstrated superior electrochemical performance; however, it is imperative to underscore the necessity for further optimization studies to refine its efficacy.

• Journal of Korean Society of Rural Planning
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• v.30 no.1
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• pp.67-79
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• 2024

The Comprehensive Rural Village Development Project, a resident-led bottom-up regional development project, began in 2004. This study investigated difficulties and problems in the operation process after the completion of the project, and future improvement plans, through in-depth interviews with the former and current chairman of the steering committee, steering committee members, and office managers, targeting three regions in Jeollanam-do, 15 years after the completion of the project. As a result of the survey and analysis, it was effective in improving the living environment and characteristics of each village and revitalizing the area. And while there were well-run facilities depending on the type of project, there were also many idle facilities. In the case of communal facilities, there was a high possibility of problems in operation and management when the scale of the new building was large. Conflicts occurred between villages in the process of independently operating the area after the completion of the project. Therefore, it is necessary to provide an S/W project program to prepare for after completion. Local governments need to utilize City and County Capacity Enhancement Projects to support regional leaders to participate in educational programs after completion and provide guidance and supervision for village operations.

• Journal of muscle and joint health
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• v.31 no.1
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• pp.22-30
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• 2024

Purpose: In-hospital cardiac arrest is rare, but often results in high mortality rates. Early and effective cardiopulmonary resuscitation (CPR) is crucial for survival and nurses are often the first responders. This study aimed to investigate how inter-professional attitudes and educational burdens affect self-efficacy related to CPR performance following team-based CPR simulation training. Methods: This retrospective observational study analyzed data from a satisfaction survey conducted after team-based CPR training sessions between January and November 2022. Of the 454 nurses surveyed, 238 were included in the study after excluding those with ambiguous responses. Multiple regression analysis was performed to assess factors influencing CPR self-efficacy. The factors examined included inter-professional attitudes and educational burden. Results: Higher levels of inter-professional attitudes, particularly regarding teamwork roles and responsibilities, lower educational burden, and a positive perception of CPR competence were all associated with improved CPR-related self-efficacy. Participants who reported higher engagement in teamwork, lower task load, and greater confidence in their CPR abilities demonstrated higher self-efficacy in performing CPR. Conclusion: Enhancing the competencies of nurses who may act as initial responders in CPR situations within or outside hospital settings can help save lives and support public health.

• Geomechanics and Engineering
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• v.37 no.4
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• pp.307-321
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• 2024

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.

• The Journal of Engineering Geology
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• v.34 no.1
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• pp.107-115
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• 2024

This study assesses forest fire susceptibility in Gangwon-do, South Korea, which hosts the largest forested area in the nation and constitutes ~21% of the country's forested land. With 81% of its terrain forested, Gangwon-do is particularly susceptible to wildfires, as evidenced by the fact that seven out of the ten most extensive wildfires in Korea have occurred in this region, with significant ecological and economic implications. Here, we analyze 480 historical wildfire occurrences in Gangwon-do between 2003 and 2019 using 17 predictor variables of wildfire occurrence. We utilized three machine learning algorithms—random forest, logistic regression, and support vector machine—to construct wildfire susceptibility prediction models and identify the best-performing model for Gangwon-do. Forest and soil map data were integrated as important indicators of wildfire susceptibility and enhanced the precision of the three models in identifying areas at high risk of wildfires. Of the three models examined, the random forest model showed the best predictive performance, with an area-under-the-curve value of 0.936. The findings of this study, especially the maps generated by the models, are expected to offer important guidance to local governments in formulating effective management and conservation strategies. These strategies aim to ensure the sustainable preservation of forest resources and to enhance the well-being of communities situated in areas adjacent to forests. Furthermore, the outcomes of this study are anticipated to contribute to the safeguarding of forest resources and biodiversity and to the development of comprehensive plans for forest resource protection, biodiversity conservation, and environmental management.

• Steel and Composite Structures
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• v.52 no.2
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• pp.145-163
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• 2024

This paper presents six novel hybrid machine learning (ML) models that combine support vector machines (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with the Harris Hawks Optimization (HHO) algorithm. These models, namely HHO-SVM, HHO-DT, HHO-RF, HHO-GB, HHO-XGB, and HHO-CGB, are designed to predict the ultimate strength of both rectangular and circular reinforced concrete (RC) columns. The prediction models are established using a comprehensive database consisting of 325 experimental data for rectangular columns and 172 experimental data for circular columns. The ML model hyperparameters are optimized through a combination of cross-validation technique and the HHO. The performance of the hybrid ML models is evaluated and compared using various metrics, ultimately identifying the HHO-CGB model as the top-performing model for predicting the ultimate shear strength of both rectangular and circular RC columns. The mean R-value and mean a20-index are relatively high, reaching 0.991 and 0.959, respectively, while the mean absolute error and root mean square error are low (10.302 kN and 27.954 kN, respectively). Another comparison is conducted with four existing formulas to further validate the efficiency of the proposed HHO-CGB model. The Shapely Additive Explanations method is applied to analyze the contribution of each variable to the output within the HHO-CGB model, providing insights into the local and global influence of variables. The analysis reveals that the depth of the column, length of the column, and axial loading exert the most significant influence on the ultimate shear strength of RC columns. A user-friendly graphical interface tool is then developed based on the HHO-CGB to facilitate practical and cost-effective usage.