• Korean Medical Education Review
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• v.23 no.2
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• pp.80-89
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• 2021

Professional identity formation (PIF) is an essential concept in professional education. Many scholars have explored conceptual frameworks of PIF and conducted empirical studies to advance an understanding of the construct in medical education. Despite its importance, it is unclear what educational approaches and assessment practices are actually implemented in medical education settings. Therefore, we conducted a literature review of empirical studies reporting educational practices for medical learners' PIF. We searched the Web of Science database using keywords and chose 37 papers for analysis based on inclusion and exclusion criteria. Thematic analysis was conducted. Most empirical papers (92%) were from North America and Western Europe and used qualitative research methods, including mixed methods (99%). The papers reported the use of reflection activities and elective courses for specific purposes, such as art as an educational activity. Patient and healthcare experiences were also found to be a central theme in medical learners' PIF. Through an iterative analysis of the key themes that emerged from the PIF studies, we derived the following key concepts and implications: (1) the importance of creating informal and incidental learning environments, (2) ordinary yet authentic patient experiences, (3) a climate of psychosocial safety in a learning environment embracing individual learners' background and emotional development, and (4) the reconceptualization of PIF education and assessment. In conclusion, research on PIF should be diversified to include various cultural and social contexts. Theoretical frameworks should also be diversified and developed beyond Kegan's developmental framework to accommodate the nonlinear and dynamic nature of PIF.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.179-188
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• 2022

The purpose of this study is to analyze the variables affecting the institutional commitment of college students. Based on the relevant literature review, this study subdivided institutional commitment of college students into three; academic integration, social integration and institutional commitment. To achieve the goals, the hierarchical regression analysis was conducted using KELS(Korea Educational Longitudinal Survey) data collected by KEDI in 2018. Major findings are as follows: factors related to college experiences such as learning styles, negligence of learning, college climate, interaction with faculty members or peer group were found to be associated with the institutional commitment of college students. With regard to students' background, male students revealed lower level of academic integration and institutional commitment. The regression model disclosed that students from medicine demonstrated higher social integration compared to other majors. Based on the findings of the study, policy implications were discussed.

• Smart Media Journal
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• v.11 no.9
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• pp.56-63
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• 2022

Recently, the frequency and range of algae occurrence in major rivers and lakes are increasing due to the increase in water temperature due to climate change, the inflow of excessive nutrients, and changes in the river environment. Abnormal algae include green algae and red algae. Green algae is a phenomenon in which blue-green algae such as chlorophyll (Chl-a) in the water grow excessively and the color of the water changes to dark green. In this study, a 3D virtual world of digital twin was built to monitor and control water quality information measured in ecological rivers and lakes in the living environment in real time from a remote location, and a sensor measuring device for water quality information based on the Internet of Things (IOT) sensor. We propose to build a smart water environment service platform that can provide algae warning and water quality forecasting by predicting the causes and spread patterns of water pollution such as algae based on AI machine learning-based collected data analysis.

• Korean Journal of Materials Research
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• v.33 no.5
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• pp.175-188
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• 2023

Water electrolysis holds great potential as a method for producing renewable hydrogen fuel at large-scale, and to replace the fossil fuels responsible for greenhouse gases emissions and global climate change. To reduce the cost of hydrogen and make it competitive against fossil fuels, the efficiency of green hydrogen production should be maximized. This requires superior electrocatalysts to reduce the reaction energy barriers. The development of catalytic materials has mostly relied on empirical, trial-and-error methods because of the complicated, multidimensional, and dynamic nature of catalysis, requiring significant time and effort to find optimized multicomponent catalysts under a variety of reaction conditions. The ultimate goal for all researchers in the materials science and engineering field is the rational and efficient design of materials with desired performance. Discovering and understanding new catalysts with desired properties is at the heart of materials science research. This process can benefit from machine learning (ML), given the complex nature of catalytic reactions and vast range of candidate materials. This review summarizes recent achievements in catalysts discovery for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The basic concepts of ML algorithms and practical guides for materials scientists are also demonstrated. The challenges and strategies of applying ML are discussed, which should be collaboratively addressed by materials scientists and ML communities. The ultimate integration of ML in catalyst development is expected to accelerate the design, discovery, optimization, and interpretation of superior electrocatalysts, to realize a carbon-free ecosystem based on green hydrogen.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.263-272
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• 2022

As a result of climate change, the heatwave and urban heat island phenomena have become more common, and the frequency of heatwaves is expected to increase by two to six times by the year 2050. In particular, the heat sensation index felt by workers at construction sites during a heatwave is very high, and the sensation index becomes even higher if the urban heat island phenomenon is considered. The construction site environment and the situations of construction workers vulnerable to heat are not improving, and it is now imperative to respond effectively to reduce such damage. In this study, satellite imagery, land surface temperatures (LST), and long short-term memory (LSTM) were applied to analyze areas above 33 ℃, with the most vulnerable areas with increased synergistic damage from heat waves and the urban heat island phenomena then predicted. It is expected that the prediction results will ensure the safety of construction workers and will serve as the basis for a construction site early-warning system.

• Membrane and Water Treatment
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• v.14 no.4
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• pp.155-162
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• 2023

Global climate change and urbanization have various demerits, such as water pollution, flood damage, and deterioration of water circulation. Thus, attention is drawn to Nature-based Solution (NbS) that solve environmental problems in ways that imitate nature. Among the NbS, urban wetlands are facilities that perform functions, such as removing pollutants from a city, improving water circulation, and providing ecological habitats, by strengthening original natural wetland pillars. Frequent monitoring and maintenance are essential for urban wetlands to maintain their performance; therefore, there is a need to apply the Internet of Things (IoT) technology to wetland monitoring. Therefore, in this study, we attempted to develop a real-time wetland monitoring device and interface. Temperature, water temperature, humidity, soil humidity, PM1, PM2.5, and PM10 were measured, and the measurements were taken at 10-minute intervals for three days in both indoor and wetland. Sensors suitable for conditions that needed to be measured and an Arduino MEGA 2560 were connected to enable sensing, and communication modules were connected to transmit data to real-time databases. The transmitted data were displayed on a developed web page. The data measured to verify the monitoring device were compared with data from the Korea meteorological administration and the Korea environment corporation, and the output and upward or downward trend were similar. Moreover, findings from a related patent search indicated that there are a minimal number of instances where information and communication technology (ICT) has been applied in wetland contexts. Hence, it is essential to consider further research, development, and implementation of ICT to address this gap. The results of this study could be the basis for time-series data analysis research using automation, machine learning, or deep learning in urban wetland maintenance.

• Journal of the Korea Institute of Building Construction
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• v.24 no.3
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• pp.363-370
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• 2024

The escalating frequency and intensity of natural disasters and extreme weather events due to climate change have caused increasingly severe damage to societal infrastructure and buildings. Government agencies and private companies are actively working to evaluate these damages, but existing technologies and methodologies often fall short of meeting the practical demands for accurate assessment and prediction. This study proposes a novel approach to assess building damage resulting from natural disasters, focusing on typhoons-one of the most devastating natural hazards experienced in the country. The methodology leverages deep learning algorithms to evaluate typhoon-related damage, providing a comprehensive framework for assessment. The framework and outcomes of this research can provide foundational data for the evaluation of natural disaster-induced damage over the entire life cycle of buildings and can be applied in various other industries and research areas for assessing risk of damage.

• Advanced Industrial SCIence
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• v.3 no.1
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• pp.17-22
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• 2024

History, which began with the emergence of mankind, has a means of recording. Today, we can check the past through data. Generated data may only be generated and stored at a certain moment, but it is not only continuously generated over a certain time interval from the past to the present, but also occurs in the future, so making predictions using it is an important task. In order to find out trends in the use of time series data among numerous data, this paper analyzes the concept of time series data, analyzes Recurrent Neural Network and Long-Short Term Memory, which are mainly used for time series data analysis in the machine learning field, and analyzes the use of these models. Through case studies, it was confirmed that it is being used in various fields such as medical diagnosis, stock price analysis, and climate prediction, and is showing high predictive results. Based on this, we will explore ways to utilize it in the future.

• Health Policy and Management
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• v.24 no.3
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• pp.291-300
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• 2014

Background: The efficient knowledge management in hospital organization is generally known as the important activities relevant to employees' knowledge sharing behavior and work performance. This research examined factors affecting employees' knowledge sharing behavior and work performance in top 4 university hospitals. This study is based on individual factors such as incentives, reciprocity, behavioral control, and subjective norms. Also, there are organizational factors such as CEO support, learning climate, IT system, rewards system, and trust. Methods: Data was collected from employees who are working at 3 hospitals university in Seoul and 1 university hospital in Gyeonggi-Do through the self-administered questionnaires. A total of 779 questionnaires were analyzed by PASW SPSS ver. 18.0. (SPSS Inc., Chicago, IL, USA). Results: The significant variables affecting knowledge sharing behavior are behavioral control (in individual factor) and CEO, IT system, and trust (in organization factor). Also the significant variables affecting work performance are incentives, reciprocity, subjective norms, and behavioral control (in individual factor) and CEO support, IT system, reward system, and trust (in organization factor). Conclusion: The personality and organization characteristics factors is important to improve knowledge sharing behavior and work performance of hospital employees. Therefore, to make more efficient knowledge management is to build and system knowledge sharing culture, system, and leadership and to develop practical strategies.

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.609-626
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• 2020

Satellite-based land surface temperature (LST) has been used as one of the major parameters in various climate and environmental models. Especially, Moderate Resolution Imaging Spectroradiometer (MODIS) LST is the most widely used satellite-based LST product due to its spatiotemporal coverage (1 km spatial and sub-daily temporal resolutions) and longevity (> 20 years). However, there is an increasing demand for LST products with finer spatial resolution (e.g., 10-250 m) over regions such as urban areas. Therefore, various methods have been proposed to produce high-resolution MODIS-like LST less than 250 m (e.g., 100 m). The purpose of this review is to provide a comprehensive overview of recent research trends and challenges for the downscaling of MODIS LST. Based on the recent literature survey for the past decade, the downscaling techniques classified into three groups-kernel-driven, fusion-based, and the combination of kernel-driven and fusion-based methods-were reviewed with their pros and cons. Then, five open issues and challenges were discussed: uncertainty in LST retrievals, low thermal contrast, the nonlinearity of LST temporal change, cloud contamination, and model generalization. Future research directions of LST downscaling were finally provided.