The state-of-the-art Earth system model as a virtual Earth is required for studies of current and future climate change or climate crises. This complex numerical model can account for almost all human activities and natural phenomena affecting the atmosphere of Earth. The Unified Model (UM) from the United Kingdom Meteorological Office (UK Met Office) is among the best Earth system models as a scientific tool for studying the atmosphere. However, owing to the expansive numerical integration cost and substantial output size required to maintain the UM, individual research groups have had to rely only on supercomputers. The limitations of computer resources, especially the computer environment being blocked from outside network connections, reduce the efficiency and effectiveness of conducting research using the model, as well as improving the component codes. Therefore, this study has presented detailed guidance for installing a new version of the UM on high-performance parallel computers (Linux clusters) owned by individual researchers, which would help researchers to easily work with the UM. The numerical integration performance of the UM on Linux clusters was also evaluated for two different model resolutions, namely N96L85 (1.875° ×1.25° with 85 vertical levels up to 85 km) and N48L70 (3.75° ×2.5° with 70 vertical levels up to 80 km). The one-month integration times using 256 cores for the AMIP and CMIP simulations of N96L85 resolution were 169 and 205 min, respectively. The one-month integration time for an N48L70 AMIP run using 252 cores was 33 min. Simulated results on 2-m surface temperature and precipitation intensity were compared with ERA5 re-analysis data. The spatial distributions of the simulated results were qualitatively compared to those of ERA5 in terms of spatial distribution, despite the quantitative differences caused by different resolutions and atmosphere-ocean coupling. In conclusion, this study has confirmed that UM can be successfully installed and used in high-performance Linux clusters.
Mi Na Choi;Do-Hun Lee;Moon-Jeong Jang;Dong Ju Kim;Sun Mi Lee;Yoon Jung Moon;Yong Sung Kwon
Korean Journal of Environmental Biology
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v.41
no.1
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pp.18-30
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2023
Forest destruction is an inevitable result of the development processes. According to the environmental impact assessment, over 10% of the destroyed trees need to be recycled and transplanted to minimize the impact of forest destruction. However, the rate of successful transplantation is low, leading to a high rate of tree death. This is attributable to a lack of consideration for environmental factors when choosing a temporary site for transplantation and inadequate management. To monitor transplanted trees, a field survey is essential; however, the spatio-temporal aspect is limited. This study evaluated the applicability of remote sensing for the effective monitoring of transplanted trees. Vegetation indices based on satellite remote sensing were derived to detect time-series changes in the status of the transplanted trees at three temporary transplantation sites. The mortality rate and vitality of transplanted trees before and after the transplant have a similar tendency to the changes in the vegetation indicators. The findings of this study showed that vegetation indices increased after transplantation of trees and decreased as the death rate increased and vitality decreased over time. This study presents a method for assessing newly transplanted trees using satellite images. The approach of utilizing satellite photos and the vegetation index is expected to detect changes in trees that have been transplanted across the country and help to manage tree transplantation for the environmental impact assessment.
Ocean currents play the most important role in causing and controlling global climate change. The water depth of the Yellow Sea is very shallow compared to the East Sea, and the circulation and currents of seawater are quite complicated owing to the influence of various wind fields, ocean currents, and river discharge with low-salinity seawater. The Yellow Sea Warm Current (YSWC) is one of the most representative currents of the Yellow Sea in winter and is closely related to the weather of the southwest coast of the Korean Peninsula, so it needs to be treated as important in secondary-school textbooks. Based on the 2015 revised national educational curriculum, secondary-school science and earth science textbooks were analyzed for content related to the YSWC. In addition, a questionnaire survey of secondary-school science teachers was conducted to investigate their perceptions of the temporal variability of ocean currents. Most teachers appeared to have the incorrect knowledge that the YSWC moves north all year round to the west coast of the Korean Peninsula and is strong in the summer like a general warm current. The YSWC does not have strong seasonal variability in current strength, unlike the North Korean Cold Current (NKCC), but does not exist all year round and appears only in winter. These errors in teachers' subject knowledge had a background similar to why they had a misconception that the NKCC was strong in winter. Therefore, errors in textbook contents on the YSWC were analyzed and presented. In addition, to develop students' and teachers' data literacy, class materials on the YSWC that can be used in inquiry activities were developed. A graphical user interface (GUI) program that can visualize the sea surface temperature of the Yellow Sea was introduced, and a program displaying the spatial distribution of water temperature and salinity was developed using World Ocean Atlas (WOA) 2018 oceanic in-situ measurements of water temperature and salinity data and ocean numerical model reanalysis field data. This data visualization materials using oceanic data is expected to improve teachers' misunderstandings and serve as an opportunity to cultivate both students and teachers' ocean and data literacy.
Journal of the Korean Society of Earth Science Education
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v.16
no.3
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pp.340-350
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2023
This study analyzed the difficulty level of class on the seasonal change unit for 84 students at a university of education. The conclusions of this study are as follows. First, if we first present the four topics that make up the seasonal changes in elementary science, the subjects that have the greatest difficulty in teaching for prospective elementary school teachers are 'Why do seasonal changes occur?' (Teaching difficulty level 4.05), 'The sun changes depending on the season' What is the difference between the southern altitude and the length of day and night?' (difficulty level of class, 3.12), 'What is the relationship between the altitude of the sun, length of shadow, and temperature during the day?' (difficulty level of class, 2.85), 'How does the temperature change depending on the season?' (class difficulty level 2.80). As a result, in the elementary science season change unit, the class on the four topics 'Why do seasons change?', which is classified as a class topic that requires the concept of spatial perception, showed a higher level of class difficulty than other units. Second, in the seasonal change unit, various factors of class difficulty appeared depending on the class topic. When pre-service elementary school teachers look at the factors that make class difficult when teaching a lesson on seasonal changes in order of frequency, 42 (50%) said 'Experimental instruction for comparing the altitude of solar masculine according to the tilt of the axis of rotation', followed by 'Solar masculine'. 38 people (45%) answered 'Difficulty in explaining mid-high altitude and the length of day and night', 27 people (32%) answered 'Difficulty in explaining the concept of mid-high altitude', and 24 people (32%) answered 'Difficulty in explaining seasonal changes in the sun's position.' 29%), 20 people (24%) said 'Explain the reasonable reason why the height of the light should be adjusted when measuring the solar altitude', and 16 people (19%) said 'It is difficult to explain the reason for the discrepancy between the solar altitude and the maximum temperature'. ), 'difficulties in measuring sand (ground) temperature' were mentioned by 12 people (14%). Third, when analyzing the factors of class difficulty, there were more curriculum factors than teacher factors. In this context, the exploratory activities on 'Why do seasonal changes occur?', the fourth topic of the seasonal change unit in which elementary school pre-service teachers showed the greatest difficulty in teaching, need improvement in terms of the curriculum.
Sea ice currently covers approximately 7% of the world's ocean area, primarily concentrated in polar and high-altitude regions, subject to seasonal and annual variations. It is very important to analyze the area and type classification of sea ice through time series monitoring because sea ice is formed in various types on a large spatial scale, and oil and gas exploration and other marine activities are rapidly increasing. Currently, research on the type and area of sea ice is being conducted based on high-resolution satellite images and field measurement data, but there is a limit to sea ice monitoring by acquiring field measurement data. High-resolution optical satellite images can visually detect and identify types of sea ice in a wide range and can compensate for gaps in sea ice monitoring using Geostationary Ocean Color Imager-II (GOCI-II), an ocean satellite with short time resolution. This study tried to find out the possibility of utilizing sea ice monitoring by training a rule-based machine learning model based on learning data produced using high-resolution optical satellite images and performing detection on GOCI-II images. Learning materials were extracted from Liaodong Bay in the Bohai Sea from 2021 to 2022, and a Random Forest (RF) model using GOCI-II was constructed to compare qualitative and quantitative with sea ice areas obtained from existing normalized difference snow index (NDSI) based and high-resolution satellite images. Unlike NDSI index-based results, which underestimated the sea ice area, this study detected relatively detailed sea ice areas and confirmed that sea ice can be classified by type, enabling sea ice monitoring. If the accuracy of the detection model is improved through the construction of continuous learning materials and influencing factors on sea ice formation in the future, it is expected that it can be used in the field of sea ice monitoring in high-altitude ocean areas.
Proceedings of the Korean Society of Crop Science Conference
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2023.04a
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pp.7-7
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2023
Currently, the main technologies of various fourth industries are big data, the Internet of Things, artificial intelligence, blockchain, mixed reality (MR), and drones. In particular, "digital twin," which has recently become a global technological trend, is a concept of a virtual model that is expressed equally in physical objects and computers. By creating and simulating a Digital twin of software-virtualized assets instead of real physical assets, accurate information about the characteristics of real farming (current state, agricultural productivity, agricultural work scenarios, etc.) can be obtained. This study aims to streamline agricultural work through automatic water management, remote growth forecasting, drone control, and pest forecasting through the operation of an integrated control system by constructing digital twin data on the main production area of the nojinot industry and designing and building a smart farm complex. In addition, it aims to distribute digital environmental control agriculture in Korea that can reduce labor and improve crop productivity by minimizing environmental load through the use of appropriate amounts of fertilizers and pesticides through big data analysis. These open-field agricultural technologies can reduce labor through digital farming and cultivation management, optimize water use and prevent soil pollution in preparation for climate change, and quantitative growth management of open-field crops by securing digital data for the national cultivation environment. It is also a way to directly implement carbon-neutral RED++ activities by improving agricultural productivity. The analysis and prediction of growth status through the acquisition of the acquired high-precision and high-definition image-based crop growth data are very effective in digital farming work management. The Southern Crop Department of the National Institute of Food Science conducted research and development on various types of open-field agricultural smart farms such as underground point and underground drainage. In particular, from this year, commercialization is underway in earnest through the establishment of smart farm facilities and technology distribution for agricultural technology complexes across the country. In this study, we would like to describe the case of establishing the agricultural field that combines digital twin technology and open-field agricultural smart farm technology and future utilization plans.
TAE SOO CHANG;EUNIL LEE;DO-SEONG BYUN;HWAYOUNG LEE;SEUNG-GYUN BAEK
The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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v.29
no.1
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pp.14-27
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2024
Unlike the shelf sand ridges moribund in motion, nearshore sand ridges are highly mobile, sensitive to changes in ocean environments, thereby becoming of particular interest with respect to morphological changes. About 5 km off the Daesan port, the Jangan Sand Ridge has been undergoing severe subsea morphological change over the past two decades. Understanding the nature of sand ridges is critical to elucidate the causes of morphological changes. In this context, this study aims at understanding the characteristics and distribution patterns of surface sediments of the ridge and its vicinity. For this purpose, 227 sediment samples were acquired using a grab-sampler, the grain sizes being analysed by the sieve-pipette method. In addition, comparison of grain sizes in sediments between 1997 and 2021 was made in order to investigate the 25-years change in sediment composition. Surface sediments along the ridge axis are fine to medium sands with 2-3 phi in mean grain size, whereas, in the trough of ridge, the sediments are composed of gravels and muddy sandy gravels with mean sizes of -2 to -6 phi. Sediments in the crest of the ridge are well-sorted with normal distribution, on the other hand, the basal sediments are poorly-sorted and positively skewed. Along the ridge crest, the sediments are negatively skewed. From 1997 to 2021, the ridge sediments became largely coarser about 0.5 phi. Such coarsening trend in mean grain size can be explained either by elimination of fine sediments during high waves in winter or elimination of fines suspended during sand mining activities in the past. Spatial distribution pattern of surface sediments shows that ca. 30 m thick of the sand ridge itself overlies the thin relict gravels. The strong asymmetry of sand ridge, the exposure of ridge base, and reworked gravel lags suggest that Jangan sand ridge is probably sediment-deficit and hence erosive in nature at present.
Recently, Metaverse technology has emerged as an important topic in various fields. Metaverse refers to a three-dimensional virtual space in which social and economic activities similar to the real world are possible. Among the 235 third-year students who applied the Metaversity app in the radiology department of this university from September to December 2023, 200 participated in a survey to determine the difference in student response and satisfaction when applying the Metaversity app. analyzed. First, the most satisfactory VOD viewing method was viewing through the Metaversity app, followed by viewing through the LMS. Second, 'I think online videos are appropriate for holiday reinforcement.' showed the highest score at 4.35±0.60, 'I want face-to-face classes and online classes to be held simultaneously.' was 4.25±0.87, and 'I think meta. 'I watched it well through the Metaversity app' was the lowest at 4.10±0.30, and 'VOD viewing through the Metaversity app was used appropriately in class' was the lowest at 3.99±0.75. Also, there was no significant difference in the response to the teaching method (p>0.05). Third, in terms of satisfaction with VOD viewing using the Metaversity app, 'Applying the Metaversity app was interesting and fun' ranked the highest at 4.24±0.88. The score was high, with 'Better improvement is needed to actively utilize the metaversity app' at 4.00±0.45, and 'I hope the metaversity app is implemented in other remote classes' at 3.77±0.88. appear. 'VOD classes through the Metaversity app are better than the existing LMS method.' was found to be 3.44±0.66. Additionally, there was no significant difference in satisfaction with classes according to age and gender (p>0.05). The correlation between response and satisfaction with the metaversity app is 0.601, which can be considered very significant (p>0.001). As a limitation of this study, although we surveyed students' satisfaction with using the Metaversity app, we were unable to investigate the satisfaction of instructors who interact with students. In the future, we did not consider the instructor's satisfaction in classes using the Metaversity app. Research must be conducted, and universities must have institutional support and continued interest until metaversity apps are selected and used to prepare for distance learning.
Asia-Pacific Journal of Business Venturing and Entrepreneurship
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v.19
no.2
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pp.81-96
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2024
This study analyzed the differences in the effects on entrepreneurship and entrepreneurial willingness of college students under the coronavirus pandemic by dividing theoretical education into practical education, face-to-face education, and non-face-to-face education, and analyzed the differences in the effects on entrepreneurship and entrepreneurship willingness according to the education method. This study conducted entrepreneurship education for 552 students at a comprehensive university in Chungcheong-do, Korea, and analyzed the sample by dividing it into theoretical and practical education, face-to-face education, and non-face-to-face education. In addition, a two-way repeated measures ANOVA was conducted to determine whether there were differences in the entrepreneurship education course operation form according to the pre- and post-education time points. The results showed that, first, the difference between the effectiveness of entrepreneurship education before and after theoretical and practical education was significant, and the entrepreneurship of practical education was higher than that of theoretical education after education. In the test of pre- and post-training differences in entrepreneurial intention, the difference in effectiveness was significant only in practical training. Second, the results of the repeated measures ANOVA analysis of the course operation type of theoretical and practical courses according to the difference between the pre- and post-education time points showed that there were differences in the entrepreneurship effectiveness of theoretical and practical courses according to the time point of education. Third, the difference in the effectiveness of entrepreneurship education according to face-to-face and non-face-to-face education was significant, and only the effect of non-face-to-face education on entrepreneurial intention was significant before and after education. Fourth, the results of repeated measures ANOVA analysis of face-to-face and non-face-to-face course operation type showed that the effect of face-to-face and non-face-to-face entrepreneurship education differed depending on the time of education. The pre-post difference in entrepreneurial intention was significant only for the non-face-to-face program. The implication of this study is that in order to increase the effectiveness of entrepreneurship and entrepreneurial will among university students, it is necessary to expand the amount of practical classes in which students actively participate in activities related to entrepreneurship. In addition, in order to increase the effectiveness of entrepreneurial will, a non-face-to-face education method that utilizes the metaverse space and increases the role of each student can contribute to increasing the effectiveness of entrepreneurial will.
This study aims to design mathematics-integrated classes that cultivate artificial intelligence (AI) thinking and to analyze students' AI thinking within these classes. To do this, four classes were designed through the integration of the AI4K12 Initiative's AI Big Ideas with the 2015 revised elementary mathematics curriculum. Implementation of three classes took place with 5th and 6th grade elementary school students. Leveraging the computational thinking taxonomy and the AI thinking components, a comprehensive framework for analyzing of AI thinking was established. Using this framework, analysis of students' AI thinking during these classes was conducted based on classroom discourse and supplementary worksheets. The results of the analysis were peer-reviewed by two researchers. The research findings affirm the potential of mathematics-integrated classes in nurturing students' AI thinking and underscore the viability of AI education for elementary school students. The classes, based on AI Big Ideas, facilitated elementary students' understanding of AI concepts and principles, enhanced their grasp of mathematical content elements, and reinforced mathematical process aspects. Furthermore, through activities that maintain structural consistency with previous problem-solving methods while applying them to new problems, the potential for the transfer of AI thinking was evidenced.
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