• KOREAN JOURNAL OF CROP SCIENCE
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• v.39 no.2
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• pp.165-170
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• 1994

High quality rapeseed cultivars including Nojeokchae, Yeongsanyuchae Halla-yuchae and Tamrayuchae have been released and recommended as a zero erucic acid variety to Cheju farmers for 13 years, where is a major rapeseed production area in korea. However, rapeseeds produced in Cheju island in 1992 and 1993 contained 47.7% and 37.0% of erucic acid respectively resulting in poor quality oil being not adequate for edible oil. It was considered that the zero erucic acid varieties did not have an opportunity to be cultivated in Cheju island by farmers living in the Island. Thus, the new rapeseed varieties without erucic acid should be bred and recommended to the farmers of southern area of Korea as a multiple cropping crop just after rice harvest, and for large scale mechanized and labour-serving rapeseed culture. The change of rapeseed breeding goal would be desirable for fatty acid composition improvement of rapeseed to develop varieties adaptable to southern part of Korea, and to produce rapeseed oil directly used as an edible oil safely.

• Korean Journal of Ecology and Environment
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• v.56 no.1
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• pp.45-56
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• 2023

In stream ecosystem assessment, RIVPACS, which makes a simple but clear evaluation based on macroinvertebrate community, is widely used. In this study, a preliminary study was conducted to develop a RIVPACS-type model suitable for Korean streams nationwide. Reference streams were classified into two types(upstream and downstream), and a prediction model for macroinvertebrates was developed based on each family. A model for upstream was divided into 7 (train): 3 (test), and that for downstream was made using a leave-one-out method. Variables for the models were selected by non-metric multidimensional scaling, and seven variables were chosen, including elevation, slope, annual average temperature, stream width, forest ratio in land use, riffle ratio in hydrological characteristics, and boulder ratio in substrate composition. Stream order classified 3,224 sites as upstream and downstream, and community compositions of sites were predicted. The prediction was conducted for 30 macroinvertebrate families. Expected (E) and observed fauna (O) were compared using an ASPT biotic index, which is computed by dividing the BMWPK score into the number of families in a community. EQR values (i.e. O/E) for ASPT were used to assess stream condition. Lastly, we compared EQR to BMI, an index that is commonly used in the assessment. In the results, the average observed ASPT was 4.82 (±2.04 SD) and the expected one was 6.30 (±0.79 SD), and the expected ASPT was higher than the observed one. In the comparison between EQR and BMI index, EQR generally showed a higher value than the BMI index.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.61-79
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• 2023

This study is a case study of overseas banana farms as a global agricultural startup that has hardly been attempted so far in terms of paradigm shift in the industry, beyond regional limitations. It was researched for the purpose of revealing the success factors of 'global agricultural startup' in terms of business process, entrepreneurship, and management dimensions learned through direct participation and observation at the local level. In order to study global agricultural startups, this study also conducted a comparative analysis of global startups (global startups) and global agricultural startups(global agricultural startups). In fact, the analysis consists of 'definition', 'components', and 'success factors', and we want to confirm the difference between the two concepts that can be distinguished. The case analysis tried to maximize the advantages of 'participatory action research' by directly observing and experiencing banana farms. In the case of banana farm cases, by dividing them into preparation process for farm development and farm development and management process, various variables considered in farm management were explained through the whole process of farm management. Through the process of overcoming and responding to specific failure cases, we tried to secure the reliability and validity of the research, and the case studies related to entrepreneurship, management, and organization analyzed by applying them by subdividing them into theoretical areas belonging to components and management that were theorized in existing preceding studies. This study is almost the first study on the process of creating a local entry business by directly moving the head office overseas rather than entering overseas agriculture as a subsidiary, joint venture or overseas corporation. In particular, it is a unique case that corresponds to agriculture in terms of region(Africa), scale(startup), and industry that have not been introduced so far as a global agricultural startup. In terms of entrepreneurship, it also concretely exemplified how entrepreneurship components such as innovativeness, risk-taking propensity, proactiveness, vision sharing, social contribution, leadership, etc., which have not been attempted so far in agricultural cases, are manifested and effective. The management and cultural aspects also went beyond the argument that only cultural aspects are important in overseas business, and also confirmed individual failure cases and their responses in recruitment, job, wage, retirement, development, organizational structure management, etc. As a result, there is significance and implications of this study in that it provides theoretical confirmation as well as practical and responsive basis for 'entrepreneurship', 'farming management', and 'management' aspects in overseas agricultural startup business operation.

• Journal of The Korean Association For Science Education
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• v.26 no.7
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• pp.813-825
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• 2006

The purpose of this study was to identify students' conceptions and their conceptualization traits in terms of the explanation of subjects: human, animals, and plants. A questionnaire was specially developed to make sure students' conceptions of evolution, students' explanation patterns with the five evolutionary explanations: creationism, internal will explanation, theological explanations, use and disuse explanation and natural selection after mutation and perceived ideas of evolution in terms of the genetic characters of human, animals and plants. 1,540 elementary, middle and high school students participated in responding to the questions. The data was collected and analyzed cross-sectionally by age. Results showed that students' evolutionary explanations were different among each subject: human, animals and plants. Students had concentrated with more 'theological explanations' than 'internal will explanation' and 'use and disuse explanation' about plant evolution. 'Natural selection after mutation explanation' was less represented in explaining human characters. This result showed that the anthropocentric thoughts had influenced students' evolution conceptions. Accordingly, as student's age, 'theological explanation' and 'internal will explanation' became least used in explaining the theory of creation. 'Use and disuse explanation' attained more representation in their explanation. In addition, the diversity of students' evolutionary explanations was getting less attention as age increases. Among youngers, 'theological explanation', 'internal will explanation' and 'use and disuse explanation' showed an even number of responses. The elders responded with more 'use and disuse explanation'. This result let us infer that 'use and disuse explanation' was strongly adhered to by student cognitive structures. Many students recognized that evolution was a kind of scientific hypothesis with little evidence. They have had a little interest in evolution and conceptualized it through informal educational sources. This study mentions that to teach evolution more effectively, teachers should make 'use and disuse explanation' adhere strongly to students' cognitive structure.

• Journal of the Korean earth science society
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• v.45 no.2
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• pp.157-171
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• 2024

The purposes of this study were: (1) to revalidate the revised Systems Thinking Measuring Instrument (Re_STMI) reported by Lee et al. (2024) among Vietnamese high school students and (2) to investigate the differences in systems thinking abilities between Korean and Vietnamese high school students. To achieve this, data from 234 Vietnamese high school students who responded to translated Re_STMI consisting of 20 items and an Scale consisting of 20 items were used. Validity analysis was conducted through item response analysis (Item Reliability, Item Map, Infit and Outfit MNSQ, DIF between male and female) and exploratory factor analysis (principal axis factor analysis using Promax). Furthermore, structural equation modeling was employed with data from 475 Korean high school students to verify the latent mean analysis. The results were as follows: First, in the item response analysis of the 20 translated Re_STMI items in Vietnamese, the Item Reliability was .97, and the Infit MNSQ ranged from .67 to 1.38. The results from the Item Map and DIF analysis align with previous findings. In the exploratory factor analysis, all items were loaded onto intended sub-factors, with sub-factor reliabilities ranging from .662 to .833 and total reliability at .876. Confirmatory factor analysis for latent mean analysis between Korean and Vietnamese students yielded acceptable model fit indices (χ²/df: 2.830, CFI: .931, TLI: .918, SRMR: .043, RMSEA: .051). Lastly, the latent mean analysis between Korean and Vietnamese students revealed a small effect size in systems analysis, mental models, team learning, and shared vision factors, whereas a medium effect size was observed in personal mastery factors, with Vietnamese high school students showing significantly higher results in systems thinking. This study confirmed the reliability and validity of the Re_STMI items. Furthermore, international comparative studies on systems thinking using Re_STMI translated into Vietnamese, English, and other languages are warranted in the context of students' systems thinking analysis.

• The Mathematical Education
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• v.63 no.2
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• pp.255-272
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• 2024

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.

• The Korean Journal of Archival Studies
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• no.79
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• pp.5-32
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• 2024

The Korean government ranked 3rd among 193 UN member countries in the UN's 2022 e-Government Development Index. Korea, which has consistently been evaluated as a top country, can clearly be said to be a leading country in the world of e-government. The lubricant of e-government is data. Data itself is neither information nor a record, but it is a source of information and records and a resource of knowledge. Since administrative actions through electronic systems have become widespread, the production and technology of data-based records have naturally expanded and evolved. Technology may seem value-neutral, but in fact, technology itself reflects a specific worldview. The digital order of new technologies, armed with hyper-connectivity and super-intelligence, not only has a profound influence on traditional power structures, but also has an a similar influence on existing information and knowledge transmission media. Moreover, new technologies and media, including data-based generative artificial intelligence, are by far the hot topic. It can be seen that the all-round growth and spread of digital technology has led to the augmentation of human capabilities and the outsourcing of thinking. This also involves a variety of problems, ranging from deep fakes and other fake images, auto profiling, AI lies hallucination that creates them as if they were real, and copyright infringement of machine learning data. Moreover, radical connectivity capabilities enable the instantaneous sharing of vast amounts of data and rely on the technological unconscious to generate actions without awareness. Another irony of the digital world and online network, which is based on immaterial distribution and logical existence, is that access and contact can only be made through physical tools. Digital information is a logical object, but digital resources cannot be read or utilized without some type of device to relay it. In that respect, machines in today's technological society have gone beyond the level of simple assistance, and there are points at which it is difficult to say that the entry of machines into human society is a natural change pattern due to advanced technological development. This is because perspectives on machines will change over time. Important is the social and cultural implications of changes in the way records are produced as a result of communication and actions through machines. Even in the archive field, what problems will a data-based archive society face due to technological changes toward a hyper-intelligence and hyper-connected society, and who will prove the continuous activity of records and data and what will be the main drivers of media change? It is time to research whether this will happen. This study began with the need to recognize that archives are not only records that are the result of actions, but also data as strategic assets. Through this, author considered how to expand traditional boundaries and achieves reterritorialization in a data-driven society.

• Journal of The Korean Association For Science Education
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• v.44 no.1
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• pp.11-27
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• 2024

This study aimed to investigate the role of scientific empathy in influencing students' productive disciplinary engagement in scientific activities and analyze the key factors of scientific empathy that manifest during this process. Twelve fifth-grade students were divided into three subgroups based on their general empathic abilities. Lessons promoting productive disciplinary engagement, integrating design thinking processes, were conducted. Subgroup discourse analysis during idea generation and prototype stages, two of five problem-solving steps, enabled observation of scientific empathy and practice aspects. The results showed that applying scientific empathy effectively through design thinking facilitated students' productive disciplinary engagement in science. In the idea generation stage, we observed an initial increase followed by a decrease in scientific empathy and practice utterances, while during the prototyping stage, utterance frequency increased, particularly in the later part. However, subgroups with lower empathic abilities displayed decreased discourse frequency in scientific empathy and practice during the prototype stage due to a lack of collaborative communication. Across all empathic ability levels, the students articulated all five key factors of scientific empathy through their utterances in situations involving productive science engagement. In the high empathic ability subgroup, empathic understanding and concern were emphasized, whereas in the low empathic ability subgroup, sensitivity, scientific imagination, and situational interest, factors of empathizing with the research object, were prominent. These results indicate that experiences of scientific empathy with research objects, beyond general empathetic abilities, serve as a distinct and crucial factor in stimulating diverse participation and sustaining students' productive engagement in scientific activities during science classes. By suggesting the potential multidimensional impact of scientific empathy on productive disciplinary engagement, this study contributes to discussions on the theoretical structure and stability of scientific empathy in science education.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.151-164
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• 2024

In water treatment plants supplying potable water, the management of chlorine concentration in water treatment processes involving pre-chlorination or intermediate chlorination requires process control. To address this, research has been conducted on water quality prediction techniques utilizing AI technology. This study developed an AI-based predictive model for automating the process control of chlorine disinfection, targeting the prediction of residual chlorine concentration downstream of sedimentation basins in water treatment processes. The AI-based model, which learns from past water quality observation data to predict future water quality, offers a simpler and more efficient approach compared to complex physicochemical and biological water quality models. The model was tested by predicting the residual chlorine concentration downstream of the sedimentation basins at Plant, using multiple regression models and AI-based models like Random Forest and LSTM, and the results were compared. For optimal prediction of residual chlorine concentration, the input-output structure of the AI model included the residual chlorine concentration upstream of the sedimentation basin, turbidity, pH, water temperature, electrical conductivity, inflow of raw water, alkalinity, NH₃, etc. as independent variables, and the desired residual chlorine concentration of the effluent from the sedimentation basin as the dependent variable. The independent variables were selected from observable data at the water treatment plant, which are influential on the residual chlorine concentration downstream of the sedimentation basin. The analysis showed that, for Plant, the model based on Random Forest had the lowest error compared to multiple regression models, neural network models, model trees, and other Random Forest models. The optimal predicted residual chlorine concentration downstream of the sedimentation basin presented in this study is expected to enable real-time control of chlorine dosing in previous treatment stages, thereby enhancing water treatment efficiency and reducing chemical costs.