• Journal of Korean Elementary Science Education
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• v.43 no.1
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• pp.136-147
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• 2024

It has recently be emphasized in science education that lessons that can develop "scientific participation and action" should be implemented to scientifically recognize various problems and respond to them as well as risks that occur in real life. This study aims to analyze the characteristics of scientific participation and action lessons as perceived by the preservice primary school teachers. To do that, the researchers collected and analyzed the lesson plans designed by the preservice teachers based on the achievement standard related to noise for grades 3-4 in 2022 revised science curriculum. Focusing on the stages of "problem recognition," "data collection and analysis," and "implementation and sharing," the results identity the four main characteristics as problem-solving activity, inquiry activity, investigative activity, and activity that encourages practical actions. The two or three features were found to be combinated in a lesson depending on its context. In some cases, only one feature was seen in a lesson. Based on the results, educational implications were discussed in terms of the teaching and learning methods and teacher education for implementing scientific participation and action.

• Information Systems Review
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• v.23 no.4
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• pp.45-67
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• 2021

As the transaction volume of the C2C second-hand market is growing, the number of frauds, which intend to earn unfair gains by sending products different from specified ones or not sending them to buyers, is also increasing. This study explores the model that can identify frauds in the online C2C second-hand market by examining the postings for transactions. For this goal, this study collected 145,536 field data from actual C2C second-hand market. Then, the model is built with the characteristics from postings such as the topic and the linguistic characteristics of the product description, and the characteristics of products, postings, sellers, and transactions. The constructed model is then trained by the machine learning algorithm XGBoost. The final analysis results show that fraudulent postings have less information, which is also less specific, fewer nouns and images, a higher ratio of the number and white space, and a shorter length than genuine postings do. Also, while the genuine postings are focused on the product information for nouns, delivery information for verbs, and actions for adjectives, the fraudulent postings did not show those characteristics. This study shows that the various features can be extracted from postings written in C2C second-hand transactions and be used to construct an effective model for frauds. The proposed model can be also considered and applied for the other C2C platforms. Overall, the model proposed in this study can be expected to have positive effects on suppressing and preventing fraudulent behavior in online C2C markets.

• Journal of The Korean Association For Science Education
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• v.26 no.1
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• pp.40-48
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• 2006

The purpose of this study was to investigate 6th grade pupil's thoughts during classification activities. Two suitable tools in classification activity achievement were developed to achieve this purpose. The first was an artificial stimulus card in which the attribute was prominent; and the other a natural stimulus card in which the attribute was less prominent. Participants of the study were 8 6th grade pupils from D elementary school in Yeongdeungpo-gu, Seoul. Data were collected from interviews with the pupils, the pupil's recordings of classification, the investigator's observation of pupil's actions, and video recordings of the pupil's subject classification process. Results found in this study were as following. First, when doing classification 6th grade pupils considered attribute observation, attribute estimation, preliminary inspection, criteria selection, and sample identification. Second, 6th grade pupil classification thought process was found to be repetitive, passing through the steps of attribute observation, attribute estimation, preliminary inspection, criteria selection, and lastly, sample identification. Third, 6th grade pupils took advantage of cognitive economic efficiency. Study findings also revealed guidance for the teaching and learning of scientific classification. First, once teachers understand the classification thought process of students, more effective classification guidance will be possible. Second, it is necessary that guidance fit each step of the classification thought process.

• Journal of The Korean Association For Science Education
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• v.27 no.7
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• pp.663-675
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• 2007

Chemical reactions in cells are so complicated and abstract that students have difficulty in understanding them. In this study, classes with the application of systematic analogies used at the interpretation of experimental results were taught to 10th-grade students in order to help them to understand the concept of enzymes, which play an important role in chemical reactions in cells. Effects of the classes on their understanding of the concept of enzymes and the role of systematic analogies were analyzed. The gap of understanding between the test group and the control group was significant at 0.05, indicating that systematic analogies are effective for students' understanding of the concept of enzymes. Looking into the concept of enzymes by individual element, the effect of systematic analogies was shown to be large for equilibrium-like processes, such as the enzyme structure change caused by temperature and pH; and the continuous and random actions of enzymes, which students have difficulty in understanding. For these processes, systematic analogies played a positive role in improving their conceptual status. The visualizations and familiarity of analogs increased their intelligibility regarding the concept of enzyme. Also, the systematic analogies increases their plausibility by helping to connect phenomena, taking place in the enzyme reaction experiments, with scientific concepts as scaffold. Accordingly, it was possible to explain experimental results as scientific concepts in a consistent manner. In addition, analogies familiar to students played a positive role from the affective perspective by promoting students' interest and helping them to approach hard scientific concepts.

• Journal of the Daesoon Academy of Sciences
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• v.47
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• pp.105-138
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• 2023

The purpose of this study is to examine the significance of various figures in The Canonical Scripture and their contents. The Canonical Scripture (jeon-gyeong 典經) is a record of the beliefs and deeds of Kang Jeungsan, composed through the memories of the followers of Kang Jeungsan who followed the words of The Canonical Scripture. In other words, The Canonical Scripture is understood as the scripture of Daesoon Jinrihoe that contains the religious deeds and teachings of Kang Jeungsan. It is divided into seven parts and 17 chapters. Those seven parts, some of which contain more than one chapter, are as follows: Acts, Reordering Works, Progress of the Order, Dharma, Authority and Foreknowledge, Saving Lives, and Prophetic Elucidations. In particular, The Canonical Scripture records the deeds of historical figures from both China and Korea, and this prominently includes the life history of Kang Jeungsan, and this is an especially pronounced feature of the sections Acts, Progress of the Order, and Prophetic Elucidations. In addition, each chapter describes the teachings and faith-inspiring acts of Kang Jeungsan and presents the gist of the Daesoon Thought while referring to the lives and actions of various historical figures. In this paper, introductions to the figures that appear in each section are provided to help readers better understand the contents of The Canonical Scripture. Therefore, this study focuses on the major figures introduced in The Canonical Scripture in connection with the religious values of Daesoon Thought. Through this, a contribution is made to the academic development of Daesoon Thought by specifically exploring and examining the contents of the figures who appear in The Canonical Scripture. This is a surprisingly underdeveloped area of study in Daesoon Thought.

• Smart Media Journal
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• v.13 no.2
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• pp.32-43
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• 2024

Game agents which are behavioral agent for game playing are a crucial component of game satisfaction. However it takes a lot of time and effort to create game agents for various game levels, environments, and players. In addition, when the game environment changes such as adding contents or updating characters, new game agents need to be developed and the development difficulty gradually increases. And it is important to have a game agent that can be customized for different levels of players. This is because a game agent that can play games of various levels is more useful and can increase the satisfaction of more players than a high-level game agent. In this paper, we propose a method for learning and controlling the level of play of game agents that can be rapidly developed and fine-tuned for various game environments and changes. At this time, reinforcement learning applies a policy-based distributed reinforcement learning method IMPALA for flexible processing and fast learning of various behavioral structures. Once reinforcement learning is complete, we choose actions by sampling based on Softmax-Temperature method. From this result, we show that the game agent's play level decreases as the Temperature value increases. This shows that it is possible to easily control the play level.

• Journal of the Korean Society of Radiology
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• v.18 no.1
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• pp.53-63
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• 2024

The purpose of this study was to analyze radiological technologists' (RT) task perceptions of intravenous injection behavior of contrast medium and use it as basic data for future workforce response plans. We surveyed a total of 172 RT using questionnaire terms consisting of demographic characteristics, job priorities, and RT' task perceptions of intravenous injection behavior. Statistical analysis was performed using descriptive statistics, frequency analysis, independent samples T-test, and ANOVA analysis. As a result, first, current clinical RT were highly aware of the need for intravenous injection behavior as a response to the future workforce of them, and the workload burden resulting from this was evaluated as low. Second, the fear of intravenous injection behavior was found to be significant, so it is judged to be useful to perform them as selective job actions rather than all RT' task. Third, the need for training courses and certification for RT' intravenous injection behavior is being raised, and additional specific research on this is required. Last, RT' positive perception of intravenous injection behavior could be expected as a foundation for improving national medical services, strengthening RT expertise, and expanding tasks.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.1
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• pp.49-56
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• 2024

An automated dynamic structural analysis module stands as a crucial element within a structural integrated mitigation system. This module must deliver prompt real-time responses to enable timely actions, such as evacuation or warnings, in response to the severity posed by the structural system. The finite element method, a widely adopted approximate structural analysis approach globally, owes its popularity in part to its user-friendly nature. However, the computational efficiency and accuracy of results depend on the user-provided finite element mesh, with the number of elements and their quality playing pivotal roles. This paper introduces a computationally efficient adaptive mesh generation scheme that optimally combines the h-method of node movement and the r-method of element division for mesh refinement. Adaptive mesh generation schemes automatically create finite element meshes, and in this case, representative strain values for a given mesh are employed for error estimates. When applied to dynamic problems analyzed in the time domain, meshes need to be modified at each time step, considering a few hundred or thousand steps. The algorithm's specifics are demonstrated through a standard cantilever beam example subjected to a concentrated load at the free end. Additionally, a portal frame example showcases the generation of various robust meshes. These examples illustrate the adaptive algorithm's capability to produce robust meshes, ensuring reasonable accuracy and efficient computing time. Moreover, the study highlights the potential for the scheme's effective application in complex structural dynamic problems, such as those subjected to seismic or erratic wind loads. It also emphasizes its suitability for general nonlinear analysis problems, establishing the versatility and reliability of the proposed adaptive mesh generation scheme.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.203-217
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• 2024

The Land Use, Land-Use Change and Forestry (LULUCF) sector of the National Greenhouse Gas Inventory is crucial for obtaining data on carbon sinks, necessitating accurate estimations. This study analyzes cases of countries applying the LULUCF sector at the Tier 3 level to propose enhanced methodologies for carbon sink estimation. In nations like Japan and Western Europe, satellite spatial information such as SPOT, Landsat, and Light Detection and Ranging (LiDAR)is used alongside national statistical data to estimate LULUCF. However, in Korea, the lack of land use change data and the absence of integrated management by category, measurement is predominantly conducted at the Tier 1 level, except for certain forest areas. In this study, Space-borne LiDAR Global Ecosystem Dynamics Investigation (GEDI) was used to calculate forest canopy heights based on Relative Height 100 (RH100) in the cities of Icheon, Gwangju, and Yeoju in Gyeonggi Province, Korea. These canopy heights were compared with the 1:5,000 scale forest maps used for the National Inventory Report in Korea. The GEDI data showed a maximum canopy height of 29.44 meters (m) in Gwangju, contrasting with the forest type maps that reported heights up to 34 m in Gwangju and parts of Icheon, and a minimum of 2 m in Icheon. Additionally, this study utilized Ordinary Least Squares(OLS)regression analysis to compare GEDI RH100 data with forest stand heights at the eup-myeon-dong level using ArcGIS, revealing Standard Deviations (SDs)ranging from -1.4 to 2.5, indicating significant regional variability. Areas where forest stand heights were higher than GEDI measurements showed greater variability, whereas locations with lower tree heights from forest type maps demonstrated lower SDs. The discrepancies between GEDI and actual measurements suggest the potential for improving height estimations through the application of high-resolution remote sensing techniques. To enhance future assessments of forest biomass and carbon storage at the Tier 3 level, high-resolution, reliable data are essential. These findings underscore the urgent need for integrating high-resolution, spatially explicit LiDAR data to enhance the accuracy of carbon sink calculations in Korea.

• Journal of Life Science
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• v.34 no.7
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• pp.493-499
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• 2024

Antimicrobial peptides are antimicrobial substances inherent in animals and plants, with strong antibacterial activity even in small amounts and with various other functions such as antiviral and antioxidant actions. Plants can be grown with just water and sunlight, allowing for their mass production at low costs. However, transforming a chloroplast into one that produces antimicrobial peptides, rather than growing plants, increases the amount of protein expression and minimizes contamination of the ecosystem because gene transfer by pollen does not occur. In that context, using transgenic plant chloroplasts to produce recombinant proteins increases protein degradation and reduces the solubility of proteins. To solve this problem, we fused SUMO, a fusion protein, with a recombinant protein. We also used a 6xHis tag to purify the fusion protein. The antimicrobial peptide stomoxyn is an antibacterial substance found in stable flies. Stomoxyn has an α-helix structure and is amphiphilic, which allows it to dissolve bacterial cell membranes. In this study, we constructed a transformation vector to express stomoxyn in both plant chloroplasts and Escherichia coli and used this vector to confirm the expression of stomoxyn in E. coli. The expression of the protein was then confirmed in E. coli using a transformation vector. The expressed stomoxyn was purified by nickel column and SUMOase treatment, and its antibacterial activity was confirmed using an agar diffusion assay. The EGFP gene was used to ensure that the transformed vector was inserted into the chloroplast.