• Fourth Industrial Review
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• v.2 no.1
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• pp.9-17
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• 2022

Purpose -In general, elementary school is the time to take the first social step away from family relationships with parents or siblings. Recently, AI technology has been widely used in everyday life and society. The purpose of this study is to propose a program that can cultivate AI literacy and self-expression for elementary school students according to the trend of the times. Research design, data, and methodology - In this study, prior to developing a self-expression class program for cultivating AI literacy, we looked at the related literature on what AI literacy is. In addition, the digital learning program was analyzed considering that the current AI literacy is based on the cutting edge of digital technology and is located in the same area as digital literacy. Result -This study developed a curriculum for self-expression and AI literacy cultivation. The main feature of this study is that the education program of this study allows 3rd, 4th, and 5th graders of elementary school to express themselves and to express their career problems by combining culture and art with AI programs. Conclusion -Self-expression activity education for cultivating AI literacy should be oriented toward holistic education and should be education as a way to express oneself in order to improve the quality of life of learners

• Journal of Gastric Cancer
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• v.23 no.3
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• pp.388-399
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• 2023

Gastric cancer remains a significant global health concern, coercing the need for advancements in imaging techniques for ensuring accurate diagnosis and effective treatment planning. Artificial intelligence (AI) has emerged as a potent tool for gastric-cancer imaging, particularly for diagnostic imaging and body morphometry. This review article offers a comprehensive overview of the recent developments and applications of AI in gastric cancer imaging. We investigated the role of AI imaging in gastric cancer diagnosis and staging, showcasing its potential to enhance the accuracy and efficiency of these crucial aspects of patient management. Additionally, we explored the application of AI body morphometry specifically for assessing the clinical impact of gastrectomy. This aspect of AI utilization holds significant promise for understanding postoperative changes and optimizing patient outcomes. Furthermore, we examine the current state of AI techniques for the prognosis of patients with gastric cancer. These prognostic models leverage AI algorithms to predict long-term survival outcomes and assist clinicians in making informed treatment decisions. However, the implementation of AI techniques for gastric cancer imaging has several limitations. As AI continues to evolve, we hope to witness the translation of cutting-edge technologies into routine clinical practice, ultimately improving patient care and outcomes in the fight against gastric cancer.

• Journal of Environmental Impact Assessment
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• v.30 no.1
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• pp.1-12
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• 2021

This study focused on verifying the identification of freshwater fish species in Korea using Environmental DNA (eDNA) technique. The research of DNA is increasing in the field of ecology, since this is more sensitive of identify rather than traditional investigation method. Which is difficult to detect species hidden in water and be easily influenced by diverse factors (sites, bad weather, researchers and so on). We applied the pilot test in aquarium (Freshwater Fish Ecological Learning Center in Yangpyeong, Gyeonggi Do), where freshwater fish species are inhabits. We conducted to sampling and analyzing the sixteen water samples (50 species from 7 orders and 13 families) using MiFish primer set. The results showed that 45 species (90%) was investigated by eDNA. It highlight that eDNA with universal primer is possible to detect freshwater fish species of Korean. However, the errors on species identification seems to be caused by the primer that be not suited perfectly and the pollution such as aquarium, sampling collectors.

• Communications of Mathematical Education
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• v.21 no.2 s.30
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• pp.347-360
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• 2007

In this study, we made the analysis of the relation with mathematical tests and scholastic attainments of gifted students using the results of entrance end comprehensives exams and so on in science education center for gifted youths. For this, we firstly made an analysis of correlation between math and math, math and science and science and science using the test results. And then, we interviewed four students. From this, we found followings. First, in every assessment except for those carried out during the semester in the center, we saw a very low or negative correlation between the students' grades in math and that in science. Second, in contrast to the correlations among other assessments, a high correlation of the students' grades in math and science appeared in regard of the assessments carried out during the semester in the center. Third, correlations between the grades of assessments in mathematics were much lower than that in science. Fourth, many students thought the assessments in the center were not as valuable as those in their schools, which are referred to in getting into a school of high grade. So some of the students who gained excellent grades showed a relatively low achievement. Fifth, students in the center regarded a vigorous communication and inquiry learning on enriched themes as the biggest merit of attending the center.

• Journal of The Korean Association For Science Education
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• v.43 no.4
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• pp.351-368
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• 2023

The scientist-led Research and Education (R&E) program aims to strengthen gifted science students' research capabilities under the guidance of scientists. Students' actual research experiences in scientist-led R&E activities range from understanding how scientists conduct research to actively participating in research. To develop R&E that promotes student agency, i.e., student participation, this study aimed to identify the pedagogical characteristics that supported gifted science students' agentic participation in the scientist-led R&E program. We conducted interviews with learners and scientists in three teams undertaking R&E activities every three months. The interview covered their perceptions of R&E activities, student participation, and scientists' support for the activities. The recordings and transcripts of the interviews were used as primary data sources for the analysis. The trajectory of each team's activities, as well as the learners' and scientists' dynamic positioning were identified. Based on this analysis, we inductively identified the pedagogical characteristics that emerged from classes in which the scientists supported the students' learning and engagement in research. Regarding agency, three types of student participation were identified: 1) the sustained exercise of agency, 2) the initial exercise and subsequent discouragement of agency, and 3) the continuous non-exercise of agency. Two pedagogical characteristics that supported the learners' agentic participation were identified: 1) opportunities for students to take part in research management and 2) scientist-student interactions encouraging learners to present expert-level ideas. This study contributes to developing pedagogies that foster gifted science students' agentic participation in scientist-led R&E activities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2016-2029
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• 2023

Accurate prediction of critical illness is significant for ensuring the lives and health of patients. The selection of indicators affects the real-time capability and accuracy of the prediction for critical illness. However, the diversity and complexity of these indicators make it difficult to find potential connections between them and critical illnesses. For the first time, this study proposes an indicator analysis model to extract key indicators from the preoperative and intraoperative clinical indicators and laboratory results of critical illnesses. In this study, preoperative and intraoperative data of heart failure and respiratory failure are used to verify the model. The proposed model processes the datum and extracts key indicators through four parts. To test the effectiveness of the proposed model, the key indicators are used to predict the two critical illnesses. The classifiers used in the prediction are light gradient boosting machine (LightGBM) and eXtreme Gradient Boosting (XGBoost). The predictive performance using key indicators is better than that using all indicators. In the prediction of heart failure, LightGBM and XGBoost have sensitivities of 0.889 and 0.892, and specificities of 0.939 and 0.937, respectively. For respiratory failure, LightGBM and XGBoost have sensitivities of 0.709 and 0.689, and specificity of 0.936 and 0.940, respectively. The proposed model can effectively analyze the correlation between indicators and postoperative critical illness. The analytical results make it possible to find the key indicators for postoperative critical illnesses. This model is meaningful to assist doctors in extracting key indicators in time and improving the reliability and efficiency of prediction.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.31-31
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• 2023

During December 2022, the northeast monsoon, which dominates the south and the Gulf of Thailand, had significant rainfall that impacted the lower southern region, causing flash floods, landslides, blustery winds, and the river exceeding its bank. The Golok River, located in Narathiwat, divides the border between Thailand and Malaysia was also affected by rainfall. In flood management, instruments for measuring precipitation and water level have become important for assessing and forecasting the trend of situations and areas of risk. However, such regions are international borders, so the installed measuring telemetry system cannot measure the rainfall and water level of the entire area. This study aims to predict 72 hours of water level and evaluate the situation as information to support the government in making water management decisions, publicizing them to relevant agencies, and warning citizens during crisis events. This research is applied to machine learning (ML) for water level prediction of the Golok River, Lan Tu Bridge area, Sungai Golok Subdistrict, Su-ngai Golok District, Narathiwat Province, which is one of the major monitored rivers. The eXtreme Gradient Boosting (XGBoost) algorithm, a tree-based ensemble machine learning algorithm, was exploited to predict hourly water levels through the R programming language. Model training and testing were carried out utilizing observed hourly rainfall from the STH010 station and hourly water level data from the X.119A station between 2020 and 2022 as main prediction inputs. Furthermore, this model applies hourly spatial rainfall forecasting data from Weather Research and Forecasting and Regional Ocean Model System models (WRF-ROMs) provided by Hydro-Informatics Institute (HII) as input, allowing the model to predict the hourly water level in the Golok River. The evaluation of the predicted performances using the statistical performance metrics, delivering an R-square of 0.96 can validate the results as robust forecasting outcomes. The result shows that the predicted water level at the X.119A telemetry station (Golok River) is in a steady decline, which relates to the input data of predicted 72-hour rainfall from WRF-ROMs having decreased. In short, the relationship between input and result can be used to evaluate flood situations. Here, the data is contributed to the Operational support to the Special Water Resources Management Operation Center in Southern Thailand for flood preparedness and response to make intelligent decisions on water management during crisis occurrences, as well as to be prepared and prevent loss and harm to citizens.

• Journal of The Korean Society of Clinical Toxicology
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• v.1 no.1
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• pp.21-26
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• 2003

Methamphetamine (MA) is a major drug of abuse in Korea. Currently preliminary evidence suggests that MA dependence may cause long-term neural damage in human. Repeated exposure to psychostimulants such as methamphetamine results in behavioral sensitization, a paradigm thought to be relevant to drug craving and addiction in human. Sensitization alters neural circuitry involved in normal processes of incentrive, motivation, and reward. However the precise mechanism of this behavioral sensitization has not yet been fully elucidated. Repeated use of high dose MA causes neurotoxicity which is characterized by a long-lasting depletion of striatal dopamine (DA) and tyrosin hydroxylase activity of DA, DA-transporter binding sites in the striatum. The loss of DA transporters correlates with memory problems and lack of motor coordination. DA fuels motivation and pleasure, but it' s also crucial for learning and movement. This selective review provides a summary of studies that assess the neurobiological mechanisms of MA.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.227-230
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• 2009

This paper presents an architecture of various devices convergence for ubiquitous network. Integration of a variety of devices be can connect to every kind of device should not be constrained. We construct PLC to UPnP protocol architecture and UPnP Bridge Module for interconnecting Non-IP devices with heterogeneous network interfaces to UPnP devices on UPnP networks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.237-240
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• 2009

In this paper study is to present a framework for sharing medical information. information is generated on the basis of HL7 Messaging standards and shared between, message design on the Implementation of Medical Inform Based on HL7. Effective management control bundled with other devices.