Jinsoo Choi;Cho-Rong Kim;Yong-Sang Cho;Hyuk-chul Kwon;Kyu-Min Song
Nuclear Engineering and Technology
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v.55
no.10
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pp.3543-3548
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2023
Shutdown chemistry evolution is performed in nuclear power plants at each refueling outage (RFO) to establish safe conditions to open system and minimize inventory of corrosion products in the reactor coolant system (RCS). After hydrogen peroxide is added to RCS during shutdown chemistry evolution, corrosion products are released and are removed by filters and ion exchange resins in the chemical volume control system (CVCS). Shutdown chemistry evolution including RCS clean-up time to remove released corrosion products impacts the critical path schedule during RFOs. The estimation of clean-up time prior to RFO can provide more reliable actions for RCS clean-up operations and transients to operators during shutdown chemistry. Electric Power Research Institute (EPRI) shutdown calculator (SDC) enables to provide clean-up time by Co-58 peak activity through operational data from nuclear power plants (NPPs). In this study, we have investigated the results of EPRI SDC by shutdown chemistry data of Co-58 activity using NPP data from previous cycles and modeled the estimated clean-up time by EPRI SDC using average Co-58 activity of the NPP. We selected two RFO data from the NPP to evaluate EPRI SDC results using the purification time to reach to 1.3 mCi/cc of Co-58 after hydrogen peroxide addition. Comparing two RFO data, the similar purification time between actual and computed data by EPRI SDC, 0.92 and 1.74 h respectively, was observed with the deviation of 3.7-7.2%. As the modeling the estimated clean-up time, we calculated average Co-58 peak concentration for normal cycles after cycle 10 and applied two-sigma (2σ, 95.4%) for predicted Co-58 peak concentration as upper and lower values compared to the average data. For the verification of modeling, shutdown chemistry data for RFO 17 was used. Predicted RCS clean-up time with lower and upper values was between 21.05 and 27.58 h, and clean-up time for RFO 17 was 24.75 h, within the predicted time band. Therefore, our calculated modeling band was validated. This approach can be identified that the advantage of the modeling for clean-up time with SDC is that the primary prediction of shutdown chemistry plans can be performed more reliably during shutdown chemistry. This research can contribute to improving the efficiency and safety of shutdown chemistry evolution in nuclear power plants.
This article compares the psychological and behavioral profiles of adolescents on probation and high school students. A total of 253 adolescents on probation and 257 high school students completed an open-ended questionnaire developed by the present researchers and structured questionnaire that assessed moral disengagement and self-efficacy developed by Bandura (1995), delinquent behavior and academic achievement. Adolescents on probation reported that they engaged in delinquent behavior due to the fault of others and they were angry for being forced to be on probation. They are not likely to take responsibility and morally disengage from their delinquent actions. When compared to high school students, adolescents on probation are likely to report low self-efficacy, low academic grade, and less likely to feel proud of themselves. They are more likely to meet their friends in Internet and video game rooms and less likely to focus on academic achievement. Results of ANCOVA indicate that adolescents on probation have higher scores on moral disengagement, social efficacy, but lower score on efficacy for self-regulated learning. They are more likely to run away from home and have lower academic grade. The results of the discriminant analysis indicate that running away from home, social efficacy and moral disengagement are predictive of adolescents on probation and academic achievement and efficacy for self-regulated learning are predictive of high school students.
Jong In Kim;Joo Young Lee;Jio Chung;Dae Jin Shin;Dong Hyun Choi;Ki Hong Kim;Ki Jeong Hong;Sunhee Kim;Minhwa Chung
Phonetics and Speech Sciences
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v.15
no.4
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pp.109-118
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2023
Cardiac arrest is a critical medical emergency where immediate response is essential for patient survival. This is especially true for Out-of-Hospital Cardiac Arrest (OHCA), for which the actions of emergency medical services in the early stages significantly impact outcomes. However, in Korea, a challenge arises due to a shortage of dispatcher who handle a large volume of emergency calls. In such situations, the implementation of a machine learning-based OHCA detection program can assist responders and improve patient survival rates. In this study, we address this challenge by developing a machine learning-based OHCA detection program. This program analyzes transcripts of conversations between responders and callers to identify instances of cardiac arrest. The proposed model includes an automatic transcription module for these conversations, a text-based cardiac arrest detection model, and the necessary server and client components for program deployment. Importantly, The experimental results demonstrate the model's effectiveness, achieving a performance score of 79.49% based on the F1 metric and reducing the time needed for cardiac arrest detection by 15 seconds compared to dispatcher. Despite working with a limited dataset, this research highlights the potential of a cardiac arrest detection program as a valuable tool for responders, ultimately enhancing cardiac arrest survival rates.
This study aimed to address the increasing demand for technologies preventing mushroom fly damage. By monitoring the annual occurrence patterns of pests over several years and accumulating data, we conducted an analysis to evaluate the efficacy of preventive measures applied before the surge in mushroom fly infestation, typically observed in May. For preemptive control, physical measures involved installing air curtains at cultivation facility entrances and placing sticky traps and insect traps around entry points to block external entry and reduce internal insect density. Additionally, we applied an organic agricultural material, Dalmatian chrysanthemum extract, weekly alongside chemical control measures. To assess the reduction in mushroom fly populations, yellow sticky traps (15×25 cm) were placed at three locations within the mushroom cultivation facility, and the occurrence patterns before and after implementing preventive measures were compared. Compared to conventional practices, the application of preventive techniques resulted in a significant reduction, with a 60% decrease from 15 levels of mushroom flies/m2 to 6 levels of mushroom flies/m2 in May and a 40% decrease from 10 levels of mushroom flies/m2 to 6 levels of mushroom flies/m2 in June. While achieving over 50% efficacy during the peak mushroom fly season with preventive measures, we identified complementary actions such as blocking external sources (gaps in cultivation facility doors) and maintaining cleanliness around cultivation facilities (proper disposal of spent substrate) for further improvement. Comprehensive analysis and safety studies, including correlation analysis with contaminants and pathogens, are recommended to ensure the widespread adoption of mushroom fly preventive techniques for safe and stable mushroom production in the agricultural sector.
High achievement in mathematics is a very complex process in which various factors such as cognitive factors, affective factors, and social and environmental factors work respectively and complementary. A number of previous studies conducted so far have shown that there are certain factors affecting math learning and these factors have positive or negative effects on it. However, these studies were conducted with limited variables and it was not possible to present a comprehensive analysis of what would be necessary to get good achievements in mathematics learning. Therefore, in this study, we analyzed the process of experience of students who experienced success in mathematics learning using the analysis method of the grounded theory. In addition, the collected data was analyzed to explain the process of leading to the successful experience in mathematics learning. As a result of the analysis, it was revealed that students form their identity as successful learners through the processes of 'new phase stage', 'experience accumulation stage', 'stand-up stage', and 'maintenance effort stage'. Through this study, we were able to get implications for what actions are needed to experience success in math learning by looking at the process of the experience what interviewees have gone through.
Journal of the Korean Association of Geographic Information Studies
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v.26
no.4
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pp.251-265
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2023
The purpose of this study is to deduce key indicators in evaluating the pedestrian environment for the elderly in Busan, which has entered an aging society, and to propose policy improvement measures. The key indicators were selected based on prior research and surveys, and the effectiveness of those indicators were measured through evaluations conducted on three places which have the highest proportion of the elderly population in Busan. The summaries of analysis are as follow. First, the three places are hillslide residential areas, and areas of activity that the elderly have were very narrow due to the restrictions on their movement caused by slope. Second, the areas were filled with a number of illegally parked vehicles. And the degree of segregation of pedestrian and vehicle and the level of safety were very low. Third, the streets with steep slopes of the target site force the elderly to move vertically, and for this reason, the elderly are expressing difficulties in outdoor activities. Fourth, it was found that the target site lacked a space for relaxation during outdoor activities. The poor walking environment not only limit the essential and social activities of the elderly, but also adversely affects the health and quality of life of the elderly. In order to maintain the health of the elderly and improve the quality of life, actions should be taken to improve the walking factors that affect the movement and external activities of the elderly.
Asia-Pacific Journal of Business Venturing and Entrepreneurship
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v.17
no.6
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pp.127-140
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2022
The present study assumes that active and voluntary actions of organizational members in domestic enterprises can improve organizational performance as they have to seek differentiated strategies and changes in the midst of infinite competition. Considering the nature of psychological empowerment and proactive behavior, the study conducted a survey on members of companies with 100 or more workers of a certain size and used 300 samples for the study. Accordingly, this study attempted to investigate the relationship between personal factors influencing overall organizational competitiveness, i. e. job engagement, proactive behavior and psychological empowerment. Also, the mediating effects of job engagement on the relationship between psychological empowerment and proactive behavior were also examined here. The analysis established the relationship between psychological empowerment, job engagement and proactive behavior. Also, psychological empowerment was found to exert positive influence on job engagement and proactive behavior to a significant extent. Likewise, job engagement proved to have positive influence on proactive behavior to a significant degree. In addition, the mediating effects of job engagement on the relationship between psychological empowerment and proactive behavior were confirmed. Based on these findings, this study suggested relevant theoretical rationales and practical implications.
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.
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
/
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.
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