• Korean Journal of Clinical Laboratory Science
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• v.55 no.4
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• pp.253-260
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• 2023

The germanium-68/gallium-68 (⁶⁸Ge/⁶⁸Ga) generator has high spatial utilization and requires little maintenance, making it economical and easy to produce. Thus, the frequency of use of ⁶⁸Ga radiopharmaceuticals is rapidly increasing worldwide. Therefore, this study attempted to develop an automated synthesizer for the routine clinical application of ⁶⁸Ga radiopharmaceuticals. The automated synthesizer was based on a fixed tubing system and the structure was designed after adjusting the position of the parts to reflect the synthesis method. Using various components that can be supplied in Korea, the automated synthesizer was manufactured at a much lower price cost than that of a commercialized automated synthesizer sold by companies. ⁶⁸Ga-DOTA-[Tyr3]-octreotide (⁶⁸Ga-DOTATOC) was synthesized to evaluate the performance of the automated synthesizer. ⁶⁸Ga-DOTATOC could be synthesized with about 65% of non-decay corrected yield, and the synthesized ⁶⁸Ga-DOTATOC met all quality control standards. We have synthesized ⁶⁸Ga-DOTATOC more than 100 times, and only faced a few problems caused by mechanical errors. In this study, we successfully developed a simple automated synthesizer for ⁶⁸Ga radiopharmaceuticals with high reproducibility. As various ⁶⁸Ga radiopharmaceuticals have recently been developed, it is expected that the automated synthesizer developed in this study will be useful for routine clinical use.

• Industry Promotion Research
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• v.9 no.1
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• pp.231-239
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• 2024

This study aims to verify the moderated mediating effect of a growth mindset on the effect of academic burnout on academic self-efficacy through study engagement among Chinese college students. Data were collected through a survey targeting 547 college students who were purposively sampled at a junior college in China. The collected data was analyzed using SPSS PC+ Win ver. 25.0 and SPSS PROCESS macro ver. 4.2. The applied statistical methods were frequency analysis, reliability analysis, correlation analysis, and moderated mediation effect analysis. The study showed that academic burnout had a significant negative correlation with growth mindset, study engagement, and academic self-efficacy. On the other hand, growth mindset, study engagement, and academic self-efficacy showed a significant positive correlation. Second, the moderated mediating effect of a growth mindset was verified in the effect of academic burnout on academic self-efficacy through study engagement. Based on these results, this study proposed a method to protect academic self-efficacy by applying study engagement and growth mindset in situations where academic burnout among college students reduces academic self-efficacy.

• Journal of Biomedical Engineering Research
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• v.45 no.3
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• pp.128-137
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• 2024

It is necessary to develop a pulsatile Extracorporeal Membrane Oxygenator (p-ECMO) with counter-pulsation control(CPC), which ejects blood during the diastolic phase of the heart rather than the systolic phase, due to the known issues with conventional ECMO causing fatal complications such as ventricular dilation and pulmonary edema. A promising method to simultaneously detect the pulsations of the heart and p-ECMO is to analyze blood pressure waveforms using deep neural network technology(DNN). However, the accurate detection of cardiac rhythms by DNNs is challenging due to various noises such as pulsations from p-ECMO, reflected waves in the vessels, and other dynamic noises. This study aims to evaluate the accuracy of DNNs developed for CPC in p-ECMO, using human-like blood pressure waveforms reproduced in an in-vitro experiment. Especially, an experimental setup that reproduces reflected waves commonly observed in actual patients was developed, and the impact of these waves on DNN judgments was assessed using a multiple DNN (m-DNN) that provides accurate determinations along with a separate index for heartbeat recognition ability. In the experimental setup inducing reflected waves, it was observed that the shape of the blood pressure waveform became increasingly complex, which coincided with an increase in harmonic components, as evident from the Fast Fourier Transform results of the blood pressure wave. It was observed that the recognition score (RS) of DNNs decreased in blood pressure waveforms with significant harmonic components, separate from the frequency components caused by the heart and p-ECMO. This study demonstrated that each DNN trained on blood pressure waveforms without reflected waves showed low RS when faced with waveforms containing reflected waves. However, the accuracy of the final results from the m-DNN remained high even in the presence of reflected waves.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.91-101
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• 2024

The utilization of container virtualization technology ensures the consistency and portability of data-intensive and memory volatile workflows. Kubernetes serves as the de facto standard for orchestrating these container applications. Cloud users often overprovision container applications to avoid container restarts caused by resource shortages. However, overprovisioning results in decreased CPU and memory resource utilization. To address this issue, oversubscription of container resources is commonly employed, although excessive oversubscription of memory resources can lead to a cascade of container restarts due to node memory scarcity. Container restarts can reset operations and impose substantial overhead on containers with high memory volatility that include numerous stateful applications. This paper proposes a technique to mitigate container restarts in a memory oversubscription environment based on Kubernetes. The proposed technique involves identifying containers that are likely to request memory allocation on nodes experiencing high memory usage and temporarily pausing these containers. By significantly reducing the CPU usage of containers, an effect similar to a paused state is achieved. The suspension of the identified containers is released once it is determined that the corresponding node's memory usage has been reduced. The average number of container restarts was reduced by an average of 40% and a maximum of 58% when executing a high memory volatile workflow in a Kubernetes environment with the proposed method compared to its absence. Furthermore, the total execution time of a container workflow is decreased by an average of 7% and a maximum of 13% due to the reduced frequency of container restarts.

• Tuberculosis and Respiratory Diseases
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• v.87 no.3
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• pp.368-377
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• 2024

Background: Bronchiectasis is a chronic respiratory disease that leads to airway inflammation, destruction, and airflow limitation, which reflects its severity. Impulse oscillometry (IOS) is a non-invasive method that uses sound waves to estimate lung function and airway resistance. The aim of this study was to assess the usefulness of IOS in predicting the severity of bronchiectasis. Methods: We retrospectively reviewed the IOS parameters and clinical characteristics in 145 patients diagnosed with bronchiectasis between March 2020 and May 2021. Disease severity was evaluated using the FACED score, and patients were divided into mild and moderate/severe groups. Results: Forty-four patients (30.3%) were in the moderate/severe group, and 101 (69.7%) were in the mild group. Patients with moderate/severe bronchiectasis had a higher airway resistance at 5 Hz (R5), a higher difference between the resistance at 5 and 20 Hz (R5-R20), a higher resonant frequency (Fres), and a higher area of reactance (AX) than patients with mild bronchiectasis. R5 ≥0.43, resistance at 20 Hz (R20) ≥0.234, R5-R20 ≥28.3, AX ≥1.02, reactance at 5 Hz (X5) ≤-0.238, and Fres ≥20.88 revealed significant univariable relationships with bronchiectasis severity (p<0.05). Among these, only X5 ≤-0.238 exhibited a significant multivariable relationship with bronchiectasis severity (p=0.039). The receiver operating characteristic curve for predicting moderate-to-severe bronchiectasis of FACED score based on IOS parameters exhibited an area under the curve of 0.809. Conclusion: The IOS assessed by the disease severity of FACED score can effectively reflect airway resistance and elasticity in bronchiectasis patients and serve as valuable tools for predicting bronchiectasis severity.

• Korean Journal of Heritage: History & Science
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• v.57 no.3
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• pp.184-199
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• 2024

This study was conducted in response to the global trend of emphasizing the importance of "everyday landscapes", focusing on the perspective of those who have returned to rural life. With a focus on the case of Gokseong-gun in Jeollanam-do, 460 diaries written by these individuals were collected and analyzed using text mining techniques such as "frequency analysis", "topic modeling", and "sentiment analysis". The analysis of noun morphemes was interpreted from a cognitive aspect, while adjective morphemes were interpreted from an emotional aspect. In particular, this study applied semantic network analysis to overcome the limitations of existing sentiment analysis, and extracted a word network list and examined the content of nouns connected to adjectives that express emotions to identify the targets and contents of sentiments. This method represents a differentiated approach that is not commonly found in existing research. One of the intriguing findings is that the urban-to-rural migrants identified everyday landscapes such as "flowers on neighborhood walking paths", "harvest of a garden", "neighborhood events", and "cozy cafe spaces" as important. These elements all contain visual and enjoyable aspects of everyday landscapes. Currently, many rural villages are attempting to add visual elements to their everyday landscapes by unifying roof colors or painting murals on walls. However, such artificial measures do not necessarily leave a lasting impression on people. A critical review of current policies and systems is necessary. This research is significant because it is the first to study everyday landscapes from the perspective of urban-to-rural migration using diaries and text mining. With a lack of domestic research on everyday landscapes, this study hopes to contribute to the activation of related research in Korea.

• Journal of Wetlands Research
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• v.26 no.3
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• pp.279-284
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• 2024

Recently, the frequency and intensity of torential rains caused by climate change are increasing, and the damage to reservoir collapse in local governments continues to occur. Most local government reservoirs are aged reservoirs that have been built for more than 50 years, and there is a high risk of collapse due to recent heavy rainfall. In order to prevent reservoir collapse or overflow caused by heavy rainfall, a decision-making support system that can judge risks due to changes in storage capacity is needed. In this study, a reservoir discharge simulation model was constructed using a system dynamics technique that can dynamically represent causal relationships between various variables. Through discharge simulation, the change in storage capacity due to rainfall was analyzed, and the operation time and termination time of the discharge facility to prevent overflow of the reservoir were analyzed. Using the results of this study, it is possible to determine the timing of the overflow of the reservoir due to torrential rain, and also the capacity and operation timing of the discharge facility to prevent overflow can be known. hrough this, it is expected that local governments will be able to judge the risk of damage to reservoirs and establish a preliminary response plan to prevent damage.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.4
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• pp.17-34
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• 2023

Camera trapping has been used as a non-invasive survey method that minimizes anthropogenic disturbance to ecosystems. Nevertheless, it is labor-intensive and time-consuming, requiring researchers to quantify species and populations. In this study, we aimed to improve the preprocessing of camera trapping data by utilizing an object detection algorithm. Wildlife monitoring using unmanned sensor cameras was conducted in a forested urban forest and a green space on a university campus in Cheonan City, Chungcheongnam-do, Korea. The collected camera trapping data were classified by a researcher to identify the occurrence of species. The data was then used to test the performance of the YOLO-X object detection algorithm for wildlife detection. The camera trapping resulted in 10,500 images of the urban forest and 51,974 images of green spaces on campus. Out of the total 62,474 images, 52,993 images (84.82%) were found to be false positives, while 9,481 images (15.18%) were found to contain wildlife. As a result of wildlife monitoring, 19 species of birds, 5 species of mammals, and 1 species of reptile were observed within the study area. In addition, there were statistically significant differences in the frequency of occurrence of the following species according to the type of urban greenery: Parus varius(t = -3.035, p < 0.01), Parus major(t = 2.112, p < 0.05), Passer montanus(t = 2.112, p < 0.05), Paradoxornis webbianus(t = 2.112, p < 0.05), Turdus hortulorum(t = -4.026, p < 0.001), and Sitta europaea(t = -2.189, p < 0.05). The detection performance of the YOLO-X model for wildlife occurrence was analyzed, and it successfully classified 94.2% of the camera trapping data. In particular, the number of true positive predictions was 7,809 images and the number of false negative predictions was 51,044 images. In this study, the object detection algorithm YOLO-X model was used to detect the presence of wildlife in the camera trapping data. In this study, the YOLO-X model was used with a filter activated to detect 10 specific animal taxa out of the 80 classes trained on the COCO dataset, without any additional training. In future studies, it is necessary to create and apply training data for key occurrence species to make the model suitable for wildlife monitoring.

• Journal of Korean Elementary Science Education
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• v.43 no.4
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• pp.544-553
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• 2024

The purpose of this study was to analyze the trends of research focusing on artificial intelligence and the science education and derive important problems, topics, and research trends,. The analysis of the AI convergence science education research trends targeted 83 articles on the awareness of artificial intelligence, research trends, design, development, and application of the education programs related to artificial intelligence. The analysis data was collected through the RISS. The collected data was refined using Excel and Textom, and the main keywords were identified and analyzed through the frequency analysis and keyword network analysis. The connection centrality of the keywords was confirmed using the CONCOR analysis. The research results showed that the AI convergence science education research was expanding in both quantitative and qualitative aspects, and that the main keywords were identified as 'AI,' 'AI convergence education,' 'AI convergence science education,' 'AI education,' 'science education,' 'science,' 'machine learning,' 'elementary school,' 'generative AI,' and 'educational program.' Through the connection centrality analysis and CONCOR analysis, it was confirmed that the clusters were formed around the 'naming,' 'content and method,' 'elementary,' and 'data' in the AI integrated science education. Based on the results, the main topics and trends of the research integrating artificial intelligence into the science subjects were derived and the implications and directions for follow-up research were set forth.

• Journal of Practical Engineering Education
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• v.16 no.5_spc
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• pp.783-791
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• 2024

The purpose of this study was to determine the effects of a True Self meditation program on self-esteem and attention in elementary school students. In the first semester of 2021, two 6th grade classes at B elementary School in A city participated as the experimental group and two 6th grade classes at C elementary School in A city participated as the control group. Data from 46 students in the experimental group and 41 students in the control group who participated in both the pretest and posttest were used for analysis. Compared to the control group, the experimental group showed an increase in self-esteem after meditation, but the difference was not significant. On the other hand, the experimental group's attention improved significantly after meditation compared to that of the control group. Based on these results, True Self meditation can be recommended as a method to improve children's attention. In addition, we recommend replication studies that increase the duration and frequency of meditation interventions to improve self-esteem and attention in elementary school children, studies that measure the effectiveness of the program on measures of practical ability, and studies that determine the extent to which the effects of meditation are sustained.