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    ์ ˆ์ œ๊ฐ€๋Šฅ ์ทŒ๊ด€์„ ์•”์—์„œ ์ˆœํ™˜ ์ข…์–‘ DNA์˜ ๊ฒ€์ถœ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ, 2022.2. ๋ฐ•์„ฑ์„ญ.์„œ๋ก : ์ทŒ๊ด€์„ ์•”์€ ๋Œ€ํ‘œ์ ์ธ ๋‚œ์น˜์•”์œผ๋กœ, ์ง„๋‹จ ๋‹น์‹œ ํ™˜์ž์˜ 20%๋งŒ์ด ๊ทผ์น˜์  ์ ˆ์ œ์ˆ ์— ์ ํ•ฉํ•˜์ง€๋งŒ 80%๋Š” ๊ฒฐ๊ตญ ์ข…์–‘์ด ์žฌ๋ฐœํ•œ๋‹ค. ๋”ฐ๋ผ์„œ, ์กฐ๊ธฐ์— ์ข…์–‘์„ ๊ฐ์ง€ํ•˜๊ณ  ๊ทผ์น˜์  ์ ˆ์ œํ›„ ๋ฏธ์„ธ์ž”์กด์งˆํ™˜์„ ๋ชจ๋‹ˆํ„ฐ๋งํ•˜๊ธฐ ์œ„ํ•œ ๋ฏผ๊ฐํ•˜๊ณ  ํŠน์ด์ ์ธ ์ข…์–‘ ํ‘œ์ง€์ž์˜ ํ™•๋ฆฝ์ด ํ•„์š”ํ•˜๋‹ค. ์ˆœํ™˜ ์ข…์–‘ DNA๋Š” ์ตœ์†Œ ์นจ์Šต์ ์ธ ๋ฐฉ๋ฒ•์œผ๋กœ ์‰ฝ๊ฒŒ ์—ฐ์†์ ์œผ๋กœ ๋ชจ๋‹ˆํ„ฐ๋งํ•  ์ˆ˜ ์žˆ๋Š” ์œ ๋งํ•œ ๋ฐ”์ด์˜ค๋งˆ์ปค์ด์ง€๋งŒ, ์งˆ๋ณ‘์˜ ์ดˆ๊ธฐ๋‹จ๊ณ„์—์„œ ์ˆœํ™˜ ์ข…์–‘ DNA์˜ ๋ฏผ๊ฐํ•œ ๊ฒ€์ถœ ๋ฐ ์ •๋Ÿ‰ํ™”๋Š” ์–ด๋ ค์šด ์‹ค์ •์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ „ํ–ฅ์ ์œผ๋กœ ๋ชจ์ง‘ํ•œ ์ ˆ์ œ๊ฐ€๋Šฅํ•œ ์ทŒ๊ด€์„ ์•” ํ™˜์ž๊ตฐ์—์„œ ์ˆœํ™˜ ์ข…์–‘ DNA ๋ถ„์„์„ ์œ„ํ•œ ์ตœ์ ํ™”๋œ ์ฐจ์„ธ๋Œ€ ์—ผ๊ธฐ์„œ์—ด๊ธฐ๋ฒ• ๊ธฐ์ˆ ์„ ์ ์šฉํ•˜์—ฌ, ์‹ค์ œ ์ˆœํ™˜ ์ข…์–‘ DNA ๊ฒ€์ถœ ์–‘์ƒ๊ณผ ์กฐ์ง DNA ๋ถ„์„๊ณผ์˜ ์ผ์น˜๋„๋ฅผ ํ‰๊ฐ€ํ•˜์—ฌ ์ˆœํ™˜ ์ข…์–‘ DNA ๊ฒ€์ถœ์˜ ์ ˆ์ œ๊ฐ€๋Šฅ ์ทŒ๊ด€์„ ์•”์—์˜ ์ ์šฉ ๊ฐ€๋Šฅ์„ฑ์„ ์‚ดํŽด๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ๋ฐฉ๋ฒ•: ๋ณธ ์—ฐ๊ตฌ๋Š” 2020๋…„ 8์›”๋ถ€ํ„ฐ 2021๋…„ 10์›”๊นŒ์ง€ ์ทŒ์žฅ ์ข…์–‘์— ๋Œ€ํ•œ ๊ทผ์น˜์  ์ ˆ์ œ์ˆ ์„ ๋ฐ›๋Š” ์ ˆ์ œ ๊ฐ€๋Šฅํ•œ ์ทŒ๊ด€์„ ์•”์œผ๋กœ ์ง„๋‹จ๋œ ์ด 70๋ช…์˜ ํ™˜์ž๋ฅผ ์ „ํ–ฅ์  ๋ฐ ์—ฐ์†์ ์œผ๋กœ ๋“ฑ๋กํ•˜์˜€๊ณ , ์ด๋“ค ํ™˜์ž์—์„œ ์ง์ง€์–ด์ง„ ๋ง์ดˆํ˜ˆ์•ก (์ง„๋‹จ ๋‹น์‹œ, ๊ทธ๋ฆฌ๊ณ  ์ˆ˜์ˆ  ํ›„) ๋ฐ ์กฐ์ง ๊ฒ€์ฒด๋ฅผ ์ˆ˜์ง‘ํ•˜์˜€๋‹ค. ์ด ์ค‘ ์„ ์ • ๊ธฐ์ค€์— ์ ํ•ฉํ•˜์—ฌ ๋ถ„์„์ด ๊ฐ€๋Šฅํ–ˆ๋˜ 53๋ช…์˜ ํ™˜์ž์—์„œ, ๊ฐ ์‹œ์ ์˜ ์„ธํฌ ์œ ๋ฆฌ DNA์™€ ์กฐ์ง ๋ฐ ๋ฐฐ์„  DNA๋ฅผ ์ถ”์ถœํ•˜์—ฌ 77๊ฐœ์˜ ์„ ํƒ๋œ ์œ ์ „์ž๋ฅผ ํ‘œ์ ์œผ๋กœ ํ•˜๋Š” integrated digital error suppression (iDES)- CAncer Personalized Pro๏ฌling by deep Sequencing (CAPP-Seq) ๋ฐฉ๋ฒ•์œผ๋กœ ์ตœ์ ํ™”๋œ ์ฐจ์„ธ๋Œ€ ์—ผ๊ธฐ์„œ์—ด ๋ถ„์„์„ ์‹œํ–‰ํ•˜์˜€๋‹ค. ๋˜ํ•œ 16๋ช…์˜ ๊ฑด๊ฐ•ํ•œ ๋Œ€์กฐ๊ตฐ์œผ๋กœ๋ถ€ํ„ฐ ์ˆ˜์ง‘๋œ ํ˜ˆ์žฅ ๊ฒ€์ฒด์—์„œ ์ถ”์ถœํ•œ ์„ธํฌ ์œ ๋ฆฌ DNA๋„ ๊ฐ™์€ ๋ฐฉ๋ฒ•์œผ๋กœ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ: ํ‰๊ฐ€ ๊ฐ€๋Šฅํ•œ ํ™˜์ž์˜ 37.7%์—์„œ ์ˆ˜์ˆ  ์ „ ์ˆœํ™˜ ์ข…์–‘ DNA๋ฅผ ๊ฒ€์ถœํ•  ์ˆ˜ ์žˆ์—ˆ๋Š”๋ฐ, ๋ณ€์ด ๋Œ€๋ฆฝ์œ ์ „์ž ๋นˆ๋„(variant allele frequency)์˜ ์ค‘๊ฐ„ ๊ฐ’์€ 0.09% (์‚ฌ๋ถ„์œ„ ๋ฒ”์œ„, 0.04% - 0.16%)๋กœ ์ด๋Š” ์ „์ด์„ฑ ์ทŒ๊ด€์„ ์•”์—์„œ ๊ธฐ๋ณด๊ณ ๋œ ๊ฒƒ ๋ณด๋‹ค ์•ฝ 40๋ฐฐ ๋‚ฎ๋‹ค. ๋นˆ๋„ ์ˆœ์œผ๋กœ๋Š” TP53, KRAS, GNAS, SMAD4, PIK3CA, ๊ทธ๋ฆฌ๊ณ  CDKN2A ์ˆœ์ด์—ˆ๋‹ค. ์ „์ฒด์ ์œผ๋กœ, ํ™˜์ž์˜ 34.0%(18/53)๊ฐ€ ํ˜ˆ์žฅ ๊ฒ€์ฒด์™€ ์กฐ์ง ๋ชจ๋‘์—์„œ ์ž„์ƒ์ ์œผ๋กœ ๊ด€๋ จ๋œ ๋Œ์—ฐ๋ณ€์ด๊ฐ€ ๊ฒ€์ถœ๋˜์—ˆ๊ณ , 13.2%(7/53)๋Š” ์กฐ์ง DNA์—์„œ๋งŒ ๊ฒ€์ถœ๋˜์—ˆ์œผ๋ฉฐ, 3.8%(2/53)๋Š” ํ˜ˆ์žฅ ๊ฒ€์ฒด์—์„œ๋งŒ ๊ฒ€์ถœ๋˜์—ˆ๋‹ค. ๋ณ€์ด๋ณ„๋กœ ๋น„๊ต๋ฅผ ํ•ด๋ณด์•˜์„ ๋•Œ, ์กฐ์ง ๊ฒ€์ฒด์—์„œ ๊ฒ€์ถœ๋˜์ง€ ์•Š์€ 12๊ฐœ (TP53, n = 6; GNAS, n = 5; SMAD4, n = 1)์˜ ์ถ”๊ฐ€ ๋Œ์—ฐ๋ณ€์ด๊ฐ€ ํ˜ˆ์žฅ ๊ฒ€์ฒด์—์„œ ๊ฒ€์ถœ๋˜์—ˆ๋‹ค. ๋ณ€์ด ๋Œ€๋ฆฝ์œ ์ „์ž ๋นˆ๋„๋Š” ์กฐ์ง(์ค‘์•™๊ฐ’, 12.99%, ์‚ฌ๋ถ„์œ„ ๋ฒ”์œ„, 7.65% - 24.96%)๋ณด๋‹ค ์ˆœํ™˜ ์ข…์–‘ DNA์—์„œ(์ค‘์•™๊ฐ’, 0.08%; ์‚ฌ๋ถ„์œ„ ๋ฒ”์œ„, 0.04% - 0.37%)์—์„œ ์œ ์˜ํ•˜๊ฒŒ ๋‚ฎ์•˜๋‹ค (P < 0.001). ๊ทผ์น˜์  ์ ˆ์ œ์ˆ  ํ›„ ์ˆœํ™˜ ์ข…์–‘ DNA ๊ฒ€์ถœ์„ ๋ถ„์„ํ•˜์˜€์„ ๋•Œ, ๊ทผ์น˜์  ์ ˆ์ œ์ˆ  ํ›„์—๋Š” ๊ฒ€์ถœ ๊ฐ€๋Šฅํ•œ ์ˆœํ™˜ ์ข…์–‘ DNA๋ฅผ ๊ฐ€์ง„ ํ™˜์ž์˜ ๋น„์œจ์ด 15.1% ๊ฐ์†Œํ•˜์˜€๊ณ , ์ˆœํ™˜ ์ข…์–‘ DNA์˜ ๋ณ€์ด ๋Œ€๋ฆฝ์œ ์ „์ž ๋นˆ๋„๋Š” ์ˆ˜์ˆ  ํ›„ ํ˜ˆ์žฅ์—์„œ ์œ ์˜ํ•˜๊ฒŒ ๊ฐ์†Œํ–ˆ๋‹ค (P < 0.001). ์ˆ˜์ˆ  ์ „ ctDNA๊ฐ€ ๊ฒ€์ถœ๋œ 20๋ช…์˜ ํ™˜์ž ์ค‘ ์ˆ˜์ˆ  ํ›„ ctDNA๊ฐ€ ์Œ์„ฑ์œผ๋กœ ์ „ํ™˜๋œ ํ™˜์ž๋ณด๋‹ค ์ˆ˜์ˆ  ํ›„์—๋„ ์—ฌ์ „ํžˆ ctDNA๊ฐ€ ๊ฒ€์ถœ๋œ ํ™˜์ž์—์„œ 1๋…„ ํ›„ ์žฌ๋ฐœ ์œ„ํ—˜์ด ๋” ๋†’์€ ๊ฒฝํ–ฅ์„ ํ™•์ธํ•˜์˜€๋‹ค(48.6% vs. 25.0%, P=0.064). ์ถ”๊ฐ€์ ์œผ๋กœ ํ™˜์ž์˜ ๋ฐฑํ˜ˆ๊ตฌ DNA์™€ ๋น„๊ต ๋ถ„์„์„ ํ•œ ๊ฒฐ๊ณผ, ํ™˜์ž์˜ 24.5 %์—์„œ ํ˜ˆ์žฅ ๊ฒ€์ฒด์— ํด๋ก ์„ฑ ์กฐํ˜ˆ์ฆ ์œ ๋ž˜ ๋Œ์—ฐ๋ณ€์ด๊ฐ€ ๊ฒ€์ถœ๋˜์—ˆ๋‹ค. ๊ฒฐ๋ก : ์ ˆ์ œ ๊ฐ€๋Šฅํ•œ ์ทŒ๊ด€์„ ์•” ํ™˜์ž์—์„œ ๊ฒ€์ถœ๋˜๋Š” ์ˆœํ™˜ ์ข…์–‘ DNA์˜ ๋ถ„์œจ์€ ๋งค์šฐ ๋‚ฎ์•˜์œผ๋ฉฐ, ์ตœ์ ํ™”๋œ ์ฐจ์„ธ๋Œ€ ์—ผ๊ธฐ์„œ์—ด ๋ถ„์„๋ฒ•์„ ์ ์šฉํ•˜์—ฌ ์ ˆ์ œ ๊ฐ€๋Šฅํ•œ ์ทŒ๊ด€์„ ์•”์—์„œ ์ˆœํ™˜ ์ข…์–‘ DNA์˜ ๊ทน๋Œ€ํ™”๋œ ๋ฏผ๊ฐํ•œ ๊ฒ€์ถœ์ด ๊ฐ€๋Šฅํ•จ์„ ํ™•์ธํ•˜์˜€๊ณ , ์ด๋Š” ์กฐ์ง DNA ๋ถ„์„๊ณผ ๋ณ‘๋ ฌ์ ์œผ๋กœ ์‚ฌ์šฉ๋˜์—ˆ์„ ๋•Œ ์ถ”๊ฐ€์ ์ธ ์ด์ ์„ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค. ๋˜ํ•œ, ์ž„์ƒ์ ์œผ๋กœ ์˜๋ฏธ์žˆ๋Š” ์ˆœํ™˜ ์ข…์–‘ DNA๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ๊ฒ€์ถœํ•˜๊ธฐ ์œ„ํ•ด ์ง์ง€์–ด์ง„ ๋ฐฑํ˜ˆ๊ตฌ DNA์˜ ์—ผ๊ธฐ์„œ์—ด๋ถ„์„์ด ํ•„์š”ํ•  ๊ฒƒ์œผ๋กœ ์ƒ๊ฐ๋œ๋‹ค.Introduction: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies, where only 20% of the patients are suitable for curative resection at the time of diagnosis, but 80% of them eventually have tumor recurrence. Therefore, reliable biomarkers for the detection of tumors especially in the early stages of PDAC and for the monitoring of residual tumors after curative resection are critical for PDAC treatment. Circulating tumor DNA (ctDNA) is a promising blood-based biomarker because of its easily and serially accessible nature, but accurate quantification of ctDNA in the early stage of disease is challenging. To overcome this challenge, we applied an optimized next-generation sequencing (NGS) technology for ctDNA analysis in a prospective cohort of patients with resectable PDAC. In this study, we aimed to investigate the feasibility of ctDNA profiling using optimized NGS for resectable PDAC. Methods: A total of 70 consecutive patients diagnosed with resectable PDAC and undergoing curative resection for pancreatic tumor were enrolled from August 2020 through October 2021. We performed CAncer Personalized Pro๏ฌling by deep Sequencing (CAPP-Seq) NGS with integrated digital error suppression (iDES) of triple-matched samples [plasma cell-free DNA (cfDNA), tumor tissue, and germline DNA] targeting 77 selected genes. cfDNA isolated from pooled plasma samples of 16 healthy control individuals was also sequenced. Results: We were able to detect preoperative ctDNA in 37.7% of the evaluable patients, with a median variant allele frequency (VAF) of 0.09% [interquartile range (IQR), 0.04% - 0.16%], which was approximately 40-fold lower than that previously reported for metastatic PDAC. TP53 gene (29.1%) was most frequently identified, followed by KRAS (18.9%), GNAS (11.3%), SMAD4 (1.9%), PIK3CA (1.9%), and CDKN2A (1.9%). In total, 34.0% (18/53) of patients had clinically relevant mutations detected in both plasma and tissue, 13.2% (7/53) were detected exclusively in tissue analysis, and 3.8% (2/53) were detected exclusively in ctDNA analysis. In addition, 12 additional oncogenic mutations (TP53, n = 6; GNAS, n = 5; SMAD4, n = 1) that were not detected in tissue samples were detected in ctDNA. In addition, we observed a 15.1% decrease in the proportion of patients with detectable ctDNA after curative resection. VAF in each patient was significantly decreased in postoperative plasma (preoperative ctDNA, median 0.08%, IQR 0.04%โ€“0.15%; postoperative ctDNA, median 0.00%, IQR 0.00%โ€“0.03%; P < 0.001). Among 20 patients with detectable ctDNA before surgery, the risk of recurrence at 1 year tended to be higher in patients with still detectable ctDNA after surgery than in patients with negative conversion of ctDNA after surgery (48.6% vs. 25.0%, P = 0.064). We also found that cfDNA from 24.5% of patients had features compatible with clonal hematopoiesis. Conclusion: The fraction of ctDNA in resectable PDAC patients was very low. However, an optimized NGS approach may add value beyond tissue analysis through a highly sensitive detection of ctDNA in resectable PDAC. ctDNA analysis after surgery could be a potential prognostic marker. Moreover, paired sequencing of matched leukocytes may be required to accurately detect clinically relevant ctDNA.Introduction 1 Materials and Methods 3 Results 10 Discussion 33 References 42 Abstract in Korean 47๋ฐ•

    ํ•œ๊ตญ ๋…ธ์ธ์˜ ์ง€๊ฐ๋œ ์ŠคํŠธ๋ ˆ์Šค๊ฐ€ ์ธ์ง€๊ธฐ๋Šฅ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ: ์ •์„œ์  ์œ ๋Œ€๊ฐ์˜ ๋งค๊ฐœํšจ๊ณผ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์‚ฌํšŒ๊ณผํ•™๋Œ€ํ•™ ์‹ฌ๋ฆฌํ•™๊ณผ, 2023. 2. ์ตœ์ง„์˜.Perceived stress is known to negatively affect cognitive function in late life and has been reported to have a negative correlation with social network characteristics. In terms of social network characteristics, people with a larger quantity and better quality of social network showed better performance of the cognitive function. Despite the importance of perceived stress and social network characteristics in cognitive function in late life, studies that examined the relationships between these three variables are absent. Therefore, this study aimed to examine whether social network mediates the effect of perceived stress on cognitive function in the older adults in Korea. Also, since neuroticism and depression revealed a very high correlation both with the perceived stress level and cognitive function, this study excluded the effects of the two factors in the whole analysis to focus on the effects of stress on cognitive functions. One hundred seventy-six community-dwelling adults aged 60 years or older from Korean Social Life, Health, and Aging Project study (KSHAP) underwent perceived stress assessment, social network surveys, and neuropsychological tests. In the result, perceived stress has been shown to have a negative effect on the categorical fluency of executive functions, and emotional closeness of social network quality mediated the association between perceived stress and long-term recall index. An additional mediation analysis was conducted to figure out which of the two subscales of perceived stress had a more significant effect in the mediation model between cognitive function and emotional closeness. Between lack of perceived self-efficacy (LSE) and perceived helplessness (PH), the mediation effect of emotional closeness was significant only in LSE. These results suggest that emotional closeness may be an important factor in explaining the association between perceived stress, especially lack of perceived self-efficacy, and cognitive function.๋…ธ๋…„๊ธฐ๋Š” ์ŠคํŠธ๋ ˆ์Šค์›์ด ์ฆ๊ฐ€ํ•˜๋Š” ์‹œ๊ธฐ์ด๋ฉฐ, ๊ฐœ์ธ์˜ ์ง€๊ฐ๋œ ์ŠคํŠธ๋ ˆ์Šค๋Š” ๋…ธ๋…„๊ธฐ ์ธ์ง€์ €ํ•˜์˜ ๋Œ€ํ‘œ์ ์ธ ์œ„ํ—˜์š”์ธ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๋˜ํ•œ, ์„ ํ–‰์—ฐ๊ตฌ๋“ค์— ๋”ฐ๋ฅด๋ฉด ์ง€๊ฐ๋œ ์ŠคํŠธ๋ ˆ์Šค๋Š” ๊ฐœ์ธ์˜ ์‚ฌํšŒ์—ฐ๊ฒฐ๋ง์˜ ์งˆ์ , ์–‘์ ์ธ ํŠน์„ฑ๊ณผ๋„ ๋ถ€์  ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๊ฐ€์ง„๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํ˜„์žฌ๊นŒ์ง€ ์ง€๊ฐ๋œ ์ŠคํŠธ๋ ˆ์Šค์™€ ๋…ธ๋…„๊ธฐ ์ธ์ง€๊ธฐ๋Šฅ, ๊ทธ๋ฆฌ๊ณ  ์‚ฌํšŒ์—ฐ๊ฒฐ๋ง์˜ ํŠน์„ฑ ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ํƒ์ƒ‰ํ•œ ์—ฐ๊ตฌ๋Š” ๋ถ€์žฌํ•˜๋‹ค. ๋” ๋‚˜์•„๊ฐ€ ๊ธฐ์กด์˜ ์—ฐ๊ตฌ๋“ค์—์„œ๋Š” ์ง€๊ฐ๋œ ์ŠคํŠธ๋ ˆ์Šค์™€ ๊ด€๋ จ์„ฑ์ด ๋งค์šฐ ๋†’์€ ์‹ ๊ฒฝ์ฆ ๋ฐ ์šฐ์šธ ์ˆ˜์ค€์„ ํ†ต์ œํ•˜์ง€ ์•Š์•˜๋‹ค๋Š” ํ•œ๊ณ„์ ์ด ์กด์žฌํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ํ•œ๊ณ„์ ์„ ๋ฐ”ํƒ•์œผ๋กœ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ง€๊ฐ๋œ ์ŠคํŠธ๋ ˆ์Šค์™€ ์ธ์ง€๊ธฐ๋Šฅ์˜ ๊ด€๊ณ„์—์„œ ์‚ฌํšŒ์—ฐ๊ฒฐ๋ง์˜ ์งˆ์ , ์–‘์  ์†์„ฑ์ด ๋งค๊ฐœํšจ๊ณผ๋ฅผ ๊ฐ€์ง€๋Š”์ง€ ์‚ดํŽด๋ณด์•˜๋‹ค. ๋ณธ ๋ถ„์„์€ ์‹ ๊ฒฝ์ฆ๊ณผ ์šฐ์šธ์ˆ˜์ค€์„ ํ†ต์ œํ•œ ์ƒํƒœ์—์„œ ์ง„ํ–‰๋˜์—ˆ๋‹ค. ํ•œ๊ตญ์ธ์˜ ์‚ฌํšŒ์  ์‚ถ, ๊ฑด๊ฐ•๊ณผ ๋…ธํ™”์— ๋Œ€ํ•œ ์กฐ์‚ฌ (KSHAP)์— ์ฐธ์—ฌํ•œ 60์„ธ ์ด์ƒ ์ง€์—ญ์‚ฌํšŒ ๊ฑฐ์ฃผ ๋…ธ์ธ 176๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ์ง€๊ฐ๋œ ์ŠคํŠธ๋ ˆ์Šค, ์‹ฌ๋ฆฌ์„ค๋ฌธ, ์‚ฌํšŒ์—ฐ๊ฒฐ๋ง ์กฐ์‚ฌ ๋ฐ ์‹ ๊ฒฝ์‹ฌ๋ฆฌ๊ฒ€์‚ฌ๋ฅผ ์‹ค์‹œํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ์ง€๊ฐ๋œ ์ŠคํŠธ๋ ˆ์Šค๋Š” ์ง‘ํ–‰๊ธฐ๋Šฅ ์ค‘ ๋ฒ”์ฃผ ์œ ์ฐฝ์„ฑ์— ๋ถ€์ •์  ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋งค๊ฐœ๋ถ„์„ ๊ฒฐ๊ณผ, ์‚ฌํšŒ์—ฐ๊ฒฐ๋ง์˜ ์งˆ์  ์ธก๋ฉด์„ ๋‚˜ํƒ€๋‚ด๋Š” ์ •์„œ์  ์œ ๋Œ€๊ฐ์€ ์ง€๊ฐ๋œ ์ŠคํŠธ๋ ˆ์Šค์™€ ์žฅ๊ธฐ๊ธฐ์–ต์ง€์ˆ˜์™€์˜ ๊ด€๊ณ„๋ฅผ ์™„์ „๋งค๊ฐœํ•˜์˜€๋‹ค. ๋” ๋‚˜์•„๊ฐ€ ์ง€๊ฐ๋œ ์ŠคํŠธ๋ ˆ์Šค์˜ ๋‘ ๊ฐ€์ง€ ํ•˜์œ„์š”์ธ์ธ ์ง€๊ฐ๋œ ์ž๊ธฐํšจ๋Šฅ๊ฐ์˜ ๊ฒฐ์—ฌ์™€ ์ง€๊ฐ๋œ ๋ฌด๊ธฐ๋ ฅ๊ฐ ์ค‘ ์–ด๋–ค ์š”์ธ์ด ๋…ธ๋…„๊ธฐ ์ธ์ง€๊ธฐ๋Šฅ๊ณผ ์‚ฌํšŒ์—ฐ๊ฒฐ๋ง๊ฐ„์˜ ๊ด€๊ณ„์— ์žˆ์–ด ๋” ํฐ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€ ์ถ”๊ฐ€๋ถ„์„์„ ํ†ตํ•ด ์‚ดํŽด๋ณด์•˜๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ๋‘ ํ•˜์œ„์ฒ™๋„ ์ค‘, ์ •์„œ์  ์œ ๋Œ€๊ฐ์˜ ๋งค๊ฐœํšจ๊ณผ๋Š” ์ง€๊ฐ๋œ ์ž๊ธฐํšจ๋Šฅ๊ฐ์˜ ๊ฒฐ์—ฌ์™€ ์žฅ๊ธฐ๊ธฐ์–ต์ง€์ˆ˜์˜ ๊ด€๊ณ„์—์„œ๋งŒ ์œ ์˜ํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ์‚ฌํšŒ์—ฐ๊ฒฐ๋ง์˜ ์งˆ์ ์ธ ์ธก๋ฉด, ํŠนํžˆ ์ •์„œ์  ์œ ๋Œ€๊ฐ์ด ์ง€๊ฐ๋œ ์ŠคํŠธ๋ ˆ์Šค์™€ ์ธ์ง€๊ธฐ๋Šฅ๊ณผ์˜ ๊ด€๊ณ„๋ฅผ ์„ค๋ช…ํ•˜๋Š” ๊ธฐ์ „์ž„์„ ์‹œ์‚ฌํ•˜๋ฉฐ, ํŠนํžˆ ์ง€๊ฐ๋œ ์ŠคํŠธ๋ ˆ์Šค ์ค‘ ์ž๊ธฐํšจ๋Šฅ๊ฐ์˜ ๊ฒฐ์—ฌ๊ฐ€ ๋…ธ๋…„๊ธฐ ์ธ์ง€๊ธฐ๋Šฅ์— ์žˆ์–ด ์ค‘์š”ํ•œ ์š”์ธ์ž„์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค.Chapter 1. Introduction ๏ผ‘ 1.1. Definition of Stress ๏ผ’ 1.2. Measurement of Stress ๏ผ“ 1.3. Stress in Late Life ๏ผ” 1.4. Stress and Cognitive Function ๏ผ• 1.5. Stress and Social Network ๏ผ– 1.6. Social Network and Cognitive Function ๏ผ˜ 1.7. Purpose of this Study and Hypothesis ๏ผ™ Chapter 2. Methodology ๏ผ‘๏ผ‘ 2.1. Participants ๏ผ‘๏ผ‘ 2.2. Measures ๏ผ‘๏ผ’ 2.2.1. Psychological Measurements ๏ผ‘๏ผ’ 2.2.2. Neuropsychological Assessment ๏ผ‘๏ผ” 2.2.3. Social Network Characteristic ๏ผ’๏ผ 2.3. Analysis ๏ผ’๏ผ’ Chapter 3. Results ๏ผ’๏ผ” Chapter 4. Discussion ๏ผ”๏ผ Bibliography ๏ผ”๏ผ˜ Korean Abstract ๏ผ•๏ผ—์„

    ์‚ฐํ™”์ฒ  ๋‚˜๋…ธ์ž…์ž์˜ ๊ธˆ์†-์ ˆ์—ฐ์ฒด ์ „์ด ํ˜„์ƒ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€, 2019. 2. ํ˜„ํƒํ™˜.๋‚˜๋…ธ์ž…์ž๋Š” ์ ์–ด๋„ ํ•œ์ชฝ ๋ฐฉํ–ฅ์˜ ๋„ˆ๋น„๊ฐ€ 100 nm, ๋‹ค์‹œ ๋งํ•ด ์ฒœ๋งŒ ๋ถ„์˜ 1๋ฏธํ„ฐ ์ดํ•˜์˜ ํฌ๊ธฐ๋ฅผ ๊ฐ€์ง„ ์ž…์ž์ด๋‹ค. ๋‚˜๋…ธ์ž…์ž๋Š” ๊ธฐ์กด์˜ ๋ฒŒํฌ ๋ฌผ์งˆ๊ณผ๋Š” ๋‹ฌ๋ฆฌ, ํ•˜๋‚˜์˜ ์ž…์ž๋ฅผ ๊ตฌ์„ฑํ•˜๋Š” ์›์ž ๊ฐœ์ˆ˜๊ฐ€ ๋งค์šฐ ์ ๋‹ค. ์ด๋กœ ์ธํ•ด์„œ ๊ธฐ์กด๊ณผ๋Š” ๋งค์šฐ ๋‹ค๋ฅธ ๋ฌผ๋ฆฌ์ , ํ™”ํ•™์  ์„ฑ์งˆ์„ ์ง€๋‹ ์ˆ˜ ์žˆ๋‹ค. ์ง€๋‚œ ์ˆ˜์‹ญ ๋…„์— ๊ฑธ์ณ์„œ ์ด๋Ÿฌํ•œ ์ฐจ์ด๋ฅผ ํ™•์ธํ•˜๋Š” ์—ฐ๊ตฌ ๋ฐ ๊ณตํ•™์ ์œผ๋กœ ์ด์šฉํ•˜๋ ค๋Š” ์—ฐ๊ตฌ๊ฐ€ ๋งŽ์ด ์ง„ํ–‰๋˜๊ณ  ์žˆ๋‹ค. ํ•œํŽธ, ์›ํ•˜๋Š” ์„ฑ์งˆ์˜ ๋‚˜๋…ธ์ž…์ž๋ฅผ ์‹ค์งˆ์ ์œผ๋กœ ํ™œ์šฉํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋‚˜๋…ธ์ž…์ž๋ฅผ ๊ท ์ผํ•˜๋ฉด์„œ๋„ ์›ํ•˜๋Š” ๋ชจ์–‘์œผ๋กœ ๋งŒ๋“ค ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์˜ ์ฒซ ๋ฒˆ์งธ ์žฅ์—์„œ๋Š” ์–ด๋–ป๊ฒŒ ๋‚˜๋…ธ์ž…์ž๋ฅผ ํ•ฉ์„ฑํ•ด์•ผ ๊ท ์ผํ•˜๋ฉด์„œ๋„ ์›ํ•˜๋Š” ๋ชจ์–‘์œผ๋กœ ๋งŒ๋“ค ์ˆ˜ ์žˆ๋Š”์ง€ ์•Œ์•„๋ณด์•˜๋‹ค. ์ด๋ ‡๊ฒŒ ํ•ฉ์„ฑ๋œ ๋‚˜๋…ธ์ž…์ž๋ฅผ ๊ฐ€์ง€๊ณ  ๋‚˜๋…ธ์ž…์ž๋งŒ์ด ๊ฐ€์ง€๋Š” ํŠน์ดํ•œ ์„ฑ์งˆ์„ ์—ฐ๊ตฌํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋‘ ๋ฒˆ์งธ ์žฅ์—์„œ๋Š” ๋‹ค์–‘ํ•œ ํฌ๊ธฐ๋กœ ํ•ฉ์„ฑ๋œ ์‚ฐํ™”์ฒ  ๋‚˜๋…ธ์ž…์ž๋ฅผ ๊ฐ€์ง€๊ณ  ๊ธˆ์†-์ ˆ์—ฐ์ฒด ์ „์ด ํ˜„์ƒ์„ ๊ด€์ฐฐํ•˜์˜€๋‹ค. ๋‚˜๋…ธ์ž…์ž์˜ ํฌ๊ธฐ๊ฐ€ ์ž‘์•„์งˆ์ˆ˜๋ก ๊ธˆ์†-์ ˆ์—ฐ์ฒด ์ „์ด ํ˜„์ƒ์— ํฐ ๋ณ€ํ™”๊ฐ€ ์ƒ๊ธฐ๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์„ธ ๋ฒˆ์งธ ์žฅ์—์„œ๋Š” ์‚ฐํ™”์ฒ  ๋‚˜๋…ธ์ž…์ž์— ๊ป์งˆ๊ตฌ์กฐ๋ฅผ ๋งŒ๋“ค์–ด์„œ ๊ธˆ์†-์ ˆ์—ฐ์ฒด ์ „์ด ํ˜„์ƒ์˜ ๋ณ€ํ™”๋ฅผ ๊ด€์ฐฐํ•˜์˜€๋‹ค. ๊ป์งˆ ๋ฌผ์งˆ์˜ ์ข…๋ฅ˜ ๋ฐ ๋‘๊ป˜๊ฐ€ ๋‹ฌ๋ผ์ง์— ๋”ฐ๋ผ์„œ ๊ธˆ์†-์ ˆ์—ฐ์ฒด ์ „์ด ํ˜„์ƒ์—๋„ ๋ณ€ํ™”๊ฐ€ ์ƒ๊ธฐ๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค.Nanocrystals are particles that are at least 100 nm wide in one direction. Nanocrystals, unlike their bulk counterparts, contain very few atoms that make up a single particle, leading to very different physical and chemical properties. In order to utilize the desired nanocrystals, nanocrystals must be able to be synthesized in a uniform and desired shape. In the first chapter of this thesis, I will discuss how nanocrystals can be synthesized in a uniform and desired shape. These synthesized nanocrystals allow us to study their unusual properties. In the second chapter, I studied size-dependent metal-insulator transition phenomena on uniform-sized iron oxide (magnetite) nanocrystals with various sizes. The smaller the nanocrystals, the greater the change in metal-insulator transition phenomena. In the third chapter, I synthesized core/shell Fe3O4/ferrite nanocrystals and investigated their metal-insulator transition phenomena. As the types and thickness of the shell materials are varied, changes in the metal-insulator transition phenomena were observed.Chapter 1. Introduction: Nucleation and growth of inorganic nanoparticles 1 1.1 Introduction 1 1.2 Molecule-to-solid transition 5 1.2.1 Structure of molecular clusters 5 1.3 Prenucleation and nucleation periods 9 1.3.1 Nucleation models 9 1.3.2 Stepwise phase transitions 12 1.3.3 Aggregation of nuclei 18 1.4 Growth by assembly and merging 21 1.4.1 Oriented attachment 22 1.4.2 Mesocrystals formation 34 1.5 Heterogeneous nucleation 36 1.5.1 Heterogeneous nucleation process 36 1.5.2 Interface energy minimization and property tuning by lattice strain 42 1.6 Conclusions 50 1.7 References 54 Chapter 2. Size Dependence of Metalโ€“Insulator Transition in Stoichiometric Fe3O4 Nanocrystals 71 2.1 Introduction 71 2.2 Experimental section 73 2.3 Synthesis of uniform sized Fe3O4 nanocrystals 81 2.4 Metal-insulator transition of Fe3O4 nanocrystals 98 2.5 Conclusions 108 2.6 References 110 โ€ƒ Chapter 3. Metalโ€“Insulator Transition of Fe3O4 Nanocrystals by Shell Formation 117 3.1 Introduction 117 3.2 Experimental section 121 3.3 Fe3O4-Fe3O4 core-shell nanocrystals 128 3.4 Fe3O4-MFe2O4 (M= Mn, Co, Ni, CU, Zn) core-shell nanocrystals 131 3.5 Conclusions 144 3.6 References 145 Bibliography 151 ๊ตญ๋ฌธ ์ดˆ๋ก (Abstract in Korean) 154Docto

    MOOC ์„œ๋น„์Šค๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ํ•œ ์›น ๊ธฐ๋ฐ˜ ํ•™์Šต์˜ ๊ธฐ์ˆ ์ˆ˜์šฉ์˜๋„์™€ ํ˜์‹ ์ €ํ•ญ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๊ธฐ์ˆ ๊ฒฝ์˜ยท๊ฒฝ์ œยท์ •์ฑ…์ „๊ณต, 2019. 2. ํ™ฉ์ค€์„.This research aims to identify the factors that lead users to use MOOC, the web-based education platform, by using the concept of technology acceptance and innovation resistance. The UTAUT2 model is adopted to provide explanation on the basic premise of users acceptance towards emerging technology, and afterwards, modifications on the existing model of UTAUT2 is done by adopting the concept of innovation resistance. Innovation resistance is considered as a form of construct affecting the users intention to use, which is thought to have influence on human s behavior towards new technology. The survey is conducted on the usage of the web-based education platform by collecting the data from 427 respondents, and the data is analyzed by using structural equation modeling (SEM) as the methodology. The exploratory factor analysis illustrates that performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating condition (FC), hedonic motivation (HM), habit (HB) are the potential constructs which can affect the innovation resistance (IR) and intention to use (IU). After proceeding with the confirmatory factor analysis and path analysis, the empirical results show that effort expectancy, social influence, facilitating condition, hedonic motivation, and habit have a significant negative influence on innovation resistance, and habit, facilitating condition and innovation resistance have a significant negative influence on intention to use. Also, respondents with experience on using MOOC and those do not have been separated to carry out the comparison between groups. Tests highlight that the total sample results follow the results from the experienced group. By identifying the significant factors of technology acceptance, this paper presents implication and insights to those interested in utilizing MOOC.MOOC๋Š” ๊ธฐ์กด์˜ ํ•™์Šต๊ด€๋ฆฌ์‹œ์Šคํ…œ ์ƒ์œ„์— ๊ณต๊ฐœ๊ต์œก์ž๋ฃŒ, ์˜จ๋ผ์ธ ๋™์˜์ƒ ์ฝ˜ํ…์ธ ๋ฅผ ์ถ”๊ฐ€ํ•˜์—ฌ ๊ฐ•์˜๋ฅผ ์ˆ˜๋งŽ์€ ๋Œ€์ค‘์—๊ฒŒ ์ œ๊ณตํ•˜๋Š” ์„œ๋น„์Šค๋กœ 2010๋…„ ์ดํ›„ ์ „์„ธ๊ณ„์ ์œผ๋กœ ํฐ ์ธ๊ธฐ๋ฅผ ๋Œ๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿผ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  MOOC ์„œ๋น„์Šค ๋‚ด์˜ ๊ฐ•์ขŒ ์ˆ˜๊ฐ•์„ ์™„๋ฃŒํ•˜๋Š” ์ธ์› ๋น„์œจ์ด ํ˜„์ €ํ•˜๊ฒŒ ๋–จ์–ด์ง€๊ธฐ ๋•Œ๋ฌธ์— ๊ทธ ์‹คํšจ์„ฑ์— ๋Œ€ํ•œ ํ† ๋ก ์ด ์ง„ํ–‰๋˜๊ณ  ์žˆ๋Š” ์‹ค์ •์ด๋ฉฐ, ์ด๋Ÿฌํ•œ ๋งฅ๋ฝ์—์„œ ํ•™์Šต์ž์˜ MOOC์˜ ์ˆ˜์šฉ์˜๋„๋ฅผ ์กฐ์‚ฌํ•˜๋Š” ์—ฌ๋Ÿฌ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋œ ๋ฐ” ์žˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๋™์ผํ•œ ๋งฅ๋ฝ์—์„œ MOOC ์„œ๋น„์Šค๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ํ•˜์—ฌ ์›น ๊ธฐ๋ฐ˜ ํ•™์Šต์˜ ๊ธฐ์ˆ ์ˆ˜์šฉ์˜๋„๋ฅผ ์กฐ์‚ฌํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๊ธฐ์ˆ ์ˆ˜์šฉ๋ชจ๋ธ(TAM)๊ณผ ํ˜์‹ ์ €ํ•ญ์„ ์ฐจ์šฉํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ด์šฉ์ž์˜ MOOC์— ๊ด€ํ•œ ์‚ฌ์šฉ์˜๋„๋ฅผ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ด 427๋ช…์˜ ์„ค๋ฌธ ๋ฐ์ดํ„ฐ๋ฅผ ํ™•๋ณดํ•˜์˜€์œผ๋ฉฐ, UTAUT2 ๋ชจํ˜•์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์„ฑ๊ณผ๊ธฐ๋Œ€, ๋…ธ๋ ฅ๊ธฐ๋Œ€, ์ด‰์ง„์กฐ๊ฑด, ์‚ฌํšŒ์  ์˜ํ–ฅ, ์˜ค๋ฝ์  ๋™๊ธฐ, ์Šต๊ด€ ๋“ฑ์˜ ์š”์ธ์ด ํ˜์‹ ์ €ํ•ญ ๋ฐ ์‚ฌ์šฉ์˜๋„์— ์–ด๋– ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€ ๊ตฌ์กฐ๋ฐฉ์ •์‹ ๋ชจํ˜•์œผ๋กœ ๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ๋Œ€๋ถ€๋ถ„์˜ ์ž ์žฌ๋ณ€์ธ์ด ํ˜์‹ ์ €ํ•ญ ๋ฐ ์‚ฌ์šฉ์˜๋„์— ์˜ํ–ฅ์„ ๋ฏธ์ณค์ง€๋งŒ, ์„ฑ๊ณผ๊ธฐ๋Œ€์˜ ๊ฒฝ์šฐ ํ˜์‹ ์ €ํ•ญ์— ๋Œ€ํ•˜์—ฌ ์œ ์˜๋ฏธํ•œ ๋ถ€์˜ ์˜ํ–ฅ์„ ์ฃผ์ง€ ๋ชปํ•œ๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์˜จ๋ผ์ธ ๊ณต๊ฐœ๊ฐ•์ขŒ์— ๋Œ€ํ•œ ๊ณผ๊ฑฐ ๊ฒฝํ—˜์— ๋”ฐ๋ผ ๋ชจํ˜•์˜ ํ•ด์„์ด ๋‹ฌ๋ผ์ง„๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, ๊ฒฝํ—˜์ด ์žˆ๋Š” ๊ฒฝ์šฐ ์ „์ฒด ๋ชจํ˜•์„ ๋”์šฑ ์ž˜ ๋”ฐ๋ฅธ๋‹ค๋Š” ๊ฒƒ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค.Contents Abstract i Contents iii List of Tables vi List of Figures vii Chapter 1. Introduction 1 Chapter 2. Literature Review 4 2.1 Massive Open Online Course 4 2.1.1 General Concept 4 2.1.2 History of MOOCs Development 5 2.1.3 Problems of MOOC Adoption 8 2.2 Technology Acceptance Model 10 2.2.1 Concept 10 2.2.2 Previous Researches 13 2.3 Innovation Resistance 15 2.3.1 Concept 15 Chapter 3. Model and Hypothesis 18 3.1 Research Model 18 3.1.1 Modification of the Existing Model 19 3.1.2 Intersectional Concept between Models 19 3.2 Variables and Hypotheses 19 3.2.1 Description of Variables 19 3.2.2 Operational Definition of Variables and Hypotheses 22 3.2.3 Summary of Hypotheses 23 Chapter 4. Research Methodology 26 4.1 Survey 26 4.2 Data Collection 26 4.3 Data Analysis 30 Chapter 5. Results and Discussion 31 5.1 Reliability and Validity Tests 31 5.2 Exploratory Factor Analysis 32 5.3 Confirmatory Factor Analysis 38 5.4 Structural Equation Modeling Results 39 5.4.1 Total Sample 39 5.4.2 Comparison of Group 43 Chapter 6. Conclusion 47 6.1 Research Summary 47 6.2 Implications 47 6.2.1 Theoretical Implications 47 6.2.2 Practical Implications 48 6.3 Limitations 49 6.3.1 Future Research 49 Appendix: Survey Sheet 57Maste

    Memory management technique for deep learning training with GPU

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2021. 2. ์ด์žฌ์ง„.ํ˜„๋Œ€์— ์ž์ฃผ ์‚ฌ์šฉ๋˜๋Š” ๋”ฅ ๋Ÿฌ๋‹ ํ•™์Šต ํ”„๋ ˆ์ž„์›Œํฌ๋“ค์€ GPU๋ฅผ ํƒ‘์žฌํ•œ ์ด์ข… ์‹œ์Šคํ…œ์„ ํ™œ์šฉํ•˜์—ฌ ๋‹จ๊ธฐ๊ฐ„์— ๋†’์€ ํ•™์Šต ์„ฑ๋Šฅ์„ ๋‹ฌ์„ฑํ•˜๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ์ƒˆ๋กญ๊ฒŒ ๊ฐœ๋ฐœ๋˜๋Š” DNN๋“ค์˜ ํฌ๊ธฐ๊ฐ€ ์ ์  ์ปค์ง์— ๋”ฐ๋ผ GPU์˜ ์ž‘์€ ๋ฉ”๋ชจ๋ฆฌ ์šฉ๋Ÿ‰์ด ํ•™์Šต์— ๋ฌธ์ œ๊ฐ€ ๋˜๊ณ  ์žˆ๋‹ค. ๋ฐฐ์น˜ ํฌ๊ธฐ๋Š” ํ•™์Šต์˜ ์„ฑ๋Šฅ์—๋„ ์ง์ ‘์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ธฐ ๋•Œ๋ฌธ์— GPU ๋ฉ”๋ชจ๋ฆฌ์˜ ํ•œ๊ณ„๋Š” DNN์˜ ํ•™์Šต ์„ฑ๋Šฅ์— ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ์‚ฌ์šฉ์ž๋“ค์€ ๊ธฐ์กด์˜ ๋”ฅ ๋Ÿฌ๋‹ ํ•™์Šต ํ”„๋ ˆ์ž„์›Œํฌ์—์„œ๋„ CPU ๋ฉ”๋ชจ๋ฆฌ๋ฅผ GPU ๋ฉ”๋ชจ๋ฆฌ์˜ ์Šค์™‘ ๋ฉ”๋ชจ๋ฆฌ๋กœ์จ ์‚ฌ์šฉํ•˜์—ฌ GPU ๋ฉ”๋ชจ๋ฆฌ ๋ฌธ์ œ๋ฅผ ๊ทน๋ณตํ•  ์ˆ˜ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ์‚ฌ์šฉ์ž๊ฐ€ ๋ช…์‹œ์ ์œผ๋กœ CPU์™€ GPU๊ฐ„ ๋ฉ”๋ชจ๋ฆฌ ์ „์†ก์„ ์Šค์ผ€์ค„๋งํ•˜๋Š” ๊ฒƒ์€ ์‚ฌ์šฉ์ž์—๊ฒŒ ํฐ ์ „๋ฌธ์„ฑ์„ ์š”๊ตฌํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์‚ฌ์šฉ์ž์˜ ๊ฐœ์ž… ์—†์ด ์ž๋™์œผ๋กœ CPU ๋ฉ”๋ชจ๋ฆฌ๋ฅผ GPU ๋ฉ”๋ชจ๋ฆฌ์˜ ์Šค์™‘ ๋ฉ”๋ชจ๋ฆฌ๋กœ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ๋ฉ”๋ชจ๋ฆฌ๋ฅผ ๊ด€๋ฆฌํ•˜๋Š” ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์‹œํ•œ ๊ธฐ๋ฒ•์˜ ์‹คํšจ์„ฑ์„ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•ด, ๋”ฅ ๋Ÿฌ๋‹ ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ์ธ SnuDNN์—์„œ ์ œ์‹œํ•œ ๊ธฐ๋ฒ•์„ ๊ตฌํ˜„ํ•˜๊ณ  ์‹คํ—˜์„ ํ†ตํ•ด ์ด๋ฅผ ๊ฒ€์ฆํ•˜์˜€๋‹ค.Widely used deep learning frameworks utilize heterogeneous systems with GPUs to achieve high learning performance in a short period of time. However, as the recent DNNs become larger and deeper, the limitation of the GPU memory size is becoming an issue. Users can overcome the limitation by using the CPU memory as the swap memory of the GPU memory even in the existing deep learning frameworks. However, it requires great expertise for users to explicitly schedule memory transfers between CPU and GPU. This paper proposes a memory management technique so that CPU memory can automatically be utilized as swap memory of GPU memory without user intervention. The proposed technique is implemented by modifying the SnuDNN and verified through experiment.์ œ1์žฅ ์„œ๋ก  1 ์ œ2์žฅ ๊ธฐ์กด ํ”„๋ ˆ์ž„์›Œํฌ์˜ GPU ๋ฉ”๋ชจ๋ฆฌ ํ™œ์šฉ 3 ์ œ3์žฅ ๊ด€๋ จ ์—ฐ๊ตฌ 6 ์ œ4์žฅ ๋ฉ”๋ชจ๋ฆฌ ์Šค์ผ€์ค„๋ง ๊ธฐ๋ฒ• 8 4.1 ๋”ฅ ๋Ÿฌ๋‹ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์˜ ํŠน์ง• 8 4.2 ๋”ฅ ๋Ÿฌ๋‹ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์˜ ํŒจํ„ด ๊ฒ€์ถœ 10 4.3 ํ”„๋กœํŒŒ์ผ๋ง ๋‹จ๊ณ„์™€ ๋ฉ”๋ชจ๋ฆฌ ์Šค์ผ€์ค„๋ง ๋‹จ๊ณ„ 14 4.4 ILP๋ฅผ ํ†ตํ•œ ๋ฉ”๋ชจ๋ฆฌ ์Šค์ผ€์ค„๋ง 16 4.5 ํœด๋ฆฌ์Šคํ‹ฑ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ํ†ตํ•œ ๋ฉ”๋ชจ๋ฆฌ ์Šค์ผ€์ค„๋ง 21 ์ œ5์žฅ ๊ตฌํ˜„ 22 5.1 SnuDNN 22 5.2 OpenCL 22 5.3 ๋ฉ”๋ชจ๋ฆฌ ๊ฐ์ฒด ๊ด€๋ฆฌ ๋ฐ ํ”„๋กœํŒŒ์ผ๋ง ๋‹จ๊ณ„ 23 5.4 ํŒจํ„ด ๊ฒ€์ถœ 25 ์ œ6์žฅ ์‹คํ—˜ ๊ฒฐ๊ณผ ๋ฐ ๋ถ„์„ 29 6.1 ์‹คํ—˜ ํ™˜๊ฒฝ 29 6.2 ์‹คํ—˜ ๊ฒฐ๊ณผ 30 6.2.1 ํ”„๋กœํŒŒ์ผ๋ง ๋‹จ๊ณ„์—์„œ์˜ ์„ฑ๋Šฅ 30 6.2.2 ๋ฉ”๋ชจ๋ฆฌ ์Šค์ผ€์ค„๋ง ๋‹จ๊ณ„์—์„œ์˜ ์„ฑ๋Šฅ 30 ์ œ7์žฅ ๊ฒฐ๋ก  32 ์ฐธ๊ณ ๋ฌธํ—Œ 33 Abstract 35Maste

    Dynamic Effects of electronic Word-of-Mouth on Product Performance: Comparison of Online Consumer Reviews and Social Media Buzz

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฒฝ์˜๋Œ€ํ•™ ๊ฒฝ์˜ํ•™๊ณผ, 2019. 2. ๊น€์ƒํ›ˆ.๋ณธ ์—ฐ๊ตฌ๋Š” ์˜จ๋ผ์ธ ๊ตฌ๋งค์ž ๋ฆฌ๋ทฐ์™€ ์†Œ์…œ ๋ฏธ๋””์–ด๊ฐ„์˜ ๋น„๊ต๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ์˜จ๋ผ์ธ ๊ตฌ์ „์ด ์ œํ’ˆ ์„ฑ๊ณผ์— ๋ฏธ์น˜๋Š” ๋™ํƒœ์  ์˜ํ–ฅ์„ ๋ฐํžˆ๊ณ  ์žˆ๋‹ค. ์˜จ๋ผ์ธ ๊ตฌ๋งค์ž ๋ฆฌ๋ทฐ์™€ ์†Œ์…œ ๋ฏธ๋””์–ด๋Š” ๊ตฌ๋งค์ž์˜ ์ฑ„ํƒ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ •๋ณด์›์˜ ์—ญํ• ์„ ํ•œ๋‹ค๋Š” ์ ์—์„œ๋Š” ์œ ์‚ฌํ•˜์ง€๋งŒ, ๊ตํ™˜๋˜๋Š” ์ •๋ณด์˜ ์„ฑ๊ฒฉ๊ณผ ์ •๋ณด๊ฐ€ ์ „ํŒŒ๋˜๋Š” ๋ฐฉ์‹ ๋“ฑ์— ์žˆ์–ด์„œ๋Š” ์ƒ๋‹นํ•œ ์ฐจ์ด๋ฅผ ๋ณด์ธ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ตฌ์ „ ๋งค์ฒด๋กœ์„œ์˜ ์˜จ๋ผ์ธ ๊ตฌ๋งค์ž ๋ฆฌ๋ทฐ์™€ ์†Œ์…œ ๋ฏธ๋””์–ด๋ฅผ ๊ฐœ๋…์ ์œผ๋กœ ๋Œ€๋น„ํ•  ๋ฟ ์•„๋‹ˆ๋ผ, ๋‘ ๋งค์ฒด์˜ ๊ตฌ์ „ ํšจ๊ณผ๊ฐ€ ์‹œ๊ฐ„์˜ ํ๋ฆ„์— ๋”ฐ๋ผ ๋‹ค๋ฅด๊ฒŒ ๋‚˜ํƒ€๋‚จ์„ ์ž…์ฆํ•˜๊ณ  ์žˆ๋‹ค. ๋˜ํ•œ, ์˜จ๋ผ์ธ ๊ตฌ๋งค์ž ๋ฆฌ๋ทฐ์™€ ์†Œ์…œ ๋ฏธ๋””์–ด ์ƒ ๊ตฌ์ „์˜ ํ•ฉ์ž‘ ํšจ๊ณผ(joint effect)๋ฅผ ๋ชจํ˜•์— ๋ฐ˜์˜ํ•จ์œผ๋กœ์จ ๊ฐ ๋งค์ฒด๊ฐ€ ์„œ๋กœ ๋‹ค๋ฅธ ๋งค์ฒด์˜ ํšจ๊ณผ๋ฅผ ์กฐ์ ˆํ•˜๋Š” ์ง€ ์—ฌ๋ถ€๋ฅผ ๊ฒ€์ฆํ•˜์˜€๋‹ค. 137ํŽธ ์˜ํ™”์˜ ํŒ๋งค ์„ฑ๊ณผ, ์˜จ๋ผ์ธ ๊ตฌ๋งค์ž ๋ฆฌ๋ทฐ, ์†Œ์…œ ๋ฏธ๋””์–ด ์ƒ์˜ ๊ฒŒ์‹œ๋ฌผ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•œ ์‹ค์ฆ ๋ถ„์„์—์„œ๋Š” ์ œํ’ˆ ์„ฑ๊ณผ์™€ ์˜จ๋ผ์ธ ๊ตฌ์ „๊ฐ„์˜ ๋‚ด์ƒ์„ฑ์„ ๊ณ ๋ คํ•œ ์—ฐ๊ตฌ ๋ชจํ˜•์„ ์ˆ˜๋ฆฝํ•˜๊ณ , ์ด๋ฅผ 3SLS ๋ฐฉ์‹์œผ๋กœ ์ถ”์ •ํ•˜์˜€๋‹ค. ์‹ค์ฆ ๋ถ„์„์„ ํ†ตํ•ด ๋ฐœ๊ฒฌํ•œ ์ฃผ์š” ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์˜จ๋ผ์ธ ๊ตฌ๋งค์ž ๋ฆฌ๋ทฐ์˜ ๊ฒฝ์šฐ, ๋ฆฌ๋ทฐ์˜ ๊ทœ๋ชจ(volume)์™€ ๋ฐฉํ–ฅ์„ฑ(valence)์ด ๋ชจ๋‘ ์ œํ’ˆ ์„ฑ๊ณผ๋ฅผ ๋†’์ด๋Š” ๋ฐ์— ๊ธฐ์—ฌํ•˜๋ฉฐ, ๊ทธ ์˜ํ–ฅ๋ ฅ์€ ์ œํ’ˆ ์ถœ์‹œ ํ›„ ์‹œ๊ฐ„์ด ์ง€๋‚ ์ˆ˜๋ก ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‘˜์งธ, ์†Œ์…œ ๋ฏธ๋””์–ด ๊ตฌ์ „์˜ ๊ฒฝ์šฐ, ๊ตฌ์ „์˜ ๊ทœ๋ชจ(volume)๋งŒ์ด ์ œํ’ˆ ์„ฑ๊ณผ๋ฅผ ๋†’์ด๋Š” ๋ฐ์— ์œ ์˜ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์ณค์œผ๋ฉฐ, ๋ฐฉํ–ฅ์„ฑ(valence)์˜ ํšจ๊ณผ๋Š” ์œ ์˜ํ•˜์ง€ ์•Š์•˜๋‹ค. ์˜จ๋ผ์ธ ๊ตฌ๋งค์ž ๋ฆฌ๋ทฐ์™€ ๋‹ฌ๋ฆฌ, ์†Œ์…œ ๋ฏธ๋””์–ด ๊ตฌ์ „์˜ ์˜ํ–ฅ๋ ฅ์€ ์ œํ’ˆ ์ถœ์‹œ ์งํ›„์— ๊ฐ€์žฅ ๋†’์œผ๋ฉฐ ์‹œ๊ฐ„์ด ์ง€๋‚ ์ˆ˜๋ก ๊ฐ์†Œํ•˜๋Š” ๊ฒƒ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ์…‹์งธ, ์˜จ๋ผ์ธ ๊ตฌ๋งค์ž ๋ฆฌ๋ทฐ์™€ ์†Œ์…œ ๋ฏธ๋””์–ด ๊ตฌ์ „ ๊ฐ„์—๋Š” ์–‘์˜ ์ƒํ˜ธ์ž‘์šฉ ํšจ๊ณผ๊ฐ€ ๋ฐœ๊ฒฌ๋จ์œผ๋กœ์จ ํ•œ ๋งค์ฒด์˜ ๊ตฌ์ „ ๊ทœ๋ชจ๊ฐ€ ํด์ˆ˜๋ก ๋‹ค๋ฅธ ๋งค์ฒด์˜ ๊ตฌ์ „ ํšจ๊ณผ๊ฐ€ ์ฆ๋Œ€๋œ๋‹ค๋Š” ๊ฒƒ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ํ•œํŽธ, ์ด๋“ค๊ฐ„์˜ ์‹œ๋„ˆ์ง€ ํšจ๊ณผ๋Š” ์ œํ’ˆ ์ถœ์‹œ ์งํ›„์— ๊ฐ€์žฅ ๋†’์œผ๋ฉฐ ์‹œ๊ฐ„์ด ์ง€๋‚ ์ˆ˜๋ก ์ ์ฐจ ๊ฐ์†Œํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์‹œ์‚ฌ์ ์„ ์ง€๋‹Œ๋‹ค. ์ฒซ์งธ, ์˜จ๋ผ์ธ ๊ตฌ์ „๊ณผ ๊ด€๋ จํ•œ ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ๋Š” ์ฃผ๋กœ ์˜จ๋ผ์ธ ๊ตฌ๋งค์ž ๋ฆฌ๋ทฐ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ๊ทธ ํšจ๊ณผ๋ฅผ ๊ฒ€์ฆํ•ด์™”๊ธฐ์— ์ƒ๋Œ€์ ์œผ๋กœ ์†Œ์…œ ๋ฏธ๋””์–ด ๊ตฌ์ „์˜ ํšจ๊ณผ์— ๋Œ€ํ•œ ์ดํ•ด๊ฐ€ ๋งค์šฐ ๋ถ€์กฑํ–ˆ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ถฉ๋ถ„ํ•œ ์ˆ˜์˜ ํŒจ๋„์„ ํ™œ์šฉํ•ด ์†Œ์…œ ๋ฏธ๋””์–ด ์ƒ์˜ ๊ตฌ์ „์˜ ํšจ๊ณผ๋ฅผ ์‹ค์ฆ์ ์œผ๋กœ ๊ฒ€์ฆํ–ˆ์„ ๋ฟ ์•„๋‹ˆ๋ผ, ์˜จ๋ผ์ธ ๊ตฌ๋งค์ž ๋ฆฌ๋ทฐ์™€์˜ ํ•ฉ์ž‘ ํšจ๊ณผ๋ฅผ ๊ฒ€์ฆํ•จ์œผ๋กœ์จ ์˜จ๋ผ์ธ ๊ตฌ์ „์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ ํ™•์žฅํ•˜๋Š” ๋ฐ์— ๊ธฐ์—ฌํ•˜๊ณ  ์žˆ๋‹ค. ๋‘˜์งธ, ์ด ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ์‹ค๋ฌด์ ์œผ๋กœ๋„ ์ค‘์š”ํ•œ ์‹œ์‚ฌ์ ์„ ์ œ์‹œํ•œ๋‹ค. ์ฆ‰, ์˜จ๋ผ์ธ ๊ตฌ๋งค์ž ๋ฆฌ๋ทฐ์™€ ์†Œ์…œ ๋ฏธ๋””์–ด ์ƒ์˜ ๊ตฌ์ „์ด ์ œํ’ˆ ์„ฑ๊ณผ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ด ์‹œ๊ฐ„์— ๋”ฐ๋ผ ๋ณ€ํ™”ํ•œ๋‹ค๋Š” ๊ฒฐ๊ณผ๋Š” ์˜จ๋ผ์ธ ๊ตฌ์ „ ๊ด€๋ฆฌ์— ์žˆ์–ด ๊ฐ ๋งค์ฒด ๊ฐ„ ์‹œ๋„ˆ์ง€๋ฅผ ๊ณ ๋ คํ•œ ํ†ตํ•ฉ์ ์ธ ๊ด€์ ์˜ ์ ‘๊ทผ์ด ํ•„์š”ํ•˜๋ฉฐ, ์ œํ’ˆ ์ถœ์‹œ ์ดํ›„ ์‹œ๊ฐ„์˜ ๊ฒฝ๊ณผ์— ๋”ฐ๋ผ ๊ฐ ๋งค์ฒด์— ์ฐจ๋ณ„์ ์ธ ์ค‘์š”๋„๋ฅผ ๋ถ€์—ฌํ•ด์•ผ ํ•จ์„ ์‹œ์‚ฌํ•˜๊ณ  ์žˆ๋‹ค.This research aims to examine the dynamics of electronic word-of-mouth effects on product's financial performance considering two types of platforms (i.e., online consumer reviews and social media). Our joint model not only captures the time varying effects of each channel, but also investigates whether online consumer reviews and social media buzz assist or hinder one another in increasing product's financial performances. The panel data of 137 movies with daily revenues, audiences reviews, and posts on Twitter and Instagram is used in the empirical analysis. The results show that both the volume and the valence of online consumer reviews have impacts on increasing movie revenues. Whereas, only the volume, not the valence, of social media buzz affects movie revenues. Interestingly, while the influence of online consumer reviews increases over time, that of social media buzz decreases over time. In addition, the results indicate that there is a synergy effect of two platforms of word-of-mouth. This research suggests implications for managing electronic word-of-mouth of various sources from an integrated point of view.Chapter 1. Introduction 1 Chapter 2. Theoretical Backgrounds 4 2.1. electronic Word-of-Mouth (eWOM) 4 2.2. Social Media Buzz 7 2.3. Comparison of Online Consumer Reviews and Social Media Buzz 10 Chapter 3. Hypotheses 14 3.1. Influences of Online Consumer Reviews on Product Performance 14 3.2. Influences of Social Media Buzz on Product Performance 16 3.3. Interplay of Online Consumer Reviews and Social Media Buzz 19 Chapter 4. Empirical Analysis 21 4.1. Research Methods 21 4.1.1. Data Collection 21 4.1.2. Definition of Variables 24 4.1.3. Empirical Model Specification 29 4.2. Analysis 32 4.2.1. Data Description 32 4.2.2. Estimation Results 37 4.2.3. Sensitivity Analysis 41 4.2.4. Subsample Analysis 44 Chapter 5. Discussion 48 5.1. Summary of the Results 48 5.2. Implications 49 5.3. Limitations and Future Research 51 References 53 Abstract in Korean 65Docto

    ์ˆ˜์ž์› ์ „๋ง์˜ ๋‹จ๊ณ„๋ณ„ ๋ถˆํ™•์‹ค์„ฑ ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ํ†ต๊ณ„ํ•™๊ณผ, 2019. 2. ๊น€์šฉ๋Œ€.The prospect of various resources considering climate change is academically meaningful but also politically important. Among them, the importance of water resources can not be overstressed at all, because there are not so many important resources like water to human beings. As recent droughts and floods become frequent and severe, there is a growing need to predict the outlook for water resources due to climate change. Water resources forecasts due to climate change are largely the emission scenarios, global circulation models, downscaling technique, and hydrological models. It is also important to quantify the uncertainty of each outlook, as well as forecasts interaction with each outlook phase. In this paper, we propose a method to quantify the uncertainty of each forecasting stage, with the sum of each forecasting step being equal to the total uncertainty. It also suggests ways in which uncertainty at each stage of the forecast can be resolved once more at various stages of the forecasting phase to enable comparisons within the forecasting stage.๊ธฐํ›„ ๋ณ€ํ™”๋ฅผ ๊ณ ๋ คํ•œ ์—ฌ๋Ÿฌ ์ž์›์˜ ์ „๋ง์€ ํ•™๋ฌธ์ ์œผ๋กœ๋„ ์˜์˜๊ฐ€ ์žˆ์ง€๋งŒ ์ •์ฑ…์ ์œผ๋กœ๋„ ์ค‘์š”ํ•œ ์‚ฌ์•ˆ์ด๋‹ค. ๊ทธ ์ค‘ ์ˆ˜์ž์›์˜ ์ค‘์š”์„ฑ์€ ๋‹จ์—ฐ ๊ฐ•์กฐํ•ด๋„ ์ง€๋‚˜์น˜์ง€ ์•Š์€๋ฐ ์ธ๊ฐ„์—๊ฒŒ ๋ฌผ์ฒ˜๋Ÿผ ์ค‘์š”ํ•œ ์ž์›์€ ๊ทธ๋ ‡๊ฒŒ ๋งŽ์ง€ ์•Š๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์ตœ๊ทผ์˜ ๊ฐ€๋ญ„์ด๋‚˜ ํ™์ˆ˜๊ฐ€ ๋นˆ๋ฒˆํ•ด์ง€๊ณ  ์‹ฌํ•ด์ง์— ๋”ฐ๋ผ ๊ธฐํ›„ ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ์ˆ˜์ž์› ์ „๋ง์— ๋Œ€ํ•œ ์˜ˆ์ธก ํ•„์š”์„ฑ์ด ์ ์  ๋Œ€๋‘๋˜๊ณ  ์žˆ๋‹ค. ๊ธฐํ›„ ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ์ˆ˜์ž์› ์ „๋ง์€ ํฌ๊ฒŒ ๋ฐฐ์ถœ ์‹œ๋‚˜๋ฆฌ์˜ค, ์ „์ง€๊ตฌ์  ์ˆœํ™˜ ๋ชจํ˜•, ์ƒ์„ธํ™” ๊ธฐ๋ฒ•, ์ˆ˜๋ฌธ ๋ชจํ˜•์ด ์žˆ๋‹ค. ๊ฐ ์ „๋ง ๋‹จ๊ณ„๋“ค์„ ํ†ตํ•œ ์˜ˆ์ธก๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๊ฐ ์ „๋ง์˜ ๋ถˆํ™•์‹ค์„ฑ์„ ๊ณ„๋Ÿ‰ํ™”ํ•˜๋Š” ๊ฒƒ ๋˜ํ•œ ์ค‘์š”ํ•˜๋‹ค. ๊ฐ ์ „๋ง ๋‹จ๊ณ„์˜ ๋ถˆํ™•์‹ค์„ฑ์„ ๊ณ„๋Ÿ‰ํ™”ํ•˜๋Š” ๋ฐฉ๋ฒ•์€ ์ œ์‹œ๋˜์—ˆ์ง€๋งŒ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ฐ ์ „๋ง ๋‹จ๊ณ„์˜ ํ•ฉ์ด ์ด ๋ถˆํ™•์‹ค์„ฑ๊ณผ ๊ฐ™๊ฒŒ ๊ณ„๋Ÿ‰ํ™”๋œ ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋˜ํ•œ ๊ฐ ์ „๋ง ๋‹จ๊ณ„์˜ ๋ถˆํ™•์‹ค์„ฑ์„ ์ „๋ง ๋‹จ๊ณ„์˜ ์—ฌ๋Ÿฌ ๋ฐฉ๋ฒ•์— ๋”ฐ๋ผ ํ•œ ๋ฒˆ ๋” ๋ถ„ํ•ดํ•ด ์ „๋ง ๋‹จ๊ณ„ ๋‚ด์—์„œ์˜ ๋น„๊ต๊ฐ€ ๊ฐ€๋Šฅํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค.Contents 1 Introduction 1 2 Preliminaries 3 2.1 Detailed description of the data 3 2.2 Uncertainty decomposition modeling 5 3 Results 8 3.1 The result of decomposing uncertainty 8 3.2 Break down into smaller steps 10 4 Conclusion 15 Bibliography 17 Abstract in Korean 19Maste

    <ไธŠๅฎฎ่จ˜้€ธๆ–‡>์— ๊ด€ํ•œ ไธ€่€ƒ

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    Beginning of DPRK: Revolutionary Base or Common State?

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    Dscam ๋ฐ Vps13b ์œ ์ „์ž ๋Œ์—ฐ๋ณ€์ด ์ƒ์ฅ์˜ ์ „๊ธฐ์ƒ๋ฆฌํ•™์  ํŠน์„ฑ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๋‡Œ๊ณผํ•™์ „๊ณต,2019. 8. ๊ฐ•๋ด‰๊ท .์žํ ์ŠคํŽ™ํŠธ๋Ÿผ ์žฅ์• ๋Š” ์‚ฌํšŒ์  ์ƒํ˜ธ์ž‘์šฉ, ์ธ์ง€๊ธฐ๋Šฅ์— ์†์ƒ์ด ์žˆ๊ฑฐ๋‚˜ ๋ฐ˜๋ณต์ ์ธ ํ–‰๋™์„ ๋ณด์ด๋Š” ๊ฒƒ์„ ํŠน์ง•์œผ๋กœ ํ•œ๋‹ค. ์ตœ๊ทผ ์—ฌ๋Ÿฌ ์œ ์ „์ž ๋ถ„์„ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์žํ ์ŠคํŽ™ํŠธ๋Ÿผ ์žฅ์• ๋ฅผ ๊ฐ€์ง„ ํ™˜์ž๋“ค์—์„œ VPS13B ์œ ์ „์ž์˜ ๋ณ€์ด์™€ DSCAM ์œ ์ „์ž์˜ ๋ณ€์ด๊ฐ€ ๋ฐœ๊ฒฌ๋˜์—ˆ๋‹ค๋Š” ๋ณด๊ณ ๊ฐ€ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ณตํ•ฉ์ ์ธ ์‹œ๊ฐ„์ , ๋น„์šฉ์ , ์œค๋ฆฌ์  ๋ฌธ์ œ๋กœ ์ธํ•ด ์ธ๊ฐ„ ๋ชจ๋ธ์ด ์•„๋‹Œ, ์ธ๊ฐ„๊ณผ ์œ ์‚ฌํ•œ ํŠน์ง•์„ ๊ฐ€์ง„ ๋งˆ์šฐ์Šค ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ ๋‘ ์œ ์ „์ž์˜ ์ „๊ธฐ์ƒ๋ฆฌํ•™์ ์ธ ํŠน์„ฑ์„ ์‚ดํŽด๋ณด์•˜๋‹ค. ์ด๋ฅผ ํ†ตํ•ด Vps13b ์œ ์ „์ž ์ ์ค‘ ๋งˆ์šฐ์Šค์—์„œ๋Š” ์•ผ์ƒํ˜• ๋งˆ์šฐ์Šค์™€ ๋น„๊ตํ•˜์˜€์„ ๋•Œ ๋ฏธ์„ธ ํฅ๋ถ„์„ฑ ์‹œ๋ƒ…์Šค ํ›„ ์ „๋ฅ˜์˜ ์ง„๋™์ˆ˜๊ฐ€ ์ฆ๊ฐ€ํ•˜์—ฌ ์‹œ๋ƒ…์Šค ์ „ ๋‰ด๋Ÿฐ์—์„œ ์‹ ๊ฒฝ์ „๋‹ฌ๋ฌผ์งˆ์˜ ๋ถ„๋น„๊ฐ€ ์ฆ๊ฐ€ํ•˜์˜€์„ ๊ฒƒ์ž„์„ ํ™•์ธํ•˜์˜€๋‹ค. Dscam ์œ ์ „์ž ์ ์ค‘ ๋งˆ์šฐ์Šค์˜ ๊ฒฝ์šฐ, Vps13b ์œ ์ „์ž ์ ์ค‘ ๋งˆ์šฐ์Šค์™€ ๋‹ค๋ฅด๊ฒŒ ๋ฏธ์„ธ ํฅ๋ถ„์„ฑ ์‹œ๋ƒ…์Šค ํ›„ ์ „๋ฅ˜์˜ ์ง„๋™์ˆ˜์™€ ์ง„ํญ์—์„œ ์œ ์ „์žํ˜• ๊ฐ„์— ํฐ ์ฐจ์ด๋Š” ์—†์—ˆ๋‹ค. ํ•˜์ง€๋งŒ, ๊ธฐ์กด์˜ ์žํ ์ŠคํŽ™ํŠธ๋Ÿผ ์งˆ๋ณ‘ ์—ฐ๊ตฌ๋“ค์—์„œ์™€ ๋™์ผํ•˜๊ฒŒ NMDA/AMPA ๋น„์œจ์ด ๊ฐ์†Œํ•œ ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. Dscam ์œ ์ „์ž ์ ์ค‘ ๋งˆ์šฐ์Šค์˜ ๊ฒฝ์šฐ NMDA ์ˆ˜์šฉ์ฒด ๊ตฌ์„ฑ์š”์†Œ์˜ ๋‹จ๋ฐฑ์งˆ ์–‘์—์„œ๋Š” ์œ ์˜๋ฏธํ•œ ์ฐจ์ด๊ฐ€ ์—†์—ˆ์œผ๋‚˜, ๋‡Œ์˜ ์‹ ํ˜ธ ์ „๋‹ฌ ๊ณผ์ •์—์„œ ํ™•์—ฐํ•œ ๋ณ€ํ™”๊ฐ€ ์žˆ์Œ์„ ๋ณผ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ, Dscam ์œ ์ „์ž ์ ์ค‘ ๋งˆ์šฐ์Šค๊ฐ€ ์žํ ์ŠคํŽ™ํŠธ๋Ÿผ ์žฅ์• ์˜ ๋งˆ์šฐ์Šค ์งˆ๋ณ‘ ๋ชจ๋ธ๋กœ์„œ ์‚ฌ์šฉ ๊ฐ€๋Šฅ์„ฑ์„ ๊ฐ€์ง์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค.Features of Autism Spectrum Disorder (ASD) include deficits in social communication skills, impaired cognition, and repetitive behavior. Recent findings reported that ASD patients have mutations in VPS13B gene or DSCAM gene. In this study, two mouse models were used to identify electrophysiological changes because mice share similar genetic characteristics with humans. The frequency of miniature excitatory postsynaptic currents (mEPSC) was increased in Vps13b KO mice, entailing an increase in the neurotransmitter release. However, there were no differences in basal synaptic transmission in Dscam KO mice. Instead, the NMDA/AMPA ratio was significantly decreased in Dscam KO mice. These results are consistent with previous research on ASD. The DSCAM gene strongly effected electrophysiological properties although there were no changes in protein level of NMDA receptor components. Therefore, Dscam KO mice have potential as an ASD mouse model.Table of Contents Page List of Figures โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 2 Abstract โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 3 Introduction โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 5 Materials and Methods โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 8 Results โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 14 Figures โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 20 Discussion โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 34 References โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 37 Abstract in Korean โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 45Maste
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