29 research outputs found

    ์ฝ”๋กœ๋‚˜19๊ฐ€ ์ขŒ์‹ ์ƒํ™œ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ๊ณผ ์ด์— ๋”ฐ๋ฅธ ํ•œ๊ตญ ์„ฑ์ธ์˜ ๋น„๋งŒ์— ๋Œ€ํ•œ ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๋ณด๊ฑด๋Œ€ํ•™์› ๋ณด๊ฑดํ•™๊ณผ(๋ณด๊ฑด์ •์ฑ…๊ด€๋ฆฌํ•™์ „๊ณต), 2023. 2. ์ •์™„๊ต.Background: Starting from March 2020, South Korea has enforced a strict Social Distancing in order to minimize the spread of COVID-19 pandemic. The plan included contact tracing, quarantine, social, and remote working, bringing significant changes to the lifestyles of Korean adults; people spent more time indoors and subsequent increase in sedentary time was found during the pandemic. Meanwhile, sedentary lifestyle is strongly associated with obesity and causes various serious medical complications. Therefore, this study aimed to investigate the effects of COVID-19 pandemic on sedentary lifestyles and subsequent changes in obesity rates among South Korean adults. Methods: Data were included from the 2018, 2019, and 2020 Korean National Health and Nutrition Examination Survey of 9,476 Korean adults. The KNHANES is conducted by a national institution based on random cluster sampling, and therefore, the data gained from it is statistically reliable and representative in comparison to surveys performed by private institutions. COVID-19 pandemic and sedentary lifestyle were the main independent variables. The dependent variable, presence of obesity, was categorized based on the Korean guideline using both BMI and waist circumference as the indicators. Multiple logistic regression and ordinary least squares regression analyses were performed to examine target associations. Results: The rate of conducting sedentary lifestyles among the participants increased from 30.4% before COVID-19 to 36.6% during the pandemic. During COVID-19, the odd ratio of obesity increased for both BMI and waist circumference participants (Obesity (BMI): OR = 1.16, 95% CI = 1.04โ€“1.30; Obesity (WC): OR = 1.31, 95% CI = 1.17โ€“1.46). On the same token, sedentary lifestyles also increased obesity categorized by the two indicators (Obesity (BMI): OR = 1.17, 95% CI = 1.04โ€“1.31; Obesity (WC): OR = 1.15, 95% CI = 1.03โ€“1.29). Lastly, white collar workers who indicated the most increase in sedentary time during COVID-19 had the highest risk of obesity when compared to those of people from other occupations. Conclusions: This study found that during COVID-19 pandemic, sedentary lifestyles increased among South Korean adults, which eventually increased risk of obesity.์ด๋ก ์  ๋ฐฐ๊ฒฝ: 2020๋…„ 3์›”๋ถ€ํ„ฐ ํ•œ๊ตญ์€ ์ฝ”๋กœ๋‚˜-19 ํ™•์‚ฐ์„ ๋ฐฉ์ง€ํ•˜๊ธฐ ์œ„ ํ•ด ์‚ฌํšŒ์  ๊ฑฐ๋ฆฌ๋‘๊ธฐ ์ •์ฑ…์„ ์‹œํ–‰ํ•˜์˜€๋‹ค. ํ•ด๋‹น ์ •์ฑ…์€ ํ™•์ง„์ž ๋™์„  ์ถ”์ , ๊ฒฉ๋ฆฌ, ์›๊ฒฉ ๊ทผ๋ฌด ๋“ฑ์„ ํฌํ•จํ•˜์˜€๊ณ , ์ด๋Š” ํ•œ๊ตญ ์„ฑ์ธ๋“ค์˜ ์ƒํ™œ์— ๋งŽ์€ ๋ณ€ํ™”๋ฅผ ๋ถˆ๋Ÿฌ ์ผ์œผ์ผฐ๋‹ค. ์‚ฌ๋žŒ๋“ค์€ ์‹ค๋‚ด์—์„œ ๋งŽ์€ ์‹œ๊ฐ„์„ ๋ณด๋‚ด๊ฒŒ ๋˜์—ˆ๊ณ , ์ด์— ์•‰์•„์„œ ์‹œ๊ฐ„์„ ๋ณด๋‚ด๋Š” ์ขŒ์‹ ์ƒํ™œ์ด ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ์ขŒ์‹ ์ƒํ™œ์€ ๋น„๋งŒ๊ณผ ๋ฐ€์ ‘ํ•œ ์ƒ๊ด€๊ด€๊ณ„๊ฐ€ ์žˆ๋Š”๋ฐ, ๋น„๋งŒ์€ ๋งŽ์€ ์˜ํ•™์  ํ•ฉ๋ณ‘์ฆ์„ ์•ผ๊ธฐํ•˜๋Š” ์‹ฌ๊ฐํ•œ ๋ฌธ์ œ์ด๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ํ•ด๋‹น ์—ฐ๊ตฌ๋Š” ์ฝ”๋กœ๋‚˜-19๊ฐ€ ์ขŒ์‹ ์ƒํ™œ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ๊ณผ ๊ทธ์— ๋”ฐ๋ฅธ ํ•œ๊ตญ ์„ฑ์ธ์˜ ๋‹ฌ๋ผ์ง„ ๋น„๋งŒ์œจ์„ ๋ถ„์„ํ•˜๊ณ ์ž ํ•œ๋‹ค. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•: ํ•ด๋‹น ์—ฐ๊ตฌ๋Š” 2018, 2019, 2020๋…„ ๊ตญ๋ฏผ๊ฑด๊ฐ•์˜์–‘์กฐ์‚ฌ ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•ด, 9,476๋ช…์˜ ๋Œ€์ƒ์ž๋ฅผ ํฌํ•จํ•˜์˜€๋‹ค. ์ฝ”๋กœ๋‚˜-19์™€ ์ขŒ์‹ ์ƒํ™œ์ด ์ฃผ๋œ ๋…๋ฆฝ ๋ณ€์ˆ˜๋กœ ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ์ข…์† ๋ณ€์ˆ˜์ธ ๋น„๋งŒ์€ ๋Œ€ํ•œ๋น„๋งŒํ•™ํšŒ์˜ ๊ธฐ์ค€์— ๋”ฐ๋ผ BMI์™€ ํ—ˆ๋ฆฌ๋‘˜๋ ˆ๋ฅผ ๋ชจ๋‘ ์‚ฌ์šฉํ•˜์—ฌ ์ •์˜ํ•˜์˜€๋‹ค. ์ข…์†๋ณ€์ˆ˜์™€ ๋…๋ฆฝ๋ณ€์ˆ˜ ๊ฐ„์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๊ตฌํ•˜๊ธฐ ์œ„ํ•ด Multiple logistic regression analysis ๊ณผ ordinary least squares regression analysis ๊ฐ€ ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ: ์ขŒ์‹ ์ƒํ™œ์„ ํ•˜๋Š” ๋Œ€์ƒ์ž์˜ ๋น„์œจ์ด ์ฝ”๋กœ๋‚˜-19 ์ด์ „ 30.4%์—์„œ ๋™์•ˆ์— 36.6%๋กœ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ์ฝ”๋กœ๋‚˜-19 ๊ธฐ๊ฐ„๋™์•ˆ ๊ฐ๊ฐ BMI์™€ ํ—ˆ๋ฆฌ ๋‘˜๋ ˆ๋ฅผ ์ด์šฉํ•œ ๋น„๋งŒ ๋ณ€์ˆ˜ ๋ชจ๋‘์—์„œ ์ฆ๊ฐ€๋œ ์œ„ํ—˜์„ ํ™•์ธํ•˜์˜€๋‹ค (Obesity (BMI): OR = 1.16, 95% CI = 1.04โ€“1.30; Obesity (WC): OR = 1.31, 95% CI = 1.17โ€“1.46). ๋˜ํ•œ, ์ขŒ์‹ ์ƒํ™œ์—์„œ ์—ญ์‹œ ๋‘ ๊ธฐ์ค€์„ ๋”ฐ๋ฅธ ๋น„๋งŒ์—์„œ ์ฆ๊ฐ€๋œ ์œ„ํ—˜์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค (Obesity (BMI): OR = 1.17, 95% CI = 1.04โ€“1.31; Obesity (WC): OR = 1.15, 95% CI = 1.03โ€“1.29). ๋งˆ์ง€๋ง‰์œผ๋กœ ํ™”์ดํŠธ ์นผ๋ผ ๊ทผ๋ฌด์ž๊ฐ€ ์ฝ”๋กœ๋‚˜-19 ๊ธฐ๊ฐ„๋™์•ˆ ๊ฐ€์žฅ ๋งŽ์€ ์ขŒ์‹ ์ƒํ™œ์ธ์˜ ์ฆ๊ฐ€๊ฐ€ ์žˆ์—ˆ๋Š”๋ฐ, ๊ธฐํƒ€ ๋‹ค๋ฅธ ๊ทผ๋ฌด์ž ๊ทธ๋ฃน์— ๋น„ํ•ด ๊ฐ€์žฅ ๋†’์€ ๋น„๋งŒ์˜ ์œ„ํ—˜์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ฒฐ๋ก : ํ•ด๋‹น ์—ฐ๊ตฌ๋Š” ์ฝ”๋กœ๋‚˜-19 ๊ธฐ๊ฐ„๋™์•ˆ ๋Œ€์ƒ์ž์˜ ์ขŒ์‹ ์ƒํ™œ ์ฆ๊ฐ€๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์ขŒ์‹ ์‹œ๊ฐ„ ๋ณ€ํ™”๋กœ ์ธํ•ด ์ฆ๊ฐ€ํ•œ ๋น„๋งŒ์˜ ์œ„ํ—˜์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค.Chapter 1. Introduction 1 1.1 Study Background 1 1.2 Purpose of Research 4 Chapter 2. Literature Review 5 2.1. Trend of Obesity and Problems associated with it 5 2.2. Trend of Sedentary Lifestyle and Problems associated with it 7 2.3. Obesity Interventions 9 2.4. COVID-19 pandemic and Obesity 10 Chapter 3. Material and Methods 12 3.1. Framework of the Study Design 12 3.2. Data and Study Population 13 3.3. Variables 15 3.4. Statistical analysis for Obesity Using BMI 18 3.5 Ethics Statement 19 Chapter 4. Results 20 4.1. Differences in General Characteristics of the Study population Before and During COVID-19 20 4.1.1 Analyses of BMI across Different Study Population Groups 24 4.1.2 Factors associated with Obesity (BMI) 27 4.1.3 Factors associated with BMI 30 4.1.4 Subgroup Analysos for Obesity Risk (BMI) according to the Occupational Classification 33 4.1.5 Factors associated with BMI with an interaction term 35 4.2.1 Analyses of Waist Circumference across Different Population Groups 39 4.2.2 Factors associated with Obesity (Waist Circumference) 42 4.2.3 Factors associated with Waist Circumference 45 4.2.4 Subgroup Analysis for Obesity Risk (Waist Circumference) according to the Occupational Classification 48 4.2.5 Factors associated with Waist Circumference with an interaction term 50 4.3. Falsification Test 53 Chapter 5. Discussion 54 5.1. Discussion of the Study Methods 54 5.2. Discussion of the Results 56 5.3. Policy Implications 58 5.4. Limitations 60 Chapter 6. Conclusion 62 6.1. Conclusion 62 Bibilography 64 Korean abstract 67์„

    ๊ท ์ผํ•œ ํ•™์Šต๊ณผ ๋ถˆํ™•์‹ค์„ฑ ์ถ”์ •์— ์˜ํ•œ ์ธ๊ณต ์‹ ๊ฒฝ๋ง ํผํ…์…œ ์ ์šฉ๋ฒ”์œ„์˜ ํ™•์žฅ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์žฌ๋ฃŒ๊ณตํ•™๋ถ€, 2021. 2. ํ•œ์Šน์šฐ.ํ˜„์žฌ ๋‚˜๋…ธ ์Šค์ผ€์ผ ์†Œ์žฌ ์—ฐ๊ตฌ์—์„œ ํ™œ๋ฐœํžˆ ์‚ฌ์šฉ๋˜๋Š” ์ œ์ผ์›๋ฆฌ๊ณ„์‚ฐ(First-principles calculations)์€ ๊ณผ๋‹คํ•œ ์ปดํ“จํŒ… ์ž์›์„ ์š”๊ตฌํ•˜์—ฌ ๋ชจ๋ธ๋ง ์‚ฌ์ด์ฆˆ๊ฐ€ ์ˆ˜ nm๋ฅผ ๋„˜๊ธฐ ํž˜๋“ค๋‹ค๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ๋ฐ˜๋Œ€๋กœ ๊ณ ์ „ ์›์ž๊ฐ„ ํผํ…์…œ(Classical interatomic potential)์˜ ๊ฒฝ์šฐ ์ •ํ•ด์ง„ ํ•จ์ˆ˜ ํ˜•ํƒœ๋ฅผ ํ†ตํ•ด์„œ ์›์ž ๊ฐ„ ๊ฒฐํ•ฉ์„ ๊ธฐ์ˆ ํ•˜์—ฌ ๊ณ„์‚ฐ ์†๋„๊ฐ€ ๋งค์šฐ ๋น ๋ฅด์ง€๋งŒ, ์ •ํ™•๋„๊ฐ€ ์ œํ•œ์ ์ด๊ณ  ๋ณต์žกํ•œ ํ™”ํ•™ ๊ฒฐํ•ฉ์„ ๊ธฐ์ˆ ํ•˜๋Š” ํ•จ์ˆ˜ ํ˜•ํƒœ๋ฅผ ๊ฐœ๋ฐœํ•˜๋Š” ๊ฒƒ์ด ๋งค์šฐ ์–ด๋ ต๋‹ค๋Š” ๋‹จ์ ์ด ์žˆ๋‹ค. ๊ธฐ๊ณ„ํ•™์Šต ๋ชจ๋ธ์„ ํ†ตํ•ด ์ œ์ผ์›๋ฆฌ๊ณ„์‚ฐ์„ ํ•™์Šตํ•˜์—ฌ ์ฃผ์–ด์ง„ ์‹œ์Šคํ…œ์˜ ํผํ…์…œ ์—๋„ˆ์ง€ ํ‘œ๋ฉด(Potential energy surface)์„ ๊ธฐ์ˆ ํ•˜๋Š”, ์ผ๋ช… ๊ธฐ๊ณ„ํ•™์Šต ํผํ…์…œ์€ ์•ž์„œ ์–ธ๊ธ‰ํ•œ ๋‘ ๋ฐฉ๋ฒ•๋ก ์˜ ์žฅ์ ์„ ์กฐํ•ฉํ•˜์—ฌ ๋†’์€ ์ •ํ™•๋„์˜ ๊ณ„์‚ฐ์„ ํ›จ์”ฌ ์ ์€ ๊ณ„์‚ฐ ๋น„์šฉ์œผ๋กœ ์–ป์„ ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•๋ก ์œผ๋กœ ๋งŽ์€ ๊ด€์‹ฌ์„ ๋ฐ›๊ณ  ์žˆ๋‹ค. ๊ทธ ์ค‘ ํŠนํžˆ ์ธ๊ณต์‹ ๊ฒฝ๋ง ๋ชจ๋ธ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ๊ณ ์ฐจ์› ์ธ๊ณต์‹ ๊ฒฝ๋ง ํผํ…์…œ(High-dimensional neural network potential, NNP)์€ ๊ธˆ์†, ์‚ฐํ™”๋ฌผ, ๋ฐ˜๋„์ฒด, ๋ถ„์ž ๊ฐ„ ๋ฐ˜์‘ ๋“ฑ ๊ด‘๋ฒ”์œ„ํ•œ ๋ฌผ์งˆ๊ณ„์— ์„ฑ๊ณต์ ์œผ๋กœ ์‘์šฉ๋œ ์‚ฌ๋ก€๊ฐ€ ์ด๋ฏธ ๋ณด๊ณ ๋˜์—ˆ์œผ๋ฉฐ ์ ์ฐจ ๊ทธ ์‘์šฉ ๋ถ„์•ผ๋ฅผ ๋„“ํ˜€๊ฐ€๋Š” ๋ชจ์Šต์ด๋‹ค. ํ•˜์ง€๋งŒ ์—ฌ์ „ํžˆ ์—ฐ๊ตฌ์ž๋“ค ์‚ฌ์ด์— ๊ธฐ๊ณ„ํ•™์Šต ํผํ…์…œ์— ๋Œ€ํ•œ ๊ทผ๋ณธ์ ์ธ ์ดํ•ด๊ฐ€ ๋งŽ์ด ๋ถ€์กฑํ•œ๋ฐ ์ƒํ™ฉ์ธ๋ฐ, ๊ทธ์— ๋”ฐ๋ผ ํ”ํ•œ ์ €์ง€๋ฅด๋Š” ์˜ค๋ฅ˜๋Š” ๊ธฐ๊ณ„ํ•™์Šต ํผํ…์…œ์„ ๊ณ ์ „์ ์ธ ์›์ž๊ฐ„ ํผํ…์…œ์˜ ์ผ์ข…์œผ๋กœ ์ƒ๊ฐํ•˜์—ฌ ๊ทธ ๋‘˜์„ ๋น„์Šทํ•œ ๋ฐฉ์‹์œผ๋กœ ํ•ด์„ํ•˜๋ ค๋Š” ๊ฒƒ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ณ ์ „์ ์ธ ์›์ž๊ฐ„ ํผํ…์…œ๊ณผ ๊ธฐ๊ณ„ํ•™์Šต ํผํ…์…œ ๊ฐ„์—๋Š” ๋”์šฑ ๊ทผ๋ณธ์ ์ธ ์ฐจ์ด๊ฐ€ ์กด์žฌํ•œ๋‹ค. ๊ทธ๊ฒƒ์€ ๋ฐ”๋กœ ๊ณ ์ „์ ์ธ ํผํ…์…œ์ด ์›์ž ๊ฐ„์˜ ํ™”ํ•™ ๊ฒฐํ•ฉ์„ ๋ช…์‹œ์ ์ธ ํ•จ์ˆ˜ ํ˜•ํƒœ๋กœ ๊ทผ์‚ฌํ•˜๋Š” ๋ฐ˜๋ฉด ๊ธฐ๊ณ„ํ•™์Šต ํผํ…์…œ์€ ์ด๋Ÿฌํ•œ ๊ทผ์‚ฌ ์—†์ด ๋‹จ์ง€ ํ•™์Šต ์„ธํŠธ(Training set)๋กœ ์ฃผ์–ด์ง€๋Š” ์ œ์ผ์›๋ฆฌ๊ณ„์‚ฐ์˜ ์ „์ฒด ์—๋„ˆ์ง€(Total energy)๋ฅผ ๊ฐ ์›์ž ์—๋„ˆ์ง€(Atomic energy)์˜ ํ•ฉ์œผ๋กœ ๋งตํ•‘(mapping)ํ•œ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ์ด๋Ÿฌํ•œ ํŠน์ˆ˜์„ฑ ๋•Œ๋ฌธ์— ๊ธฐ๊ณ„ํ•™์Šต ํผํ…์…œ์€ ๊ณ ์ „์  ์›์ž๊ฐ„ ํผํ…์…œ๊ณผ๋Š” ์ƒ๋‹นํžˆ ๋‹ค๋ฅธ ๋ฐฉ์‹์œผ๋กœ ์ž‘๋™ํ•˜๊ฒŒ ๋˜๋ฉฐ ๋”ฐ๋ผ์„œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์ค‘ ์˜ค๋ฅ˜๋ฅผ ์œ ๋ฐœํ•˜๋Š” ์š”์ธ ๋˜ํ•œ ์ƒˆ๋กœ์ด ์ดํ•ดํ•˜๊ณ  ๊ทธ์— ๋งž๋Š” ํ•ด๊ฒฐ์ฑ…์„ ์ œ์‹œํ•˜์—ฌ์•ผ ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋น„๊ต์  ๋ณต์žกํ•œ ์‹œ์Šคํ…œ์„ ๋‹ค๋ฃจ๋Š” ๊ธฐ๊ณ„ํ•™์Šต ํผํ…์…œ์„ ๊ฐœ๋ฐœํ•  ๋•Œ ์‰ฝ๊ฒŒ ๋งˆ์ฃผํ•  ์ˆ˜ ์žˆ๋Š” ๋‚ด์žฌ์  ์–ด๋ ค์›€ ์ค‘ ์„ธ ๊ฐ€์ง€ ์ฃผ์š”ํ•œ ์ฃผ์ œ๋ฅผ ๋‹ค๋ฃจ๊ณ  ๊ทธ ๋ฌธ์ œ๋“ค์— ๋Œ€ํ•œ ํ•ด๊ฒฐ์ฑ…์„ ์ œ์‹œํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ๋…ผ์˜ํ•˜๋Š” ์ฒซ ๋ฒˆ์งธ ์ฃผ์ œ๋Š” ํ•™์Šต ๋ฐ์ดํ„ฐ์˜ ํŽธ์ค‘๋œ ๋ถ„ํฌ์— ์˜ํ•œ ๋ถˆ๊ท ํ˜•์ ์ธ ํ•™์Šต์˜ ๋ฌธ์ œ์ด๋‹ค. ์ด ๋ฌธ์ œ๋Š” ํŠนํžˆ ๊ณต๊ณต(Vacancy), ์นจ์ž…(Interstitial) ๋“ฑ ๊ฒฐํ•จ(Defect)์ด ํฌํ•จ๋œ ์›์ž ๊ตฌ์กฐ๋ฅผ ๋‹ค๋ฃฐ ๋•Œ ํฌ๊ฒŒ ๋‘๋“œ๋Ÿฌ์ง€๋Š”๋ฐ ๊ทธ๊ฒƒ์€ ์‰ฝ๊ฒŒ ๋งํ•ด ๊ฒฐํ•จ์ด ํฌํ•จ๋œ ์›์ž ๊ตฌ์กฐ๋ฅผ ๊ธฐ๊ณ„ํ•™์Šต ํผํ…์…œ๋กœ ํ•™์Šตํ•  ๊ฒฝ์šฐ ํ•™์Šต ์—๋Ÿฌ๊ฐ€ ๋ชจ๋“  ์›์ž์— ๊ณ ๋ฅด๊ฒŒ ๋ถ„ํฌํ•˜์ง€ ์•Š๊ณ  ์ ๊ฒŒ ์ƒ˜ํ”Œ ๋˜๋Š” ๊ฒฐํ•จ ์›์ž์— ํŽธ์ค‘๋œ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ์ด๋Ÿฌํ•œ ์—๋Ÿฌ์˜ ํŽธ์ค‘์€ ๋งŽ์€ ๊ฒฝ์šฐ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์ •ํ™•์„ฑ์„ ํฌ๊ฒŒ ํ•˜๋ฝ์‹œํ‚ค๋ฉฐ ๊ฒฐํ•จ ์—๋„ˆ์ง€ (Defect energy) ๋“ฑ์˜ ๋ฌผ๋ฆฌ๋Ÿ‰ ์˜ˆ์ธก์— ์žˆ์–ด์„œ ํฐ ์˜ค๋ฅ˜๋ฅผ ์œ ๋ฐœํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” ํ›ˆ๋ จ ํฌ์ธํŠธ์˜ ๋ฐ€๋„๋ฅผ ์ •๋Ÿ‰ํ™”ํ•˜๋Š” ๊ฐ€์šฐ์Šค ๋ฐ€๋„ ํ•จ์ˆ˜๋ฅผ ์ƒˆ๋กญ๊ฒŒ ์ œ์‹œํ•˜๊ณ  ์ด ํ•จ์ˆ˜๋ฅผ ํ†ตํ•œ ๊ฐ€์ค‘์น˜ ๋ถ€์—ฌ ๋ฐฉ๋ฒ•๋ก ์„ ์ ์šฉํ•จ์œผ๋กœ์จ ๋ถˆ๊ท ํ˜•์  ํ•™์Šต ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋‹ค์Œ์œผ๋กœ ๋‹ค๋ฃจ๋Š” ์ฃผ์ œ๋Š” ์˜ˆ์ธก ๋ถˆํ™•์‹ค์„ฑ์˜ ์ •๋Ÿ‰ํ™” ๋ฌธ์ œ์ด๋‹ค. ๊ณ ์ „์ ์ธ ์›์ž ๊ฐ„ ํผํ…์…œ์ด ๋ฌผ๋ฆฌ์  ๊ธฐ๋ฐ˜์„ ๊ฐ€์ง„ ํ•จ์ˆ˜๋ฅผ ๊ทผ๊ฐ„์œผ๋กœ ํ•˜๋Š” ๊ฒƒ์— ๋ฐ˜ํ•ด ๊ธฐ๊ณ„ํ•™์Šต ํผํ…์…œ์€ ๊ทธ๋Ÿฌํ•œ ๋ฌผ๋ฆฌ์  ๊ทผ๊ฐ„์ด ๋ถ€์กฑํ•˜๋ฏ€๋กœ ์˜ˆ์ธก ๊ฒฐ๊ณผ์˜ ๋†’์€ ์‹ ๋ขฐ์„ฑ์„ ๋ณด์žฅํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ๋”ฐ๋ผ์„œ ๊ธฐ๊ณ„ํ•™์Šต ๋ฐฉ๋ฒ•๋ก ์˜ ์‹ ๋ขฐ์„ฑ ํ™•๋ณด๋ฅผ ์œ„ํ•ด์„œ๋Š” ์˜ˆ์ธก ๋ถˆํ™•์‹ค์„ฑ์˜ ์ •๋Ÿ‰ํ™”๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์ด์— ์šฐ๋ฆฌ๋Š” ๋ณต์ œ ์ธ๊ณต์‹ ๊ฒฝ๋ง ์•™์ƒ๋ธ”(Replica NNP ensemble)์„ ํ†ตํ•œ ์˜ˆ์ธก ๋ถˆํ™•์‹ค์„ฑ์˜ ์ •๋Ÿ‰ํ™” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ๋ณต์ œ ์ธ๊ณต์‹ ๊ฒฝ๋ง ์•™์ƒ๋ธ” ๋ฐฉ๋ฒ•์€ ์ธ๊ณต์‹ ๊ฒฝ๋ง ํผํ…์…œ์ด ๋ฌผ์งˆ ์‹œ์Šคํ…œ์˜ ๊ธฐ์ €์— ๊น”๋ฆฐ ์›์ž ๋‹จ์œ„์˜ ์—๋„ˆ์ง€๋ฅผ ํ•™์Šตํ•  ๋•Œ ํ•„์—ฐ์ ์œผ๋กœ ๋ฐœ์ƒํ•˜๋Š” ๋ถˆํ™•์‹ค์„ฑ์„ ๋ฐฐ์ œํ•จ์œผ๋กœ์จ ์›์ž ๋‹จ์œ„์—์„œ ์˜ˆ์ธก ๋ถˆํ™•์‹ค์„ฑ์„ ์ •ํ™•ํ•˜๊ฒŒ ์ •๋Ÿ‰ํ™”ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฐ•๋ ฅํ•œ ์žฅ์ ์„ ์ง€๋‹Œ๋‹ค. ์šฐ๋ฆฌ๋Š” ์ด ๋ฐฉ๋ฒ•๋ก ์„ ๋‹ˆ์ผˆ-์‹ค๋ฆฌ์ฝ˜ ๊ณ ์ฒด ๊ณ„๋ฉด๋ฐ˜์‘ ๋ชจ์‚ฌ๋ฅผ ์œ„ํ•œ ์ธ๊ณต์‹ ๊ฒฝ๋ง ํผํ…์…œ ๊ฐœ๋ฐœ ๊ณผ์ •์— ์ ์šฉํ•˜์˜€๊ณ  ํ•ด๋‹น ๋ฐฉ๋ฒ•๋ก ์˜ ํšจ์šฉ์„ฑ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์šฐ๋ฆฌ๋Š” ๊ธฐ๊ณ„ํ•™์Šต ํผํ…์…œ์„ ์‚ฌ์šฉํ•˜์—ฌ ํ™”ํ•™์‹ ์ด์™ธ์˜ ์–ด๋– ํ•œ ์„ ํ–‰ ์ •๋ณด๋„ ์—†์ด ์•ˆ์ •ํ•œ ๊ฒฐ์ • ๊ตฌ์กฐ๋ฅผ ์ฐพ์„ ๋•Œ ๋ฐœ์ƒํ•˜๋Š” ์–ด๋ ค์›€์— ๋Œ€ํ•ด ๋…ผ์˜ํ•œ๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ ๊ธฐ๊ณ„ํ•™์Šต ํผํ…์…œ์„ ๊ฐœ๋ฐœํ•  ๋•Œ๋Š” ๋ชฉํ‘œ๋กœ ํ•˜๋Š” ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์ค‘ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ๋Š” ๊ตญ์†Œ์ ์ธ ์›์ž ๊ตฌ์กฐ๋ฅผ ์˜ˆ์ธกํ•˜์—ฌ ํ•™์Šต ์„ธํŠธ๋ฅผ ๊ตฌ์ถ•ํ•˜๊ฒŒ ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ธฐ๊ณ„ํ•™์Šต ํผํ…์…œ์„ ๋ฏธ์ง€์˜ ๊ฒฐ์ • ๊ตฌ์กฐ ํƒ์ƒ‰์— ์ ์šฉํ•œ๋‹ค๋ฉด ๊ตฌ์กฐ ํƒ์ƒ‰ ์ค‘ ์–ด๋– ํ•œ ๊ฒฐ์ • ๊ตฌ์กฐ๊ฐ€ ๋‚˜ํƒ€๋‚ ์ง€ ์˜ˆ์ธกํ•  ์ˆ˜ ์—†๋‹ค๋Š” ๋ฌธ์ œ๊ฐ€ ๋ฐœ์ƒํ•œ๋‹ค. ์ด์— ๋”ฐ๋ผ ์šฐ๋ฆฌ๋Š” ์•ก์ƒ๊ณผ ๋น„์ •์งˆ ๊ตฌ์กฐ๋กœ๋งŒ ํ•™์Šต ์„ธํŠธ๋ฅผ ๊ตฌ์„ฑํ•˜์—ฌ ๊ฒฐ์ • ๊ตฌ์กฐ ๋‚ด์— ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ๋Š” ๋‹ค์–‘ํ•œ ๊ตญ์ง€์  ์›์ž ํ™˜๊ฒฝ์„ ์ƒ˜ํ”Œ๋งํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ๋ฐฉ๋ฒ•๋ก ์„ ์„ธ ๊ฐ€์ง€์˜ 3์„ฑ๋ถ„๊ณ„(Ternary system)์™€ ํ•œ ๊ฐ€์ง€ 4์„ฑ๋ถ„๊ณ„(Quaternary system) ๋ฌผ์งˆ์˜ ์•ˆ์ •ํ•œ ๊ฒฐ์ •๊ตฌ์กฐ ํƒ์ƒ‰์— ์ ์šฉํ–ˆ์„ ๋•Œ ๋น„๋ก ๊ธฐ๊ณ„ํ•™์Šต ํผํ…์…œ์ด ์•ก์ƒ๊ณผ ๋น„์ •์งˆ ๊ตฌ์กฐ๋งŒ์„ ํ•™์Šตํ•˜์˜€์Œ์—๋„ ๊ฒฐ์ • ๊ตฌ์กฐ ์˜ˆ์ธก ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์ค‘ ๋‚˜ํƒ€๋‚˜๋Š” ์ˆ˜๋งŽ์€ ๋‹ค์–‘ํ•œ ๊ฒฐ์ • ๊ตฌ์กฐ๋“ค์˜ ์—๋„ˆ์ง€๋ฅผ ์ œ์ผ์›๋ฆฌ๊ณ„์‚ฐ์— ๋น„ํ•˜์—ฌ ์ •ํ™•ํžˆ ์˜ˆ์ธกํ•œ๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ธฐ๊ณ„ํ•™์Šต ํผํ…์…œ์ด ๊ฐ€์ง„ ํŠน์ง•์ ์ธ ๋ฌธ์ œ ์ค‘ ์„ธ ๊ฐ€์ง€ ์ฃผ์š”ํ•œ ์ฃผ์ œ๋ฅผ ๋‹ค๋ฃจ๊ณ  ํ•ด๊ฒฐ์ฑ…์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์šฐ๋ฆฌ๋Š” ํ•ด๋‹น ๋…ผ์˜๊ฐ€ ๊ธฐ๊ณ„ํ•™์Šต ํผํ…์…œ์˜ ์ ์šฉ ๋ฒ”์œ„ ํ™•์žฅ์— ๊ธฐ์—ฌํ•  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€ํ•œ๋‹ค.The machine learning approach to develop interatomic potential attracts considerable attention because they can achieve simulation accuracy comparable to the reference first-principles calculations, with a much lower calculation cost. Of the many choices of machine learning potentials, high-dimensional neural network potential is highly anticipated due to its successful demonstrations in a wide range of materials, including metals, oxides, semiconductors, and molecular reactions. Despite its success and attractiveness, machine learning potentials are often regarded as a black-box method, and efforts to understand the basic foundation are lacking. The difficulty in understanding machine learning potentials stems from the difference in traits that distinguish it from the traditional classical potentials. Due to the fundamental differences, machine learning potentials present unique challenges that must be overcome to achieve high accuracy when used. In this dissertation, we address three significant challenges of machine learning potentials and, with neural network potential, in particular, suggest the solution to the challenges. First, we discuss an unbalanced training problem coming from a biased distribution of training points. We provide various examples of biased sampling and how it undermines the accuracy of the simulation. Using the Gaussian density function that quantifies the sparsity of training points, we propose a weighting scheme to solve the unbalanced training problem. Next, we focus on the establishment of prediction uncertainty indicators. Because machine learning potentials do not have physics-based functions like conventional classical potentials, their reliability can be questionable. Therefore, a prediction uncertainty indicator is essential for machine learning potentials. The uncertainty indicator should have atomic-level resolution to identify the exact local atomic environment lacking in the training set. To this end, we propose a replica ensemble method that can ensure the atomic-level resolution of uncertainty estimation by excluding uncertainties arising from atomic energy mapping. We demonstrate this method to run molecular dynamics simulations of the Ni-Si interface reaction. Finally, we touch on one of the grand challenges in machine learning potential application, which is applying machine learning potentials to search for new crystal structures without any preceding information other than the chemical composition. In usual practices, machine learning potentials are first trained over structures derived from known structures. However, such information is not available at the outset in exploring unknown crystals. As we will address that machine learning potentials can effectively map the atomic energies from the reference total energy, we find that they can sample diverse local orders that can appear in crystals from a training set composed only of disordered structures. We prove this analogy on four different multinary crystal systems over experimental phases as well as low-energy crystal structures. By addressing and overcoming the inherent challenges in machine learning potentials, this dissertation will extend the application range of the machine learning potentials.Abstract i Contents iii List of Tables vi List of Figures vii 1 Introdunction 1 1.1 Overview of machine learning potential . . . . . . . . . . . . . . . . 1 1.2 Goal of the dissertation . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Organization of the dissertation . . . . . . . . . . . . . . . . . . . . . 5 2 Theoretical background 6 2.1 Density functional theory . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.1 Born-Oppenheimer approximation . . . . . . . . . . . . . . . 6 2.1.2 Hohenberg-Kohn theorem . . . . . . . . . . . . . . . . . . . 8 2.1.3 Kohn-Sham equation . . . . . . . . . . . . . . . . . . . . . . 9 2.1.4 Exchange-correlation energy . . . . . . . . . . . . . . . . . . 12 2.2 Classical potentials . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3 Machine learning potentials . . . . . . . . . . . . . . . . . . . . . . . 17 2.3.1 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.3.2 Descriptors . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.3.3 The training techniques for NNP . . . . . . . . . . . . . . . . 34 2.3.4 Atomic energy mapping of machine learning potentials . . . . 37 3 Unbalanced training problem 41 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.2 Examples of sampling bias . . . . . . . . . . . . . . . . . . . . . . . 42 3.3 GDF weighting scheme . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4 Prediction uncertainty quantification 57 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.2 Replica ensemble . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.3 Application: Ni silicidation simulation . . . . . . . . . . . . . . . . . 61 4.4 Training set and validation of reference NNP . . . . . . . . . . . . . 63 4.5 Large-scale Ni silicidation simulation . . . . . . . . . . . . . . . . . 70 4.6 Replica NNP training . . . . . . . . . . . . . . . . . . . . . . . . . . 74 4.7 Uncertainty quantification on Ni silicidation simulation . . . . . . . . 77 4.8 Schottky barrier height estimation . . . . . . . . . . . . . . . . . . . 82 4.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 5 Crystal structure prediction by machine learning potentials 85 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 5.2 Test materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 5.3 Construction of training set . . . . . . . . . . . . . . . . . . . . . . . 90 5.4 Crystal structure prediction by genetic algorithm . . . . . . . . . . . 91 5.5 Comparison of NNP and DFT energies . . . . . . . . . . . . . . . . . 92 5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 6 Conclusion 97 Bibliography 99 Abstract (In Korean) 109 Acknowlegement 112Docto

    ์„ธ๋ผ๋ฏน ์šฉ๊ธฐ์— ์ •์กฑ์ˆ˜ ๊ฐ์ง€์–ต์ œ ๋ฏธ์ƒ๋ฌผ์˜ ๊ณ ์ •ํ™” ๋ฐ ๋ถ„๋ฆฌ๋ง‰-์ƒ๋ฌผ๋ฐ˜์‘๊ธฐ์—์„œ ์ƒ๋ฌผ๋ง‰ ์˜ค์—ผ์ œ์–ด๋ฅผ ์œ„ํ•œ ํ™œ์šฉ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€, 2014. 2. ์ด์ •ํ•™.์ตœ๊ทผ 20๋…„ ๋ถ„๋ฆฌ๋ง‰-์ƒ๋ฌผ๋ฐ˜์‘๊ธฐ์˜ ์ƒ์—…์ ์ธ ํ™œ์šฉ์€ ํƒ์›”ํ•œ ์ฒ˜๋ฆฌ์ˆ˜์งˆ๊ณผ ์ ์€ ์†Œ์š”๋ถ€์ง€ ๋ฉด์ ์œผ๋กœ ์ธํ•˜์—ฌ ๊ธ‰๊ฒฉํžˆ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ๋ถ„๋ฆฌ๋ง‰ ํ‘œ๋ฉด์— ์ž์—ฐ์ ์œผ๋กœ ์Œ“์—ฌ ์—ฌ๊ณผ์„ฑ๋Šฅ์„ ๋–จ์–ด๋œจ๋ฆฌ๋Š” ์ƒ๋ฌผ๋ง‰ ์˜ค์—ผ์€ ์ด ๋ถ„์•ผ์—์„œ ์—ฌ์ „ํžˆ ํ’€์ง€ ๋ชปํ•œ ๋‚œ์ œ์ด๋ฉฐ ๋ถ„๋ฆฌ๋ง‰-์ƒ๋ฌผ๋ฐ˜์‘๊ธฐ์˜ ํšจ์œจ์„ ๋–จ์–ด๋œจ๋ฆฌ๋Š” ์ฃผ์š” ์›์ธ์ด๋‹ค. ์ตœ๊ทผ ํ์ˆ˜์ฒ˜๋ฆฌ๋ฅผ ์œ„ํ•œ ๋ถ„๋ฆฌ๋ง‰-์ƒ๋ฌผ๋ฐ˜์‘๊ธฐ์—์„œ ๋ฏธ์ƒ๋ฌผ ์šฉ๊ธฐ๋ฅผ ์ด์šฉํ•œ ์ •์กฑ์ˆ˜ ๊ฐ์ง€์–ต์ œ ๊ธฐ์ˆ ์€ ๊ฒฝ์ œ์ ์œผ๋กœ ์ƒ๋ฌผ๋ง‰ ์˜ค์—ผ์„ ์ œ์–ดํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ ์ฃผ๋ชฉ ๋ฐ›๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ •์กฑ์ˆ˜ ๊ฐ์ง€์–ต์ œ์— ์‚ฌ์šฉํ•˜๋Š” ๋ฏธ์ƒ๋ฌผ ์šฉ๊ธฐ ๋‚ด๋ถ€์˜ ๋‚ฎ์€ F/M ๋น„๋ฅผ ๊ฐœ์„ ํ•˜๊ณ ์ž ์„ธ๋ผ๋ฏน ๋ฏธ์ƒ๋ฌผ ์šฉ๊ธฐ๋ฅผ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์„ธ๋ผ๋ฏน ๋ฏธ์ƒ๋ฌผ ์šฉ๊ธฐ๋Š” ์ƒ์šฉ ์„ธ๋ผ๋ฏน ๋ฉค๋ธŒ๋ ˆ์ธ๊ณผ ์ •์กฑ์ˆ˜ ๊ฐ์ง€์–ต์ œ ์„ธ๊ท ์ธ Pseudomonas sp. 1A1์„ ์ด์šฉํ•˜์—ฌ ๊ตฌ์ถ•ํ•˜์˜€๋‹ค. ๋‚ด๋ถ€์˜์–‘๊ณต๊ธ‰๋ฐฉ์‹(inner flow feeding mode)์€ ์„ธ๋ผ๋ฏน ๋ฏธ์ƒ๋ฌผ ์šฉ๊ธฐ๋ฅผ ์‚ฌ์šฉํ•œ ์ฃผ๋ชฉ์ ์œผ๋กœ์„œ, ์„ธ๋ผ๋ฏน ๋ฏธ์ƒ๋ฌผ ์šฉ๊ธฐ ๋‚ด๋ถ€์— ์œ„์น˜ํ•œ ์ •์กฑ์ˆ˜ ๊ฐ์ง€์–ต์ œ ๋ฏธ์ƒ๋ฌผ์—๊ฒŒ ์˜์–‘๋ฌผ์งˆ์„ ์šฉ์ดํ•˜๊ฒŒ ์ „๋‹ฌํ•˜์—ฌ ๋ณด๋‹ค ์˜ค๋žœ ๊ธฐ๊ฐ„ ๋™์•ˆ ์„ธํฌ์˜ ํ™œ์„ฑ์„ ์œ ์ง€ํ•˜๋„๋ก ํ•˜์˜€๋‹ค. Pseudomonas sp. 1A1์„ ๋‹ด์ฒดํ•œ ์„ธ๋ผ๋ฏน ๋ฏธ์ƒ๋ฌผ ์šฉ๊ธฐ(CMV)๋Š” ๋‚ด๋ถ€์˜์–‘๊ณต๊ธ‰๋ฐฉ์‹์œผ๋กœ ์šด์ „ํ•˜์˜€์„ ๋•Œ ๋ถ„๋ฆฌ๋ง‰ ํ‘œ๋ฉด์˜ ์ƒ๋ฌผ๋ง‰ ์˜ค์—ผ์„ ๋ณด๋‹ค ํšจ๊ณผ์ ์œผ๋กœ ์ œ์–ดํ•˜์˜€์œผ๋ฉฐ, ๋˜ํ•œ MBR ๋‚ด์—์„œ ์žฅ๊ธฐ๊ฐ„ ์ •์กฑ์ˆ˜๊ฐ์ง€์–ต์ œ ํšจ๊ณผ๋ฅผ ์œ ์ง€ํ•˜์˜€๋‹ค. ์ด๋Š” ๊ณ ๋ถ„์ž ์—ฌ๊ณผ๋ง‰์˜ ์ฐจ์••์ƒ์Šน๊ณผ ๊ณต์ดˆ์ ์ฃผ์‚ฌํ˜„๋ฏธ๊ฒฝ์— ์˜ํ•œ ์ƒ๋ฌผ๋ง‰ ๋ฐ ๋ฏธ์ƒ๋ฌผ์˜ ์ง์ ‘ ๊ด€์ฐฐ๋กœ ํ™•์ธํ•˜์˜€๋‹ค. ์‹คํ—˜์‹ค ๊ทœ๋ชจ MBR์—์„œ์˜ ์„ฑ๊ณต์ ์ธ ์ƒ๋ฌผ๋ง‰ ์˜ค์—ผ์ œ์–ด๋Š” ๋‚ด๋ถ€์˜์–‘๊ณต๊ธ‰๋ฐฉ์‹์„ ์ด์šฉํ•œ ์„ธ๋ผ๋ฏน ๋ฏธ์ƒ๋ฌผ ์šฉ๊ธฐ๊ฐ€ ์‹ค๊ทœ๋ชจ MBR ์—์„œ๋„ ํšจ๊ณผ์ ์œผ๋กœ ์ƒ๋ฌผ๋ง‰ ์˜ค์—ผ์„ ์ œ์–ดํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์—ฌ ์ฃผ์—ˆ๊ณ  ๋‹ค์–‘ํ•œ ํ™˜๊ฒฝ๊ณตํ•™ ๋ถ„์•ผ์— ์‘์šฉ๊ฐ€๋Šฅ์„ฑ์„ ๊ธฐ๋Œ€ํ•˜๊ฒŒ ํ•œ๋‹ค.Table of Contents Abstract i List of Figures ix List of Tables xiii 1. Introduction 1 1.1. Backgrounds 3 1.2. Objectives 6 2. Literature Review 9 2.1. Biological wastewater treatment 11 2.1.1. Historical overview 11 2.1.2. Biological wastewater process 15 2.1.3. Operation parameters 17 2.2. Membrane Bioreactor (MBR) 22 2.2.1. MBR History 22 2.2.2. Market and Research 25 2.2.3. Advantage and Disadvantage of MBR 29 2.3. Quorum Sensing (QS) 31 2.3.1. Definition and Mechanism 31 2.3.2. Role of QS in Biofilm 47 2.3.3. Quorum sensing control Strategy 51 2.4. Quorum Quenching (QQ) 62 2.4.1. Enzymatic Biological processes 62 2.4.2. Application of QQ to the Control of Biofouling 68 3. Design of Quorum Quenching Microbial Vessel to Enhance Cell Viability in MBR 73 3.1. Introduction 75 3.2. Experimental Section 77 3.2.1. Design of Ceramic Microbial Vessel (CMV) 77 3.2.2. Measurement of the nutrient transport rate 80 3.2.3. Analysis of cell viability in the CMV 83 3.3. Results and Discussion 85 3.3.1. Investigation of the morphology of CMVs 85 3.3.2. Effect of the inner flow feeding mode on the rate of nutrient transfer to the CMV 87 3.3.3. Comparison of cell viability of CMVs under the different feeding mode 91 3.4. Conclusions 93 4. Isolation and Identification of Indigenous Bacteria producing Quorum Quenching Enzyme 95 4.1. Introduction 97 4.2. Experimental Section 98 4.2.1. Indigenous QQ bacteria 98 4.2.2. Comparison of QQ enzyme localization between strain 1A1 and BH4 98 4.2.3. Characterization of QQ bacteria 99 4.2.4. Bioassay for detecting AHL molecules 100 4.3. Results and Discussion 101 4.3.1 Localization of quorum quenching enzyme of the strain, Pseudomonas sp. 1A1 101 4.3.2 Molecular size of AHL-acylase of Pseudomonas sp. 1A1 103 4.3.3 Comparison of the Growth rate of Pseudomonas sp. 1A1 105 4.3.4 QQ activity of Pseudomonas sp. 1A1 according to the Growth rate 110 4.3.5 Degradation of various AHLs by Pseudomonas sp. 1A1 112 4.4 Conclusions 114 5. Application of CMV encapsulated Pseudomonas sp. 1A1 for biofouling control in MBR 115 5.1 Introduction 117 5.2 Experimental Section 118 5.2.1 Preparation of the ceramic microbial vessel (CMV) 118 5.2.2 AHL bioluminescence assay 118 5.2.3 Measurement of the QQ activity of the CMV 119 5.2.4 MBR operation 120 5.2.5 Analytical methods 124 5.3 Results and Discussion 127 5.3.1 Quorum quenching activity of the ceramic microbial vessel 127 5.3.2 Effect of the CMV on MBR biofouling 129 5.3.3 Effect of the inner flow feeding mode on TMP rise-up in QQ MBR at constant flux 133 5.3.4 Effect of the CMV on SMP and EPS production in the MBR 137 5.3.5 Effect of the CMV on the biodegradation of organics in MBRs 142 5.3.6 Stability of the QQ activity in the CMV 144 5.4 Conclusions 146 Recommendations for improving this research 147 ์ดˆ ๋ก 149 References 151Docto

    A Case Study on Process of Change in Male High School Students Sport Literacy through Art Convergence Physical Education Class

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฌ๋ฒ”๋Œ€ํ•™ ์ฒด์œก๊ต์œก๊ณผ, 2018. 8. ์ตœ์˜์ฐฝ.๋ณธ ์—ฐ๊ตฌ๋Š” ์˜ˆ์ˆ ์œตํ•ฉ ์ฒด์œก์ˆ˜์—…์„ ํ†ตํ•œ ๋‚จ์ž๊ณ ๋“ฑํ•™์ƒ์˜ ์šด๋™์†Œ์–‘ ๋ณ€ํ™” ๊ณผ์ •์„ ํƒ์ƒ‰ํ•˜๋Š” ๋ฐ ๋ชฉ์ ์ด ์žˆ๋‹ค. ๋‚จ์ž๊ณ ๋“ฑํ•™์ƒ๋“ค์˜ ์šด๋™์†Œ์–‘ ํ•จ์–‘์„ ์œ„ํ•œ ์˜ˆ์ˆ ์œตํ•ฉ ์ฒด์œก์ˆ˜์—…์„ ๊ตฌ์„ฑํ•˜์—ฌ ์‹ค์ฒœํ•˜๊ณ , ์šด๋™์†Œ์–‘์ด ์–ด๋–ป๊ฒŒ ๋ณ€ํ™”๋˜๋Š”์ง€ ๊ทธ ๊ณผ์ •์„ ์‚ดํŽด๋ด„์œผ๋กœ์„œ, ์šด๋™์†Œ์–‘ ๋ณ€ํ™”์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์„ ๋ถ„์„ํ•˜๊ณ ์ž ํ•œ๋‹ค. ์—ฐ๊ตฌ ๋ฌธ์ œ๋Š” ์ฒซ์งธ, ๋‚จ์ž๊ณ ๋“ฑํ•™์ƒ์„ ์œ„ํ•œ ์˜ˆ์ˆ ์œตํ•ฉ ์ฒด์œก์ˆ˜์—…์€ ์–ด๋–ป๊ฒŒ ๊ตฌ์„ฑ๋˜๊ณ  ์‹ค์ฒœ๋˜๋Š”๊ฐ€, ๋‘˜์งธ, ์˜ˆ์ˆ ์œตํ•ฉ ์ฒด์œก์ˆ˜์—…์—์„œ ๋ณ€ํ™”๋˜๋Š” ๋‚จ์ž๊ณ ๋“ฑํ•™์ƒ์˜ ์šด๋™์†Œ์–‘์€ ๋ฌด์—‡์ด๋ฉฐ, ๊ทธ ๊ณผ์ •์€ ์–ด๋– ํ•œ๊ฐ€, ์…‹์งธ, ์˜ˆ์ˆ ์œตํ•ฉ ์ฒด์œก์ˆ˜์—…์—์„œ ๋‚จ์ž๊ณ ๋“ฑํ•™์ƒ์˜ ์šด๋™์†Œ์–‘ ๋ณ€ํ™”์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์€ ๋ฌด์—‡์ธ๊ฐ€๋กœ ์„ค์ •ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๋Š” ์งˆ์  ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์œผ๋กœ 2017๋…„ 1์›”๋ถ€ํ„ฐ 2018๋…„ 1์›”๊นŒ์ง€ ์•ฝ 12๊ฐœ์›” ๋™์•ˆ ์ˆ˜ํ–‰๋˜์—ˆ์œผ๋ฉฐ, ์‚ฌํšŒ๊ตฌ์„ฑ์ฃผ์˜ ํŒจ๋Ÿฌ๋‹ค์ž„์„ ๋ฐ”ํƒ•์œผ๋กœ ํ•˜์—ฌ ์ฐธ์—ฌ์  ์‹คํ–‰์—ฐ๊ตฌ ๋ฐ ์‚ฌ๋ก€ ์—ฐ๊ตฌ์˜ ํ˜•ํƒœ๋กœ ์ง„ํ–‰๋˜์—ˆ๋‹ค. ์—ฐ๊ตฌ์˜ ์ ˆ์ฐจ๋Š” ADDIE ๋ชจํ˜•์— ๊ทผ๊ฑฐํ•˜์—ฌ ๋ถ„์„, ์„ค๊ณ„, ๊ฐœ๋ฐœ, ์‹คํ–‰, ํ‰๊ฐ€ ์ˆœ์œผ๋กœ ์ด๋ฃจ์–ด์กŒ๋‹ค. ๋ฌธํ—Œ๋ถ„์„, ์ „๋ฌธ๊ฐ€ ํ˜‘์˜, ์‹ฌ์ธต๋ฉด๋‹ด์„ ํ†ตํ•ด ๊ฐœ๋ฐœ ์›๋ฆฌ๋ฅผ ๋„์ถœํ•œ ๋‹ค์Œ, ์˜ˆ์ˆ ์œตํ•ฉ ์ฒด์œก์ˆ˜์—…์„ ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ์ฐธ์—ฌ์ž๋Š” ์ž๋ฐœ์  ๋™์˜์— ์˜ํ•œ ๋‚จ์ž๊ณ ๋“ฑํ•™์ƒ 10๋ช…์ด๋ฉฐ, ์ž๋ฃŒ ์ˆ˜์ง‘์€ ์‚ฌ์ „ ์‹ฌ์ธต๋ฉด๋‹ด, ์‚ฌํ›„ ์‹ฌ์ธต๋ฉด๋‹ด, ์ฐธ์—ฌ๊ด€์ฐฐ, ์˜ค์„ฑ์ง€์ˆ˜๊ฒ€์‚ฌ ๋“ฑ์œผ๋กœ ์ด๋ฃจ์–ด์กŒ๋‹ค. ์ž๋ฃŒ ๋ถ„์„์€ ํฌ๊ด„์  ๋ถ„์„์ ˆ์ฐจ์˜ ๋ฐฉ๋ฒ•์œผ๋กœ ์ด๋ฃจ์–ด์กŒ์œผ๋ฉฐ, ๋‹ค๊ฐ์  ์ž๋ฃŒ ์ˆ˜์ง‘์„ ํ†ตํ•œ ๊ฒ€์ฆ, ์—ฐ๊ตฌ ์ฐธ์—ฌ์ž์— ์˜ํ•œ ๊ฒ€ํ† , ๋™๋ฃŒ ๊ฐ„ ํ˜‘์˜๋ฅผ ํ†ตํ•ด ์—ฐ๊ตฌ์˜ ํƒ€๋‹น๋„์™€ ์‹ ๋ขฐ๋„๋ฅผ ํ™•๋ณดํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ์ž๋Š” ์†Œ์† ๋Œ€ํ•™๊ต์˜ ์—ฐ๊ตฌ์œค๋ฆฌ์‹ฌ์˜์œ„์›ํšŒ ์Šน์ธ์„ ๋ฐ”ํƒ•์œผ๋กœ ์—ฐ๊ตฌ ์œค๋ฆฌ๋ฅผ ์ค€์ˆ˜ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ, ์ฒซ์งธ, ์˜ˆ์ˆ ์œตํ•ฉ ์ฒด์œก์ˆ˜์—…์˜ ๊ตฌ์„ฑ ๋ฐ ์‹ค์ฒœ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ๋จผ์ €, ๊ตฌ์„ฑ ์›๋ฆฌ๋Š” 1) ๋ฐฐ์›€์ด ์žˆ๋Š” ์ˆ˜์—…, 2) ์ฆ๊ฑฐ์›€์ด ์žˆ๋Š” ์ˆ˜์—…, 3) ์†Œํ†ต์ด ์žˆ๋Š” ์ˆ˜์—…์œผ๋กœ ๋„์ถœ๋˜์—ˆ์œผ๋ฉฐ ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ตฌ์ฒด์ ์ธ ์ˆ˜์—…์˜ ๋ชฉํ‘œ, ๋‚ด์šฉ, ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์ „์ธ(whole person)๊ต์œก์„ ๋ชฉํ‘œ๋กœ ์„ค์ •ํ•˜๊ณ , ๋ฐฐ๋“œ๋ฏผํ„ด์„ ์†Œ์žฌ๋กœ ์ด 16์ฐจ์‹œ์˜ ์ˆ˜์—…์„ ์ง„ํ–‰ํ•˜์˜€์œผ๋ฉฐ, ๊ต์‚ฌ์˜ ์ง์ ‘์ง€๋„ํ–‰๋™๊ณผ ๊ฐ„์ ‘์ง€๋„ํ–‰๋™์„ ์ ์ ˆํ•˜๊ฒŒ ํ™œ์šฉํ•˜์˜€๋‹ค. ๋‘˜์งธ, ์˜ˆ์ˆ ์œตํ•ฉ ์ฒด์œก์ˆ˜์—…์„ ํ†ตํ•œ ๋‚จ์ž๊ณ ๋“ฑํ•™์ƒ์˜ ์šด๋™์†Œ์–‘ ๋ณ€ํ™”๋Š” 1) ๋Šฅ์†Œ์–‘ ๋ณ€ํ™”, 2) ์ง€์†Œ์–‘ ๋ณ€ํ™”, 3) ์‹ฌ์†Œ์–‘ ๋ณ€ํ™”๋กœ ๊ตฌ๋ถ„๋˜์—ˆ๋‹ค. ๋Šฅ์†Œ์–‘์˜ ๋ณ€ํ™”๋ฅผ ๋ณด์ธ ํ•™์ƒ๋“ค์€ ์ฐธ์—ฌ๋„ ๋ฐ ํฅ๋ฏธ๋„์˜ ์ฆ๊ฐ€, ๊ฒฝ๊ธฐ์ˆ˜ํ–‰๋Šฅ๋ ฅ์˜ ํ–ฅ์ƒ์„ ๋‚˜ํƒ€๋‚ด์—ˆ๋‹ค. ์ง€์†Œ์–‘์˜ ๋ณ€ํ™”๋ฅผ ๋ณด์ธ ํ•™์ƒ๋“ค์€ ๋‹ค๊ฐ์  ์ดํ•ด, ์ง€์‹์˜ ์žฌ๊ตฌ์„ฑ, ๋ถ„์„๋ ฅ ํ–ฅ์ƒ์˜ ๋ชจ์Šต์„ ๋‚˜ํƒ€๋‚ด์—ˆ๋‹ค. ์‹ฌ์†Œ์–‘ ๋ณ€ํ™”๋ฅผ ๋ณด์ธ ํ•™์ƒ๋“ค์€ ์„ค๋ ˜๊ณผ ๊ธฐ๋‹ค๋ฆผ ์ง€์†, ์˜ˆ์ˆ ์  ์˜๊ฐ ํš๋“, ๋ˆ๋ˆํ•œ ๊ต์šฐ๊ด€๊ณ„ ํ˜•์„ฑ, ์ฐฝ์ž‘์˜ ๊ธฐ์จ ๋งŒ๋ฝ, ์—ฐ๊ฒฐ๊ณ ๋ฆฌ ํ˜•์„ฑ์˜ ๋ชจ์Šต์„ ๋‚˜ํƒ€๋‚ด์—ˆ๋‹ค. ๊ฐœ๋…์ ์œผ๋กœ๋Š” ์šด๋™๋Šฅยท์ง€ยท์‹ฌ์˜ ๋ณ€ํ™”๋ฅผ ๊ตฌ๋ถ„ํ•˜์—ฌ ์ œ์‹œํ–ˆ์ง€๋งŒ, ์‹ค์ œ ๋ณ€ํ™”๋Š” ๋ณตํ•ฉ์ ์œผ๋กœ ๋ฐœ์ƒํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์•˜๋‹ค. ์ด 10๋ช…์˜ ์—ฐ๊ตฌ ์ฐธ์—ฌ์ž ์ค‘, 9๋ช…์˜ ์šด๋™์†Œ์–‘์ด ๊ธ์ •์ ์œผ๋กœ ๋ณ€ํ™”ํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, 1๋ช…์˜ ์ฐธ์—ฌ์ž๋Š” ์ง€์†Œ์–‘๊ณผ ์‹ฌ์†Œ์–‘์€ ์ฆ๊ฐ€ํ•˜์˜€์ง€๋งŒ ์‹ฌ์†Œ์–‘์˜ ๊ฐ์†Œ๋กœ ์ธํ•ด ์ „์ฒด์ ์ธ ์šด๋™์†Œ์–‘ ์ˆ˜์ค€์€ ๋‹ค์†Œ ๊ฐ์†Œํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์˜ˆ์ˆ ์œตํ•ฉ ์ฒด์œก์ˆ˜์—…์ด ๋‚จ์ž๊ณ ๋“ฑํ•™์ƒ์˜ ์šด๋™์†Œ์–‘ ๋ณ€ํ™”์— ๊ธ์ •์  ์˜ํ–ฅ์„ ์ค„ ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฐ€๋Šฅ์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์…‹์งธ, ์šด๋™์†Œ์–‘ ๋ณ€ํ™”์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์˜ˆ์ˆ ์œตํ•ฉ ์ฒด์œก์ˆ˜์—… ์š”์ธ์€ 1) ๋‚ด์šฉ ์š”์ธ, 2) ํ™˜๊ฒฝ ์š”์ธ, 3) ๊ต์‚ฌ ์š”์ธ์œผ๋กœ ๋„์ถœ๋˜์—ˆ๋‹ค. ๋‚ด์šฉ ์š”์ธ์€ ๋‹ค์–‘์„ฑ๊ณผ ๊ฐœ๋ฐฉ์„ฑ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‹ค์–‘์„ฑ์€ ํ•™์ƒ๋“ค์ด ๊ฒฝํ—˜ํ•œ ๋‹ค์ฑ„๋กœ์šด ์ˆ˜์—… ๋‚ด์šฉ์œผ๋กœ๋ถ€ํ„ฐ ๋„์ถœ๋˜์—ˆ์œผ๋ฉฐ, ๊ฐœ๋ฐฉ์„ฑ์€ ์ฐฝ์˜๋ ฅ์„ ์ž๊ทนํ•˜๊ณ  ์ƒˆ๋กœ์šด ์ธ์‹์„ ํ˜•์„ฑํ•˜๊ฒŒ ํ•˜์˜€์Œ์„ ์˜๋ฏธํ•œ๋‹ค. ํ™˜๊ฒฝ ์š”์ธ์€ ์ถ•์ œ์„ฑ ๋ฐ ๋ฌธํ™”์„ฑ, ํ˜‘๋™์„ฑ ๋ฐ ๋น„๊ฒฝ์Ÿ์„ฑ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ถ•์ œ์„ฑ ๋ฐ ๋ฌธํ™”์„ฑ์€ ์ฆ๊ฒ๊ณ  ์‹ ๋‚˜๋Š” ์ˆ˜์—…์˜ ๋ถ„์œ„๊ธฐ๋กœ๋ถ€ํ„ฐ, ํ˜‘๋™์„ฑ ๋ฐ ๋น„๊ฒฝ์Ÿ์„ฑ์€ ๋‹คํˆผ ์—†์ด ์„œ๋กœ ๋ฐฐ๋ คํ•˜๋ฉด์„œ ํ•จ๊ป˜ ํ•˜๋Š” ๋ถ„์œ„๊ธฐ๋กœ๋ถ€ํ„ฐ ๋„์ถœ๋˜์—ˆ๋‹ค. ๊ต์‚ฌ ์š”์ธ์€ ์—ด์ • ๋ฐ ์„ฑ์‹ค์„ฑ, ๋ฐฐ๋ ค ๋ฐ ๊ฐ์ˆ˜์„ฑ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์—ด์ • ๋ฐ ์„ฑ์‹ค์„ฑ์€ ์–ธ์ œ๋‚˜ ์—ด๊ณผ ์„ฑ์„ ๋‹คํ•˜๋Š” ๊ต์‚ฌ์˜ ๋ชจ์Šต์—์„œ, ๋ฐฐ๋ ค ๋ฐ ๊ฐ์ˆ˜์„ฑ์€ ์„ฌ์„ธํ•˜๊ณ  ์ž์ƒํ•œ ๊ต์‚ฌ์˜ ๋ชจ์Šต์œผ๋กœ๋ถ€ํ„ฐ ๋„์ถœ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๋ก ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์˜ˆ์ˆ ์œตํ•ฉ ์ฒด์œก์ˆ˜์—…์˜ ๊ตฌ์„ฑ ๋ฐ ์‹ค์ฒœ์€ ๊ต์œก๊ณผ์ •๊ณผ์˜ ์—ฐ๊ณ„์„ฑ์„ ๋ฐ”ํƒ•์œผ๋กœ ํ•ด์•ผ ํ•˜๋ฉฐ ๋‚จ์ž๊ณ ๋“ฑํ•™์ƒ๋“ค์˜ ์‹ ์ฒด์ ยท์ธ์ง€์ ยท์ •์„œ์  ํŠน์„ฑ์„ ํ•จ๊ป˜ ๊ณ ๋ คํ•ด์•ผ ํ•œ๋‹ค. ๊ธฐ๋Šฅ์  ์š”์†Œ๊ฐ€ ๊ฐ•์กฐ๋˜๋Š” ์ผ๋ฐ˜์ ์ธ ์ฒด์œก์ˆ˜์—…๊ณผ ๊ตฌ๋ถ„๋˜๋Š” ์ ์œผ๋กœ, ํŠนํžˆ ์˜ˆ์ˆ ์œตํ•ฉ ์ฒด์œก์ˆ˜์—…์—์„œ๋Š” ๊ต์‚ฌ์™€ ํ•™์ƒ ๋ชจ๋‘์˜ ์‹ฌ์„ฑ์  ์ธก๋ฉด์ด ์ฃผ๋ชฉ๋˜์—ˆ๋‹ค. ๋‘˜์งธ, ์˜ˆ์ˆ ์œตํ•ฉ ์ฒด์œก์ˆ˜์—…์„ ํ†ตํ•ด ๋‚จ์ž๊ณ ๋“ฑํ•™์ƒ์˜ ์šด๋™์†Œ์–‘์€ ๋Šฅยท์ง€ยท์‹ฌ์˜ ์ฐจ์›์—์„œ ๋ชจ๋‘ ๋ณ€ํ™”๊ฐ€ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ์˜ค์„ฑ์ ์ˆ˜์˜ ์‚ฌ์ „ยท์‚ฌํ›„ ์ธก์ •์„ ํ†ตํ•ด์„œ๋„ ์ผ์ • ๋ถ€๋ถ„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ํŠนํžˆ, ์šด๋™์†Œ์–‘ ๋ณ€ํ™”๋Š” ๋ณตํ•ฉ์ ์œผ๋กœ ๋ฐœ์ƒํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋Œ€๋ถ€๋ถ„์ด๋ฏ€๋กœ ์—ฐ๊ตฌ์ž๋Š” ์ด์— ๋Œ€ํ•ด ์„ธ์‹ฌํ•œ ๊ด€์ฐฐ์ด ํ•„์š”ํ•˜๋‹ค. ์…‹์งธ, ๋‚จ์ž๊ณ ๋“ฑํ•™์ƒ์˜ ๊ธ์ •์  ์šด๋™์†Œ์–‘ ๋ณ€ํ™”๋ฅผ ์ด๋„๋Š” ์˜ˆ์ˆ ์œตํ•ฉ ์ฒด์œก์ˆ˜์—… ์š”์ธ์€ ๋‚ด์šฉยทํ™˜๊ฒฝยท๊ต์‚ฌ ์ฐจ์›์œผ๋กœ ๊ตฌ๋ถ„๋˜๋ฉฐ ์ด๋ฅผ ๋ฐ”๋ฅด๊ฒŒ ์ดํ•ดํ•˜์—ฌ ์ ์šฉํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์˜ˆ์ˆ ์œตํ•ฉ ์ฒด์œก์ˆ˜์—… ์‹ค์ฒœ ์ธก๋ฉด์—์„œ ์ œ์–ธํ•˜๋ฉด, ์ฒซ์งธ, ๋‚จ์ž๊ณ ๋“ฑํ•™์ƒ์˜ ์‹ ์ฒด์ ยท์ธ์ง€์ ยท์ •์„œ์  ํŠน์„ฑ์— ๋Œ€ํ•œ ์„ฌ์„ธํ•œ ์ดํ•ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ธด๋ฐ€ํ•œ ์†Œํ†ต์ด ๊ฐ•ํ™”๋˜์–ด์•ผ ํ•œ๋‹ค. ๋‘˜์งธ, ๋ณธ๋ณด๊ธฐ๋กœ์„œ ๊ต์‚ฌ๋Š” ์šด๋™์†Œ์–‘์„ ํฌํ•จํ•œ ์ง€์†์  ์ „๋ฌธ์„ฑ ๊ฐœ๋ฐœ(continuing professional development, CPD)์— ๊ด€์‹ฌ์„ ๊ฐ–๊ณ  ๋…ธ๋ ฅ์„ ๊ธฐ์šธ์—ฌ์•ผ ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์…‹์งธ, ์ˆ˜์—…์˜ ์‹ค์ฒœ์— ์ˆ˜๋ฐ˜๋˜๋Š” ํ™˜๊ฒฝ์  ์ œ์•ฝ์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•œ ๋„“๊ณ  ๊ธด ํ˜ธํก๊ณผ ์ธ๋‚ด์‹ฌ์ด ํ•„์š”ํ•˜๋‹ค. ํ›„์† ์—ฐ๊ตฌ ์ธก๋ฉด์—์„œ ์ œ์–ธํ•˜๋ฉด, ์ฒซ์งธ, ๋‹ค์–‘ํ•œ ์กฐ๊ฑด๊ณผ ํ™˜๊ฒฝ์—์„œ์˜ ์˜ˆ์ˆ ์œตํ•ฉ ์ฒด์œก์ˆ˜์—… ์‚ฌ๋ก€ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๋‘˜์งธ, ์˜ˆ์ˆ ์ฒด์œก ์œตํ•ฉ์˜ ๊ต์œก์  ํšจ๊ณผ์— ๋Œ€ํ•œ ์ด๋ก ์  ์—ฐ๊ตฌ๋ฅผ ํ˜„์žฅ๊ณผ ์—ฐ๊ฒฐ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ์‹ค์ฒœ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค.I. ์„œ ๋ก  1 1. ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 1 2. ์—ฐ๊ตฌ ๋ชฉ์  6 3. ์—ฐ๊ตฌ ๋ฌธ์ œ 6 4. ์šฉ์–ด์˜ ์ •์˜ 7 5. ์—ฐ๊ตฌ์˜ ์ œํ•œ์  8 II. ์ด๋ก ์  ๋ฐฐ๊ฒฝ 9 1. ์ธ๋ฌธ์  ์ฒด์œก๊ต์œก๊ณผ ์šด๋™์†Œ์–‘ 9 ๊ฐ€. ์ธ๋ฌธ์  ์ฒด์œก๊ต์œก 9 ๋‚˜. ์šด๋™์†Œ์–‘ 13 ๋‹ค. ๊ต์œก๊ณผ์ •๊ณผ ์šด๋™์†Œ์–‘ 15 2. ๋‚จ์ž๊ณ ๋“ฑํ•™์ƒ์˜ ํŠน์„ฑ 17 ๊ฐ€. ๋‚จ์ž๊ณ ๋“ฑํ•™์ƒ์˜ ์‹ ์ฒด์  ํŠน์„ฑ 17 ๋‚˜. ๋‚จ์ž๊ณ ๋“ฑํ•™์ƒ์˜ ์ธ์ง€์  ํŠน์„ฑ 19 ๋‹ค. ๋‚จ์ž๊ณ ๋“ฑํ•™์ƒ์˜ ์ •์„œ์  ํŠน์„ฑ 21 ๋ผ. ๋‚จ์ž๊ณ ๋“ฑํ•™์ƒ์˜ ์ฒด์œก ์ˆ˜์—… 22 3. ์œตํ•ฉ์  ์ฒด์œก๊ต์œก 23 ๊ฐ€. ์œตํ•ฉ์˜ ๊ฐœ๋…๊ณผ ํ•„์š”์„ฑ 23 ๋‚˜. ์œตํ•ฉ์˜ ์œ ํ˜• 25 ๋‹ค. ์„œ์‚ฌ์  ์ ‘๊ทผ์„ ํ†ตํ•œ ์œตํ•ฉ 28 ๋ผ. ์„ ํ–‰์—ฐ๊ตฌ ๋ถ„์„ 30 III. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 31 1. ์—ฐ๊ตฌ ์„ค๊ณ„ 31 ๊ฐ€. ์—ฐ๊ตฌ์˜ ํŒจ๋Ÿฌ๋‹ค์ž„ ๋ฐ ๋…ผ๋ฆฌ 31 ๋‚˜. ์—ฐ๊ตฌ ์ ˆ์ฐจ ๋ฐ ๋‹จ๊ณ„ 32 2. ์—ฐ๊ตฌ ํ™˜๊ฒฝ 34 3. ์—ฐ๊ตฌ ์ฐธ์—ฌ์ž 35 ๊ฐ€. ์—ฐ๊ตฌ์ž๋กœ์„œ์˜ ์ง€๋„๊ต์‚ฌ 35 ๋‚˜. ์—ฐ๊ตฌ ์ฐธ์—ฌ ํ•™์ƒ 37 4. ์ž๋ฃŒ ์ˆ˜์ง‘ 38 ๊ฐ€. ์‹ฌ์ธต๋ฉด๋‹ด 39 ๋‚˜. ์ฐธ์—ฌ๊ด€์ฐฐ 41 ๋‹ค. ๊ต์‚ฌ ๋ฐ˜์„ฑ์ผ์ง€ 42 ๋ผ. ํ˜„์ง€๋ฌธ์„œ 43 ๋งˆ. ๊ต๊ณผ๊ต์‚ฌ ์˜๊ฒฌ 43 5. ์ž๋ฃŒ ๋ถ„์„ 46 6. ์—ฐ๊ตฌ์˜ ์ง„์‹ค์„ฑ 47 ๊ฐ€. ๋‹ค๊ฐ์  ์ž๋ฃŒ ์ˆ˜์ง‘์„ ํ†ตํ•œ ๊ฒ€์ฆ 47 ๋‚˜. ์—ฐ๊ตฌ ์ฐธ์—ฌ์ž ๊ฒ€ํ†  48 ๋‹ค. ๋™๋ฃŒ ๊ฐ„ ํ˜‘์˜ 48 7. ์—ฐ๊ตฌ์˜ ์œค๋ฆฌ 48 โ…ฃ. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ ๋ฐ ๋…ผ์˜ 50 1. ์˜ˆ์ˆ ์œตํ•ฉ ์ฒด์œก์ˆ˜์—…์˜ ๊ตฌ์„ฑ ๋ฐ ์‹ค์ฒœ 50 ๊ฐ€. ์˜ˆ์ˆ ์œตํ•ฉ ์ฒด์œก์ˆ˜์—…์˜ ๊ตฌ์„ฑ 50 ๋‚˜. ์˜ˆ์ˆ ์œตํ•ฉ ์ฒด์œก์ˆ˜์—…์˜ ์‹ค์ฒœ 58 2. ์˜ˆ์ˆ ์œตํ•ฉ ์ฒด์œก์ˆ˜์—…์„ ํ†ตํ•œ ์šด๋™์†Œ์–‘ ๋ณ€ํ™” 80 ๊ฐ€. ๋Šฅ์†Œ์–‘ ๋ณ€ํ™” 80 ๋‚˜. ์ง€์†Œ์–‘ ๋ณ€ํ™” 87 ๋‹ค. ์‹ฌ์†Œ์–‘ ๋ณ€ํ™” 92 ๋ผ. ์˜ค์„ฑ์ ์ˆ˜๋ฅผ ํ†ตํ•ด ๋ณธ ์šด๋™์†Œ์–‘ ๋ณ€ํ™” 100 ๋งˆ. ์ข…ํ•ฉ 105 3. ์šด๋™์†Œ์–‘ ๋ณ€ํ™”์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์˜ˆ์ˆ ์œตํ•ฉ ์ฒด์œก์ˆ˜์—… ์š”์ธ 106 ๊ฐ€. ๋‚ด์šฉ ์š”์ธ 106 ๋‚˜. ํ™˜๊ฒฝ ์š”์ธ 110 ๋‹ค. ๊ต์‚ฌ ์š”์ธ 114 4. ๋…ผ์˜ 118 ๊ฐ€. ์˜ˆ์ˆ ์œตํ•ฉ ์ฒด์œก์ˆ˜์—… ๊ตฌ์„ฑ๊ณผ ์‹ค์ฒœ์— ๊ด€ํ•œ ๋…ผ์˜ 118 ๋‚˜. ๋‚จ์ž๊ณ ๋“ฑํ•™์ƒ์˜ ์šด๋™์†Œ์–‘ ๋ณ€ํ™”์— ๊ด€ํ•œ ๋…ผ์˜ 120 ๋‹ค. ๊ต์‚ฌ์˜ ์šด๋™์†Œ์–‘์— ๊ด€ํ•œ ๋…ผ์˜ 122 โ…ค. ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 124 1. ๊ฒฐ๋ก  124 2. ์ œ์–ธ 126 ๊ฐ€. ์˜ˆ์ˆ ์œตํ•ฉ ์ฒด์œก์ˆ˜์—… ์‹ค์ฒœ์„ ์œ„ํ•œ ์ œ์–ธ 127 ๋‚˜. ํ›„์† ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•œ ์ œ์–ธ 129 ์ฐธ๊ณ ๋ฌธํ—Œ 130 ๋ถ€ ๋ก 141 Abstract 157Maste

    (The) effect of separation of medicine and pharmacy policy on health centers in rural districts.

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    ๋ณ‘์›ํ–‰์ •ํ•™๊ณผ/์„์‚ฌ[ํ•œ๊ธ€] ์ด ์—ฐ๊ตฌ๋Š” ์˜์•ฝ๋ถ„์—…์„ ์ „ํ›„ํ•œ ๋†, ์–ด์ดŒ ์ง€์—ญ์˜ ๋ณด๊ฑด์ง€์†Œ ์ด์šฉ๋„์˜ ๋ณ€ํ™”์™€ ์ด๋Ÿฌํ•œ ๋ณ€ํ™”๋ฅผ ๊ฐ€์ ธ์˜จ ํ™˜์ž๋“ค์˜ ํŠน์„ฑ์„ ์กฐ์‚ฌํ•˜๊ธฐ ์œ„ํ•ด์„œ, ๊ฐ•์›๋„ ํ‰์ฐฝ๊ตฐ ๋‚ด์˜ 3๊ฐœ ๋ณด๊ฑด์ง€์†Œ(์˜์›๊ณผ ์•ฝ๊ตญ์ด ์žˆ๋Š” ์˜์•ฝ๋ถ„์—… ์‹ค์‹œ์ง€์—ญ, ์˜์›๊ณผ ์•ฝ๊ตญ์ด ์—†๋Š” ์˜์•ฝ๋ถ„์—… ์˜ˆ์™ธ์ง€์—ญ, ์˜์›๊ณผ ์•ฝ๊ตญ์ด ์žˆ์ง€๋งŒ ์˜์•ฝ๋ถ„์—…์—์„œ ์ œ์™ธ๋œ ์ง€์—ญ)๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์กฐ์‚ฌ๋ถ„์„ ํ•˜์˜€๋‹ค. ์˜์›๊ณผ ์•ฝ๊ตญ์ด ์—†๋Š” ์˜ˆ์™ธ์ง€์—ญ์€ ๊ตฐ์ฒญ์†Œ์žฌ์ง€์™€ ์ธ์ ‘ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ๊ตฐ์ฒญ์†Œ์žฌ์ง€๋Š” ์˜์•ฝ๋ถ„์—…์ด ์‹ค์‹œ๋œ ์ง€์—ญ์ด๋‹ค. ์˜์•ฝ๋ถ„์—… ์‹ค์‹œ ์ด์ „์ธ 2000๋…„ 1์›”๊ณผ 2์›” ๋ฐ ์˜์•ฝ๋ถ„์—… ์‹ค์‹œ ์ดํ›„์ธ 2001๋…„ 1์›”๊ณผ2์›”์˜ ๋ณด๊ฑด์ง€์†Œ ํ™˜์ž ๋‚ด์› ์ž๋ฃŒ๋ฅผ ์กฐ์‚ฌํ•˜์—ฌ ๋ถ„์„ํ•œ ์ฃผ์š” ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์˜์•ฝ๋ถ„์—…์„ ์‹ค์‹œํ•œ ์ง€์—ญ์—์„œ๋Š” ํ™˜์ž์ˆ˜๊ฐ€ 65๏ผ… ๊ฐ์†Œํ•˜์˜€๊ณ , ์˜์› ๋ฐ ์•ฝ๊ตญ์ด ์—†๊ณ  ์ธ์ ‘์ง€์—ญ์ด ์˜์•ฝ๋ถ„์—… ์‹ค์‹œ์ง€์—ญ์ธ ์˜์•ฝ๋ถ„์—… ์˜ˆ์™ธ์ง€์—ญ์—์„œ๋Š” ํ™˜์ž์ˆ˜๊ฐ€ 76๏ผ… ์ฆ๊ฐ€ํ•˜์˜€์œผ๋ฉฐ, ์˜์›๊ณผ์•ฝ๊ตญ์ด ์žˆ๋Š” ์ œ์™ธ์ง€์—ญ์˜ ๋ณด๊ฑด์ง€์†Œ๋ฅผ ๋‚ด์›ํ•œ ํ™˜์ž์ˆ˜๋Š” 11๏ผ… ๊ฐ์†Œํ•˜์˜€๋‹ค. ์˜์•ฝ๋ถ„์—…์„ ์‹ค์‹œํ•œ ์ง€์—ญ์—์„œ๋Š” ํ‰๊ท  ํˆฌ์•ฝ์ผ์ˆ˜๊ฐ€ ๋‘ ๋ฐฐ ์ฆ๊ฐ€ํ•˜์˜€๊ณ , ๋‹ค๋ฅธ ์ง€์—ญ์€ ํฌ๊ฒŒ ๋ณ€ํ•˜์ง€ ์•Š์•˜์œผ๋‚˜ ๋ณธ์ธ ๋ถ€๋‹ด๊ธˆ ์ƒ์Šน์— ๋”ฐ๋ฅธ ์˜ํ–ฅ์„ ๋ฐ›์•˜๋‹ค. ์˜์•ฝ๋ถ„์—…์ด ์‹ค์‹œ๋œ ์ง€์—ญ์˜ ๋ณด๊ฑด์ง€์†Œ๋Š” ํ™˜์ž์ˆ˜๊ฐ€ ๊ธ‰๊ฐํ•˜์˜€๋Š”๋ฐ, ํŠนํžˆ ๊ณ„์† ์•ฝ์„ ๋ณต์šฉํ•ด์•ผํ•˜๋Š” ๋งŒ์„ฑ ์งˆํ™˜์ž์™€ ๋ณธ์ธ์ด ๋น„์šฉ์˜ ์ผ๋ถ€๋ฅผ ๋ถ€๋‹ดํ•˜๋Š” ์˜๋ฃŒ๋ณดํ—˜ ๋Œ€์ƒ์ž๋“ค์ด ํฐ ํญ์œผ๋กœ ๊ฐ์†Œํ•˜์˜€๋‹ค. ๋ฐ˜๋ฉด, ์˜์•ฝ๋ถ„์—… ์˜ˆ์™ธ์ง€์—ญ์˜ ๋ณด๊ฑด์ง€์†Œ๋Š” ์ธ์ ‘์ง€์—ญ์˜ ์˜์•ฝ๋ถ„์—… ์‹ค์‹œ์— ํฌ๊ฒŒ ์˜ํ–ฅ์„ ๋ฐ›์•„์„œ ํ™˜์ž์ˆ˜๊ฐ€ ๊ธ‰๊ฒฉํžˆ ์ฆ๊ฐ€ํ•˜์˜€๋Š”๋ฐ, ์ด๊ฒƒ์€ ์˜์•ฝ๋ถ„์—… ์‹ค์‹œ์ง€์—ญ์œผ๋กœ ๊ฐ€๋˜ ํ™˜์ž๋“ค์ด ์˜ˆ์™ธ์ง€์—ญ ๋ณด๊ฑด์ง€์†Œ๋ฅผ ์ด์šฉํ–ˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๋ฐ˜๋ฉด, ์˜์›๊ณผ ์•ฝ๊ตญ์ด ์žˆ๋Š” ์ œ์™ธ์ง€์—ญ์—์„œ๋Š” ๋‹ค๋ฅธ ์ง€์—ญ ์˜์•ฝ๋ถ„์—… ์‹ค์‹œ์— ๋Œ€ํ•œ ์˜ํ–ฅ์„ ์ ๊ฒŒ๋ฐ›์•˜๋‹ค. ์ด์ƒ์˜ ๊ฒฐ๊ณผ๋ฅผ ์ข…ํ•ฉํ•ด๋ณด๋ฉด, ์˜์•ฝ๋ถ„์—… ์‹ค์‹œ์™€ ๊ด€๋ จํ•œ ํ™˜์ž๋“ค์˜ ์ด์šฉ๋„์˜ ๋ณ€ํ™”๋Š”, ๋ณด๊ฑด์ง€์†Œ๋ฅผ ์ด์šฉํ•  ๋•Œ์— ๋ณธ์ธ์ด ๋ถ€๋‹ดํ•˜๋Š” ๋น„์šฉ์˜ ๋‹ค๊ณผ์— ๋งŽ์€ ์˜ํ–ฅ์„ ๋ฐ›์•˜๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ํ™˜์ž๋“ค์ด ๊ฒช๊ฒŒ ๋œ ์ƒ๋Œ€์  ๋ถˆํŽธํ•จ์—๋„ ์˜ํ–ฅ์„ ๋ฐ›์€ ๊ฒƒ์œผ๋กœ ํ’€์ด๋œ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ์ธก์ • ๊ฐ€๋Šฅํ–ˆ๋˜ ๋ณ€์ˆ˜๊ฐ€ ์ œํ•œ๋˜์–ด ์žˆ๊ณ  ๋†์ดŒ ์ง€์—ญ ์ฃผ๋ฏผ ์˜๋ฃŒ ์ด์šฉ๋„์˜ ๋ชจ๋“  ๋ณ€ํ™”๋ฅผ ํŒŒ์•…ํ•  ์ˆ˜ ์—†์—ˆ๋‹ค๋Š” ์ œํ•œ์ ์ด ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ, ์˜์•ฝ๋ถ„์—…์„ ์ „ํ›„ํ•œ ๊ธฐ๊ฐ„์˜ ์˜๋ฃŒ ์ด์šฉ ๋ณ€ํ™”์— ๋Œ€ํ•œ ๋‹ค์–‘ํ•œ ์—ฐ๊ตฌ๊ฐ€ ๋’ค๋”ฐ๋ผ์•ผ ํ•  ๊ฒƒ์œผ๋กœ ์ƒ๊ฐ๋œ๋‹ค. [์˜๋ฌธ] This study investigated three health centers in Pyungchang-Gun, Kangwon Province ( one in a district practicing medicine-pharmacy separation, one in a district with no clinic and pharmacy - an exception to the practice of medicine - pharmacy separation, and one in a district with clinics and pharmacies, though excluded from the practice medicine-pharmacy separation) to evaluate the change in use of health centers in agricultural and fishing districts before and after the practice medicine - pharmacy separation and the general characteristics of the people who have brought about this change. The excluded districts with no clinics and pharmacies are close to districts with a district office. Towns with the district office practice medicine - pharmacy separation. The investigation of data on the patients who were admitted to the subject health centers in January and February of 2000, period before the medicine - pharmacy separation, and in January and February of 2001, period after medicine - pharmacy separation, revealed the following main findings: The number of patients reduced by 65๏ผ… at the subject health center, located in a district practicing medicine - pharmacy separation; increased by 76๏ผ… at the subject health center located in an excluded district with no clinics and pharmacies and close to a district practicing medicine - pharmacy separation; and reduced by 11% at the subject health center located in an excluded district with clinics and pharmacies. The average number of medication-distribution days at the subject health center located in the district practicing medicine - pharmacy separation increased by two-fold. There was no marked change in this respect at the other subject health centers, although the patient's "out-of-pocket" expenses increased. The number of patients at the subject health center located in the district practicing medicine - pharmacy separation reduced sharply, especially the numbers of chronically ill patients who needed regular medication and medically insured patients who paid for part of their medical fees. On the other hand, the number of patients at the subject health center located in the excluded district not practicing medicine-pharmacy separation increased sharply, and among these, the number of chronically ill patients increased sharply compared with the number of other types of patients; the reason being that patients who had gone to a health center in a district practicing medicine-pharmacy separation transferred and used the subject health center in excluded district. However, excluded districts with clinics and pharmacies were less affected by the practice of medicine - pharmacy separation in other districts. To summarize, the patients' "out-of-pocket" expenses and relative inconvenience significantly affected the change in the use of health centers under the practice of the medicine - pharmacy separation. The limitation in the number of variables which could be measured prevented evaluation of all areas of change in medical use of people living in agricultural districts. Therefore, more extensive research should be conducted on the change in medical use of the people in these districts before and after the practice of medicine-pharmacy separation.ope

    Topographic anatomy of the median nerve and the pronator teres and the flexor digitorum superficialis muscles

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    ์˜๊ณผํ•™๊ณผ/์„์‚ฌ[ํ•œ๊ธ€] ์ •์ค‘์‹ ๊ฒฝ์ด ์ฃผ์œ„์˜ ๊ตฌ์กฐ์— ์˜ํ•ด ๋ˆŒ๋ฆด ๋•Œ ์›์—Ž์นจ๊ทผ์˜ ํ†ต์ฆ๊ณผ ์ •์ค‘์‹ ๊ฒฝ์ด ๋ถ„ํฌํ•˜๋Š” ๊ทผ์œก์˜ ํž˜์ด ์•ฝํ•ด์ง€๋Š” ์›์—Ž์นจ๊ทผ์ฆํ›„๊ตฐ์ด ์ƒ๊ธด๋‹ค. ์ •์ค‘์‹ ๊ฒฝ์„ ๋ˆŒ๋Ÿฌ ์›์—Ž์นจ๊ทผ์ฆํ›„๊ตฐ์„ ์œ ๋ฐœํ•  ์ˆ˜ ์žˆ๋Š” ๊ตฌ์กฐ๋กœ ์›์—Ž์นจ๊ทผ๊ณผ ์–•์€์†๊ฐ€๋ฝ๊ตฝํž˜๊ทผ์ด ์ œ์‹œ๋œ๋‹ค. ์ด ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ์›์—Ž์นจ๊ทผ์ฆํ›„๊ตฐ์ด ์ƒ๊ธธ ์ˆ˜ ์žˆ๋Š” ๋ถ€์œ„์—์„œ ์‹ ๊ฒฝ๊ณผ ๊ทผ์œก์˜ ๊ตญ์†Œํ•ด๋ถ€ํ•™์  ๊ด€๊ณ„๋ฅผ ๋ฐํžˆ๋Š”๋ฐ ์žˆ๋‹ค.์žฌ๋ฃŒ๋กœ๋Š” ํ•œ๊ตญ ์„ฑ์ธ ์‹œ์‹  79๊ตฌ์˜ 148์ชฝ์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์›์—Ž์นจ๊ทผ์„ ๊ณ„์ธกํ•˜๊ณ  ์ •์ค‘์‹ ๊ฒฝ๊ณผ ์ ‘ํ•˜๋Š” ์›์—Ž์นจ๊ทผ์˜ ๊ตฌ์„ฑ์„ฑ๋ถ„์„ ๊ด€์ฐฐํ•œ ๋’ค ์ •์ค‘์‹ ๊ฒฝ๊ณผ ์›์—Ž์นจ๊ทผ์˜ ์œ„์น˜๊ด€๊ณ„๋ฅผ ๋ถ„๋ฅ˜ํ•˜์˜€๋‹ค. ์ •์ค‘์‹ ๊ฒฝ์˜ ๊ทผ์œก๊ฐ€์ง€๊ฐ€ ์ผ์–ด๋‚˜๋Š” ์œ„์น˜๋ฅผ ์–‘์ชฝ ์œ„๊ด€์ ˆ์œต๊ธฐ๋ฅผ ์ž‡๋Š” ์„ ์—์„œ ์ˆ˜์ง๊ฑฐ๋ฆฌ๋กœ ์ธก์ •ํ•˜์˜€๋‹ค. ์–•์€์†๊ฐ€๋ฝ๊ตฝํž˜๊ทผ์˜ ๊ทผ์œกํž˜์ค„ํ™œ์˜ ์œ„์น˜์™€ ๊ตฌ์„ฑ์„ฑ๋ถ„, ๋…ธ๊ฐˆ๋ž˜์˜ ํ˜•ํƒœ๋ฅผ ๋ถ„๋ฅ˜ํ•˜์˜€๊ณ  ์–•์€์†๊ฐ€๋ฝ๊ตฝํž˜๊ทผ์˜ ๋ผˆ์‚ฌ์ด๋ง‰ ๊ฐˆ๋ž˜์™€ ๊ธด์—„์ง€๊ตฝํž˜๊ทผ, ๊นŠ์€์†๊ฐ€๋ฝ๊ตฝํž˜๊ทผ์˜ ๋ง๊ฐˆ๋ž˜์˜ ๋นˆ๋„๋ฅผ ์กฐ์‚ฌํ•˜์˜€๋‹ค.์›์—Ž์นจ๊ทผ์€ ์–‘์ชฝ ์œ„๊ด€์ ˆ์œต๊ธฐ๋ฅผ ์ž‡๋Š” ์„ ์œผ๋กœ๋ถ€ํ„ฐ ํ‰๊ท  61.3 mm๋˜๋Š” ๊ณณ์—์„œ ์œ„ํŒ”๊ฐˆ๋ž˜์™€ ์ž๊ฐˆ๋ž˜๊ฐ€ ๋งŒ๋‚˜ ํ‰๊ท  141.5 mm๋˜๋Š” ๊ณณ์—์„œ ๋…ธ๋ผˆ์˜ ๊ฐ€์ชฝ๋ฉด์— ๋‹ฟ์•˜๋‹ค. ์ •์ค‘์‹ ๊ฒฝ๊ณผ ์ ‘ํ•˜๋Š” ์ž๊ฐˆ๋ž˜์˜ ์ž์ชฝ๋ฉด์€ ๊ทผ์œก์œผ๋กœ๋งŒ ์ด๋ฃจ์–ด์ง„ ๊ฒฝ์šฐ๊ฐ€ 8.4%, ๊ทผ์œก๊ณผ ํž˜์ค„์ด ์„ž์—ฌ์žˆ๋Š” ๊ฒฝ์šฐ๊ฐ€ 20.8%, ํž˜์ค„๋กœ๋งŒ ์ด๋ฃจ์–ด์ง„ ๊ฒฝ์šฐ๊ฐ€ 70.8%์˜€๋‹ค. ์ •์ค‘์‹ ๊ฒฝ๊ณผ ์ ‘ํ•˜๋Š” ์œ„ํŒ”๊ฐˆ๋ž˜์˜ ๋…ธ์ชฝ๋ฉด์€ ๊ทผ์œก์œผ๋กœ๋งŒ ์ด๋ฃจ์–ด์ง„ ๊ฒฝ์šฐ๊ฐ€ 56.0%, ๊ทผ์œก๊ณผ ํž˜์ค„์ด ์„ž์—ฌ์žˆ๋Š” ๊ฒฝ์šฐ๊ฐ€ 30.0%, ํž˜์ค„๋กœ๋งŒ ์ด๋ฃจ์–ด์ง„ ๊ฒฝ์šฐ๊ฐ€ 14.0%์˜€๋‹ค. ์ •์ค‘์‹ ๊ฒฝ์€ ์–‘์ชฝ ์œ„๊ด€์ ˆ์œต๊ธฐ๋ฅผ ์ž‡๋Š” ์„ ์œผ๋กœ๋ถ€ํ„ฐ ํ‰๊ท  26.5 mm๋˜๋Š” ๊ณณ์—์„œ ์›์—Ž์นจ๊ทผ์˜ ์œ„ํŒ”๊ฐˆ๋ž˜์— ๋ฎ์ด๊ฒŒ ๋˜๊ณ  ํ‰๊ท  54.2 mm๋˜๋Š” ๊ณณ์—์„œ ์›์—Ž์นจ๊ทผ์˜ ๋‘ ๊ฐˆ๋ž˜ ์‚ฌ์ด๋กœ ๋“ค์–ด๊ฐ€ ํ‰๊ท  25.5 mm(๋ฒ”์œ„ 20.0 mm - 36.9 mm)๋ฅผ ๋‹ฌ๋ฆฌ๊ณ  ๋น ์ ธ๋‚˜์™”๋‹ค. ์ •์ค‘์‹ ๊ฒฝ์ด ์›์—Ž์นจ๊ทผ์˜ ๋‘ ๊ฐˆ๋ž˜ ์‚ฌ์ด๋กœ ์ง€๋‚˜๊ฐ€๋Š” ๊ฒฝ์šฐ๋Š” 93.9%, ๊ฐˆ๋ž˜์˜ ์•„๋ž˜๋กœ ์ง€๋‚˜๊ฐ€๋Š” ๊ฒฝ์šฐ๋Š” 2.7%, ์œ„ํŒ”๊ฐˆ๋ž˜๋ฅผ ๋šซ๊ณ  ์ง€๋‚˜๊ฐ€๋Š” ๊ฒฝ์šฐ๋Š” 2.0%, ์ž๊ฐˆ๋ž˜๋ฅผ ๋šซ๊ณ  ์ง€๋‚˜๊ฐ€๋Š” ๊ฒฝ์šฐ๋Š” 0.7%์˜€๋‹ค. ์ •์ค‘์‹ ๊ฒฝ์—์„œ ์œ„ํŒ”๊ฐˆ๋ž˜๋กœ ๊ฐ€๋Š” ๊ฐ€์ง€๊ฐ€ ์ผ์–ด๋‚˜๋Š” ์œ„์น˜๋Š” ์–‘์ชฝ ์œ„๊ด€์ ˆ์œต๊ธฐ๋ฅผ ์ž‡๋Š” ์„ ์œผ๋กœ๋ถ€ํ„ฐ ํ‰๊ท  16.8 mm์˜€๋‹ค. ์ž๊ฐˆ๋ž˜๋กœ ๊ฐ€๋Š” ๊ฐ€์ง€๊ฐ€ ์ผ์–ด๋‚˜๋Š” ์œ„์น˜๋Š” ์–‘์ชฝ ์œ„๊ด€์ ˆ์œต๊ธฐ๋ฅผ ์ž‡๋Š” ์„ ์œผ๋กœ๋ถ€ํ„ฐ ํ‰๊ท  26.8 mm์˜€๊ณ  ๋…ธ์ชฝ์†๋ชฉ๊ตฝํž˜๊ทผ๊ณผ ๊ธด์†๋ฐ”๋‹ฅ๊ทผ์— ๋ถ„ํฌํ•˜๋Š” ์‹ ๊ฒฝ์ด ์ผ์–ด๋‚˜๋Š” ์œ„์น˜๋Š” ๊ฐ๊ฐ ํ‰๊ท  43.8 mm, 30.2 mm์˜€๋‹ค. ์•ž๋ผˆ์‚ฌ์ด์‹ ๊ฒฝ์€ ์–‘์ชฝ ์œ„๊ด€์ ˆ์œต๊ธฐ๋ฅผ ์ž‡๋Š” ์„ ์œผ๋กœ๋ถ€ํ„ฐ ํ‰๊ท  56.8 mm์—์„œ ์ผ์–ด๋‚ฌ์œผ๋ฉฐ 16.1%์—์„œ ์›์—Ž์นจ๊ทผ์˜ ์ž๊ฐˆ๋ž˜์™€ ์œ„ํŒ”๊ฐˆ๋ž˜ ์‚ฌ์ด๋กœ ๋“ค์–ด๊ฐ€๊ธฐ ์ „์— ์ผ์–ด๋‚ฌ๋‹ค. ์–•์€์†๊ฐ€๋ฝ๊ตฝํž˜๊ทผ์˜ ๊ทผ์œกํž˜์ค„ํ™œ์€ ์–‘์ชฝ ์œ„๊ด€์ ˆ์œต๊ธฐ๋ฅผ ์ž‡๋Š” ์„ ์œผ๋กœ๋ถ€ํ„ฐ ํ‰๊ท  80.9 mm๋–จ์–ด์ ธ ์žˆ์—ˆ๊ณ  ๊ทผ์œก์œผ๋กœ ์ด๋ฃจ์–ด์ง„ ๊ฒฝ์šฐ๊ฐ€ 30.6%, ํž˜์ค„๋กœ ์ด๋ฃจ์–ด์ง„ ๊ฒฝ์šฐ๊ฐ€ 69.4%์˜€๋‹ค. ์–•์€์†๊ฐ€๋ฝ๊ตฝํž˜๊ทผ์˜ ๋…ธ๊ฐˆ๋ž˜๊ฐ€ ์›์—Ž์นจ๊ทผ์ด ๋…ธ๋ผˆ์— ๋‹ฟ๋Š” ๋จผ์ชฝ๋ถ€์œ„์—์„œ ์ด๋Š” ๊ฒฝ์šฐ๊ฐ€ 29.4%, ๊ทธ๋ณด๋‹ค ๋จผ์ชฝ์—์„œ ์ด๋Š” ๊ฒฝ์šฐ๊ฐ€ 55.9%, ๊ทธ๋ณด๋‹ค ๋ชธ์ชฝ์—์„œ ์ด๋Š” ๊ฒฝ์šฐ๊ฐ€ 14.7%์˜€๋‹ค. ๊ธด์—„์ง€๊ตฝํž˜๊ทผ์˜ ๋ง๊ฐˆ๋ž˜๋Š” 60.0%์—์„œ ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. ๊นŠ์€์†๊ฐ€๋ฝ๊ตฝํž˜๊ทผ์˜ ๋ง๊ฐˆ๋ž˜๋Š” 14.0%์—์„œ ๊ด€์ฐฐ๋˜์—ˆ๋‹ค.์ด์ƒ์˜ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํŒ”์˜ค๊ธˆ๋ถ€์œ„์—์„œ ์ •์ค‘์‹ ๊ฒฝ์˜ ์ฃ„์ž„์ฆํ›„๊ตฐ์„ ์ผ์œผํ‚ฌ ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ๋Š” ๊ตฌ์กฐ์˜ ๊ตญ์†Œํ•ด๋ถ€ํ•™์  ๋ณ€์ด์— ๋Œ€ํ•˜์—ฌ ๊ณ ์ฐฐํ•˜์˜€๋‹ค. [์˜๋ฌธ]The pronator teres syndrome occurs when the median nerve is compressed by the adjacent structures in the proximal forearm. The pronator teres muscle and the flexor digitorum superficialis muscle have been postulated to contribute to this syndrome. This study was performed to obtain clinically useful data by clarifying the topographical relationship between the median nerve, the pronator teres muscle and the flexor digitorum superficialis muscle in 79 Korean adult cadavers (148 sides). The composition of the two heads of the pronator teres muscle was observed. 91.6% of the pronator muscles had a fibrous band in the ulnar side of the ulnar head. 44.0% of the specimens had a fibrous band in the radial aspects of its humeral head. The median nerve was completely covered by the pronator muscle within 26.5 mm from the Hueter''s line(line through the epicondyles of the humerus) and entered between the two heads of the pronator muscle at 54.2 mm from the line and exited it after running 25.5 mm. The relationship of the median nerve to the pronator teres muscle as it pass through the muscle was classified. The median nerve ran between the two heads of the pronator teres in 93.9% and it ran deep to the two heads in 2.7%. It pierced the humeral head in 2.0% and ulnar head in 0.7%. The distance between the origins of the median nerve branches and Hueter''s line was measured. The branch to the humeral head and ulnar head of the pronator teres muscle originated at 16.8 mm and 26.8 mm from the Hueter''s line, respectively. The branch to the flexor carpi radialis muscle and the palmaris longus muscle was divided at 43.8 mm and 30.2 mm from the Hueter''s line. The anterior interosseous nerve was branched at 56.8 mm from the Hueter''s line. The musculotendinous arch of the flexor digitorum superficialis muscle was located at 80.9 mm from the Hueter''s line. The radial head of the flexor digitorum superficialis muscle originated from the distal point where the pronator teres inserted to the radius in 29.4%, distal from that point in 55.9%, and proximal to that point in 14.7%. The accessory head of the flexor policis longus muscle existed in 60.0% and the accessory head of the flexor digitorum profundus muscle was found in 14.0%.ope

    (A)Study on low use condition and fact of childrens playground in public rental apartment complex

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :ํ˜‘๋™๊ณผ์ • ๋„์‹œ์„ค๊ณ„ํ•™์ „๊ณต,2007.Maste
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