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    ์ˆœ์„œ์˜์กด์  ์ž‘์—…์ค€๋น„๊ฐ€ ์žˆ๋Š” ์ƒ์‚ฐ๊ณ„ํš ๋ฌธ์ œ์— ๋Œ€ํ•œ ์ •์ˆ˜ ์ตœ์ ํ™” ๋ฐ ๊ทผ์‚ฌ ๋™์  ๊ณ„ํš๋ฒ• ๊ธฐ๋ฐ˜ ํ•ด๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ, 2022. 8. ์ด๊ฒฝ์‹.Lot-sizing and scheduling problem, an integration of the two important decision making problems in the production planning phase of a supply chain, determines both the production amounts and sequences of multiple items within a given planning horizon to meet the time-varying demand with minimum cost. Along with the growing importance of coordinated decision making in the supply chain, this integrated problem has attracted increasing attention from both industrial and academic communities. However, despite vibrant research over the recent decades, the problem is still hard to be solved due to its inherent theoretical complexity as well as the evolving complexity of the real-world industrial environments and the corresponding manufacturing processes. Furthermore, when the setup activity occurs in a sequence-dependent manner, it is known that the problem becomes considerably more difficult. This dissertation aims to propose integer optimization and approximate dynamic programming approaches for solving the lot-sizing and scheduling problem with sequence-dependent setups. Firstly, to enhance the knowledge of the structure of the problem which is strongly NP-hard, we consider a single-period substructure of the problem. By analyzing the polyhedron defined by the substructure, we derive new families of facet-defining inequalities which are separable in polynomial time via solving maximum flow problems. Through the computational experiments, these inequalities are demonstrated to provide much tighter lower bounds than the existing ones. Then, using these results, we provide new integer optimization models which can incorporate various extensions of the lot-sizing and scheduling problem such as setup crossover and carryover naturally. The proposed models provide tighter linear programming relaxation bounds than standard models. This leads to the development of an efficient linear programming-based heuristic algorithm which provides a primal feasible solution quickly. Finally, we devise an approximate dynamic programming algorithm. The proposed algorithm incorporates the value function approximation approach which makes use of both the tight lower bound obtained from the linear programming relaxation and the upper bound acquired from the linear programming-based heuristic algorithm. The results of computational experiments indicate that the proposed algorithm has advantages over the existing approaches.๊ณต๊ธ‰๋ง์˜ ์ƒ์‚ฐ ๊ณ„ํš ๋‹จ๊ณ„์—์„œ์˜ ์ฃผ์š”ํ•œ ๋‘ ๊ฐ€์ง€ ๋‹จ๊ธฐ ์˜์‚ฌ๊ฒฐ์ • ๋ฌธ์ œ์ธ Lot-sizing ๋ฌธ์ œ์™€ Scheduling ๋ฌธ์ œ๊ฐ€ ํ†ตํ•ฉ๋œ ๋ฌธ์ œ์ธ Lot-sizing and scheduling problem (LSP)์€ ๊ณ„ํš๋Œ€์ƒ๊ธฐ๊ฐ„ ๋™์•ˆ ์ฃผ์–ด์ง„ ๋ณต์ˆ˜์˜ ์ œํ’ˆ์— ๋Œ€ํ•œ ์ˆ˜์š”๋ฅผ ์ตœ์†Œ์˜ ๋น„์šฉ์œผ๋กœ ๋งŒ์กฑ์‹œํ‚ค๊ธฐ ์œ„ํ•œ ๋‹จ์œ„ ๊ธฐ๊ฐ„ ๋ณ„ ์ตœ์ ์˜ ์ƒ์‚ฐ๋Ÿ‰ ๋ฐ ์ƒ์‚ฐ ์ˆœ์„œ๋ฅผ ๊ฒฐ์ •ํ•œ๋‹ค. ๊ณต๊ธ‰๋ง ๋‚ด์˜ ๋‹ค์–‘ํ•œ ์š”์†Œ์— ๋Œ€ํ•œ ํ†ตํ•ฉ์  ์˜์‚ฌ ๊ฒฐ์ •์˜ ์ค‘์š”์„ฑ์ด ์ปค์ง์— ๋”ฐ๋ผ LSP์— ๋Œ€ํ•œ ๊ด€์‹ฌ ์—ญ์‹œ ์‚ฐ์—…๊ณ„์™€ ํ•™๊ณ„ ๋ชจ๋‘์—์„œ ์ง€์†์ ์œผ๋กœ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ตœ๊ทผ ์ˆ˜์‹ญ ๋…„์— ๊ฑธ์นœ ํ™œ๋ฐœํ•œ ์—ฐ๊ตฌ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ๋ฌธ์ œ ์ž์ฒด๊ฐ€ ๋‚ดํฌํ•˜๋Š” ์ด๋ก ์  ๋ณต์žก์„ฑ ๋ฐ ์‹ค์ œ ์‚ฐ์—… ํ™˜๊ฒฝ๊ณผ ์ œ์กฐ ๊ณต์ •์˜ ๊ณ ๋„ํ™”/๋ณต์žกํ™” ๋“ฑ์œผ๋กœ ์ธํ•ด LSP๋ฅผ ํ•ด๊ฒฐํ•˜๋Š” ๊ฒƒ์€ ์—ฌ์ „ํžˆ ์–ด๋ ค์šด ๋ฌธ์ œ๋กœ ๋‚จ์•„์žˆ๋‹ค. ํŠนํžˆ ์ˆœ์„œ์˜์กด์  ์ž‘์—…์ค€๋น„๊ฐ€ ์žˆ๋Š” ๊ฒฝ์šฐ ๋ฌธ์ œ๊ฐ€ ๋”์šฑ ์–ด๋ ค์›Œ์ง„๋‹ค๋Š” ๊ฒƒ์ด ์ž˜ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ˆœ์„œ์˜์กด์  ์ž‘์—…์ค€๋น„๊ฐ€ ์žˆ๋Š” LSP๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ์ •์ˆ˜ ์ตœ์ ํ™” ๋ฐ ๊ทผ์‚ฌ ๋™์  ๊ณ„ํš๋ฒ• ๊ธฐ๋ฐ˜์˜ ํ•ด๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋จผ์ €, ์ด๋ก ์ ์œผ๋กœ ๊ฐ•์„ฑ NP-hard์— ์†ํ•œ๋‹ค๋Š” ์‚ฌ์‹ค์ด ์ž˜ ์•Œ๋ ค์ง„ LSP์˜ ๊ทผ๋ณธ ๊ตฌ์กฐ์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ ๋†’์ด๊ธฐ ์œ„ํ•˜์—ฌ ๋‹จ์ผ ๊ธฐ๊ฐ„๋งŒ์„ ๊ณ ๋ คํ•˜๋Š” ๋ถ€๋ถ„๊ตฌ์กฐ์— ๋Œ€ํ•ด ๋‹ค๋ฃฌ๋‹ค. ๋‹จ์ผ ๊ธฐ๊ฐ„ ๋ถ€๋ถ„๊ตฌ์กฐ์— ์˜ํ•ด ์ •์˜๋˜๋Š” ๋‹ค๋ฉด์ฒด์— ๋Œ€ํ•œ ์ด๋ก ์  ๋ถ„์„์„ ํ†ตํ•ด ์ƒˆ๋กœ์šด ์œ ํšจ ๋ถ€๋“ฑ์‹ ๊ตฐ์„ ๋„์ถœํ•˜๊ณ  ํ•ด๋‹น ์œ ํšจ ๋ถ€๋“ฑ์‹๋“ค์ด ๊ทน๋Œ€๋ฉด(facet)์„ ์ •์˜ํ•  ์กฐ๊ฑด์— ๋Œ€ํ•ด ๋ฐํžŒ๋‹ค. ๋˜ํ•œ, ๋„์ถœ๋œ ์œ ํšจ ๋ถ€๋“ฑ์‹๋“ค์ด ๋‹คํ•ญ์‹œ๊ฐ„ ๋‚ด์— ๋ถ„๋ฆฌ ๊ฐ€๋Šฅํ•จ์„ ๋ณด์ด๊ณ , ์ตœ๋Œ€ํ๋ฆ„๋ฌธ์ œ๋ฅผ ํ™œ์šฉํ•œ ๋‹คํ•ญ์‹œ๊ฐ„ ๋ถ„๋ฆฌ ์•Œ๊ณ ๋ฆฌ๋“ฌ์„ ๊ณ ์•ˆํ•œ๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ์ œ์•ˆํ•œ ์œ ํšจ ๋ถ€๋“ฑ์‹๋“ค์ด ๋ชจํ˜•์˜ ์„ ํ˜•๊ณ„ํš ํ•˜ํ•œ๊ฐ•๋„๋ฅผ ๋†’์ด๋Š” ๋ฐ ํฐ ์˜ํ–ฅ์„ ์คŒ์„ ํ™•์ธํ•œ๋‹ค. ๋˜ํ•œ ํ•ด๋‹น ๋ถ€๋“ฑ์‹๋“ค์ด ๋ชจ๋‘ ์ถ”๊ฐ€๋œ ๋ชจํ˜•๊ณผ ์ด๋ก ์ ์œผ๋กœ ๋™์ผํ•œ ํ•˜ํ•œ์„ ์ œ๊ณตํ•˜๋Š” ํ™•์žฅ ์ˆ˜๋ฆฌ๋ชจํ˜•(extended formulation)์„ ๋„์ถœํ•œ๋‹ค. ์ด๋ฅผ ํ™œ์šฉํ•˜์—ฌ, ์‹ค์ œ ์‚ฐ์—…์—์„œ ๋ฐœ์ƒํ•˜๋Š” LSP์—์„œ ์ข…์ข… ๊ณ ๋ คํ•˜๋Š” ์ฃผ์š”ํ•œ ์ถ”๊ฐ€ ์š”์†Œ๋“ค์„ ๋‹ค๋ฃฐ ์ˆ˜ ์žˆ๋Š” ์ƒˆ๋กœ์šด ์ˆ˜๋ฆฌ ๋ชจํ˜•์„ ์ œ์•ˆํ•˜๋ฉฐ ํ•ด๋‹น ๋ชจํ˜•์ด ๊ธฐ์กด์˜ ๋ชจํ˜•์— ๋น„ํ•ด ๋”์šฑ ๊ฐ•ํ•œ ์„ ํ˜•๊ณ„ํš ํ•˜ํ•œ์„ ์ œ๊ณตํ•จ์„ ๋ณด์ธ๋‹ค. ์ด ๋ชจํ˜•์„ ๋ฐ”ํƒ•์œผ๋กœ ๋น ๋ฅธ ์‹œ๊ฐ„ ๋‚ด์— ๊ฐ€๋Šฅํ•ด๋ฅผ ์ฐพ์„ ์ˆ˜ ์žˆ๋Š” ์„ ํ˜•๊ณ„ํš ๊ธฐ๋ฐ˜ ํœด๋ฆฌ์Šคํ‹ฑ ์•Œ๊ณ ๋ฆฌ๋“ฌ์„ ๊ฐœ๋ฐœํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ํ•ด๋‹น ๋ฌธ์ œ์— ๋Œ€ํ•œ ๊ทผ์‚ฌ ๋™์  ๊ณ„ํš๋ฒ• ์•Œ๊ณ ๋ฆฌ๋“ฌ์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ๋“ฌ์€ ๊ฐ€์น˜ํ•จ์ˆ˜ ๊ทผ์‚ฌ ๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•˜๋ฉฐ ํŠน์ • ์ƒํƒœ์˜ ๊ฐ€์น˜๋ฅผ ๊ทผ์‚ฌํ•˜๊ธฐ ์œ„ํ•ด ํ•ด๋‹น ์ƒํƒœ์—์„œ์˜ ๊ทผ์‚ฌํ•จ์ˆ˜์˜ ์ƒํ•œ ๋ฐ ํ•˜ํ•œ์„ ํ™œ์šฉํ•œ๋‹ค. ์ด ๋•Œ, ์ข‹์€ ์ƒํ•œ ๋ฐ ํ•˜ํ•œ๊ฐ’์„ ๊ตฌํ•˜๊ธฐ ์œ„ํ•ด ์ œ์•ˆ๋œ ๋ชจํ˜•์˜ ์„ ํ˜•๊ณ„ํš ์™„ํ™”๋ฌธ์ œ์™€ ์„ ํ˜•๊ณ„ํš ๊ธฐ๋ฐ˜ ํœด๋ฆฌ์Šคํ‹ฑ ์•Œ๊ณ ๋ฆฌ๋“ฌ์„ ์‚ฌ์šฉํ•œ๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ์ œ์•ˆํ•œ ์•Œ๊ณ ๋ฆฌ๋“ฌ์ด ๊ธฐ์กด์˜ ๋ฐฉ๋ฒ•๋“ค๊ณผ ๋น„๊ตํ•˜์—ฌ ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ๋ณด์ž„์„ ํ™•์ธํ•œ๋‹ค.Abstract i Contents iii List of Tables vii List of Figures ix Chapter 1 Introduction 1 1.1 Backgrounds 1 1.2 Integrated Lot-sizing and Scheduling Problem 6 1.3 Literature Review 9 1.3.1 Optimization Models for LSP 9 1.3.2 Recent Works on LSP 14 1.4 Research Objectives and Contributions 16 1.5 Outline of the Dissertation 19 Chapter 2 Polyhedral Study on Single-period Substructure of Lot-sizing and Scheduling Problem with Sequence-dependent Setups 21 2.1 Introduction 22 2.2 Literature Review 27 2.3 Single-period Substructure 30 2.3.1 Assumptions 31 2.3.2 Basic Polyhedral Properties 32 2.4 New Valid Inequalities 37 2.4.1 S-STAR Inequality 37 2.4.2 Separation of S-STAR Inequality 42 2.4.3 U-STAR Inequality 47 2.4.4 Separation of U-STAR Inequality 49 2.4.5 General Representation of the Inequalities 52 2.5 Extended Formulations 55 2.5.1 Single-commodity Flow Formulations 55 2.5.2 Multi-commodity Flow Formulations 58 2.5.3 Time-ow Formulations 59 2.6 Computational Experiments 63 2.6.1 Experiment Settings 63 2.6.2 Experiment Results on Single-period Instances 65 2.6.3 Experiment Results on Multi-period Instances 69 2.7 Summary 73 Chapter 3 New Optimization Models for Lot-sizing and Scheduling Problem with Sequence-dependent Setups, Crossover, and Carryover 75 3.1 Introduction 76 3.2 Literature Review 78 3.3 Integer Optimization Models 80 3.3.1 Standard Model (ST) 82 3.3.2 Time-ow Model (TF) 84 3.3.3 Comparison of (ST) and (TF) 89 3.3.4 Facility Location Reformulation 101 3.4 LP-based Naive Fixing Heuristic Algorithm 104 3.5 Computational Experiments 108 3.5.1 Test Instances 108 3.5.2 LP Bound 109 3.5.3 Computational Performance with MIP Solver 111 3.5.4 Performance of LPNF Algorithm 113 3.6 Summary 115 Chapter 4 Approximate Dynamic Programming Algorithm for Lot-sizing and Scheduling Problem with Sequence-dependent Setups 117 4.1 Introduction 118 4.1.1 Markov Decision Process 118 4.1.2 Approximate Dynamic Programming Algorithms 121 4.2 Markov Decision Process Reformulation 124 4.3 Approximate Dynamic Programming Algorithm 127 4.4 Computational Experiments 131 4.4.1 Comparison with (TF-FL) Model 131 4.4.2 Comparison with Big Bucket Model 134 4.5 Summary 138 Chapter 5 Conclusion 139 5.1 Summary and Contributions 139 5.2 Future Research Directions 141 Bibliography 145 Appendix A Pattern-based Formulation in Chapter 2 159 Appendix B Detailed Test Results in Chapter 2 163 Appendix C Detailed Test Results in Chapter 3 169 ๊ตญ๋ฌธ์ดˆ๋ก 173๋ฐ•

    (The) relationship between job stress and psychological distress.

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    ์‚ฐ์—…๋ณด๊ฑดํ•™๊ณผ/์„์‚ฌope

    ๋งŒ์„ฑ์‹ ์งˆํ™˜ ๋žซ๋“œ์—์„œ ์น˜์ฃผ์—ผ์ด ์‹ ์žฅ ์„ฌ์œ ํ™” ๋ฐ ๋Œ€์‹์„ธํฌ ์นจ์œค์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

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    ๋งŒ์„ฑ์‹ ์งˆํ™˜์€ ์‹ ์žฅ์˜ ๊ตฌ์กฐ์™€ ๊ธฐ๋Šฅ์— ์ง€์†์ ์ธ ์ด์ƒ์„ ๋‚˜ํƒ€๋‚ธ๋‹ค. ๋Œ€ํ‘œ์ ์ธ ์ฆ์ƒ์€ ํ˜ˆ์•ก์˜ ํฌ๋ ˆ์•„ํ‹ฐ๋‹Œ (Creatinine; Cre) ๋ฐ ํ˜ˆ์ค‘์š”์†Œ์งˆ์†Œ (Blood urea nitrogen; BUN)์˜ ์ฆ๊ฐ€์ด๋‹ค. ์—ผ์ฆ์€ ์‹ ์žฅ์˜ ํ˜•ํƒœ์  ๋ณ€ํ™”๋ฅผ ์œ ๋ฐœํ•˜๋Š” ์š”์ธ ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ์น˜์ฃผ์—ผ์€ ์„ธ๊ท ์— ์˜ํ•œ ์—ผ์ฆ์งˆํ™˜์œผ๋กœ ์น˜์กฐ๊ณจ ์†Œ์‹ค์„ ๋ณด์ธ๋‹ค. ์น˜์ฃผ์—ผ ์‹œ ์น˜์ฃผ ์กฐ์ง์˜ ์„ธ๊ท  ๋ฐ ์„ธ๊ท ์— ์˜ํ•˜์—ฌ ๋ฐœํ˜„๋œ ๋ฌผ์งˆ์ด ํ˜ˆ๊ด€์„ ํ†ตํ•ด ์ด๋™ํ•˜์—ฌ ๋‹ค๋ฅธ ์žฅ๊ธฐ์— ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ์Œ์ด ์ œ์‹œ๋˜์–ด ์น˜์ฃผ์—ผ๊ณผ ๋งŒ์„ฑ์‹ ์งˆํ™˜์˜ ์—ฐ๊ด€์„ฑ์— ๋Œ€ํ•œ ์ž„์ƒ์—ฐ๊ตฌ๊ฐ€ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ์œผ๋‚˜ ๋‘ ์งˆํ™˜์˜ ์—ฐ๊ด€์„ฑ์— ๋Œ€ํ•œ ๊ธฐ์ „์€ ๋ช…ํ™•ํžˆ ๋ฐํ˜€์ ธ ์žˆ์ง€ ์•Š๋‹ค. ๋”ฐ๋ผ์„œ, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ •์ƒ ๋˜๋Š” ์‹ ์žฅ์„ ์ ์ถœํ•œ ์‹ ์งˆํ™˜ ๋žซ๋“œ ๋ชจ๋ธ์—์„œ ์น˜์ฃผ์—ผ์ด ์‹ ์žฅ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๋žซ๋“œ๋ฅผ ๋Œ€์กฐ๊ตฐ (Sham), ์น˜์ฃผ์—ผ๊ตฐ (ShamL), ์‹ ์งˆํ™˜๊ตฐ (Nx) ๋ฐ ์‹ ์งˆํ™˜ ๋™๋ฐ˜ ์น˜์ฃผ์—ผ๊ตฐ (NxL)์œผ๋กœ ๋‚˜๋ˆ„์–ด ์‹คํ—˜์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. 5์ฃผ๋ น ๋ฐ 6์ฃผ๋ น์— ์‹ ์žฅ์„ ์ ์ถœํ•œ ์‹ ์งˆํ™˜ ๋žซ๋“œ๋ฅผ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ, 16์ฃผ๋ น์— Sham ๋ฐ Nx๊ตฐ์˜ BUN ๋ฐ Cre ์„ ์ธก์ •ํ•˜์—ฌ ์‹ ์งˆํ™˜ ์œ ๋ฐœ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์น˜์ฃผ์—ผ์€ 16์ฃผ๋ น์— ํ•˜์•… ์ œ1๋Œ€๊ตฌ์น˜๋ฅผ ๊ฒฐ์ฐฐํ•˜์—ฌ ์œ ๋„ํ•˜์˜€์œผ๋ฉฐ, 20์ฃผ๋ น (๊ฒฐ์ฐฐ 4์ฃผ ํ›„)์— Sham, ShamL ๋ฐ NxL ๊ตฐ์˜ ํ•˜์•…์„ ์ฑ„์ทจํ•˜์—ฌ ์น˜์ฃผ์—ผ ์œ ๋ฐœ ์—ฌ๋ถ€๋ฅผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์น˜์ฃผ์—ผ ์œ ๋ฐœ์€ Hematoxylin & Eosin (H&E)๊ณผ TRAP ์—ผ์ƒ‰์„ ํ†ตํ•ด ์น˜์กฐ๊ณจ ์†Œ์‹ค๊ณผ ํŒŒ๊ณจ์„ธํฌ ํ˜•์„ฑ์„ ์ธก์ •ํ•˜์—ฌ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์น˜์ฃผ์—ผ์ด ์‹ ์žฅ์— ๋ฏธ์น˜๋Š” ์กฐ์ง๋ณ‘๋ฆฌํ•™์  ๋ณ€ํ™”๋ฅผ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด 20์ฃผ๋ น์— ์‹ ์žฅ์„ ์ฑ„์ทจํ•˜์˜€๋‹ค. H&E ์—ผ์ƒ‰์œผ๋กœ ์‚ฌ๊ตฌ์ฒด ์ˆ˜, Jones ์—ผ์ƒ‰์œผ๋กœ Bowmanโ€™s capsule membrane (BCM)์˜ ๋‘๊ป˜ ๋ฐ ์†Œ์ฒด ๋Œ€๋น„ ์‚ฌ๊ตฌ์ฒด ๋น„์œจ์„ ํ‰๊ฐ€ํ•˜์˜€๊ณ , Masson trichrome ์—ผ์ƒ‰์œผ๋กœ ์„ธ๋‡จ๊ด€ ๊ฐ„์งˆ ์„ฌ์œ ํ™”๋ฅผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋ฉด์—ญํ™”ํ•™์—ผ์ƒ‰์œผ๋กœ ์‹ ์žฅ์˜ ๋Œ€์‹์„ธํฌ ์นจ์œค๊ณผ TNFฮฑ์˜ ๋ฐœํ˜„์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. Nx๊ตฐ์€ Sham๊ตฐ๋ณด๋‹ค ๋†’์€ BUN ๋ฐ Cre ์ˆ˜์น˜๋ฅผ ๋ณด์˜€๋‹ค. ShamL ๋ฐ NxL ๊ตฐ์˜ ์น˜์กฐ๊ณจ ๋ฉด์ ์€ Sham๊ตฐ ๋ณด๋‹ค ๊ฐ์†Œํ•˜์˜€๊ณ , ํŒŒ๊ณจ์„ธํฌํ˜•์„ฑ์€ ์ฆ๊ฐ€ํ•˜์˜€์œผ๋‚˜ ๋‘ ๊ตฐ ๊ฐ„์—๋Š” ์ฐจ์ด๊ฐ€ ์—†์—ˆ๋‹ค. ์‹ ์žฅ์—์„œ Nx ๊ตฐ์˜ ์‚ฌ๊ตฌ์ฒด ์ˆ˜๋Š” Sham ๊ตฐ์— ๋น„ํ•ด ๊ฐ์†Œํ•˜์˜€๊ณ , BCM์˜ ๋‘๊ป˜, ์„ธ๋‡จ๊ด€ ๊ฐ„์งˆ ์„ฌ์œ ํ™” ๋ฐ ๋Œ€์‹์„ธํฌ ์นจ์œค์€ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ์‚ฌ๊ตฌ์ฒด ์ˆ˜๋Š” NxL๊ตฐ์—์„œ Nx๊ตฐ์— ๋น„ํ•ด ๊ฐ์†Œํ•˜๋Š” ๋ฐ˜๋ฉด, Sham๊ตฐ๊ณผ ShamL๊ตฐ ์‚ฌ์ด์—๋Š” ์ฐจ์ด๋ฅผ ๋ณด์ด์ง€ ์•Š์•˜๋‹ค. ์†Œ์ฒด ๋Œ€๋น„ ์‚ฌ๊ตฌ์ฒด ๋น„์œจ์—์„œ๋Š” NxL๊ตฐ๋งŒ์ด Sham๊ตฐ์— ๋น„ํ•ด ๊ฐ์†Œ๋ฅผ ๋ณด์˜€๋‹ค. ํฅ๋ฏธ๋กญ๊ฒŒ๋„, ์น˜์ฃผ์—ผ ์œ ๋ฐœ ๊ตฐ๋“ค์˜ ์„ธ๋‡จ๊ด€ ๊ฐ„์งˆ ์„ฌ์œ ํ™” ๋ฐ ๋Œ€์‹์„ธํฌ ์นจ์œค์€ ์น˜์ฃผ์—ผ์„ ์œ ๋ฐœํ•˜์ง€ ์•Š์€ ๊ตฐ๋“ค ๋ณด๋‹ค ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ๋˜ํ•œ NxL ๊ตฐ์€ ์‹ ์žฅ ์†Œ์ฒด์™€ ์„ธ๋‡จ๊ด€ ๊ฐ„์งˆ ๋ชจ๋‘์—์„œ Sham ๊ตฐ์— ๋น„ํ•ด ๋†’์€ TNFฮฑ ๋ฐœํ˜„์„ ๋ณด์˜€์œผ๋‚˜, ๋‹ค๋ฅธ ๊ตฐ๋“ค์€ Sham๊ณผ ์ฐจ์ด๋ฅผ ๋ณด์ด์ง€ ์•Š์•˜๋‹ค. ์‹ ์งˆํ™˜์€ ์น˜์ฃผ์—ผ์— ์˜ํ•œ ์น˜์กฐ๊ณจ ์†Œ์‹ค์— ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ์•Š์•˜๋‹ค. ์น˜์ฃผ์—ผ์€ ์‹ ์งˆํ™˜ ์กด์žฌ ์—ฌ๋ถ€์™€ ์ƒ๊ด€ ์—†์ด ๋ชจ๋“  ๊ฒฝ์šฐ์— ์‹ ์žฅ์˜ ์„ธ๋‡จ๊ด€ ๊ฐ„์งˆ ์„ฌ์œ ํ™” ๋ฐ ๋Œ€์‹์„ธํฌ ์นจ์œค์„ ์ฆ๊ฐ€์‹œ์ผฐ๋‹ค. ์น˜์ฃผ์—ผ์€ ์‹ ์งˆํ™˜์ด ์กด์žฌํ•˜๋Š” ๊ฒฝ์šฐ์—๋งŒ ์‚ฌ๊ตฌ์ฒด ์ˆ˜, ์†Œ์ฒด ๋Œ€๋น„ ์‚ฌ๊ตฌ์ฒด ๋น„์œจ ๋ฐ TNFฮฑ ๋ฐœํ˜„์— ๋ณ€ํ™”๋ฅผ ์œ ๋„ํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ์น˜์ฃผ์—ผ์ด ์‹ ์žฅ์˜ ํ˜•ํƒœ์  ๋ณ€ํ™”๋ฅผ ์œ ๋„ํ•˜๋ฉฐ, ์น˜์ฃผ์—ผ์— ์˜ํ•œ ํ˜•ํƒœํ•™์  ๋ณ€ํ™”๋Š” ์‹ ์งˆํ™˜์ด ์กด์žฌํ•˜๋Š” ๊ฒฝ์šฐ ๋”์šฑ ์•…ํ™”๋  ์ˆ˜ ์žˆ์Œ์„ ๋‚˜ํƒ€๋‚ธ๋‹ค. ๋˜ํ•œ, ์ด๋Š” ์น˜์ฃผ์—ผ์— ์˜ํ•œ ๋Œ€์‹์„ธํฌ ์นจ์œค ๋ฐ TNFฮฑ ๋ฐœํ˜„ ์ฆ๊ฐ€๊ฐ€ ์‹ ์žฅ์˜ ํ˜•ํƒœํ•™์  ๋ณ€ํ™”์™€ ๊ด€๋ จ์ด ์žˆ์„ ์ˆ˜ ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค. Chronic kidney disease (CKD) presents continuous abnormalities in structure and function of the kidney. Its representative symptoms are elevated blood urea nitrogen (BUN) and creatinine (Cre) levels. Inflammation is one of the factors that induces morphological changes in CKD. Periodontitis is a bacteria-induced inflammatory disease characterized by alveolar bone loss. Bacteria and bacteria-induced substances that are localized in the periodontal tissues may enter the system via blood vessels, and have deleterious effects on distant organs. In clinical studies, there are ongoing studies regarding the association between periodontitis and CKD. However, the mechanisms related to the association between the two diseases have not been clearly elucidated. Researches using animal models of periodontitis and CKD are useful in understanding not only the association, but also the related mechanisms between CKD and periodontitis. Therefore, this study investigated the effects of periodontitis on kidneys in rats with or without nephrectomy (Nx)-induced CKD. Rats were divided into sham surgery group (Sham group), sham surgery group with tooth ligation (ShamL group), Nx group (Nx group), and Nx group with tooth ligation (NxL group). Two-steps sham and 4/6Nx surgeries were conducted on weeks 5 and 6 of age rats. To confirm the induction of CKD, levels of plasma BUN and Cre of the Sham and Nx groups were evaluated at 16 weeks of age. At 16 weeks, periodontitis was induced by ligating the mandibular first molars with dental floss. Four weeks after ligation, mandibles of the Sham, ShamL and NxL groups were extracted. To determine successful induction of periodontitis, alveolar bone area and osteoclast numbers (No.) were measured using Hematoxylin and Eosin (H&E) and TRAP stains, respectively. To determine the effects of periodontitis on kidneys, kidneys were extracted at 20 weeks of age. Histopathological changes in renal corpuscles were analyzed using H&E and Jones stains. Tubulointerstitial fibrosis was analyzed using Masson trichrome stain. In addition, macrophage infiltrations and TNFฮฑ expressions were evaluated using immunohistochemistry. The Nx group showed higher plasma BUN and Cre levels than the Sham group. While the ShamL and NxL groups both showed reduced alveolar bone areas and increased osteoclast numbers than the Sham group, there were no differences between both groups. In kidney, the Nx group showed decreased glomerulus No. and increased Bowmanโ€™s capsule membrane (BCM) thickness, tubulointerstitial fibrosis, and macrophage infiltration compared to those of Sham group. The NxL group had reduced glomerulus No. compared to the Nx group, whereas there were no differences between the Sham and ShamL groups. Only the NxL group showed reduced glomerular area ratio relative to the Sham group. Interestingly, the ligated groups had increased tubulointerstitial fibrosis and macrophage infiltration than the non-ligated groups. In addition, only the NxL group had elevated TNFฮฑ expressions in both renal corpuscle and tubulointerstitium compared to the Sham group. In the mandibles, CKD did not affect alveolar bone loss by periodontitis. In the kidneys, periodontitis induced increase in tubulointerstitial fibrosis and macrophage infiltration in both cases of absence and presence of CKD. Additionally, periodontitis altered glomerulus No., glomerulus area ratio, and TNFฮฑ expression only when CKD was present. These results suggest that periodontitis induce morphological changes in the kidney, but these changes are further exacerbated in the presence of CKD. In addition, macrophage infiltration and TNFฮฑ expression in the presence of periodontitis may be associated with the morphological alterations in the kidney.open์„

    The Development of an Ethical Thinking Test (ETT) for Elementary School Students

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    ๋น„์ƒ์šฉ ๊ณ ๋ถ„์ž ๊ณ„๋ฉด์—์„œ์˜ ๋ฐ˜์‘์„ฑ ๊ณ ๋ถ„์ž์˜ ๋ฐ˜์‘๊ณผ ๊ณ„๋ฉด ์ ‘์ฐฉ๋ ฅ ํ–ฅ์ƒ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    Thesis (doctoral)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :ํ™”ํ•™๊ณตํ•™๊ณผ,1997.Docto

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    A Study on the Enhancement of the Interfacial Adhesion between Amorphous Polyamide and Polystyrene Using End-functionalized Polystyrene as a Reactive Compatiblizer

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    ๋ฐ˜์‘์ƒ์šฉํ™” (reactive compatibilization)์— ์˜ํ•œ ๊ณ„๋ฉด์ ‘์ฐฉ๋ ฅ ํ–ฅ์ƒ๊ธฐ๊ตฌ๋ฅผ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด ๋ชจ๋ธ ๊ณ ๋ถ„์ž๋กœ์„œ ๋ถ„์ž ๋์— ๋ฌด์ˆ˜ ๋ง๋ ˆ์ธ์‚ฐ (MAH) ๊ธฐ๋Šฅ๊ธฐ๋ฅผ ํ•˜๋‚˜์”ฉ ๊ฐ–๋Š” Polystyrene(PS)์ธ end-functionalized PS(ef-PS)๋ฅผ ๋น„์ƒ์šฉ์ธ ๋น„๊ฒฐ์ •์„ฑ ๋‚˜์ผ๋ก ๊ณผ PS ๊ณ„๋ฉด์— ํˆฌ์ž…ํ•˜์—ฌ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ef-PS๋Š” ์Œ์ด์˜จ ์ค‘ํ•ฉ์— ์˜ํ•ด ์ˆ˜ํ‰๊ท  ๋ถ„์ž๋Ÿ‰ 60,000(60K)๊ณผ 100,000(l00K)์˜ ๋‘๊ฐ€์ง€๋ฅผ ํ•ฉ์„ฑํ•˜์˜€์œผ๋ฉฐ, ์ ‘์ฐฉ๋ ฅ์˜ ์ธก์ •์„ ์œ„ํ•ด asymrnetric fracture test๋ฅผ ์ด์šฉํ•˜์˜€๋‹ค. 190โ„ƒ, 2์‹œ๊ฐ„์˜ ์ ‘์ฐฉ์กฐ๊ฑด์—์„œ 60 K์™€ 100 K์˜ ef-PS ๋ชจ๋‘ ์ƒ์—…์šฉ SMA ๊ณต์ค‘ํ•ฉ์ฒด์˜ ๊ฒฝ์šฐ๋ณด๋‹ค ๊ณ„๋ฉด์— ํ›จ์”ฌ ๋งŽ์€ ์–‘์„ ํˆฌ์ž…ํ•˜์—ฌ์•ผ ์ ‘์ฐฉ ํšจ๊ณผ๋ฅผ ๋‚˜ํƒ€๋ƒˆ์œผ๋ฉฐ ์ ‘์ฐฉ๋ ฅ์˜ ๊ฐ’๋„ ๋‚ฎ์•˜๋‹ค. ์ด๊ฒƒ์€ MAH ๊ธฐ๋Šฅ๊ธฐ์˜ ์ ˆ๋Œ€๋Ÿ‰์ด ์ž‘์•„์„œ ๊ณ„๋ฉด์—์„œ ๋ฐ˜์‘ํ•  ํ™•๋ฅ ์ด ๋‚ฎ๊ธฐ ๋•Œ๋ฌธ์ด๋ฉฐ, ๋น„๊ฒฐ์ •์„ฑ ๋‚˜์ผ๋ก ์˜ Tg์™€ ๊ทผ์ ‘ํ•œ 150โ„ƒ์—์„œ๋Š” ์ด ํšจ๊ณผ๊ฐ€ ๋”์šฑ ์ค‘์š”ํ•จ์„ ์•Œ์•˜๋‹ค. ๋˜ํ•œ 60 K์˜ ef-PS๋Š” 100K ๋ณด๋‹ค ๊ณ„๋ฉด์— ํˆฌ์ž…ํ•˜๋Š” ์–‘์ด ์ž‘์€ ๊ฐ’์—์„œ ๋ถ€ํ„ฐ ์ ‘์ฐฉ ํšจ๊ณผ๋ฅผ ๋‚˜ํƒ€๋ƒˆ์œผ๋‚˜, ๊ณ„๋ฉด์— ํˆฌ์ž…๋˜๋Š” ๊ธฐ๋Šฅ๊ธฐ์˜ ์–‘์— ๋น„๋ก€ํ•˜๋Š” ๋‘๊ป˜/๋ถ„์ž๋Ÿ‰ ์œผ๋กœ๋Š” ๊ฐ™์€ ๊ฐ’์—์„œ ์ ‘์ฐฉํšจ๊ณผ๊ฐ€ ๋‚˜ํƒ€๋‚จ์œผ๋กœ์จ ๊ณ„๋ฉด์—์„œ์˜ ๊ธฐ๋Šฅ๊ธฐ์˜ ์ ˆ๋Œ€๋Ÿ‰์ด ์ ‘์ฐฉํšจ๊ณผ์— ์ค‘์š”ํ•จ์„ ์•Œ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ณ„๋ฉด์— ํˆฌ์ž…ํ•œ ์–‘์ด ๋งŽ์€ ๊ฒฝ์šฐ์—๋Š” ๋ถ„๋ผ๋Ÿ‰์ด ํฐ 100 K๊ฐ€ ์ ‘์ฐฉ๋ ฅ์ด ๋” ํฐ ๊ฐ’์œผ๋กœ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ์ด๊ฒƒ์˜ ์›์ธ์œผ๋กœ๋Š” ํŒŒ๊ดด์—๋„ˆ์ง€์— ๋Œ€ํ•œ ๋ถ„์ž๋Ÿ‰์˜ ์˜ํ–ฅ๊ณผ ํ™•์‚ฐ๊ธฐ๊ตฌ ์ฐจ์ด์— ์˜ํ•œ ๊ณ„๋ฉด์—์„œ์˜ ๋ฐ˜์‘๋Ÿ‰์˜ ์ฐจ์ด๋ฅผ ๊ณ ๋ คํ•˜์˜€๋‹ค. ; The enhancement of the interfacial adhesion between immiscible amorphous polyamids and polystyrene(PS) by the addition of a model reactive compatibilizer was investigated. The model reactive compatibilizer, which were maleic anhydride(MAH) terminated PS with two different molecular weights(60K and 100k), were prepared by anionic polymerization. Fracture toughness of the interface was measured by employing the asymmetric fracture test. Under the annealing condition of 190โ„ƒ for 2 hrs the amount of the reactive compatibilizer required to see any adhesion improvement was increased to large extent and the toughness value remained low compared to the commercially available styren-maleic anhydride random copolymer. This is due to the small amount of the functional groups(HAM) which are able to react with amine end-groups of the amorphous polyamide to from in-situ copolymers in case of the end-functionalized PS(ef-PS). It was noted that smaller amount of the 60K ef-PS was required to inprove the interfacial adhesion when compared with the 100K ef-PS. When the amount(thickness) of the ef-PS was replotted with thickness/molecular weight, which is proportional to the amount of the functional group, the two different molecular at the interface is a crucial factor. The difference in the plateau value of the fracture toughness for two different molecular weight ef-PS was interpreted in terms of either the molecular weight dependence of the fracture energy or the residence time of the ef-PS at the interface during diffusion.๋ณธ ์—ฐ๊ตฌ๋Š” ํ•œ๊ตญ๊ณผํ•™์žฌ๋‹จ(KOSEF 923-1000-004-2)์˜ ์ง€์›์œผ๋กœ ์ˆ˜ํ–‰๋˜์—ˆ๊ธฐ์— ์ด์— ๊ฐ์‚ฌ๋“œ๋ฆฝ๋‹ˆ๋‹ค
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