205 research outputs found
λΆλ₯΄κ°± μ κ·Ήλ ν¨μ μ 리μ νμ₯
νμλ
Όλ¬Έ(λ°μ¬) -- μμΈλνκ΅λνμ : μμ°κ³Όνλν μ리과νλΆ, 2023. 8. μ΄μν.The estimates for maximal functions play important roles in various problems in mathematical analysis such as those in partial differential equations, geometric measure theory, and harmonic analysis. Since the 1950s, the maximal functions defined by averages have been extensively studied in the field of classical harmonic analysis and a huge literature has been devoted to the subject. In 1976, Stein proved his seminal result: bound on the spherical maximal operator on the optimal range for every dimension bigger than 2. Its two-dimensional counterpart, the bound on the circular maximal function, turned out to be more difficult since the traditional based argument did not work. In 1986, however, Bourgain settled the problem by proving his celebrated theorem: the circular maximal operator is bounded on for . In this thesis, we prove three results which strengthen Bourgains circular maximal theorem. First, we establish on the sharp range of the -- boundedness of the circular maximal operator on the Heisenberg group for Heisenberg radial functions. Secondly, we obtain the sharp -- boundedness of the two-parametric maximal operator defined by averages over tori. Lastly, we prove estimates on the elliptic maximal operators which are multiparametric maximal operators given by averages over ellipses.κ·Ήλ ν¨μμ λν κ³μΈ‘μ νΈλ―ΈλΆλ°©μ μ, κΈ°ννμ μΈ‘λμ΄λ‘ , μ‘°νν΄μνκ³Ό κ°μ μ리ν΄μνμ μ¬λ¬ λΆμΌμ λ¬Έμ μμ μ€μν μν μ νλ€. 1950λ
λ μ΄ν, νκ· μΌλ‘ μ μλ κ·Ήλ ν¨μλ κ³ μ μ μ‘°νν΄μν λΆμΌμμ κ΄λ²μνκ² μ°κ΅¬λμ΄μκ³ , νμ¬ μ΄ μ£Όμ μ μ°κ΅¬μ κ΄λ ¨ν λ°©λν λ¬Ένμ΄ μ‘΄μ¬νλ€. 1976λ
μ μ€νμΈμ `3 μ΄μμ λͺ¨λ μ°¨μμμ ꡬ면 κ΅λ ν¨μμ κ³μΈ‘'μ κ·λͺ
νλ κ°μ°½μ κ²°κ³Όλ₯Ό μ¦λͺ
νμλ€. 2μ°¨μ λ¬Έμ μ ν΄λΉνλ μ κ·Ήλ ν¨μμ μ κ³μ±μ, κ³ μ μ μΈ λ°©λ²μ νκ³λ‘ μΈνμ¬ λ§€μ° μ΄λ €μ΄ κ²μΌλ‘ μλ €μ Έ μμλ€. κ·Έλ¬λ 1986λ
μ λΆλ₯΄κ°±μ `μ κ·Ήλ μ°μ°μλ κ° 2λ³΄λ€ ν΄ λ μμ μ κ³μ΄λ€'λΌλ κ·Έμ μ λͺ
ν μ κ·Ήλ ν¨μ μ 리λ₯Ό μ¦λͺ
ν¨μΌλ‘μ¨ μ΄ λ¬Έμ μ λ§μΉ¨νλ₯Ό μ°μλ€. μ΄ νμ λ
Όλ¬Έμμλ λΆλ₯΄κ°± μ κ·Ήλ ν¨μ μ 리λ₯Ό λμ± κ°ννλ μΈ κ°μ§ κ²°κ³Όλ₯Ό μ¦λͺ
νλ€. 첫째, νμ΄μ λ² λ₯΄κ·Έ κ΅° μμμμ μ λμΉ ν¨μμ λν΄μ μ κ·Ήλ μ°μ°μμ -- μ κ³λ₯Ό μ΅μ μμμμ μ»λλ€. λμ§Έ, μν체 μμ νκ· μ μν΄ μ μλ 2κ°μ 맀κ°λ³μλ₯Ό κ°μ§λ κ·Ήλ μ°μ°μμ μ΅μ -- μ κ³μ±μ κ·λͺ
νλ€. λ§μ§λ§μΌλ‘, νμμ μν΄ μ μλλ λ€μ€λ³μ κ·Ήλ μ°μ°μμΈ νμ κ·Ήλ μ°μ°μμ κ³μΈ‘μ μ¦λͺ
νλ€.Abstract i
1 Introduction 1
1.1 Maximal averages over rectangles 1
1.2 Maximal averages over submanifolds 3
1.3 The circular maximal function and L^p improving property 5
1.4 Maximal averages on the Heisenberg group 6
1.5 Two parameter maximal averages over tori 7
1.6 Multiparameter maximal averages over ellipses 9
1.7 Notations 10
2 Preliminaries 12
2.1 Decoupling inequalities 12
2.2 Local smoothing estimates of the wave operator 16
3 The Heisenberg circular maximal operator 19
3.1 Heisenberg radial functions and main estimates 21
3.2 Local maximal estimates 23
3.3 Global maximal estimates 27
3.4 Proof of main estimates 30
3.5 Proof of Proposition 3.1.131
3.6 Proof of Proposition 3.1.2 34
3.7 Sharpness of the range of p, q 41
4 Two parameter averages over tori 42
4.1 Comparison with one parameter maximal average 43
4.2 Local smoothing estimates of averages over tori 44
4.3 Two parameter propagator 45
4.4 Estimates for the averaging operator A_s^t 50
4.5 Global maximal estimates 58
4.6 Local maximal estimates 64
4.7 Proof of smoothing estimates 66
4.8 Optimality of the estimates 75
5 Multiparameter averages over ellipses 79
5.1 Local smoothing estimates for averaging operators over ellipses. 80
5.2 Proof of maximal bounds 82
5.3 Variable coe cient decoupling inequalities 87
5.4 Proof of local smoothing estimates 92
5.5 Proof of Theorem 5.3.2 99
5.6 Optimality of the estimates 106
Abstract (in Korean) i
Acknowledgement (in Korean) iiλ°
The Effects of Self-esteem of Elementary School Students on Multicultural Acceptability
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Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : μ¬λ²λν μ¬νκ΅μ‘κ³Ό, 2018. 2. λͺ¨κ²½ν.μμ½(κ΅λ¬Έμ΄λ‘)
1980λ
λ μ νλ₯Ό κΈ°μ μΌλ‘, μΈκ΅μΈ λ
Έλμμ κ²°νΌ μ΄μ£Όλ―Όμ μ μ
μ΄ ν¬κ² λμ΄λλ©΄μ νκ΅μ λ€λ¬Έν μ¬νλ‘ κΈκ²©ν λ³νλμ΄κ°κ³ μλ€. μ΄μ λ°λΌ κ΅λ΄ μ΄β€μ€β€κ³ λ±νκ΅μ μ¬ν μ€μΈ λ€λ¬Έν νμ μλ μ¦κ°νκ³ μλ€. λ°λΌμ λ€μν λ―Όμ‘±μ , λ¬Ένμ νΉμ±μ μ§λ νμΈμ μμ©νκ³ μ‘΄μ€νλ λ€λ¬Έν μλ―Όμ± ν¨μμ΄ μꡬλλ€.
μ΄λ¬ν νλ¦μ λ§μΆ° λ€λ¬Ένκ΅μ‘μ λͺ©ν, λ°©ν₯, λ΄μ© λ±μ λν μ΄λ‘ μ β€μ€μ²μ μ°κ΅¬κ° νλ°ν μ§νλμ΄ μλ€. νμ§λ§ κ΅μ‘ νμ₯μμλ μ¬μ ν μ£Όλ₯λ¬Ένμ§λ¨ μλλ€μ΄ μμλ¬Ένμ§λ¨ μλλ€μ μ°¨λ³νκ³ νΈκ²¬μ μμ μΌλ‘ λνκ³ μλ€λ λ¬Έμ κ° μ κΈ°λμλ€. λΏλ§ μλλΌ μΌλ°©μ μΌλ‘ μ£Όλ₯μ§λ¨μ λ¬Ένμ μ©ν΄λκΈ°λ₯Ό λ°λΌλ μΌλ°©μ λνκΈ°λ, κ²½μ λ°μ μμ€κ³Ό λ¬Ένμ κ°μΉμ λ±κΈνμ λ°λΌ μ΄μ€μ μΈ μ£λλ‘ μμλ¬Ένμ§λ¨μ λνλ μ΄μ€μ νκ°μ κ²½ν₯ λν κ°μ λμ§ μμ κ²μΌλ‘ λλ¬λ¬λ€.
μ΄λ κΈ°μ‘΄μ λ€λ¬Ένκ΅μ‘μ΄ κ΅κ° μ£Όλμ λνμ£Όμμ β€μ£Όμ§μ£Όμμ μ±ν₯μ λκ³ μμΌλ©°, κ·Έ λ΄μ© λν λ¬Ένμ ννΌμ μΈ λ©΄λ§ λ€λ£¨λλ° κ·Έμ³ μμλ¬Ένμ§λ¨μ λν κ·Όλ³Έμ μΈ μΈμκ³Ό νλμ κ°μ μ΄ μ΄λ£¨μ΄μ§μ§ μμκΈ° λλ¬ΈμΌλ‘ λΆμλλ€. μ΄λ¬ν λ€λ¬Ένκ΅μ‘μ νκ³λ₯Ό 극볡νκ³ μ€μ§μ μΈ λ€λ¬Έν μμ©μ±μ λ΄λ©΄νλ₯Ό μν΄ κ°μΈ λ΄μ μμΈμ λ°λ¬μ μ£Όμμ μ λ λ€λ¬Ένκ΅μ‘μ΄ μ΄λ£¨μ΄μ ΈμΌ νλ€λ μ°κ΅¬κ° λ€μ λ³΄κ³ λμλ€. μ¦, κ°μΈ λ°λ¬ λ° μμμ‘΄μ€κ° ν립μ λ°νμΌλ‘ λ€λ¬Έν μλ μ¬ν ν΅ν©μ κΈ°μ¬ ν μ μλ λ―Όμ£Ό μλ―Ό μμ±μ λͺ©νλ‘ νλ λ€λ¬Ένκ΅μ‘μ΄ κΈ°μ‘΄ κ΅μ‘μ λμμ΄ λ μ μλ€.
μμμ‘΄μ€κ°μ΄λ μμ μ΄ μ λ₯νκ³ κ°μΉ μλ€κ³ λ―Ώλ μ λλ₯Ό λνλΌ λΏλ§ μλλΌ, μ£Όμ νκ²½κ³Ό μ¬νμ κ΄κ³μμ μμ κ°κ³Ό μμκ°μ λλΌλλ° μ€μν μμΈμΌλ‘ μμ©νλ€. λμ μμμ‘΄μ€κ°μ μΈμ§λ¨μ λν κ΄μ©μ΄λ μμ©μ μ μ‘°κ° λ μ μμΌλ―λ‘, μμμ‘΄μ€κ°μ λμ λ€λ₯Έ λ¬Ένμ λ°°κ²½μ μ§λ νμΈ λ° λ€λ₯Έ λ¬Ένλ₯Ό μμ©νλ νλμΈ λ€λ¬Έν μμ©μ±μλ μν₯μ λ―ΈμΉλ€κ³ ν μ μλ€.
λν λ°λ¬ λ¨κ³μ μλκΈ°λ μμμ§λ¨κ³Ό μ¬νμ λ¬Ένμ κ°μΉλ₯Ό λ΄λ©΄ννκ³ , κ΅μ°κ΄κ³λ₯Ό ν΅ν΄ μΈκ°κ΄μ ν립νκ² λλ μ€μν μκΈ°μ΄λ€. κ·Έ κ³Όμ μμ νμ±λ λ€λ₯Έ μΈμ’
κ³Ό λ―Όμ‘±μ λν νλκ° ν₯ν μ±μΈμ΄ λμ΄μλ μ μ§λλ κ²μΌλ‘ λ°νμ‘λ€. λ°λΌμ μ΄λ±νμμ μμμ‘΄μ€κ°μ λ€λ¬Ένκ΅μ‘ κ°μ μ μ€λ§λ¦¬λ‘ μΌκ³ , λ€λ¬Έν μμ©μ±μ λ―ΈμΉλ μν₯μ κ²μ¦ν΄ λ³Ό νμκ° μλ€.
μ΄λ¬ν λ¬Έμ μμ νμ λ³Έ μ°κ΅¬λ μ΄λ±νμμ μμμ‘΄μ€κ°μ΄ λμμλ‘ λ€λ¬Έν μμ©μ±μ΄ λμ κ²μ΄λ€.λ μ°κ΅¬ κ°μ€μ μ€μ νμλ€. νΉν λ€λ¬Έν μμ©μ±μ λ€μμ±, κ΄κ³μ±, 보νΈμ± λ± μΈ κ°μ§ μ°¨μμΌλ‘ λλμ΄ λΆμν κ²°κ³Όλ₯Ό λ°νμΌλ‘ κΈ°μ‘΄ μ ν μ°κ΅¬λ₯Ό 보μνκ³ , ν₯ν ꡬ체μ μΈ λ€λ¬Ένκ΅μ‘ λ°©μμ μ μΈνκ³ μ νλ€. μ΄λ₯Ό κ²μ¦νκΈ° μν΄ μ€μ ν νμ μ°κ΅¬ κ°μ€μ λ€μκ³Ό κ°λ€.
μ΄λ±νμμ μμμ‘΄μ€κ°μ΄ λμμλ‘ λ€μμ± μ°¨μμ λ€λ¬Έν μμ©μ±μ΄ λμ κ²μ΄λ€.
μ΄λ±νμμ μμμ‘΄μ€κ°μ΄ λμμλ‘ κ΄κ³μ± μ°¨μμ λ€λ¬Έν μμ©μ±μ΄ λμ κ²μ΄λ€.
μ΄λ±νμμ μμμ‘΄μ€κ°μ΄ λμμλ‘ λ³΄νΈμ± μ°¨μμ λ€λ¬Έν μμ©μ±μ΄ λμ κ²μ΄λ€.
μμ μ°κ΅¬ κ°μ€μ κ²μ¦νκΈ° μν΄ μμΈμ§μ μ΄λ±νκ΅μ μ¬ν μ€μΈ 6νλ
νμ 421λͺ
μ λμμΌλ‘ μ€λ¬Έμ μ§ννμλ€. λΆμ κ²°κ³Ό, μμμ‘΄μ€κ°μ λ€λ¬Έν μμ©μ± μ 체 λ° 3κ° νμ μ°¨μ(λ€μμ±, κ΄κ³μ±, 보νΈμ±)μ λ€λ¬Έν μμ©μ±μ p=.000 μμ€μμ μ μλ―Έν μν₯μ λ―ΈμΉλ κ²μΌλ‘ λλ¬λ¬λ€. μμμ‘΄μ€κ°μ΄ κ°μ₯ ν° μν₯μ λ―ΈμΉλ λ³μΈμ λ€λ¬Έν μμ©μ± μ 체μμΌλ©°, λ€μμ± μ°¨μμ λ€λ¬Έν μμ©μ±, 보νΈμ± μ°¨μμ λ€λ¬Έν μμ©μ±, κ΄κ³μ± μ°¨μμ λ€λ¬Έν μμ©μ±μ΄ κ·Έ λ€λ₯Ό μ΄μλ€. λ°λΌμ λ³Έ μ°κ΅¬ κ²°κ³Όλ μ΄λ±νμμ λμ μμμ‘΄μ€κ°μ΄ λ€λ¬Έν μμ©μ±μ ν¨μνλλ° μ€μν μν μ ν μ μμμ μμ¬νλ€.
λ³Έ μ°κ΅¬ κ²°κ³Όλ₯Ό λ°νμΌλ‘ μλμ κ°μ λ€λ¬Ένκ΅μ‘ λ°©ν₯μ μ μνκ³ μ νλ€.
첫째, κ°νλ¬Έμ λ€λ¬Ένκ΅μ‘ λ°©λ²μ μ μ©νμ¬ κ°κ°μΈμ΄ νλ¬Ένβ€νμΈμ’
μ λν΄ κ°λ νλμ μΈμμ μ΄ν΄νκ³ , λ€λ¬Έν μλ λ―Όμ£Όμλ―ΌμΌλ‘μ μμ ν¨λ₯κ°μ λμΌ μ μλ κ΅μ‘ λ°©μμ μ μνλ€. μ΄λ₯Ό ν΅ν΄ λνμ£Όμμ Β·μ£Όμ§μ£Όμμ λ€λ¬Ένκ΅μ‘μ νκ³λ₯Ό 극볡ν μ μμ λΏλ§ μλλΌ, μκΈ° μ΄ν΄λ₯Ό λ°νμΌλ‘ μμ μ λν 건μ ν νκ°λ₯Ό νκ² λκ³ μ΄λ 곧 λμ μμμ‘΄μ€κ° ν¨μμΌλ‘ μ΄μ΄μ§ μ μλ€. μμμ‘΄μ€κ°μ΄ λμμλ‘ λ€λ₯Έ λ¬Έν λ° μΈμ’
μ λν΄ κ΄μ©μ μΌλ‘ λνλ κ²½ν₯μ΄ μμΌλ―λ‘, κ°νλ¬Έμ λ€λ¬Ένκ΅μ‘μ λ€λ¬Έν μμ©μ±μ λ΄λ©΄νν μ μλ λ°©μμ΄ λ κ²μ΄λ€.
λμ§Έ, νμ΅μμ ν₯λ―Έ, μꡬμ λ§λ λ€λ¬Έν κ΄λ ¨ μ¬νβ€λ¬Ένμ λ§₯λ½μ ν¬ν¨ν κ΅μ‘ 컨ν
μΈ λ₯Ό μ 곡νμ¬μΌ νλ€. μλμ μμ μ΄ κ°μ§ λ€λ¬Έν μΈμ λ° μμ© νλλ₯Ό μ΄ν΄νκ³ νκ°νλ©΄μ, κ°μΈμ΄ μ§λ μμλ¬Ένμ§λ¨μ λν μ΄μ€μ νλ, μΌλ°©μ λνκΈ°λ κ²½ν₯μ μ§λ©΄ν μ μλ€. μ΄λ¬ν κ³Όμ μμ λ€λ¬Έν μ¬ν λ―Όμ£Ό μλ―ΌμΌλ‘ μ§λ
μΌ ν μμ§μ λν΄ μ€μ€λ‘ κ³ μ°°νκ³ , λ€λ¬Έν μμ©μ±μ λ΄λ©΄ν ν μ μκ² λλ€. ν΄λΉ λ€λ¬Ένκ΅μ‘μ μν΄ νμ μ£Όλμ νμ΅μ΄ μ΄λ£¨μ΄ μ§ μ μλλ‘ κ΅κ³Όμ λ΄μ©μ ꡬ쑰ννκ³ , λν νμ΅μκ° μ²ν μν©μ΄λ ν₯λ―Έμ λ§κ² μ λμ μΌλ‘ λ³ν κ°λ₯ν ννλ‘ μ μλμ΄μΌ νλ€.
μ
μ§Έ, νλνμ΅, νλ‘μ νΈ νμ΅, κ°λ³ν κ΅μλ² λ±μ μ μ©ν λ€λ¬Ένκ΅μ‘μ μ μνλ€. νμλ€μ΄ λ₯λμ μΌλ‘ μ°Έμ¬νκ³ , λ κ°μΈμ κ²½νμ ν΅ν΄ μλ―Έλ₯Ό ꡬμ±νλ κ³Όμ μμ μμ ν¨λ₯κ°μ΄ λ°λ¬ν μ μλ€. μ΄λ¬ν μμ μ λν μ΄ν΄μ μμ ν¨λ₯κ°μ λ°λ¬μ λ°νμΌλ‘ μμμ‘΄μ€κ°μ΄ λμμ§ μ μμΌλ©°, μ΄λ 곧 λ€λ¬Έν μμ©μ±μ ν¨μμλ μν₯μ λ―ΈμΉ μ μλ€.
λ³Έ μ°κ΅¬μ μ°κ΅¬κ²°κ³Όλ₯Ό λ°νμΌλ‘ ν₯ν λ€λ¬Έν μμ©μ±κ³Ό μμμ‘΄μ€κ°μ κ΄λ ¨λ νμ μ°κ΅¬λ₯Ό μνμ¬ λ€μκ³Ό κ°μ΄ μ μΈνλ€.
첫째, μ΄λ±νμμ λμμΌλ‘ ν μ κ΅μ μΈ λ€λ¬Έν μμ©μ± μ‘°μ¬ μ°κ΅¬κ° νμνλ€. ν΄λΉ μ°κ΅¬λ₯Ό ν΅ν΄ λ³΄λ€ κ΅¬μ²΄μ μΈ λ€λ¬Ένκ΅μ‘μ λ°©ν₯μ λν μ μΈμ΄ κ°λ₯ν κ²μΌλ‘ 보μΈλ€.
λμ§Έ, μ΄λ±νμμ μμμ‘΄μ€κ°μ λ€ κ°μ§ μ°¨μμΌλ‘ λλμ΄ κ°κ° λ€λ¬Έν μμ©μ±μ λ―ΈμΉλ μν₯μ νμ
νλ€λ©΄, ν₯ν νμ κ°κ°μΈμ νΉμ±μ λ°λΌ ν¨κ³Όλ₯Ό λνλΌ μ μλ λ€λ¬Ένκ΅μ‘λ² κ΅¬μμ λμμ΄ λ μ μλ€.
μ
μ§Έ, λ€λ¬Έν μ μ΄ κ²½νμ λμ λ€λ¬Έν μμ©μ±κ°μ κ΄κ³λ₯Ό μμ β€μ§μ μΌλ‘ μ°κ΅¬ν΄ λ³Ό νμκ° μλ€.
λ·μ§Έ, μμλ¬Ένμ§λ¨ μλλ€λΌλ¦¬ μλ‘μ λ¬Ένλ₯Ό μ΄λ»κ² μμ©νλμ§μ λν μμ β€μ§μ μ°κ΅¬κ° μ§νλλ€λ©΄ λ€λ¬Έν μ¬νμμ λ³΄λ€ μ κ·Ήμ μ΄κ³ 민첩ν κ΅μ‘μ λμ λ° μ²μΉκ° μ΄λ£¨μ΄μ§ μ μλ€.
μ£Όμμ΄ : μμμ‘΄μ€κ°, λ€λ¬Έν μμ©μ±, λ€λ¬Ένκ΅μ‘, μλ―Όκ΅μ‘, μΈκ° λ°λ¬, μ΄λ± κ΅μ‘
ν λ² : 2016-21543β
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1. μ°κ΅¬μ νμμ±κ³Ό λͺ©μ 1
2. μ°κ΅¬κ°μ€ λ° μ°κ΅¬λ΄μ© 5
3. μ°κ΅¬μ μμ λ° νκ³ 6
β
‘. μ΄λ‘ μ λ°°κ²½ 9
1. μμμ‘΄μ€κ° 9
1) μμμ‘΄μ€κ°μ μ μ λ° μ°κ΅¬ λν₯ 9
(1) μμμ‘΄μ€κ°μ μ μ 9
(2) μμμ‘΄μ€κ°μ μ°κ΅¬ λν₯ 14
2) μμμ‘΄μ€κ°κ³Ό μλ―Όκ΅μ‘ 16
2. λ€λ¬Έν μμ©μ± 22
1) λ€λ¬Έν μμ©μ± 22
(1) λ€λ¬Έν μμ©μ±μ κ°λ
λ° μ€ν 22
(2) λ€λ¬Έν μμ©μ±μ μν₯μ λ―ΈμΉλ μμΈ λΆμ 31
2) λ€λ¬Έν μμ©μ±κ³Ό λ€λ¬Ένκ΅μ‘ 35
(1) λ€λ¬Ένκ΅μ‘μ κ°λ
κ³Ό λͺ©ν 35
(2) λ€λ¬Ένκ΅μ‘κ³Ό λ€λ¬Έν μμ©μ± 38
3. μμμ‘΄μ€κ°κ³Ό λ€λ¬Έν μμ©μ± 41
1) μμμ‘΄μ€κ°κ³Ό λ€μμ± μ°¨μμ λ€λ¬Έν μμ©μ± 41
2) μμμ‘΄μ€κ°κ³Ό κ΄κ³μ± μ°¨μμ λ€λ¬Έν μμ©μ± 44
3) μμμ‘΄μ€κ°κ³Ό 보νΈμ± μ°¨μμ λ€λ¬Έν μμ©μ± 45
4. μ νμ°κ΅¬ κ²ν 47
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3. μ°κ΅¬λ°©λ² 56
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2) λΆμ λ°©λ² 59
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2) μ΄λ±νμμ λ€λ¬Έν μμ©μ± 61
2. μμμ‘΄μ€κ°μ΄ λ€λ¬Έν μμ©μ±μ λ―ΈμΉλ μν₯ 64
1) μκ΄κ΄κ³ λΆμ 64
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2) μμμ‘΄μ€κ°μ΄ λ€λ¬Έν μμ©μ±μ λ―ΈμΉλ μν₯ 65
(1) μμμ‘΄μ€κ°κ³Ό λ€λ¬Έν μμ©μ± 66
(2) μμμ‘΄μ€κ°κ³Ό λ€μμ± μ°¨μμ λ€λ¬Έν μμ©μ± 67
(3) μμμ‘΄μ€κ°κ³Ό κ΄κ³μ± μ°¨μμ λ€λ¬Έν μμ©μ± 68
(4) μμμ‘΄μ€κ°κ³Ό 보νΈμ± μ°¨μμ λ€λ¬Έν μμ©μ± 70
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2) μ΄λ‘ μ μ μΈ 80
μ°Έκ³ λ¬Έν 83
λΆλ‘ 99
μμμ‘΄μ€κ° λ° λ€λ¬Έν μμ©μ± μ€λ¬Έμ§ 99
Abstract 105Maste
μ΄λΆμ°ννμ‘μ μ λ³νμ μ±μ§κ³Ό λ―ΈμΈκ΅¬μ‘° ν΄μ
νμλ
Όλ¬Έ (λ°μ¬)-- μμΈλνκ΅ λνμ : ννμ물곡νλΆ, 2016. 2. μκ²½ν.Coating fluids that include highly size-asymmetric particles are widely used in many applications such as Li-ion batteries, multi-layer ceramic capacitors, and electrical conductors. They include different types of particles to improve the performance of the product. In these liquids, the size of the particles is very differentone is micrometer and the other nanometer scale, for example. Because of distinctively different size of the particles, these bimodal dispersions show more complex flow behavior compared to colloids of a single size. However, most studies for bi-modal dispersions assumed well-stabilized nanoparticles, thus they have not been relevant to the behavior of the materials used in industry.
In this study, a model system consisting of two kinds of particles with highly asymmetric size was designed to offer a valuable description of complex behavior of the slurries, using a suspension consisting of polystyrene latex (500 nm) and alumina-coated silica (12 nm) particles. The surface potential of small particles was tuned by varying the solution pH, causing them to be repulsive to each other, attractive to each other, and oppositely charged to the large particles, while the large particles remained electrostatically stabilized.
The effect of addition of small particles on the rheological properties of bimodal suspensions was investigated in terms of surface chemistry and concentration of small particles. The rheological properties were dramatically changed from viscous to gel-like depending on the surface potential and concentration of small particles. A colloidal gel was induced by small particles when the small particles had the opposite charge to the large particles and a volume fraction of , and when the small particles were attractive to each other above a critical threshold, . Cryo-SEM distinguished the gel structures to be either short bridging gels produced by oppositely charged small particles, or long bridging gels or dense gels produced by attractive small particles. On the basis of this rheological behavior and microstructure, a phase diagram of highly size-asymmetric bimodal colloids was presented with respect to the surface chemistry and concentration of small particles.
When the mutually attractive small particles are added to suspension of highly charged large particles, a new type of colloidal gel was induced which is not described by the typical power - law scaling for fractal clusters. Their elastic moduli have a unique scaling behavior on particle volume fraction with two distinct power - law indices. The unique scaling behavior arises from the non-fractal networks of large particles that are bridged by small particle clusters in the region between the lower and the upper critical boundary of small particle volume fraction.
Because the model fluid in this study is similar to the slurries used in industry with respect to the size ratio, the range of concentration, and the surface potential, it is expected that the slurries encountered in practice have analogous mechanical behavior and microstructure to this model fluid. This study consequently provides a guideline for the design of such complex fluids and understanding of their complex flow behavior.Chapter 1. Introduction 1
1.1. Coating material: bimodal suspensions 2
1.2. Outline of the thesis 6
Chapter 2. Background 9
2.1. The importance of particle size variation in colloidal suspension 10
2.2. Bimodal suspension in industrial coating process 13
2.3. Overview of bimodal suspensions in previous studies 22
2.4. Scaling behavior and microstructure of typical colloidal gels 26
Chapter 3. Experimental methods 28
3.1. Sample preparation 29
3.2. Measurement of suspension rheology 30
3.3. Characterization of suspension microstructure 31
Chapter 4. Results and discussion 32
4.1. Characterization of PS/alumina coated silica suspension 33
4.1.1. Surface potential of PS/alumina coated silica at various pH 33
4.1.2. Stability of PS suspension 35
4.2. Effect of surface chemistry of small particles 37
4.2.1. Rheological behavior at various pH 37
4.2.2. Microstructural analysis 51
4.2.3. Origin of nanoparticle induced gelation 54
4.2.4. Phase diagram 60
4.3. Attractive nanoparticle induced gelation 65
4.3.1. The onset of gelation 65
4.3.2. Scaling behavior 67
4.3.3. Microstructural analysis 70
4.3.4. Phase diagram 79
Chapter 5. Summary 82
Bibliography 85
κ΅λ¬Έ μ΄λ‘ 92Docto
Conversion of the organic breakdown products of glucosinolate to thiocyanate anions and their effects on thyroid hormone production
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Όλ¬Έ (λ°μ¬)-- μμΈλνκ΅ λνμ : μνμμνκ³Ό, 2015. 2. κΆνμ .λμ λλμ κΈλ£¨μ½μλλ μ΄νΈλ λλ¬Όμμ κ°μμ νΈλ₯΄λͺ¬ λμ¬λ₯Ό μ ν΄ν¨μΌλ‘μ¨ κ°μμ νΈλ₯΄λͺ¬ κ²°νμ¦μμ μΌκΈ°νλ€. κΈλ£¨μ½μλλ μ΄νΈκ° κ°μμ νΈλ₯΄λͺ¬ λμ¬μ μν₯μ λ―ΈμΉλ λ° μ£Όμ λ¬Όμ§λ‘ μμ©νλ κ²μΌλ‘λ κ·Έ λΆν΄μ°λ¬ΌμΈ goitrinκ³Ό ν°μ€μμμ° μμ΄μ¨(thiocyanate anion, SCN-)μ΄ μλ €μ Έ μλ€. μ΄μΈμ μΌλΆ λ€λ₯Έ λΆν΄μ°λ¬Όμ μν λ°κ°μμ μν₯μ κ°λ₯μ±μ΄ λ³΄κ³ λ λ° μμΌλ λ€μν κ³μ¬μ¬ ꡬ쑰μ μμ©κΈ°λ₯Ό κ³ λ €ν 체κ³μ μΈ μ°κ΅¬κ° λΆμ‘±ν μν©μ΄λ€. μ΄μ λ³Έ μ°κ΅¬μμλ λ€μν ꡬ쑰μ κΈλ£¨μ½μλλ μ΄νΈ(10 μ’
λ₯)λ₯Ό μνλ¬Όμ§λ‘ μ μ νμ¬ in vitro μνμ μ΄μ©νμ¬ κ° λΆν΄μ°λ¬Όμ SCN-μΌλ‘μ λμ¬μ μ ν, κ°μμ νΈλ₯΄λͺ¬ μμ±μ κ΄μ¬νλ ν¨μμΈ thyroid peroxidase (TPO)μ νμ± μ ν΄ λ° κ°μμ μΈν¬ λ΄λ‘μ μμ€λ μ μ
μ ν΄ν¨κ³Όλ₯Ό μΈ‘μ νκ³ , λ¨ν ν¬μ¬ ν λ«λμμ λνλλ κΈμ± λ³νλ₯Ό κ΄μ°°ν¨μΌλ‘μ¨ κ°μμ νΈλ₯΄λͺ¬ μμ±κ³Όμ μ λ―ΈμΉλ μν₯μ μ΄ν΄λ³΄μλ€.
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μ κΈλ£¨μ½μλλ μ΄νΈ λΆν΄μ°λ¬Όμ human liver S9 λΆνκ³Ό rhodaneseλ₯Ό μ¬μ©ν in vitro λμ¬νκ²½μ μ μ©ν κ²°κ³Ό, μΌλΆ organic nitrile(aliphatic, benzyl, indolyl)λ€κ³Ό organic thiocyanateλ€λ‘λΆν° μμμ°μμ΄μ¨(CN-)μ΄ μμ±λμμΌλ©°, μμ±λ CN-μ λλΆλΆ SCN-μΌλ‘ μ νλμλ€. Allyl ITCμ organic thiocyanateλ S9μ μνμ¬ SCN-μΌλ‘μ μ§μ μ μΈ μ νμ΄ μΌμ΄λ¬μΌλ©°, 4-(methylthio)butyl thiocyanateμ κ²½μ°μλ μμ©μ‘ μμμ μλ°μ μΈ λΆν΄κ° μΌμ΄λ SCN-μ μμ±νμλ€.
24μ’
μ κΈλ£¨μ½μλλ μ΄νΈ λΆν΄μ°λ¬Ό μ€ 2-hydroxy-3-butenyl cyanide, 3-indoleacetonitrile, goitrin, 4-(methylthio)butyl thiocyanate, indole-3-carbinol (I3C), κ·Έλ¦¬κ³ SCN-μ μνμ¬ λΌμ§ κ°μμ μ‘°μ§μ microsomal TPOμ νμ±μ΄ λμ‘°κ΅° λλΉ μ½ 2%~78%λ‘ μ μμ μΌλ‘ κ°μλμμΌλ©°, μ΄ μ€ goitrinμ TPOμ λΉκ°μμ λΆνμ±νλ₯Ό μΌκΈ°νλ κ²μΌλ‘ λνλ¬λ€. λν κ°μμ μΈν¬μ£Ό(FRTL-5 rat thyroid cell)μ 100 Β΅Mμ SCN-, benzyl ITC, 2-phenylethyl ITC, 4-(methylthio)butyl thiocyanateμ μ²λ¦¬ν κ΅°μμ μΈν¬ λ΄λ‘μ μμ€λ μ μ
λμ΄ λμ‘°κ΅° λλΉ μ½ 39%~78%λ‘ μ μμ μΌλ‘ κ°μνμλ€.
μμ in vitro μνμμ μ¬μ©ν 24μ’
λ₯μ κΈλ£¨μ½μλλ μ΄νΈ λΆν΄μ°λ¬Ό μ€ 2-hydroxy-3-butenyl cyanide, 3-indoleacetonitrile, goitrin, benzyl ITC, 2-phenylethyl ITC, benzyl thiocyanate, I3C, SCN-, allyl ITCλ₯Ό μ μ νμ¬ F344 λ«λμ ν λ§λ¦¬λΉ 50 Β΅molμ© κ° κΈλ£¨μ½μλλ μ΄νΈ λΆν΄μ°λ¬Όμ λ¨ν 경ꡬν¬μ¬νμλ€. 14μκ° ν goitrin, allyl ITC, benzyl ITCλ₯Ό ν¬μ¬ν κ΅°μμ μ μμ μ΄μ§λ μμΌλ λμ‘°κ΅° λλΉ 84%~93% μμ€μ κ°μμ μ‘°μ§ λ΄ TPO νμ±μ΄ μΈ‘μ λμλ€. κ°μμ μ‘°μ§ λ΄μ T3λ goitrinκ³Ό SCN-μ ν¬μ¬ν κ΅°μμ λμ‘°κ΅° λλΉ κ°κ° μ½ 53, 77%λ‘ μ μμ μΌλ‘ κ°μνμμΌλ©°, μ²΄λ΄ λμ¬μ μνμ¬ λμ νμ€ SCN-μ λνλΈ λΆν΄μ°λ¬Ό(allyl ITC, benzyl thiocyanate, 3-indoleacetonitrile)μ κ²½μ° SCN-ν¬μ¬κ΅°κ³Ό μ μ¬ν μμ€μΌλ‘ λνλ¬λ€(λμ‘°κ΅° λλΉ 77%~81%).
λ³Έ μ°κ΅¬κ²°κ³Ό μΌλΆ κΈλ£¨μ½μλλ μ΄νΈ λΆν΄μ°λ¬Όλ€μ κ²½μ° in vitro μν 쑰건μμ κ°μμ νΈλ₯΄λͺ¬ μμ± λ¨κ³μ λνμ¬ μ½ν μ ν΄ν¨κ³Όκ° κ΄μ°°λμλ€. κ·Έλ¬λ in vivoμμμλ goitrinκ³Ό SCN-, κ·Έλ¦¬κ³ μ²΄λ΄ λμ¬μ μνμ¬ SCN-μΌλ‘ μ νλλ κΈλ£¨μ½μλλ μ΄νΈ λΆν΄μ°λ¬Όμ μν μν₯μ΄ λ μ°μΈνμμΌλ©° μ΄λ μ΄λ€μ κ³ λλλ‘ μμ·¨ν μ μμ€λμ κ²°ν μ¬λΆμ κ΄κ³μμ΄ κ°μμ νΈλ₯΄λͺ¬ μμ±μ μ ν΄ν μ μμμ μμ¬νλ€. λν κ΅λ΄ μμνκ³Ό μ±μμ μμ·¨μμ€μ λ°μν κ²°κ³Ό μνν 10μ’
λ₯μ κΈλ£¨μ½μλλ μ΄νΈ μ€ progoitrin, glucobrassicin, sinigrinμ΄ μ΄λ€μ μμ±νλ μ£Όμ μ ꡬ물μ§μμ μ μ μμλ€. μΌλ°μ μΈ μμ΄μμ·¨ μ λ³΄λ€ κ±΄κ°κΈ°λ₯μνμΌλ‘μ μμ·¨νκ² λ κ²½μ° λμ μμ€μ λ
ΈμΆμ΄ κ°λ₯ν΄μ§λ©°, λν μν λ΄ κΈλ£¨μ½μλλ μ΄νΈ λΆν΄μ°λ¬Όμ μ‘°μ± λ° ν¨λμ 쑰리βκ°κ³΅ 쑰건μ λ°λΌ λ¬λΌμ§κ² λλ―λ‘ λΆν΄βμ ν κ²°κ³Ό μ λΆν΄μ°λ¬Όλ€μ ν¨λμ΄ λμμ§μλ‘ κΈλ£¨μ½μλλ μ΄νΈ μμ·¨μ μν κ°μμ νΈλ₯΄λͺ¬ μμ± μ ν΄μ λν μν΄λκ° λμμ§κ² λλ€.κ΅λ¬Έμ΄λ‘ β
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μλ‘ 1
1μ₯. λ¬Ένκ³ μ°° 7
1.1. κ΅λ΄ κΈλ£¨μ½μλλ μ΄νΈμ μμ·¨μμ€κ³Ό μμνκ³Ό μ±μ λ΄μ ν¨λ 8
1.2. κΈλ£¨μ½μλλ μ΄νΈ λΆν΄μ°λ¬Όμ μμ±κ³Όμ 14
1.3. κΈλ£¨μ½μλλ μ΄νΈκ° μ체μ λ―ΈμΉλ μν₯ 18
1.4. κ°μμ νΈλ₯΄λͺ¬μ μν κ³Ό μ²΄λ΄ λμ¬ 23
2μ₯. In vitro λμ¬ νκ²½μμ κΈλ£¨μ½μλλ μ΄νΈ λΆν΄μ°λ¬Όμ ν°μ€μμμ° μμ΄μ¨(SCN-)μΌλ‘μ μ ν 28
2.1. μλ‘ 29
2.2. μ€νμ¬λ£ λ° λ°©λ² 35
2.2.1. μμ½ 35
2.2.2. κΈλ£¨μ½μλλ μ΄νΈ λΆν΄μ°λ¬Ό ν©μ± 36
2.2.2.1. 곡ν΅μ¬ν 36
2.2.2.2. Allyl thiocyanate ν©μ± 38
2.2.2.3. 2-Hydroxy-3-butenyl cyanide ν©μ± 40
2.2.2.4. 4-(methylthio)butyl cyanide ν©μ± 40
2.2.2.5. 4-(methylthio)butyl thiocyanate ν©μ± 41
2.2.2.6. 4-(methylsulfinyl)butyl cyanide ν©μ± 41
2.2.3. κΈλ£¨μ½μλλ μ΄νΈ λΆν΄μ°λ¬Όμ in vitro λμ¬ 42
2.2.4. SCN-κ³Ό CN- λΆμ 43
2.3. κ²°κ³Ό 44
2.3.1. λμ¬ν¨μ μμ©μ μν SCN-μμ± 44
2.3.2. μλ°μ λΆν΄μ μν SCN-μμ± 47
2.3.3. λμ¬ν¨μ μμ©μ μν CN-μμ±μ κ±°μΉ SCN-μμ± 49
2.4. κ³ μ°° 51
3μ₯. κΈλ£¨μ½μλλ μ΄νΈ λΆν΄μ°λ¬Όμ΄ thyroid peroxidaseμ νμ± λ° κ°μμ μΈν¬λ‘μ μμ€λ μ μ
μ λ―ΈμΉλ μν₯ 56
3.1. μλ‘ 57
3.2. μ€νμ¬λ£ λ° λ°©λ² 61
3.2.1. μμ½ 61
3.2.2. TPO νμ±λ³ν μΈ‘μ 62
3.2.3. κ°μμ μΈν¬μ£Ό FRTL-5λ₯Ό μ¬μ©ν μΈν¬ λ΄ μμ€λ μ μ
λ μΈ‘μ 64
3.2.4. κΈλ£¨μ½μλλ μ΄νΈ λΆν΄μ°λ¬Όμ ν¬μ¬ν λ«λμμμ λμ¬μ κ°μμ μ‘°μ§ λ΄ TPO νμ± λ° κ°μμ νΈλ₯΄λͺ¬ λ³ν μΈ‘μ 67
3.2.5. ν΅κ³λΆμ 70
3.3. κ²°κ³Ό 71
3.3.1. κΈλ£¨μ½μλλ μ΄νΈ λΆν΄μ°λ¬Όμ μν TPOμ νμ± λ³ν 71
3.3.2. κΈλ£¨μ½μλλ μ΄νΈ λΆν΄μ°λ¬Όμ κ°μμ μΈν¬(FRTL-5)μ λν μΈν¬μ¬λ©Έν¨κ³Ό(MTT assay) 77
3.3.3. κΈλ£¨μ½μλλ μ΄νΈ λΆν΄μ°λ¬Όμ μν κ°μμ μΈν¬(FRTL-5)μ μμ€λ μ μ
λ λ³ν 80
3.3.4. κΈλ£¨μ½μλλ μ΄νΈ λΆν΄μ°λ¬Όμ ν¬μ¬ν λ«λμ νμ²κ³Ό λ¨μμμ λμ¬λ¬Ό μΈ‘μ 86
3.3.5. λΆν΄μ°λ¬Όμ ν¬μ¬ν λ«λμ TPO νμ± λ³ν 91
3.3.6. λΆν΄μ°λ¬Όμ ν¬μ¬ν λ«λμ κ°μμ μ‘°μ§ λ΄ κ°μμ νΈλ₯΄λͺ¬ (T3, T4) λ³ν 93
3.4. κ³ μ°° 95
4μ₯. μμ½ λ° κ²°λ‘ 106
μ°Έκ³ λ¬Έν 116Docto
ν¨μ μλΉμμ μ¨λΌμΈ ν΄μΈμ§κ΅¬ νλμλμ κ΄ν μ°κ΅¬
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Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : μλ₯νκ³Ό, 2015. 8. μΆνΈμ .2014λ
μ¨λΌμΈμ ν΅ν κ±°λμ‘μ 45μ‘°2μ²440μ΅μμΌλ‘ μ¬μ μ΅μ΄λ‘ μ¨λΌμΈ μ±λμ΄ λνλ§νΈ κ±°λμ‘μ λ°μ΄λμλ€. μ ν΅μ±λ 1μμ μμ μ ν¬κ² μΌμ‘°ν μ¨λΌμΈμ ν΅ν ν΄μΈ μ§μ ꡬ맀(ν΄μΈμ§κ΅¬)λ κ΄μΈμ² μ§κ³ κΈ°μ€μΌλ‘ μλ
νν΄ μ½ 1μ‘°6μ²600μ΅μμΌλ‘, μ¨λΌμΈ ν΄μΈμ§κ΅¬λ 2010λ
μ΄ν ν΄λ§λ€ 40-50%μ μ±μ₯λ₯ μ 보μ¬μλ€. κ΅λ΄ μλΉμκ° ν΄μΈ μ¬μ΄νΈμμ ꡬ맀νλ μ§κ΅¬μ λλΆμ΄, ν΄μΈμλΉμκ° κ΅λ΄ μ¨λΌμΈ μΌνλͺ°μμ μ§μ ꡬ맀νλ μμ§κ΅¬ λν νμ°λκ³ μλ μΆμΈμ΄λ€. μ¨λΌμΈ ν΄μΈμ§κ΅¬λ 2040 μ¬μ±λ€μ μ μ λ¬Όμ΄μ νΈλ λλ₯Ό λμ΄μ λ νλμ κ°λ ₯ν μ ν΅μ±λλ‘ μ리λ₯Ό μ‘κ² λμμΌλ©° κ΅κ²½μ λμ΄μ 보νΈμ μΈ μΌννλμ΄ λμλ€. νΉν νλͺ©λ³ μ¨λΌμΈ ν΄μΈμ§κ΅¬ μμλ₯Ό μ΄ν΄λ³΄λ©΄ μλ₯κ° 19%λ‘ κ°μ₯ λ§μκ³ μ΄μ΄ μ λ°(13%, 3μ), νμ₯ν(11%, 5μ) μμΌλ‘ ν¨μ
μ νμ΄ μ¨λΌμΈ ν΄μΈμ§κ΅¬ μ ν μΉ΄ν
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λ° μ ν΅κ΅¬μ‘°μ λ³νμ μλΉμ νλμ λ°μνλ μ£Όμ μ΄μμμλ λΆκ΅¬νκ³ μ¨λΌμΈ ν΄μΈμ§κ΅¬μ λν νμ μ μΈ μ°κ΅¬λ μ νμ μ΄λ€. μ΄μ λ³Έ μ°κ΅¬λ μ νμ°κ΅¬λ₯Ό λ°νμΌλ‘ μ¨λΌμΈ ν΄μΈμ§κ΅¬λ₯Ό μ΄μ©νλ μλΉμλ€μ νλμλ λκΈ°λ₯Ό νμΈν΄ λ³΄κ³ μ νμλ€.
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μΈ ν¨μ©μ λκΈ°, μ¬νμ λκΈ°, μΎλ½μ λκΈ°λ₯Ό ν¨μ
μ ν μ¨λΌμΈ ν΄μΈμ§κ΅¬ λκΈ°λ‘ μ μ νκ³ , μλΉμκ° μ§κ°νλ ν¨μ
μ νμ μ¨λΌμΈ ν΄μΈμ§κ΅¬ μνμ λ°λΌ μ¨λΌμΈ ν΄μΈμ§κ΅¬ λκΈ°κ° νλμλμ λ―ΈμΉλ μν₯μ΄ μ‘°μ λλμ§ μ΄ν΄λ³΄μλ€. 2014λ
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μ νμ ꡬ맀ν κ²½νμ΄ μλ 20λ~40λ μ¬μ± μλΉμλ₯Ό λμμΌλ‘ μ¨λΌμΈ μ€λ¬Έμ μ€μνμμΌλ©°, μμ§λ μλ΅ μλ£μ λΆμμ ν΅ν΄ λμΆν λ³Έ μ°κ΅¬μ κ²°κ³Όλ λ€μκ³Ό κ°λ€.
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μ νμ ꡬ맀νλ μλΉμλ€μ ν¨μ©μ λκΈ°, μ¬νμ λκΈ°, μΎλ½μ λκΈ°κ° λμμλ‘ μ¨λΌμΈ ν΄μΈμ§κ΅¬λ₯Ό λΉλ²νκ² νλ©°, μΎλ½μ λκΈ°κ° μ¨λΌμΈ ν΄μΈμ§κ΅¬ νλμλμ κ°μ₯ ν° μν₯μ λ―ΈμΉλ κ²μΌλ‘ νμΈλμλ€.
λμ§Έ, ν¨μ
μλΉμκ° κ°λ μν μ§κ°μ΄ μ¨λΌμΈ ν΄μΈμ§κ΅¬ νλμλμ μν₯μ λ―ΈμΉμ§ μλ κ²μ νμΈνμλ€. μ¨λΌμΈ ν΄μΈμ§κ΅¬λ‘ ν¨μ
μ νμ ꡬ맀νλ μλΉμλ€μ μ¨λΌμΈ μ±λμ μνμ μ΅μνκ³ , μ±λκ³Ό κ±°λ μμ λ¨μ μ κ°μνκ³ μλΌλ ν¨μ©μ ννμ΄ λμΌλ©΄ μ¨λΌμΈ ν΄μΈμ§κ΅¬ νλμλλ₯Ό νλ κ²μΌλ‘ λνλ¬λ€.
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μ ν μ¨λΌμΈ ν΄μΈμ§κ΅¬ λκΈ°μ νλμλ κ° μλΉμκ° μΈμ§νλ μν μ§κ°μ μ‘°μ ν¨κ³Όλ₯Ό λ°νλ€. μ¨λΌμΈ ν΄μΈμ§κ΅¬ νλμλ λκΈ° μ€ ν¨μ©μ λκΈ°μ μν₯λ ₯λ§μ΄ μν μ§κ°μΌλ‘ μ‘°μ λμλ€. ν¨μ©μ λκΈ°μ μ±λ μν, κ±°λ μνμ μνΈμμ© ν¨κ³Όλ₯Ό μ΄ν΄λ³Έ κ²°κ³Ό, μ±λ μν μ§κ° μμ€μ΄ λμ μλΉμλ€μ ν¨μ©μ λκΈ°μ μμ€μ μλμ μΌλ‘ μ μ μν₯μ λ°μ μ¨λΌμΈ ν΄μΈμ§κ΅¬ νλμλλ₯Ό κ°μ§λ λ°λ©΄ κ±°λ μν μ§κ° μμ€μ΄ λμ μλΉμλ€μ ν¨μ©μ λκΈ°μ κ³ μ μ λ°λΌ μ¨λΌμΈ ν΄μΈμ§κ΅¬ νλμλμ ν° μ°¨μ΄λ₯Ό 보μ΄λ κ²μ νμΈνμλ€.
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ν¨μΌλ‘μ¨ ν¨μ
λΈλλλ 리ν
μΌλ¬λ€μ΄ μ¨λΌμΈ ν΄μΈμ§κ΅¬ μ¨λΌμΈ μ¬μ΄νΈλ₯Ό μ μν λ μ¬λ―Έλ₯Ό λΆλ¬μΌμΌν€λ ννμ΄μ§ μ μμ΄ μ ν©νλ€λ μ€λ¬΄μ μμ¬μ μ μ 곡ν μ μμ κ²μ΄λ€.According to Korea Customs Service, the total sales record of cross-border online shopping in Korea last year was 1.5 billion U.S. dollars. Since 2010, the average growth rate of the cross-border online shopping market was 40~50% per year. In addition to Koreans purchasing foreign goods via online channel, Yeok-jikgu or inverse cross-border online shopping is also prevalent phenomenon. Cross-border online shopping is no longer a trendy act amongst 20s~40s female consumers, but has become a strong distributional channel as well as a global consumer behavior. Among many categories being imported via cross-border online shopping channel, fashion items including clothing & apparel(19%), shoes(13%), and cosmetics(11%) rank number one. Although cross-border online shopping clearly is an issue that reflects changes in fashion industry and distribution channel, and consumer behavior, academic researches related to cross-border online shopping are very limited. The purpose of this study is to find out the factors affecting fashion consumers purchasing intentions on cross-border online shopping.
In order to empirically prove research questions, this study adopted the three outshopping motivation dimensionsutilitarian, social, and hedonic. Furthermore, the moderating effect of consumers perceived risk between cross-border online shopping motivation and behavioral intention was tested. Online survey was conducted on Korean female consumers in their 20s to 40s, with the experience of purchasing fashion goods through cross-border online shopping channel within a year. The findings of analysis are the following.
First, the three motivations have positive effects on cross-border online shopping purchase intention. Plus, fashion consumers are affected greatly by hedonic motivation compared to utilitarian and social motivations.
Second, perceived risk does not have negative effect on cross-border online shopping purchase intention. Cross-border online shopping uses the Internet as transaction channel, and many consumers well adapted, long-time online shoppers. Thus, fashion consumers tend to shop through cross-border online shopping channel even if they perceive risk, as long as there is utilitarian motivation. Finally, moderating effect of perceived risk exists between utilitarian motivation and cross-border online shopping purchase intention. The interaction effect of the independent and moderating factor proved that only utilitarian motivation is moderated with perceived risks when consumers are purchasing fashion goods on cross-border online shopping channel.
This study found the motivational and moderating factors that influence fashion consumers to shop through cross-border online shopping channel. It academically contributes by extending the research range of cross-border online shopping into fashion. Moreover, marketers and retailers should take note that fashion consumers are affected most by hedonic motivation when shopping in cross-border online malls.μ 1 μ₯ μ λ‘ 1
μ 1 μ μ°κ΅¬μ νμμ± λ° μμ 1
μ 2 μ μ°κ΅¬μ λͺ©μ 7
μ 3 μ μ°κ΅¬μ κ΅¬μ± 8
μ 2 μ₯ μ΄λ‘ μ λ°°κ²½ 9
μ 1 μ μμμΌνκ³Ό μ¨λΌμΈ ν΄μΈμ§κ΅¬ 9
1. μμμΌνκ³Ό μ¨λΌμΈ ν΄μΈμ§κ΅¬μ κ°λ
9
2. μμμΌνκ³Ό μ¨λΌμΈ ν΄μΈμ§κ΅¬μ λκΈ° 14
μ 2 μ μν μ§κ° 22
1. μν μ§κ°μ κ°λ
λ° μ νμ°κ΅¬ 22
2. μν μ§κ°μ μ‘°μ ν¨κ³Ό 24
μ 3 μ νμ₯λ ν΅ν©κΈ°μ μμ©μ΄λ‘ 26
1. νμ₯λ ν΅ν©κΈ°μ μμ©μ΄λ‘ μ κ°λ
26
2. νμ₯λ ν΅ν©κΈ°μ μμ©μ΄λ‘ μ κ΄ν μ νμ°κ΅¬ 31
μ 3 μ₯ μ°κ΅¬ λ°©λ² λ° μ μ°¨ 36
μ 1 μ μ°κ΅¬ κ°μ€ λ° μ°κ΅¬ λͺ¨ν 36
1. μ°κ΅¬ λ¬Έμ λ° κ°μ€ μ€μ 36
2. μ°κ΅¬ λͺ¨ν 38
μ 2 μ μ€μ¦μ μ°κ΅¬ λ°©λ² 39
1. μ°κ΅¬ λμ λ° λ°©λ² 39
2. μ€λ¬Έμ§ λ¬Ένμ κ΅¬μ± 39
μ 3 μ μλ£ μμ§ λ° λΆμ λ°©λ² 44
1. μλ£μ μμ§κ³Ό νλ³Έμ κ΅¬μ± 44
2. μλ£μ λΆμ λ°©λ² 44
μ 4 μ₯ μ°κ΅¬ κ²°κ³Ό λ° λ
Όμ 46
μ 1 μ μ‘°μ¬ λμμ νΉμ± 46
1. μΈκ΅¬ν΅κ³νμ νΉμ± 46
2. ν¨μ
μ ν μ¨λΌμΈ ν΄μΈμ§κ΅¬ κ²½ν 47
μ 2 μ μΈ‘μ λ³μμ μ λ’°μ± λ° νλΉμ± κ²μ¦ 52
1. κ° λ³μμ λν μ λ’°μ± λ° νλΉμ± κ²μ¦ 53
μ 3 μ λ³μμ κΈ°μ ν΅κ³ 56
μ 4 μ ν¨μ©μ λκΈ°, μ¬νμ λκΈ°, μΎλ½μ λκΈ°κ° ν¨μ
μ ν
μ¨λΌμΈ ν΄μΈμ§κ΅¬ νλμλμ λ―ΈμΉλ μν₯ 57
μ 5 μ₯ κ²° λ‘ 64
μ 1 μ μμ½ λ° κ²°λ‘ 64
μ 2 μ μ°κ΅¬μ μμ¬μ 67
1. μ°κ΅¬μ νλ¬Έμ μμ¬μ 67
2. μ°κ΅¬μ μ€λ¬΄μ μμ¬μ 68
μ 3 μ μ°κ΅¬μ νκ³ λ° νμ μ°κ΅¬λ₯Ό μν μ μΈ 70
μ°Έκ³ λ¬Έν 72
Abstract 86
λΆ λ‘ 89Maste
Absence of Cytosolic 2-Cys Prx Subtypes I and II Exacerbates TNF-Ξ±-Induced Apoptosis via Different Routes
There are abundant peroxiredoxin (Prx) enzymes, but an increase of cellular H2O2 level always happens in apoptotic cells. Here, we show that cellular H2O2 switches different apoptosis pathways depending on which type of Prx enzyme is absent. TNF alpha-induced H2O2 burst preferentially activates the DNA damage-dependent apoptosis pathway in the absence of PrxI. By contrast, the same H2O2 burst stimulates the RIPK1-dependent apoptosis pathway in the absence of PrxII by inducing the destruction of cIAP1 in caveolar membrane. Specifically, H2O2 induces the oxidation of Cys308 residue in the cIAP1-BIR3 domain, which induces the dimerization-dependent E3 ligase activation. Thus, the reduction in cIAP level by the absence of PrxII triggers cell-autonomous apoptosis in cancer cells and tumors. Such differential functions of PrxI and PrxII are mediated by interaction with H2AX and cIAP1, respectively. Collectively, this study reveals the distinct switch roles of 2-Cys Prx isoforms in apoptosis signaling.ope
Age Classification based on Machine Learning
νμλ
Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : μΈλ¬Έλν μΈμ΄νκ³Ό, 2018. 8. μ λ―Όν.λ³Έ μ°κ΅¬λ λκ²μ°°μ²μμ μμ§ν νκ΅μΈ λκ·λͺ¨ μμ± μ½νΌμ€λ₯Ό μ¬μ©νμ¬ κΈ°κ³ νμ΅ λͺ¨λΈμ ν΅ν΄ μ°λ Ήμ λΆλ₯νλ κ²μ λͺ©μ μΌλ‘ νλ€. νμ΅ λͺ¨λΈμ 20λ, 30~40λ, 50λ μ΄μμΌλ‘ 3λΆλ₯λ₯Ό νλ€.
μ€νμ μν΄ λ¬΅μ(silence)μ κΈ°μ€μΌλ‘ μμ± μ½νΌμ€λ₯Ό 378,684κ°μ λ°μ΄ν°λ‘ λΆμ νμμΌλ©°, λ°ν μ νκ³Ό μ±λ³λ‘ λ°μ΄ν°λ₯Ό ꡬλΆνμλ€. μμ±μΌλ‘λΆν° Mel Frequency Cepstral Coefficients(MFCCs), fundamental frequency(F0), i-vector, jitter, shimmer, λ°νμλλ₯Ό μΆμΆνμ¬ κΈ°κ³ νμ΅ λͺ¨λΈμΈ Long Short Term Memory(LSTM) λͺ¨λΈμ ν΅ν΄ μ°λ Ήμ λΆλ₯νμλ€. λν, feature selection μκ³ λ¦¬μ¦μ ν΅ν΄ κ° μμ± νΉμ§μ μν₯μ νμΈνμ¬ νΉμ§λ§λ€ κ°μ€μΉλ₯Ό λ¬λ¦¬ν μ€νλ μ§ννμλ€.
μ€νμμλ μμ± νΉμ§λ³ μ±λ₯κ³Ό μμ± νΉμ§μ μ‘°ν©μ μ±λ₯μΌλ‘ λλμ΄ ννμλ€. κ·Έ κ²°κ³Ό, κ°λ³ μμ± νΉμ§μ κ²½μ° MFCCλ‘ νμ΅νμμ λ 76.01%λ‘ κ°μ₯ λμμΌλ©°, μμ± νΉμ§μ μ‘°ν©μ κ²½μ° λͺ¨λ μμ± νΉμ§μ νμ΅νμμ λ 80.01%λ‘ κ°μ₯ λμλ€. λν, Recursive Feature Elimination (RFE)λ Extra Tree Classifier (ETC)μ κ°μ feature selection μκ³ λ¦¬μ¦μ μ μ©νμμ λλ 80.87%λ‘ λ³Έ μ°κ΅¬μμ κ°μ₯ λμ μ±λ₯μ 보μλ€.1. μλ‘ 1
2. μ ν μ°κ΅¬ 3
3. μ°κ΅¬ λ°©λ² 8
3.1 μμ± μ½νΌμ€ 8
3.1.1 μμ± μ½νΌμ€μ κ΅¬μ± 8
3.1.2 λ°μ΄ν° λΆμ 12
3.2 μ€ν λͺ¨λΈ 15
3.3 μμ± νΉμ§ μΆμΆ 17
3.3.1 Mel Frequency Cepstral Coefficients (MFCCs) 17
3.3.2 i-vector 21
3.3.3 Fundamental frequency (F0) 24
3.3.4 Jitter 25
3.3.5 Shimmer 27
3.3.6 λ°νμλ 29
4. μ€ν 31
4.1 μ€ν μ€κ³ 31
4.2 μ€ν κ²°κ³Ό 33
4.2.1 μμ± νΉμ§λ³ μ±λ₯ 34
4.2.2 μ‘°ν© μ±λ₯ 35
4.2.3 feature selection μ μ© ν μ±λ₯ 36
4.3 ν μ 39
5. κ²°λ‘ 43
μ°Έκ³ λ¬Έν 44
λΆλ‘ 48
μμ± νΉμ§λ³ νΌλ νλ ¬ 48
μμ± μ½νΌμ€ λ°ν μ ν 52
Abstract 59Maste
<μΉ μ±νμ΄> μ ν μμ¬λ¬΄κ°μ λ΄μ¬λ ν¬μμλ‘μ λμμ
So far, type Shamanic Epic has been interpreted mainly as a family
myth. However, considering that many myths use family relationships as symbols to
explain various themes, since it is a family incident, it is not displayed only by talking
about the family. There is also a need to explore possibilities that have different
meanings.
When classified according to the type of story, it is roughly divided into Salpoori and
Sungshingut in the northern region, Chilseongpoori in the southwestern region, and
MunjonBonpoori in Jeju Island. In all versions, the conflict between the stepmother and
the child of the ex-wife appears in a major event. For Chilseongpoori and
MunjonBonpoori, the separation of the parents and the resurrection of the real mother
were added.
The conflict between the stepmother and the child of the ex-wife was compared to
the story of the origin of the golden fleece in ancient Greece, which is similar in
content. The story of the origin of the golden fleece is about eliminating famine in the
city, and trying to solve the problem with a ritual of human sacrifice. The story shows
ritual traces of the murder of a prince on behalf of the king, who is responsible for
resolving famine, was interpreted as being replaced by a royal woman represented by
stepmother. She had an allegory of the Hinuwelle type, because she was a redemption
for the abundance of crops.
The separation and resurrection of the real mother shows that the range of redemption
of the stepmother has been extended to the real mother. When redeeming, the original
offering must be hidden from God. So the real mother was separated from her family
and hid to death, but when stepmother died and the redemption was over, she was
revived and redisplayed. The stepmother, who had been read as a wicked woman, could
be read again as a redemptor for a family. When interpreted as an ancient human
sacrifice ritual, the stepmother was a sacred being who promised family affluence and
prosperity as a sacrifice
Secondhand Smoke Exposure and Depressive Symptoms among Korean Adolescents: JS High School Study
INTRODUCTION: Increasing evidence suggests that secondhand smoke exposure (SHSE) may affect not only physical health, but also mental health. Therefore, we evaluated the association between SHSE and depressive symptoms among Korean adolescents.
METHODS: The JS High School Study enrolled 1071 high school freshmen from a rural community of South Korea. The current analysis was limited to 989 adolescents (495 male and 494 female adolescents), after excluding 48 ever-smokers, 3 students with physician-diagnosed depression, and 31 students who did not complete the depression questionnaire. SHSE was assessed using a self-reported questionnaire and was classified into three groups: none, occasional exposure, and regular exposure. Depressive symptoms were assessed according to the Beck Depression Inventory (BDI) score, ranging from 0 to 63, and the presence of depressive symptoms was defined as a BDI score β₯10.
RESULTS: Overall, adolescents with SHSE were more likely to have depressive symptoms than those without SHSE (p = 0.042).In a sex-specific analysis treating the BDI score as a continuous variable, regular SHSE was independently associated with higher BDI scores in male adolescents (Ξ² = 2.25, p = 0.026), but not in female adolescents (Ξ² = 1.11, p = 0.253). Compared to no SHSE, the odds ratio for having depressive symptoms among male adolescents with regular SHSE was 2.17 (95% confidence interval, 1.11 to 4.25) after adjusting for age, body mass index, and study year, and 3.65 (95% confidence interval, 1.52 to 8.73) after adjusting for age, body mass index, study year, exercise, and household income.
CONCLUSION: Regular exposure to secondhand smoke was associated with having depressive symptoms among Korean male adolescents.ope
λμμΈμμΈκ±°λ¦¬λ₯Ό ν΅ν΄ λ°λΌλ³Έ κ°λ‘νκ²½ κ°μ μ¬μ μ λν κ³ μ°°
νμλ
Όλ¬Έ (μμ¬)-- μμΈλνκ΅ νκ²½λνμ : νκ²½μ‘°κ²½νκ³Ό, 2017. 2. μμλ‘.λμμΈμμΈκ±°λ¦¬ μ¬μ
μ λμμΈμμΈμ μ λ μ¬μ
μΌλ‘μ 곡곡λμμΈ κ°μ΄λλΌμΈμ νκ΅ μ΅μ΄λ‘ κ·μ ννμ¬ λμ
Β·μννλ€. μ΄ν λ§μ κ²½κ΄ κ΄λ ¨ μ¬μ
κ³Ό μ μ±
λ€μ΄ μ κ΅μ μΌλ‘ μνλκΈ° μμνλλ° λμμΈμμΈκ±°λ¦¬λ μ΄λ κ² νκ΅μ μλ§μ κ²½κ΄ μ¬μ
λ€μ μ΄μμ΄ λμλ€λ μ μμ μ€μν μλ―Έλ₯Ό λ΄μ§νκ³ μλ€. μΆμ§λ°°κ²½μ κ·Έλμ μνλμλ 거리νκ²½κ°μ μ¬μ
μ 거리μμλ€μ΄ κ°λ³μ μΌλ‘ μ€μΉλμ΄ κΈ°λ₯μ μΈ ν΅ν©μ±μ΄ λΆμ‘±νλ€λ μ μ λ¬Έμ μ μΌλ‘ μΈμν κ²μ΄μλ€. λ°λΌμ μ΄λ₯Ό κ°μ ν 거리경κ΄μ μ‘°μ±νκΈ° μν΄ κ±°λ¦¬μ λͺ¨λ ꡬμ±μμλ€μ ν΅ν©μ μΌλ‘ λμμΈνμ¬ μμΈμ μ¬λ¬ λν거리λ€μ μΎμ νκ³ μλ¦λ€μ΄ 거리νκ²½μΌλ‘ λ§λ€κ³ μ νμλ€. μ΄ μ¬μ
μ ν΅ν΄ μλ¦λ€μ΄ 거리경κ΄μ μ‘°μ±νμ¬ μμΈμ λνμ μΈ λ¬ΈνμνμΌλ‘μ μμμ κ°μΆκ³ μ νμ§λ§ 거리경κ΄μ νμΌνλΌλ λΉνμμ μμ λ‘μ§ λͺ»νλ€. λν μ¬μ
μ μν΄ λ§μ μμ°μ λ€μμ§λ§ λ¨κΈ°μ± μ¬μ
μΌλ‘ λλ¬λ€λ μ μμ λ§μ λΉνμ λ°μμ λΏλ§ μλλΌ μλ§μ μ¬νμ μ΄μκΉμ§ μλ°νλ€λ μ μ ν΅ν΄ λ¬Έμ μμμ κ°μ§κ² λμλ€. μ΄λ¬ν κ³ λ―Όμ μ€μ¬μΌλ‘ λ³Έ μ°κ΅¬λ₯Ό ν΅ν΄ μμΌλ‘ λ λμ 거리νκ²½μ λ§λ€κΈ° μν κ°λ₯μ±μ λ°κ²¬ν μ μλ κ³κΈ°λ₯Ό λ§λ ¨ν΄λ³΄κ³ μ νμλ€.
μ΄λ₯Ό μν΄ λ¨Όμ μ’μ κ°λ‘ νκ²½μ λν κ³ μ°°κ³Ό λλΆμ΄ λμμΈμμΈκ±°λ¦¬ μ¬μ
μ μ€μ¬λ΄μ©μΈ 곡곡λμμΈμ μ€μ¬μΌλ‘ 곡곡μ±μ λ³Έμ§μ λν΄μλ κ³ μ°°ν΄λ³΄μλ€. μ΄λ₯Ό ν΅ν΄ μ’μ κ°λ‘ 곡κ°μ μ‘°μ±νκΈ° μν΄μλ λ¨μν λμμΈμ ν΅ν΄ 곡κ°μ 물리μ νκ²½μ μ§λ§ λμ΄λ κ²μ΄ μλλΌ κ³΅κ³΅λμμΈμ΄ μκ³ μλ 곡곡μ±μ κ°μΉλ₯Ό μ μ€μ²νλ κ²λ μ€μνλ€λ κ²μ μ μ μμλ€. μ΄ μ μ λ°νμΌλ‘ λμμΈμμΈκ±°λ¦¬λ₯Ό μ΄ν΄λ³΄λ©΄ μμΈμμμλ κΆκ·Ήμ μΌλ‘ μ’μ 곡곡λμμΈμ ν΅ν΄ μ’μ κ°λ‘ 곡κ°μ μ‘°μ±νκ³ μ νμ§λ§ μ΄λ₯Ό μν κ°μ΄λλΌμΈμ μΈμ°λ κ³Όμ μμ 곡곡μ μ±
κ°μ μλ―Ό μ¬μ΄μ λ
Όμ κ³Όμ μ΄ μμλ€λ μ μμ 곡곡μ±μ κ°μΉλ₯Ό μ€ννμ§ λͺ»νμμ μ§μ ν΄ λ³Ό μ μμλ€. κ·Έλ¦¬κ³ μμΈμκ° μ’μ 곡곡λμμΈμ μ΄λ
μ λ°μν γ곡곡λμμΈ κ°μ΄λλΌμΈγμ μ립νμ¬ κ°λ‘ νκ²½μ μ λΉνμ§λ§ λ§μ μ¬λλ€μ΄ μ΄μ 곡κ°νμ§ μμλ€λ μ μμ μ’μ κ°λ‘ 곡κ°μ μλ―Έλ 곡곡μ μ±
κ°μ 거리μ΄μ©μμ κ΄μ μ λ°λΌ λ€λ₯΄κ² λ³΄μΌ μ μλ€λ λΆλΆλ μκ°ν΄λ³Ό μ μμλ€.
λμμΈμμΈκ±°λ¦¬ μ¬μ
μ λ¬Έμ λ₯Ό λ³΄λ€ κ·Όλ³Έμ μΈ μ°¨μμμ μ΄ν΄νκΈ° μν΄ κ·Έλμ μμΈμμμ μΆμ§ν΄μλ κ°λ‘νκ²½κ°μ μ¬μ
μ μμ¬μ λ§₯λ½μ ν΅ν΄ λΆμνλ©΄μ λλ¬λλ κ·Όμμ μΈ νκ³μ λ¬Έμ λ€μ μ΄ν΄λ³΄μλ€. κ°λ‘νκ²½ κ°μ μ¬μ
μ νλ¦μ μλ§μμ΄λ μΌνμΌλ‘ μνλμλ 1970λ
λ 거리νκ²½λ―Έν μΊ νμΈλΆν° 2007λ
λμμΈμμΈκ±°λ¦¬μ¬μ
μ μ΄λ₯΄κΈ°κΉμ§ ν΅μμ μΌλ‘ μ΄ν΄λ³΄λ©΄μ λμμΈμμΈκ±°λ¦¬ μ¬μ
μ κΈ°μ‘΄ κ°λ‘νκ²½ κ°μ μ¬μ
μ κ²½ν₯μ μ΄μ΄λ°μ κ°μ₯ λ°μ μ μ΄κ³ μ λ¬Έμ μΌλ‘ μΆμ§λμλ μ¬μ
μ΄μμμ μ μ μμλ€.
λμμΈμμΈκ±°λ¦¬ λμμ§λ₯Ό ν΅ν΄ κ°λ‘νκ²½ κ°μ μ¬μ
μ νκ³μ λ¬Έμ λ€μ μ€μ¦μ μΌλ‘ μ΄ν΄λ³΄κ³ μ κ°λ¨λλ‘ λμμΈκ±°λ¦¬μ μ΄νμλμμΈκ±°λ¦¬ λ κ°μ λμμ§λ₯Ό μ μ νμ¬ κ΄μ°°Β·λΆμν΄ λ³΄μλ€. μ°κ΅¬μ λ°©λ²μ λμμΈμμΈκ±°λ¦¬ μ¬μ
μ΄ μ§ν₯νλ 4λ μ€μ²μ λ΅μ μ€μ¬μΌλ‘ κ²½κ΄μ±, 곡곡μ±, μ§μμ±μ μΆμΆνμ¬ μ΄ κ°μΉκ° μ μ€νλμλμ§λ₯Ό νμ
νλ©° λμμ§λ₯Ό μ΄ν΄λ³΄μλ€. λ λμμ§ κ°λ‘νκ²½μ κ²½κ΄μ±μ μ΄ν΄λ³΄κΈ° μν΄ μ§μ νΉμ±μ μ‘°μ¬νκ³ μκ°λλ³λ‘ 거리경κ΄μ κ΄μ°°ν΄λ³΄μκ³ , 곡곡μ±μ μ΄ν΄λ³΄κΈ° μν΄ μ¬μ
μ μΆμ§λ΄μ©μμ λ€μν μ£Όμ²΄κ° μ°Έμ¬νλ ννΈλμμ΄ νμ±λμλμ§ νμΈν΄ 보μλ€. λν μ§μμ±μ νμΈνκΈ° μν΄ λμμΈμμΈκ±°λ¦¬ μ¬μ
μ μ£Όμλ΄μ©μΈ κ°λ‘μμ€λ¬Όκ³Ό μ₯μΈκ΄κ³ λ¬Ό μ λΉλ₯Ό μ€μ¬μΌλ‘ 곡곡μμ€λ¬Ό μ€μΉ λ³νμ μ μ§κ΄λ¦¬ νν©κ³Ό 곡곡μμ€λ¬Όμ μ μ©λ κ·μ λ³νλ₯Ό μ΄ν΄λ³΄μλ€. μ΄λ κ² κ°λ¨λλ‘μ μ΄νμ λμμΈκ±°λ¦¬λ₯Ό μ΄ν΄λ³Έ κ²°κ³Ό λ μ§μ λͺ¨λ μλ―Όμ°Έμ¬κ° κ²°μ¬λ 곡곡주λ μ¬μ
λ°©μμ΄ μ§μλλ©΄μ 곡곡μ±μ κ°μΉκ° μ€μ²λμ§ μμλ€λ κ²μ μ μ μμκ³ , μ΄ μ μμ κ°λ‘νκ²½ κ°μ μ¬μ
μ λ³Έμ§μ μΈ λ¬Έμ μ μ΄ λ°λ³΅λκ³ μμμ μ μ μμλ€. κ·Έλ¦¬κ³ κ³΅κ³΅κ³Ό λ―Όκ°μ κ΄λ¦¬μ£Όμ²΄μ λ°λΌ 곡곡μμ€λ¬Όμ λ³ν μμλ λ¬λΌμ§κ³ μμΌλ©°, 곡곡μμ€λ¬Όμ μ‘°μ±κ³Όμ κ³Ό μ μ§Β·κ΄λ¦¬μμ 곡곡기κ΄μ μ§λμΉ κ΄λ£μ±μ΄ λνλκ³ μμμ νμΈν μ μμλ€. νΉν κ° μ§μμ νΉμ±μ λ°λΌ μ₯μΈκ΄κ³ λ¬Όμ μ΄μ©ννκ° κΈκ²©ν λ³ννκ³ μλ€λ μ μ μ μ μμλλ°, ννΈμΌλ‘λ μ§μμ λ¬Ένλ₯Ό 곡곡μμ€λ¬Όμ ν΅ν΄ νμΆν μ μλ κ°λ₯μ±λ λ°κ²¬ν μ μμλ€.
λ°λΌμ λμμΈμμΈκ±°λ¦¬ μ¬μ
μ μμΈμ κ°λ‘νκ²½ κ°μ μ¬μ
μ λ¬Έμ μ νκ³λ₯Ό 극볡νκΈ° μν΄ μ’μ 곡곡곡κ°μ μ‘°μ±νκ³ μ νμ§λ§ μ¬μ
μ λͺ©νλ₯Ό λ¨κΈ°μ μΌλ‘ μ립νμ¬ μμΈμ κ°λ‘νκ²½ κ°μ μ¬μ
μ κ·Όλ³Έμ μΈ νκ³μΈ 곡곡μ±κ³Ό μ§μμ±μ κ°μΉλ₯Ό μ μ€μ²νμ§ λͺ»νλ€. μ΄λ¬ν νκ³λ₯Ό 극볡νκ³ λ μ’μ κ°λ‘νκ²½μΌλ‘ κ±°λλκΈ° μν΄μλ μ¬μ
μ κ³νμμ λΆν° 곡곡μ μ±
κ°λ€κ³Ό λ―Όκ° μ΄ μνΈκ° μ‘°μ¨ν μ μλ κ³Όμ μ μ κ·Ήμ μΌλ‘ λ§λ ¨νκ³ , μ₯κΈ°μ μΈ κ΄μ μμ 곡곡μμμ ꡬμΆνλ©΄μ 곡곡μ±μ λ°νν μ μμ΄μΌ νλ€. κ·Έλ¦¬κ³ λ³΄λ€ λμ 곡곡λμμΈμ μ€ννκΈ° μν΄ μλ―Όμ°Έμ¬μ κΈ°νλ₯Ό λ€λ°©λ©΄μΌλ‘ νμ§ μ΄μ΄μ μ₯κΈ°μ μΈ μ§μκ°λ₯μ±μ μλ―Όκ³Ό ν¨κ» λ§λ€μ΄κ°μΌ ν κ²μ΄λ€.μ 1μ₯ μλ‘ 1
1μ . μ°κ΅¬μ λ°°κ²½ λ° λͺ©μ 1
1. μ°κ΅¬μ λ°°κ²½ 1
2. μ°κ΅¬μ λͺ©μ 2
2μ . μ νμ°κ΅¬ λΆμκ³Ό μ°κ΅¬μ μ°¨λ³μ± 3
μ 2μ₯ λ¬Έμ μ κΈ°μ μ΄λ‘ μ κ³ μ°° 5
1μ . λμμΈμμΈκ±°λ¦¬μ λν λ¬Έμ μ κΈ° 5
2μ . κ°λ‘ 곡κ°μμ 곡곡λμμΈμ μλ―Έ 9
1. μ’μ κ°λ‘ 곡κ°μ λν μ°κ΅¬ 9
2. 곡곡λμμΈμ μλ―Έ 14
3. κ°λ‘ 곡κ°μμ 곡곡λμμΈμ 곡곡μ±κ³Ό μ¬νΒ·λ¬Ένμ κ³ μ°° 18
μ 3μ₯ κ°λ‘νκ²½ κ°μ μ¬μ
μ μμ¬μ κ³ μ°° 26
1μ . κ°λ‘νκ²½ κ°μ μ¬μ
μ μμ¬μ νλ¦ 26
1. 1970λ
λ κ°λ‘νκ²½ μ λΉμ¬μ
26
2. 1980λ
λ κ°λ‘νκ²½ μ λΉμ¬μ
28
3. 1990λ
λ κ°λ‘νκ²½ μ λΉμ¬μ
33
4. 2000λ
λ κ°λ‘νκ²½ κ°μ μ¬μ
40
2μ . 2007λ
μ΄ν λμμΈμμΈκ±°λ¦¬ μ¬μ
κ³ μ°° 48
1. λμμΈμμΈκ±°λ¦¬ μ¬μ
μ μΆμ§κ³Ό μ±κ²© 48
2. λμμΈμμΈκ±°λ¦¬ μ¬μ
μ λ΄μ©κ³Ό νν© 54
3μ . κ°λ‘νκ²½ κ°μ μ¬μ
μ μμ¬λ₯Ό ν΅ν νΉμ± λμΆ 60
μ 4μ₯ μ°κ΅¬μ λ°©λ²κ³Ό λμμ§ μ μ 64
1μ . μ°κ΅¬μ λΆμνκ³Ό μ°κ΅¬λ°©λ² 64
1. μ°κ΅¬μ λΆμν 64
2. μ°κ΅¬μ λ°©λ² 66
2μ . λμμ§μ μ μ 68
μ 5μ₯ κ°λ¨λλ‘ U-Street 71
1μ . κ°λ¨λλ‘ U-Streetμ μΆμ§λ΄μ© 71
2μ . κ°λ‘ κ²½κ΄μ νΉμ± 77
3μ . κ°λ‘μμ€λ¬Όμ νν©κ³Ό νΉμ± 80
1. κ°λ‘μμ€λ¬Όμ κ°μ΄λλΌμΈ κ·μ κ³Ό μ€μΉνν© 81
2. λμμΈμμΈκ±°λ¦¬μ¬μ
μ΄ν κ°λ‘μμ€λ¬Όμ λ³μ² 96
4μ . μ₯μΈκ΄κ³ λ¬Όμ νν©κ³Ό νΉμ± 97
1. κ°νμ λΉμ¬μ
μ μΆμ§ λ° μ‘°λ‘ 97
2. μ₯μΈκ΄κ³ λ¬Ό κ°μ΄λλΌμΈκ·μ κ³Ό λΆλ²κ΄κ³ λ¬Ό 100
5μ . μ κ²° 103
μ 6μ₯ μ΄νμ λμμΈκ±°λ¦¬ 106
1μ . μ΄νμ λμμΈκ±°λ¦¬μ μΆμ§λ΄μ© 106
2μ . κ°λ‘ κ²½κ΄μ νΉμ± 110
3μ . κ°λ‘μμ€λ¬Όμ νν©κ³Ό νΉμ± 114
1. κ°λ‘μμ€λ¬Όμ κ°μ΄λλΌμΈ κ·μ κ³Ό μ€μΉνν© 114
2. λμμΈμμΈκ±°λ¦¬μ¬μ
μ΄ν κ°λ‘μμ€λ¬Όμ λ³μ² 118
4μ . μ₯μΈκ΄κ³ λ¬Όμ νν©κ³Ό νΉμ± 119
1. κ°νμ λΉμ¬μ
μ μΆμ§ λ° μ‘°λ‘ 119
2. μ₯μΈκ΄κ³ λ¬Ό κ°μ΄λλΌμΈκ·μ κ³Ό λΆλ²κ΄κ³ λ¬Ό 122
5μ . μ κ²° 126
μ 7μ₯ κ²° λ‘ 129
μ°Έκ³ λ¬Έν 139
Abstract 144Maste
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