18 research outputs found
κ΅λ΄ λμ₯λΆλ¦¬ λμ₯κ· μ νμμ λ΄μ±μ ν λ° λ΄μ±μ μ μμ μ λ¬μ κ΄ν λΆμ
νμλ
Όλ¬Έ (λ°μ¬)-- μμΈλνκ΅ λνμ : μμκ³Όλν μμνκ³Ό μμλ―Έμλ¬Όνμ 곡, 2016. 2. μ νμ.Escherichia coli is a significant reservoir of antimicrobial resistance determinants which can spread pathogenic bacteria to human and animals. E. coli strains are able to efficiently exchange genetic mobile material such as integrons, transposomes and plasmid of pathogens. Acquired resistance mechanism mediated by these determinants play on important role in acquisition and dissemination of resistance mechanism. Thus in order to investigate and analyze the prevalence and transferability of antimicrobial resistance in farm animals, E. coli strains isolated from the pathogenic lesions and fecal samples during the year 2009-2015 from beef cattle, pigs and chickens were included in this study.
The first study on the prevalence and characterization of E. coli isolated from beef cattle farms showed the diverse patterns of phenotype and genotype in antimicrobial resistance and pathogenicity. The most dominant virulence gene was f17. The 152 isolates showed multidrug-resistance. Antimicrobial susceptibility test determined that the most frequent resistance phenotype was streptomycin (63.1%), followed by tetracycline 54.5%), cephalothin (32.8%), and sulphamethoxazole/trimethoprim (16.6%). PCR and sequencing showed the prevalence of associated resistance determinants as follows: strA-strB (39.0%, 113/290), tet(A) (27.6%, 80/290), blaTEM (23.8%, 69/290), and sul2 (22.1%, 97/290). PFGE and O serotyping identified that E. coli isolates in this study showed the high degree of clonal diversity in genetic relation.
Second study was focused on ampicillin-resistant bovine E. coli strains harboring Ξ²-lactamases which have possibility to evolve into Extended-spectrum Ξ²-lactamase (ESBL) or plasmid-mediated AmpC Ξ²-lactamase. In this study, 78 E. coli isolates from beef cattle were included in this study. In the disc diffusion test with Ξ²-lactams, 38.5% of the isolates showed resistance to ampicillin, amoxicillin, and cephalothin, together. However, none of the isolates had determined to produce ESBL or AmpC Ξ²-lactamases by double disc synergy method. All isolates encoded genes for TEM-1-type Ξ²-lactamase. In plasmid replicon typing, IncFIB and IncFIA were identified in 71.4% and 41.0% of plasmids, respectively. Of transferable replicon, IncFIB and IncFIA were the most frequent type detected (61.5% and 41.0%, respectively). Based on these results, we might suggest that the transferable plasmids could provide significant effect on the acquisition and dissemination of Ξ²-lactam resistance as well as selection pressure although the level of antimicrobial usage in beef cattle is relatively low compared to those in other livestock animals in Korea.
In third study, the prevalence and transferability of resistance in tetracycline-resistant E. coli isolates from beef cattle in South Korea were carried out. Among 155 E. coli isolates, 146 were confirmed to be resistant to tetracycline. The tetracycline resistance gene tet(A) (46.5%) was the most prevalent. Ninety-one (62.3%) isolates were determined to be multidrug-resistant by the disc diffusion method. MIC testing using the principal tetracyclines, revealed that isolates carrying tet(B) had higher MIC values than isolates carrying tet(A). Conjugation assays showed that 121/155 (82.9%) isolates could transfer a tetracycline resistance gene to a recipient via the IncFIB replicon (65.1%, 95/155). This study suggests that the high prevalence of tetracycline-resistant E. coli isolates in beef cattle might be due to the transferability of tetracycline resistance genes between E. coli populations which have survived the selective pressure caused by the use of antimicrobial agents.
In final study, a total of 281 E. coli strains isolated from pigs and chickens were investigated for ESBL-production. Fourteen E. coli isolates were identified to produce ESBL. The most common CTX-M- and CMY-types were CTX-M-15 (8/14) and CMY-2 (3/14). All ESBL-producing isolates showed resistance to the extent of the fourth-generation cephalosporins, along with multi-drug resistance. A conjugation assay demonstrated that blaCTX-M and blaCMY genes have the potential to be transferred to non-resistant E. coli. The horizontal dissemination of blaCTX-M and blaCMY genes was mediated mainly by Frep and IncI1 plasmids. PFGE revealed that isolates tested in this study were very diverse, clonally. To our knowledge, this is the first report of E. coli isolate possessing blaCMY-6 from chickens in South Korea.
Distribution of resistance determinants in transferable plasmid of E. coli investigated in these studies could be critical in the public health. In addition future use of antimicrobial agents for human and veterinary purpose should be limited because of the increase in antimicrobial resistance for E. coli in human and farm animals. Thus reasonable use and long-term surveillance are needed for minimizing the emergence and spread of antimicrobial resistance in E. coli.General introduction 17
Chapter I. Literature review 20
1.1. Antimicrobial resistance 20
1.1.1. Resistance mechanism of bacteria 21
1.1.2. Molecular mechanisms of resistance 23
1.2. Trend of antimicrobial consumption in food animal 27
1.2.1. Global trend of antimicrobial consumption 27
1.2.2. Trend of antimicrobial consumption in Korea 30
1.3. Prevalence of antimicrobial resistance 33
1.4. Emerging trends of resistance in E. coli 39
1.4.1. Extended-spectrum Ξ²-lactamases (ESBLs) 39
1.4.2. Plasmid-mediated AmpC-Ξ²-lactamase 41
1.4.3. Metallo-Ξ²-lactamases (MBLs) 44
1.5. Plasmid mediated transfer of antimicrobial resistance among E. coli 48
1.5.1. Plasmids mediated ESBLs in E. coli of animal origin 48
1.5.2. Plasmids mediated AmpC Ξ²-lactamases in E. coli 50
1.5.3. Plasmid mediated carbapenem resistance in E. coli 51
1.5.4. Plasmids mediated quinolone and/or aminoglycoside resistance 52
Chapter II. Antimicrobial resistance, virulence gene and PFGE-profiling of Escherichia coli isolates from cattle farms 55
Abstract 55
Introduction 56
Materials and Methods 59
Results 64
Discussion 67
Chapter III. Profiling of antimicrobial resistance and plasmid replicon types in Ξ²-lactamase producing Escherichia coli isolated from beef cattle 81
Abstract 81
Introduction 82
Materials and Methods 84
Results 88
Discussion 90
Chapter IV. Prevalence of antimicrobial resistance and transfer of tetracycline resistance genes in Escherichia coli isolates from beef cattle 98
Abstract 98
Introduction 99
Materials and Methods 101
Results 105
Discussion 108
Chapter V. Prevalence and characterization of CTX-M- and CMY-type extended-spectrum Ξ²-lactamase producing Escherichia coli isolates from pigs and chickens 119
Abstract 119
Introduction 120
Materials and Methods 122
Results 127
Discussion 131
General conclusions 139
References 141
κ΅λ¬Έμ΄λ‘ 170Docto
μ ν΄ννλ¬Όμ§λ°°μΆλ μλ£μ GIS νμ©λ°©μμ κ΄ν μ°κ΅¬
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Όλ¬Έ(μμ¬)--μμΈλνκ΅ λ³΄κ±΄λνμ :ν경보건νκ³Ό μμ§κ΄λ¦¬μ 곡,2002.Maste
(A) study on the environmental awareness and behavior of the college studies in Seoul
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νλ‘ μΈν μμ° μμκ³Ό μν μμ λ³νμ νμ°μ μΈ κ²°κ³Όμ΄λ€.μ°μ
μ΄ λ°μ νλ©΄μ λλ μμ° λ° μλΉ, μ΄μ λ°λ₯Έ νκΈ°λ¬Ό λ°μκ³Ό μμ λ¨μ©μ λ¬Έμ κ° λ°μνμμΌλ©° κ·Έ κ²°κ³Ό μμ° μνκ³μ νκ΄΄μ νκ²½ μ€μΌμ΄ μ λ°λλ©΄μ μ°λ¦¬μ μν νκ²½μ μννκ³ μλ€.
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νμ¬ λΆμν¨μΌλ‘μ¨ κΆκ·Ήμ μΌλ‘ νμλ€μ νκ²½ κ΄λ¦¬ νμμ μ΄νμ ν₯μμν€κΈ° μν λ°©μμ λ§λ ¨νκ³ , ν₯ν λ³΄λ€ ν¨μ¨μ μΈ νκ²½ κ΅μ‘μ μν κΈ°μ΄ μλ£λ₯Ό μ»κ³ μ μ€νλμλ€.
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4μ 20μΌ λΆν° 1998λ
4μ 25μΌκΉμ§ μμΈ μλ΄ ν λνμ νλΆμ 282λͺ
μ λμμΌλ‘ μ€λ¬Έ μ‘°μ¬λ₯Ό μννμμΌλ©°, μμ§λ μλ£λ₯Ό ν΅κ³ λΆμνμ¬ κ²°κ³Όλ₯Ό μ μνμλ€.
λ³Έ μ°κ΅¬μ μ£Όμ κ²°κ³Όλ₯Ό μμ½νλ©΄ λ€μκ³Ό κ°λ€.
1. νκ²½ μμκ³Ό νκ²½ κ΄λ¦¬ νμμ μμ€μ κ° μ 곡 λ³λ‘ μ μν μ°¨μ΄λ₯Ό 보μλλ°(p-value=0.0001), νΉν 6κ°μ§λ‘ λΆλ₯ν μ 곡(μκ²½ μ¬ν κ³μ΄, 곡ν κ³μ΄, μ΄λ¬Έ κ³μ΄, μν μ 곡,κ°νΈν μ 곡, κ°μ κ³μ΄) μ€μμ μν, κ°νΈν μ 곡 νμμ΄ νμ 곡 νμλ³΄λ€ νκ· μ μΈ
νκ²½ μμκ³Ό νκ²½ κ΄λ¦¬ νμ μ μκ° λκ² λνλ¬μΌλ©° ν΅κ³μ μΌλ‘ 보μμ λμλ μ μν μκ΄μ±μ λνλ΄μλ€.
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, μ±λ³, μ£Όκ±° νν, λΆλͺ¨μ μ΅μ’
νλ ₯, μ΄λ¨Έλμ μ§μ
λ±μ΄μλ€(p-valueγ 0.05).
3. νκ²½ μμμ λ°λ₯Έ νκ²½ κ΄λ¦¬ νμ μ λλ₯Ό 보μμ λ, μκ΄ κ³μκ° 0.28λ‘ λνλ λλ³μκ°μ μ 체μ μΈ μκ΄μ±μ κ·Έλ€μ§ μ μνμ§ μλ€κ³ λ³Ό μ μμΌλ, μ΄λ₯Ό 곡λΆμ°λΆμ(ANCOVA)μΌλ‘ λ€μ 보μμ κ²½μ°μλ p-valueκ° 0.0001λ‘ μμ£Ό μ μν¨μ μ μ μλ€. μ΄λ
νκ²½ μμμ΄ λμμ λ°λΌμ νκ²½ κ΄λ¦¬ νμ μ λλ λμμ§λ€λ κ²μ 보μ¬μ£Όκ³ μλ€.
4. μ‘°μ¬ λμμμκ² κ°μ₯ μν₯μ λΌμΉλ λμ€ λ§€μ²΄λ‘λ μ λ¬Έ, μ‘μ§λ₯Ό κΌ½μ μ μμμΌλ©°(53.5%), λμ€ λ§€μ²΄κ° μ‘°μ¬ λμμμκ² λΌμΉλ μν₯μ 'κ·Έμ κ·Έλ λ€'λ λλ΅μ΄ κ°μ₯ λ§μλ€(41.8%). λν μ‘°μ¬ λμμμ νκ²½μ λν μ§μμ΄λ μ 보μ μν₯μ μ£Όλ κ²½λ‘λ‘ TV, λΌλμ€, μ λ¬Έ, μ‘μ§ λ±μ 맀μ€μ»΄μ΄ κ°μ₯ λ§μ΄ μ νλμλ€(64.9%).
μ΄μμ μ°κ΅¬ κ²°κ³Όμ μνλ©΄, λμ νκ²½ μμκ³Ό μ΄μ μμλλ νκ²½ κ΄λ¦¬ νμμ μ€μ² μ λκ° λ°μ ν μκ΄μ±μ 보μ΄λ κ²μΌλ‘ νκ°λμμΌλ―λ‘ νκ²½ κ΅μ‘μ΄λ TV, μ λ¬Έ, μ‘μ§ λ±μ 맀μ€μ»΄μ μν ν보 λ±μΌλ‘ νκ²½ μμμ λμΈλ€λ©΄ κ·Έμ λ°λ₯Έ νκ²½ κ΄λ¦¬ νμ μ λλ
λμμ§ μ μμμ μ μ μλ€. κ·Έλ¬λ, νκ²½ κ΄λ ¨ μμ
μ 무μ νκ²½ μμ λ° νκ²½ κ΄λ¦¬ νμκ°μ κ΄κ³κ° μ μνμ§ μμ κ²μΌλ‘ λνλ¬μΌλ―λ‘ νμ¬ ννμ¬μ§κ³ μλ νκ²½ κ΄λ ¨ μμ
μ΄ μμΌλ‘ μ’λ ν¨κ³Όμ μΌλ‘ μ΄λ£¨μ΄μ§ μ μλλ‘ λ
Έλ ₯κ³Ό κ°μ μ΄ μꡬλμ΄μ§λ€.
[μλ¬Έ]
Nowadays, environmental pollution has reached its peak and the environmental education become important. This study was conducted to evaluate the status of awareness and behavior of college students regarding environment, and to understand how the environmental awareness and behavior of college students differs from their age, gender, grade, major, socio-economic status, birthplace, dwelling, religion, parents' school career, parents' occupation, and presence or absence of the environmental education in college. This survey was done from April 20 to April 25 1998, and the number of cases were 282, from selected college students from a university in Seoul.
The specific objectives are as follows :
1. To investigate how the environmental awareness of college students affect their environmental behavior.
2. To assess the relationship between environmental awareness and environmental behavior.
3. To investigate how the major of college students affect their environmental awareness and behavior. Data were analysed using ANOVA, ANCOVA and t-test. The demographic and socio-economic variables were set as independent variables and environmental awareness and behavior, dependent variables.
The major findings are as follows:
1. Analysis of the data revealed that there were significant differences among majors in terms of environmental awareness and behavior(P=0.0001). Especially, with regard to environmental awareness and behavior, students with health related
majors scored higher than those students whose majors are not related to health.
2. The correlation between environmental awareness and behavior and other demographic and socio-economic variables were statistically significant in some items such as age, grade, gender, dwelling, parents' school career, mother's occupation(Pγ0.05).
3. There was a significant correlation between environmental awareness and behavior in ANCOVA(P=0.0001).
4. The most frequent source of environmental concern was mass-media such as TV, radio, newspaper, magazine(64.9%).
In conclusion, it is noted that the higher students' environmental awareness are, the more they do environmental behavior. Accordingly, the environmental education should be arranged systemically and an effort to improve environmental awareness of student is needed.prohibitio
PIV μκ³ λ¦¬λ¬ κ°λ°κ³Ό 2 μ μ²΄λ Έμ¦ μ λ μΈ‘μ μμ μμ©
νμλ
Όλ¬Έ(μμ¬)--μμΈλνκ΅ λνμ :κΈ°κ³κ³΅νκ³Ό,1998.Maste
The Effects of high School Students' Digital Literacy on School Educational Performance Through Learning Strategy
νμλ
Όλ¬Έ(μμ¬) -- μμΈλνκ΅λνμ : μ¬λ²λν κ΅μ‘νκ³Ό, 2023. 2. λ°±μκ·Ό.In the era of the Fourth Industrial Revolution, where everything is digitalized, digital literacy is regarded as one of the core competencies required in all fields, such as reading and writing, and the interest and importance of digital literacy is growing. The importance of digital literacy and learning strategies continues to increase for high school students as the learning environment and contents change rapidly, such as the increasing spread of educational smart devices and the application of digital literacy to the school curriculum through the 2022 revised curriculum.
In the meantime, studies on high school students' digital literacy, learning strategies, and school educational performance (class satisfaction, academic achievement) have been steadily conducted, but studies to confirm the structural relationship between each variable or three variables are insufficient. Therefore, it is necessary to analyze the structural relationship between the three variables empirically based on previous studies on the relationship between each variable. This study analyzed the structural relationship between three variables using Structural Equation Medeling. In this study, it was assumed that high school students' digital literacy and learning strategies would affect school educational performance (class satisfaction, academic achievement), and the specific research questions are as follows.
1. How does high school students' digital literacy affect their learning strategies?
2. How do high school students' learning strategies affect school educational outcomes?
3. How does high school students' digital literacy affect school educational outcomes?
4. How does high school students' digital literacy affect school educational performance through learning strategies?
Methods and procedures for solving these research problems are as follows. First, through the review of domestic and international precedent studies and evaluation by experts, the 'digital literacy scale' and 'learning strategy scale' of high school students were created and validated. The sub-domains of the 'digital literacy scale' were composed of 'digital device use', 'information collection', and 'information analysis and utilization', and a total of 18 items were produced, 6 items for each sub-area. The sub-domains of 'Learning strategy scale' were composed of 'learning goal setting', 'learning method selection', and 'learning inspection and reflection', and a total of 18 questions were produced, 6 questions for each sub-domain. The construct validity of each scale was checked through confirmatory factor analysis, and the reliability was checked through Cronbach's alpha. As a result, the validity and reliability of both scales were good.
In order to collect data, the test was conducted on 2nd year high school students attending Y High School located in Chungcheongbuk-do. In order to identify the causal relationship between each variable, the 'digital literacy scale of high school students' and the 'learning strategy scale of high school students' were tested in September 2022, and the 'class satisfaction' test was conducted four weeks later in November. Also Midterm exam grades were collected with class satisfaction. Using the collected data, descriptive statistics, correlations, and structural equation models were analyzed to examine the structural relationship between digital literacy, learning strategies, and school education outcomes.
The main results of this study are summarized as follows.
First, it was found that high school students' digital literacy had a positive effect on school education performance. As a result of the structural equation model analysis, the standardized coefficient for the effect of digital literacy on school educational performance was 0.485 in the model without controlling for the control variable and 0.514 for the model with the control variable, all of which were statistically significant (p. <.001).
Second, high school students' digital literacy was found to have a positive effect on learning strategies. As a result of the analysis of the structural equation model, the standardization coefficient for the effect of digital literacy on learning strategies was 0.271 in the model without setting the control variable and 0.276 in the model with setting the control variable, both of which were found to be statistically significant ( p<.001).
Third, It was found that high school students' learning strategies had a positive effect on school educational performance. As a result of the analysis of the structural equation model, the standardization coefficient for the effect of learning strategy on school educational performance was 0.397 in the model without the control variable and 0.318 in the model with the control variable set, both of which were statistically significant. (p<.01, p<.05).
Fourth, it was found that high school students' learning strategies mediated the effect of digital literacy on school educational performance. In this study, in order to verify whether the mediating effect of the learning strategy is statistically significant, the statistical significance of direct effects (digital literacy β school educational outcomes) and indirect effects (digital literacy β learning strategies β school educational outcomes) was verified by bootstrapping verification. confirmed. As a result, both direct and indirect effects of high school students' digital literacy on school educational performance were found to be statistically significant. When the control variable is not set, the standardized coefficient of the indirect effect of digital literacy on school educational performance is 0.107, the total effect is 0.592, and when the control variable is set, the standardized coefficient of the indirect effect is 0.087, the total effect is 0.601 (p< .01).
In short, in this study, digital literacy of high school students had a positive effect on learning strategy and school educational performance, regardless of the socioeconomic status of their families and whether or not private tutoring time was controlled. It was empirically scientifically verified that partly mediates the effect of school education on school education. These findings suggest that efforts should be made to increase the level of digital literacy and learning strategies of high school students.λͺ¨λ κ²μ΄ λμ§νΈν λλ μ 4μ°¨ μ°μ
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μλμ λμ§νΈ 리ν°λ¬μ(digital literacy)λ μ½κΈ°, μ°κΈ°μ κ°μ΄ λͺ¨λ λΆμΌμμ νμν ν΅μ¬ κΈ°μ΄μλμ νλλ‘ κ°μ£Όλκ³ μμΌλ©° λμ§νΈ 리ν°λ¬μμ λν κ΄μ¬κ³Ό κ·Έ μ€μμ±μ μ μ 컀μ§κ³ μλ€. κ΅μ‘μ© μ€λ§νΈ κΈ°κΈ°μ 보κΈμ΄ λκ³ 2022 κ°μ κ΅μ‘κ³Όμ μ ν΅ν΄ λμ§νΈ 리ν°λ¬μκ° νκ΅ κ΅μ‘κ³Όμ μ μ μ©λλ λ± νμ΅ νκ²½κ³Ό λ΄μ©μ΄ κΈκ²©νκ² λ³νν¨μ λ°λΌ κ³ λ±νμμκ² λμ§νΈ 리ν°λ¬μμ νμ΅μ λ΅μ κ·Έ μ€μμ±μ΄ κ³μνμ¬ λμμ§κ³ μλ€.
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λ§μ‘±λ, νμ
μ±μ·¨λ)μ λν μ°κ΅¬λ κΎΈμ€ν μ΄λ£¨μ΄μ Έ μμΌλ κ° λ³μΈ κ° νΉμ μΈ λ³μΈ κ°μ ꡬ쑰μ κ΄κ³λ₯Ό νμΈνλ μ°κ΅¬λ λ―Έν‘ν μ€μ μ΄λ€. λ°λΌμ κ° λ³μΈ κ°μ κ΄κ³μ λν μ νμ°κ΅¬λ₯Ό κΈ°μ΄λ‘ μΈ λ³μΈ κ°μ ꡬ쑰μ κ΄κ³λ₯Ό κ²½νκ³Όνμ μΌλ‘ λΆμν νμκ° μλ€. μ΄ μ°κ΅¬λ ꡬ쑰방μ μ λͺ¨ν(Structural Equation Medeling, SEM)μ νμ©νμ¬ μΈ λ³μΈ κ°μ ꡬ쑰μ κ΄κ³λ₯Ό λΆμν κ²μ΄λ€. μ΄ μ°κ΅¬μμλ κ³ λ±νμμ λμ§νΈ 리ν°λ¬μμ νμ΅μ λ΅μ΄ νκ΅κ΅μ‘μ±κ³Ό(μμ
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1. κ³ λ±νμμ λμ§νΈ 리ν°λ¬μλ νμ΅μ λ΅μ μ΄λ ν μν₯μ λ―ΈμΉλκ°?
2. κ³ λ±νμμ νμ΅μ λ΅μ νκ΅κ΅μ‘μ±κ³Όμ μ΄λ ν μν₯μ λ―ΈμΉλκ°?
3. κ³ λ±νμμ λμ§νΈ 리ν°λ¬μλ νκ΅κ΅μ‘μ±κ³Όμ μ΄λ ν μν₯μ λ―ΈμΉλκ°?
4. κ³ λ±νμμ λμ§νΈ 리ν°λ¬μλ νμ΅μ λ΅μ 맀κ°λ‘ νμ¬ νκ΅κ΅μ‘μ±κ³Όμ μ΄λ ν μν₯μ λ―ΈμΉλκ°?
μ΄λ¬ν μ°κ΅¬ λ¬Έμ λ₯Ό ν΄κ²°νκΈ° μν λ°©λ² λ° μ μ°¨λ λ€μκ³Ό κ°λ€. λ¨Όμ μ νμ°κ΅¬ κ²ν μ μ λ¬Έκ°μ μλ©΄νκ°λ₯Ό ν΅ν΄ κ³ λ±νμμ λμ§νΈ 리ν°λ¬μ μ²λμ νμ΅μ λ΅ μ²λλ₯Ό μ μνκ³ νλΉννμλ€. λμ§νΈ 리ν°λ¬μ μ²λμ νμμμμ λμ§νΈ κΈ°κΈ° μ¬μ©, μ 보μμ§, μ 보 λΆμ λ° νμ©μΌλ‘ ꡬμ±νμκ³ , νμμμλ³λ‘ 6κ° λ¬Ένμ© μ΄ 18κ° λ¬Ένμ μ μνμλ€.νμ΅μ λ΅ μ²λμ νμμμμ νμ΅ λͺ©ν μ€μ , νμ΅ λ°©λ² μ μ , νμ΅ μ κ² λ° μ±μ°°λ‘ ꡬμ±νμκ³ , νμμμλ³λ‘ 6κ° λ¬Ένμ© μ΄ 18κ° λ¬Ένμ μ μνμλ€. κ° μ²λμ ꡬμΈνλΉλλ νμΈμ μμΈλΆμμ ν΅ν΄ μ κ²νμκ³ , μ λ’°λλ Cronbachs alphaλ₯Ό ν΅ν΄ μ κ²ν κ²°κ³Ό λ μ²λ λͺ¨λ νλΉλμ μ λ’°λκ° μνΈνμλ€.
μλ£λ₯Ό μμ§νκΈ° μν΄ μΆ©μ²λΆλ μμ¬ Yκ³ λ±νκ΅μ μ¬ν μ€μΈ κ³ λ±νκ΅ 2νλ
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λ§μ‘±λ κ²μ¬λ₯Ό μ€μνμκ³ 2νκΈ° μ€κ°κ³ μ¬ μ±μ μ μμ§νμλ€. μμ§ν λ°μ΄ν°λ₯Ό νμ©νμ¬ κΈ°μ ν΅κ³, μκ΄κ΄κ³, ꡬ쑰방μ μ λͺ¨ν λ±μ λΆμν¨μΌλ‘μ¨ λμ§νΈ 리ν°λ¬μ, νμ΅μ λ΅, νκ΅κ΅μ‘μ±κ³Όμ ꡬ쑰μ μΈ κ΄κ³λ₯Ό μ κ²νμλ€.
μ΄ μ°κ΅¬μ μ£Όμ κ²°κ³Όλ₯Ό μμ½νλ©΄ λ€μκ³Ό κ°λ€.
첫째, κ³ λ±νμμ λμ§νΈ 리ν°λ¬μλ νκ΅κ΅μ‘μ±κ³Όμ κΈμ μ μΈ μν₯μ λ―ΈμΉλ κ²μΌλ‘ λνλ¬λ€. ꡬ쑰방μ μ λͺ¨ν λΆμ κ²°κ³Ό λμ§νΈ 리ν°λ¬μκ° νκ΅κ΅μ‘μ±κ³Όμ λ―ΈμΉλ μν₯μ λν νμ€νκ³μλ ν΅μ λ³μΈμ ν΅μ νμ§ μμ λͺ¨νμμ 0.485, ν΅μ λ³μΈμ μ€μ ν λͺ¨νμμλ 0.514λ‘ λνλ¬μΌλ©° λͺ¨λ ν΅κ³μ μΌλ‘ μ μν κ²μΌλ‘ λνλ¬λ€(p<.001).
λμ§Έ, κ³ λ±νμμ λμ§νΈ 리ν°λ¬μλ νμ΅μ λ΅μ κΈμ μ μΈ μν₯μ λ―ΈμΉλ κ²μΌλ‘ λνλ¬λ€. ꡬ쑰방μ μ λͺ¨νμ λΆμ κ²°κ³Ό, λμ§νΈ 리ν°λ¬μκ° νμ΅μ λ΅μ λ―ΈμΉλ μν₯μ λν νμ€νκ³μλ ν΅μ λ³μΈμ μ€μ νμ§ μμ λͺ¨νμμ 0.271, ν΅μ λ³μΈμ μ€μ ν λͺ¨νμμλ 0.276μΌλ‘ λνλ¬μΌλ©° λͺ¨λ ν΅κ³μ μΌλ‘ μ μν κ²μΌλ‘ λνλ¬λ€(p<.001).
μ
μ§Έ. κ³ λ±νμμ νμ΅μ λ΅μ νκ΅κ΅μ‘μ±κ³Όμ κΈμ μ μν₯μ λ―ΈμΉλ κ²μΌλ‘ λνλ¬λ€. ꡬ쑰방μ μ λͺ¨νμ λΆμ κ²°κ³Ό, νμ΅μ λ΅μ΄ νκ΅κ΅μ‘μ±κ³Όμ λ―ΈμΉλ μν₯μ λν νμ€νκ³μλ ν΅μ λ³μΈμ μ€μ νμ§ μμ λͺ¨νμμ 0.397, ν΅μ λ³μΈμ μ€μ ν λͺ¨νμμλ 0.318λ‘ λνλ¬μΌλ©° λͺ¨λ ν΅κ³μ μΌλ‘ μ μν κ²μΌλ‘ λνλ¬λ€(p<.01, p<.05).
λ·μ§Έ, κ³ λ±νμμ νμ΅μ λ΅μ΄ λμ§νΈ 리ν°λ¬μκ° νκ΅κ΅μ‘μ±κ³Όμ λ―ΈμΉλ μν₯μ 맀κ°νλ κ²μΌλ‘ λνλ¬λ€. μ΄ μ°κ΅¬μμλ νμ΅μ λ΅μ 맀κ°ν¨κ³Όκ° ν΅κ³μ μΌλ‘ μ μνμ§ κ²μ¦νκΈ° μν΄ λΆμ€νΈλν(bootstrapping) κ²μ¦μΌλ‘ μ§μ ν¨κ³Ό(λμ§νΈλ¦¬ν°λ¬μβνκ΅κ΅μ‘μ±κ³Ό)μ κ°μ ν¨κ³Ό(λμ§νΈλ¦¬ν°λ¬μβνμ΅μ λ΅βνκ΅κ΅μ‘μ±κ³Ό)μ ν΅κ³μ μ μμ±μ νμΈνμλ€. κ·Έ κ²°κ³Ό, κ³ λ±νμμ λμ§νΈ 리ν°λ¬μκ° νκ΅κ΅μ‘μ±κ³Όμ λ―ΈμΉλ μ§μ ν¨κ³Όμ κ°μ ν¨κ³Όλ λͺ¨λ ν΅κ³μ μΌλ‘ μ μν κ²μΌλ‘ λνλ¬λ€. ν΅μ λ³μΈμ μ€μ νμ§ μμ κ²½μ° λμ§νΈ 리ν°λ¬μκ° νκ΅κ΅μ‘μ±κ³Όμ λ―ΈμΉλ κ°μ ν¨κ³Όμ νμ€ν κ³μλ 0.107, μ΄ ν¨κ³Όλ 0.592μ΄λ©° ν΅μ λ³μΈμ μ€μ ν κ²½μ° κ°μ ν¨κ³Όμ νμ€ν κ³μλ 0.087, μ΄ ν¨κ³Όλ 0.601μ΄λ€(p<.01).
μ컨λ, μ΄ μ°κ΅¬μμλ λΆλͺ¨μ μ¬νκ²½μ μ μ§μμ μ¬κ΅μ‘ μκ°μ ν΅μ μ¬λΆμ 무κ΄νκ² κ³ λ±νμμ λμ§νΈ 리ν°λ¬μκ° νμ΅μ λ΅κ³Ό νκ΅κ΅μ‘μ±κ³Όμ κΈμ μ μΈ μν₯μ λ―Έμ³€μΌλ©°, κ° λ³μΈ κ° μ’
ν©μ μΈ κ΄κ³μμ νμ΅μ λ΅μ΄ λμ§νΈ 리ν°λ¬μκ° νκ΅κ΅μ‘μ λ―ΈμΉλ μν₯μ λΆλΆμ μΌλ‘ 맀κ°νλ κ²μ κ²½νκ³Όνμ μΌλ‘ κ²μ¦νμλ€. μ΄λ¬ν μ°κ΅¬ κ²°κ³Όλ κ³ λ±νμμ λμ§νΈ 리ν°λ¬μμ νμ΅μ λ΅ μμ€μ μ μ₯ λ° λ°μ μν€κΈ° μν΄ λ
Έλ ₯ν΄μΌ ν¨μ μμ¬νλ€.β
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1.1 μ°κ΅¬μ νμμ± λ° λͺ©μ 1
1.2μ°κ΅¬ λ¬Έμ 3
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2.1 λμ§νΈ 리ν°λ¬μ 5
2.2 νμ΅μ λ΅ 15
2.3 νκ΅κ΅μ‘μ±κ³Ό 22
2.4 λ³μ κ°μ κ΄κ³ 27
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3.1 μ£Όμ λ³μΈ μ€μ λ° μ μ 33
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4.1 μ°κ΅¬ λμ 38
4.2 μΈ‘μ λꡬ 38
4.3 μ°κ΅¬ μ μ°¨ 62
4.4 μλ£ λΆμ λ°©λ² 63
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€. μ°κ΅¬ κ²°κ³Ό 65
5.1 κΈ°μ΄ν΅κ³ 65
5.2 ꡬ쑰방μ μ λͺ¨ν λΆμ 71
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Abstract 105μ
Blowdown Analysis of System Composed of Pipelines Only using HYSYS Depressuring Utility and HYSYS Dynamic Simulation
Masterν΄μνλνΈ μ€κ³μμ νλμ΄ μμ€ν
μ νλνΈμ μμ ν μ΄μ μ μν΄ νμνλ€. λΉμ κ°μμ λ°°μΆλλ μ 체μ μμ΄ νλμ΄ μμ€ν
μ€κ³μ κ°μ₯ ν° μν₯μ λ―ΈμΉκ² λλ€. νμ¬μ λΉμ κ°μμ κ²½μ°κ° κ°μ₯ μ¬κ°ν κ²½μ°μ΄λ€. λ°°μΆλμ κ³μ°μ μν΄ HYSYS Depressuring Utility λ₯Ό μ¬μ©νμ¬ ν΄μμ μννλ κ²μ΄ μΌλ°μ μ΄λ€. νμ§λ§ HYSYS Depressuring Utility λ μ€μ System μ νμκ³Ό ꡬμ±μ μν 볡μ‘μ±μ μκ΄μμ΄ νλμ μλ ₯ μ©κΈ°λ‘ λ¨μν ν ν ν΄μμ μννκΈ° λλ¬Έμ μ€μ μμ€ν
μ΄ λν μλ ₯ μ©κΈ°κ° μμΌλ©° μ΄ μ©κΈ°μ 체μ μ΄ μ 체 μμ€ν
μ 체μ μ λλΆλΆμ μ°¨μ§νλ κ²½μ° κ°μ₯ μ ν© ν μ μλ€. λ§€μ° λ³΅μ‘ν μμ€ν
μ κ²½μ°μλ λ¨μΌ μλ ₯ μ©κΈ°λ‘ λ¨μν νμ¬ ν΄μμ μννλ μ κ·Όλ²μ μ ν©νμ§ μμ μ μλ€. λ³Έ λ
Όλ¬Έμμλ μ€μ μμ€ν
μ κ·Έλλ‘ λͺ¨λΈλ§μ ν ν λμ ν΄μμ μννμ¬ κ·Έ κ²°κ³Όμ HYSYS Depressuing Utility λ₯Ό μ¬μ©ν ν΄μμ λΉκ΅ λΆμνμλ€.Flare system is designed to ensure safe system operation in offshore plant. Flow from the case of emergency depressurization can be the one mainly influence flare system design. Depressurization under the fire event is the most extreme case. HYSYS Depressuring Utility introduce single vessel regardless how the actual system configured and what component are in the real system. HYSYS Depressuring Utility is appropriate for the system where large vessel accounts for the majority of system volume. Since representing real complex system with single vessel is inaccurate considering rate of change of some parameters, it might not be proper to replace real complex system with single vessel. Firstly, HYSYS model almost similar with real system is built. Then dynamic
simulation is carried out with fire event, not using HYSYS Depressuring Utility