18 research outputs found

    κ΅­λ‚΄ 농μž₯뢄리 λŒ€μž₯균의 ν•­μƒμ œ λ‚΄μ„±μœ ν˜• 및 λ‚΄μ„±μœ μ „μžμ˜ 전달에 κ΄€ν•œ 뢄석

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    ν•™μœ„λ…Όλ¬Έ (박사)-- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : μˆ˜μ˜κ³ΌλŒ€ν•™ μˆ˜μ˜ν•™κ³Ό μˆ˜μ˜λ―Έμƒλ¬Όν•™μ „κ³΅, 2016. 2. μœ ν•œμƒ.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 ꡭ문초둝 170Docto

    μœ ν•΄ν™”ν•™λ¬Όμ§ˆλ°°μΆœλŸ‰ 자료의 GIS ν™œμš©λ°©μ•ˆμ— κ΄€ν•œ 연ꡬ

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    ν•™μœ„λ…Όλ¬Έ(석사)--μ„œμšΈλŒ€ν•™κ΅ λ³΄κ±΄λŒ€ν•™μ› :ν™˜κ²½λ³΄κ±΄ν•™κ³Ό μˆ˜μ§ˆκ΄€λ¦¬μ „κ³΅,2002.Maste

    Simulation Analysis and Luminous Efficiency of coplanar AC-PDP system

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    (A) study on the environmental awareness and behavior of the college studies in Seoul

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    ν™˜κ²½κ΄€λ¦¬ν•™κ³Ό/석사[ν•œκΈ€] μ˜€λŠ˜λ‚ μ˜ ν™˜κ²½ λ¬Έμ œλŠ” μ‚°μ—…ν™”λ‘œ μΈν•œ 생산 양식과 μƒν™œ 양식 λ³€ν™”μ˜ 필연적인 결과이닀.산업이 λ°œμ „ν•˜λ©΄μ„œ λŒ€λŸ‰ 생산 및 μ†ŒλΉ„, 이에 λ”°λ₯Έ 폐기물 λ°œμƒκ³Ό μžμ› λ‚¨μš©μ˜ λ¬Έμ œκ°€ λ°œμƒν•˜μ˜€μœΌλ©° κ·Έ κ²°κ³Ό μžμ—° μƒνƒœκ³„μ˜ νŒŒκ΄΄μ™€ ν™˜κ²½ μ˜€μ—Όμ΄ μœ λ°œλ˜λ©΄μ„œ 우리의 μƒν™œ ν™˜κ²½μ„ μœ„ν˜‘ν•˜κ³  μžˆλ‹€. 이에 λ³Έ μ—°κ΅¬λŠ” ν™˜κ²½ μ˜€μ—Όμ— λŒ€ν•œ 심각성과 이에 λŒ€ν•œ ν™˜κ²½ ꡐ윑의 μ€‘μš”μ„±μ„ κ°μ•ˆν•˜μ—¬ μ„œμšΈ μ‹œλ‚΄ 일뢀 λŒ€ν•™μƒλ“€μ˜ ν™˜κ²½ μ˜μ‹ 및 ν™˜κ²½ 관리 ν–‰μœ„μ— κ΄€ν•œ μ‹€νƒœλ₯Ό μ „λ°˜μ μœΌλ‘œ νŒŒμ•…ν•˜μ—¬ λΆ„μ„ν•¨μœΌλ‘œμ¨ ꢁ극적으둜 ν•™μƒλ“€μ˜ ν™˜κ²½ 관리 ν–‰μœ„μ˜ 이행을 ν–₯μƒμ‹œν‚€κΈ° μœ„ν•œ λ°©μ•ˆμ„ λ§ˆλ ¨ν•˜κ³ , ν–₯ν›„ 보닀 효율적인 ν™˜κ²½ κ΅μœ‘μ„ μœ„ν•œ 기초 자료λ₯Ό μ–»κ³ μž μ‹€ν–‰λ˜μ—ˆλ‹€. 1998λ…„ 4μ›” 20일 λΆ€ν„° 1998λ…„ 4μ›” 25μΌκΉŒμ§€ μ„œμšΈ μ‹œλ‚΄ ν•œ λŒ€ν•™μ˜ 학뢀생 282λͺ…을 λŒ€μƒμœΌλ‘œ μ„€λ¬Έ 쑰사λ₯Ό μ‹œν–‰ν•˜μ˜€μœΌλ©°, μˆ˜μ§‘λœ 자료λ₯Ό 톡계 λΆ„μ„ν•˜μ—¬ κ²°κ³Όλ₯Ό μ œμ‹œν•˜μ˜€λ‹€. λ³Έ μ—°κ΅¬μ˜ μ£Όμš” κ²°κ³Όλ₯Ό μš”μ•½ν•˜λ©΄ λ‹€μŒκ³Ό κ°™λ‹€. 1. ν™˜κ²½ μ˜μ‹κ³Ό ν™˜κ²½ 관리 ν–‰μœ„μ˜ μˆ˜μ€€μ€ 각 전곡 λ³„λ‘œ μœ μ˜ν•œ 차이λ₯Ό λ³΄μ˜€λŠ”λ°(p-value=0.0001), 특히 6κ°€μ§€λ‘œ λΆ„λ₯˜ν•œ 전곡(상경 μ‚¬νšŒ 계열, 곡학 계열, μ–΄λ¬Έ 계열, μ˜ν•™ 전곡,κ°„ν˜Έν•™ 전곡, κ°€μ • 계열) μ€‘μ—μ„œ μ˜ν•™, κ°„ν˜Έν•™ 전곡 학생이 타전곡 학생보닀 평균적인 ν™˜κ²½ μ˜μ‹κ³Ό ν™˜κ²½ 관리 ν–‰μœ„ μ μˆ˜κ°€ λ†’κ²Œ λ‚˜νƒ€λ‚¬μœΌλ©° ν†΅κ³„μ μœΌλ‘œ λ³΄μ•˜μ„ λ•Œμ—λ„ μœ μ˜ν•œ 상관성을 λ‚˜νƒ€λ‚΄μ—ˆλ‹€. 2. 인ꡬ 및 μ‚¬νšŒκ²½μ œμ  νŠΉμ„±μ— λ”°λ₯Έ ν™˜κ²½ μ˜μ‹, ν™˜κ²½ 관리 ν–‰μœ„ μ •λ„μ—μ„œ ν†΅κ³„μ μœΌλ‘œ μœ μ˜ν•œ 차이λ₯Ό 보인 ν•­λͺ©μ€ 쑰사 λŒ€μƒμžμ˜ λ‚˜μ΄, ν•™λ…„, 성별, μ£Όκ±° ν˜•νƒœ, λΆ€λͺ¨μ˜ μ΅œμ’… ν•™λ ₯, μ–΄λ¨Έλ‹ˆμ˜ 직업 λ“±μ΄μ—ˆλ‹€(p-valueγ€ˆ 0.05). 3. ν™˜κ²½ μ˜μ‹μ— λ”°λ₯Έ ν™˜κ²½ 관리 ν–‰μœ„ 정도λ₯Ό λ³΄μ•˜μ„ λ•Œ, 상관 κ³„μˆ˜κ°€ 0.28둜 λ‚˜νƒ€λ‚˜ λ‘λ³€μˆ˜κ°„μ˜ 전체적인 상관성은 그닀지 μœ μ˜ν•˜μ§€ μ•Šλ‹€κ³  λ³Ό 수 μžˆμœΌλ‚˜, 이λ₯Ό 곡뢄산뢄석(ANCOVA)으둜 λ‹€μ‹œ λ³΄μ•˜μ„ κ²½μš°μ—λŠ” p-valueκ°€ 0.0001둜 μ•„μ£Ό μœ μ˜ν•¨μ„ μ•Œ 수 μžˆλ‹€. μ΄λŠ” ν™˜κ²½ μ˜μ‹μ΄ λ†’μŒμ— λ”°λΌμ„œ ν™˜κ²½ 관리 ν–‰μœ„ 정도도 λ†’μ•„μ§„λ‹€λŠ” 것을 보여주고 μžˆλ‹€. 4. 쑰사 λŒ€μƒμžμ—κ²Œ κ°€μž₯ 영ν–₯을 λΌμΉ˜λŠ” λŒ€μ€‘ λ§€μ²΄λ‘œλŠ” μ‹ λ¬Έ, μž‘μ§€λ₯Ό 꼽을 수 μžˆμ—ˆμœΌλ©°(53.5%), λŒ€μ€‘ 맀체가 쑰사 λŒ€μƒμžμ—κ²Œ λΌμΉ˜λŠ” 영ν–₯은 'κ·Έμ € κ·Έλ ‡λ‹€'λŠ” λŒ€λ‹΅μ΄ κ°€μž₯ λ§Žμ•˜λ‹€(41.8%). λ˜ν•œ 쑰사 λŒ€μƒμžμ˜ ν™˜κ²½μ— λŒ€ν•œ μ§€μ‹μ΄λ‚˜ 정보에 영ν–₯을 μ£ΌλŠ” 경둜둜 TV, λΌλ””μ˜€, μ‹ λ¬Έ, μž‘μ§€ λ“±μ˜ 맀슀컴이 κ°€μž₯ 많이 μ„ νƒλ˜μ—ˆλ‹€(64.9%). μ΄μƒμ˜ 연ꡬ 결과에 μ˜ν•˜λ©΄, 높은 ν™˜κ²½ μ˜μ‹κ³Ό 이에 μƒμ‘λ˜λŠ” ν™˜κ²½ 관리 ν–‰μœ„μ˜ μ‹€μ²œ 정도가 λ°€μ ‘ν•œ 상관성을 λ³΄μ΄λŠ” κ²ƒμœΌλ‘œ ν‰κ°€λ˜μ—ˆμœΌλ―€λ‘œ ν™˜κ²½ κ΅μœ‘μ΄λ‚˜ TV, μ‹ λ¬Έ, μž‘μ§€ λ“±μ˜ λ§€μŠ€μ»΄μ— μ˜ν•œ 홍보 λ“±μœΌλ‘œ ν™˜κ²½ μ˜μ‹μ„ 높인닀면 그에 λ”°λ₯Έ ν™˜κ²½ 관리 ν–‰μœ„ 정도도 λ†’μ•„μ§ˆ 수 μžˆμŒμ„ μ•Œ 수 μžˆλ‹€. κ·ΈλŸ¬λ‚˜, ν™˜κ²½ κ΄€λ ¨ μˆ˜μ—… μœ λ¬΄μ™€ ν™˜κ²½ μ˜μ‹ 및 ν™˜κ²½ 관리 ν–‰μœ„κ°„μ˜ 관계가 μœ μ˜ν•˜μ§€ μ•Šμ€ κ²ƒμœΌλ‘œ λ‚˜νƒ€λ‚¬μœΌλ―€λ‘œ ν˜„μž¬ ν–‰ν•˜μ—¬μ§€κ³  μžˆλŠ” ν™˜κ²½ κ΄€λ ¨ μˆ˜μ—…μ΄ μ•žμœΌλ‘œ 쒀더 효과적으둜 μ΄λ£¨μ–΄μ§ˆ 수 μžˆλ„λ‘ λ…Έλ ₯κ³Ό κ°œμ„ μ΄ μš”κ΅¬λ˜μ–΄μ§„λ‹€. [영문] 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(Pγ€ˆ0.05). 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 μ•Œκ³ λ¦¬λ“¬ 개발과 2 μœ μ²΄λ…Έμ¦ μœ λ™ μΈ‘μ •μ—μ˜ μ‘μš©

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    ν•™μœ„λ…Όλ¬Έ(석사)--μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› :기계곡학과,1998.Maste

    The Effects of high School Students' Digital Literacy on School Educational Performance Through Learning Strategy

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    ν•™μœ„λ…Όλ¬Έ(석사) -- μ„œμšΈλŒ€ν•™κ΅λŒ€ν•™μ› : μ‚¬λ²”λŒ€ν•™ κ΅μœ‘ν•™κ³Ό, 2023. 2. 백순근.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“  것이 디지털화 λ˜λŠ” 제4μ°¨ μ‚°μ—…ν˜λͺ… μ‹œλŒ€μ— 디지털 λ¦¬ν„°λŸ¬μ‹œ(digital literacy)λŠ” 읽기, 쓰기와 같이 λͺ¨λ“  λΆ„μ•Όμ—μ„œ ν•„μš”ν•œ 핡심 κΈ°μ΄ˆμ—­λŸ‰μ˜ ν•˜λ‚˜λ‘œ κ°„μ£Όλ˜κ³  있으며 디지털 λ¦¬ν„°λŸ¬μ‹œμ— λŒ€ν•œ 관심과 κ·Έ μ€‘μš”μ„±μ€ 점점 컀지고 μžˆλ‹€. ꡐ윑용 슀마트 기기의 보급이 늘고 2022 κ°œμ • κ΅μœ‘κ³Όμ •μ„ 톡해 디지털 λ¦¬ν„°λŸ¬μ‹œκ°€ 학ꡐ κ΅μœ‘κ³Όμ •μ— μ μš©λ˜λŠ” λ“± ν•™μŠ΅ ν™˜κ²½κ³Ό λ‚΄μš©μ΄ κΈ‰κ²©ν•˜κ²Œ 변화함에 따라 κ³ λ“±ν•™μƒμ—κ²Œ 디지털 λ¦¬ν„°λŸ¬μ‹œμ™€ ν•™μŠ΅μ „λž΅μ€ κ·Έ μ€‘μš”μ„±μ΄ κ³„μ†ν•˜μ—¬ 높아지고 μžˆλ‹€. κ·Έ λ™μ•ˆ κ³ λ“±ν•™μƒμ˜ 디지털 λ¦¬ν„°λŸ¬μ‹œ, ν•™μŠ΅μ „λž΅ 그리고 ν•™κ΅κ΅μœ‘μ„±κ³Ό(μˆ˜μ—…λ§Œμ‘±λ„, 학업성취도)에 λŒ€ν•œ μ—°κ΅¬λŠ” κΎΈμ€€νžˆ 이루어져 μ™”μœΌλ‚˜ 각 변인 κ°„ ν˜Ήμ€ μ„Έ 변인 κ°„μ˜ ꡬ쑰적 관계λ₯Ό ν™•μΈν•˜λŠ” μ—°κ΅¬λŠ” λ―Έν‘ν•œ 싀정이닀. λ”°λΌμ„œ 각 변인 κ°„μ˜ 관계에 λŒ€ν•œ 선행연ꡬλ₯Ό 기초둜 μ„Έ 변인 κ°„μ˜ ꡬ쑰적 관계λ₯Ό κ²½ν—˜κ³Όν•™μ μœΌλ‘œ 뢄석할 ν•„μš”κ°€ μžˆλ‹€. 이 μ—°κ΅¬λŠ” ꡬ쑰방정식 λͺ¨ν˜•(Structural Equation Medeling, SEM)을 ν™œμš©ν•˜μ—¬ μ„Έ 변인 κ°„μ˜ ꡬ쑰적 관계λ₯Ό λΆ„μ„ν•œ 것이닀. 이 μ—°κ΅¬μ—μ„œλŠ” κ³ λ“±ν•™μƒμ˜ 디지털 λ¦¬ν„°λŸ¬μ‹œμ™€ ν•™μŠ΅μ „λž΅μ΄ ν•™κ΅κ΅μœ‘μ„±κ³Ό(μˆ˜μ—…λ§Œμ‘±λ„, 학업성취도)에 영ν–₯을 λ―ΈμΉ  것이라고 μƒμ •ν•˜μ˜€μœΌλ©°, ꡬ체적인 연ꡬ λ¬Έμ œλŠ” λ‹€μŒκ³Ό κ°™λ‹€. 1. κ³ λ“±ν•™μƒμ˜ 디지털 λ¦¬ν„°λŸ¬μ‹œλŠ” ν•™μŠ΅μ „λž΅μ— μ–΄λ– ν•œ 영ν–₯을 λ―ΈμΉ˜λŠ”κ°€? 2. κ³ λ“±ν•™μƒμ˜ ν•™μŠ΅μ „λž΅μ€ ν•™κ΅κ΅μœ‘μ„±κ³Όμ— μ–΄λ– ν•œ 영ν–₯을 λ―ΈμΉ˜λŠ”κ°€? 3. κ³ λ“±ν•™μƒμ˜ 디지털 λ¦¬ν„°λŸ¬μ‹œλŠ” ν•™κ΅κ΅μœ‘μ„±κ³Όμ— μ–΄λ– ν•œ 영ν–₯을 λ―ΈμΉ˜λŠ”κ°€? 4. κ³ λ“±ν•™μƒμ˜ 디지털 λ¦¬ν„°λŸ¬μ‹œλŠ” ν•™μŠ΅μ „λž΅μ„ 맀개둜 ν•˜μ—¬ ν•™κ΅κ΅μœ‘μ„±κ³Όμ— μ–΄λ– ν•œ 영ν–₯을 λ―ΈμΉ˜λŠ”κ°€? μ΄λŸ¬ν•œ 연ꡬ 문제λ₯Ό ν•΄κ²°ν•˜κΈ° μœ„ν•œ 방법 및 μ ˆμ°¨λŠ” λ‹€μŒκ³Ό κ°™λ‹€. λ¨Όμ € 선행연ꡬ 검토와 μ „λ¬Έκ°€μ˜ μ„œλ©΄ν‰κ°€λ₯Ό 톡해 κ³ λ“±ν•™μƒμ˜ 디지털 λ¦¬ν„°λŸ¬μ‹œ 척도와 ν•™μŠ΅μ „λž΅ 척도λ₯Ό μ œμž‘ν•˜κ³  νƒ€λ‹Ήν™”ν•˜μ˜€λ‹€. 디지털 λ¦¬ν„°λŸ¬μ‹œ μ²™λ„μ˜ ν•˜μœ„μ˜μ—­μ€ 디지털 κΈ°κΈ° μ‚¬μš©, μ •λ³΄μˆ˜μ§‘, 정보 뢄석 및 ν™œμš©μœΌλ‘œ κ΅¬μ„±ν•˜μ˜€κ³ , ν•˜μœ„μ˜μ—­λ³„λ‘œ 6개 λ¬Έν•­μ”© 총 18개 문항을 μ œμž‘ν•˜μ˜€λ‹€.ν•™μŠ΅μ „λž΅ μ²™λ„μ˜ ν•˜μœ„μ˜μ—­μ€ ν•™μŠ΅ λͺ©ν‘œ μ„€μ •, ν•™μŠ΅ 방법 μ„ μ •, ν•™μŠ΅ 점검 및 μ„±μ°°λ‘œ κ΅¬μ„±ν•˜μ˜€κ³ , ν•˜μœ„μ˜μ—­λ³„λ‘œ 6개 λ¬Έν•­μ”© 총 18개 문항을 μ œμž‘ν•˜μ˜€λ‹€. 각 μ²™λ„μ˜ κ΅¬μΈνƒ€λ‹Ήλ„λŠ” 확인적 μš”μΈλΆ„μ„μ„ 톡해 μ κ²€ν•˜μ˜€κ³ , μ‹ λ’°λ„λŠ” Cronbachs alphaλ₯Ό 톡해 μ κ²€ν•œ κ²°κ³Ό 두 척도 λͺ¨λ‘ 타당도와 신뒰도가 μ–‘ν˜Έν•˜μ˜€λ‹€. 자료λ₯Ό μˆ˜μ§‘ν•˜κΈ° μœ„ν•΄ 좩청뢁도 μ†Œμž¬ Y고등학ꡐ에 μž¬ν•™ 쀑인 고등학ꡐ 2ν•™λ…„ 학생을 λŒ€μƒμœΌλ‘œ 검사λ₯Ό μ‹œν–‰ν•˜μ˜€λ‹€. 각 변인 κ°„μ˜ 인과 관계λ₯Ό νŒŒμ•…ν•˜κΈ° μœ„ν•΄ 2022λ…„ 9월에 κ³ λ“±ν•™μƒμ˜ 디지털 λ¦¬ν„°λŸ¬μ‹œ 척도와 κ³ λ“±ν•™μƒμ˜ ν•™μŠ΅μ „λž΅ 척도검사λ₯Ό μ‹€μ‹œν•˜κ³ , 4μ£Ό ν›„ 11월에 μˆ˜μ—…λ§Œμ‘±λ„ 검사λ₯Ό μ‹€μ‹œν•˜μ˜€κ³  2ν•™κΈ° 쀑간고사 성적을 μˆ˜μ§‘ν•˜μ˜€λ‹€. μˆ˜μ§‘ν•œ 데이터λ₯Ό ν™œμš©ν•˜μ—¬ κΈ°μˆ ν†΅κ³„, 상관관계, ꡬ쑰방정식 λͺ¨ν˜• 등을 λΆ„μ„ν•¨μœΌλ‘œμ¨ 디지털 λ¦¬ν„°λŸ¬μ‹œ, ν•™μŠ΅μ „λž΅, ν•™κ΅κ΅μœ‘μ„±κ³Όμ˜ ꡬ쑰적인 관계λ₯Ό μ κ²€ν•˜μ˜€λ‹€. 이 μ—°κ΅¬μ˜ μ£Όμš” κ²°κ³Όλ₯Ό μš”μ•½ν•˜λ©΄ λ‹€μŒκ³Ό κ°™λ‹€. 첫째, κ³ λ“±ν•™μƒμ˜ 디지털 λ¦¬ν„°λŸ¬μ‹œλŠ” ν•™κ΅κ΅μœ‘μ„±κ³Όμ— 긍정적인 영ν–₯을 λ―ΈμΉ˜λŠ” κ²ƒμœΌλ‘œ λ‚˜νƒ€λ‚¬λ‹€. ꡬ쑰방정식 λͺ¨ν˜• 뢄석 κ²°κ³Ό 디지털 λ¦¬ν„°λŸ¬μ‹œκ°€ ν•™κ΅κ΅μœ‘μ„±κ³Όμ— λ―ΈμΉ˜λŠ” 영ν–₯에 λŒ€ν•œ ν‘œμ€€ν™”κ³„μˆ˜λŠ” ν†΅μ œ 변인을 ν†΅μ œν•˜μ§€ μ•Šμ€ λͺ¨ν˜•μ—μ„œ 0.485, ν†΅μ œλ³€μΈμ„ μ„€μ •ν•œ λͺ¨ν˜•μ—μ„œλŠ” 0.514둜 λ‚˜νƒ€λ‚¬μœΌλ©° λͺ¨λ‘ ν†΅κ³„μ μœΌλ‘œ μœ μ˜ν•œ κ²ƒμœΌλ‘œ λ‚˜νƒ€λ‚¬λ‹€(p<.001). λ‘˜μ§Έ, κ³ λ“±ν•™μƒμ˜ 디지털 λ¦¬ν„°λŸ¬μ‹œλŠ” ν•™μŠ΅μ „λž΅μ— 긍정적인 영ν–₯을 λ―ΈμΉ˜λŠ” κ²ƒμœΌλ‘œ λ‚˜νƒ€λ‚¬λ‹€. ꡬ쑰방정식 λͺ¨ν˜•μ˜ 뢄석 κ²°κ³Ό, 디지털 λ¦¬ν„°λŸ¬μ‹œκ°€ ν•™μŠ΅μ „λž΅μ— λ―ΈμΉ˜λŠ” 영ν–₯에 λŒ€ν•œ ν‘œμ€€ν™”κ³„μˆ˜λŠ” ν†΅μ œ 변인을 μ„€μ •ν•˜μ§€ μ•Šμ€ λͺ¨ν˜•μ—μ„œ 0.271, ν†΅μ œλ³€μΈμ„ μ„€μ •ν•œ λͺ¨ν˜•μ—μ„œλŠ” 0.276으둜 λ‚˜νƒ€λ‚¬μœΌλ©° λͺ¨λ‘ ν†΅κ³„μ μœΌλ‘œ μœ μ˜ν•œ κ²ƒμœΌλ‘œ λ‚˜νƒ€λ‚¬λ‹€(p<.001). μ…‹μ§Έ. κ³ λ“±ν•™μƒμ˜ ν•™μŠ΅μ „λž΅μ€ ν•™κ΅κ΅μœ‘μ„±κ³Όμ— 긍정적 영ν–₯을 λ―ΈμΉ˜λŠ” κ²ƒμœΌλ‘œ λ‚˜νƒ€λ‚¬λ‹€. ꡬ쑰방정식 λͺ¨ν˜•μ˜ 뢄석 κ²°κ³Ό, ν•™μŠ΅μ „λž΅μ΄ ν•™κ΅κ΅μœ‘μ„±κ³Όμ— λ―ΈμΉ˜λŠ” 영ν–₯에 λŒ€ν•œ ν‘œμ€€ν™”κ³„μˆ˜λŠ” ν†΅μ œ 변인을 μ„€μ •ν•˜μ§€ μ•Šμ€ λͺ¨ν˜•μ—μ„œ 0.397, ν†΅μ œλ³€μΈμ„ μ„€μ •ν•œ λͺ¨ν˜•μ—μ„œλŠ” 0.318둜 λ‚˜νƒ€λ‚¬μœΌλ©° λͺ¨λ‘ ν†΅κ³„μ μœΌλ‘œ μœ μ˜ν•œ κ²ƒμœΌλ‘œ λ‚˜νƒ€λ‚¬λ‹€(p<.01, p<.05). λ„·μ§Έ, κ³ λ“±ν•™μƒμ˜ ν•™μŠ΅μ „λž΅μ΄ 디지털 λ¦¬ν„°λŸ¬μ‹œκ°€ ν•™κ΅κ΅μœ‘μ„±κ³Όμ— λ―ΈμΉ˜λŠ” 영ν–₯을 λ§€κ°œν•˜λŠ” κ²ƒμœΌλ‘œ λ‚˜νƒ€λ‚¬λ‹€. 이 μ—°κ΅¬μ—μ„œλŠ” ν•™μŠ΅μ „λž΅μ˜ λ§€κ°œνš¨κ³Όκ°€ ν†΅κ³„μ μœΌλ‘œ μœ μ˜ν•œμ§€ κ²€μ¦ν•˜κΈ° μœ„ν•΄ λΆ“μŠ€νŠΈλž˜ν•‘(bootstrapping) κ²€μ¦μœΌλ‘œ μ§μ ‘νš¨κ³Ό(λ””μ§€ν„Έλ¦¬ν„°λŸ¬μ‹œβ†’ν•™κ΅κ΅μœ‘μ„±κ³Ό)와 κ°„μ ‘νš¨κ³Ό(λ””μ§€ν„Έλ¦¬ν„°λŸ¬μ‹œβ†’ν•™μŠ΅μ „λž΅β†’ν•™κ΅κ΅μœ‘μ„±κ³Ό)의 톡계적 μœ μ˜μ„±μ„ ν™•μΈν•˜μ˜€λ‹€. κ·Έ κ²°κ³Ό, κ³ λ“±ν•™μƒμ˜ 디지털 λ¦¬ν„°λŸ¬μ‹œκ°€ ν•™κ΅κ΅μœ‘μ„±κ³Όμ— λ―ΈμΉ˜λŠ” μ§μ ‘νš¨κ³Όμ™€ κ°„μ ‘νš¨κ³ΌλŠ” λͺ¨λ‘ ν†΅κ³„μ μœΌλ‘œ μœ μ˜ν•œ κ²ƒμœΌλ‘œ λ‚˜νƒ€λ‚¬λ‹€. ν†΅μ œ 변인을 μ„€μ •ν•˜μ§€ μ•Šμ€ 경우 디지털 λ¦¬ν„°λŸ¬μ‹œκ°€ ν•™κ΅κ΅μœ‘μ„±κ³Όμ— λ―ΈμΉ˜λŠ” κ°„μ ‘νš¨κ³Όμ˜ ν‘œμ€€ν™” κ³„μˆ˜λŠ” 0.107, 총 νš¨κ³ΌλŠ” 0.592이며 ν†΅μ œ 변인을 μ„€μ •ν•œ 경우 κ°„μ ‘νš¨κ³Όμ˜ ν‘œμ€€ν™” κ³„μˆ˜λŠ” 0.087, 총 νš¨κ³ΌλŠ” 0.601이닀(p<.01). μš”μ»¨λŒ€, 이 μ—°κ΅¬μ—μ„œλŠ” λΆ€λͺ¨μ˜ μ‚¬νšŒκ²½μ œμ  μ§€μœ„μ™€ μ‚¬κ΅μœ‘ μ‹œκ°„μ˜ ν†΅μ œ 여뢀와 λ¬΄κ΄€ν•˜κ²Œ κ³ λ“±ν•™μƒμ˜ 디지털 λ¦¬ν„°λŸ¬μ‹œκ°€ ν•™μŠ΅μ „λž΅κ³Ό ν•™κ΅κ΅μœ‘μ„±κ³Όμ— 긍정적인 영ν–₯을 미쳀으며, 각 변인 κ°„ 쒅합적인 κ΄€κ³„μ—μ„œ ν•™μŠ΅μ „λž΅μ΄ 디지털 λ¦¬ν„°λŸ¬μ‹œκ°€ ν•™κ΅κ΅μœ‘μ— λ―ΈμΉ˜λŠ” 영ν–₯을 λΆ€λΆ„μ μœΌλ‘œ λ§€κ°œν•˜λŠ” 것을 κ²½ν—˜κ³Όν•™μ μœΌλ‘œ κ²€μ¦ν•˜μ˜€λ‹€. μ΄λŸ¬ν•œ 연ꡬ κ²°κ³ΌλŠ” κ³ λ“±ν•™μƒμ˜ 디지털 λ¦¬ν„°λŸ¬μ‹œμ™€ ν•™μŠ΅μ „λž΅ μˆ˜μ€€μ„ μ‹ μž₯ 및 λ°œμ „μ‹œν‚€κΈ° μœ„ν•΄ λ…Έλ ₯ν•΄μ•Ό 함을 μ‹œμ‚¬ν•œλ‹€.β… . μ„œλ‘  1 1.1 μ—°κ΅¬μ˜ ν•„μš”μ„± 및 λͺ©μ  1 1.2연ꡬ 문제 3 β…‘. 이둠적 λ°°κ²½ 5 2.1 디지털 λ¦¬ν„°λŸ¬μ‹œ 5 2.2 ν•™μŠ΅μ „λž΅ 15 2.3 ν•™κ΅κ΅μœ‘μ„±κ³Ό 22 2.4 λ³€μˆ˜ κ°„μ˜ 관계 27 β…’. 연ꡬ κ°€μ„€ 33 3.1 μ£Όμš” 변인 μ„€μ • 및 μ •μ˜ 33 3.2 연ꡬ κ°€μ„€ 및 연ꡬ λͺ¨ν˜• 35 β…£. 연ꡬ 방법 38 4.1 연ꡬ λŒ€μƒ 38 4.2 μΈ‘μ • 도ꡬ 38 4.3 연ꡬ 절차 62 4.4 자료 뢄석 방법 63 β…€. 연ꡬ κ²°κ³Ό 65 5.1 κΈ°μ΄ˆν†΅κ³„ 65 5.2 ꡬ쑰방정식 λͺ¨ν˜• 뢄석 71 β…₯. μš”μ•½ 및 λ…Όμ˜ 82 5.1 μš”μ•½ 82 5.2 λ…Όμ˜ 85 μ°Έκ³ λ¬Έν—Œ 88 뢀둝(섀문지) 98 Abstract 105석

    Blowdown Analysis of System Composed of Pipelines Only using HYSYS Depressuring Utility and HYSYS Dynamic Simulation

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    Masterν•΄μ–‘ν”ŒλžœνŠΈ μ„€κ³„μ—μ„œ ν”Œλž˜μ–΄ μ‹œμŠ€ν…œμ€ ν”ŒλžœνŠΈμ˜ μ•ˆμ „ν•œ μš΄μ „μ„ μœ„ν•΄ ν•„μš”ν•˜λ‹€. 비상 κ°μ••μ‹œ λ°°μΆœλ˜λŠ” 유체의 양이 ν”Œλž˜μ–΄ μ‹œμŠ€ν…œ μ„€κ³„μ‹œ κ°€μž₯ 큰 영ν–₯을 미치게 λœλ‹€. ν™”μž¬μ‹œ 비상 κ°μ••μ˜ κ²½μš°κ°€ κ°€μž₯ μ‹¬κ°ν•œ κ²½μš°μ΄λ‹€. λ°°μΆœλŸ‰μ˜ 계산을 μœ„ν•΄ HYSYS Depressuring Utility λ₯Ό μ‚¬μš©ν•˜μ—¬ 해석을 μˆ˜ν–‰ν•˜λŠ” 것이 μΌλ°˜μ μ΄λ‹€. ν•˜μ§€λ§Œ HYSYS Depressuring Utility λŠ” μ‹€μ œ System 의 ν˜•μƒκ³Ό ꡬ성에 μ˜ν•œ λ³΅μž‘μ„±μ— 상관없이 ν•˜λ‚˜μ˜ μ••λ ₯ 용기둜 λ‹¨μˆœν™” ν•œ ν›„ 해석을 μˆ˜ν–‰ν•˜κΈ° λ•Œλ¬Έμ— μ‹€μ œ μ‹œμŠ€ν…œμ΄ λŒ€ν˜• μ••λ ₯ μš©κΈ°κ°€ 있으며 이 용기의 체적이 전체 μ‹œμŠ€ν…œμ˜ 체적의 λŒ€λΆ€λΆ„μ„ μ°¨μ§€ν•˜λŠ” 경우 κ°€μž₯ 적합 ν•  수 μžˆλ‹€. 맀우 λ³΅μž‘ν•œ μ‹œμŠ€ν…œμ˜ κ²½μš°μ—λ„ 단일 μ••λ ₯ 용기둜 λ‹¨μˆœν™” ν•˜μ—¬ 해석을 μˆ˜ν–‰ν•˜λŠ” 접근법은 μ ν•©ν•˜μ§€ μ•Šμ„ 수 μžˆλ‹€. λ³Έ λ…Όλ¬Έμ—μ„œλŠ” μ‹€μ œ μ‹œμŠ€ν…œμ„ κ·ΈλŒ€λ‘œ λͺ¨λΈλ§μ„ ν•œ ν›„ 동적 해석을 μˆ˜ν–‰ν•˜μ—¬ κ·Έ 결과와 HYSYS Depressuing Utility λ₯Ό μ‚¬μš©ν•œ 해석을 비ꡐ λΆ„μ„ν•˜μ˜€λ‹€.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
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