24 research outputs found

    CAPT๋ฅผ ์œ„ํ•œ ๋ฐœ์Œ ๋ณ€์ด ๋ถ„์„ ๋ฐ CycleGAN ๊ธฐ๋ฐ˜ ํ”ผ๋“œ๋ฐฑ ์ƒ์„ฑ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ธ๋ฌธ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ์ธ์ง€๊ณผํ•™์ „๊ณต,2020. 2. ์ •๋ฏผํ™”.Despite the growing popularity in learning Korean as a foreign language and the rapid development in language learning applications, the existing computer-assisted pronunciation training (CAPT) systems in Korean do not utilize linguistic characteristics of non-native Korean speech. Pronunciation variations in non-native speech are far more diverse than those observed in native speech, which may pose a difficulty in combining such knowledge in an automatic system. Moreover, most of the existing methods rely on feature extraction results from signal processing, prosodic analysis, and natural language processing techniques. Such methods entail limitations since they necessarily depend on finding the right features for the task and the extraction accuracies. This thesis presents a new approach for corrective feedback generation in a CAPT system, in which pronunciation variation patterns and linguistic correlates with accentedness are analyzed and combined with a deep neural network approach, so that feature engineering efforts are minimized while maintaining the linguistically important factors for the corrective feedback generation task. Investigations on non-native Korean speech characteristics in contrast with those of native speakers, and their correlation with accentedness judgement show that both segmental and prosodic variations are important factors in a Korean CAPT system. The present thesis argues that the feedback generation task can be interpreted as a style transfer problem, and proposes to evaluate the idea using generative adversarial network. A corrective feedback generation model is trained on 65,100 read utterances by 217 non-native speakers of 27 mother tongue backgrounds. The features are automatically learnt in an unsupervised way in an auxiliary classifier CycleGAN setting, in which the generator learns to map a foreign accented speech to native speech distributions. In order to inject linguistic knowledge into the network, an auxiliary classifier is trained so that the feedback also identifies the linguistic error types that were defined in the first half of the thesis. The proposed approach generates a corrected version the speech using the learners own voice, outperforming the conventional Pitch-Synchronous Overlap-and-Add method.์™ธ๊ตญ์–ด๋กœ์„œ์˜ ํ•œ๊ตญ์–ด ๊ต์œก์— ๋Œ€ํ•œ ๊ด€์‹ฌ์ด ๊ณ ์กฐ๋˜์–ด ํ•œ๊ตญ์–ด ํ•™์Šต์ž์˜ ์ˆ˜๊ฐ€ ํฌ๊ฒŒ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์Œ์„ฑ์–ธ์–ด์ฒ˜๋ฆฌ ๊ธฐ์ˆ ์„ ์ ์šฉํ•œ ์ปดํ“จํ„ฐ ๊ธฐ๋ฐ˜ ๋ฐœ์Œ ๊ต์œก(Computer-Assisted Pronunciation Training; CAPT) ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์— ๋Œ€ํ•œ ์—ฐ๊ตฌ ๋˜ํ•œ ์ ๊ทน์ ์œผ๋กœ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿผ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ํ˜„์กดํ•˜๋Š” ํ•œ๊ตญ์–ด ๋งํ•˜๊ธฐ ๊ต์œก ์‹œ์Šคํ…œ์€ ์™ธ๊ตญ์ธ์˜ ํ•œ๊ตญ์–ด์— ๋Œ€ํ•œ ์–ธ์–ดํ•™์  ํŠน์ง•์„ ์ถฉ๋ถ„ํžˆ ํ™œ์šฉํ•˜์ง€ ์•Š๊ณ  ์žˆ์œผ๋ฉฐ, ์ตœ์‹  ์–ธ์–ด์ฒ˜๋ฆฌ ๊ธฐ์ˆ  ๋˜ํ•œ ์ ์šฉ๋˜์ง€ ์•Š๊ณ  ์žˆ๋Š” ์‹ค์ •์ด๋‹ค. ๊ฐ€๋Šฅํ•œ ์›์ธ์œผ๋กœ์จ๋Š” ์™ธ๊ตญ์ธ ๋ฐœํ™” ํ•œ๊ตญ์–ด ํ˜„์ƒ์— ๋Œ€ํ•œ ๋ถ„์„์ด ์ถฉ๋ถ„ํ•˜๊ฒŒ ์ด๋ฃจ์–ด์ง€์ง€ ์•Š์•˜๋‹ค๋Š” ์ , ๊ทธ๋ฆฌ๊ณ  ๊ด€๋ จ ์—ฐ๊ตฌ๊ฐ€ ์žˆ์–ด๋„ ์ด๋ฅผ ์ž๋™ํ™”๋œ ์‹œ์Šคํ…œ์— ๋ฐ˜์˜ํ•˜๊ธฐ์—๋Š” ๊ณ ๋„ํ™”๋œ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค๋Š” ์ ์ด ์žˆ๋‹ค. ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ CAPT ๊ธฐ์ˆ  ์ „๋ฐ˜์ ์œผ๋กœ๋Š” ์‹ ํ˜ธ์ฒ˜๋ฆฌ, ์šด์œจ ๋ถ„์„, ์ž์—ฐ์–ด์ฒ˜๋ฆฌ ๊ธฐ๋ฒ•๊ณผ ๊ฐ™์€ ํŠน์ง• ์ถ”์ถœ์— ์˜์กดํ•˜๊ณ  ์žˆ์–ด์„œ ์ ํ•ฉํ•œ ํŠน์ง•์„ ์ฐพ๊ณ  ์ด๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ์ถ”์ถœํ•˜๋Š” ๋ฐ์— ๋งŽ์€ ์‹œ๊ฐ„๊ณผ ๋…ธ๋ ฅ์ด ํ•„์š”ํ•œ ์‹ค์ •์ด๋‹ค. ์ด๋Š” ์ตœ์‹  ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜ ์–ธ์–ด์ฒ˜๋ฆฌ ๊ธฐ์ˆ ์„ ํ™œ์šฉํ•จ์œผ๋กœ์จ ์ด ๊ณผ์ • ๋˜ํ•œ ๋ฐœ์ „์˜ ์—ฌ์ง€๊ฐ€ ๋งŽ๋‹ค๋Š” ๋ฐ”๋ฅผ ์‹œ์‚ฌํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ๋จผ์ € CAPT ์‹œ์Šคํ…œ ๊ฐœ๋ฐœ์— ์žˆ์–ด ๋ฐœ์Œ ๋ณ€์ด ์–‘์ƒ๊ณผ ์–ธ์–ดํ•™์  ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ์™ธ๊ตญ์ธ ํ™”์ž๋“ค์˜ ๋‚ญ๋…์ฒด ๋ณ€์ด ์–‘์ƒ๊ณผ ํ•œ๊ตญ์–ด ์›์–ด๋ฏผ ํ™”์ž๋“ค์˜ ๋‚ญ๋…์ฒด ๋ณ€์ด ์–‘์ƒ์„ ๋Œ€์กฐํ•˜๊ณ  ์ฃผ์š”ํ•œ ๋ณ€์ด๋ฅผ ํ™•์ธํ•œ ํ›„, ์ƒ๊ด€๊ด€๊ณ„ ๋ถ„์„์„ ํ†ตํ•˜์—ฌ ์˜์‚ฌ์†Œํ†ต์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ค‘์š”๋„๋ฅผ ํŒŒ์•…ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ์ข…์„ฑ ์‚ญ์ œ์™€ 3์ค‘ ๋Œ€๋ฆฝ์˜ ํ˜ผ๋™, ์ดˆ๋ถ„์ ˆ ๊ด€๋ จ ์˜ค๋ฅ˜๊ฐ€ ๋ฐœ์ƒํ•  ๊ฒฝ์šฐ ํ”ผ๋“œ๋ฐฑ ์ƒ์„ฑ์— ์šฐ์„ ์ ์œผ๋กœ ๋ฐ˜์˜ํ•˜๋Š” ๊ฒƒ์ด ํ•„์š”ํ•˜๋‹ค๋Š” ๊ฒƒ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ๊ต์ •๋œ ํ”ผ๋“œ๋ฐฑ์„ ์ž๋™์œผ๋กœ ์ƒ์„ฑํ•˜๋Š” ๊ฒƒ์€ CAPT ์‹œ์Šคํ…œ์˜ ์ค‘์š”ํ•œ ๊ณผ์ œ ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ด ๊ณผ์ œ๊ฐ€ ๋ฐœํ™”์˜ ์Šคํƒ€์ผ ๋ณ€ํ™”์˜ ๋ฌธ์ œ๋กœ ํ•ด์„์ด ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ๋ณด์•˜์œผ๋ฉฐ, ์ƒ์„ฑ์  ์ ๋Œ€ ์‹ ๊ฒฝ๋ง (Cycle-consistent Generative Adversarial Network; CycleGAN) ๊ตฌ์กฐ์—์„œ ๋ชจ๋ธ๋งํ•˜๋Š” ๊ฒƒ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. GAN ๋„คํŠธ์›Œํฌ์˜ ์ƒ์„ฑ๋ชจ๋ธ์€ ๋น„์›์–ด๋ฏผ ๋ฐœํ™”์˜ ๋ถ„ํฌ์™€ ์›์–ด๋ฏผ ๋ฐœํ™” ๋ถ„ํฌ์˜ ๋งคํ•‘์„ ํ•™์Šตํ•˜๋ฉฐ, Cycle consistency ์†์‹คํ•จ์ˆ˜๋ฅผ ์‚ฌ์šฉํ•จ์œผ๋กœ์จ ๋ฐœํ™”๊ฐ„ ์ „๋ฐ˜์ ์ธ ๊ตฌ์กฐ๋ฅผ ์œ ์ง€ํ•จ๊ณผ ๋™์‹œ์— ๊ณผ๋„ํ•œ ๊ต์ •์„ ๋ฐฉ์ง€ํ•˜์˜€๋‹ค. ๋ณ„๋„์˜ ํŠน์ง• ์ถ”์ถœ ๊ณผ์ •์ด ์—†์ด ํ•„์š”ํ•œ ํŠน์ง•๋“ค์ด CycleGAN ํ”„๋ ˆ์ž„์›Œํฌ์—์„œ ๋ฌด๊ฐ๋… ๋ฐฉ๋ฒ•์œผ๋กœ ์Šค์Šค๋กœ ํ•™์Šต๋˜๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ, ์–ธ์–ด ํ™•์žฅ์ด ์šฉ์ดํ•œ ๋ฐฉ๋ฒ•์ด๋‹ค. ์–ธ์–ดํ•™์  ๋ถ„์„์—์„œ ๋“œ๋Ÿฌ๋‚œ ์ฃผ์š”ํ•œ ๋ณ€์ด๋“ค ๊ฐ„์˜ ์šฐ์„ ์ˆœ์œ„๋Š” Auxiliary Classifier CycleGAN ๊ตฌ์กฐ์—์„œ ๋ชจ๋ธ๋งํ•˜๋Š” ๊ฒƒ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ด ๋ฐฉ๋ฒ•์€ ๊ธฐ์กด์˜ CycleGAN์— ์ง€์‹์„ ์ ‘๋ชฉ์‹œ์ผœ ํ”ผ๋“œ๋ฐฑ ์Œ์„ฑ์„ ์ƒ์„ฑํ•จ๊ณผ ๋™์‹œ์— ํ•ด๋‹น ํ”ผ๋“œ๋ฐฑ์ด ์–ด๋–ค ์œ ํ˜•์˜ ์˜ค๋ฅ˜์ธ์ง€ ๋ถ„๋ฅ˜ํ•˜๋Š” ๋ฌธ์ œ๋ฅผ ์ˆ˜ํ–‰ํ•œ๋‹ค. ์ด๋Š” ๋„๋ฉ”์ธ ์ง€์‹์ด ๊ต์ • ํ”ผ๋“œ๋ฐฑ ์ƒ์„ฑ ๋‹จ๊ณ„๊นŒ์ง€ ์œ ์ง€๋˜๊ณ  ํ†ต์ œ๊ฐ€ ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ์žฅ์ ์ด ์žˆ๋‹ค๋Š” ๋ฐ์— ๊ทธ ์˜์˜๊ฐ€ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์„ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด์„œ 27๊ฐœ์˜ ๋ชจ๊ตญ์–ด๋ฅผ ๊ฐ–๋Š” 217๋ช…์˜ ์œ ์˜๋ฏธ ์–ดํœ˜ ๋ฐœํ™” 65,100๊ฐœ๋กœ ํ”ผ๋“œ๋ฐฑ ์ž๋™ ์ƒ์„ฑ ๋ชจ๋ธ์„ ํ›ˆ๋ จํ•˜๊ณ , ๊ฐœ์„  ์—ฌ๋ถ€ ๋ฐ ์ •๋„์— ๋Œ€ํ•œ ์ง€๊ฐ ํ‰๊ฐ€๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์˜€์„ ๋•Œ ํ•™์Šต์ž ๋ณธ์ธ์˜ ๋ชฉ์†Œ๋ฆฌ๋ฅผ ์œ ์ง€ํ•œ ์ฑ„ ๊ต์ •๋œ ๋ฐœ์Œ์œผ๋กœ ๋ณ€ํ™˜ํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€๋Šฅํ•˜๋ฉฐ, ์ „ํ†ต์ ์ธ ๋ฐฉ๋ฒ•์ธ ์Œ๋†’์ด ๋™๊ธฐ์‹ ์ค‘์ฒฉ๊ฐ€์‚ฐ (Pitch-Synchronous Overlap-and-Add) ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์‚ฌ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋น„ํ•ด ์ƒ๋Œ€ ๊ฐœ์„ ๋ฅ  16.67%์ด ํ™•์ธ๋˜์—ˆ๋‹ค.Chapter 1. Introduction 1 1.1. Motivation 1 1.1.1. An Overview of CAPT Systems 3 1.1.2. Survey of existing Korean CAPT Systems 5 1.2. Problem Statement 7 1.3. Thesis Structure 7 Chapter 2. Pronunciation Analysis of Korean Produced by Chinese 9 2.1. Comparison between Korean and Chinese 11 2.1.1. Phonetic and Syllable Structure Comparisons 11 2.1.2. Phonological Comparisons 14 2.2. Related Works 16 2.3. Proposed Analysis Method 19 2.3.1. Corpus 19 2.3.2. Transcribers and Agreement Rates 22 2.4. Salient Pronunciation Variations 22 2.4.1. Segmental Variation Patterns 22 2.4.1.1. Discussions 25 2.4.2. Phonological Variation Patterns 26 2.4.1.2. Discussions 27 2.5. Summary 29 Chapter 3. Correlation Analysis of Pronunciation Variations and Human Evaluation 30 3.1. Related Works 31 3.1.1. Criteria used in L2 Speech 31 3.1.2. Criteria used in L2 Korean Speech 32 3.2. Proposed Human Evaluation Method 36 3.2.1. Reading Prompt Design 36 3.2.2. Evaluation Criteria Design 37 3.2.3. Raters and Agreement Rates 40 3.3. Linguistic Factors Affecting L2 Korean Accentedness 41 3.3.1. Pearsons Correlation Analysis 41 3.3.2. Discussions 42 3.3.3. Implications for Automatic Feedback Generation 44 3.4. Summary 45 Chapter 4. Corrective Feedback Generation for CAPT 46 4.1. Related Works 46 4.1.1. Prosody Transplantation 47 4.1.2. Recent Speech Conversion Methods 49 4.1.3. Evaluation of Corrective Feedback 50 4.2. Proposed Method: Corrective Feedback as a Style Transfer 51 4.2.1. Speech Analysis at Spectral Domain 53 4.2.2. Self-imitative Learning 55 4.2.3. An Analogy: CAPT System and GAN Architecture 57 4.3. Generative Adversarial Networks 59 4.3.1. Conditional GAN 61 4.3.2. CycleGAN 62 4.4. Experiment 63 4.4.1. Corpus 64 4.4.2. Baseline Implementation 65 4.4.3. Adversarial Training Implementation 65 4.4.4. Spectrogram-to-Spectrogram Training 66 4.5. Results and Evaluation 69 4.5.1. Spectrogram Generation Results 69 4.5.2. Perceptual Evaluation 70 4.5.3. Discussions 72 4.6. Summary 74 Chapter 5. Integration of Linguistic Knowledge in an Auxiliary Classifier CycleGAN for Feedback Generation 75 5.1. Linguistic Class Selection 75 5.2. Auxiliary Classifier CycleGAN Design 77 5.3. Experiment and Results 80 5.3.1. Corpus 80 5.3.2. Feature Annotations 81 5.3.3. Experiment Setup 81 5.3.4. Results 82 5.4. Summary 84 Chapter 6. Conclusion 86 6.1. Thesis Results 86 6.2. Thesis Contributions 88 6.3. Recommendations for Future Work 89 Bibliography 91 Appendix 107 Abstract in Korean 117 Acknowledgments 120Docto

    DHPV์˜ ๋…ธํ™”์„ฑ ๊ทผ๊ฐ์†Œ ์–ต์ œ ๋ฐ ๊ทผ์œก๋ถ„ํ™” ์ด‰์ง„ ํšจ๋Šฅ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ๋†์ƒ๋ช…๊ณตํ•™๋ถ€, 2018. 2. ์ด๊ธฐ์›.Procyanidins have many health benefits. After intake, procyanidins are degraded into small metabolites by gastrointestinal microorganisms. According to a previous study, 5-(3',4-Dihydroxyphenyl)-ฮณ-valerolactone is the most abundant among these metabolites. The object of this study is to determine the effects of DHPV on muscle and whether DHPV could recover aged muscle to normal. TGF-ฮฒ is connected to muscle aging and attenuates the muscle differentiation and inhibits myogenesis. In this study, I measured myoD, myogenin, and the myosin heavy chain expression level to determine whether DHPV improved the myogenesis of C2C12, which had been inhibited by a TGF-ฮฒ treatment. The results show that the RNA and protein expression level of these 3 biomarkers increases in the DHPV treated group in comparison to TGF-ฮฒ only treated. In addition, DHPV modulated the TGF-ฮฒ signaling pathway and C2C12 myogenesis by regulating p-smad2/3, p-JNK, p-ERK and p-p38 expression. Taken together, it may be suggested that DHPV is an ideal therapeutic candidate for recovering myogenesis and muscle differentiation evoked by aging.I. INTRODUCTION 1 II. MATERLIALS AND METHODS 4 1. Chemicals and reagents 4 2. Cell culture and treatments 5 3. Sulforhodamine B assay 6 4. Hematoxylin and Eosin staining (H&E staining) 6 5. Western blot assay 7 6. Real-time quantitative PCR 8 7. Statistical analysis 10 III. RESULTS 11 1. DHPV promotes myotube formation and has no toxic effect on C2C12 murine myoblasts 11 2. DHPV improves the myoD expression compared to TGF-ฮฒ treatment 15 3. DHPV up-regulates the expression of myogenin in spite of TGF-ฮฒ treatment on C2C12 myoblasts 19 4. DHPV treatment counteracts the inhibition of myosin heavy chain expression induced by TGF-ฮฒ 22 5. DHPV donw-regulates p-smad2/3 expressions even in TGF-ฮฒ treated group 26 6. The summary of the myogenic role of DHPV on C2C 12 myogenesis against TGF-ฮฒ 29 IV. DISCUSSION 30 V. REFERENCES 34 VI. ๊ตญ๋ฌธ ์ดˆ๋ก 38Maste

    Why has Financial Reporting Become More Conservative over Time?

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฒฝ์˜ํ•™๊ณผ, 2015. 2. ํ™ฉ์ด์„.Accounting conservatism has significantly increased over the last several decades, but little is known about causes for these changes. I find that the overall increase in conservatism is largely driven by a subset of young firms rather than old firms in the firm population. I then examine whether the different conservatism trends between old and young firms are due to changes in accounting standards, changes in demand for conservatism, or changes in firms economic environments. My evidence suggests that the adoption of new accounting standards is responsible for changes in conservatism as most prior studies expect, but changes in demand for conservatism driven by the passage of regulations also play an important role in explaining conservatism trends. In contrast, changes in economic environments seem to have had little effect on conservatism.1. INTRODUCTION 2. CHANGES IN THE ACCOUNTING CONSERVATISM 3. WHO DRIVES THE OVERALL TREND IN CONSERVATISM? 4. EXPLANATIONS FOR THE DIFFERENT CONSERVATISM TREND BETWEEN OLD AND YOUNG FIRMS 4.1 Three possible sources of the changes in conservatism 4.2 Changes in accounting standards and changes in the demand for conservatism 4.3 Changes in firms economic environments 5. CONCLUSION REFERENCES APPENDIXMaste

    Thrombolytic Effects of the Snake Venom Disintegrin Saxatilin Determined by Novel Assessment Methods: A FeCl3-Induced Thrombosis Model in Mice.

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    Saxatilin, a novel disintegrin purified and cloned from the venom of the Korean snake Gloydius saxatilis, strongly inhibits activation and aggregation of platelets. Glycoprotein (GP) IIb/IIIa receptor antagonists can resolve thrombus, so saxatilin might also have thrombolytic effects. We investigated the thrombolytic effects of saxatilin in mice using a ferric chloride-induced carotid arterial thrombosis model. Thrombotic occlusion and thrombus resolution were evaluated quantitatively by measuring blood flow in the carotid artery with an ultrasonic flow meter and calculating the degree of flow restoration on a minute-by-minute basis; results were confirmed by histological examination. Saxatilin dissolved thrombi in a dose-dependent manner. Saxatilin at 5 mg/kg restored blood flow to baseline levels. As saxatilin dose increased, time to recanalization decreased. A bolus injection of 10% of a complete dose with continuous infusion of the remaining dose for 60 minutes resulted in effective recanalization without reocclusion. The thrombolytic effect of saxatilin was also demonstrated in vitro using platelet aggregometry by administering saxatilin in preformed thrombi. Bleeding complications were observed in 2 of 71 mice that received saxatilin. Fibrin/fibrinogen zymography and platelet aggregometry studies indicated that saxatilin does not have fibrinolytic activity, but exerted its action on platelets. Integrin-binding assays showed that saxatilin inhibited multiple integrins, specifically ฮฑ2bฮฒ3 (GP IIb/IIIa), ฮฑ5ฮฒ1, ฮฑvฮฒ3, ฮฑvฮฒ1, and ฮฑvฮฒ5, which act on platelet adhesion/aggregation. Saxatilin inhibited multiple integrins by acting on platelets, and was safe and effective in resolving thrombi in mice.ope

    ๊ธฐ์—… ์ž๋ณธ์กฐ๋‹ฌ ์˜์‚ฌ๊ฒฐ์ • ๋ฐ ํšŒ๊ณ„๋ณด์ˆ˜์ฃผ์˜์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ฒฝ์˜๋Œ€ํ•™ ๊ฒฝ์˜ํ•™๊ณผ,2019. 8. ํ™ฉ์ด์„.๋ณธ ๋…ผ๋ฌธ์€ ๊ธฐ์—…์˜ ์ž๋ณธ์กฐ๋‹ฌ ์˜์‚ฌ๊ฒฐ์ •๊ณผ ํšŒ๊ณ„๋ณด์ˆ˜์ฃผ์˜์— ๋Œ€ํ•œ ๋‘ ๊ฐœ์˜ ๋…๋ฆฝ์ ์ธ ๋…ผ๋ฌธ์œผ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ ๋…ผ๋ฌธ์€ ๊ธฐ์—…์˜ ์„ฑ์žฅ์„ฑ์— ๋Œ€ํ•œ ๋ถˆํ™•์‹ค์„ฑ์ด ๋ถ€์ฑ„์™€ ์ž๊ธฐ์ž๋ณธ์˜ ๊ฐ€์น˜ํ‰๊ฐ€๋ฅผ ๋งค๊ฐœ๋กœ ํ•˜์—ฌ ๊ธฐ์—…์˜ ์ž๋ณธ์กฐ๋‹ฌ ์˜์‚ฌ๊ฒฐ์ •์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•˜์—ฌ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ํšจ์œจ์  ์‹œ์žฅ์— ๋Œ€ํ•œ ๊ฐ€์ •์— ๊ธฐ๋ฐ˜ํ•œ ๊ฐ€์น˜ํ‰๊ฐ€๋ชจํ˜•์— ๋”ฐ๋ฅด๋ฉด, ๋†’์€ ๋ถˆํ™•์‹ค์„ฑ์€ ์ฃผ์‹์˜ ๋ณผ๋ก์„ฑ์œผ๋กœ ์ธํ•ด ์ž๊ธฐ์ž๋ณธ์˜ ๊ฐ€์น˜๋ฅผ ๋†’์ด๋Š” ๋ฐ˜๋ฉด, ๋ถ€์ฑ„์˜ ์˜ค๋ชฉ์„ฑ์œผ๋กœ ์ธํ•ด ๋ถ€์ฑ„์˜ ๊ฐ€์น˜๋Š” ๋–จ์–ด๋œจ๋ฆฐ๋‹ค. ์ด๋Ÿฌํ•œ ๊ด€๊ณ„๋Š” ์ •๋ณด ๋น„๋Œ€์นญ์ด๋‚˜ ๊ฐ€์น˜ํ‰๊ฐ€์˜ค๋ฅ˜๊ฐ€ ์กด์žฌํ•˜์ง€ ์•Š๋Š” ๊ฒฝ์šฐ์—๋„ ์„ฑ๋ฆฝํ•œ๋‹ค. ์ด๋กœ ์ธํ•ด ๋ถˆํ™•์‹ค์„ฑ์€ ์ž๊ธฐ์ž๋ณธ์กฐ๋‹ฌ์˜ ์ƒ๋Œ€์  ๋งค๋ ฅ๋„๋ฅผ ๋†’์—ฌ ๊ถ๊ทน์ ์œผ๋กœ ๊ธฐ์—…์˜ ์ž๋ณธ์กฐ๋‹ฌ ์˜์‚ฌ๊ฒฐ์ •์— ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๋‹ค. 1980๋…„๋ถ€ํ„ฐ 2017๋…„๊นŒ์ง€ ์™ธ๋ถ€์ž๋ณธ์„ ์กฐ๋‹ฌํ•œ ๋ฏธ๊ตญ ๊ธฐ์—…์„ ๋Œ€์ƒ์œผ๋กœ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ๊ธฐ๋Œ€ ๋ถˆํ™•์‹ค์„ฑ์ด ๋†’์€ ๊ธฐ์—…์€ ํƒ€์ธ์ž๋ณธ๋ณด๋‹ค๋Š” ์ž๊ธฐ์ž๋ณธ ์กฐ๋‹ฌ์„ ์„ ํ˜ธํ•˜๋ฉฐ, ์ž๊ธฐ์ž๋ณธ ์กฐ๋‹ฌ์•ก์€ ๋” ๋†’์œผ๋‚˜ ํƒ€์ธ์ž๋ณธ ์กฐ๋‹ฌ์•ก์€ ๋” ๋‚ฎ์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Ÿฌํ•œ ํšจ๊ณผ๋Š” ์ฃผ์‹๊ณผ ๋ถ€์ฑ„์˜ ๋น„์„ ํ˜•์„ฑ์ด ๋‘๋“œ๋Ÿฌ์ง€๋Š” ๊ธฐ์—…์—์„œ ๋”์šฑ ๊ฐ•ํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ์ž๋ณธ์กฐ๋‹ฌ๋น„์šฉ, ๊ฐ€์น˜ํ‰๊ฐ€์˜ค๋ฅ˜, ์žฌ๋ฌด์  ๊ณค๊ฒฝ๊ณผ ๊ฐ™์€ ๋Œ€์ฒด์ ์ธ ์š”์ธ์— ์˜ํ•ด ์„ค๋ช…๋˜์ง€ ์•Š์•˜๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋“ค์€ ๊ธฐ์—…์ด ์˜ˆ์ƒ๋˜๋Š” ๋ถˆํ™•์‹ค์„ฑ์— ๋”ฐ๋ผ ์ž๋ณธ์กฐ๋‹ฌ ๊ตฌ์กฐ๋ฅผ ์กฐ์ •ํ•˜๋ฉฐ, ๊ทธ ์ฃผ๋œ ์›์ธ์ด ๋ถˆํ™•์‹ค์„ฑ์ด ์ฃผ์‹ ๋ฐ ๋ถ€์ฑ„์˜ ๊ฐ€์น˜ํ‰๊ฐ€์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ž„์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ ๋…ผ๋ฌธ์€ ํšŒ๊ณ„ ๋ณด์ˆ˜์ฃผ์˜๊ฐ€ ์ง€๋‚œ ๋ช‡์‹ญ ๋…„ ๋™์•ˆ ๋ณ€ํ™”ํ•œ ์›์ธ์— ๋Œ€ํ•˜์—ฌ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ๋…ผ๋ฌธ์˜ ์ฒซ ๋ฒˆ์งธ ๋ถ€๋ถ„์—์„œ๋Š” ๋จผ์ € ํšŒ๊ณ„ ๋ณด์ˆ˜์ฃผ์˜์˜ ํŠน์„ฑ์ด 1981๋…„๋ถ€ํ„ฐ 2017๋…„๊นŒ์ง€ ์–ด๋–ป๊ฒŒ ๋ณ€ํ•˜์˜€๋Š”์ง€ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ์„ ํ–‰์—ฐ๊ตฌ๊ฐ€ ์ œ์‹œํ•œ ๊ฒฐ๊ณผ์™€ ์ผ๊ด€๋˜๊ฒŒ ๋ณด์ˆ˜์ฃผ์˜๊ฐ€ ์œ ์˜ํ•˜๊ฒŒ ์ฆ๊ฐ€ํ•˜์˜€์Œ์„ ๋ฐœ๊ฒฌํ•˜์˜€์œผ๋ฉฐ, ๋™ ์ฆ๊ฐ€๊ฐ€ ์˜์—…ํ˜„๊ธˆํ๋ฆ„์ด ์•„๋‹Œ ๋ฐœ์ƒ์•ก ํ•ญ๋ชฉ์—, ์˜์—…์†์ต์ด ์•„๋‹Œ ๋น„์˜์—…์†์ต ํ•ญ๋ชฉ์— ๊ธฐ์ธํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ, ๋ณด์ˆ˜์ฃผ์˜์˜ ์ฆ๊ฐ€๋Š” ํ‘œ๋ณธ์— ์ƒˆ๋กญ๊ฒŒ ํŽธ์ž…๋œ ์ƒˆ๋กœ์šด ๊ธฐ์—…๋“ค์— ๊ธฐ์ธํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋…ผ๋ฌธ์˜ ๋‘ ๋ฒˆ์งธ ๋ถ€๋ถ„์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๋ณ€ํ™”๊ฐ€ ๋‚˜ํƒ€๋‚œ ์›์ธ์— ๋Œ€ํ•˜์—ฌ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ, ์„ฑ์ˆ™๊ธฐ์—…๊ณผ ์ƒˆ๋กœ์šด ๊ธฐ์—…์ด ์„œ๋กœ ๋‹ค๋ฅธ ๋ณด์ˆ˜์ฃผ์˜ ์ถ”์„ธ๋ฅผ ๋ณด์ด๋Š” ์›์ธ์„ ์‹๋ณ„ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ, ์„ ํ–‰์—ฐ๊ตฌ์˜ ์˜ˆ์ธก๋Œ€๋กœ ํšŒ๊ณ„๊ธฐ์ค€์˜ ๋ณ€ํ™”๊ฐ€ ๋ณด์ˆ˜์ฃผ์˜์˜ ๋ณ€ํ™”, ํŠนํžˆ ๊ทธ ์ฆ๊ฐ€์ถ”์„ธ์™€ ์œ ์˜ํ•œ ๊ด€๋ จ์„ฑ์„ ๋ณด์˜€๋‹ค. ์ด์™€ ๋”๋ถˆ์–ด ์ œ๋„ ๋ณ€ํ™”๋กœ ์ด‰๋ฐœ๋œ ๋ณด์ˆ˜์ฃผ์˜์— ๋Œ€ํ•œ ์ˆ˜์š” ๋ณ€ํ™” ์—ญ์‹œ ๋ณด์ˆ˜์ฃผ์˜์˜ ์ถ”์„ธ๋ฅผ ์„ค๋ช…ํ•˜๋Š”๋ฐ ์ค‘์š”ํ•œ ์š”์ธ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋Š”๋ฐ, ์ผ๋ถ€ ๊ธฐ๊ฐ„์—๋Š” ์ด๋Ÿฌํ•œ ์š”์ธ์ด ํšŒ๊ณ„๊ธฐ์ค€์˜ ํšจ๊ณผ๋ฅผ ์™„์ „ํžˆ ์ƒ์‡„ํ•˜๊ธฐ๋„ ํ•˜์˜€๋‹ค. ๋ฐ˜๋ฉด, ๊ธฐ์—…์˜ ๊ฒฝ์ œ์  ํ™˜๊ฒฝ ๋ณ€ํ™”๋Š” ๋ณด์ˆ˜์ฃผ์˜์˜ ๋ณ€ํ™”๋ฅผ ์ดˆ๋ž˜ํ•œ ์ฃผ๋œ ์›์ธ์€ ์•„๋‹Œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค.This dissertation is comprised of two essays on corporate financing choices and accounting conservatism. The first essay, entitled Growth Uncertainty and the Choice between Debt and Equity: A Valuation Perspective, examines the effect of uncertainty about firms growth on financing decisions through its effect on debt and equity valuations. According to the rational asset pricing model, higher uncertainty contributes to higher equity valuations (lower debt valuations) due to the convexity (concavity) in equity (debt) value, even in the absence of asymmetric information or mispricing. This increases the relative attractiveness of equity financing, potentially affecting firms financing choices. Analyzing firms raising external capital during the period 1980-2017, I find that firms with higher expected uncertainty are more likely to issue equity as opposed to debt and raise larger amount of equity financing and smaller amount of debt financing. Consistent with a valuation channel, the effect is more pronounced for firms predicted to have greater nonlinearities in equity and debt values. Investigating several alternative explanations, I show that my main findings are not likely to be explained by cost of capital, mispricing, or financial distress channel. Overall, I provide evidence that firms adjust their financing structures in response to the level of uncertainty expected to exist, since uncertainty has differential effects on equity and debt valuations. The second essay, entitled The Changing Properties of Accounting Conservatism: Why Has Financial Reporting Become More Conservative?, investigates causes for the over-time change in accounting conservatism. In the first part of the paper, I investigate how the properties of accounting conservatism have changed for the period 1981-2017. I find that conservatism has substantially increased over time as prior studies document, and that this increase is primarily driven by the increase in the asymmetric timeliness of accruals rather than cash flow from operations, and by the increased recognition of non-operating expenses. Moreover, the increasing trend in conservatism is largely led by young firms that are newly incorporated into the sample. In the second part of the paper, I examine what causes the change in conservatism by investigating the source of different conservatism trends observed for old and young firms. My findings suggest that changes in accounting standards account for the change, especially an increase, in conservatism as most prior studies expect, but changes in demand for conservatism caused by the introduction of related regulations also play an important role, even offsetting the effect of accounting standards in some years. In contrast, changes in economic environments do not seem to be the major source of time-series variation in conservatism.Essay 1. Growth Uncertainty and the Choice between Debt and Equity: A Valuation Perspective 1 1. Introduction . 2 2. Literature Review and Hypothesis Development . 6 2.1. Literature Review . 6 2.1.1. Value Implications of Economic Uncertainty . 7 2.1.2. The Determinants of Corporate Financing Decisions 9 2.2. Hypothesis Development 12 3. Research Design 14 3.1. Sample 14 3.2. Measuring Growth Uncertainty 15 3.3. External Financing Decisions . 16 4. Empirical Results 18 4.1. Descriptive Statistics 18 4.2. The Average Effect of Growth Uncertainty on Financing Choices 19 4.3. The Valuation Channel 21 4.3.1. Growth Uncertainty and Valuations of Equity and Debt 21 4.3.2. The Role of Convexity (Concavity) of Equity (Debt) Value in the Relation between Growth Uncertainty and Financing Choices 25 4.4. Alternative Explanations . 28 4.4.1. Cost of Capital 29 4.4.2. Mispricing . 31 4.4.3. Financial Distress . 33 5. Additional Analyses 34 5.1. Frequent Issuers vs. One-Time Issuers . 34 5.2. The Effect of Macro Uncertainty 35 5.3. The Use of Cash Proceeds from External Financing 37 5.4. Alternative Proxies for Growth Uncertainty . 38 6. Conclusions . 39 References . 41 Appendix . 86 Essay 2. The Changing Properties of Accounting Conservatism: Why Has Financial Reporting Become More Conservative 90 1. Introduction . 91 2. Changes in the Accounting Conservatism . 97 2.1. Overall Trends in Accounting Conservatism 97 2.2. Changing Properties of Accounting Conservatism . 99 2.2.1. Asymmetric Timeliness of Earnings in Reflecting Current and Past News 100 2.2.2. Asymmetric Timeliness of Various Components of Earnings. 102 2.2.3. Changes in the Sample Composition. 106 3. Why Has Financial Reporting Become More Conservative . 109 3.1. Three Possible Sources of the Changes in Conservatism . 109 3.2. Changes in Accounting Standards and Demand for Conservatism 110 3.3. Changes in Firms Economic Environments 118 4. Additional Analyses 120 4.1. Changes in Firm Characteristics . 120 4.2. Confounding Effect of Cost Stickiness . 122 4.3. Non-Linearity in the Asymmetric Timeliness of Earnings . 123 5. Conclusions . 125 References . 127 Appendix . 164Docto

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    The Effects of Learner Centered Instruction in Nursing: A Meta-Analysis

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    ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ฐ„ํ˜ธํ•™ ๋ถ„์•ผ ํ•™์Šต์ž ์ค‘์‹ฌ ์ˆ˜์—…์˜ ํšจ๊ณผ๋ฅผ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด ๋ฌธ์ œ์ค‘์‹ฌํ•™์Šต, ์•ก์…˜๋Ÿฌ๋‹, ํ”Œ๋ฆฝ๋Ÿฌ๋‹, ํŒ€ ๊ธฐ๋ฐ˜ ํ•™์Šต์„ ์ ์šฉํ•œ ์—ฐ๊ตฌ 84ํŽธ์„ ๋Œ€์ƒ์œผ๋กœ ๋ฉ”ํƒ€๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ, ๊ฐ„ํ˜ธํ•™ ๋ถ„์•ผ ํ•™์Šต์ž ์ค‘์‹ฌ ์ˆ˜์—…์˜ ์ „์ฒด ํšจ๊ณผํฌ๊ธฐ๋Š” 0.624๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Š” ํ•™์Šต์ž ์ค‘์‹ฌ ์ˆ˜์—…์ด ์ค‘๊ฐ„ ํšจ๊ณผํฌ๊ธฐ๋ฅผ ๊ฐ–๊ณ  ์žˆ์Œ์„ ์˜๋ฏธํ•œ๋‹ค. ์ˆ˜์—…๋ชจํ˜•์— ๋”ฐ๋ผ์„œ๋Š” ํŒ€ ๊ธฐ๋ฐ˜ ํ•™์Šต, ์•ก์…˜๋Ÿฌ๋‹, ํ”Œ๋ฆฝ๋Ÿฌ๋‹, ๋ฌธ์ œ์ค‘์‹ฌํ•™์Šต์˜ ์ˆœ์œผ๋กœ ํšจ๊ณผํฌ๊ธฐ๊ฐ€ ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด 15๊ฐ€์ง€์˜ ์ข…์†๋ณ€์ธ ์ค‘ ํฐ ํšจ๊ณผํฌ๊ธฐ๋ฅผ ๋ณด์ธ ๊ฒƒ์€ ์ž„์ƒ์ˆ˜ํ–‰๋Šฅ๋ ฅ์ด์—ˆ๋‹ค. ๊ฐ„ํ˜ธ๋ถ„์•ผ ์ง€์‹, ์ฐฝ์˜์„ฑ, ์ž๊ธฐ์ฃผ๋„ํ•™์Šต, ์˜์‚ฌ์†Œํ†ต๋Šฅ๋ ฅ, ๋Œ€์ธ๊ด€๊ณ„๋Šฅ๋ ฅ, ๋ฌธ์ œํ•ด๊ฒฐ๋ ฅ, ๋น„ํŒ์  ์‚ฌ๊ณ  ๋“ฑ์˜ ์ข…์†๋ณ€์ˆ˜์—์„œ๋Š” ์ค‘๊ฐ„ ํšจ๊ณผํฌ๊ธฐ๊ฐ€ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ข…์†๋ณ€์ˆ˜๋ฅผ ์„ธ ๊ฐ€์ง€ ์˜์—ญ์œผ๋กœ ๋ฌถ์—ˆ์„ ๋•Œ, ์ „๊ณต๋Šฅ๋ ฅ์˜ ํšจ๊ณผํฌ๊ธฐ๊ฐ€ ๊ฐ€์žฅ ์ปธ์œผ๋ฉฐ, ๊ฐœ์ธ์—ญ๋Ÿ‰๊ณผ ํ•™์Šตํƒœ๋„์˜ ์ˆœ์œผ๋กœ ํšจ๊ณผ๊ฐ€ ์žˆ์—ˆ๋‹ค. ์ด ๋ฐ–์—๋„ ํ•™์Šต์ž ์ค‘์‹ฌ ์ˆ˜์—…์˜ ์šด์˜ ๋ฐฉ๋ฒ•๊ณผ ๊ด€๋ จ๋œ ์กฐ์ ˆ๋ณ€์ˆ˜์— ๋”ฐ๋ฅธ ํšจ๊ณผํฌ๊ธฐ์˜ ์ฐจ์ด๋ฅผ ์‚ดํŽด๋ณด์•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์ „ํ†ต์  ๊ต์ˆ˜์ž ์ค‘์‹ฌ ์ˆ˜์—…๋ณด๋‹ค ํ•™์Šต์ž ์ค‘์‹ฌ ์ˆ˜์—…์ด ๋” ํšจ๊ณผ์ ์ž„์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•™์Šต์ž ์ค‘์‹ฌ์˜ ์ƒˆ๋กœ์šด ๊ต์œก๋ฐฉ๋ฒ•์„ ์ ์šฉํ•˜๊ธฐ๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค.The purpose of this study is to analyze the effect of learner centered instruction in the nursing field by using meta-analysis of 84 studies with problem based learning, action learning, flipped learning, and team based learning. As a result, the total effect size of learner centered class in nursing was 0.624. This means that learner centered instruction has a medium effect size. According to the instructional model, the effect size was higher in order of team based learning, action learning, flipped learning, and problem based learning. Among the total 15 dependent variables, the greatest effect size was in clinical performance. The medium effect size was in the dependent variables such as nursing knowledge, creativity, self directed learning, communication ability, interpersonal ability, problem solving ability, critical thinking. When the dependent variables were grouped into three domains, the effect size of the major competency was the largest, followed by the individual competency and the learning attitude. In addition, we examined the effect sizes of the moderate variables in learner centered instruction. This study shows that learner centered instruction is more effective than traditional teacher centered instruction. Based on the results of this study, we propose applying a new method of learner centered education

    ์ค‘๊ตญ์ธ ํ•™์Šต์ž์˜ ํ•œ๊ตญ์–ด ๋ถ„์ ˆ์Œ ๋ณ€์ด์–‘์ƒ๊ณผ ๋ฐœ์Œ๋ชจ๋ธ๋ง

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ˜‘๋™๊ณผ์ • ์ธ์ง€๊ณผํ•™์ „๊ณต, 2016. 2. ์ •๋ฏผํ™”.Learners of second foreign language stand to benefit greatly from Computer-Assisted Pronunciation Training (CAPT) systems which can offer automatic mispronunciation detection and individualized corrective feedback. The increasing demand for learning Korean as a foreign language yields a strong need for a Korean CAPT system development. However, recognition accuracy for non-native speech is often too low to make practical use of Automatic Speech Recognition (ASR) technology in language learning interfaces, and there is limited research on Koran pronunciation produced by non-natives. As a preliminary research towards developing a CAPT system for Mandarin Chinese learners of Korean, the first part of the study surveys major agreements and disagreements among related works. By conducting a corpus-based experiment, these disagreements are resolved and segmental variations patterns are analyzed, in which flap sounds show the highest variation rate of 35%. The second part of this study discusses quantitative modeling of these variation patterns for adapting Korean ASR system to Chinese learners. Using context-dependent variation rules describing substitutions, insertions, deletions, and phonological knowledge, extended pronunciation dictionary is generated. With the proposed approach, 21.2% and 1.3% relative WER reduction is obtained from consonantal and vocalic models. The result verifies the corpus-based variation pattern analysis. This study lays the groundwork for Korean CAPT system for various linguistic backgrounds.์ปดํ“จํ„ฐ๊ธฐ๋ฐ˜ ๋ฐœ์Œ๊ต์œก (Computer-Assisted Pronunciation Training, CAPT) ์‹œ์Šคํ…œ์€ ํ•™์Šต์ž์˜ ๋ฐœ์Œ์˜ค๋ฅ˜๋ฅผ ์ž๋™์œผ๋กœ ์ธ์‹ ๋ฐ ๊ฒ€์ถœํ•˜๊ณ  ๊ฐœ์ธํ™”๋œ ๊ต์ • ํ”ผ๋“œ๋ฐฑ์„ ์ œ๊ณตํ•จ์œผ๋กœ์จ, ํšจ์œจ์ ์ธ ์ œ 2์™ธ๊ตญ์–ด ํ•™์Šต์„ ์ œ๊ณตํ•œ๋‹ค. ์ตœ๊ทผ ์ฆ๊ฐ€ํ•˜๋Š” ํ•œ๊ตญ์–ด ๊ต์œก์— ๋Œ€ํ•œ ์ˆ˜์š”๋Š” ํ•œ๊ตญ์–ด CAPT ์‹œ์Šคํ…œ ๊ฐœ๋ฐœ์˜ ํ•„์š”์„ฑ์„ ์˜๋ฏธํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ๋น„์›์–ด๋ฏผ ํ•œ๊ตญ์–ด ๋ฐœํ™”์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๋ถ€์กฑํ•œ ์‹ค์ •์ด๋ฉฐ, ํ•™์Šต์ž์˜ ๋ฐœ์Œ์— ๋Œ€ํ•œ ๋น„์›์–ด๋ฏผ ์Œ์„ฑ์ธ์‹ ์„ฑ๋Šฅ์ด ํ–ฅ์ƒ๋จ์œผ๋กœ์จ ํ•™์Šต ์ธํ„ฐํŽ˜์ด์Šค์— ๊ตฌํ˜„์ด ๊ฐ€๋Šฅํ•  ๊ฒƒ์ด๋‹ค. ์ค‘๊ตญ์ธ ํ•™์Šต์ž๋ฅผ ์œ„ํ•œ ํ•œ๊ตญ์–ด CAPT ์‹œ์Šคํ…œ ๊ฐœ๋ฐœ์˜ ์ดˆ๊ธฐ ์—ฐ๊ตฌ๋กœ์จ, ๋ณธ๊ณ ์˜ ์ดˆ๋ฐ˜๋ถ€๋Š” ์„ ํ–‰์—ฐ๊ตฌ ์‹คํ—˜๊ฒฐ๊ณผ์˜ ์ผ์น˜ ๋ฐ ๋ถˆ์ผ์น˜ ์–‘์ƒ์„ ์ •๋ฆฌํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ , ์ฝ”ํผ์Šค ๊ธฐ๋ฐ˜ ์‹คํ—˜์„ ์ง„ํ–‰ํ•˜์—ฌ ๋ถˆ์ผ์น˜ ์–‘์ƒ์„ ํ•ด์†Œํ•˜์—ฌ ์ฃผ์š” ๋ณ€์ด์–‘์ƒ์„ ๋ถ„์„ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ์‹คํ—˜๊ฒฐ๊ณผ ํƒ„์„ค์Œ์˜ ์„ค์ธก์Œํ™” ๋ณ€์ด์œจ์ด 35%๋กœ ๊ฐ€์žฅ ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ณธ๊ณ ์˜ ํ›„๋ฐ˜๋ถ€๋Š” ๋ฐœ์Œ๋ชจ๋ธ๋ง๊ณผ ์Œ์„ฑ์ธ์‹ ์‹คํ—˜๊ฒฐ๊ณผ๋ฅผ ์ œ์‹œํ•œ๋‹ค. ์ •๋Ÿ‰ํ™”๋œ ๋ณ€์ด์–‘์ƒ์˜ ๋ฌธ๋งฅ์„ ๊ณ ๋ คํ•˜์—ฌ ๋Œ€์น˜, ์‚ฝ์ž…, ์‚ญ์ œ, ์Œ์šด๋ณ€๋™ ํŒจํ„ด๊ทœ์น™์œผ๋กœ ๊ตฌ๋ถ„ํ•˜์˜€๋‹ค. ์ด ๊ทœ์น™์„ ์‚ฌ์šฉํ•˜์—ฌ ํ™•์žฅ๋œ ๋ฐœ์Œ์—ด์„ ๊ตฌ์ถ•ํ•˜์—ฌ ์ธ์‹ ์‹คํ—˜์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์ด ๋ฐฉ๋ฒ•์œผ๋กœ ์ž์Œ ๋ฐ ๋ชจ์Œ ๋ชจ๋ธ์—์„œ ์ƒ๋Œ€ ์ธ์‹๋ฅ  21.2%๊ณผ 1.3%์˜ ๊ฐœ์„ ์ด ๊ฐ๊ฐ ํ™•์ธ๋˜์—ˆ๋‹ค. ์ธ์‹์‹คํ—˜ ๊ฒฐ๊ณผ๋Š” ์ฝ”ํผ์Šค ๊ธฐ๋ฐ˜ ๋ณ€์ด์–‘์ƒ ๋ถ„์„ ๊ฒฐ๊ณผ์™€ ๋ฐœ์Œ๋ชจ๋ธ๋ง ๊ธฐ๋ฒ•์„ ๊ฒ€์ฆํ•œ๋‹ค.1. Introduction 6 1.1. Purpose of Research 7 1.2. Research Scope 9 1.3. Outline of Research 9 2. Survey of Related Work 10 2.1. Contrastive Analysis of Chinese and Korean 10 2.2. Survey of Previous Studies 13 3. Segmental Variations Produced by Chinese Learners 21 3.1. Experiment Design for Corpus-based Analysis 21 3.2. Experiment Results 24 3.3. Comparison with the Related Works 26 3.4. Analysis of Frequent Segmental Variations 28 4. Modeling Pronunciation Variation for Speech Recognition 31 4.1. Rule Derivation 32 4.2 Dictionary Generation 35 4.3 Speech Recognition Experiment 36 4.4 Speech Recognition Result and Discussion 37 5. Conclusion 43 References 45 Appendix 48 ๊ตญ๋ฌธ์ดˆ๋ก 57Maste

    Role of tumour necrosis factor receptor-1 and nuclear factor-ฮบB in production of TNF-ฮฑ-induced pro-inflammatory microparticles in endothelial cells

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    Tumour necrosis factor-ฮฑ (TNF-ฮฑ) is upregulated in many inflammatory diseases and is also a potent agent for microparticle (MP) generation. Here, we describe an essential role of TNF-ฮฑ in the production of endothelial cell-derived microparticles (EMPs) in vivo and the function of TNF-ฮฑ-induced EMPs in endothelial cells. We found that TNF-ฮฑ rapidly increased blood levels of EMPs in mice. Treatment of human umbilical vein endothelial cells (HUVECs) with TNF-ฮฑ also induced EMP formation in a time-dependent manner. Silencing of TNF receptor (TNFR)-1 or inhibition of the nuclear factor-ฮบB (NF-ฮบB) in HUVECs impaired the production of TNF-ฮฑ-induced EMP. Incubation of HUVECs with PKH-67-stained EMPs showed that endothelial cells readily engulfed EMPs, and the engulfed TNF-ฮฑ-induced EMPs promoted the expression of pro-apoptotic molecules and upregulated intercellular adhesion molecule-1 level on the cell surface, which led to monocyte adhesion. Collectively, our findings indicate that the generation of TNF-ฮฑ-induced EMPs was mediated by TNFR1 or NF-ฮบB and that EMPs can contribute to apoptosis and inflammation of endothelial cells.ope

    Plasma osteoprotegerin levels increase with the severity of cerebral artery atherosclerosis

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    OBJECTIVES: Osteoprotegerin (OPG) is a member of the tumor necrosis factor receptor superfamily and suggested as a marker of atherosclerosis. We investigated whether plasma OPG levels were associated with the presence and severity of cerebral atherosclerosis. DESIGN AND METHODS: We used an enzyme-linked immunosorbent assay to measure the plasma OPG levels of 107 patients with acute cerebral infarction. We compared the plasma OPG levels according to the presence and number of arteries with cerebral atherosclerosis (โ‰ฅ 50% stenosis). RESULTS: Of 107 patients, 73 (68.2%) had cerebral atherosclerosis. OPG levels were increased in patients with cerebral atherosclerosis (374.69 ยฑ 206.48 vs 261.17 ยฑ 166.91 pg/mL, p=0.006). OPG levels showed positive correlation with the number of cerebral arteries with atherosclerosis (Spearman's rho=0.342, p229.9 pg/mL was independently associated with the presence [OR 4.61, 95% CI 1.57-13.55, p=0.005, binary logistic regression] of cerebral atherosclerosis and number [OR 3.20, 95% CI 1.26-8.12, p=0.014, ordinal logistic regression] of arteries with cerebral atherosclerosis. CONCLUSIONS: Plasma OPG levels were significantly associated with the presence and severity of cerebral atherosclerosis. This finding suggests that plasma OPG might have a role in cerebral atherosclerosis.ope
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