35 research outputs found

    ํฌํ† ๋ฆฌ์†Œ๊ทธ๋ž˜ํ”ผ ๊ฒ€์‚ฌ ์‹œ์Šคํ…œ์˜ ์ด๋ฏธ์ง€ ๋ถ„ํ• ์„ ์œ„ํ•œ ์ƒˆ๋กœ์šด ๊นŠ์€ ์•„ํ‚คํ…์ฒ˜

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์œตํ•ฉ๊ณผํ•™๋ถ€(์ง€๋Šฅํ˜•์œตํ•ฉ์‹œ์Šคํ…œ์ „๊ณต), 2021.8. ํ™์„ฑ์ˆ˜.In semiconductor manufacturing, defect detection is critical to maintain high yield. Typically, the defects of semiconductor wafer may be generated from the manufacturing process. Most computer vision systems used in semiconductor photolithography process inspection still have adopt to image processing algorithm, which often occur inspection faults due to sensitivity to external environment changes. Therefore, we intend to tackle this problem by means of converging the advantages of image processing algorithm and deep learning. In this dissertation, we propose Image Segmentation Detector (ISD) to extract the enhanced feature-maps under the situations where training dataset is limited in the specific industry domain, such as semiconductor photolithography inspection. ISD is used as a novel backbone network of state-of-the-art Mask R-CNN framework for image segmentation. ISD consists of four dense blocks and four transition layers. Especially, each dense block in ISD has the shortcut connection and the concatenation of the feature-maps produced in layer with dynamic growth rate for more compactness. ISD is trained from scratch without using recently approached transfer learning method. Additionally, ISD is trained with image dataset pre-processed by means of our designed image filter to extract the better enhanced feature map of Convolutional Neural Network (CNN). In ISD, one of the key design principles is the compactness, plays a critical role for addressing real-time problem and for application on resource bounded devices. To empirically demonstrate the model, this dissertation uses the existing image obtained from the computer vision system embedded in the currently operating semiconductor manufacturing equipment. ISD achieves consistently better results than state-of-the-art methods at the standard mean average precision which is the most common metric used to measure the accuracy of the instance detection. Significantly, our ISD outperforms baseline method DenseNet, while requiring only 1/4 parameters. We also observe that ISD can achieve comparable better results in performance than ResNet, with only much smaller 1/268 parameters, using no extra data or pre-trained models. Our experimental results show that ISD can be useful to many future image segmentation research efforts in diverse fields of semiconductor industry which is requiring real-time and good performance with only limited training dataset.๋ฐ˜๋„์ฒด ์ œ์กฐ์—์„œ ๊ฒฐํ•จ ๊ฒ€์ถœ์€ ๋†’์€ ์ˆ˜์œจ์„ ์œ ์ง€ํ•˜๋Š”๋ฐ ์ค‘์š”ํ•ฉ๋‹ˆ๋‹ค. ์ „ํ˜•์ ์œผ๋กœ, ๋ฐ˜๋„์ฒด ์›จ์ดํผ์˜ ๊ฒฐํ•จ์€ ์ œ์กฐ ๊ณต์ •์—์„œ ๋ฐœ์ƒํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ๋ฐ˜๋„์ฒด ํฌํ† ๋ฆฌ์†Œ๊ทธ๋ž˜ํ”ผ ๊ณต์ • ๊ฒ€์‚ฌ์— ์‚ฌ์šฉ๋˜๋Š” ๋Œ€๋ถ€๋ถ„์˜ ์ปดํ“จํ„ฐ ๋น„์ „ ์‹œ์Šคํ…œ๋“ค์€ ์—ฌ์ „ํžˆ ์™ธ๋ถ€ ํ™˜๊ฒฝ ๋ณ€ํ™”์— ๋ฏผ๊ฐํ•œ ์ด๋ฏธ์ง€ ์ฒ˜๋ฆฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์‚ฌ์šฉํ•˜๊ณ  ์žˆ์–ด์„œ ๊ฒ€์‚ฌ ์˜ค๋ฅ˜๊ฐ€ ์ž์ฃผ ๋ฐœ์ƒํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ, ์ด๋ฏธ์ง€ ์ฒ˜๋ฆฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์žฅ์ ๊ณผ ๋”ฅ ๋Ÿฌ๋‹์˜ ์žฅ์ ์„ ์œตํ•ฉํ•˜์—ฌ ์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋ ค๊ณ  ํ•ฉ๋‹ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ ์šฐ๋ฆฌ๋Š” ๋ฐ˜๋„์ฒด ํฌํ† ๋ฆฌ์†Œ๊ทธ๋ž˜ํ”ผ ๊ฒ€์‚ฌ์™€ ๊ฐ™์ด ํ›ˆ๋ จ ๋ฐ์ดํ„ฐ ์„ธํŠธ๊ฐ€ ์ œํ•œ๋œ ์ƒํ™ฉ์—์„œ ํ–ฅ์ƒ๋œ ๊ธฐ๋Šฅ ๋งต์„ ์ถ”์ถœํ•˜๊ธฐ ์œ„ํ•ด ์ด๋ฏธ์ง€ ๋ถ„ํ•  ๊ฒ€์ถœ๊ธฐ(Image Segmentation Detector, ์ดํ•˜ ISD)๋ฅผ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ISD๋Š” ์ด๋ฏธ์ง€ ๋ถ„ํ• ์„ ์œ„ํ•œ ์ตœ์‹  Mask R-CNN ํ”„๋ ˆ์ž„ ์›Œํฌ์˜ ์ƒˆ๋กœ์šด ๋ฐฑ๋ณธ ๋„คํŠธ์›Œํฌ๋กœ ์‚ฌ์šฉํ•ฉ๋‹ˆ๋‹ค. ISD๋Š” 4 ๊ฐœ์˜ ์กฐ๋ฐ€ํ•œ ๋ธ”๋ก๊ณผ 4 ๊ฐœ์˜ ์ „ํ™˜ ๋ ˆ์ด์–ด๋กœ ๊ตฌ์„ฑํ•ฉ๋‹ˆ๋‹ค. ํŠนํžˆ, ISD์˜ ๊ฐ ์กฐ๋ฐ€ํ•œ ๋ธ”๋ก์€ ๋ณด๋‹ค ์ปดํŒฉํŠธํ•จ์„ ์œ„ํ•ด ๋‹จ์ถ• ์—ฐ๊ฒฐ ๋ฐ ๋™์  ์„ฑ์žฅ๋ฅ ์„ ๊ฐ€์ง€๊ณ  ๋ ˆ์ด์–ด์—์„œ ์ƒ์„ฑ๋œ ํ”ผ์ณ ๋งต์„ ๊ฒฐํ•ฉํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ISD๋Š” ์ตœ๊ทผ ์ ์šฉํ•˜๊ณ  ์žˆ๋Š” ์ „์ด ํ•™์Šต ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์ง€ ์•Š๊ณ  ์ฒ˜์Œ๋ถ€ํ„ฐ ํ›ˆ๋ จํ•ฉ๋‹ˆ๋‹ค. ๋˜ํ•œ, ISD๋Š” ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง(Convolutional Neural Network, ์ดํ•˜ CNN)์˜ ํ–ฅ์ƒ๋œ ๊ธฐ๋Šฅ ๋งต์„ ์ถ”์ถœํ•˜๊ธฐ ์œ„ํ•ด ์šฐ๋ฆฌ๊ฐ€ ์„ค๊ณ„ํ•œ ์ด๋ฏธ์ง€ ํ•„ํ„ฐ๋ฅผ ํ†ตํ•ด ์‚ฌ์ „ ์ฒ˜๋ฆฌ๋œ ์ด๋ฏธ์ง€ ๋ฐ์ดํ„ฐ ์„ธํŠธ๋กœ ํ›ˆ๋ จ์„ ํ•ฉ๋‹ˆ๋‹ค. ISD์˜ ์„ค๊ณ„ ํ•ต์‹ฌ ์›์น™ ์ค‘ ํ•˜๋‚˜๋Š” ์†Œํ˜•ํ™”๋กœ ์‹ค์‹œ๊ฐ„ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ณ  ๋ฆฌ์†Œ์Šค์— ์ œํ•œ์ด ์žˆ๋Š” ์žฅ์น˜์— ์ ์šฉํ•˜๋Š”๋ฐ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๊ฒŒ ํ•ฉ๋‹ˆ๋‹ค. ๋ชจ๋ธ์„ ์‹ค์ฆ์ ์œผ๋กœ ์ž…์ฆํ•˜๊ธฐ ์œ„ํ•ด ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ํ˜„์žฌ ์šด์˜ ์ค‘์ธ ๋ฐ˜๋„์ฒด ์ œ์กฐ ์žฅ๋น„์— ๋‚ด์žฅ๋œ ์ปดํ“จํ„ฐ ๋น„์ „ ์‹œ์Šคํ…œ์—์„œ ํš๋“ํ•œ ์‹ค์ œ ์ด๋ฏธ์ง€๋ฅผ ์‚ฌ์šฉํ•ฉ๋‹ˆ๋‹ค. ISD๋Š” ๊ฐ€์žฅ ์ผ๋ฐ˜์ ์ธ ์„ฑ๋Šฅ ์ธก์ • ์ง€ํ‘œ์ธ ํ‰๊ท  ์ •๋ฐ€๋„์—์„œ ์ตœ์ฒจ๋‹จ ๋ฐฑ๋ณธ ๋„คํŠธ์›Œํฌ ๋ณด๋‹ค ์ผ๊ด€๋˜๊ฒŒ ๋” ๋‚˜์€ ์„ฑ๋Šฅ์„ ์–ป์Šต๋‹ˆ๋‹ค. ํŠนํžˆ, ISD๋Š” ๋ฒ ์ด์Šค ๋ผ์ธ์œผ๋กœ ์‚ผ์€ DenseNet ๋ณด๋‹ค ํŒŒ๋ผ๋ฏธํ„ฐ๋“ค์ด 4๋ฐฐ ๋” ์ ์ง€๋งŒ, ์„ฑ๋Šฅ์ด ์šฐ์ˆ˜ ํ•ฉ๋‹ˆ๋‹ค. ์šฐ๋ฆฌ๋Š” ๋˜ํ•œ ISD๊ฐ€ Mask R-CNN ๋ฐฑ๋ณธ ๋„คํŠธ์›Œํฌ๋กœ ์ฃผ๋กœ ์‚ฌ์šฉํ•˜๋Š” ResNet ๋ณด๋‹ค 268๋ฐฐ ํ›จ์”ฌ ๋” ์ ์€ ํŒŒ๋ผ๋ฏธํ„ฐ๋“ค์„ ๊ฐ€์ง€๊ณ , ์ถ”๊ฐ€ ๋ฐ์ดํ„ฐ ๋˜๋Š” ์‚ฌ์ „ ํ›ˆ๋ จ๋œ ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์ง€ ์•Š๊ณ , ์„ฑ๋Šฅ์—์„œ ๋น„์Šทํ•˜๊ฑฐ๋‚˜ ๋” ๋‚˜์€ ๊ฒฐ๊ณผ๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ์Œ์„ ๊ด€์ฐฐํ•ฉ๋‹ˆ๋‹ค. ์šฐ๋ฆฌ์˜ ์‹คํ—˜ ๊ฒฐ๊ณผ๋“ค์€ ISD๊ฐ€ ์ œํ•œ๋œ ํ›ˆ๋ จ ๋ฐ์ดํ„ฐ ์„ธํŠธ๋งŒ์œผ๋กœ ์‹ค์‹œ๊ฐ„ ๋ฐ ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ์š”๊ตฌํ•˜๋Š” ๋ฐ˜๋„์ฒด ์‚ฐ์—…์˜ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ๋“ค์—์„œ ๋งŽ์€ ๋ฏธ๋ž˜์˜ ์ด๋ฏธ์ง€ ๋ถ„ํ•  ์—ฐ๊ตฌ ๋…ธ๋ ฅ์— ์œ ์šฉํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์—ฌ์ค๋‹ˆ๋‹ค.Chapter 1. Introduction ๏ผ‘ 1.1. Background and Motivation ๏ผ” Chapter 2. Related Work ๏ผ‘๏ผ’ 2.1. Inspection Method ๏ผ‘๏ผ’ 2.2. Instance Segmentation ๏ผ‘๏ผ– 2.3. Backbone Structure ๏ผ’๏ผ” 2.4. Enhanced Feature Map ๏ผ“๏ผ• 2.5. Detection Performance Evaluation ๏ผ”๏ผ— 2.6. Learning Network Model from Scratch ๏ผ•๏ผ Chapter 3. Proposed Method ๏ผ•๏ผ’ 3.1. ISD Architecture ๏ผ•๏ผ’ 3.2. Pre-processing ๏ผ–๏ผ“ 3.3. Model Training ๏ผ—๏ผ‘ 3.4. Training Objective ๏ผ—๏ผ“ 3.5. Setting and Configurations ๏ผ—๏ผ• Chapter 4. Experimental Evaluation ๏ผ—๏ผ˜ 4.1. Classification Results on ISD ๏ผ˜๏ผ‘ 4.2. Comparison with Pre-processing ๏ผ˜๏ผ• 4.3. Image Segmentation Results on ISD ๏ผ™๏ผ” 4.3.1. Results on Suck-back State ๏ผ™๏ผ” 4.3.2. Results on Dispensing State ๏ผ‘๏ผ๏ผ” 4.4. Comparison with State-of-the-art Methods ๏ผ‘๏ผ‘๏ผ“ Chapter 5. Conclusion ๏ผ‘๏ผ’๏ผ‘ Bibliography ๏ผ‘๏ผ’๏ผ— ์ดˆ๋ก ๏ผ‘๏ผ”๏ผ–๋ฐ•

    Factors affecting pouch-related outcomes after restorative proctocolectomy.

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    PURPOSES: Restorative proctocolectomy (RPC) with ileal pouch anal anastomosis (IPAA) is the procedure of choice for patients with familial adenomatous polyposis (FAP) and ulcerative colitis (UC) despite morbidities that can lead to pouch failure. We aimed to identify factors associated with pouch-related morbidities. METHODS: A retrospective analysis of patients who underwent RPC with IPAA was performed. To investigate the factors associated with pouch-related morbidities, patients' preoperative demographic and clinical factors, and intraoperative factors were included in the analysis. RESULTS: A total of 49 patients with UC, FAP, and colorectal cancer were included. Twenty patients (40.8%) experienced leakage-related, functional, and/or pouchitis-related morbidities. Patients with American Society of Anesthesiologists (ASA) grade 2 or 3 had a higher risk of functional morbidity than those with grade 1. Intraoperative blood loss exceeding 300.0 mL was associated with an increased risk of pouchitis-related morbidity. CONCLUSIONS: Our study demonstrated associations of higher ASA grade and increased intraoperative blood loss with poor functional outcomes and pouchitis, respectively.ope

    The number of retrieved lymph nodes needed for accurate staging differs based on the presence of preoperative chemoradiation for rectal cancer

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    The aim of this study is to investigate if retrieval of 12 lymph nodes (LNs) is sufficient to avoid stage migration as well as to evaluate the prognostic impact of insufficient LN retrieval in different treatment settings of rectal cancer, particularly in the case of preoperative chemoradiotherapy (pCRT).The data of all patients with biopsy proven rectal adenocarcinoma who underwent curative surgery between January 2005 and December 2012 were analyzed. Univariate and multivariate analyses for oncologic outcomes were performed in LN metastasis or no LN metastasis (LN-) group. Subgroup analyses were performed according to whether a patient had received pCRT.A total of 1825 patients were enrolled into the study. The maximal Chi-square method revealed the minimum number of harvested LNs required to be 12. Univariate and multivariate analyses found LNs?โ‰ฅ?12 to be an independent prognostic factor for both overall survival (OS) (hazard ratio [HR] = 0.5, 95% confidence intervals [CIs]: 0.3-0.8; P = 0.002) and disease-free survival (DFS) (HR = 0.6, 95% CI: 0.4-0.7; P?<?0.001) in the LN- group. In the LN- group, LNs?โ‰ฅ?12 continued to be a significant prognostic factor both for OS and DFS in the subgroup of patients who did not undergo pCRT. However, in the subgroup of the LN- patients who underwent pCRT, LN?โ‰ฅ?8 was significant for DFS and OS.Retrieval of LNs?โ‰ฅ?12 and LNs?โ‰ฅ?8 should be achieved to obtain accurate staging and optimal treatment for the non-pCRT and pCRT groups in rectal cancer, respectively.ope

    Do audit fees and audit hours influence credit ratings?: A comparative analysis of Big4 vs Non-Big4

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    We examine the relationship between credit ratings / changes and audit fees (hours) for Big4 and Non-Big4 firms. Audit fee (hours) may be considered as a default risk metric for credit ratings agencies. However, firms audited by Big4 are larger, better performing and operate with lower leverage compared to firms followed by Non-Big4. Therefore, the association between audit fee (hours) may be different for firms followed by Big4 and Non-Big4 audit firms. We find that there is a negative association between audit fees and credit ratings for firms followed by Big4 audit firms. However, we find an insignificant relation for firms followed by Non-Big4. We conjecture the different association due to the Big4 firms having more robust accounting procedures; Big4 firms must offer competitive audit fees because they are engaged in fierce competition with other Big4 firms. Moreover, Big4 and Non-Big4 firms have different relationships with their clients because Non-Big4 firms are more income dependent on their clients. Using a sample of 1,717 firmโ€“year observations between 2002 and 2013, we establish a relation between audit fees in period t and credit ratings in period t+1, for firms followed by Big4 auditors. We do not find a significant relation for firms followed by Non-Nig4 firms, suggesting that credit ratings agencies perceive audit fee differently for Big4 and Non-Big4 firms. Client firms followed by Big4 auditors that experience a credit rating change in period t+1 pay lower audit fees in period t compared to firms that do not experience a credit rating change. Our additional analysis suggests a different association between firms audit fees and firm performance for firms that experience a credit rating increase and decrease. Firms that experience a credit ratings increase in period t+1 have strong performance and lower audit fees in period t. On the other hand, firms that experience a credit rating decrease have weak financial performance and negative audit fees compared to firms that do not experience a credit ratings change. Our results suggest that audit fees combined with financial performance influence a credit ratings agency' perception of default risk

    ์—ฌ์„ฑ ๋‡Œ์กธ์ค‘ ํ™˜์ž๋ฅผ ๋Œ๋ณด๋Š” ๋‚จํŽธ ๊ฐ„ํ˜ธ์ž์˜ ๋ถ€๋‹ด๊ฐ๊ณผ ๊ฑด๊ฐ• ๊ด€๋ จ ์‚ถ์˜ ์งˆ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฐ„ํ˜ธํ•™๊ณผ, 2012. 2. ๋ฐ•์—ฐํ™˜.๋‡Œ์กธ์ค‘์€ ์žฅ๊ธฐ์ ์ธ ๊ฐ„ํ˜ธ๋ฅผ ์š”ํ•˜๋Š” ์งˆํ™˜์œผ๋กœ ๋‡Œ์กธ์ค‘ ํ™˜์ž ๊ฐ„ํ˜ธ๋Š” ๋Œ€๋ถ€๋ถ„ ๊ฐ€์กฑ์— ์˜ํ•ด ์‹œํ–‰๋˜๋ฉฐ ์ด๋“ค์„ ๋Œ๋ณด๋Š” ๊ฐ€์กฑ ๊ฐ„ํ˜ธ์ž์˜ ๋ถ€๋‹ด๊ฐ์€ ๋†’์€ ํŽธ์ด๋‹ค. ๊ฐ€์กฑ ์ค‘ ๋ฐฐ์šฐ์ž๋Š” ๊ฐ€์žฅ ๋น„์œจ์ด ๋†’์€๋ฐ ๋ฐฐ์šฐ์ž๋Š” ๋‹ค๋ฅธ ๊ฐ€์กฑ ๊ตฌ์„ฑ์›์— ๋น„ํ•ด ํ™˜์ž ๊ฐ„ํ˜ธ์— ๋Œ€ํ•œ ๋ถ€๋‹ด๊ฐ์ด ๋†’์œผ๋ฉฐ, ๋‡Œ์กธ์ค‘ ํ™˜์ž๋ฅผ ๋Œ๋ณด๋Š” ๋ฐฐ์šฐ์ž๋Š” ๋Œ€๋ถ€๋ถ„ ๋…ธ์ธ์œผ๋กœ ์Šค์Šค๋กœ์˜ ๊ฑด๊ฐ• ๋ฌธ์ œ๊นŒ์ง€ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ๋‚จํŽธ ๊ฐ„ํ˜ธ์ž๋Š” ์—ฌ์„ฑ๊ณผ ๋‹ฌ๋ฆฌ ์Šค์Šค๋กœ์˜ ๊ฑด๊ฐ• ๊ด€๋ฆฌ์˜ ์งˆ์ด ๋–จ์–ด์ง€๋ฉฐ ๊ฐ€์‚ฌ์ผ์—๋„ ์–ด๋ ค์›€์„ ๊ฒช์–ด ๋ถ€๋‹ด๊ฐ์ด ๋†’์„ ์ˆ˜ ์žˆ์œผ๋‚˜ ์ด์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๋ถ€์กฑํ•˜๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ์—ฌ์„ฑ ๋‡Œ์กธ์ค‘ ํ™˜์ž๋ฅผ ๋Œ๋ณด๋Š” ๋‚จํŽธ ๊ฐ„ํ˜ธ์ž์˜ ๋ถ€๋‹ด๊ฐ๊ณผ ๊ฑด๊ฐ• ๊ด€๋ จ ์‚ถ์˜ ์งˆ๊ณผ์˜ ๊ด€๊ณ„๋ฅผ ํŒŒ์•…ํ•˜๊ธฐ ์œ„ํ•ด ์‹œํ–‰๋˜์—ˆ๋‹ค. ์—ฐ๊ตฌ ๋Œ€์ƒ์ž๋Š” 2011๋…„ 2์›”~4์›” ์‚ฌ์ด ์„œ์šธ์— ์œ„์น˜ํ•œ ์ผ๊ฐœ ์ข…ํ•ฉ๋ณ‘์› ์‹ ๊ฒฝ๊ณผ ์™ธ๋ž˜์— ๋‚ด์›ํ•œ ๋‡Œ์กธ์ค‘ ํ™˜์ž์™€ ์ฃผ ๊ฐ„ํ˜ธ์ž์ธ 65์„ธ ์ด์ƒ์˜ ๋‚จํŽธ ๊ฐ„ํ˜ธ์ž๋กœ ์ตœ์ข… 121์Œ์ด ์—ฐ๊ตฌ๋˜์—ˆ๋‹ค. ๋‚จํŽธ ๊ฐ„ํ˜ธ์ž์˜ ๋ถ€๋‹ด๊ฐ์€ 63.3ยฑ9.8์ ์œผ๋กœ ๋ณดํ†ต๋ณด๋‹ค ์•ฝ๊ฐ„ ๋‚ฎ์€ ์ƒํƒœ์˜€์œผ๋ฉฐ ๋ถ€๋‹ด๊ฐ์˜ ํ•˜์œ„ ํ•ญ๋ชฉ ์ค‘ ๋ณดํ˜ธ ํ™œ๋™์˜ ๊ฒฐ๊ณผ๋กœ ์ƒ๊ธฐ๋Š” ๋ถ€๋‹ด๊ฐ์ด ๊ฐ€์žฅ ๋†’์•˜๋‹ค. ๋ถ€๋‹ด๊ฐ์ด ๋†’์€ ๋ฌธํ•ญ์€ ๋Œ€๋ถ€๋ถ„ ๋ณธ์ธ์ด ๋ณดํ˜ธ๋ฅผ ์ˆ˜ํ–‰ํ•˜๋Š” ๋ฐ์— ์žˆ์–ด์„œ์˜ ๋ถ€๋‹ด๊ฐ์— ์†ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋ถ€๋‹ด๊ฐ์˜ ๋ฌธํ•ญ ์ค‘ ํ™˜์ž์™€์˜ ๊ด€๊ณ„๊ฐ€ ๋‚˜์•„์ง€๊ธฐ๋ฅผ ์›ํ•œ๋‹ค๋Š” ์ •๋„๊ฐ€ ๋†’์€ ์ง‘๋‹จ์ด ์ •๋„๊ฐ€ ๋‚ฎ์€ ์ง‘๋‹จ์— ๋น„ํ•ด ๋ถ€๋‹ด๊ฐ์ด ๋” ๋†’์•˜๋‹ค. ๋‚จํŽธ ๊ฐ„ํ˜ธ์ž์˜ ๊ฐ„ํ˜ธ ํ™œ๋™ ์‹œ๊ฐ„์€ ํ‰๊ท  58.5ยฑ5.5๋ถ„/์ผ์ด์—ˆ์œผ๋ฉฐ ๊ฐ€์žฅ ๋งŽ์ด ํ•˜๋Š” ๊ฐ„ํ˜ธ ํ™œ๋™์€ ์ด๋™(์›€์ง์ด๋Š” ๊ฒƒ) ๋„์™€์ฃผ๊ธฐ(51.2%), ์‹์‚ฌ ๋ณด์กฐ(50.4%), ํ™˜์ž์˜ ์•ฝ๋ฌผ ๋ณต์šฉ ๋ณด์กฐ(50.4%)์˜€๋‹ค. ๊ฐ„ํ˜ธ ํ™œ๋™์˜ ์ผ ํ‰๊ท  ์†Œ์š” ์‹œ๊ฐ„์€ ์‹์‚ฌ ๋ณด์กฐ(11.3๋ถ„/์ผ), ์‹์‚ฌ ์ค€๋น„(11.2๋ถ„/์ผ), ํ™˜์ž์˜ ๊ตํ†ต ์ˆ˜๋‹จ(๋ฒ„์Šค, ํƒ์‹œ, ์ง€ํ•˜์ฒ  ๋“ฑ) ์ด์šฉ ๋ณด์กฐ(11.2๋ถ„/์ผ)์˜ ์ˆœ์„œ๋กœ ์†Œ์š”๋˜์—ˆ๋‹ค. ๋ถ€๋‹ด๊ฐ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์€ ๋‚จํŽธ ๊ฐ„ํ˜ธ์ž์˜ ์šฐ์šธ(B=.805, p=.000), ๋‡Œ์กธ์ค‘ ํ™˜์ž์˜ ์ธ์ง€ ๊ธฐ๋Šฅ(B=-.179, p=.014), ๋‡Œ์กธ์ค‘ ํ™˜์ž์˜ ์ผ์ƒ ์ƒํ™œ ์˜์กด๋„(B=1.869, p=.008), ๊ทธ๋ฆฌ๊ณ  ๋‚จํŽธ ๊ฐ„ํ˜ธ์ž์˜ ๊ฐ„ํ˜ธ ํ™œ๋™ ์‹œ๊ฐ„(B=.039, p=.013).์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‚จํŽธ ๊ฐ„ํ˜ธ์ž์˜ ๊ฑด๊ฐ• ๊ด€๋ จ ์‚ถ์˜ ์งˆ ์ •๋„๋Š” ์‹ ์ฒด์  ๊ฑด๊ฐ•๊ณผ(47.49์ ) ์ •์‹ ์  ๊ฑด๊ฐ•(47.33์ ) ๋ชจ๋‘ good grade๋กœ ๋น„์Šทํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ ๋ถ€๋‹ด๊ฐ์€ ์‹ ์ฒด์  ๊ฑด๊ฐ• (r=-.566, p<.01) ๊ณผ ์ •์‹ ์  ๊ฑด๊ฐ•(r=-.565, p<.01)์— ๋ชจ๋‘ ์Œ์˜ ์ƒ๊ด€ ๊ด€๊ณ„๋ฅผ ๋ณด์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ์—ฌ์„ฑ ๋‡Œ์กธ์ค‘ ํ™˜์ž๋ฅผ ๋Œ๋ณด๋Š” ๋‚จํŽธ ๊ฐ„ํ˜ธ์ž์˜ ๋ถ€๋‹ด๊ฐ ๊ฐ์†Œ๋ฅผ ์œ„ํ•ด์„œ๋Š” ๋‚จํŽธ ๊ฐ„ํ˜ธ์ž์—๊ฒŒ ์ •์„œ์  ์ƒ๋‹ด๊ณผ ์ž์กฐ ์ง‘๋‹จ๊ณผ์˜ ๋ชจ์ž„์ด ํ•„์š”ํ•˜๋ฉฐ ๋‡Œ์กธ์ค‘ ํ™˜์ž์˜ ์ผ์ƒ ์ƒํ™œ ์˜์กด์— ๋Œ€ํ•œ ์‚ฌํšŒ์  ์ง€์ง€๊ฐ€ ํ•„์š”ํ•จ์„ ์ œ์–ธํ•œ๋‹ค. ๋˜ํ•œ ๋‚จํŽธ ๊ฐ„ํ˜ธ์ž์˜ ๊ฐ€์‚ฌ์ผ์— ๋Œ€ํ•œ ๊ต์œก๊ณผ ์ง€์ง€๊ฐ€ ์ œ๊ณต๋˜์–ด์•ผ ํ•˜๋ฉฐ ๋‡Œ์กธ์ค‘์œผ๋กœ ์ธํ•œ ์ธ์ง€ ๊ธฐ๋Šฅ ์ €ํ•˜์˜ ํŠน์„ฑ๊ณผ ํ•จ๊ป˜ ์ด์— ๋Œ€ํ•œ ๋Œ€์ฒ˜ ๋ฐฉ๋ฒ•์„ ๊ต์œกํ•˜๋Š” ๊ฒƒ์ด ๋’ท๋ฐ›์นจ๋˜์–ด์•ผ ํ•˜๊ฒ ๋‹ค. ๋˜ํ•œ ์—ฌ์„ฑ ๋‡Œ์กธ์ค‘ ํ™˜์ž๋ฅผ ๋Œ๋ณด๋Š” ๋‚จํŽธ ๊ฐ„ํ˜ธ์ž์˜ ๊ฐ„ํ˜ธ ํ™œ๋™ ์ข…๋ฅ˜์— ๋Œ€ํ•œ ์ •ํ™•ํ•œ ๋„๊ตฌ์˜ ๊ฐœ๋ฐœ๊ณผ ํ•จ๊ป˜ ๋‡Œ์กธ์ค‘์˜ ์ง„๋‹จ๋ช…, ๋‡Œ์กธ์ค‘ ํ™˜์ž์˜ ์ฆ์ƒ ๋ฐ ์šฐ์šธ ์ •๋„, ๋‹ค๋ฅธ ๊ฐ€์กฑ์˜ ์ง€์ง€ ์—ฌ๋ถ€ ๊ทธ๋ฆฌ๊ณ  ๋‹ค๋ฅธ ๊ฐ€์กฑ์˜ ๊ฐ„ํ˜ธ ์—ญํ•  ๋ฐฐ๋ถ„ ๋“ฑ์— ๋”ฐ๋ฅธ ๋ถ€๋‹ด๊ฐ์— ๋Œ€ํ•˜์—ฌ ์ถ”ํ›„ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๊ฒ ๋‹ค.Stroke is a disease which needs long term care and most family members care their stroke patients so that family caregivers burden is high. Almost family caregivers are spouses and spouses burden is much higher than other family caregivers. Stroke patients spouses are almost elders so they have many disease problems. In male spouses, they are relatively poor health status and they are different from female spouses, however there are seldom studies about stroke patients male caregivers. The purpose of this study is to investigate caregiver burden and health related quality of life in male spouses who care female stroke patients. The subjects of the final analysis are 121 stroke patients and their male spouses who visited the neurology outpatients department in a tertiary hospital located in Seoul between February and April in 2011. Male spouses are primary caregivers and over 65 years. The data was analyzed with descriptive statistics, t-tests, chrobach a, oneway ANOVA, Scheffes method and pearsons correlation using SPSS(ver 18.0). The mean score of caregiver burden is 63.3ยฑ9.8. The mean score(standardized score) of burden to patients is 14.9ยฑ3.6 (2.13ยฑ0.50), burden to care is 27.7ยฑ3.3(2.30ยฑ0.27) and burden to sacrifice of personal life is 20.7ยฑ3.6(2.59ยฑ0.54). The average of caregiver activity time per day is 58.5ยฑ5.5min. The most caregiver activities are Ambulation(51.2%), Eating(50.4%), Medication(50.4%), Bathing(47.9%) and Dressing(42.9%). The most time required activities are Eating(11.3min/day), Food preparation(11.2min/day), Using transportation(11.2min/day), Walking distance(11.1min/day) and Housekeeping(11.0min/day). The related factors of caregiver burden are caregivers depression(B=.805, p=.000), stroke patients cognitive status(B=-.179, p=.014), stroke patients dependencies(B=1.869, p=.008) and caregivers activity time(B=.039, p=.013). Caregivers health related quality of life are physical health and mental health. The mean score of physical health is 47.49 and mental health is 47.33. Both mental and physical health are good grades. Caregiver burden is related with physical health(r=-.566, p<.01) and mental health(r=-.565, p<.01). The conclusions in this study are below. In conclusion, male spouses caregiver burden is moderate and the score of burden to sacrifice of personal life is the highest. The related factors of caregiver burden is caregivers depression, stroke patients cognitive status, stroke patients dependencies and caregivers activity time, whereas caregivers subjective health status and relationship are not affected to caregiver burden. Male spouses physical health and mental health are moderate. Caregiver burden is negative correlation with health related quality of life. To reduce male spouses caregiver burden, emotional support and activation of self-help group meeting are needed to decrease caregivers depression. Social supports of stroke patients dependencies, education of housework to male spouses are also required. Moreover it is needed that nursing education about the characteristics of stroke patients cognitive impairment and how to cope with reduced cognitive function to the state. Further studies are necessary to validate male spouses caregiver burden and to develop the nursing activities.Maste

    An Empirical Study on the Effectiveness of Regional Innovation Cluster Policy: the Firm`s Character and Network Activities for Innovation Using of Social Capital

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    ์ง€์—ญํด๋Ÿฌ์Šคํ„ฐ ์œก์„ฑ ์ •์ฑ…์˜ ์„ฑํŒจ์—ฌ๋ถ€๋Š” ํด๋Ÿฌ์Šคํ„ฐ ๋‚ด ๊ธฐ์—…๋“ค์ด ์–ผ๋งˆ๋‚˜ ํ™œ๋ฐœํ•œ ํ˜์‹ ํ™œ๋™์„ ํ•˜๋Š๋ƒ์— ๋‹ฌ๋ ค ์žˆ๋‹ค. ํ˜์‹ ํ™œ๋™์˜ ๊ฒฐ๊ณผ, ๊ธฐ์—…์€ ์ง€์†์ ์œผ๋กœ ๊ฒฝ์Ÿ๋ ฅ์„ ํ™•๋ณดํ•˜๊ณ , ์ธ๊ทผ ์ง€์—ญ์€ ์ด๋ฅผ ํ†ตํ•˜์—ฌ ๊ฒฝ์ œ์  ๋ฐœ์ „์„ ์ถ”๊ตฌํ•˜๋ฉฐ, ๊ตญ๊ฐ€์ ์œผ๋กœ๋Š” ์‚ฐ์—…๋ฐœ์ „์„ ๊พธ์ค€ํžˆ ์ง€์†์‹œํ‚ค๋Š”๋ฐ ์žˆ๋‹ค. ๊ฒฝ์Ÿ ํ™˜๊ฒฝ์ด ๊ธ€๋กœ๋ฒŒํ™” ๋˜๋ฉด์„œ, ๊ฐœ๋ณ„๊ธฐ์—…์ด ๊ธฐ์ˆ ํ˜์‹ ์— ํ•„์š”ํ•œ ๋ชจ๋“  ๊ฒƒ์„ ๋ณด์œ ํ•˜๊ธฐ๋ž€ ํ˜„์‹ค์ ์œผ๋กœ ๋ถˆ๊ฐ€๋Šฅํ•˜๋‹ค. ํด๋Ÿฌ์Šคํ„ฐ๋ž€ ์ผ์ •ํ•œ ์ง€๋ฆฌ์  ๊ณต๊ฐ„ ๋‚ด์— ๊ธฐ์—…, ๋Œ€ํ•™, ์—ฐ๊ตฌ์†Œ, ๊ณต๊ณต๊ธฐ๊ด€ ๋“ฑ์˜ ์ง‘์ ์„ ํ†ตํ•œ ์ด๋“ค ๊ธฐ๊ด€๊ณผ์˜ ๋„คํŠธ์›Œํฌ์™€ ์ƒํ˜ธ์ž‘์šฉ์œผ๋กœ ๊ธฐ์—…์˜ ๊ฒฝ์Ÿ๋ ฅ์„ ๊ฐ•ํ™”ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๊ธฐ๊ด€๋“ค์˜ ์ง€๋ฆฌ์  ์ง‘์ ์ด ํ•˜๋“œ์›จ์–ด์ ์ธ ์ธํ”„๋ผ๊ตฌ์ถ• ์ด๋ผ๋ฉด ํด๋Ÿฌ์Šคํ„ฐ ๋‚ด ๊ตฌ์„ฑ์›๋“ค์˜ ๊ด€๊ณ„๋ฅผ ์ด์–ด์ฃผ๋Š” ๊ฐ€๊ต์—ญํ• ์˜ ์†Œํ”„ํŠธ์›จ์–ด์  ๊ตฌ์ถ•์œผ๋กœ ์‚ฌํšŒ์ ์ž๋ณธ์ด ํ•„์š”ํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํด๋Ÿฌ์Šคํ„ฐ ๋‚ด์— ๊ตฌ์ถ•๋œ ์‚ฌํšŒ์  ์ž๋ณธ์ด ๊ธฐ์—…์˜ ํ˜์‹ ํ™œ๋™์„ ์ด์–ด์ฃผ๋Š” ์—ญํ• ์„ ํ•  ๊ฒƒ ์ด๋ผ๋Š” ๊ฐ€์„ค์—์„œ ์ถœ๋ฐœํ•˜์˜€๋‹ค. ์‹ค์ฆ๋ถ„์„๊ฒฐ๊ณผ ์ค‘๊ฒฌ๊ธฐ์—… ์ด์ƒ ๊ทœ๋ชจ์˜ ๊ธฐ์—…์€ ๊ตฌ์„ฑ์› ๊ฐ„ ์‚ฌํšŒ์  ์ž๋ณธ์ธ ์‹ ๋ขฐ๋ณ€์ˆ˜๊ฐ€ ๋†’๋‹ค๊ณ  ์ธ์‹ํ•˜๊ณ  ์žˆ๋Š” ๋ฐ˜๋ฉด, ๊ทœ๋ชจ๊ฐ€ ์ž‘์€ ๊ธฐ์—…์€ ๊ทธ๋ ‡์ง€ ์•Š์•˜๋‹ค. ๊ฐ ๊ตฌ์„ฑ์› ๊ฐ„์˜ ๊ด€๊ณ„์˜์—ญ์—์„œ๋Š” ๊ทœ๋ชจ๊ฐ€ ์ž‘์€ ๊ธฐ์—…์€ ๋Œ€ํ•™, ์—ฐ๊ตฌ์†Œ์™€์˜ ํ˜‘๋ ฅํ™œ๋™์„ ์ถ”๊ตฌํ•˜๋ ค๋Š” ๊ฒฝํ–ฅ์ด ์žˆ์ง€๋งŒ ์ค‘๊ฒฌ๊ธฐ์—… ์ด์ƒ์˜ ๊ทœ๋ชจ๊ฐ€ ํฐ ๊ธฐ์—…์€ ๊ทธ๋ ‡์ง€ ๋ชปํ–ˆ๋‹ค. ๊ณต๊ณต๊ธฐ๊ด€ ๊ฐ„์˜ ๊ด€๊ณ„์—์„œ๋„ ๋™์ผํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์—ฌ์ฃผ๊ณ  ์žˆ์œผ๋ฉฐ, ํ˜์‹ ์„ ์œ„ํ•œ ํ™œ๋™์—์„œ๋Š” ์˜คํžˆ๋ ค ๊ทœ๋ชจ๊ฐ€ ์ž‘์€ ๊ธฐ์—…์ด ํ˜์‹ ์— ๋ณด๋‹ค ์ ๊ทน์„ฑ์„ ๋ณด์—ฌ์ฃผ๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฐ ๊ฒฐ๊ณผ๋ฅผ ๋ณผ ๋•Œ, ์—ฐ๊ตฌ๊ฐ€์„ค์—์„œ ์‚ฌ์šฉํ•œ ์‹ ๋ขฐ, ๊ทœ๋ฒ”, ๋ชฉํ‘œ๊ณต์œ  ๋“ฑ์˜ ์‚ฌํšŒ์  ์ž๋ณธ์ด์™ธ์— ์ง€์‹, ์ •๋ณด ๋ฐ ๋…ธํ•˜์šฐ ๋“ฑ ํด๋Ÿฌ์Šคํ„ฐ๊ฐ€ ๊ฐ€์ง€๋Š” ํŠน์ • ์„ฑ๊ฒฉ์—์„œ ๋ฐฐํƒœ(embeddendess)๋˜๋Š” ํด๋Ÿฌ์Šคํ„ฐ ๊ณ ์œ ์˜ ์‚ฌํšŒ์ ์ธ ์ž๋ณธ์ด ๊ตฌ์ถ•๋˜์–ด์•ผ ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด์„œ ๊ธฐ์กด์˜ ์ค‘์†Œ๊ธฐ์—… ์œก์„ฑ๋ฟ ์•„๋‹Œ, ์‚ฌํšŒ์ ์ธ ์ž๋ณธ์ฐฝ์ถœ์— ๋ณด๋‹ค ์ ๊ทน์ ์ธ ์ค‘๊ฒฌ๊ธฐ์—… ์œก์„ฑ์ •์ฑ…์ˆ˜๋‹จ๋„ ๊ฐ•๊ตฌํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. The success and failure of the clusters pursued by government depends on how much their members interact with one another. Clusters are currently being pursued by the government and cover a diverse spectrum, ranging from export complexes to R&D institutes. Regional and national economic development is expected due to the effectiveness of fostering clusters. Each firm is pursing innovation using of face-to-face contact. social capital in the form of trust, norms, and shared goals facilitates the sharing of knowledge and informahon when people meet. This paper shows that upper structure, which mean the role of social capital and the relationship of each establishment is not performed. Medium size firms feel more trust than small firms. However, small firms have stronger to relationships with establishments like universities, research institutes, and public agencis than medium firms. Small firms are more proactive than medium ones with regard to innovative activities. According to the empirical results, boasting the role of social capital for continuing interaction among each actor for innovation is necessary. Policy support for dusters should change from low structure clusters to upper structure clusters

    ๋ช…๋ชฉ์†Œ๋“ ํƒ€๊ฒŸํŒ…์ „๋žต๊ณผ ๊ทธ ์šด์šฉ์ค€์น™์— ๊ด€ํ•œ ์ผ๊ณ ์ฐฐ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ฒฝ์ œํ•™๋ถ€ ๊ฒฝ์ œํ•™์ „๊ณต,1997.Maste

    ์ผ๋ณธ์‚ฐ ์ˆ˜์ž…์‹ํ’ˆ ์•ˆ์ „์„ฑ ๊ทœ์ œ์— ๋”ฐ๋ฅธ ๋ฌด์—ญ๋ณ€ํ™”์— ๋Œ€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๊ตญ์ œ๋Œ€ํ•™์› : ๊ตญ์ œํ•™๊ณผ, 2015. 2. ์•ˆ๋•๊ทผ.Japan relies heavily on import products including semiconductor, vehicles, computers, and most importantly the daily agricultural products. They also heavily rely on the export commodities for their economic growth and stability. Among many traded commodities, Japans food sectors consist of seafood, vegetable, and meat products. Of all the food products being exported, Japan is a leading exporter of seafood products, with the amount totaling up to about 40% of all food exports. However, since the Fukushima accident, the atmosphere in export has been changing. The impact of Japans earthquake and tsunami has left serious damage in many parts of Japan. It has made physical damage amounting up to 195billionto195 billion to 305 billion. 23,000 people went missing or were reported killed. The main side-effect led to Fukushima Dai-ichi nuclear power plant accident. KOTRA reported that the Japan production companies are facing 20 to 50% decrease in manufacturing products, depending on each commodities, due to the Japans Dai-ichi nuclear plant incident. And there were more than 400,000 damaged buildings. It has been three years and eight months since the incident of the Japans earthquake and Fukushima Dai-ichi nuclear power plants accident, however, the worrisome eyes towards the food supplies being produced in Japan is growing bigger as the time pass. This is mainly because there are continuous reports stating that the leakage from nuclear power plant has not stopped and is still on going. Japan has been showing trend of decrease in the trade volume of agricultural and seafood products since the 2011 Fukushima power plant accident. The Japanese government are aware of the possible danger in their food products. They have announced that fishery activities should not resume in the area of Fukushima prefecture. Naturally, many countries, including Canada, China, European Union, Korea, and United States has raised their surveillance levels on the imported food from Japan. Japan has been requesting their trading partners to remove any discriminating policies and unreasonable increase in surveillance level of their food imports. It is true that in the previous studies it has been proven that the policies on food safety level of one country does impact the amount of trade volume of agricultural and seafood products. However, is the phenomenon of export volume decrease in Japan also due to the policy imposed on the Japanese food imports from neighboring countries? If it is, is it significant enough to blame the decrease in export volume on the regulations of the other countries? The policies imposed on Japanese imported goods vary from one country to another, some having stricter restrictions as opposed to others not having any sort of barriers. This study will find to see if the strictness of the policy imposed due to the Fukushima power plant accidents has significant impact on the change in export volume of Japanese agricultural and seafood commodities.ABSTRACT i TABLE OF CONTENTS iii LIST OF TABLES AND FIGURES iv LIST OF ABBREVIATIONS v I. Introduction 1 1) Background in Japans Dai-ichi nuclear plant incident 2 2) Japans Export Food Safety 4 3) Objective 6 II. Regulations 7 1) Japans governmental intervention 7 2) Regulations between Japan and other countries 8 2.1 Korea and Japan 9 2.2 China and Japan 13 2.3 United States of America and Japan 14 2.4 Canada and Japan 16 2.5 European Union and Japan 17 III. Literature Review 19 1) Gravity Model 19 2) Customers Survey on Food Safety 20 IV. Analysis on the Impact of Regulations on Export of Agricultural and Seafood Supplies in Japan 21 1) Gravity Model 22 2) Empirical Model and Data Collection 23 3) Empirical Results 25 V. Conclusion and Recommendations 29 VI. References 31 ๊ตญ๋ฌธ์ดˆ๋ก 34Maste
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