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    ์‹ ์ƒ ๋ฐฑ์„œ๋ฅผ ์ด์šฉํ•œ ์ €์‚ฐ์†Œ์„ฑ ํ—ˆํ˜ˆ์„ฑ ๋‡Œ์†์ƒ ๋ชจ๋ธ์—์„œ ๋™๋งฅ์Šคํ•€ํ‘œ์ง€ ๊ด€๋ฅ˜ ์ž๊ธฐ๊ณต๋ช…์˜์ƒ๋ฒ•์„ ํ†ตํ•œ ๋‡Œํ˜ˆ๋ฅ˜ ๋ณ€ํ™” ํ‰๊ฐ€

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ,2020. 2. ์ฒœ์ •์€.Purpose The purpose of this study was to evaluate cerebral blood flow (CBF) changes over time in the animal model of neonatal hypoxic-ischemic brain injury using arterial spin labeling (ASL) and correlate CBF changes with development of diffusion restriction on diffusion-weighted imaging (DWI). Materials and methods Six 7-day-old neonatal rats with unilateral carotid artery ligation underwent multiple ASL and DWI MRI scans at 9.4 T before and during hypoxia (8% O2). One 7-day-old rat underwent ASL MRI without surgical ligation or hypoxia as a normal CBF control. Delayed T2-weighted MR imaging and histological examination were performed on day 3 post-hypoxia. CBF values on ASL were measured in four brain areas (i.e., ipsilateral and contralateral cortical and deep areas). The development of diffusion restriction was also evaluated in each of the four areas (i.e., DWI-positive[+] vs. DWI-negative[-] areas). Regional CBF changes over time were evaluated. Regional CBF values and their changes over time were compared between the DWI(+) and DWI(-) areas. Results Regional CBF values before hypoxia were significantly lower than those of the normal control (CBF in normal control vs. CBF before hypoxia: 147.8 vs. 39.2 ยฑ 19.7 in ipsilateral cortex, p < 0.01; 151.5 vs. 49.2 ยฑ 21.2 in ipsilateral deep area, p < 0.01; 150.0 vs. 108.2 ยฑ 22.2 in contralateral cortex, p = 0.014; and 165.0 vs. 104.22 ยฑ 26.0 in contralateral deep area, p < 0.01). After exposure to hypoxia, CBF values decreased in all areas (mean CBF difference: -25.5 in ipsilateral cortex, p = 0.057; -21.5 in ipsilateral deep area, p = 0.012; -52.2 in contralateral cortex, p<0.01; -36.4 in contralateral deep area, p<0.01). Eleven areas with diffusion restriction were included in the DWI(+) area group, whereas 13 areas showing no diffusion restriction were included in the DWI(-) area group. The regional CBF values in the DWI(+) area were estimated to be 34.6 ml/100 g/min lower than those in the DWI(-) area. On delayed T2-weighted MRI, the diffusion-restricted areas presented as areas of bright signal intensity or heterogeneous mixed signal intensity with volume loss, which correlated to areas of infarction or ischemia on histology. Conclusion The ASL perfusion MRI technique made it possible to evaluate regional CBF changes over time during exposure to hypoxia in neonatal rats with unilateral carotid artery ligation. Damaged brain areas that matched well with the diffusion restricted areas had significantly lower CBF values at all time points, compared to preserved areas without diffusion restriction. CBFs measured with ASL may be utilized as a useful imaging indicator of subsequent hypoxic ischemic brain damage.๋ชฉ์  ์‹ ์ƒ ๋ฐฑ์„œ๋ฅผ ์ด์šฉํ•œ ์ €์‚ฐ์†Œ์„ฑ ํ—ˆํ˜ˆ์„ฑ ๋‡Œ์†์ƒ ๋ชจ๋ธ์—์„œ ๋™๋งฅ์Šคํ•€ํ‘œ์ง€(ASL) ๊ด€๋ฅ˜์ž๊ธฐ๊ณต๋ช…์˜์ƒ์„ ์ด์šฉํ•˜์—ฌ ์‹œ๊ฐ„ ๊ฒฝ๊ณผ์— ๋”ฐ๋ฅธ ๋Œ€๋‡Œํ˜ˆ๋ฅ˜(CBF)์˜ ๋ณ€ํ™”๋ฅผ ํ‰๊ฐ€ํ•˜๊ณ , ๋‡Œํ˜ˆ๋ฅ˜ ๋ณ€ํ™”์™€ ํ™•์‚ฐ๊ฐ•์กฐ์˜์ƒ(DWI) ๋ฐ ์กฐ์งํ•™์  ์†Œ๊ฒฌ์„ ๋น„๊ต ํ‰๊ฐ€ํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋ฐฉ๋ฒ• ์ƒํ›„ 7์ผ๋œ ์‹ ์ƒ์•„ ์ฅ 6๋งˆ๋ฆฌ์—์„œ ์ผ์ธก ๊ฒฝ๋™๋งฅ์„ ๊ฒฐ์ฐฐํ•œ ๋’ค ์ €์‚ฐ์†Œ(8% O2)๋ฅผ ๊ฐ€ํ•˜๋ฉด์„œ 9.4T MRI์—์„œ ASL ๋ฐ DWI MRI ๊ฒ€์‚ฌ๋ฅผ ์‹œํ–‰ํ•˜์˜€๋‹ค. ์ƒํ›„ 7์ผ๋œ ์ฅ ํ•œ ๋งˆ๋ฆฌ๋ฅผ ์™ธ๊ณผ์  ๊ฒฐ์ฐฐ์ด๋‚˜ ์ €์‚ฐ์†Œ์ฆ์—†์ด ASL MRI๋ฅผ ์‹œํ–‰ํ•˜์—ฌ ์ •์ƒ ๋‡Œํ˜ˆ๋ฅ˜ ๋Œ€์กฐ๊ตฐ์œผ๋กœ ์‚ฌ์šฉํ•˜์˜€๋‹ค. 3์ผ ๋’ค T2๊ฐ•์กฐ์˜์ƒ๊ณผ ์กฐ์ง๊ฒ€์‚ฌ๋ฅผ ์‹œํ–‰ํ•˜์˜€๋‹ค. ASL CBF ๊ฐ’์„ 4๊ฐœ์˜ ๋‡Œ์˜์—ญ(์ฆ‰, ๋™์ธก๊ณผ ๋ฐ˜๋Œ€์ธก ํ”ผ์งˆ ๋ฐ ์‹ฌ๋ถ€ ์˜์—ญ)์—์„œ ์ธก์ •ํ•˜์˜€๋‹ค. ๊ฐ ์˜์—ญ์—์„œ DWI์ƒ ์ œํ•œํ™•์‚ฐ์˜ ๋ฐœ์ƒ ์—ฌ๋ถ€๋ฅผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค (์ฆ‰, DWI-์–‘์„ฑ ๋Œ€ DWI-์Œ์„ฑ๊ตฐ). ์‹œ๊ฐ„ ๊ฒฝ๊ณผ์— ๋”ฐ๋ฅธ CBF ๋ณ€ํ™”๋ฅผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์‹œ๊ฐ„ ๊ฒฝ๊ณผ์— ๋”ฐ๋ฅธ CBF ๋ณ€ํ™” ์–‘์ƒ์„ DWI์–‘์„ฑ๊ตฐ๊ณผ ์Œ์„ฑ๊ตฐ๊ฐ„์— ๋น„๊ตํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ ์ผ์ธก ๊ฒฝ๋™๋งฅ ๊ฒฐ์ฐฐ ํ›„ CBF ๊ฐ’์€ ์ •์ƒ ๋Œ€์กฐ๊ตฐ์— ๋น„ํ•ด ์œ ์˜ํ•˜๊ฒŒ ๋‚ฎ์•„์กŒ๋‹ค (์ •์ƒ ๋Œ€์กฐ๊ตฐ CBF vs.๋Œ€ ์ €์‚ฐ์†Œ์ฆ ์ „ CBF: ๋™์ธก ๋Œ€๋‡Œํ”ผ์งˆ, 147.8 vs. 39.2 ยฑ 19.7, p <0.01; ๋™์ธก ์‹ฌ๋ถ€์˜์—ญ, 151.5 vs. 49.2 ยฑ 21.2, p <0.01; ๋ฐ˜๋Œ€์ธก ํ”ผ์งˆ, 150.0 vs. 108.2 ยฑ 22.2, p = 0.014; ๋ฐ˜๋Œ€์ธก ์‹ฌ๋ถ€์˜์—ญ, 165.0 vs 104.22 ยฑ 26.0, p <0.01). ์ €์‚ฐ์†Œ์— ๋…ธ์ถœ ๋œ ํ›„ CBF ๊ฐ’์€ ๋ชจ๋“  ์˜์—ญ์—์„œ ๊ฐ์†Œํ–ˆ๋‹ค (ํ‰๊ท  CBF ์ฐจ์ด, ๋™์ธก ํ”ผ์งˆ์—์„œ -25.5, p = 0.057; ๋™์ธก ์‹ฌ๋ถ€์˜์—ญ์—์„œ -21.5, p = 0.012, ๋ฐ˜๋Œ€์ชฝ ํ”ผ์งˆ์—์„œ -52.2, p <0.01, ๋ฐ˜๋Œ€์ธก ์‹ฌ๋ถ€์˜์—ญ์—์„œ -36.4, p <0.01). DWI์–‘์„ฑ๊ตฐ์—๋Š” ํ™•์‚ฐ์ œํ•œ์ด ์žˆ๋Š” 11๊ฐœ์˜ ์˜์—ญ์ด ํฌํ•จ๋˜์—ˆ๊ณ  DWI์Œ์„ฑ๊ตฐ์—๋Š” ํ™•์‚ฐ์ œํ•œ์ด ์—†์—ˆ๋˜ 13๊ฐœ์˜ ์˜์—ญ์ด ํฌํ•จ๋˜์—ˆ๋‹ค. DWI-์–‘์„ฑ๊ตฐ์˜ CBF ๊ฐ’์€ DWI-์Œ์„ฑ๊ตฐ๋ณด๋‹ค ํ‰๊ท  34.6 ml/100g/min ๋‚ฎ์•˜๋‹ค. ์ง€์—ฐ T2๊ฐ•์กฐ MRI์—์„œ ํ™•์‚ฐ ์ œํ•œ์„ ๋ณด์˜€๋˜ ์˜์—ญ์€ ๋ฐ์€ ์‹ ํ˜ธ ๊ฐ•๋„ ๋˜๋Š” ๋ถ€ํ”ผ ๊ฐ์†Œ๋ฅผ ๋™๋ฐ˜ํ•œ ํ˜ผํ•ฉ ์‹ ํ˜ธ ๊ฐ•๋„ ์˜์—ญ์œผ๋กœ ๋ณด์˜€์œผ๋ฉฐ ์กฐ์งํ•™์ƒ์˜ ๊ฒฝ์ƒ‰์ด๋‚˜ ํ—ˆํ˜ˆ ์˜์—ญ๊ณผ ์ผ์น˜ํ•˜์˜€๋‹ค. ๊ฒฐ๋ก  ASL ๊ด€๋ฅ˜์ž๊ธฐ๊ณต๋ช…์˜์ƒ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ์‹ ์ƒ๋ฐฑ์„œ์—์„œ ์ผ์ธก ๊ฒฝ๋™๋งฅ ๋™๋งฅ ๊ฒฐ์ฐฐ ํ›„ ์ €์‚ฐ์†Œ์ฆ์— ๋…ธ์ถœ๋˜๋Š” ๋™์•ˆ, ์‹œ๊ฐ„์— ๋”ฐ๋ฅธ CBF ๋ณ€ํ™”๋ฅผ ํ‰๊ฐ€ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ํ™•์‚ฐ ์ œํ•œ ์˜์—ญ๊ณผ ์ž˜ ์ผ์น˜ํ•˜๋Š” ์†์ƒ ๋‡Œ์˜์—ญ์€ ํ™•์‚ฐ ์ œํ•œ์ด ์—†์—ˆ๋˜ ๋ณด์กด ๋‡Œ์˜์—ญ์— ๋น„ํ•ด ๋ชจ๋“  ์‹œ์ ์—์„œ CBF ๊ฐ’์ด ํ˜„์ €ํžˆ ๋‚ฎ์•˜๋‹ค. ASL ๊ธฐ๋ฒ•์œผ๋กœ ์ธก์ •๋œ CBF๊ฐ’์€ ํ–ฅํ›„ ์ €์‚ฐ์†Œ์„ฑ ํ—ˆํ˜ˆ์„ฑ ๋‡Œ ์†์ƒ ๋ฐœ์ƒ์„ ์˜ˆ์ธกํ•˜๋Š”๋ฐ ์œ ์šฉํ•œ ์˜์ƒ์ง€ํ‘œ๋กœ์„œ ์ด์šฉ๋  ์ˆ˜ ์žˆ๋‹ค.Introduction 9 Materials & Methods 12 Results 18 Discussion 29 Conclusion 35 Reference 36 ๊ตญ๋ฌธ์ดˆ๋ก 39 Figure 1 15 Figure 2 20 Figure 3 24 Figure 4 25 Figure 5 26 Figure 6 28 Table 1 19 Table 2 22 Table 3 23Docto

    ์•„์‹œ์•„๊ณ„ ์ทŒ์žฅ์•” ํ™˜์ž์—์„œ Excision Repair Cross-Complementation Group 6 ์œ ์ „์ž ๋‹คํ˜•์„ฑ๊ณผ FOLFIRINOX ํ•ญ์•”ํ™”ํ•™์š”๋ฒ•์— ๋Œ€ํ•œ ๋ฐ˜์‘์„ฑ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ, 2021.8. ์ตœ์˜ํ›ˆ.FOLFIRINOX is currently one of the standard chemotherapy regimens for pancreatic cancer patients, and is known to be more effective in the presence of the BRCA mutation, one of the DNA damage repair (DDR) gene mutations. However, BRCA mutations are less common in pancreatic cancer patients, especially in Asians. We performed a study to discover novel DNA damage repair (DDR) gene variants associated with the response to FOLFIRINOX chemotherapy in patients with pancreatic cancer. We queried a cohort of pancreatic cancer patients who received FOLFIRINOX chemotherapy as the first treatment and who had tissue obtained through an endoscopic ultrasound-guided biopsy that was suitable for DNA sequencing. We explored variants of 148 DDR genes based on whole exome sequencing and performed multivariate Cox regression to find genetic variants associated with progression-free survival (PFS). Overall, 103 patients were included. Among 2384 variants of 141 DDR genes, 612 non-synonymous variants of 123 genes were selected for Cox regression analysis. The multivariate Cox model showed that rs2228528 in ERCC6 was significantly associated with improved PFS (hazard ratio 0.54, p = 0.001). The median PFS was significantly longer in patients with rs2228528 genotype AA vs. genotype GA and GG (23.5 vs. 16.2 and 8.6 months; log-rank p < 0.001). This study suggests that rs2228528 in ERCC6 could be a potential predictor of response to FOLFIRINOX chemotherapy in patients with pancreatic cancer.FOLFIRINOX๋Š” ํ˜„์žฌ ์ทŒ์žฅ์•” ํ™˜์ž์˜ ํ‘œ์ค€ ํ•ญ์•”ํ™”ํ•™์š”๋ฒ• ์ค‘์˜ ํ•˜๋‚˜๋กœ, DNA ์†์ƒ ๋ณต๊ตฌ ์œ ์ „์ž ์ค‘ ํ•˜๋‚˜์ธ BRCA ์œ ์ „์ž์— ๋ณ€์ด๊ฐ€ ์žˆ๋Š” ์ทŒ์žฅ์•” ํ™˜์ž์—์„œ ๋” ํšจ๊ณผ์ ์ธ ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ BRCA ์œ ์ „์ž ๋ณ€์ด๋Š” ์ทŒ์žฅ์•” ํ™˜์ž, ํŠนํžˆ ์•„์‹œ์•„๊ณ„ ํ™˜์ž์—์„œ๋Š” ๋“œ๋ฌผ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์ทŒ์žฅ์•” ํ™˜์ž์˜ FOLFIRINOX ํ•ญ์•”ํ™”ํ•™์š”๋ฒ•์— ๋Œ€ํ•œ ๋ฐ˜์‘๊ณผ ๊ด€๋ จ๋œ ์ƒˆ๋กœ์šด DNA ์†์ƒ ๋ณต๊ตฌ ์œ ์ „์ž ๋ณ€์ด๋ฅผ ๋ฐœ๊ฒฌํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๋Œ€์ƒ์€ FOLFIRINOX ํ•ญ์•”ํ™”ํ•™์š”๋ฒ•์„ ์ฒซ ๋ฒˆ์งธ ์น˜๋ฃŒ๋กœ ๋ฐ›์•˜์œผ๋ฉฐ, ๋‚ด์‹œ๊ฒฝ ์ดˆ์ŒํŒŒ ์œ ๋„ ์ƒ๊ฒ€์„ ํ†ตํ•ด ์ฑ„์ทจํ•œ ์กฐ์ง์ด DNA ์—ผ๊ธฐ ์„œ์—ด ๋ถ„์„์— ์ ํ•ฉํ–ˆ๋˜ ์ทŒ์žฅ์•” ํ™˜์ž ์ฝ”ํ˜ธํŠธ๋ฅผ ์ด์šฉํ–ˆ๋‹ค. ์ „์žฅ์—‘์†œ์—ผ๊ธฐ์„œ์—ด๋ถ„์„์„ ๊ธฐ๋ฐ˜์œผ๋กœ 148 ๊ฐœ์˜ DNA ์†์ƒ ๋ณต๊ตฌ ์œ ์ „์ž ๋ณ€์ด๋ฅผ ํƒ์ƒ‰ํ•˜๊ณ , ๋‹ค๋ณ€๋Ÿ‰ Cox ํšŒ๊ท€ ๋ถ„์„์„ ์‹œํ–‰ํ•˜์—ฌ ๋ฌด์ง„ํ–‰์ƒ์กด๊ธฐ๊ฐ„๊ณผ ๊ด€๋ จ๋œ ์œ ์ „์ž ๋ณ€์ด๋ฅผ ์ฐพ๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด 103 ๋ช…์˜ ํ™˜์ž๊ฐ€ ์—ฐ๊ตฌ์— ํฌํ•จ๋˜์—ˆ์œผ๋ฉฐ, 141 ๊ฐœ์˜ DNA ์†์ƒ ๋ณต๊ตฌ ์œ ์ „์ž์˜ 2,384 ๊ฐœ ๋ณ€์ด๋“ค ์ค‘ 123 ๊ฐœ ์œ ์ „์ž์—์„œ ๋ฐœ๊ฒฌ๋œ 612 ๊ฐœ์˜ ๋น„๋™์˜ (non-synonymous) ๋ณ€์ด๊ฐ€ Cox ํšŒ๊ท€ ๋ถ„์„์„ ์œ„ํ•ด ์„ ํƒ๋˜์—ˆ๋‹ค. ๋‹ค๋ณ€๋Ÿ‰ Cox ๋ชจ๋ธ์—์„œ ERCC6 ์œ ์ „์ž์˜ rs2228528์ด ๊ฐœ์„ ๋œ ๋ฌด์ง„ํ–‰์ƒ์กด๊ธฐ๊ฐ„ (์œ„ํ—˜๋น„, 0.54, p = 0.001) ๊ณผ ์œ ์˜ํ•˜๊ฒŒ ์—ฐ๊ด€๋˜์–ด ์žˆ์—ˆ๋‹ค. ๋ฌด์ง„ํ–‰์ƒ์กด๊ธฐ๊ฐ„ ์ค‘์•™๊ฐ’์€ rs2228528 ์œ ์ „์žํ˜• AA์ธ ํ™˜์ž (23.5 ๊ฐœ์›”)๊ฐ€ ์œ ์ „์žํ˜• GA (16.2 ๊ฐœ์›”) ๋ฐ GG (8.6 ๊ฐœ์›”)์ธ ํ™˜์ž๋ณด๋‹ค ์œ ์˜ํ•˜๊ฒŒ ๊ธธ์—ˆ๋‹ค (๋กœ๊ทธ ์ˆœ์œ„ p < 0.001). ์ด ์—ฐ๊ตฌ๋Š” ERCC6์˜ rs2228528์ด ์ทŒ์žฅ์•” ํ™˜์ž์˜ FOLFIRINOX ํ•ญ์•”ํ™”ํ•™์š”๋ฒ•์— ๋Œ€ํ•œ ๋ฐ˜์‘์˜ ์ž ์žฌ์ ์ธ ์˜ˆ์ธก์ธ์ž๊ฐ€ ๋  ์ˆ˜ ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค.Chapter 1. Introduction 1 Chapter 2. Methods 3 Chapter 3. Results 7 Chapter 4. Discussion 10 Chapter 5. Conclusions 16 References 17 Tables 24 Figures 29 Abstract in Korean 34๋ฐ•

    A Study on the Integrated Dispatch System for Efficient Truck Operation

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    Container trucking company can expect the reduction of logistics costs by efficient dispatching and operating trucks. This requires a best method of operating trucks which considers freight traffic and frequency of transportation by regions. This is a study on the efficient method of operating trucks for integrated dispatch system, and on this study, to search the way to minimize the logistics costs, the comparison and its analysis method is used for various transportation way which can be caused by changing the present dispatch system which was run by region, to integrated system. First, transit distances to intercity, medium-distance, long-distance, and truck types into their own trucks, consigned trucks, and other company trucks will be classified. Second, the optimum number of trucks and optimal operating system for the certain amount of container cargo will be determined. Finally for the comparison, K Company's transportation data will be used to show the cost efficiency.์ œ 1 ์žฅ ์„œ๋ก  ์ œ 1 ์ ˆ ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• ๋ฐ ๊ตฌ์„ฑ 2 ์ œ 2 ์žฅ ์ปจํ…Œ์ด๋„ˆ ์ฐจ๋Ÿ‰ ์šด์†ก ๋ฐ ๋ฐฐ์ฐจ ์‹œ์Šคํ…œ์˜ ๊ฐœ๋… ์ œ 1 ์ ˆ ํ™”๋ฌผ์ž๋™์ฐจ์šด์†ก์˜ ๊ฐœ๋… ๋ฐ ํ˜„ํ™ฉ 5 1. ํ™”๋ฌผ์ž๋™์ฐจ์šด์†ก์˜ ๊ฐœ๋… 5 2. ํ™”๋ฌผ์ž๋™์ฐจ์šด์†ก์˜ ์ข…๋ฅ˜ 6 3. ํ™”๋ฌผ์ž๋™์ฐจ์šด์†ก์˜ ๋ถ„๋ฅ˜ 12 ์ œ 3 ์ ˆ ์ปจํ…Œ์ด๋„ˆ ์ฐจ๋Ÿ‰์˜ ์šด์†ก ๋ฐฉ์‹ 16 1. ์ปจํ…Œ์ด๋„ˆ ํ™”๋ฌผ์˜ ์šด์†ก ํ˜•ํƒœ 16 2. ์ˆ˜์ž… ์šด์†ก ๋ฐฉ์‹ 17 3. ์ˆ˜์ถœ ์šด์†ก ๋ฐฉ์‹ 18 4. ๋ณตํ™” ์šด์†ก ๋ฐฉ์‹ 19 5. ์…”ํ‹€ ์šด์†ก ๋ฐฉ์‹ 20 ์ œ 4 ์ ˆ ์ปจํ…Œ์ด๋„ˆ ์ฐจ๋Ÿ‰ ๋ฐฐ์ฐจ ์‹œ์Šคํ…œ์˜ ๊ฐœ๋… 22 1. ์ฐจ๋Ÿ‰ ๋ฐฐ์ฐจ 22 2. ๋ฐฐ์ฐจ ์—…๋ฌด 23 3. ํ†ตํ•ฉ ๋ฐฐ์ฐจ 26 ์ œ 3 ์žฅ ํšจ๊ณผ์ ์ธ ์šด์˜์„ ์œ„ํ•œ ์ฐจ๋Ÿ‰ ํ˜•ํƒœ ๋ฐ ๋Œ€์ˆ˜ ๊ฒฐ์ • ์ œ 1 ์ ˆ ์ฐจ๋Ÿ‰ ์šด์˜ ๋ฐฉ์‹์— ๊ด€ํ•œ ์„ ํ–‰ ์—ฐ๊ตฌ 28 ์ œ 2 ์ ˆ ์ฐจ๋Ÿ‰ ๋Œ€์ˆ˜ ๊ฒฐ์ •๋ฒ• 31 1. ์—ฐ๊ฐ„ ํ‰๊ท  ๋ฌผ๋™๋Ÿ‰์— ๋”ฐ๋ฅธ ์ฐจ๋Ÿ‰ ์‚ฐ์ถœ ๊ธฐ๋ฒ• 32 2. ํŠน์ •๋ฌผ๋Ÿ‰์— ๋”ฐ๋ฅธ ํ”„๋กœ์ ํŠธ ๊ธฐ๋ฒ• 36 3. ์‹œ๊ฐ„ ์ฐฝ์„ ์ด์šฉํ•œ ํ…ŒํŠธ๋ฆฌ์Šค ๊ธฐ๋ฒ• 36 ์ œ 3 ์ ˆ ์ฐจ๋Ÿ‰ ํ˜•ํƒœ ๋ฐ ๋น„์šฉ ์ •์˜ 40 1. ์ฐจ๋Ÿ‰ ํ˜•ํƒœ 40 2. ์ฐจ๋Ÿ‰ ์šด์˜๋น„์šฉ ์ •์˜ 42 ์ œ 4 ์žฅ ์ฐจ๋Ÿ‰ ์šด์˜ ๋ฐฉ์‹ ์ œ 1 ์ ˆ K์‚ฌ ์ฐจ๋Ÿ‰ ์šด์˜ ๋ฐฉ์‹ 45 1. ํŒŒํŠธ๋ณ„ ๊ตฌ๋ถ„๊ณผ ์ฐจ๋Ÿ‰ ํ˜•ํƒœ 45 2. ํŒŒํŠธ๋ณ„ ๋ฌผ๋™๋Ÿ‰ ๋ถ„์„ 48 ์ œ 2 ์ ˆ ํŒŒํŠธ๋ณ„ ์ ์ • ๊ฐ€๋™ ์ฐจ๋Ÿ‰ 49 1. ํŒŒํŠธ๋ณ„ ์ž‘์—… ๊ฐ€๋Šฅํ•œ ์ˆ˜๋Ÿ‰ 49 2. ํŒŒํŠธ๋ณ„ ์ฐจ๋Ÿ‰ ํ•„์š”ํ•œ ์ฐจ๋Ÿ‰ ๋Œ€์ˆ˜ 51 ์ œ 3 ์ ˆ K์‚ฌ์˜ ์ฐจ๋Ÿ‰ ์šด์˜ ํ˜•ํƒœ ๋ฐ ๋ฌธ์ œ์  53 1. K์‚ฌ์˜ ์ฐจ๋Ÿ‰ ๋ฐฐ์ฐจ ์‹œ์Šคํ…œ 53 2. K์‚ฌ์˜ ์ฐจ๋Ÿ‰ ์šด์˜ ํ˜•ํƒœ 54 3. K์‚ฌ์˜ ์ฐจ๋Ÿ‰ ์šด์˜ ๋ฐฉ์‹์˜ ๋ฌธ์ œ์  55 ์ œ 5 ์žฅ ํ†ตํ•ฉ ๋ฐฐ์ฐจ ์‹œ์Šคํ…œ์˜ ํšจ์œจํ™” ๋ฐฉ์•ˆ ์ œ 1 ์ ˆ ํ†ตํ•ฉ ๋ฐฐ์ฐจ ์‹œ์Šคํ…œ์˜ ๊ฐœ๋… 58 1. ํ†ตํ•ฉ ๋ฐฐ์ฐจ ์‹œ์Šคํ…œ 58 2. ํ†ตํ•ฉ ๋ฐฐ์ฐจ ์ฐจ๋Ÿ‰ ์šด์˜ ํ˜•ํƒœ 59 3. ํ†ตํ•ฉ ๋ฐฐ์ฐจ ํ”„๋กœ์„ธ์Šค 60 ์ œ 2 ์ ˆ ํ†ตํ•ฉ ๋ฐฐ์ฐจ ์‹œ์Šคํ…œ์˜ ํšจ์œจ์„ฑ ๋น„๊ต ์—ฐ๊ตฌ 61 1. ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•œ ์ฐจ๋Ÿ‰ ์žฌ๋ฐฐ์น˜ ๋ฐ ํšจ๊ณผ ๋ถ„์„ 62 2. ํ†ตํ•ฉ ๋ฐฐ์ฐจ ์‹œ์Šคํ…œ์˜ ์‹œ์‚ฌ์  63 ์ œ 6 ์žฅ ๊ฒฐ๋ก  ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ์š”์•ฝ 65 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„์  66 ์ฐธ๊ณ ๋ฌธํ—Œ 67Maste

    ๊ฐ•๋™๊ตฌ์ง€์—ญ์— ๋Œ€ํ•œ ์ €๋ฅ˜์กฐ ์„ค๊ณ„๋ฅผ ์ค‘์ ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๋„์‹œ์„ค๊ณ„ํ•™์ „๊ณต,2019. 8. ๊ฐ•์ค€์„.As urbanization and climate change have surged all over the world, many cities and governments have been trying to prepare the protective design for cities and infrastructures resilient to natural disasters but flooding in the city is still one of the biggest disasters. In 2018, total amount for water disaster reached 1 trillion Yen in Japan. Although large-scale reservoirs and underground drainage systems are being installed at the national level, it is hard to construct these kinds of facilities due to high cost and time-consuming approval process. It, therefore, is needed to develop the preventive design methodologies maximizing the safety of urban areas as well as minimizing the construction cost of facilities. The objective of this study was to suggest the preventive designs for urban flood-risky areas adopted by local governments. The types of floods can be classified into 1) urban floods caused by the increase in impervious area, 2) river floods, 3) coastal floods occurring at coastal areas, and 4) sudden floods caused by sudden rainfall at steep slopes have. This study focused on urban floods, and Gangdong โ€“ gu in Seoul was chosen as a target area as it has been historically frequent floods. The disaster response technologies for flood are mainly the reservoir, the extension of the pipe, and the pitcher packing. However, because of the expensive construction cost and a large number of complaints, the packing of pavement and the extension of the pipe are difficult to be adopted. The location analyses of Gangdong - gu were executed by ArcGIS, and the types of land use were classified based on the public data of the national geographic information platform. These include schools, parking lots, public facilities, and underground warehouses. The hydrological analysis was carried out through ArcGIS's ArcHydro plug-in using DEM data. The flooded area and the watershed in Seoul were divided into several types based on the analyses. The flooded area was in the middle of the water flow, and the houses and commercial buildings were most damaged. The affecting areas were houses and commercial buildings, which were the most common types. Gangdong-gu can be represented as these two types. The technologies such as rainwater storage facilities, inundation sites, storage facilities, ecological water storage, and distributed rainwater storage were utilized for the flood-preventive design of Gangdong - gu. In the case of rainfall of 100mm per hour, which was the largest concentration of existing intensive rainfall, the flooded area without technological substitution was 84,802ใŽก, but the area was decreased by 8,682ใŽก with applying these technologies to the selected locations. The reduction rate of flooded area is 97%, so it is concluded that the proposed design is highly effective to mitigate the flooding risk. Climate change scenarios and the probability of rainfall intensity were also considered for the proposed design. In the case of 40-year rainfall at RCP 8.5 (84 mm/h), no flooding occurred, but in the case of 70-year rainfall at RCP 8.5 (126 mm/h), half of the existing flooded area was flooded, which would provide a guideline for decision-making when the existing design is to be revised. Finally, Hazard Capacity Factor Design (HCFD) concept was proposed to generalize current proposed methods for urban flooding, later on, for applying to coastal flood or river flood. The HCFD is composed of quantifying both hazard from natural disasters and capacity of target areas, considering the climate change scenario, deterioration of infrastructure and system, contributions of the applied technologies etc. The basic concept of this method compares the hazard with the capacity to evaluate the safety factor of the city or site regarding the disaster-resisting potential. Nevertheless, this study has limitations such as insufficient information for the characteristics of pavement and underground drainage lines. It, however, would provide a methodology to design flood-resisting systems for local governments, which is based on the climate change scenario, topographic information, and site analyses. The proposed methodology, therefore, would help decision making of project implementation for mid-term and long-tern plan.๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ธฐํ›„๋ณ€ํ™”์— ๋”ฐ๋ฅธ ๊ฐ•์šฐ๋Ÿ‰ ์ฆ๊ฐ€๋กœ ์ธํ•ด ํ™์ˆ˜์˜ ๊ทœ๋ชจ๊ฐ€ ์ปค์งˆ ๊ฒƒ์— ๋Œ€๋น„ํ•ด ์ง€์ž์ฒด์—์„œ ์ž์ฒด์ ์œผ๋กœ ์žฌ๋‚œ์— ๋Œ€๋น„ํ•  ์ˆ˜ ์žˆ๋Š” ๋Œ€์‘ ๋ฐฉ์•ˆ์„ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ์ „ ์„ธ๊ณ„์ ์œผ๋กœ ๋„์‹œํ™”๊ฐ€ ๊ธ‰์ฆํ•˜๋ฉด์„œ ๊ฐ ์ข… ์žฌ๋‚œ์— ๋Œ€๋น„ํ•œ ์‹œ์„ค์ด ๋งŽ์ด ๋Š˜์–ด๋‚˜๊ณ  ์žˆ์ง€๋งŒ ํ”ผํ•ด๋Š” ์ค„๊ณ  ์žˆ์ง€ ์•Š๋‹ค. ๊ทธ ์ค‘ ๋ฌผ ์žฌํ•ด๋กœ ์ธํ•œ ํ”ผ๊ฐ€ ๊ฐ€์žฅ ํ”ผํ•ด๊ฐ€ ํฐ๋ฐ 2018๋…„ ์ผ๋ณธ์˜ ๊ฒฝ์šฐ ๊ทธ ํ”ผํ•ด์•ก์ด 10์กฐ์›์— ๋‹ค๋‹ค๋ž๋‹ค. ์ฃผ๋กœ ์ด๋Ÿฐ ์žฌ๋‚œ์— ๋Œ€ํ•œ ๋Œ€์‘์ฑ…์œผ๋กœ ๊ตญ๊ฐ€์ฐจ์›์—์„œ ๋Œ€๊ทœ๋ชจ ์ €๋ฅ˜์กฐ ๋“ฑ์„ ์„ค์น˜ํ•˜๊ณ  ์žˆ์ง€๋งŒ ์ง€์ž์ฒด ์ž…์žฅ์—์„œ์˜ ๋Œ€์‘์ฑ…์€ ํ˜„์‹ค์ ์œผ๋กœ ์–ด๋ ต๊ธฐ์— ๋ถ€์‹คํ•œ ์ƒํ™ฉ์ด๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ ์ง€๋ถ„์„๊ณผ ์ˆ˜๋ฌธํ•™ ๋ถ„์„์„ ํ†ตํ•ด ์ตœ์†Œํ•œ์˜ ์žฌํ™”๋กœ ์ตœ๋Œ€ํ•œ์˜ ํšจ๊ณผ๋ฅผ ๋‚ด๋Š” ์„ค๊ณ„๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์šฐ์„  ํ™์ˆ˜์˜ ์œ ํ˜•์„ ์ •๋ฆฌํ•˜๋ฉด ๋ถˆํˆฌ์ˆ˜๋ฉด์ ์˜ ์ฆ๊ฐ€๋กœ ์ธํ•ด ๋ฐœ์ƒํ•˜๋Š” ๋„์‹œํ™์ˆ˜, ๊ฐ•์ด๋‚˜ ํ•˜์ฒœ์˜ ๋ฒ”๋žŒ์œผ๋กœ ๋ฐœ์ƒํ•˜๋Š” ํ•˜์ฒœํ™์ˆ˜, ํ•ด์•ˆ๊ฐ€์—์„œ ๋ฐœ์ƒํ•˜๋Š” ํ•ด์•ˆํ™์ˆ˜ ๊ทธ๋ฆฌ๊ณ  ๊ธ‰๊ฒฝ์‚ฌ์ง€์—์„œ ๊ฐ‘์ž‘์Šค๋Ÿฌ์šด ๊ฐ•์šฐ๋กœ ์ธํ•ด ๋ฐœ์ƒํ•˜๋Š” ๋Œ๋ฐœํ™์ˆ˜๊ฐ€ ์žˆ๋‹ค. ์ด ์ค‘ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋„์‹œํ™์ˆ˜์— ์ง‘์ค‘ํ•˜์˜€๊ณ  ๋Œ€์ƒ์ง€๋กœ๋Š” ๊ฐ•๋™๊ตฌ๋ฅผ ์„ ์ •ํ•˜์˜€๋‹ค. ํ™์ˆ˜์— ๋Œ€ํ•œ ์žฌ๋‚œ ๋Œ€์‘ ๊ธฐ์ˆ ์€ ์ฃผ๋กœ ์ €๋ฅ˜์กฐ, ๊ด€๊ฑฐ ํ™•์žฅ, ํˆฌ์ˆ˜ํฌ์žฅ์ด ์žˆ์ง€๋งŒ ํˆฌ์ˆ˜ํฌ์žฅ๊ณผ ๊ด€๊ฑฐ ํ™•์žฅ์€ ๋น„์‹ผ ๊ณต์‚ฌ๋น„ ๊ทธ๋ฆฌ๊ณ  ๋‹ค๋Ÿ‰์˜ ๋ฏผ์›์œผ๋กœ ์ธํ•ด ์‹ค์งˆ์ ์œผ๋กœ ์ ์šฉ์ด ์–ด๋ ค์›Œ ์ €๋ฅ˜์กฐ ์„ค์น˜๋ฅผ ์ฃผ๋œ ์ ์‘ ๊ธฐ์ˆ ๋กœ ์„ ์ •ํ•˜์˜€๋‹ค. ๊ฐ•๋™๊ตฌ์˜ ์ ์ง€๋ถ„์„์€ ArcGIS๋กœ ํ•˜์˜€์œผ๋ฉฐ ๊ตญํ† ์ง€๋ฆฌ์ •๋ณด ํ”Œ๋žซํผ์˜ ๊ณต๊ณต๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์šฉ๋„๋ฅผ ๋ถ„๋ฅ˜ํ•˜์˜€๋‹ค. ์ด์— ํ•ด๋‹น๋˜๋Š” ์šฉ๋„๋Š” ํ•™๊ต, ์ฃผ์ฐจ์žฅ, ๊ณต๊ณต์‹œ์„ค, ์ง€ํ•˜์ฐฝ๊ณ  ๋“ฑ์ด ํ•ด๋‹น๋œ๋‹ค. ์ˆ˜๋ฌธํ•™ ๋ถ„์„์€ DEM์ž๋ฃŒ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ArcGIS์˜ ํ”Œ๋Ÿฌ๊ทธ์ธ์ธ ArcHydro๋ฅผ ํ†ตํ•ด ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์„œ์šธ์‹œ ์ „์ฒด๋ฅผ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ 3๊ฐ€์ง€์˜ ๊ฒฐ๊ณผ๋ฅผ ์–ป์„ ์ˆ˜์žˆ์—ˆ๋‹ค. ์ฒซ ์งธ, ์นจ์ˆ˜์ง€์—ญ๊ณผ ์˜ํ–ฅ์ง€์—ญ(Watershed)๋Š” ๋ช‡ ๊ฐ€์ง€ ์œ ํ˜•์œผ๋กœ ๋‚˜๋‰˜์—ˆ๋Š”๋ฐ ์นจ์ˆ˜์ง€์—ญ์€ ๋ฌผ์˜ ํ๋ฆ„ ์ค‘๊ฐ„์— ์žˆ์œผ๋ฉฐ ์ฃผํƒ๊ณผ ์ƒ๊ฐ€๊ฑด๋ฌผ์ด ํ”ผํ•ด๋ฅผ ์ž…์€ ์œ ํ˜•์ด ๊ฐ€์žฅ ๋งŽ์•˜๊ณ , ์˜ํ–ฅ์ง€์—ญ์€ ์ฃผํƒ๊ณผ ์ƒ๊ฐ€ ๊ฑด๋ฌผ์ด ๋Œ€๋ถ€๋ถ„์ธ ์œ ํ˜•์ด ๊ฐ€์žฅ ๋งŽ์•˜๋‹ค. ๊ฐ•๋™๊ตฌ๋Š” ์ด ๋‘๊ฐ€์ง€ ์œ ํ˜•์— ๋ชจ๋‘ ์ ํ•ฉํ•˜์—ฌ ๋Œ€ํ‘œ์„ฑ์„ ๋„๋Š” ๊ตฌ์ด๋‹ค. ๋‘˜ ์งธ, ๊ฐ•๋™๊ตฌ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ๋น—๋ฌผ ์ €๋ฅ˜์‹œ์„ค, ์นจ์ˆ˜์ง€, ์ €๋ฅ˜์‹œ์„ค, ์ƒํƒœ์ˆ˜๋กœ ๊ทธ๋ฆฌ๊ณ  ๋ถ„์‚ฐํ˜• ๋น—๋ฌผ ์ €๋ฅ˜์กฐ๋ฅผ ๋ฐฐ์น˜ ์„ค๊ณ„ ํ•˜์˜€๊ณ  ๊ทธ ํšจ๊ณผ๋ฅผ ๋ณด์•˜๋‹ค. ๊ธฐ์กด ์ง‘์ค‘ ๊ฐ•์šฐ ์ค‘ ๊ฐ€์žฅ ์ปธ๋˜ ์‹œ๊ฐ„๋‹น 100mm์˜ ๊ฐ•์šฐ ์‹œ ๊ธฐ์ˆ  ๋Œ€์ž… ์ „ ๋Œ€๋น„ ๊ธฐ์ˆ  ๋„์ž… ํ›„ ์ด 97%์˜ ๋ฉด์ ์ด ์ค„์–ด ์„ค๊ณ„์•ˆ์˜ ํšจ๊ณผ๊ฐ€ ์ ์ ˆํ–ˆ๋‹ค๋Š” ๊ฒฐ๋ก ์ด ๋‚˜์™”๋‹ค. ์…‹ ์งธ, ์ด ์„ค๊ณ„์•ˆ์„ ๊ธฐํ›„๋ณ€ํ™” ์‹œ๋‚˜๋ฆฌ์˜ค์™€ ํ™•๋ฅ ๊ฐ•์šฐ๊ฐ•๋„ ๋นˆ๋„์™€ ์—ฐ๊ณ„ํ•˜์—ฌ ๋น„๊ตํ–ˆ์„ ๋•Œ๋Š” ์กฐ๊ธˆ ๋‹ค๋ฅธ ๊ฒฐ๊ณผ๊ฐ€ ๋‚˜์™”๋‹ค. RCP 8.5์˜ 40๋…„ ๋นˆ๋„ ๊ฐ•์šฐ(84mm/h)์˜ ๊ฒฝ์šฐ๋Š” ์นจ์ˆ˜๊ฐ€ ๋ฐœ์ƒํ•˜์ง€ ์•Š์•˜์ง€๋งŒ, RCP 8.5์˜ 70๋…„ ๋นˆ๋„ ๊ฐ•์šฐ(126mm/h)์˜ ๊ฒฝ์šฐ์—๋Š” ๊ธฐ์กด ์นจ์ˆ˜์ง€์—ญ์˜ ์ ˆ๋ฐ˜์ด ์นจ์ˆ˜๋˜์—ˆ๋‹ค. ์‹œ๋‚˜๋ฆฌ์˜ค์™€์˜ ๋น„๊ต๋ฅผ ํ†ตํ•ด ์–ด๋Š ์‹œ์ ์— ์ƒˆ๋กœ์šด ๊ธฐ์ˆ ์ด ๋„์ž…๋˜์–ด์•ผ ํ•˜๋Š”์ง€์— ๋Œ€ํ•œ ๊ธฐ์ค€์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ๋Š” ์žฌ๋‚œ ์„ฑ๋Šฅ ์ง€์ˆ˜ ์„ค๊ณ„(Hazard Capacity Factor Design) ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜์—ฌ ์ผ๋ฐ˜ํ™” ํ•˜์˜€๋‹ค. ์ด ๋ชจ๋ธ๋กœ ๋„์‹œํ™์ˆ˜ ๋Œ€์‘์˜ ๋ฐฉ๋ฒ•๋ก ์„ ์ผ๋ฐ˜ํ™”ํ•˜์—ฌ ํ•ด์•ˆํ™์ˆ˜, ํ•˜์ฒœํ™์ˆ˜์— ๋Œ€์ž…ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ถ„์„ ํ”„๋กœ๊ทธ๋žจ์˜ ์ •ํ™•์„ฑ, ์ •๋ณด์˜ ์ œ์•ฝ ๋“ฑ์˜ ํ•œ๊ณ„๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์ง€๋งŒ ์ง€์ž์ฒด์—์„œ ๊ตฌํ•˜๊ธฐ ์‰ฌ์šด ์ž๋ฃŒ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ธฐํ›„๋ณ€ํ™”์— ๋”ฐ๋ฅธ ์žฌํ•ด ๋Œ€๋น„ ๋ฐฉ๋ฒ•๋ก ์„ ์ •๋ฆฝํ•˜๊ณ  ์‚ฌ์—… ์‹œํ–‰์˜ ์˜์‚ฌ๊ฒฐ์ •์— ๋„์›€์„ ์ค„ ์ˆ˜ ์žˆ๋‹ค๋Š”๋ฐ ์˜์˜๊ฐ€ ์žˆ๋‹ค. ์ถ”ํ›„์— ๋ถ„์„ ๋ฐฉ๋ฒ•์ด ๋” ์ •ํ™•ํ•ด ์ง€๊ณ  ๋‹ค์–‘ํ•œ ์žฌ๋‚œ์— ๋Œ€ํ•œ ๋ฐฉ๋ฒ•๋ก ์ด ์ •๋ฆฝ๋˜์–ด ๊ฐ„๋‹ค๋ฉด ๋” ๋‚˜์€ ๋ชจ๋ธ์ด ๊ตฌ์ถ•๋˜์–ด ๊ฐˆ ๊ฒƒ์ด๋‹ค.์ œ 1์žฅ ์„œ๋ก  1 1.1 ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1 1.2 ํ˜„ํ™ฉ ์กฐ์‚ฌ . 3 1.2.1 ๊ตญํ†  ์ฐจ์›์—์„œ์˜ ๋Œ€์‘ . 3 1.2.2 ์‹œ๊ตฌ ์ฐจ์›์—์„œ์˜ ๋Œ€์‘ 5 1.3 ์—ฐ๊ตฌ์˜ ๋ชฉ์  7 1.4 ์—ฐ๊ตฌ์˜ ๊ตฌ์„ฑ ๋ฐ ๊ฐœ์š” 7 ์ œ 2์žฅ ๋ฌธํ—Œ์กฐ์‚ฌ 8 2.1 ํ™์ˆ˜์™€ ๊ด€๋ จ๋œ ์—ฐ๊ตฌ 8 2.2 ํ™์ˆ˜์˜ ์œ ํ˜•๊ณผ ์›์ธ 11 2.3 ๊ธฐ์ˆ ์˜ ์œ ํ˜• 13 2.4 ์ €๋ฅ˜์กฐ์˜ ๋ถ„๋ฅ˜ 16 2.4.1 ๋ฌผ์˜ ์œ ์ž…์— ๋”ฐ๋ฅธ ๋ถ„๋ฅ˜ 16 2.4.2 ์„ค์น˜ ์œ„์น˜์— ๋”ฐ๋ฅธ ๋ถ„๋ฅ˜ 17 2.4.3 ๋Œ€ํ‘œ์ ์ธ ๊ธฐ์ˆ  19 ์ œ 3์žฅ ๋ฐฉ๋ฒ•๋ก  . 23 3.1 ์—ฐ๊ตฌ ํ๋ฆ„๋„ 23 3.2 ๋ถ„์„ ํ”„๋กœ๊ทธ๋žจ์˜ ์„ ์ • 24 3.3. ๊ธฐ๋ณธ DEM(Digital Elevation Model) . 27 3.4 ์ˆ˜๋ฌธํ•™ ๋ถ„์„ ๋ฐฉ๋ฒ• . 29 3.5 ๊ธฐํ›„๋ณ€ํ™” ์‹œ๋‚˜๋ฆฌ์˜ค 31 ์ œ 4์žฅ ๋ถ„์„ ๊ฒฐ๊ณผ 35 4.1 ๋ถ„์„ ๋Œ€์ƒ์ง€ 35 4.2 ์นจ์ˆ˜ ์ง€์—ญ . 36 4.3 ์˜ํ–ฅ ์ง€์—ญ . 42 ์ œ 5์žฅ ๋Œ€์ƒ์ง€ ์„ค๊ณ„ 47 5.1 ์„ค๊ณ„ ๋Œ€์ƒ์ง€ ์„ ์ • ๋ฐ ์ƒ์„ธ ๋ถ„์„ . 47 5.1.1 ๊ฑด๋ฌผ ํ˜•ํƒœ์™€ ๋…น์ง€ . 48 5.1.2 ์ƒ์Šต ์นจ์ˆ˜ ์ง€์—ญ 49 5.1.3 ์นจ์ˆ˜์ง€์—ญ์˜ ๊ฑด๋ฌผ ์šฉ๋„ 50 5.1.4 ์นจ์ˆ˜์ง€์—ญ ๊ฑด๋ฌผ ์œ ํ˜• . 51 5.1.5 ์˜ํ–ฅ์ง€์—ญ ๋ถ„ํฌ 52 5.1.6 ์˜ํ–ฅ์ง€์—ญ ๊ฑด๋ฌผ ์šฉ๋„ . 53 5.1.7 ์˜ํ–ฅ์ง€์—ญ ๊ฑด๋ฌผ ์œ ํ˜• . 54 5.1.8 Drainage Line . 55 5.1.9 ๊ฐ•๋™๊ตฌ์˜ ๋ฏธ๋ž˜ ๊ณ„ํš . 56 5.2 ์„ค๊ณ„ ๋…ผ๋ฆฌ . 58 5.2.1 ๊ฐ•์šฐ ์‹œ๋‚˜๋ฆฌ์˜ค์— ๋”ฐ๋ฅธ ํ”ผํ•ด ๋ฉด์ ๊ณผ ๋ณ€๋™ 58 5.2.2 ๊ธฐ์ˆ  ์ ์šฉ ๋…ผ๋ฆฌ 60 5.2.3 ๊ธฐ์ˆ  ํšจ๊ณผ ์‚ฐ์ • 62 5.2.4 ๊ฐ€์šฉ์ง€ ์„ ์ • . 63 5.3 ๋งˆ์Šคํ„ฐ ํ”Œ๋žœ 68 5.4 ์—ฐ๋„๋ณ„ RCP ์‹œ๋‚˜๋ฆฌ์˜ค์— ๋”ฐ๋ฅธ ๊ธฐ์ˆ ํšจ๊ณผ . 70 5.5 Hazard Capacity Factor Design(HCFD) Model 73 ์ œ 6์žฅ ๊ฒฐ๋ก  75 ์ฐธ๊ณ  ๋ฌธํ—Œ 77 Abstract . 80 ๋ถ€๋ก . 83Maste

    ์œ ๋™์„ฑ ํ”„๋ฆฌ๋ฏธ์—„์ด ์ฃผ๊ฐ€์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

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    Doppler sonographic findings in an experimental rabbit model of necrotizing enterocolitis

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    Regulation of TRPC6 channels by antidepressants

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    ์˜๊ณผํ•™๊ณผ/๋ฐ•์‚ฌ[ํ•œ๊ธ€]TRPC6 ์ด์˜จ์ฑ„๋„์€ canonical transient receptor potential (TRPC) ๊ณ„์—ด์— ์†ํ•˜๋Š” ์นผ์Š˜ ํˆฌ๊ณผ๋„๋ฅผ ์ง€๋‹Œ ๋น„์„ ํƒ์  ์–‘์ด์˜จ ์ฑ„๋„๋กœ์„œ G-๋‹จ๋ฐฑ์งˆ ์—ฐ๊ฒฐ ์ˆ˜์šฉ์ฒด ๋˜๋Š” tyrosine kinase ์ˆ˜์šฉ์ฒด์˜ ํ™œ์„ฑ์„ ํ†ตํ•ด PLC-์˜์กด์„ฑ ๊ธฐ์ „์„ ํ†ตํ•˜์—ฌ ์ž๊ทน๋œ๋‹ค. TRPC6๋Š” ํ˜ˆ๊ด€ ๋‚ดํ”ผ ์„ฑ์žฅ์š”์ธ์กฐ์ ˆ์„ ํ†ตํ•œ ํ˜ˆ๊ด€์ƒ์„ฑ, ํ˜ˆ๊ด€ ํ‰ํ™œ๊ทผ ์„ธํฌ์˜ ์ˆ˜์ถ• ๋ฐ ์‹ฌ์žฅ ๋น„๋Œ€์— ์ฃผ์š”ํ•œ ์—ญํ• ์„ ๋‹ด๋‹นํ•˜๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ง„๋‹ค. ํ•ญ์šฐ์šธ์ œ๋Š” ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ์กฐ์ง์— ๋ฐœํ˜„๋˜์–ด ์žˆ๋Š” ๋‹ค์–‘ํ•œ ์ด์˜จ ์ฑ„๋„์— ์–ต์ œํšจ๊ณผ๋ฅผ ๋‚˜ํƒ€๋‚ด๋ฉฐ ํŠนํžˆ ์‹ฌํ˜ˆ๊ด€๊ณ„์— ๋งŽ์€ ๋ถ€์ž‘์šฉ์„ ์ดˆ๋ž˜ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋ณด๊ณ ๋œ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ธ์ฒด์—์„œ ์œ ๋ž˜๋œ TRPC6์„ STIM1(stromal interaction molecule 1) ๋ฐ M3 ์ˆ˜์šฉ์ฒด(muscarinic receptor type 3)์™€ ํ•จ๊ป˜ HEK293์„ธํฌ์— ๊ณผ ๋ฐœํ˜„์‹œํ‚จ ํ›„ ์ „์„ธํฌ ๋ฐ ๋‹จ์ผ ์ด์˜จ์ฑ„๋„ ํŒจ์น˜ํด๋žจํ”„ ๋ฐฉ๋ฒ•์œผ๋กœ ํ•ญ์šฐ์šธ์ œ์˜ ์˜ํ–ฅ์„ ์•Œ์•„๋ณด์•˜๋‹ค. ์„ธํฌ ๋‚ด GTPฮณs๋ฅผ ์ฃผ์ž… ๋˜๋Š” M3 ๋ฌด์Šค์นด๋ฆฐ ์ˆ˜์šฉ์ฒด ์ž๊ทน์„ ํ†ตํ•ด TRPC6 ์ „๋ฅ˜๋ฅผ ํ™œ์„ฑํ™” ์‹œํ‚ค๊ณ , ์ด์— ๋Œ€ํ•œ ํ•ญ์šฐ์šธ์ œ์˜ ์–ต์ œํšจ๊ณผ๋ฅผ ์ „๋ฅ˜ ์ „์••๊ณก์„ ์—์„œ ํ™•์ธํ•˜์˜€๋‹ค. ๊ฐœ์—ด๋ณ„ ๋‹ค์–‘ํ•œ ์ข…๋ฅ˜์˜ ํ•ญ์šฐ์šธ์ œ๋Š” TRPC6 ์ด์˜จ์ฑ„๋„ ํ™œ์„ฑ ์ „๋ฅ˜๋ฅผ ์–ต์ œํ•˜๋ฉฐ ๋†๋„-์˜์กด ๊ณก์„ ์„ ํ†ตํ•œ IC50์˜ (logM)๋†๋„๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์•˜๋‹ค. Venlafaxine(SNRI); -4.99ยฑ0.03, fluoxetine(SSRI); -4.94ยฑ0.02, amitriptyline (TCA); -4.84ยฑ0.02, bupropion(NDRI); -4.81ยฑ0.03, trazodone(SRI); -4.58ยฑ0.01. Fluoxetine ์€ GTPฮณs ์œ ๋ฐœ์„ฑ ์ตœ๋Œ€ ์ „๋ฅ˜์— ์žˆ์–ด ๋†๋„-์˜์กด์ ์œผ๋กœ ์–ต์ œํšจ๊ณผ๋ฅผ ๋‚˜ํƒ€๋‚ด์—ˆ์œผ๋‚˜ M3 ์ˆ˜์šฉ์ฒด ํ™œ์„ฑ์„ ํ†ตํ•œ TRPC6 ์ด์˜จ ์ฑ„๋„์˜ ๊ฐœ๋ฐฉ ํŠน์„ฑ์—๋Š” ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ์•Š์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค.์œ„์˜ ์‹คํ—˜๊ฒฐ๊ณผ๋กœ ๋ฏธ๋ฃจ์–ด ๋‹ค์–‘ํ•œ ์ข…๋ฅ˜์˜ ํ•ญ์šฐ์šธ์ œ๊ฐ€ TRPC6 ์ด์˜จ์ฑ„๋„์„ ์ง์ ‘์ ์œผ๋กœ ์–ต์ œํ•˜๋Š” ํšจ๊ณผ๋ฅผ ๊ฐ–๋Š” ๊ฒƒ์œผ๋กœ ์ƒ๊ฐ๋œ๋‹ค. [์˜๋ฌธ]ope

    (A) comparative study of Korean version of the strengths and difficulties questionnaire (SDQ-Kr) and the child behavior check list(

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    ์˜ํ•™๊ณผ/์„์‚ฌ[ํ•œ๊ธ€]๊ฐ•์ ?๋‚œ์  ์„ค๋ฌธ์ง€(the strengths and difficulties questionnaire : SDQ)๋Š” 4์„ธ-16์„ธ์˜ ์†Œ์•„ ์ฒญ์†Œ๋…„์˜ ๋ถ€๋ชจ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์ž๋…€์˜ ์ •์‹ ๋ณ‘๋ฆฌ๋ฅผ ์„ ๋ณ„ํ•˜๊ธฐ์œ„ํ•œ 25๋ฌธํ•ญ์˜ ๊ฐ„๋‹จํ•œ ์„ค๋ฌธ์ง€์ด๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ์†Œ์•„์ •์‹ ๊ณผ ์ž„์ƒ์•„๋™์ง‘๋‹จ(n=160), ์†Œ์•„๊ณผ ์ž„์ƒ์•„๋™์ง‘๋‹จ(n=105), ๊ทธ๋ฆฌ๊ณ  ์ •์ƒ ์•„๋™์ง‘๋‹จ(n=81) ๋“ฑ 4์„ธ์—์„œ 11์„ธ ์—ฐ๋ น์˜ ์•„๋™ 346๋ช…์— ๋Œ€ํ•˜์—ฌ ๋ถ€๋ชจ๋ณด๊ณ ์‹์œผ๋กœ ํ•œ๊ตญ์–ดํŒ ๊ฐ•์ ?๋‚œ์ ์„ค๋ฌธ์ง€(the Korean version of SDQ : SDQ-Kr)์™€ ํ•œ๊ตญ์–ดํŒ ์•„๋™ํ–‰๋™์กฐ์‚ฌํ‘œ(the Korean version of child and adolescent behavior checklist : K-CBCL)๋ฅผ ๋™์‹œ์— ์‹œํ–‰ํ•˜์—ฌ ๋น„๊ตํ•จ์œผ๋กœ์จ, SDQ-Kr์ด ์•„๋™ ์ •์‹ ๋ณ‘๋ฆฌ ํ‰๊ฐ€์— ํ‘œ์ค€์  ์ธก์ •๋„๊ตฌ(gold standard)๊ฐ€ ๋˜์–ด์˜จ 118๋ฌธํ•ญ์˜ K-CBCL์„ ๋Œ€์‹ ํ•  ์ˆ˜ ์žˆ๋Š”์ง€ ์กฐ์‚ฌํ•˜๊ธฐ ์œ„ํ•ด์„œ ์‹œํ–‰๋˜์—ˆ๋‹ค. ์ •์‹ ๊ณผ ์ž„์ƒ์•„๋™์ง‘๋‹จ์— ์†ํ•œ ์•„๋™๋“ค์€ DSM-โ…ฃ๋ฅผ ์ ์šฉํ•˜์—ฌ ์ง„๋‹จํ•˜์˜€์œผ๋ฉฐ ์†Œ์•„๊ณผ์  ๊ณต์กด์งˆํ™˜์ด ์žˆ๋Š” ๊ฒฝ์šฐ๋Š” ์—ฐ๊ตฌ๋Œ€์ƒ์—์„œ ๋ฐฐ์ œํ•˜์˜€๋‹ค. ๋‘ ์„ค๋ฌธ์ง€์˜ ๋น„๊ต๋ฅผ ์šฉ์ดํ•˜๊ฒŒ ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ SDQ-Kr์˜ 5๊ฐœ ์†Œ์ฒ™๋„๋Š” K-CBCL์˜ 3๊ฐœ ์†Œ์ฒ™๋„์— ๋Œ€์‘๋˜๋„๋ก ์กฐ์ •ํ•˜์˜€๋‹ค. ์กฐ์‚ฌ ๊ฒฐ๊ณผ, SDQ-Kr๊ณผ K-CBCL์€ ๊ฐ•ํ•œ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ณด์˜€์œผ๋‚˜, SDQ-Kr์˜ ๊ฐ ์†Œ์ฒ™๋„ ๊ฐ„ ์ƒ๊ด€์„ฑ์ด ๋‚ฎ๊ฒŒ ๋‚˜ํƒ€๋‚˜ K-CBCL๋ณด๋‹ค ๊ฒ€์‚ฌ๋„๊ตฌ๋กœ์„œ์˜ ํƒ€๋‹น๋„๊ฐ€ ๋†’์Œ์„ ์‹œ์‚ฌํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์†Œ์•„์ •์‹ ๊ณผ ๋ฐ ์†Œ์•„๊ณผ ์ž„์ƒ์•„๋™์ง‘๋‹จ๊ณผ ์ •์ƒ ์•„๋™์ง‘๋‹จ ๋“ฑ ์„ธ ์ง‘๋‹จ์„ ๋ณ€๋ณ„ํ•˜๋Š”๋ฐ ์žˆ์–ด์„œ SDQ-Kr์€ K-CBCL์— ๋น„ํ•ด ์†Œ์•„์ •์‹ ๊ณผ ์•„๋™๊ณผ ์†Œ์•„๊ณผ ์•„๋™์„ ๊ตฌ๋ณ„ํ•˜๋Š” ํŒ๋ณ„๋ ฅ์ด ๋” ๋†’์€ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์˜€๋‹ค. ๋ถ€๊ฐ€์ ์œผ๋กœ, SDQ-Kr์€ ์†Œ์•„์ •์‹ ๊ณผ ์ž„์ƒ์•„๋™์ง‘๋‹จ ๋‚ด์—์„œ ์ •์„œ์žฅ์• ์™€ ๊ณ ๊ธฐ๋Šฅ ์žํ์ฆ์„ ์ง„๋‹จํ•ด๋‚ด๋Š” ๋น„์œจ์ด K-CBCL ๋ณด๋‹ค ์œ ์˜ํ•˜๊ฒŒ ๋†’์•˜๋‹ค. [์˜๋ฌธ]The Strengths and Difficulties Questionnaire (SDQ) is a brief behavioral screening questionnaire for the parents or teachers of 4-16 year olds.I used the Korean version of SDQ (SDQ-Kr) for parental ratings of 346 children aged 4 through 11 from a psychiatric clinic (n=162), pediatric clinics or the community (n=101) and nonclinic(n=83). These parents also completed the Korean version of Child and Adolescent Behavior Checklist (K-CBCL). The children from the psychiatric sample received a DSM-IV diagnosis.Scores from SDQ-Kr and K-CBCL were highly correlated, but SDQ-Kr showed lower inter-scale correlation. Both questionnaires equally able to distinguish among the three samples with SDQ-Kr showing better results between psychiatric and pediatric group. Within the psychiatric sample, SDQ-Kr was significantly better than K-CBCL in detecting emotional disorder and high functioning autism.ope

    A Query method of object-oriented data model based on complex objects

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    Maste
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