1,864 research outputs found

    ARQ Protocols in Cognitive Decode-and-Forward Relay Networks: Opportunities Gain

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    In this paper, two novel automatic-repeat-request (ARQ) based protocols were proposed, which exploit coop- eration opportunity inherent in secondary retransmission to create access opportunities. If the signal was not decoded correctly in destination, another user can be acted as a relay to reduce retransmission rounds by relaying the signal. For comparison, we also propose a Direct ARQ Protocol. Specif- ically, we derive the exact closed-form outage probability of three protocols, which provides an effective means to evalu- ate the effects of several parameters. Moreover, we propose a new metric to evaluate the performance improvement for cognitive networks. Finally, Monte Carlo simulations were presented to validate the theory analysis, and a comparison is made among the three protocols

    On mountain pass theorem and its application to periodic solutions of some nonlinear discrete systems

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    We obtain a new quantitative deformation lemma, and then gain a new mountain pass theorem. More precisely, the new mountain pass theorem is independent of the functional value on the boundary of the mountain, which improves the well known results (\cite{AR,PS1,PS2,Qi,Wil}). Moreover, by our new mountain pass theorem, new existence of nontrivial periodic solutions for some nonlinear second-order discrete systems is obtained, which greatly improves the result in \cite{Z04}.Comment: 11 page

    Chemically Modified Cellulosic Materials as Multi-Functional Agents in Polymer Composites

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    Comparative flame retardancy of micro wood fiber plastic composites (WPCs) with fire retardants (1,2-bis(pentabromophenyl) ethane, metal hydroxides and nanoclay) was studied. The fire additives (1,2-bis(pentabromophenyl) ethane as well as magnesium hydroxide) obviously enhanced the fire retarding properties of WPCs. Especially, 1,2-bis(pentabromophenyl) ethane significantly reduced the total heat release as well as heat release rate. In addition, a synergistic effect of 1,2-bis(pentabromophenyl) and nanoclay was achieved for the enhanced fire retarding performance of WPCs. A copolymer of cellulose nanocrystals (CNCs) and poly(N-vinylcaprolactam) (PVCL) (PVCL-g-CNCs) for use as thermally-responsive polymers with low critical solution temperatures (LCSTs) was synthesized via atom transfer radical polymerization (ATRP). The rod like morphology of CNCs was well preserved after the grafting modification. The dynamic rheology measurement confirmed the thermally induced phase transition behavior of PVCL-g-CNCs aqueous suspensions (1.0 wt%) with the LCST value at 36 oC. The surface of CNCs was modified with poly(butyl acrylate) (PBA) and poly(methyl methacrylate) (PMMA) through ATRP technique. The successful grafting modification led to the increased thermal stability of modified CNCs (MCNCs). The increase in Youngโ€™s modulus of more than 25-fold and in tensile strength of about 3 times for 7 wt% MCNCs/PBA-co-PMMA nanocomposites was achieved compared to those of neat PBA-co-PMMA. A micro-phase separated morphology (PBA- soft domains, and PMMA- as well as CNCs- hard domains) of MCNCs/PBA-co-PMMA nanocomposites was also observed. In addition, the interfacial miscibility and phase separated morphology of PMMA-g-CNCs (PMCNCs)/PBA-co-PMMA nanocomposites were further studied. The 10 wt% PMCNCs/PBA-co-PMMA nanocomposites showed increases in Youngโ€™s modulus of more than 20-fold and in tensile strength of about 3-fold when compared to those of neat PBA-co-PMMA. Morphological analysis indicated the presence of microphase separation in PMCNCs/PBA-co-PMMA nanocomposites. Therefore, the surface modification of CNCs played a crucial role in reinforcing mechanical performance, controlling interfacial miscibility and tuning phase morphology of the nanocomposites

    ๋‹น๋‡จ ํ™˜์ž์—์„œ ๊ธฐ๊ณ„ํ•™์Šต์„ ์ด์šฉํ•œ ์ƒ๋ฆฌํ•™์  ์ง€ํ‘œ ๋ฐ ์œ„ํ—˜์š”์ธ์ด ์‹ฌํ˜ˆ๊ด€๊ณ„ ์˜ˆํ›„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•œ ๊ฒ€์ฆ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ, 2020. 8. ๊ตฌ๋ณธ๊ถŒ.Background and Objectives: Current European Society of Cardiology and European Association for Cardio-Thoracic Surgery guidelines recommend fractional flow reserve (FFR) measurement as a standard invasive method to identify the ischemia-causing coronary lesions. However, patients after therapeutic procedures still suffer adverse cardiovascular events even after deferral of revascularization according to FFR, potentially due to the presence of microvascular dysfunction that may cause ischemia or foster the progression of obstructive disease. Coronary microvascular dysfunction (CMD) is more frequently observed in patients with diabetes mellitus (DM) and is a major determinant of long-term adverse outcome. Since comprehensive physiologic assessment enables the evaluation of microvascular function which could not be fully demonstrated by angiography, we sought to investigate the prognostic implication of invasive physiologic index-defined CMD in patients with DM and coronary artery disease (part I). Increasing evidence showed that machine learning can provide tools to assist physicians during diagnosis and treatment of diverse clinical conditions, including myocardial infarction. Therefore, we sought to study using machine learning algorithms with an expanded sample size, to validate the physiologic indices and find out the valuable risk factors for cardiovascular outcomes in patients with DM and coronary artery disease (part II). Methods: Part 1: Two hundred and eighty-three patients with available FFR and index of microcirculatory resistance (IMR) were selected from the 3V FFR-FRIENDS study. Patients were classified according to the presence of DM and CMD into group A (DM-, CMD-), group B (DM-, CMD+), group C (DM+, CMD-), and group D (DM+, CMD+). Primary outcome was a major adverse cardiac event (MACE, a composite of cardiac death, myocardial infarction and ischemia-driven revascularization) at 2 years. Part 2: Seven hundred and fourteen patients (235 patients with DM) with deferred coronary revascularization according to FFR (>0.80) were included. This registry hitherto is the biggest cohort whose patients were fully assessed by comprehensive physiologic indices. Comprehensive physiologic evaluation, including coronary flow reserve (CFR), IMR and FFR, was performed at the time of revascularization deferral. The median values of CFR (2.88), FFR (0.88) and IMR (17.85) were used to classify high or low CFR, FFR, and IMR groups. Information gains of variables with 5,000-permutation resampling, minimal depth and Boruta algorithms were used for feature selection. Furthermore, prognostic models were compared using c-index. In this part, patient-oriented composite outcome (POCO) at 5 years, including all-cause death, any myocardial infarction, and any revascularization, was the primary outcome. Results: Part 1: DM population showed significantly higher risk of MACE compared with non-DM population (HR 4.88, 95% CI 1.54-15.48, p=0.003). MACE at 2-year among four groups were 2.2%, 2.0%, 7.0%, and 18.5%, respectively. Group D showed significantly higher risk of MACE compared with group A (HR 8.98, 95% CI 2.15-37.41, p=0.003). The multivariable regression analysis showed the presence of DM and CMD was an independent predictor of 2-year MACE (HR 11.24, 95% CI 2.53-49.88, p=0.002) and integrating CMD into a model with DM increased discriminant ability (C-index 0.683 vs. 0.710, p=0.010, integrated discrimination improvement 0.015, p=0.040). Part 2: Compared with non-DM population, DM population showed a higher risk of POCO at 5 years (HR 2.49, 95% CI 1.64-3.78, p<0.001). Low CFR group had a higher risk of POCO than high CFR group (HR 3.22, 95% CI 1.74-5.97, p<0.001) only in DM population. In contrast, CFR values could not differentiate the risk of POCO in non-DM population. There was a significant interaction between CFR and the presence of DM regarding the risk of POCO (interaction p=0.025). Independent predictors of POCO at 5 years were low CFR and family history of coronary artery disease in DM population, and percent diameter stenosis and multi-vessel disease in non-DM population. Among all angiographic and physiologic parameters, CFR showed the highest information gain. In DM population, CFR, consistently, was the most important feature followed by Age and FFR using Minimal Depth algorithm. Moreover, CFR was the valuable features to predict POCO using Boruta algorithm in DM population. In DM population, adding clinical risk factors (c-index 0.75 0.65-0.85, p=0.500) or clinical risk factors and invasive parameters together (c-index 0.75, 95%CI 0.65-0.85, p=0.535) into features from Boruta (c-index 0.73, 95% CI 0.63-0.83) did not show a better discriminant ability. Conclusions: The patients with DM and CMD were associated with increased risk of cardiovascular events. Integration of CMD improved risk stratification to predict the occurrence of MACE. The importance of risk factors for cardiovascular outcomes is different according to the presence of DM. CFR consistently was the important prognostic factor in patients with DM regardless of methods. Machine learning could help find out the most effective combination with acceptable numbers of features for better outcome prediction.๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์ : ์œ ๋Ÿฝ์‹ฌ์žฅํ•™ํšŒ (European Society of Cardiology) ๋ฐ ์œ ๋Ÿฝ์‹ฌ์žฅ์™ธ๊ณผํ˜‘ํšŒ (European Society of Cardio-Thoracific Academy) ์ง€์นจ์—์„œ ๊ด€์ƒ๋™๋งฅ ํ—ˆํ˜ˆ ์ง„๋‹จ์„ ์œ„ํ•œ ์นจ์Šต์  ํ‘œ์ค€ ๋ฐฉ๋ฒ•์œผ๋กœ ๋ถ„ํšํ˜ˆ๋ฅ˜์˜ˆ๋น„๋ ฅ (FFR, Fractal Flow Reserve) ์ธก์ •์„ ๊ถŒ๊ณ ํ•˜๊ณ  ์žˆ์Œ. ๊ทธ๋Ÿฌ๋‚˜ ํ‘œ์ค€์ง€์นจ์œผ๋กœ ์น˜๋ฃŒ๋ฐ›์€ ์ผ๋ถ€ ํ™˜์ž๋“ค์€ ์—ฌ์ „ํžˆ ์‹ฌํ˜ˆ๊ด€ ์‚ฌ๊ฑด์„ ๊ฒช์Œ. ์ด๋Š” ์ž ์žฌ์ ์œผ๋กœ ํ—ˆํ˜ˆ์„ ์œ ๋ฐœํ•˜๊ฑฐ๋‚˜ ํ์‡„์„ฑ ์งˆํ™˜์˜ ์ง„ํ–‰์„ ์ด‰์ง„ํ•  ์ˆ˜ ์žˆ๋Š” ๋ฏธ์„ธํ˜ˆ๊ด€ ๊ธฐ๋Šฅ์žฅ์•  ๋•Œ๋ฌธ์ž„. ์ด๋Ÿฐ ๋ฏธ์„ธ๊ธฐ๋Šฅ์žฅ์• ๋Š” ๊ด€์ƒ๋™๋งฅ ์กฐ์˜์ˆ ๋กœ ํ‰๊ฐ€ํ•  ์ˆ˜ ์—†์Œ. ๊ด€์ƒ๋™๋งฅ๋ฏธ์„ธํ˜ˆ๊ด€๊ธฐ๋Šฅ์žฅ์• ๋Š” ๋‹น๋‡จํ™˜์ž์—์„œ ๋” ์ž์ฃผ ์ƒ๊ธฐ๊ณ  ์žฅ๊ธฐ ์˜ˆํ›„์˜ ์ฃผ์š” ์œ„ํ—˜์š”์ธ์ž„. ์ข…ํ•ฉ์ ์ธ ์ƒ๋ฆฌํ•™์  ํ‰๊ฐ€๋กœ ๋ฏธ์„ธํ˜ˆ๊ด€ ๊ธฐ๋Šฅ์˜ ํ‰๊ฐ€๊ฐ€ ๊ฐ€๋Šฅํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๋‹น๋‡จ ๋ฐ ๊ด€์ƒ๋™๋งฅ์งˆํ™˜์ด ์žˆ๋Š” ํ™˜์ž์—์„œ ๋Œ€ํ•œ ์นจ์Šต์  ์ƒ๋ฆฌ์ง€ํ‘œ๋กœ ์ •์˜ํ•œ ๋ฏธ์„ธํ˜ˆ๊ด€ ์žฅ์• ๊ฐ€ ์˜ˆํ›„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์กฐ์‚ฌํ•˜๊ณ ์ž ๋ณธ ์—ฐ๊ตฌ์˜ Part 1์„ ์‹œํ–‰ํ•˜์˜€์Œ. ๋˜ ์ƒ˜ํ”Œ ํฌ๊ธฐ๊ฐ€ ํ™•์žฅ๋œ ๋ณธ ์—ฐ๊ตฌ์˜ Part 2์—์„œ๋Š” ๊ธฐ๊ณ„ํ•™์Šต์„ ์ด์šฉํ•˜์—ฌ ๋‹น๋‡จ ํ™˜์ž์—์„œ ์ƒ๋ฆฌํ•™์  ์ง€ํ‘œ ๋ฐ ์œ„ํ—˜์š”์ธ์ด ์‹ฌํ˜ˆ๊ด€๊ณ„ ์˜ˆํ›„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๊ฒ€์ฆํ•˜๊ณ ์ž ์‹œํ–‰ํ•˜์˜€์Œ. ๋ฐฉ๋ฒ•: ๋ณธ ์—ฐ๊ตฌ์˜ ์ฒซ๋ฒˆ์งธ ๋ถ€๋ถ„์€ 3V FFR-FRIENS study์—์„œ ์‚ฌ์šฉ ๊ฐ€๋Šฅํ•œ FFR ๋ฐ index of microcirculatory resistance (IMR)๊ฐ€ ์žˆ๋Š” ํ™˜์ž 283๋ช…์ด ์„ ํƒ๋จ. CMD (coronary microvascular dysfunction)๋Š” IMRโ‰ฅ25U๋กœ ์ •์˜ํ•จ. ํ™˜์ž๋Š” DM๊ณผ CMD์— ๋”ฐ๋ผ ๊ทธ๋ฃน A(DM-, CMD-), ๊ทธ๋ฃน B(DM-, CMD+), ๊ทธ๋ฃน C(DM+, CMD-), ๊ทธ๋ฃน D(DM+, CMD+)๋กœ ๋ถ„๋ฅ˜๋จ. ์ด ์„ ํ–‰ ์—ฐ๊ตฌ์—์„œ 1์ฐจ ํ‰๊ฐ€๋ณ€์ˆ˜๋Š” 2๋…„์˜ major adverse cardiac event (MACE, ์‹ฌ์žฅ์‚ฌ, ์‹ฌ๊ทผ๊ฒฝ์ƒ‰ ๋ฐ ํ—ˆํ˜ˆ์„ฑ ๊ธฐ๋ฐ˜ ํ˜ˆ๊ด€์žฌ๊ฐœํ†ต์ˆ )๋กœ ์ •์˜ํ•จ. ๋‘๋ฒˆ์งธ ๋ถ€๋ถ„์€ Korea-Japan-Spain registry์—์„œ FFR (>0.80)์— ๋”ฐ๋ผ ๊ด€์ƒ๋™๋งฅ ์žฌ๊ฐœํ†ต์ˆ ์ด ์ง€์—ฐ๋˜๊ณ  ๊ด€์ƒ๋™๋งฅํ˜ˆ๋ฅ˜์˜ˆ๋น„๋ ฅ(CFR, coronary flow reserve), IMR์„ ํฌํ•จํ•œ ์ข…ํ•ฉ์ ์ธ ์ƒ๋ฆฌํ•™์  ํ‰๊ฐ€๊ฐ€ ์ด๋ฃจ์–ด์ง„ ํ™˜์ž 714๋ช…(DM์„ ๊ฐ€์ง„ ํ™˜์ž 235๋ช…)์ด ์„ ํƒ๋จ. CFR, IMR, FFR์˜ ๋†’์€ ๊ทธ๋ฃน ๋˜๋Š” ๋‚ฎ์€ ๊ทธ๋ฃน์„ ๋ถ„๋ฅ˜ํ•˜๋Š”๋ฐ ์ค‘๊ฐ„๊ฐ’ CFR(2.88), FFR(0.88), IMR(17.85)์ด ์‚ฌ์šฉ๋จ. ์ด ๋ถ€๋ถ„์˜ 1์ฐจ ํ‰๊ฐ€๋ณ€์ˆ˜๋Š” POCO (patient-oriented composite outcome) 5๋…„ ๋‚ด์˜ ๋ชจ๋“  ์›์ธ ์‚ฌ๋ง, ์‹ฌ๊ทผ๊ฒฝ์ƒ‰, ๋ชจ๋“  ํ˜ˆ๊ด€์žฌ๊ฐœํ†ต์ˆ ๋กœ ์ •์˜ํ•จ. ๊ฒฐ๊ณผ: ์ฒซ ๋ถ€๋ถ„์—์„œ ๋‹น๋‡จ ํ™˜์ž๋“ค์€ ๋น„๋‹น๋‡จํ™˜์ž์— ๋น„ํ•ด MACE์˜ ์œ„ํ—˜์„ฑ์ด ๋†’์Œ(HR 4.88, 95% CI 1.54-15.48, p=0.003). 4๊ฐœ ๊ทธ๋ฃน์˜ 2๋…„ MACE๋Š” ๊ฐ๊ฐ 2.2%, 2.0%, 7.0%, 18.5%. ๊ทธ๋ฃน D๋Š” ๊ทธ๋ฃน A์— ๋น„ํ•ด MACE์˜ ์œ„ํ—˜๋„๊ฐ€ ํ˜„์ €ํžˆ ๋†’์Œ(HR 8.98, 95% CI 2.15-37.41, p=0.003). ๋‹ค๋ณ€๋Ÿ‰ ํšŒ๊ท€ ๋ถ„์„์—์„œ 2๋…„ MACE์˜ ๋…๋ฆฝ์ ์ธ ์˜ˆ์ธก์ธ์ž๋Š” CMD๋ฅผ ๋™๋ฐ˜ํ•œ ๋‹น๋‡จํ™˜์ž (HR 11.24, 95% CI 2.53-49.88, p=0.002). CMD๋ฅผ ๋‹น๋‡จ์— ์ถ”๊ฐ€ํ–ˆ์„๋•Œ ์˜ˆ์ธก ๋Šฅ๋ ฅ์ด ํ–ฅ์ƒ๋จ(C-index 0.683 vs 0.710, p=0.010). ๋‘๋ฒˆ์งธ ๋ถ€๋ถ„์—์„œ, ๋น„๋‹น๋‡จ๊ตฐ๊ณผ ๋น„๊ตํ–ˆ์„ ๋•Œ, ๋‹น๋‡จ๊ตฐ์€ 5๋…„ POCO์˜ ์œ„ํ—˜์„ฑ์ด ๋” ๋†’์Œ(HR 2.49, 95% CI 1.64-3.78, p<0.001). ๋‹น๋‡จ๊ตฐ์—์„œ ๋‚ฎ์€ CFR ๊ทธ๋ฃน์€ ๋†’์€ CFR ๊ทธ๋ฃน๋ณด๋‹ค POCO์˜ ์œ„ํ—˜์ด ๋†’์Œ(HR 3.22, 95% CI 1.74-5.97, p<0.001). CFR ๊ฐ’์€ ๋น„๋‹น๋‡จ๊ตฐ์—์„œ POCO์˜ ์œ„ํ—˜์„ ๊ตฌ๋ณ„ํ•  ์ˆ˜ ์—†์Œ. POCO์˜ ์œ„ํ—˜์„ฑ์„ ์˜ˆ์ธกํ•จ์— ์žˆ์–ด์„œ CFR๊ณผ ๋‹น๋‡จ ์‚ฌ์ด์— ์œ ์˜ํ•œ ์ƒํ˜ธ์ž‘์šฉ์ด ์žˆ์—ˆ๋‹ค(interaction p=0.025). 5๋…„ POCO์— ๋Œ€ํ•œ ๋…๋ฆฝ์ ์ธ ์˜ˆ์ธก ์ธ์ž๋Š” ๋‹น๋‡จ๊ตฐ์—์„œ ๋‚ฎ์€ CFR๊ณผ ๊ด€์ƒ๋™๋งฅ ๊ฐ€์กฑ๋ ฅ, ๋น„๋‹น๋‡จ๊ตฐ์—์„œ ๊ด€์ƒ๋™๋งฅ ์งˆํ™˜์˜ percent diameter stenosis์™€ ๋‹คํ˜ˆ๊ด€ ์งˆํ™˜์ž„. ๋‹น๋‡จ๊ตฐ์—์„œ POCO๋ฅผ ์˜ˆ์ธกํ•จ์— ์žˆ์–ด์„œ ๋‹ค๋ฅธ ์š”์ธ์— ๋น„ํ•ด CFR์€ ๊ฐ€์žฅ ๋งŽ์€ ์ •๋ณด๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์—ˆ์Œ. "Minimum Depth" ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์‚ฌ์šฉํ–ˆ์„ ๋•Œ CFR์€ ๊ฐ€์žฅ ์ค‘์š”ํ•œ ์˜ˆ์ธก์š”์ธ์ด๊ณ  "Boruta" ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์‚ฌ์šฉํ–ˆ์„ ๋•Œ ์˜๋ฏธ ์žˆ๋Š” ์š”์ธ์œผ๋กœ ๋‚˜ํƒ€๋‚จ. ๋‹น๋‡จ๊ตฐ์—์„œ ์ž„์ƒ์  ์œ„ํ—˜ ์ธ์ž(c-index 0.75 0.65-0.85, p=0.500) ํ˜น์€ ์ž„์ƒ์  ์‹œ์ˆ ์  ์œ„ํ—˜์ธ์ž(c-index 0.75, 95%CI 0.65-0.85, p=0.535)๋ฅผ ๋™์‹œ์— Boruta ์•Œ๊ณ ๋ฆฌ์ฆ˜์—์„œ ์„ ํƒ๋˜์–ด์ง„ ์œ„ํ—˜์ธ์ž๋กœ ๊ตฌ์„ฑ๋œ ๋ชจ๋ธ(c-index 0.73, 95% CI 0.63-0.83)์— ์ถ”๊ฐ€ํ•˜์˜€์„ ๋•Œ ๋ชจ๋ธ์˜ ์˜ˆ์ธก๋ ฅ์€ ์œ ์˜ํ•˜๊ฒŒ ๋†’์•„์ง€์ง€ ์•Š์•˜์Œ. ๊ฒฐ๋ก : CMD๋ฅผ ๋™๋ฐ˜ํ•œ ๋‹น๋‡จ๋Š” ์‹ฌํ˜ˆ๊ด€ ์งˆํ™˜ ์œ„ํ—˜์˜ ์ฆ๊ฐ€์™€ ๊ด€๋ จ์ด ์žˆ์Œ. ๋‹น๋‡จํ™˜์ž์—์„œ CMD์˜ ์ถ”๊ฐ€๋Š” MACE๋ฐœ์ƒ์˜ ์˜ˆ์ธก๋ ฅ์„ ๋†’์ž„. ๊ด€์ƒ๋™๋งฅ ์ƒ๋ฆฌํ•™์  ์ง€ํ‘œ์™€ ์œ„ํ—˜ ์ธ์ž๋“ค์ด ์˜ˆํ›„์— ๋ฏธ์น˜๋Š” ์—ญํ• ์€ ๋‹น๋‡จ์—ฌ๋ถ€์— ๋”ฐ๋ผ ๋‹ค๋ฆ„. ์–ด๋–ค ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜๋Š”์ง€ ๋ถˆ๊ตฌํ•˜๊ณ  CFR์€ ์˜ˆํ›„๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ์ค‘์š”ํ•œ ์ง€ํ‘œ์ž„. ๊ธฐ๊ณ„ํ•™์Šต์€ ๊ฐ€์žฅ ํšจ๊ณผ์ ์ด๊ณ  ํšจ์œจ์ ์ธ ๋ณ€์ˆ˜์กฐํ•ฉ์„ ์ฐพ์•„ ์˜ˆํ›„๋ฅผ ๋” ์ž˜ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ์Œ.Introduction 2 First Part 4 Methods 4 Results 7 Second Part 10 Methods 10 Results 16 Discussion 19 Conclusions 26 References 28 ๊ตญ๋ฌธ์ดˆ๋ก 58Docto
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