• Title/Summary/Keyword: Column aspect ratio

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Enzymatic Formation of Guaiacylglycerol 8-O-4'-(Coniferyl Alcohol) Ether from Coniferyl Alcohol with Enzyme Preparations of Eucommia ulmoides

  • Alam, Md. Shameul;Katayama, Takeshi;Suzuki, Toshisada;Sultana, Deeder;Sultana, Saima;Hossain, Md. Daud
    • Journal of Crop Science and Biotechnology
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    • v.11 no.1
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    • pp.45-50
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    • 2008
  • Lignans and neolignans are optically active plant secondary metabolites. Research on biosynthesis of lignans has already been advanced especially for the formation of (+) pinoresinol but information on the biosynthesis of 8-O-4'- neolignans is still limited. Moreover, the chemical structure(position of substituents on aromatic rings) and stereochemistry of 8-O-4' neolignans is not clear. Katayama and Kado discovered that incubation of cell-free extracts from E. ulmoides with coniferyl alcohol in the presence of hydrogen peroxide gave (+)-erythro- and (-)-threo- guaiacylglycerol 8-O-4'-(coniferyl alcohol) ether (GGCE)(diastereomeric ratio, 3:2) which is the first report on enzymatic formation of optically active -8-O-4' neolignans from an achiral monolignol. In this aspect, enzymatic formation of guaiacyl 8-O-4' neolignan is noteworthy to clarify its stereochemistry from incubation of coniferyl alcohol with enzyme prepared from Eucommia ulmoides. In this experiment, soluble and insoluble enzymes prepared from E. ulmoides were incubated with 30 mM coniferyl alcohol(CA) for 60 min. The enzyme catalyzed GGCE, dehydrodiconiferyl alcohol(DHCA), and pinoresinol identified by reversed phase HPLC. Consequently, diastereomeric compositions of GGCE were determined as erythro and threo isomer. Enantiomeric composition was determined by the chiral column HPLC. Both enzyme preparations enantioselectively formed (-)-erythro, (+)-erythro and (+)-threo, (-)-threo-GGCEs respectively.

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Deformability of Flat Plate Subjected to Unbalanced Moment (불균형 휨모멘트를 받는 플랫 플레이트의 변형능력)

  • Choi, Kyoung-Kyu;Park, Hong-Gun
    • Journal of the Korea Concrete Institute
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    • v.15 no.3
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    • pp.482-493
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    • 2003
  • Flat plate structures subjected to lateral load have less deformability than conventional moment frames, due to the brittle failure of plate-column connection. In the present study, parametric study using nonlinear finite element analysis was performed to investigate the deformability of flat plates. The numerical results show that as number of continuous spans increases, the deformability of flat plates considerably decreases. Therefore, existing experiments using sub-assemblages with 1 or 2 spans may overestimate the deformability of flat plates, and current design provisions based on the experiments may not be accurate in estimating the deformability. A design method estimating the deformability was developed on the basis of numerical results, and verified by comparison with existing experiment. In the proposed method, the effects of primary design parameters such as direct shear force, punching shear capacity, aspect ratio of connection, number of spans, and initial stiffness of plate can be considered.

Stereo Vision-Based Obstacle Detection and Vehicle Verification Methods Using U-Disparity Map and Bird's-Eye View Mapping (U-시차맵과 조감도를 이용한 스테레오 비전 기반의 장애물체 검출 및 차량 검증 방법)

  • Lee, Chung-Hee;Lim, Young-Chul;Kwon, Soon;Lee, Jong-Hun
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.47 no.6
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    • pp.86-96
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    • 2010
  • In this paper, we propose stereo vision-based obstacle detection and vehicle verification methods using U-disparity map and bird's-eye view mapping. First, we extract a road feature using maximum frequent values in each row and column. And we extract obstacle areas on the road using the extracted road feature. To extract obstacle areas exactly we utilize U-disparity map. We can extract obstacle areas exactly on the U-disparity map using threshold value which consists of disparity value and camera parameter. But there are still multiple obstacles in the extracted obstacle areas. Thus, we perform another processing, namely segmentation. We convert the extracted obstacle areas into a bird's-eye view using camera modeling and parameters. We can segment obstacle areas on the bird's-eye view robustly because obstacles are represented on it according to ranges. Finally, we verify the obstacles whether those are vehicles or not using various vehicle features, namely road contacting, constant horizontal length, aspect ratio and texture information. We conduct experiments to prove the performance of our proposed algorithms in real traffic situations.