864 resultados para fate and effect modelling
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Mode of access: Internet.
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Mode of access: Internet.
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By James Sedgwick, 1775-1851. Cf. NUC pre-1956 imprints.
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Mode of access: Internet.
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Mode of access: Internet.
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At head of title: 88th Cong., 1st sess. Committee print.
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Mode of access: Internet.
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Mode of access: Internet.
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Mode of access: Internet.
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A magnesium alloy of eutectic composition (33 wt-'%Al) was directionally solidified in mild steel tubes at two growth rates, 32 and 580 mum s(-1,) in a temperature gradient between 10 and 20 K mm(-1). After directional solidification, the composition of each specimen varied dramatically, from 32'%Al in the region that had remained solid to 18%Al (32 mum s(-1) specimen) and 13%Al (580 mum s(-1) specimen) at the plane that had been quenched from the eutectic temperature. As the aluminium content decreased, the microstructure contained an increasing volume fraction of primary magnesium dendrites and the eutectic morphology gradually changed from lamellar to partially divorced. The reduction in aluminium content was caused by the growth of an Al-Fe phase ahead of the Mg-Al growth front. Most of the growth of the Al-Fe phase occurred during the remelting period before directional solidification. The thickness of the Al-Fe phase increased with increased temperature and time of contact with the molten Mg-Al alloy. (C) 2003 Maney Publishing.
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For many years in the area of business systems analysis and design, practitioners and researchers alike have been searching for some comprehensive basis on which to evaluate, compare, and engineer techniques that are promoted for use in the modelling of systems' requirements. To date, while many frameworks, factors, and facets have been forthcoming, none appear to be based on a sound theory. In light of this dilemma, over the last 10 years, attention has been devoted by researchers to the use of ontology to provide some theoretical basis for the advancement of the business systems modelling discipline. This paper outlines how we have used a particular ontology for this purpose over the last five years. In particular we have learned that the understandability and the applicability of the selected ontology must be clear for IS professionals, the results of any ontological evaluation must be tempered by economic efficiency considerations of the stakeholders involved, and ontologies may have to be focused for the business purpose and type of user involved in the modelling situation.
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New tools derived from advances in molecular biology have not been widely adopted in plant breeding for complex traits because of the inability to connect information at gene level to the phenotype in a manner that is useful for selection. In this study, we explored whether physiological dissection and integrative modelling of complex traits could link phenotype complexity to underlying genetic systems in a way that enhanced the power of molecular breeding strategies. A crop and breeding system simulation study on sorghum, which involved variation in 4 key adaptive traits-phenology, osmotic adjustment, transpiration efficiency, stay-green-and a broad range of production environments in north-eastern Australia, was used. The full matrix of simulated phenotypes, which consisted of 547 location-season combinations and 4235 genotypic expression states, was analysed for genetic and environmental effects. The analysis was conducted in stages assuming gradually increased understanding of gene-to-phenotype relationships, which would arise from physiological dissection and modelling. It was found that environmental characterisation and physiological knowledge helped to explain and unravel gene and environment context dependencies in the data. Based on the analyses of gene effects, a range of marker-assisted selection breeding strategies was simulated. It was shown that the inclusion of knowledge resulting from trait physiology and modelling generated an enhanced rate of yield advance over cycles of selection. This occurred because the knowledge associated with component trait physiology and extrapolation to the target population of environments by modelling removed confounding effects associated with environment and gene context dependencies for the markers used. Developing and implementing this gene-to-phenotype capability in crop improvement requires enhanced attention to phenotyping, ecophysiological modelling, and validation studies to test the stability of candidate genetic regions.