557 resultados para Ensembles semilinéaires
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Concert Program for U.W. Percussion and Steel Drum Ensembles
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The performance of an amperometric biosensor constructed by associating tyrosinase (Tyr) enzyme with the advantages of a 3D gold nanoelectrode ensemble (GNEE) is evaluated in a flow-injection analysis (FIA) system for the analysis of l-dopa. GNEEs were fabricated by electroless deposition of the metal within the pores of polycarbonate track-etched membranes. A simple solvent etching procedure based on the solubility of polycarbonate membranes is adopted for the fabrication of the 3D GNEE. Afterward, enzyme was immobilized onto preformed self-assembled monolayers of cysteamine on the 3D GNEEs (GNEE-Tyr) via cross-linking with glutaraldehyde. The experimental conditions of the FIA system, such as the detection potential (−0.200 V vs. Ag/AgCl) and flow rates (1.0 mL min−1) were optimized. Analytical responses for l-dopa were obtained in a wide concentration range between 1 × 10−8 mol L−1 and 1 × 10−2 mol L−1. The limit of quantification was found to be 1 × 10−8 mol L−1 with a resultant % RSD of 7.23% (n = 5). The limit of detection was found to be 1 × 10−9 mol L−1 (S/N = 3). The common interfering compounds, namely glucose (10 mmol L−1), ascorbic acid (10 mmol L−1), and urea (10 mmol L−1), were studied. The recovery of l-dopa (1 × 10−7 mol L−1) from spiked urine samples was found to be 96%. Therefore, the developed method is adequate to be applied in the clinical analysis.
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La version intégrale de ce[te] mémoire [thèse] est disponible uniquement pour consultation individuelle à la Bibliothèque de musique de l’Université de Montréal (http://www.bib.umontreal.ca/MU).
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Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal
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Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.
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Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal
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Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal
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Réalisé en cotutelle avec l'Université Paris-Diderot.
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Le paysage philosophique en théorie de l’action contemporaine est largement façonné par l’argument de l’exclusion causale de Kim. Cet argument menace apparemment le physicalisme non réductionniste, posture ontologique qui affirme l’irréductibilité des propriétés mentales aux propriétés physiques. C’est qu’il mène à la conclusion que les propriétés mentales sont soit des propriétés physiques, soit des épiphénomènes, au sens où elles semblent dépourvues de toute efficacité causale. Dans cet article, je vais examiner une tentative récente et prétendument non réductionniste d’éviter les conclusions de l’argument de l’exclusion causale, soit la théorie de la réalisation par sous-ensembles de Shoemaker. Je vais montrer que cette théorie échappe aux critiques de Kim seulement au prix de l’abandon du non-réductionnisme. Je tenterai aussi de prouver que même en s’inspirant des idées de Pereboom sur la réalisation, Shoemaker ne peut échapper au réductionnisme.
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L’ontologie de Leśniewski est un calcul général des noms. Elle fut créée par Leśniewski pour apporter une solution naturelle au paradoxe de Russell en théorie naïve des ensembles. L’ontologie a été perçue par ses défenseurs et par ses adversaires comme une théorie incompatible avec la théorie des ensembles. Dans le présent texte, nous montrons que l’ontologie de Leśniewski permet, au contraire, de définir une théorie des ensembles qui coïncide avec la théorie de Zermelo- Fraenkel.
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Ensemble predictions are being used more frequently to model the propagation of uncertainty through complex, coupled meteorological, hydrological and coastal models, with the goal of better characterising flood risk. In this paper, we consider the issues that we judge to be important when designing and evaluating ensemble predictions, and make recommendations for the guidance of future research.
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Uncertainties associated with the representation of various physical processes in global climate models (GCMs) mean that, when projections from GCMs are used in climate change impact studies, the uncertainty propagates through to the impact estimates. A complete treatment of this ‘climate model structural uncertainty’ is necessary so that decision-makers are presented with an uncertainty range around the impact estimates. This uncertainty is often underexplored owing to the human and computer processing time required to perform the numerous simulations. Here, we present a 189-member ensemble of global river runoff and water resource stress simulations that adequately address this uncertainty. Following several adaptations and modifications, the ensemble creation time has been reduced from 750 h on a typical single-processor personal computer to 9 h of high-throughput computing on the University of Reading Campus Grid. Here, we outline the changes that had to be made to the hydrological impacts model and to the Campus Grid, and present the main results. We show that, although there is considerable uncertainty in both the magnitude and the sign of regional runoff changes across different GCMs with climate change, there is much less uncertainty in runoff changes for regions that experience large runoff increases (e.g. the high northern latitudes and Central Asia) and large runoff decreases (e.g. the Mediterranean). Furthermore, there is consensus that the percentage of the global population at risk to water resource stress will increase with climate change.
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Process-based integrated modelling of weather and crop yield over large areas is becoming an important research topic. The production of the DEMETER ensemble hindcasts of weather allows this work to be carried out in a probabilistic framework. In this study, ensembles of crop yield (groundnut, Arachis hypogaea L.) were produced for 10 2.5 degrees x 2.5 degrees grid cells in western India using the DEMETER ensembles and the general large-area model (GLAM) for annual crops. Four key issues are addressed by this study. First, crop model calibration methods for use with weather ensemble data are assessed. Calibration using yield ensembles was more successful than calibration using reanalysis data (the European Centre for Medium-Range Weather Forecasts 40-yr reanalysis, ERA40). Secondly, the potential for probabilistic forecasting of crop failure is examined. The hindcasts show skill in the prediction of crop failure, with more severe failures being more predictable. Thirdly, the use of yield ensemble means to predict interannual variability in crop yield is examined and their skill assessed relative to baseline simulations using ERA40. The accuracy of multi-model yield ensemble means is equal to or greater than the accuracy using ERA40. Fourthly, the impact of two key uncertainties, sowing window and spatial scale, is briefly examined. The impact of uncertainty in the sowing window is greater with ERA40 than with the multi-model yield ensemble mean. Subgrid heterogeneity affects model accuracy: where correlations are low on the grid scale, they may be significantly positive on the subgrid scale. The implications of the results of this study for yield forecasting on seasonal time-scales are as follows. (i) There is the potential for probabilistic forecasting of crop failure (defined by a threshold yield value); forecasting of yield terciles shows less potential. (ii) Any improvement in the skill of climate models has the potential to translate into improved deterministic yield prediction. (iii) Whilst model input uncertainties are important, uncertainty in the sowing window may not require specific modelling. The implications of the results of this study for yield forecasting on multidecadal (climate change) time-scales are as follows. (i) The skill in the ensemble mean suggests that the perturbation, within uncertainty bounds, of crop and climate parameters, could potentially average out some of the errors associated with mean yield prediction. (ii) For a given technology trend, decadal fluctuations in the yield-gap parameter used by GLAM may be relatively small, implying some predictability on those time-scales.