941 resultados para COSMO KENDA LETKF ensemble assimilation


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We study the oxygen reduction reaction (ORR), the catalytic process occurring at the cathode in fuel cells, on Pt layers prepared by electrodeposition onto an Au substrate. Using a nominal Pt layer by layer deposition method previously proposed, imperfect layers of Pt on Au are obtained. The ORR on deposited Pt layers decreases with increasing Pt thickness. In the submonolayer region, however, the ORR activity is superior to that of bulk Pt. Using density functional theory (DFT) calculations, we correlate the observed activity trend to strain, ligand, and ensemble effects. At submonolayer coverage certain atom configurations weaken the binding energies of reaction intermediates due to a ligand and ensemble effect, thus effectively increasing the ORR activity. At higher Pt coverage the activity is governed by a strain effect, which lowers the activity by decreasing the oxidation potential of water. This study is a nice example of how the influence of strain, ligand, and ensemble effects on the ORR can be deconvoluted.

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Crops growing in the Iberian Peninsula may be subjected to damagingly high temperatures during the sensitive development periods of flowering and grain filling. Such episodes are considered important hazards and farmers may take insurance to offset their impact. Increases in value and frequency of maximum temperature have been observed in the Iberian Peninsula during the 20th century, and studies on climate change indicate the possibility of further increase by the end of the 21st century. Here, impacts of current and future high temperatures on cereal cropping systems of the Iberian Peninsula are evaluated, focusing on vulnerable development periods of winter and summer crops. Climate change scenarios obtained from an ensemble of ten Regional Climate Models (multimodel ensemble) combined with crop simulation models were used for this purpose and related uncertainty was estimated. Results reveal that higher extremes of maximum temperature represent a threat to summer-grown but not to winter-grown crops in the Iberian Peninsula. The study highlights the different vulnerability of crops in the two growing seasons and the need to account for changes in extreme temperatures in developing adaptations in cereal cropping systems. Finally, this work contributes to clarifying the causes of high-uncertainty impact projections from previous studies.

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Systems biology techniques are a topic of recent interest within the neurological field. Computational intelligence (CI) addresses this holistic perspective by means of consensus or ensemble techniques ultimately capable of uncovering new and relevant findings. In this paper, we propose the application of a CI approach based on ensemble Bayesian network classifiers and multivariate feature subset selection to induce probabilistic dependences that could match or unveil biological relationships. The research focuses on the analysis of high-throughput Alzheimer's disease (AD) transcript profiling. The analysis is conducted from two perspectives. First, we compare the expression profiles of hippocampus subregion entorhinal cortex (EC) samples of AD patients and controls. Second, we use the ensemble approach to study four types of samples: EC and dentate gyrus (DG) samples from both patients and controls. Results disclose transcript interaction networks with remarkable structures and genes not directly related to AD by previous studies. The ensemble is able to identify a variety of transcripts that play key roles in other neurological pathologies. Classical statistical assessment by means of non-parametric tests confirms the relevance of the majority of the transcripts. The ensemble approach pinpoints key metabolic mechanisms that could lead to new findings in the pathogenesis and development of AD

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