Composition of Jupiter irregular satellites sheds light on their origin

Irregular satellites of Jupiter with their highly eccentric, inclined and distant orbits suggest that their capture took place just before the giant planet migration. We aim to improve our understanding of the surface composition of irregular satellites of Jupiter to gain insight into a narrow time window when our Solar System was forming. We observed three Jovian irregular satellites, Himalia, Elara, and Carme, using a medium-resolution 0.8-5.5 micro m spectrograph on the National Aeronautics and Space Administration (NASA) Infrared Telescope Facility (IRTF). Using a linear spectral unmixing model we have constrained the major mineral phases on the surface of these three bodies. Our results confirm that the surface of Himalia, Elara, and Carme are dominated by opaque materials such as those seen in carbonaceous chondrite meteorites. Our spectral modeling of NIR spectra of Himalia and Elara confirm that their surface composition is the same and magnetite is the dominant mineral. A comparison of the spectral shape of Himalia with the two large main C-type asteroids, Themis (D 176 km) and Europa (D 352 km), suggests surface composition similar to Europa. The NIR spectrum of Carme exhibits blue slope up to 1.5 microm and is spectrally distinct from those of Himalia and Elara. Our model suggests that it is compositionally similar to amorphous carbon. Himalia and Elara are compositionally similar but differ significantly from Carme. These results support the hypotheses that the Jupiter irregular satellites are captured bodies that were subject to further breakup events and clustered as families based on their similar physical and surface compositions

Support Vector Machine in Prediction of Building Energy Demand Using Pseudo Dynamic Approach

Building's energy consumption prediction is a major concern in the recent years and many efforts have been achieved in order to improve the energy management of buildings. In particular, the prediction of energy consumption in building is essential for the energy operator to build an optimal operating strategy, which could be integrated to building's energy management system (BEMS). This paper proposes a prediction model for building energy consumption using support vector machine (SVM). Data-driven model, for instance, SVM is very sensitive to the selection of training data. Thus the relevant days data selection method based on Dynamic Time Warping is used to train SVM model. In addition, to encompass thermal inertia of building, pseudo dynamic model is applied since it takes into account information of transition of energy consumption effects and occupancy profile. Relevant days data selection and whole training data model is applied to the case studies of Ecole des Mines de Nantes, France Office building. The results showed that support vector machine based on relevant data selection method is able to predict the energy consumption of building with a high accuracy in compare to whole data training. In addition, relevant data selection method is computationally cheaper (around 8 minute training time) in contrast to whole data training (around 31 hour for weekend and 116 hour for working days) and reveals realistic control implementation for online system as well.Comment: Proceedings of ECOS 2015-The 28th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems , Jun 2015, Pau, Franc

Construction d'un critère d'optimalité pour plans d'expériences numériques dans le cadre de la quantification d'incertitudes

http://archive.numdam.org/ARCHIVE/RSA/RSA_2005__53_4/RSA_2005__53_4_87_0/RSA_2005__53_4_87_0.pdfInternational audienceDe nombreux phénomènes physiques sont étudiés à l'aide de simulateurs numériques coûteux, avec lesquels une variable d'intérêt - ou "réponse" - est une fonction déterministe des variables d'entrée (les facteurs). Cependant, on est souvent amené à évaluer la réponse sous forme d'incertitudes du fait de la méconnaissance du niveau des facteurs. Ainsi en Exploration/Production pétrolière, on s'intéresse par exemple à la distribution de la production d'huile d'un réservoir dans dix ans. Dans cet article nous construisons un critère conçu pour planifier les simulations de sorte que la quantification des incertitudes sur la réponse soit la meilleure possible. Baptisé "MC-V optimalité", le critère obtenu est alors équivalent à un critère IMSE (Integrated Mean Squared Error) où l'intégration est effectuée selon la distribution des facteurs. La démarche sera illustrée par l'exposé du contexte de l'Exploration/Production pétrolière dont l'étude est à l'origine de ce critère

Genetic distance predicts trait differentiation at the subpopulation but not the individual level in eelgrass, Zostera marina.

Ecological studies often assume that genetically similar individuals will be more similar in phenotypic traits, such that genetic diversity can serve as a proxy for trait diversity. Here, we explicitly test the relationship between genetic relatedness and trait distance using 40 eelgrass (Zostera marina) genotypes from five sites within Bodega Harbor, CA. We measured traits related to nutrient uptake, morphology, biomass and growth, photosynthesis, and chemical deterrents for all genotypes. We used these trait measurements to calculate a multivariate pairwise trait distance for all possible genotype combinations. We then estimated pairwise relatedness from 11 microsatellite markers. We found significant trait variation among genotypes for nearly every measured trait; however, there was no evidence of a significant correlation between pairwise genetic relatedness and multivariate trait distance among individuals. However, at the subpopulation level (sites within a harbor), genetic (FST) and trait differentiation were positively correlated. Our work suggests that pairwise relatedness estimated from neutral marker loci is a poor proxy for trait differentiation between individual genotypes. It remains to be seen whether genomewide measures of genetic differentiation or easily measured "master" traits (like body size) might provide good predictions of overall trait differentiation

Identification of the dominant recombination process for perovskite solar cells based on machine learning

Over the past decade, perovskite solar cells have become one of the major research interests of the photovoltaic community, and they are now on the brink of catching up with the classical inorganic solar cells, with efficiency now reaching up to 25%. However, significant improvements are still achievable by reducing recombination losses. The aim of this work is to develop a fast and easy-to-use tool to pinpoint the main losses in perovskite solar cells. We use large-scale drift-diffusion simulations to get a better understanding of the light intensity dependence of the open-circuit voltage and how it correlates to the dominant recombination process. We introduce an automated identification tool using machine learning methods to pinpoint the dominant loss using the light intensity-dependent performances as an input. The machine learning was trained using >2 million simulations and gives an accuracy of the prediction up to 82%. Le Corre et al. demonstrate the application of machine learning methods to identify the dominant recombination process in perovskite solar cells with 82% accuracy. The machine learning algorithms are trained and tested using large-scale drift-diffusion simulations, and their applicability on real solar cells is also demonstrated on devices previously reported

Charge Carrier Extraction in Organic Solar Cells Governed by Steady-State Mobilities

Charge transport in organic photovoltaic (OPV) devices is often characterized by steady-state mobilities. However, the suitability of steady-state mobilities to describe charge transport has recently been called into question, and it has been argued that dispersion plays a significant role. In this paper, the importance of the dispersion of charge carrier motion on the performance of organic photovoltaic devices is investigated. An experiment to measure the charge extraction time under realistic operating conditions is set up. This experiment is applied to different blends and shows that extraction time is directly related to the geometrical average of the steady-state mobilities. This demonstrates that under realistic operating conditions the steady-state mobilities govern the charge extraction of OPV and gives a valuable insight in device performance

Extended-spectrum β-lactamase Enterobacteriaceae (ESBLE) in intensive care units: strong correlation with the ESBLE colonization pressure in patients but not same species

Sink drains of six intensive care units (ICUs) were sampled for screening contamination with extended-spectrum β-lactamase-producing Enterobacteriaceae (ESBLE). A high prevalence (59.4%) of sink drain contamination was observed. Analysing the data by ICU, the ratio \u27number of ESBLE species isolated in sink drains/total number of sink drains sampled\u27 was highly correlated (Spearman coefficient: 0.87; P = 0.02) with the ratio \u27number of hospitalization days for patients with ESBLE carriage identified within the preceding year/total number of hospitalization days within the preceding year\u27. Concurrently, the distribution of ESBLE species differed significantly between patients and sink drains

Cholesterol metabolism is a potential therapeutic target in Duchenne muscular dystrophy

Background: Duchenne muscular dystrophy (DMD) is a lethal muscle disease detected in approximately 1:5000 male births. DMD is caused by mutations in the DMD gene, encoding a critical protein that links the cytoskeleton and the extracellular matrix in skeletal and cardiac muscles. The primary consequence of the disrupted link between the extracellular matrix and the myofibre actin cytoskeleton is thought to involve sarcolemma destabilization, perturbation of Ca homeostasis, activation of proteases, mitochondrial damage, and tissue degeneration. A recently emphasized secondary aspect of the dystrophic process is a progressive metabolic change of the dystrophic tissue; however, the mechanism and nature of the metabolic dysregulation are yet poorly understood. In this study, we characterized a molecular mechanism of metabolic perturbation in DMD. Methods: We sequenced plasma miRNA in a DMD cohort, comprising 54 DMD patients treated or not by glucocorticoid, compared with 27 healthy controls, in three groups of the ages of 4–8, 8–12, and 12–20 years. We developed an original approach for the biological interpretation of miRNA dysregulation and produced a novel hypothesis concerning metabolic perturbation in DMD. We used the mdx mouse model for DMD for the investigation of this hypothesis. Results: We identified 96 dysregulated miRNAs (adjusted P-value <0.1), of which 74 were up-regulated and 22 were down-regulated in DMD. We confirmed the dysregulation in DMD of Dystro-miRs, Cardio-miRs, and a large number of the DLK1-DIO3 miRNAs. We also identified numerous dysregulated miRNAs yet unreported in DMD. Bioinformatics analysis of both target and host genes for dysregulated miRNAs predicted that lipid metabolism might be a critical metabolic perturbation in DMD. Investigation of skeletal muscles of the mdx mouse uncovered dysregulation of transcription factors of cholesterol and fatty acid metabolism (SREBP-1 and SREBP-2), perturbation of the mevalonate pathway, and the accumulation of cholesterol in the dystrophic muscles. Elevated cholesterol level was also found in muscle biopsies of DMD patients. Treatment of mdx mice with Simvastatin, a cholesterol-reducing agent, normalized these perturbations and partially restored the dystrophic parameters. Conclusions: This investigation supports that cholesterol metabolism and the mevalonate pathway are potential therapeutic targets in DMD. 2