Shati (Photographie extraite de la série « Les vies brisées du Rana Plaza »)

Shati Blessée aux jambes et à la tête, Shati, 21 ans, ne peut rester longtemps debout et ne supporte plus la lumière intense. Elle n’a perçu que l’équivalent de 550 € qui sont bloqués sur un compte car elle a perdu la carte lui permettant d’y accéder © Jean-François Fort Le 24 avril 2013, à Savar dans la banlieue de Dhaka au Bangladesh, le Rana Plaza, immeuble de 7 étages, s’effondre sur des employés du textile, faisant au moins 1138 morts et encore plus de blessés. Près de 100 corps ne sero..

Micropositioning and Fast Transport Using a Contactless Micro-Conveyor

International audienceThe micro-conveyor is a 9 x 9 mm2 manipulation surface able to move millimeter-sized planar objects in the four cardinal directions using air flows. Thanks to a specific design, the air flow comes through a network of micro-channels connected to an array of micro-nozzles. Thus, the micro-conveyor generates an array of tilted air jets that lifts and moves the object in the required direction. In this paper, we characterize the device for transport and positioning tasks and evaluate its performances in terms of speed, resolution and repeatability. We show that the micro-conveyor is able to move the object with a speed up to 137 mm* s-1 in less than 100 ms whereas the positioning repeatability is around 17.7 µm with feedback control. The smallest step the object can do is 0.3 µm (positioning resolution). Moreover, we estimated thanks to a dynamic model that the speed could reach 456 mm* s-1 if several micro-conveyors were used to form a conveying line

Bayesian model comparison in cosmology with Population Monte Carlo

We use Bayesian model selection techniques to test extensions of the standard flat LambdaCDM paradigm. Dark-energy and curvature scenarios, and primordial perturbation models are considered. To that end, we calculate the Bayesian evidence in favour of each model using Population Monte Carlo (PMC), a new adaptive sampling technique which was recently applied in a cosmological context. The Bayesian evidence is immediately available from the PMC sample used for parameter estimation without further computational effort, and it comes with an associated error evaluation. Besides, it provides an unbiased estimator of the evidence after any fixed number of iterations and it is naturally parallelizable, in contrast with MCMC and nested sampling methods. By comparison with analytical predictions for simulated data, we show that our results obtained with PMC are reliable and robust. The variability in the evidence evaluation and the stability for various cases are estimated both from simulations and from data. For the cases we consider, the log-evidence is calculated with a precision of better than 0.08. Using a combined set of recent CMB, SNIa and BAO data, we find inconclusive evidence between flat LambdaCDM and simple dark-energy models. A curved Universe is moderately to strongly disfavoured with respect to a flat cosmology. Using physically well-motivated priors within the slow-roll approximation of inflation, we find a weak preference for a running spectral index. A Harrison-Zel'dovich spectrum is weakly disfavoured. With the current data, tensor modes are not detected; the large prior volume on the tensor-to-scalar ratio r results in moderate evidence in favour of r=0. [Abridged]Comment: 11 pages, 6 figures. Matches version accepted for publication by MNRA

Estimation of cosmological parameters using adaptive importance sampling

We present a Bayesian sampling algorithm called adaptive importance sampling or Population Monte Carlo (PMC), whose computational workload is easily parallelizable and thus has the potential to considerably reduce the wall-clock time required for sampling, along with providing other benefits. To assess the performance of the approach for cosmological problems, we use simulated and actual data consisting of CMB anisotropies, supernovae of type Ia, and weak cosmological lensing, and provide a comparison of results to those obtained using state-of-the-art Markov Chain Monte Carlo (MCMC). For both types of data sets, we find comparable parameter estimates for PMC and MCMC, with the advantage of a significantly lower computational time for PMC. In the case of WMAP5 data, for example, the wall-clock time reduces from several days for MCMC to a few hours using PMC on a cluster of processors. Other benefits of the PMC approach, along with potential difficulties in using the approach, are analysed and discussed.Comment: 17 pages, 11 figure

Intensification et requalification des centralités pour lutter contre l'étalement urbain et la dépendance à la voiture : rapport final de la subvention 2021

Intensification et requalification des centralités pour lutter contre l’étalement urbain et la dépendance à la voiture12. Responsible consumption and production13. Climate actio

Early diagnosis of Alzheimer's disease using cortical thickness: impact of cognitive reserve

Brain atrophy measured by magnetic resonance structural imaging has been proposed as a surrogate marker for the early diagnosis of Alzheimer's disease. Studies on large samples are still required to determine its practical interest at the individual level, especially with regards to the capacity of anatomical magnetic resonance imaging to disentangle the confounding role of the cognitive reserve in the early diagnosis of Alzheimer's disease. One hundred and thirty healthy controls, 122 subjects with mild cognitive impairment of the amnestic type and 130 Alzheimer's disease patients were included from the ADNI database and followed up for 24 months. After 24 months, 72 amnestic mild cognitive impairment had converted to Alzheimer's disease (referred to as progressive mild cognitive impairment, as opposed to stable mild cognitive impairment). For each subject, cortical thickness was measured on the baseline magnetic resonance imaging volume. The resulting cortical thickness map was parcellated into 22 regions and a normalized thickness index was computed using the subset of regions (right medial temporal, left lateral temporal, right posterior cingulate) that optimally distinguished stable mild cognitive impairment from progressive mild cognitive impairment. We tested the ability of baseline normalized thickness index to predict evolution from amnestic mild cognitive impairment to Alzheimer's disease and compared it to the predictive values of the main cognitive scores at baseline. In addition, we studied the relationship between the normalized thickness index, the education level and the timeline of conversion to Alzheimer's disease. Normalized thickness index at baseline differed significantly among all the four diagnosis groups (P < 0.001) and correctly distinguished Alzheimer's disease patients from healthy controls with an 85% cross-validated accuracy. Normalized thickness index also correctly predicted evolution to Alzheimer's disease for 76% of amnestic mild cognitive impairment subjects after cross-validation, thus showing an advantage over cognitive scores (range 63–72%). Moreover, progressive mild cognitive impairment subjects, who converted later than 1 year after baseline, showed a significantly higher education level than those who converted earlier than 1 year after baseline. Using a normalized thickness index-based criterion may help with early diagnosis of Alzheimer's disease at the individual level, especially for highly educated subjects, up to 24 months before clinical criteria for Alzheimer's disease diagnosis are met

An Orbitrap/Time-of-Flight Mass Spectrometer for Photofragment Ion Imaging and High-Resolution Mass Analysis of Native Macromolecular Assemblies

We discuss the design, development, and evaluation of an Orbitrap/time-of-flight (TOF) mass spectrometry (MS)-based instrument with integrated UV photodissociation (UVPD) and time/mass-to-charge ratio ( m/ z)-resolved imaging for the comprehensive study of the higher-order molecular structure of macromolecular assemblies (MMAs). A bespoke TOF analyzer has been coupled to the higher-energy collisional dissociation cell of an ultrahigh mass range hybrid quadrupole-Orbitrap MS. A 193 nm excimer laser was employed to photofragment MMA ions. A combination of microchannel plates (MCPs)-Timepix (TPX) quad and MCPs-phosphor screen-TPX3CAM assemblies have been used as axial and orthogonal imaging detectors, respectively. The instrument can operate in four different modes, where the UVPD-generated fragment ions from the native MMA ions can be measured with high-mass resolution or imaged in a mass-resolved manner to reveal the relative positions of the UVPD fragments postdissociation. This information is intended to be utilized for retrieving higher-order molecular structural details that include the conformation, subunit stoichiometry, and molecular interactions as well as to understand the dissociation dynamics of the MMAs in the gas phase

A Rouse-based method to integrate the chemical composition of river sediments : application to the Ganga basin

Author Posting. © American Geophysical Union, 2011. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 116 (2011): F04012, doi:10.1029/2010JF001947.The Ganga River is one of the main conveyors of sediments produced by Himalayan erosion. Determining the flux of elements transported through the system is essential to understand the dynamics of the basin. This is hampered by the chemical heterogeneity of sediments observed both in the water column and under variable hydrodynamic conditions. Using Acoustic Doppler Current Profiler (ADCP) acquisitions with sediment depth profile sampling of the Ganga in Bangladesh we build a simple model to derive the annual flux and grain size distributions of the sediments. The model shows that ca. 390 (±30) Mt of sediments are transported on average each year through the Ganga at Haring Bridge (Bangladesh). Modeled average sediment grain size parameters D50 and D84 are 27 (±4) and 123 (±9) μm, respectively. Grain size parameters are used to infer average chemical compositions of the sediments owing to a strong grain size chemical composition relation. The integrated sediment flux is characterized by low Al/Si and Fe/Si ratios that are close to those inferred for the Himalayan crust. This implies that only limited sequestration occurs in the Gangetic floodplain. The stored sediment flux is estimated to c.a. 10% of the initial Himalayan sediment flux by geochemical mass balance. The associated, globally averaged sedimentation rates in the floodplain are found to be ca. 0.08 mm/yr and yield average Himalayan erosion rate of ca. 0.9 mm/yr. This study stresses the need to carefully address the average composition of river sediments before solving large-scale geochemical budgets.This work was supported by INSU program “Relief de la Terre” and ANR Calimero. Valier Galy was supported by the U.S. National Science Foundation (grant OCE‐0851015)

Maximum de vraisemblance et moindre carrés pénalisés dans des modèles de durée de vie censurées

L'analyse de durées de vie censurées est utilisée dans des domaines d'application variés et différentes possibilités ont été proposées pour la modélisation de telles données. Nous nous intéressons dans cette thèse à deux types de modélisation différents, le modèle de Cox stratifié avec indicateurs de strates aléatoirement manquants et le modèle de régression linéaire censuré à droite. Nous proposons des méthodes d'estimation des paramètres et établissons les propriétés asymptotiques des estimateurs obtenus dans chacun de ces modèles. Dans un premier temps, nous considérons une généralisation du modèle de Cox qui permet à différents groupes de la population, appelés strates, de posséder des fonctions d'intensité de base différentes tandis que la valeur du paramètre de régression est commune. Dans ce modèle à intensité proportionnelle stratifié, nous nous intéressons à l'estimation des paramètres lorsque l'indicateur de strate est manquant pour certains individus de la population. Des estimateurs du maximum de vraisemblance non paramétrique pour les paramètres du modèle sont proposés et nous montrons leurs consistance et normalité asymptotique. L'efficacité du paramètre de régression est établie et des estimateurs consistants de sa variance asymptotique sont également obtenus. Pour l'évaluation des estimateurs du modèle, nous proposons l'utilisation de l'algorithme Espérance-Maximisation et le développons dans ce cas particulier. Dans un second temps, nous nous intéressons au modèle de régression linéaire lorsque la donnée réponse est censurée aléatoirement à droite. Nous introduisons un nouvel estimateur du paramètre de régression minimisant un critère des moindres carrés pénalisé et pondéré par des poids de Kaplan-Meier. Des résultats de consistance et normalité asymptotique sont obtenus et une étude de simulations est effectuée pour illustrer les propriétés de cet estimateur de type LASSO. La méthode bootstrap est utilisée pour l'estimation de la variance asymptotique.TOULOUSE3-BU Sciences (315552104) / SudocSudocFranceF