Reportage au cœur du laboratoire souterrain de l’Andra

Bienvenue au laboratoire souterrain de l’Andra, une installation en perpétuelle évolution située entre les départements de la Meuse et de la Haute-Marne. Le site, creusé dès l’an 2000, permet de préparer la construction et l’exploitation de Cigéo, le projet français de stockage géologique qui pourrait recevoir une autorisation de création en 2027

Les arrêts cardiaques pris en charge par le SMUR du Creusot (étude rétrospective selon le style d'Utstein)

DIJON-BU Médecine Pharmacie (212312103) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Extraction of coherent clusters and grid adaptation in particle-laden turbulence using wavelet filters

International audienceIn this study, a wavelet-based method for extraction of clusters of inertial particles in turbulent flows is presented that is based on decomposing Eulerian particle-number-density fields into the sum of coherent (organized) and incoherent (disorganized) components. The coherent component is associated with the clusters and is extracted by filtering the wavelet-transformed particle-number-density field based on an energy threshold. The method is applied to direct numerical simulations of homogeneous-isotropic turbulence laden with small Lagrangian particles. The analysis shows that in regimes where the preferential concentration is important, the coherent component representing the clusters can be described by just 1.6% of the total number ofwavelet coefficients, thereby illustrating the sparsity of the particle-number-density field. On the other hand, the incoherent portion is visually structureless and much less correlated than the coherent one. An application of the method, motivated by particle-laden radiative-heat-transfer simulations, is illustrated in the form of a grid-adaptation algorithm that results in nonuniform meshes with fine and coarse elements near and away from particle clusters, respectively. In regimes where preferential concentration in clusters is important, the grid adaptation leads to a significant reduction of the number of control volumes by one to two orders of magnitude

Deep Learning Prediction of Cancer Prevalence from Satellite Imagery

The worldwide growth of cancer incidence can be explained in part by changes in the prevalence and distribution of risk factors. There are geographical gaps in the estimates of cancer prevalence, which could be filled with innovative methods. We used deep learning (DL) features extracted from satellite images to predict cancer prevalence at the census tract level in seven cities in the United States. We trained the model using detailed cancer prevalence estimates from 2018 available in the CDC (Center for Disease Control) 500 Cities project. Data from 3500 census tracts covering 14,483,366 inhabitants were included. Features were extracted from 170,210 satellite images with deep learning. This method explained up to 64.37% (median = 43.53%) of the variation of cancer prevalence. Satellite features are highly correlated with individual socioeconomic and health measures that are linked to cancer prevalence (age, smoking and drinking status, and obesity). A higher similarity between two environments is associated with better generalization of the model (p = 1.10–6). This method can be used to accurately estimate cancer prevalence at a high spatial resolution without using surveys at a fraction of the cost