[alpha/Fe] in the thin and the thick disk towards an automatic parametrization of stellar spectra

We test an automatic procedure to measure [Fe/H] and [alpha/Fe] on high resolution spectra. The test sample is the intersection of the ELODIE library and a catalogue of 830 stars having well determined abundances.Comment: Three-dimensional Universe with Gaia, 4-7 October 2004, Observatoire de Paris-Meudon, France (ESA SP-576), eds M. Perryman & C. Turo

Elemental abundances as a function of kinematics in the Milky Way's disk

We present the results of our investigation of three samples kinematically representative of the thin and thick disks and the Hercules stream using the catalogue of Soubiran & Girard (2005). We have observed abundance trends and age distribution of each component. Our results show that the two disks are chemically well separated, they overlap greatly in metallicity and both show parallel decreasing trends of alpha elements with increasing metallicity, in the interval -0.80 < [Fe/H] < -0.30. The thick disk is clearly older than the thin disk with a tentative evidence of an Age-Metallicity Relation over 2-3 Gyr and a hiatus in star formation before the formation of the thin disk. In order to improve the statistics on the disk's abundance trends, we have developed an automatic code, TGMET{\Large\alpha}; to determine (Teff, logg, [Fe/H], [\alpha/Fe]) for thousands of stellar spectra available in spectroscopic archives. We have assessed the performances of the algorithm for 350 spectra of stars being part of the abundance catalogue

Coding of shape from shading in area V4 of the macaque monkey

<p>Abstract</p> <p>Background</p> <p>The shading of an object provides an important cue for recognition, especially for determining its 3D shape. However, neuronal mechanisms that allow the recovery of 3D shape from shading are poorly understood. The aim of our study was to determine the neuronal basis of 3D shape from shading coding in area V4 of the awake macaque monkey.</p> <p>Results</p> <p>We recorded the responses of V4 cells to stimuli presented parafoveally while the monkeys fixated a central spot. We used a set of stimuli made of 8 different 3D shapes illuminated from 4 directions (from above, the left, the right and below) and different 2D controls for each stimulus. The results show that V4 neurons present a broad selectivity to 3D shape and illumination direction, but without a preference for a unique illumination direction. However, 3D shape and illumination direction selectivities are correlated suggesting that V4 neurons can use the direction of illumination present in complex patterns of shading present on the surface of objects. In addition, a vast majority of V4 neurons (78%) have statistically different responses to the 3D and 2D versions of the stimuli, while responses to 3D are not systematically stronger than those to 2D controls. However, a hierarchical cluster analysis showed that the different classes of stimuli (3D, 2D controls) are clustered in the V4 cells response space suggesting a coding of 3D stimuli based on the population response. The different illumination directions also tend to be clustered in this space.</p> <p>Conclusion</p> <p>Together, these results show that area V4 participates, at the population level, in the coding of complex shape from the shading patterns coming from the illumination of the surface of corrugated objects. Hence V4 provides important information for one of the steps of cortical processing of the 3D aspect of objects in natural light environment.</p

A novel approach for electric load curve holistic modelling and simulation

International audienceThis paper presents a novel approach of an electric load curve simulator using a set of grey box models that results to an efficient trade-off between complete and complex physical models and fast simplified statistical models. The input parameters are macroscopic data coming from large databases such as national census, DSO’s client information and meteorological data such as temperature or irradiation data. The problem of matching between the different databases is investigated to assess comparable load curves. Validation is performed using load measurements at the medium voltage level. Once the model is calibrated it can be turned into a good prediction tool useful for planning studies since it permits easily to incorporate the evolution of usages, the characteristics of consumption devices, as well as the evolution of the building’s characteristics

Towards a better understanding of clogged steam generators: a sensitivity analysis of dynamic themohydraulic model output

Communication available online at http://hans.wackernagel.free.fr/article_ICONE_final_140311.pdfInternational audienceTube support plate clogging of steam generators affects their operating and requires frequent maintenance operations. A diagnosis method based on dynamic behaviour analysis is under development at EDF to provide means of optimisation of maintenance strategies. Previous work showed that the dynamic response to a power transient of the wide range level measurement contains informations about the clogging state of steam generators. The diagnosis method consists of comparisons of the measured dynamic response with simulations on a mono-dimensional dynamic steam generator model for various input clogging configurations. In order to assess the potential of this method, a sensitivity analysis has been conducted through a quasi-Monte Carlo scheme to compute sensitivity indices for each half tube support plate's clogging ratio. Sensitivity indices are usually defined for scalar model outputs. Principal component analysis has been used to determine a small subset of variables that condense the information about the shape of the response curves. Finally, estimation variability was assessed by construction of bootstrap confidence intervals. The results showed that half of the preselected input variables have negligible influence and allowed to rank the most important ones. Interactions of input variables have been estimated to exert only a small influence on the output. The effects of clogging on the steam generator dynamics has been characterised qualitatively and quantitatively

Population pharmacokinetic model selection assisted by machine learning

A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learning algorithms. We compared the classical pharmacometric approach with two machine learning methods, genetic algorithm and neural networks, in different scenarios based on simulated pharmacokinetic data. Genetic algorithm performance was assessed using a fitness function based on log-likelihood, whilst neural networks were trained using mean square error or binary cross-entropy loss. Machine learning provided a selection based only on statistical rules and achieved accurate selection. The minimization process of genetic algorithm was successful at allowing the algorithm to select plausible models. Neural network classification tasks achieved the most accurate results. Neural network regression tasks were less precise than neural network classification and genetic algorithm methods. The computational gain obtained by using machine learning was substantial, especially in the case of neural networks. We demonstrated that machine learning methods can greatly increase the efficiency of pharmacokinetic population model selection in case of large datasets or complex models requiring long run-times. Our results suggest that machine learning approaches can achieve a first fast selection of models which can be followed by more conventional pharmacometric approaches

3-D Magnetotelluric Investigations for geothermal exploration in Martinique (Lesser Antilles). Characteristic Deep Resistivity Structures, and Shallow Resistivity Distribution Matching Heliborne TEM Results

Within the framework of a global French program oriented towards the development of renewable energies, Martinique Island (Lesser Antilles, France) has been extensively investigated (from 2012 to 2013) through an integrated multi-methods approach, with the aim to define precisely the potential geothermal ressources, previously highlighted (Sanjuan et al., 2003). Amongst the common investigation methods deployed, we carried out three magnetotelluric (MT) surveys located above three of the most promising geothermal fields of Martinique, namely the Anses d'Arlet, the Montagne Pel{\'e}e and the Pitons du Carbet prospects. A total of about 100 MT stations were acquired showing single or multi-dimensional behaviors and static shift effects. After processing data with remote reference, 3-D MT inversions of the four complex elements of MT impedance tensor without pre-static-shift correction, have been performed for each sector, providing three 3-D resistivity models down to about 12 to 30 km depth. The sea coast effect has been taken into account in the 3-D inversion through generation of a 3-D resistivity model including the bathymetry around Martinique from the coast up to a distance of 200 km. The forward response of the model is used to calculate coast effect coefficients that are applied to the calculated MT response during the 3-D inversion process for comparison with the observed data. 3-D resistivity models of each sector, which are inherited from different geological history, show 3-D resistivity distribution and specificities related to its volcanological history. In particular, the geothermal field related to the Montagne Pel{\'e}e strato-volcano, is characterized by a quasi ubiquitous conductive layer and quite monotonic typical resistivity distribution making interpretation difficult in terms of geothermal targets. At the opposite, the resistivity distribution of Anse d'Arlet area is radically different and geothermal target is thought to be connected to a not so deep resistive intrusion elongated along a main structural axis. Beside these interesting deep structures, we demonstrate, after analyzing the results of the recent heliborne TEM survey covering the whole Martinique, that surface resistivity distribution obtained from 3-D inversion reproduce faithfully the resistivity distribution observed by TEM. In spite of a very different sampling scale, this comparison illustrates the ability of 3-D MT inversion to take into account and reproduce static shift effects in the sub-surface resistivity distribution.Comment: Wordl Geothermal Congress 2015, Apr 2015, Melbourne, Australi

Semimechanistic Clearance Models of Oncology Biotherapeutics and Impact of Study Design: Cetuximab as a Case Study

This study aimed to explore the currently competing and new semimechanistic clearance models for monoclonal antibodies and the impact of clearance model misspecification on exposure metrics under different study designs exemplified for cetuximab. Six clearance models were investigated under four different study designs (sampling density and single/multiple-dose levels) using a rich data set from two cetuximab clinical trials (226 patients with metastatic colorectal cancer) and using the nonlinear mixed-effects modeling approach. A two-compartment model with parallel Michaelis-Menten and time-decreasing linear clearance adequately described the data, the latter being related to post-treatment response. With respect to bias in exposure metrics, the simplified time-varying linear clearance (CL) model was the best alternative. Time-variance of the linear CL component should be considered for biotherapeutics if response impacts pharmacokinetics. Rich sampling at steady-state was crucial for unbiased estimation of Michaelis-Menten elimination in case of the reference (parallel Michaelis-Menten and time-varying linear CL) model