Improving the surface brightness-color relation for early-type stars using optical interferometry

The aim of this work is to improve the SBC relation for early-type stars in the $-1 \leq V-K \leq 0$ color domain, using optical interferometry. Observations of eight B- and A-type stars were secured with the VEGA/CHARA instrument in the visible. The derived uniform disk angular diameters were converted into limb darkened angular diameters and included in a larger sample of 24 stars, already observed by interferometry, in order to derive a revised empirical relation for O, B, A spectral type stars with a V-K color index ranging from -1 to 0. We also took the opportunity to check the consistency of the SBC relation up to $V-K \simeq 4$ using 100 additional measurements. We determined the uniform disk angular diameter for the eight following stars: $\gamma$ Ori, $\zeta$ Per, $8$ Cyg, $\iota$ Her, $\lambda$ Aql, $\zeta$ Peg, $\gamma$ Lyr, and $\delta$ Cyg with V-K color ranging from -0.70 to 0.02 and typical precision of about $1.5\%$. Using our total sample of 132 stars with $V-K$ colors index ranging from about $-1$ to $4$, we provide a revised SBC relation. For late-type stars ($0 \leq V-K \leq 4$), the results are consistent with previous studies. For early-type stars ($-1 \leq V-K \leq 0$), our new VEGA/CHARA measurements combined with a careful selection of the stars (rejecting stars with environment or stars with a strong variability), allows us to reach an unprecedented precision of about 0.16 magnitude or $\simeq 7\%$ in terms of angular diameter.Comment: 13 pages, 5 figures, accepted for publication in A&

Application of Feedforward Neural Network for Induction Machine Rotor Faults Diagnostics using Stator Current

Faults and failures of induction machines can lead to excessive downtimes and generate large losses in terms of maintenance and lost revenues. This motivates motor monitoring, incipient fault detection and diagnosis. Non-invasive, inexpensive, and reliable fault detection techniques are often preferred by many engineers. In this paper, a feedforward neural network based fault detection system is developed for performing induction motors rotor faults detection and severity evaluation using stator current. From the motor current spectrum analysis and the broken rotor bar specific frequency components knowledge, the rotor fault signature is extracted and monitored by neural network for fault detection and classification. The proposed methodology has been experimentally tested on a 5.5Kw/3000rpm induction motor. The obtained results provide a satisfactory level of accuracy

Line positions and intensities in the ν2 fundamental band of sulfuryl fluoride using the C2 top data system

International audienc

Making Green(s) With Black and White: Constructing Soils for Urban Agriculture Using Earthworms, Organic and Mineral Wastes

International audienceUrban agriculture has been of growing interest for a decade because it can address many economic and societal issues in the development of modern cities. However, urban agriculture is often limited by the availability of fertile and non-contaminated soils in the cities. Recycling excavated mineral wastes from building activities to construct fertile soils may be a more sustainable alternative than the importation of topsoils from rural zones. The present study aims to evaluate the possibility to grow green vegetables on soils made with excavated deep horizon of soils and green waste compost. During three consecutive seasons, we tested in situ the effects of different amounts of compost (10, 20, and 30%) and the addition of an earthworm species (Lumbricus terrestris) on the production of lettuce (Lactuca sativa L.), arugula (Eruca sativa Mill.), and spinach (Spinacia oleracea L.) in mono-and co-culture. Our results demonstrate that it is possible to reuse mineral and organic urban wastes to engineer soils adapted to agriculture. Here, we observed that higher doses of compost significantly increased plant biomass, especially when earthworms were introduced. For example, in the autumn, going from 10 to 30% of compost in the soil mixture allows to multiply by 2 the arugula biomass, and even by 4 in the presence of earthworms. These results were partly due to the positive effects of these two factors on soil physical properties (microand macro-porosity). This preliminary study also showed that some plants (arugula) are more adapted than others (lettuce) to the soil properties and that it only takes few months to get the highest yields. These promising results for the development of urban agricultures encourage to test many other combination of plant and earthworm species but also to conduct experiments over long-term periods