Effects of cold-rolling deformation on texture evolution and mechanical properties of Ti-29Nb-9Ta-10Zr alloy

International audienceThe crystallographic texture of Ti-29Nb-9Ta-10Zr alloy is studied after cold-rolling with different amounts of thickness reduction, up to 60%. The major texture components developed during cold-rolling were: γ-fibre components: {111}〈View the MathML source〉, {111}〈View the MathML source〉, {111}〈View the MathML source〉 and {111}〈View the MathML source〉; texture component: {112}〈View the MathML source〉 and texture components: {001}〈View the MathML source〉 and {010}〈001〉. Besides crystallographic texture the resulted mechanical properties were studied by nanoindentation. It was showed that the decrease in Young's modulus after different cold-rolling stages is mainly attributed to the stress-induced α″-Ti phase formation. At 60% cold-rolling thickness reduction obtained an elastic modulus close to 45.29±3.81 GPa, coupled with an average Vickers microhardness close to 279.83±4.28 HV

β1A Integrin Is a Master Regulator of Invadosome Organization and Function

Use of patterned surfaces, reverse genetics, and time-controlled photoinactivation showed that β1 but not β3 integrins are required for invadosome formation, self-assembly, and stabilization into a ring structure. The activation state of β1 as well as its phosphorylation by protein kinase C on Ser785 control these process and link to the degradative function

Texture evolution during ARB (Accumulative Roll Bonding) processing of Ti-10Zr-5Nb-5Ta alloy

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Texture evolution in a Ti-Ta-Nb alloy processed by severe plastic deformation

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Walking on a Capacitive Sensing Floor

International audienceA capacitive proximity device SensFloor [1] was installed in the HUman at home project [2] appartment in Montpellier, France. Floor activations related to participants' movements are recorded with a frequency of 868Hz. One issue of this low-cost floor is its poor spatial precision. Actually, the least spatial element is a triangle of 25 × 50 cm. Moreover, this capacitive signal is not proportional to the weight of the object. Thus, it is challenging to organize this space-temporal signals into human behavioral events such as static, trample, walk. These events are identified by our Walk@Home algorithm presented here. In the core of this algorithm is a space-temporal window that scans the raw signals and organize them into a dynamic graph containing the eventual trajectories. Even then, the result is an approximation of the movement of the center of the gravity of a human. A good identification of trajectories is validated with data from controlled movements on the floor