407 research outputs found
Scale Modelling of Soil Structure Interaction during Earthquakes Using a Programmed Series of Explosions during Centrifugation
Scale models of Nuclear Power plants were constructed to study soil structure interaction during Earthquakes. The centrifuge of the C.E.S.T.A. Center near Bordeaux, France, was used to simulate gravity at 100 g (length scale 1: 100) on a 1000 kg net weight of soil and structure. The Earthquake was simulated by a surface wave created by a programmed series of small explosions suitably modified so that the free field signals of horizontal and vertical accelerometers had spectra resembling those of real Earthquakes according to similitude laws. The problem of echoes and suitability of a confined structure was studied without models of structures. In tests including models of structures movements and stresses in the soil and in the structure were measured using transducers of acceleration displacement pressure and deformation. The overall stability was studied
Mechanical identification of layer-specific properties of mouse carotid arteries using 3D-DIC and a hyperelastic anisotropic constitutive model
The role of mechanics is known to be of primary order in many arterial
diseases; however, determining mechanical properties of arteries remains a
challenge. This paper discusses the identifiability of the passive mechanical
properties of a mouse carotid artery, taking into account the orientation of
collagen fibres in the medial and adventitial layers. On the basis of 3D
digital image correlation measurements of the surface strain during an
inflation/extension test, an inverse identification method is set up. It
involves a 3D finite element mechanical model of the mechanical test and an
optimisation algorithm. A two-layer constitutive model derived from the
Holzapfel model is used, with five and then seven parameters. The
five-parameter model is successfully identified providing layer-specific fibre
angles. The seven-parameter model is over parameterised, yet it is shown that
additional data from a simple tension test make the identification of refined
layer-specific data reliable.Comment: PB-CMBBE-15.pd
Optimization of Piezoelectric Electrical Generators Powered by Random Vibrations
This paper compares the performances of a vibrationpowered electrical
generators using PZT piezoelectric ceramic associated to two different power
conditioning circuits. A new approach of the piezoelectric power conversion
based on a nonlinear voltage processing is presented and implemented with a
particular power conditioning circuit topology. Theoretical predictions and
experimental results show that the nonlinear processing technique may increase
the power harvested by a factor up to 4 compared to the Standard optimization
technique. Properties of this new technique are analyzed in particular in the
case of broadband, random vibrations, and compared to those of the Standard
interface.Comment: Submitted on behalf of TIMA Editions
(http://irevues.inist.fr/tima-editions
Vibration stabilization for a cantilever magnet prototype at the subnanometer scale
In the future linear colliders, the size of the beams is in the nanometer range, which requires stabilization of the final magnets before the interaction point. In order to guarantee the desired luminosity, an absolute displacement lower than 1/3 of the beam size, above a few hertz, has to be obtained. This paper describes an adapted instrumentation, the developed feedback loops dedicated to the active compensation and an adapted modelling able to simulate the behaviour of the structure. The obtained results at the subnanometer scale at the free end of a cantilever magnet prototype with a combination of the developed active compensation method and a commercial active isolation system are described
Exploring NMR ensembles of calcium binding proteins: Perspectives to design inhibitors of protein-protein interactions
<p>Abstract</p> <p>Background</p> <p>Disrupting protein-protein interactions by small organic molecules is nowadays a promising strategy employed to block protein targets involved in different pathologies. However, structural changes occurring at the binding interfaces make difficult drug discovery processes using structure-based drug design/virtual screening approaches. Here we focused on two homologous calcium binding proteins, calmodulin and human centrin 2, involved in different cellular functions via protein-protein interactions, and known to undergo important conformational changes upon ligand binding.</p> <p>Results</p> <p>In order to find suitable protein conformations of calmodulin and centrin for further structure-based drug design/virtual screening, we performed <it>in silico </it>structural/energetic analysis and molecular docking of terphenyl (a mimicking alpha-helical molecule known to inhibit protein-protein interactions of calmodulin) into X-ray and NMR ensembles of calmodulin and centrin. We employed several scoring methods in order to find the best protein conformations. Our results show that docking on NMR structures of calmodulin and centrin can be very helpful to take into account conformational changes occurring at protein-protein interfaces.</p> <p>Conclusions</p> <p>NMR structures of protein-protein complexes nowadays available could efficiently be exploited for further structure-based drug design/virtual screening processes employed to design small molecule inhibitors of protein-protein interactions.</p
Systems comparison and regression trees
A method for non parametric modelling of dynamical systems was presented in a previous paper . This work intends to propose
a new approach for addressing the problem of dynamical systems comparison and detection of abrupt changes . The algorithm
that is presented here, relies upon both d-dimensionnal histogram and regression tree estimation, and the use of f-divergences .
Illustrations on different non linear systems are provided .Nous proposons ici une application de la méthode de modélisation non linéaire non paramétrique de systÚmes dynamiques, présentée dans un précédent article. L'approche proposée dans le cadre de ce travail repose sur une partition récursive de l'espace d'état du systÚme, conduisant à un arbre de régression. Ce modÚle fournit une estimation de l'histogramme d-dimensionnel de l'espace des états du systÚme : nous montrons comment l'utilisation de distances ou de divergences entre lois de probabilité permet alors de quantifier les différences dynamiques entre systÚmes. Cette approche est illustrée sur deux exemples : la détection de changements de modÚles autorégressifs dans une série temporelle et la détection de la présence éventuelle d'un soliton de type « breather » susceptible d'apparaßtre dans le comportement d'une chaßne d'oscillateurs couplés soumis à un potentiel extérieur
Regression trees for non parametric modeling and time series prediction
We present a non-parametric approach to nonlinear modeling and prediction based on adaptive partitioning of the reconstructed
phase space associated with the process . The partitioning method is implemented with a recursive tree-structured algorithm which
successively refines the partition by binary splitting where the splitting threshold is determined by a penalized maximum entropy
criterion. An analysis of the statistical behavior of the splitting rule suggests a criterion for determining the depth of the tree . The
effectiveness of this method is illustrated through comparisons with classical approaches for nonlinear system analysis on the basis
of reconstruction error and computational complexity . An important relation between our tree-structured model for the process
and generalized non-linear thresholded AR model (ART) is established . We illustrate our method for cases where classical linear
prediction is known to be rather ineffective : chaotic signals (measured at the output of a Chua-type electronic circuit), and second
order ART signal .Nous prĂ©sentons une approche non linĂ©aire non paramĂ©trique pour la modĂ©lisation et la prĂ©diction de signaux, basĂ©e sur une mĂ©thode de partition rĂ©cursive de l'espace des phases reconstruit, associĂ© au systĂšme sur lequel le signal est prĂ©levĂ©. La partition de l'espace des phases est obtenue par un algorithme rĂ©cursif de partition binaire. Les seuils de partition sont dĂ©terminĂ©s Ă l'aide d'un critĂšre de maximum d'entropie. Une courte analyse statistique du comportement de ces seuils permet de dĂ©finir un critĂšre simple d'arrĂȘt de la partition rĂ©cursive. L'intĂ©rĂȘt de cette mĂ©thode est illustrĂ© par la comparaison avec des mĂ©thodes classiques dans le cadre de l'analyse de systĂšmes non linĂ©aires, ainsi que du point de vue du coĂ»t de calcul. Nous prĂ©sentons un lien important entre cette mĂ©thode reposant sur une partition hiĂ©rarchique (en arbre) et les modĂšles non linĂ©aires auto-rĂ©gressifs Ă seuils (ART). Dans ce contexte, la mĂ©thode prĂ©sentĂ©e est appliquĂ©e dans des cas pour lesquels les mĂ©thodes linĂ©aires Ă©chouent en gĂ©nĂ©ral : les signaux de chaos (sĂ©ries expĂ©rimentales mesurĂ©es sur des circuits Ă©lectroniques de type Chua), ainsi que sur des sĂ©ries numĂ©riques ART d'ordre deux
Mixed Analog-Digital Image Processing Cicuit Based on Hamming Artificial Neural Network Architecture
Extracting non-linear integrate-and-fire models from experimental data using dynamic IâV curves
The dynamic IâV curve method was recently introduced for the efficient experimental generation of reduced neuron models. The method extracts the response properties of a neuron while it is subject to a naturalistic stimulus that mimics in vivo-like fluctuating synaptic drive. The resulting history-dependent, transmembrane current is then projected onto a one-dimensional currentâvoltage relation that provides the basis for a tractable non-linear integrate-and-fire model. An attractive feature of the method is that it can be used in spike-triggered mode to quantify the distinct patterns of post-spike refractoriness seen in different classes of cortical neuron. The method is first illustrated using a conductance-based model and is then applied experimentally to generate reduced models of cortical layer-5 pyramidal cells and interneurons, in injected-current and injected- conductance protocols. The resulting low-dimensional neuron modelsâof the refractory exponential integrate-and-fire typeâprovide highly accurate predictions for spike-times. The method therefore provides a useful tool for the construction of tractable models and rapid experimental classification of cortical neurons
Stabilization study at the sub-nanometer level at the interaction point of the future Compact Linear Collider
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