1,068 research outputs found
Head-on collision of two solitary waves and residual falling jet formation
The head-on collision of two equal and two unequal steep solitary waves is investigated numerically. The former case is equivalent to the reflection of one solitary wave by a vertical wall when viscosity is neglected. We have performed a series of numerical simulations based on a Boundary Integral Equation Method (BIEM) on finite depth to investigate during the collision the maximum runup, phase shift, wall residence time and acceleration field for arbitrary values of the non-linearity parameter a/h, where a is the amplitude of initial solitary waves and h the constant water depth. The initial solitary waves are calculated numerically from the fully nonlinear equations. The present work extends previous results on the runup and wall residence time calculation to the collision of very steep counter propagating solitary waves. Furthermore, a new phenomenon corresponding to the occurrence of a residual jet is found for wave amplitudes larger than a threshold value
PEE8: QUALITY OF LIFE OF FRENCH PATIENTS TREATED FOR PRIMARY OPEN ANGLE GLAUCOMA AND OCULAR HYPERTENSION
Caractérisation par mesure de champ de l'hétérogénéité de comportement de cordon de soudure en alliage P91 et identification des paramètres de loi de comportement
National audienceCe premier travail s'inscrit dans le cadre du développement d'une méthodologie basée sur l'identification des mécanismes élémentaires, responsables de la déformation et de l'endommagement par fluage de matériaux polycristallins en relation avec leurs hétérogénéités microstructurales. Cette étude concerne l'acier P91, matériau potentiellement utilisé dans des applications de tenue mécanique à chaud dans le cadre des centrales thermiques à flamme et dans les centrales nucléaires de 4ème génération. Cette méthodologie repose sur l'utilisation de techniques de mesure de champs cinématiques par corrélation d'images, couplées à des simulations numériques par éléments finis afin d'optimiser des paramètres de lois de comportement de matériaux La première application concerne l'étude de structures soudées en P91 sollicitées en traction uniaxiale, afin de caractériser le comportement du cordon de soudure à l'échelle macroscopique où le gradient des propriétés mécaniques dû au soudage est observable. Une technique de microlithographie sera ensuite mise en oeuvre pour caractériser les mécanismes de glissement intergranulaire lors d'essai de fluage, en différentes zones du joint soudé
On the simulation of nonlinear bidimensional spiking neuron models
Bidimensional spiking models currently gather a lot of attention for their
simplicity and their ability to reproduce various spiking patterns of cortical
neurons, and are particularly used for large network simulations. These models
describe the dynamics of the membrane potential by a nonlinear differential
equation that blows up in finite time, coupled to a second equation for
adaptation. Spikes are emitted when the membrane potential blows up or reaches
a cutoff value. The precise simulation of the spike times and of the adaptation
variable is critical for it governs the spike pattern produced, and is hard to
compute accurately because of the exploding nature of the system at the spike
times. We thoroughly study the precision of fixed time-step integration schemes
for this type of models and demonstrate that these methods produce systematic
errors that are unbounded, as the cutoff value is increased, in the evaluation
of the two crucial quantities: the spike time and the value of the adaptation
variable at this time. Precise evaluation of these quantities therefore involve
very small time steps and long simulation times. In order to achieve a fixed
absolute precision in a reasonable computational time, we propose here a new
algorithm to simulate these systems based on a variable integration step method
that either integrates the original ordinary differential equation or the
equation of the orbits in the phase plane, and compare this algorithm with
fixed time-step Euler scheme and other more accurate simulation algorithms
Finite-size and correlation-induced effects in Mean-field Dynamics
The brain's activity is characterized by the interaction of a very large
number of neurons that are strongly affected by noise. However, signals often
arise at macroscopic scales integrating the effect of many neurons into a
reliable pattern of activity. In order to study such large neuronal assemblies,
one is often led to derive mean-field limits summarizing the effect of the
interaction of a large number of neurons into an effective signal. Classical
mean-field approaches consider the evolution of a deterministic variable, the
mean activity, thus neglecting the stochastic nature of neural behavior. In
this article, we build upon two recent approaches that include correlations and
higher order moments in mean-field equations, and study how these stochastic
effects influence the solutions of the mean-field equations, both in the limit
of an infinite number of neurons and for large yet finite networks. We
introduce a new model, the infinite model, which arises from both equations by
a rescaling of the variables and, which is invertible for finite-size networks,
and hence, provides equivalent equations to those previously derived models.
The study of this model allows us to understand qualitative behavior of such
large-scale networks. We show that, though the solutions of the deterministic
mean-field equation constitute uncorrelated solutions of the new mean-field
equations, the stability properties of limit cycles are modified by the
presence of correlations, and additional non-trivial behaviors including
periodic orbits appear when there were none in the mean field. The origin of
all these behaviors is then explored in finite-size networks where interesting
mesoscopic scale effects appear. This study leads us to show that the
infinite-size system appears as a singular limit of the network equations, and
for any finite network, the system will differ from the infinite system
Correlation between Progetto Cuore risk score and early cardiovascular damage in never treated subjects
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens
Runaway dilaton and equivalence principle violations
In a recently proposed scenario, where the dilaton decouples while
cosmologically attracted towards infinite bare string coupling, its residual
interactions can be related to the amplitude of density fluctuations generated
during inflation, and are large enough to be detectable through a modest
improvement on present tests of free-fall universality. Provided it has
significant couplings to either dark matter or dark energy, a runaway dilaton
can also induce time-variations of the natural "constants" within the reach of
near-future experiments.Comment: 4 pages, minor change
A Markovian event-based framework for stochastic spiking neural networks
In spiking neural networks, the information is conveyed by the spike times,
that depend on the intrinsic dynamics of each neuron, the input they receive
and on the connections between neurons. In this article we study the Markovian
nature of the sequence of spike times in stochastic neural networks, and in
particular the ability to deduce from a spike train the next spike time, and
therefore produce a description of the network activity only based on the spike
times regardless of the membrane potential process.
To study this question in a rigorous manner, we introduce and study an
event-based description of networks of noisy integrate-and-fire neurons, i.e.
that is based on the computation of the spike times. We show that the firing
times of the neurons in the networks constitute a Markov chain, whose
transition probability is related to the probability distribution of the
interspike interval of the neurons in the network. In the cases where the
Markovian model can be developed, the transition probability is explicitly
derived in such classical cases of neural networks as the linear
integrate-and-fire neuron models with excitatory and inhibitory interactions,
for different types of synapses, possibly featuring noisy synaptic integration,
transmission delays and absolute and relative refractory period. This covers
most of the cases that have been investigated in the event-based description of
spiking deterministic neural networks
Matter-gravity couplings and Lorentz violation
The gravitational couplings of matter are studied in the presence of Lorentz
and CPT violation. At leading order in the coefficients for Lorentz violation,
the relativistic quantum hamiltonian is derived from the gravitationally
coupled minimal Standard-Model Extension. For spin-independent effects, the
nonrelativistic quantum hamiltonian and the classical dynamics for test and
source bodies are obtained. A systematic perturbative method is developed to
treat small metric and coefficient fluctuations about a Lorentz-violating and
Minkowski background. The post-newtonian metric and the trajectory of a test
body freely falling under gravity in the presence of Lorentz violation are
established. An illustrative example is presented for a bumblebee model. The
general methodology is used to identify observable signals of Lorentz and CPT
violation in a variety of gravitational experiments and observations, including
gravimeter measurements, laboratory and satellite tests of the weak equivalence
principle, antimatter studies, solar-system observations, and investigations of
the gravitational properties of light. Numerous sensitivities to coefficients
for Lorentz violation can be achieved in existing or near-future experiments at
the level of parts in 10^3 down to parts in 10^{15}. Certain coefficients are
uniquely detectable in gravitational searches and remain unmeasured to date.Comment: 59 pages two-column REVTe
Limits and dynamics of stochastic neuronal networks with random heterogeneous delays
Realistic networks display heterogeneous transmission delays. We analyze here
the limits of large stochastic multi-populations networks with stochastic
coupling and random interconnection delays. We show that depending on the
nature of the delays distributions, a quenched or averaged propagation of chaos
takes place in these networks, and that the network equations converge towards
a delayed McKean-Vlasov equation with distributed delays. Our approach is
mostly fitted to neuroscience applications. We instantiate in particular a
classical neuronal model, the Wilson and Cowan system, and show that the
obtained limit equations have Gaussian solutions whose mean and standard
deviation satisfy a closed set of coupled delay differential equations in which
the distribution of delays and the noise levels appear as parameters. This
allows to uncover precisely the effects of noise, delays and coupling on the
dynamics of such heterogeneous networks, in particular their role in the
emergence of synchronized oscillations. We show in several examples that not
only the averaged delay, but also the dispersion, govern the dynamics of such
networks.Comment: Corrected misprint (useless stopping time) in proof of Lemma 1 and
clarified a regularity hypothesis (remark 1
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