1,107 research outputs found
Power injected in a granular gas
A granular gas may be modeled as a set of hard-spheres undergoing inelastic
collisions; its microscopic dynamics is thus strongly irreversible. As pointed
out in several experimental works bearing on turbulent flows or granular
materials, the power injected in a dissipative system to sustain a steady-state
over an asymptotically large time window is a central observable. We describe
an analytic approach allowing us to determine the full distribution of the
power injected in a granular gas within a steady-state resulting from
subjecting each particle independently either to a random force (stochastic
thermostat) or to a deterministic force proportional to its velocity (Gaussian
thermostat). We provide an analysis of our results in the light of the
relevance, for other types of systems, of the injected power to fluctuation
relations.Comment: 9 pages, 4 figures. Contribution to Proceedings of "Work,
Dissipation, and Fluctuations in Nonequilibrium Physics", Brussels, 200
Injected power and entropy flow in a heated granular gas
Our interest goes to the power injected in a heated granular gas and to the
possibility to interpret it in terms of entropy flow. We numerically determine
the distribution of the injected power by means of Monte-Carlo simulations.
Then, we provide a kinetic theory approach to the computation of such a
distribution function. Finally, after showing why the injected power does not
satisfy a Fluctuation Relation a la Gallavotti-Cohen, we put forward a new
quantity which does fulfill such a relation, and is not only applicable in a
variety of frameworks outside the granular world, but also experimentally
accessible.Comment: accepted in Europhys. Let
Two brains in action: joint-action coding in the primate frontal cortex
Daily life often requires the coordination of our actions with those of another partner. After sixty years (1968-2018) of behavioral neurophysiology of motor control, the neural mechanisms which allow such coordination in primates are unknown. We studied this issue by recording cell activity simultaneously from dorsal premotor cortex (PMd) of two male interacting monkeys trained to coordinate their hand forces to achieve a common goal. We found a population of 'joint-action cells' that discharged preferentially when monkeys cooperated in the task. This modulation was predictive in nature, since in most cells neural activity led in time the changes of the "own" and of the "other" behavior. These neurons encoded the joint-performance more accurately than 'canonical action-related cells', activated by the action per se, regardless of the individual vs. interactive context. A decoding of joint-action was obtained by combining the two brains activities, using cells with directional properties distinguished from those associated to the 'solo' behaviors. Action observation-related activity studied when one monkey observed the consequences of the partner's behavior, i.e. the cursor's motion on the screen, did not sharpen the accuracy of 'joint-action cells' representation, suggesting that it plays no major role in encoding joint-action. When monkeys performed with a non-interactive partner, such as a computer, 'joint-action cells' representation of the "other" (non-cooperative) behavior was significantly degraded. These findings provide evidence of how premotor neurons integrate the time-varying representation of the self-action with that of a co-actor, thus offering a neural substrate for successful visuo-motor coordination between individuals.SIGNIFICANT STATEMENTThe neural bases of inter-subject motor coordination were studied by recording cell activity simultaneously from the frontal cortex of two interacting monkeys, trained to coordinate their hand forces to achieve a common goal. We found a new class of cells, preferentially active when the monkeys cooperated, rather than when the same action was performed individually. These 'joint-action neurons' offered a neural representation of joint-behaviors by far more accurate than that provided by the canonical action-related cells, modulated by the action per se regardless of the individual/interactive context. A neural representation of joint-performance was obtained by combining the activity recorded from the two brains. Our findings offer the first evidence concerning neural mechanisms subtending interactive visuo-motor coordination between co-acting agents
Fluctuations of power injection in randomly driven granular gases
We investigate the large deviation function pi(w) for the fluctuations of the
power W(t)=w t, integrated over a time t, injected by a homogeneous random
driving into a granular gas, in the infinite time limit. Starting from a
generalized Liouville equation we obtain an equation for the generating
function of the cumulants mu(lambda) which appears as a generalization of the
inelastic Boltzmann equation and has a clear physical interpretation.
Reasonable assumptions are used to obtain mu(lambda) in a closed analytical
form. A Legendre transform is sufficient to get the large deviation function
pi(w). Our main result, apart from an estimate of all the cumulants of W(t) at
large times t, is that pi(w) has no negative branch. This immediately results
in the failure of the Gallavotti-Cohen Fluctuation Relation (GCFR), that in
previous studies had been suggested to be valid for injected power in driven
granular gases. We also present numerical results, in order to discuss the
finite time behavior of the fluctuations of W(t). We discover that their
probability density function converges extremely slowly to its asymptotic
scaling form: the third cumulant saturates after a characteristic time larger
than 50 mean free times and the higher order cumulants evolve even slower. The
asymptotic value is in good agreement with our theory. Remarkably, a numerical
check of the GCFR is feasible only at small times, since negative events
disappear at larger times. At such small times this check leads to the
misleading conclusion that GCFR is satisfied for pi(w). We offer an explanation
for this remarkable apparent verification. In the inelastic Maxwell model,
where a better statistics can be achieved, we are able to numerically observe
the failure of GCFR.Comment: 23 pages, 15 figure
Fluctuation relation for a L\'evy particle
We study the work fluctuations of a particle subjected to a deterministic
drag force plus a random forcing whose statistics is of the L\'evy type. In the
stationary regime, the probability density of the work is found to have ``fat''
power-law tails which assign a relatively high probability to large
fluctuations compared with the case where the random forcing is Gaussian. These
tails lead to a strong violation of existing fluctuation theorems, as the ratio
of the probabilities of positive and negative work fluctuations of equal
magnitude behaves in a non-monotonic way. Possible experiments that could probe
these features are proposed.Comment: 5 pages, 2 figures, RevTeX4; v2: minor corrections and references
added; v3: typos corrected, new conclusion, close to published versio
Activity driven fluctuations in living cells
We propose a model for the dynamics of a probe embedded in a living cell,
where both thermal fluctuations and nonequilibrium activity coexist. The model
is based on a confining harmonic potential describing the elastic cytoskeletal
matrix, which undergoes random active hops as a result of the nonequilibrium
rearrangements within the cell. We describe the probe's statistics and we bring
forth quantities affected by the nonequilibrium activity. We find an excellent
agreement between the predictions of our model and experimental results for
tracers inside living cells. Finally, we exploit our model to arrive at
quantitative predictions for the parameters characterizing nonequilibrium
activity, such as the typical time scale of the activity and the amplitude of
the active fluctuations.Comment: 6 pages, 4 figure
Dynamics of a tracer granular particle as a non-equilibrium Markov process
The dynamics of a tracer particle in a stationary driven granular gas is
investigated. We show how to transform the linear Boltzmann equation describing
the dynamics of the tracer into a master equation for a continuous Markov
process. The transition rates depend upon the stationary velocity distribution
of the gas. When the gas has a Gaussian velocity probability distribution
function (pdf), the stationary velocity pdf of the tracer is Gaussian with a
lower temperature and satisfies detailed balance for any value of the
restitution coefficient . As soon as the velocity pdf of the gas
departs from the Gaussian form, detailed balance is violated. This
non-equilibrium state can be characterized in terms of a Lebowitz-Spohn action
functional defined over trajectories of time duration . We
discuss the properties of this functional and of a similar functional
which differs from the first for a term which is non-extensive
in time. On the one hand we show that in numerical experiments, i.e. at finite
times , the two functionals have different fluctuations and
always satisfies an Evans-Searles-like symmetry. On the other hand we cannot
observe the verification of the Lebowitz-Spohn-Gallavotti-Cohen (LS-GC)
relation, which is expected for at very large times . We give
an argument for the possible failure of the LS-GC relation in this situation.
We also suggest practical recipes for measuring and
in experiments.Comment: 16 pages, 3 figures, submitted for publicatio
Granular Brownian motion
We study the stochastic motion of an intruder in a dilute driven granular
gas. All particles are coupled to a thermostat, representing the external
energy source, which is the sum of random forces and a viscous drag. The
dynamics of the intruder, in the large mass limit, is well described by a
linear Langevin equation, combining the effects of the external bath and of the
"granular bath". The drag and diffusion coefficients are calculated under few
assumptions, whose validity is well verified in numerical simulations. We also
discuss the non-equilibrium properties of the intruder dynamics, as well as the
corrections due to finite packing fraction or finite intruder mass.Comment: 19 pages, 4 figures, in press on Journal of Statistical Mechanics:
Theory and Experiment
Relevance of initial and final conditions for the Fluctuation Relation in Markov processes
Numerical observations on a Markov chain and on the continuous Markov process
performed by a granular tracer show that the ``usual'' fluctuation relation for
a given observable is not verified for finite (but arbitrarily large) times.
This suggests that some terms which are usually expected to be negligible, i.e.
``border terms'' dependent only on initial and final states, in fact cannot be
neglected. Furthermore, the Markov chain and the granular tracer behave in a
quite similar fashion.Comment: 23 pages, 5 figures, submitted to JSTA
Dark Matter searches using gravitational wave bar detectors: quark nuggets and newtorites
Many experiments have searched for supersymmetric WIMP dark matter, with null
results. This may suggest to look for more exotic possibilities, for example
compact ultra-dense quark nuggets, widely discussed in literature with several
different names. Nuclearites are an example of candidate compact objects with
atomic size cross section. After a short discussion on nuclearites, the result
of a nuclearite search with the gravitational wave bar detectors Nautilus and
Explorer is reported. The geometrical acceptance of the bar detectors is 19.5
sr, that is smaller than that of other detectors used for similar
searches. However, the detection mechanism is completely different and is more
straightforward than in other detectors. The experimental limits we obtain are
of interest because, for nuclearites of mass less than g, we find a
flux smaller than that one predicted considering nuclearites as dark matter
candidates. Particles with gravitational only interactions (newtorites) are
another example. In this case the sensitivity is quite poor and a short
discussion is reported on possible improvements.Comment: published on Astroparticle Physics Sept 25th 2016 replaced fig 1
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