19,680 research outputs found
Hydrodynamic Model for Conductivity in Graphene
Based on the recently developed picture of an electronic ideal relativistic
fluid at the Dirac point, we present an analytical model for the conductivity
in graphene that is able to describe the linear dependence on the carrier
density and the existence of a minimum conductivity. The model treats
impurities as submerged rigid obstacles, forming a disordered medium through
which graphene electrons flow, in close analogy with classical fluid dynamics.
To describe the minimum conductivity, we take into account the additional
carrier density induced by the impurities in the sample. The model, which
predicts the conductivity as a function of the impurity fraction of the sample,
is supported by extensive simulations for different values of , the
dimensionless strength of the electric field, and provides excellent agreement
with experimental data.Comment: 19 pages, 4 figure
Segregation in a fluidized binary granular mixture: Competition between buoyancy and geometric forces
Starting from the hydrodynamic equations of binary granular mixtures, we
derive an evolution equation for the relative velocity of the intruders, which
is shown to be coupled to the inertia of the smaller particles. The onset of
Brazil-nut segregation is explained as a competition between the buoyancy and
geometric forces: the Archimedean buoyancy force, a buoyancy force due to the
difference between the energies of two granular species, and two geometric
forces, one compressive and the other-one tensile in nature, due to the
size-difference. We show that inelastic dissipation strongly affects the phase
diagram of the Brazil nut phenomenon and our model is able to explain the
experimental results of Breu et al. (PRL, 2003, vol. 90, p. 01402).Comment: 5 pages, 2 figure
A Growth model for DNA evolution
A simple growth model for DNA evolution is introduced which is analytically
solvable and reproduces the observed statistical behavior of real sequences.Comment: To be published in Europhysics Letter
Inverse targeting -- an effective immunization strategy
We propose a new method to immunize populations or computer networks against
epidemics which is more efficient than any method considered before. The
novelty of our method resides in the way of determining the immunization
targets. First we identify those individuals or computers that contribute the
least to the disease spreading measured through their contribution to the size
of the largest connected cluster in the social or a computer network. The
immunization process follows the list of identified individuals or computers in
inverse order, immunizing first those which are most relevant for the epidemic
spreading. We have applied our immunization strategy to several model networks
and two real networks, the Internet and the collaboration network of high
energy physicists. We find that our new immunization strategy is in the case of
model networks up to 14%, and for real networks up to 33% more efficient than
immunizing dynamically the most connected nodes in a network. Our strategy is
also numerically efficient and can therefore be applied to large systems
Efficient algorithm to study interconnected networks
Interconnected networks have been shown to be much more vulnerable to random
and targeted failures than isolated ones, raising several interesting questions
regarding the identification and mitigation of their risk. The paradigm to
address these questions is the percolation model, where the resilience of the
system is quantified by the dependence of the size of the largest cluster on
the number of failures. Numerically, the major challenge is the identification
of this cluster and the calculation of its size. Here, we propose an efficient
algorithm to tackle this problem. We show that the algorithm scales as O(N log
N), where N is the number of nodes in the network, a significant improvement
compared to O(N^2) for a greedy algorithm, what permits studying much larger
networks. Our new strategy can be applied to any network topology and
distribution of interdependencies, as well as any sequence of failures.Comment: 5 pages, 6 figure
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