23 research outputs found
Nonextensive Statistical Mechanics Application to Vibrational Dynamics of Protein Folding
The vibrational dynamics of protein folding is analyzed in the framework of
Tsallis thermostatistics. The generalized partition functions, internal
energies, free energies and temperature factor (or Debye-Waller factor) are
calculated. It has also been observed that the temperature factor is dependent
on the non-extensive parameter q which behaves like a scale parameter in the
harmonic oscillator model. As , we also show that these approximations
agree with the result of Gaussian network model.Comment: 8 pages, 2 figure
Universal Critical Behavior of Aperiodic Ferromagnetic Models
We investigate the effects of geometric fluctuations, associated with
aperiodic exchange interactions, on the critical behavior of -state
ferromagnetic Potts models on generalized diamond hierarchical lattices. For
layered exchange interactions according to some two-letter substitutional
sequences, and irrelevant geometric fluctuations, the exact recursion relations
in parameter space display a non-trivial diagonal fixed point that governs the
universal critical behavior. For relevant fluctuations, this fixed point
becomes fully unstable, and we show the apperance of a two-cycle which is
associated with a novel critical behavior. We use scaling arguments to
calculate the critical exponent of the specific heat, which turns out
to be different from the value for the uniform case. We check the scaling
predictions by a direct numerical analysis of the singularity of the
thermodynamic free-energy. The agreement between scaling and direct
calculations is excellent for stronger singularities (large values of ). The
critical exponents do not depend on the strengths of the exchange interactions.Comment: 4 pages, 1 figure (included), RevTeX, submitted to Phys. Rev. E as a
Rapid Communicatio
Black hole thermodynamical entropy
As early as 1902, Gibbs pointed out that systems whose partition function
diverges, e.g. gravitation, lie outside the validity of the Boltzmann-Gibbs
(BG) theory. Consistently, since the pioneering Bekenstein-Hawking results,
physically meaningful evidence (e.g., the holographic principle) has
accumulated that the BG entropy of a black hole is
proportional to its area ( being a characteristic linear length), and
not to its volume . Similarly it exists the \emph{area law}, so named
because, for a wide class of strongly quantum-entangled -dimensional
systems, is proportional to if , and to if
, instead of being proportional to (). These results
violate the extensivity of the thermodynamical entropy of a -dimensional
system. This thermodynamical inconsistency disappears if we realize that the
thermodynamical entropy of such nonstandard systems is \emph{not} to be
identified with the BG {\it additive} entropy but with appropriately
generalized {\it nonadditive} entropies. Indeed, the celebrated usefulness of
the BG entropy is founded on hypothesis such as relatively weak probabilistic
correlations (and their connections to ergodicity, which by no means can be
assumed as a general rule of nature). Here we introduce a generalized entropy
which, for the Schwarzschild black hole and the area law, can solve the
thermodynamic puzzle.Comment: 7 pages, 2 figures. Accepted for publication in EPJ
Statistical characterization of an ensemble of functional neural networks
This work uses a complex network approach to analyze temporal sequences of
electrophysiological signals of brain activity from freely behaving rats. A network node
represents a neuron and a network link is included between a pair of nodes whenever their
firing rates are correlated. The framework of time varying graph (TVG) is used to deal
with a very large number (>30 000) of time dependent networks, which are set up by
taking into account correlations between neuron firing rates in a moving time lag window
of suitable width. Statistical distributions for the following network measures are
obtained: size of the largest connected cluster, number of edges, average node degree, and
average minimal path. We find that the number of networks with highly correlated activity
in distinct brain areas has a fat-tailed distribution, irrespective of the behavioral
state of the animal. This contrasts with short-tailed distributions for surrogates
obtained by shuffling the original data, and reflects the fact that neurons in the
neocortex and hippocampus often act in precise temporal coordination. Our results also
suggest that functional neuronal networks at the millimeter scale undergo statistically
nontrivial rearrangements over time, thus delimitating an empirical constraint for models
of brain activity
Integrating Computational Methods to Investigate the Macroecology of Microbiomes
Contains fulltext :
218139.pdf (publisher's version ) (Open Access)Studies in microbiology have long been mostly restricted to small spatial scales. However, recent technological advances, such as new sequencing methodologies, have ushered an era of large-scale sequencing of environmental DNA data from multiple biomes worldwide. These global datasets can now be used to explore long standing questions of microbial ecology. New methodological approaches and concepts are being developed to study such large-scale patterns in microbial communities, resulting in new perspectives that represent a significant advances for both microbiology and macroecology. Here, we identify and review important conceptual, computational, and methodological challenges and opportunities in microbial macroecology. Specifically, we discuss the challenges of handling and analyzing large amounts of microbiome data to understand taxa distribution and co-occurrence patterns. We also discuss approaches for modeling microbial communities based on environmental data, including information on biological interactions to make full use of available Big Data. Finally, we summarize the methods presented in a general approach aimed to aid microbiologists in addressing fundamental questions in microbial macroecology, including classical propositions (such as "everything is everywhere, but the environment selects") as well as applied ecological problems, such as those posed by human induced global environmental changes
Controlling self-organized criticality in complex networks
A control scheme to reduce the size of avalanches of the Bak-Tang-Wiesenfeld model on complex networks is proposed. Three network types are considered: those proposed by Erdős-Renyi, Goh-Kahng-Kim, and a real network representing the main connections of the electrical power grid of the western United States. The control scheme is based on the idea of triggering avalanches in the highest degree nodes that are near to become critical. We show that this strategy works in the sense that the dissipation of mass occurs most locally avoiding larger avalanches. We also compare this strategy with a random strategy where the nodes are chosen randomly. Although the random control has some ability to reduce the probability of large avalanches, its performance is much worse than the one based on the choice of the highest degree nodes. Finally, we argue that the ability of the proposed control scheme is related to its ability to reduce the concentration of mass on the network. Copyright EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2010