273 research outputs found
Minimal Brownian Ratchet: An Exactly Solvable Model
We develop an exactly-solvable three-state discrete-time minimal Brownian
ratchet (MBR), where the transition probabilities between states are
asymmetric. By solving the master equations we obtain the steady-state
probabilities. Generally the steady-state solution does not display detailed
balance, giving rise to an induced directional motion in the MBR. For a reduced
two-dimensional parameter space we find the null-curve on which the net current
vanishes and detailed balance holds. A system on this curve is said to be
balanced. On the null-curve, an additional source of external random noise is
introduced to show that a directional motion can be induced under the zero
overall driving force. We also indicate the off-balance behavior with biased
random noise.Comment: 4 pages, 4 figures, RevTex source, General solution added. To be
appeared in Phys. Rev. Let
Transient behavior in Single-File Systems
We have used Monte-Carlo methods and analytical techniques to investigate the
influence of the characteristics, such as pipe length, diffusion, adsorption,
desorption and reaction rates on the transient properties of Single-File
Systems. The transient or the relaxation regime is the period in which the
system is evolving to equilibrium. We have studied the system when all the
sites are reactive and when only some of them are reactive. Comparisons between
Mean-Field predictions, Cluster Approximation predictions, and Monte Carlo
simulations for the relaxation time of the system are shown. We outline the
cases where Mean-Field analysis gives good results compared to Dynamic
Monte-Carlo results. For some specific cases we can analytically derive the
relaxation time. Occupancy profiles for different distribution of the sites
both for Mean-Field and simulations are compared. Different results for slow
and fast reaction systems and different distribution of reactive sites are
discussed.Comment: 18 pages, 19 figure
What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology
Stochastic resonance is said to be observed when increases in levels of unpredictable fluctuations—e.g., random noise—cause an increase in a metric of the quality of signal transmission or detection performance, rather than a decrease. This counterintuitive effect relies on system nonlinearities and on some parameter ranges being “suboptimal”. Stochastic resonance has been observed, quantified, and described in a plethora of physical and biological systems, including neurons. Being a topic of widespread multidisciplinary interest, the definition of stochastic resonance has evolved significantly over the last decade or so, leading to a number of debates, misunderstandings, and controversies. Perhaps the most important debate is whether the brain has evolved to utilize random noise in vivo, as part of the “neural code”. Surprisingly, this debate has been for the most part ignored by neuroscientists, despite much indirect evidence of a positive role for noise in the brain. We explore some of the reasons for this and argue why it would be more surprising if the brain did not exploit randomness provided by noise—via stochastic resonance or otherwise—than if it did. We also challenge neuroscientists and biologists, both computational and experimental, to embrace a very broad definition of stochastic resonance in terms of signal-processing “noise benefits”, and to devise experiments aimed at verifying that random variability can play a functional role in the brain, nervous system, or other areas of biology
Calculations Predict That Carbon Tunneling Allows the Degenerate Cope Rearrangement of Semibullvalene to Occur Rapidly at Cryogenic Temperatures
Article on calculations predicting that carbon tunneling allows the degenerate cope rearrangement of semibullvalene to occur rapidly at cryogenic temperatures
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