Many Entropies, Many Disorders

2003 ◽  
Vol 10 (03) ◽  
pp. 281-296 ◽  
Author(s):  
Matt Davison ◽  
J. S. Shiner

To overcome the deficits of entropy as a measure for disorder when the number of states available to a system can change, Landsberg defined “disorder” as the entropy normalized to the maximum entropy. In the simplest cases, the maximum entropy is that of the equiprobable distribution, corresponding to a completely random system. However, depending on the question being asked and on system constraints, this absolute maximum entropy may not be the proper maximum entropy. To assess the effects of interactions on the “disorder” of a 1-dimensional spin system, the correct maximum entropy is that of the paramagnet (no interactions) with the same net magnetization; for a non-equilibrium system the proper maximum entropy may be that of the corresponding equilibrium system; and for hierarchical structures, an appropriate maximum entropy for a given level of the hierarchy is that of the system which is maximally random, subject to constraints deriving from the next lower level. Considerations of these examples leads us to introduce the “equivalent random system”: that system which is maximally random consistent with any constraints and with the question being asked. It is the entropy of the “equivalent random system” which should be taken as the maximum entropy in Landsberg's “disorder”.

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 433 ◽  
Author(s):  
Lee Jinwoo

Sagawa and Ueda established a fluctuation theorem of information exchange by revealing the role of correlations in stochastic thermodynamics and unified the non-equilibrium thermodynamics of measurement and feedback control. They considered a process where a non-equilibrium system exchanges information with other degrees of freedom such as an observer or a feedback controller. They proved the fluctuation theorem of information exchange under the assumption that the state of the other degrees of freedom that exchange information with the system does not change over time while the states of the system evolve in time. Here we relax this constraint and prove that the same form of the fluctuation theorem holds even if both subsystems co-evolve during information exchange processes. This result may extend the applicability of the fluctuation theorem of information exchange to a broader class of non-equilibrium processes, such as a dynamic coupling in biological systems, where subsystems that exchange information interact with each other.


Scilight ◽  
2019 ◽  
Vol 2019 (17) ◽  
pp. 170006
Author(s):  
Stacy W. Kish

Author(s):  
Chuan-ping Liu ◽  
Li Wang ◽  
Min Jia ◽  
Lige Tong

In order to study analytically the nature of the size separation in granular mixture, we present the maximum entropy production principle based on kinetic temperature of granular mixture. For simplicity we apply this principle to size separation of a sphere binary mixture in vibrated bed, and we find a new thermodynamic mechanism of size separation phenomenon. With the irreversible processes such as elastic collisions and frictions, the kinetic energy is dissipated rapidly in system, which induces the entropy production. By the fact that the entropy production rate always has the absolute maximum at the stable state of granular mixture, we find the crossover from “Brazil Nut Effect” to its reverse by changing particles size and density, and our result is about satisfied with Schnautz’s experiment.


Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 884 ◽  
Author(s):  
Rodrigo Cofré ◽  
Leonardo Videla ◽  
Fernando Rosas

Although most biological processes are characterized by a strong temporal asymmetry, several popular mathematical models neglect this issue. Maximum entropy methods provide a principled way of addressing time irreversibility, which leverages powerful results and ideas from the literature of non-equilibrium statistical mechanics. This tutorial provides a comprehensive overview of these issues, with a focus in the case of spike train statistics. We provide a detailed account of the mathematical foundations and work out examples to illustrate the key concepts and results from non-equilibrium statistical mechanics.


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