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The microscopic foundation of the generalized equilibrium statistical mechanics based on the Tsallis entropy is given by using the Gibbs idea of statistical ensembles of the classical and quantum mechanics. The equilibrium distribution…

Statistical Mechanics · Physics 2007-05-23 A. S. Parvan

The present paper studies a large class of temperature dependent probability distributions and shows that entropy and energy can be defined in such a way that these probability distributions are the equilibrium states of a generalized…

Statistical Mechanics · Physics 2015-06-24 Jan Naudts

Previous results on Renyi and Wang's formalism of the Tsallis thermostatics are founded by using an extensive variable z connected to the entropic parameter q. It is shown that in the thermodynamical limit both the Tsallis and Renyi…

Statistical Mechanics · Physics 2007-05-23 A. S. Parvan , T. S. Biro

We consider the problem of defining free energy and other thermodynamic functions when the entropy is given as a general function of the probablity distribution, including that for non extensive forms. We find that the free energy, which is…

Statistical Mechanics · Physics 2007-11-07 Fariel Shafee

We studied the thermodynamic quantities and the probability distribution, expressing the probability distribution as a function of the energy, in the canonical ensemble within the framework of the Tsallis statistics, which is characterized…

Statistical Mechanics · Physics 2025-12-16 Masamichi Ishihara

Microcanonical equations for several thermodynamic properties of a system, suitable for molecular dynamics simulations, are derived from the nonextensive Tsallis entropy functional. Two possible definitions of temperature, the usual one and…

Statistical Mechanics · Physics 2011-03-21 J. Carrete , L. M. Varela , L. J. Gallego

The original canonical ensemble formalism for the nonextensive entropy thermostatistics is reconsidered. It is shown that the unambiguous connection of the statistical mechanics with the equilibrium thermodynamics is provided if the…

Statistical Mechanics · Physics 2009-11-11 A. S. Parvan

The nonextensive statistics based on Tsallis entropy have been so far used for the systems composed of subsystems having same $q$. The applicability of this statistics to the systems with different $q$'s is still a matter of investigation.…

Statistical Mechanics · Physics 2007-05-23 Qiuping A. Wang

When at equilibrium, large-scale systems obey conventional thermodynamics because they belong to microscopic configurations (or states) that are typical. Crucially, the typical states usually represent only a small fraction of the total…

Statistical Mechanics · Physics 2025-01-14 Bernat Corominas-Murtra , Rudolf Hanel , Petr Jizba

Based on the Tsallis entropy, the nonextensive thermodynamic properties are studied as a q-deformation of classical statistical results using only probabilistic methods and straightforward calculations. It is shown that the constant in the…

Statistical Mechanics · Physics 2007-05-23 Franck Jedrzejewski

We show that Tsallis ensemble of power-law distributions provides a mechanical model of nonextensive equilibrium thermodynamics for small interacting Hamiltonian systems, i.e., using Boltzmann's original nomenclature, we prove that it is an…

Statistical Mechanics · Physics 2007-05-23 M. Campisi , G. B. Bagci

In the present paper the conditions for the validity of the Tsallis' Statistics are analyzed. The same has been done following the analogy with the traditional case: starting from the microcanonical description of the systems and taking…

Statistical Mechanics · Physics 2013-05-29 L. Velazquez , F. Guzman

We show that finite systems whose Hamiltonians obey a generalized homogeneity relation rigorously follow the nonextensive thermostatistics of Tsallis. In the thermodynamical limit, however, our results indicate that the Boltzmann-Gibbs…

Statistical Mechanics · Physics 2016-08-31 Artur B. Adib , Andre A. Moreira , Jose S. Andrade , Murilo P. Almeida

We describe in detail two numerical simulation methods valid to study systems whose thermostatistics is described by generalized entropies, such as Tsallis. The methods are useful for applications to non-trivial interacting systems with a…

Statistical Mechanics · Physics 2009-10-31 R. Salazar , R. Toral

We show that within classical statistical mechanics it is possible to naturally derive power law distributions which are of Tsallis type. The only assumption is that microcanonical distributions have to be separable from of the total system…

Statistical Mechanics · Physics 2009-11-10 Rudolf Hanel , Stefan Thurner

We consider two statistically independent systems described by the same entropy belonging to the two-parameter family of Sharma-Mittal. Assuming a weak interaction among the systems, allowing in this way an exchange of heat and work, we…

Statistical Mechanics · Physics 2009-11-11 A. M. Scarfone

The maximum entropy principle in Tsallis statistics is reformulated in the mathematical framework of the q-product, which results in the unique non self-referential q-canonical distribution. As one of the applications of the present…

Statistical Mechanics · Physics 2009-11-11 Hiroki Suyari

The thermodynamic relations in the Tsallis statistics were studied with physical quantities. An additive entropic variable related to the Tsallis entropy was introduced by assuming the form of the first law of the thermodynamics. The…

Statistical Mechanics · Physics 2023-03-15 Masamichi Ishihara

The relationships between reversible Carnot cycles, the absence of perpetual motion machines and the existence of a non-decreasing, globally unique entropy function forms the starting point of many textbook presentations of the foundations…

Statistical Mechanics · Physics 2015-05-14 O. J. E. Maroney

The nonextensive statistical ensembles are revisited for the complex systems with long-range interactions and long-range correlations. An approximation, the value of nonextensive parameter (1-q) is assumed to be very tiny, is adopted for…

Statistical Mechanics · Physics 2020-02-26 Yahui Zheng , Jiulin Du , Linxia Liu , Huijun Kong
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