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Special Issue "Selected Papers from 2nd International Electronic Conference on Entropy and Its Applications"

A special issue of Entropy (ISSN 1099-4300).

Deadline for manuscript submissions: closed (30 May 2016)

Special Issue Editor

Guest Editor
Dr. Dawn E. Holmes

Department of Statistics and Applied Probability, University of California, USA
Website | E-Mail
Interests: Bayesian networks; maximum entropy formalism; machine learning; foundations of Bayesianism

Special Issue Information

Dear Colleagues,

For more information on The 2nd International Electronic Conference on Entropy and Its Applications (ECEA-2), please go to: http://www.sciforum.net/conf/ecea-2/

Dr. Dawn E. Holmes
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Authors who have presented their article at the 2nd International Electronic Conference on Entropy and Its Applications will receive a 20% discount.

Published Papers (4 papers)

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Research

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Open AccessArticle Using Graph and Vertex Entropy to Compare Empirical Graphs with Theoretical Graph Models
Entropy 2016, 18(9), 320; https://doi.org/10.3390/e18090320
Received: 31 May 2016 / Revised: 19 July 2016 / Accepted: 24 August 2016 / Published: 5 September 2016
Cited by 2 | PDF Full-text (1647 KB) | HTML Full-text | XML Full-text
Abstract
Over the years, several theoretical graph generation models have been proposed. Among the most prominent are: the Erdős–Renyi random graph model, Watts–Strogatz small world model, Albert–Barabási preferential attachment model, Price citation model, and many more. Often, researchers working with real-world data are interested
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Over the years, several theoretical graph generation models have been proposed. Among the most prominent are: the Erdős–Renyi random graph model, Watts–Strogatz small world model, Albert–Barabási preferential attachment model, Price citation model, and many more. Often, researchers working with real-world data are interested in understanding the generative phenomena underlying their empirical graphs. They want to know which of the theoretical graph generation models would most probably generate a particular empirical graph. In other words, they expect some similarity assessment between the empirical graph and graphs artificially created from theoretical graph generation models. Usually, in order to assess the similarity of two graphs, centrality measure distributions are compared. For a theoretical graph model this means comparing the empirical graph to a single realization of a theoretical graph model, where the realization is generated from the given model using an arbitrary set of parameters. The similarity between centrality measure distributions can be measured using standard statistical tests, e.g., the Kolmogorov–Smirnov test of distances between cumulative distributions. However, this approach is both error-prone and leads to incorrect conclusions, as we show in our experiments. Therefore, we propose a new method for graph comparison and type classification by comparing the entropies of centrality measure distributions (degree centrality, betweenness centrality, closeness centrality). We demonstrate that our approach can help assign the empirical graph to the most similar theoretical model using a simple unsupervised learning method. Full article
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Open AccessArticle Exergy Analysis of a Syngas-Fueled Combined Cycle with Chemical-Looping Combustion and CO2 Sequestration
Entropy 2016, 18(9), 314; https://doi.org/10.3390/e18090314
Received: 30 May 2016 / Revised: 29 July 2016 / Accepted: 17 August 2016 / Published: 25 August 2016
Cited by 1 | PDF Full-text (1293 KB) | HTML Full-text | XML Full-text
Abstract
Fossil fuels are still widely used for power generation. Nevertheless, it is possible to attain a short- and medium-term substantial reduction of greenhouse gas emissions to the atmosphere through a sequestration of the CO2 produced in fuels’ oxidation. The chemical-looping combustion (CLC)
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Fossil fuels are still widely used for power generation. Nevertheless, it is possible to attain a short- and medium-term substantial reduction of greenhouse gas emissions to the atmosphere through a sequestration of the CO2 produced in fuels’ oxidation. The chemical-looping combustion (CLC) technique is based on a chemical intermediate agent, which gets oxidized in an air reactor and is then conducted to a separated fuel reactor, where it oxidizes the fuel in turn. Thus, the oxidation products CO2 and H2O are obtained in an output flow in which the only non-condensable gas is CO2, allowing the subsequent sequestration of CO2 without an energy penalty. Furthermore, with shrewd configurations, a lower exergy destruction in the combustion chemical transformation can be achieved. This paper focus on a second law analysis of a CLC combined cycle power plant with CO2 sequestration using syngas from coal and biomass gasification as fuel. The key thermodynamic parameters are optimized via the exergy method. The proposed power plant configuration is compared with a similar gas turbine system with a conventional combustion, finding a notable increase of the power plant efficiency. Furthermore, the influence of syngas composition on the results is investigated by considering different H2-content fuels. Full article
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Open AccessArticle Toward Improved Understanding of the Physical Meaning of Entropy in Classical Thermodynamics
Entropy 2016, 18(7), 270; https://doi.org/10.3390/e18070270
Received: 4 May 2016 / Revised: 6 July 2016 / Accepted: 20 July 2016 / Published: 22 July 2016
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Abstract
The year 2015 marked the 150th anniversary of “entropy” as a concept in classical thermodynamics. Despite its central role in the mathematical formulation of the Second Law and most of classical thermodynamics, its physical meaning continues to be elusive and confusing. This is
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The year 2015 marked the 150th anniversary of “entropy” as a concept in classical thermodynamics. Despite its central role in the mathematical formulation of the Second Law and most of classical thermodynamics, its physical meaning continues to be elusive and confusing. This is especially true when we seek a reconstruction of the classical thermodynamics of a system from the statistical behavior of its constituent microscopic particles or vice versa. This paper sketches the classical definition by Clausius and offers a modified mathematical definition that is intended to improve its conceptual meaning. In the modified version, the differential of specific entropy appears as a non-dimensional energy term that captures the invigoration or reduction of microscopic motion upon addition or withdrawal of heat from the system. It is also argued that heat transfer is a better model process to illustrate entropy; the canonical heat engines and refrigerators often used to illustrate this concept are not very relevant to new areas of thermodynamics (e.g., thermodynamics of biological systems). It is emphasized that entropy changes, as invoked in the Second Law, are necessarily related to the non-equilibrium interactions of two or more systems that might have initially been in thermal equilibrium but at different temperatures. The overall direction of entropy increase indicates the direction of naturally occurring heat transfer processes in an isolated system that consists of internally interacting (non-isolated) sub systems. We discuss the implication of the proposed modification on statements of the Second Law, interpretation of entropy in statistical thermodynamics, and the Third Law. Full article
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Review

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Open AccessReview Entropy as a Metric Generator of Dissipation in Complete Metriplectic Systems
Entropy 2016, 18(8), 304; https://doi.org/10.3390/e18080304
Received: 9 June 2016 / Revised: 31 July 2016 / Accepted: 11 August 2016 / Published: 16 August 2016
Cited by 3 | PDF Full-text (937 KB) | HTML Full-text | XML Full-text
Abstract
This lecture is a short review on the role entropy plays in those classical dissipative systems whose equations of motion may be expressed via a Leibniz Bracket Algebra (LBA). This means that the time derivative of any physical observable f of the system
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This lecture is a short review on the role entropy plays in those classical dissipative systems whose equations of motion may be expressed via a Leibniz Bracket Algebra (LBA). This means that the time derivative of any physical observable f of the system is calculated by putting this f in a “bracket” together with a “special observable” F, referred to as a Leibniz generator of the dynamics. While conservative dynamics is given an LBA formulation in the Hamiltonian framework, so that F is the Hamiltonian H of the system that generates the motion via classical Poisson brackets or quantum commutation brackets, an LBA formulation can be given to classical dissipative dynamics through the Metriplectic Bracket Algebra (MBA): the conservative component of the dynamics is still generated via Poisson algebra by the total energy H, while S, the entropy of the degrees of freedom statistically encoded in friction, generates dissipation via a metric bracket. The motivation of expressing through a bracket algebra and a motion-generating function F is to endow the theory of the system at hand with all the powerful machinery of Hamiltonian systems in terms of symmetries that become evident and readable. Here a (necessarily partial) overview of the types of systems subject to MBA formulation is presented, and the physical meaning of the quantity S involved in each is discussed. Here the aim is to review the different MBAs for isolated systems in a synoptic way. At the end of this collection of examples, the fact that dissipative dynamics may be constructed also in the absence of friction with microscopic degrees of freedom is stressed. This reasoning is a hint to introduce dissipation at a more fundamental level. Full article
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