Special Issue "Selected Papers from 4th International Electronic Conference on Entropy and Its Applications"

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

Deadline for manuscript submissions: closed (30 June 2018).

Special Issue Editor

Special Issue Information

Dear Colleagues,

Entropy held its 4th International Electronic Conference on Entropy and Its Applications (ECEA-4) from 21 November–1 December, 2017. The conference had six sessions, which reflect the inter-disciplinary nature of entropy and its applications:

  • Statistical Physics
  • Information and Complexity
  • Thermodynamics in Materials
  • Quantum Information and Foundations
  • Machine Learning
  • Astrophysics and Cosmology

For more information about the topics, please go to: http://www.sciforum.net/conf/ecea-4

All conference participants are recommended to submit a full paper to this Entropy Special issue with 20% discount of the APC charges. We also encourage other contributions from the broader community.

Prof. Dr. Philip Broadbridge
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 1600 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.

Published Papers (6 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Analysis of Basic Features in Dynamic Network Models
Entropy 2018, 20(9), 681; https://doi.org/10.3390/e20090681 - 07 Sep 2018
Cited by 1
Abstract
Time evolving Random Network Models are presented as a mathematical framework for modelling and analyzing the evolution of complex networks. This framework allows the analysis over time of several network characterizing features such as link density, clustering coefficient, degree distribution, as well as [...] Read more.
Time evolving Random Network Models are presented as a mathematical framework for modelling and analyzing the evolution of complex networks. This framework allows the analysis over time of several network characterizing features such as link density, clustering coefficient, degree distribution, as well as entropy-based complexity measures, providing new insight on the evolution of random networks. First, some simple dynamic network models, based only on edge density, are analyzed to serve as a baseline reference for assessing more complex models. Then, a model that depends on network structure with the aim of reflecting some characteristics of real networks is also analyzed. Such model shows a more sophisticated behavior with two different regimes, one of them leading to the generation of high clustering coefficient/link density ratio values when compared with the baseline values, as it happens in many real networks. Simulation examples are discussed to illustrate the behavior of the proposed models. Full article
Show Figures

Figure 1

Open AccessArticle
The Relationship between the US Economy’s Information Processing and Absorption Ratios: Systematic vs Systemic Risk
Entropy 2018, 20(9), 662; https://doi.org/10.3390/e20090662 - 02 Sep 2018
Cited by 2
Abstract
After the 2008 financial collapse, the now popular measure of implied systemic risk called the absorption ratio was introduced. This statistic measures how closely the economy’s markets are coupled. The more closely financial markets are coupled the more susceptible they are to systemic [...] Read more.
After the 2008 financial collapse, the now popular measure of implied systemic risk called the absorption ratio was introduced. This statistic measures how closely the economy’s markets are coupled. The more closely financial markets are coupled the more susceptible they are to systemic collapse. A new alternative measure of financial market health, the implied information processing ratio or entropic efficiency of the economy, was derived using concepts from information theory. This new entropic measure can also be useful in predicting economic downturns and measuring systematic risk. In the current work, the relationship between these two ratios and types of risks are explored. Potential methods of the joint use of these different measures to optimally reduce systemic and systematic risk are introduced. Full article
Show Figures

Figure 1

Open AccessArticle
On the Geodesic Distance in Shapes K-means Clustering
Entropy 2018, 20(9), 647; https://doi.org/10.3390/e20090647 - 29 Aug 2018
Cited by 3
Abstract
In this paper, the problem of clustering rotationally invariant shapes is studied and a solution using Information Geometry tools is provided. Landmarks of a complex shape are defined as probability densities in a statistical manifold. Then, in the setting of shapes clustering through [...] Read more.
In this paper, the problem of clustering rotationally invariant shapes is studied and a solution using Information Geometry tools is provided. Landmarks of a complex shape are defined as probability densities in a statistical manifold. Then, in the setting of shapes clustering through a K-means algorithm, the discriminative power of two different shapes distances are evaluated. The first, derived from Fisher–Rao metric, is related with the minimization of information in the Fisher sense and the other is derived from the Wasserstein distance which measures the minimal transportation cost. A modification of the K-means algorithm is also proposed which allows the variances to vary not only among the landmarks but also among the clusters. Full article
Show Figures

Figure 1

Open AccessArticle
Entropy and Geometric Objects
Entropy 2018, 20(6), 453; https://doi.org/10.3390/e20060453 - 09 Jun 2018
Abstract
Different notions of entropy can be identified in different scientific communities: (i) the thermodynamic sense; (ii) the information sense; (iii) the statistical sense; (iv) the disorder sense; and (v) the homogeneity sense. Especially the “disorder sense” and the “homogeneity sense” relate to and [...] Read more.
Different notions of entropy can be identified in different scientific communities: (i) the thermodynamic sense; (ii) the information sense; (iii) the statistical sense; (iv) the disorder sense; and (v) the homogeneity sense. Especially the “disorder sense” and the “homogeneity sense” relate to and require the notion of space and time. One of the few prominent examples relating entropy to both geometry and space is the Bekenstein-Hawking entropy of a Black Hole. Although this was developed for describing a physical object—a black hole—having a mass, a momentum, a temperature, an electrical charge, etc., absolutely no information about this object’s attributes can ultimately be found in the final formulation. In contrast, the Bekenstein-Hawking entropy in its dimensionless form is a positive quantity only comprising geometric attributes such as an area A—the area of the event horizon of the black hole, a length LP—the Planck length, and a factor 1/4. A purely geometric approach to this formulation will be presented here. The approach is based on a continuous 3D extension of the Heaviside function which draws on the phase-field concept of diffuse interfaces. Entropy enters into the local and statistical description of contrast or gradient distributions in the transition region of the extended Heaviside function definition. The structure of the Bekenstein-Hawking formulation is ultimately derived for a geometric sphere based solely on geometric-statistical considerations. Full article
Show Figures

Figure 1

Open AccessArticle
Maxwell’s Demon and the Problem of Observers in General Relativity
Entropy 2018, 20(5), 391; https://doi.org/10.3390/e20050391 - 22 May 2018
Cited by 2
Abstract
The fact that real dissipative (entropy producing) processes may be detected by non-comoving observers (tilted), in systems that appear to be isentropic for comoving observers, in general relativity, is explained in terms of the information theory, analogous with the explanation of the Maxwell’s [...] Read more.
The fact that real dissipative (entropy producing) processes may be detected by non-comoving observers (tilted), in systems that appear to be isentropic for comoving observers, in general relativity, is explained in terms of the information theory, analogous with the explanation of the Maxwell’s demon paradox. Full article
Show Figures

Figure 1

Open AccessArticle
On the Holographic Bound in Newtonian Cosmology
Entropy 2018, 20(2), 83; https://doi.org/10.3390/e20020083 - 25 Jan 2018
Abstract
The holographic principle sets an upper bound on the total (Boltzmann) entropy content of the Universe at around 10 123 k B ( k B being Boltzmann’s constant). In this work we point out the existence of a remarkable duality between nonrelativistic quantum [...] Read more.
The holographic principle sets an upper bound on the total (Boltzmann) entropy content of the Universe at around 10 123 k B ( k B being Boltzmann’s constant). In this work we point out the existence of a remarkable duality between nonrelativistic quantum mechanics on the one hand, and Newtonian cosmology on the other. Specifically, nonrelativistic quantum mechanics has a quantum probability fluid that exactly mimics the behaviour of the cosmological fluid, the latter considered in the Newtonian approximation. One proves that the equations governing the cosmological fluid (the Euler equation and the continuity equation) become the very equations that govern the quantum probability fluid after applying the Madelung transformation to the Schroedinger wavefunction. Under the assumption that gravitational equipotential surfaces can be identified with isoentropic surfaces, this model allows for a simple computation of the gravitational entropy of a Newtonian Universe. In a first approximation, we model the cosmological fluid as the quantum probability fluid of free Schroedinger waves. We find that this model Universe saturates the holographic bound. As a second approximation, we include the Hubble expansion of the galaxies. The corresponding Schroedinger waves lead to a value of the entropy lying three orders of magnitude below the holographic bound. Current work on a fully relativistic extension of our present model can be expected to yield results in even better agreement with empirical estimates of the entropy of the Universe. Full article
Back to TopTop