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Entropy, Volume 15, Issue 6 (June 2013) – 25 articles , Pages 1963-2463

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287 KiB  
Article
A Maximum Entropy Approach to the Realizability of Spin Correlation Matrices
by Paolo Dai Pra, Michele Pavon and Neeraja Sahasrabudhe
Entropy 2013, 15(6), 2448-2463; https://doi.org/10.3390/e15062448 - 21 Jun 2013
Viewed by 4993
Abstract
Deriving the form of the optimal solution of a maximum entropy problem, we obtain an infinite family of linear inequalities characterizing the polytope of spin correlation matrices. For n ≤ 6, the facet description of such a polytope is provided through a minimal [...] Read more.
Deriving the form of the optimal solution of a maximum entropy problem, we obtain an infinite family of linear inequalities characterizing the polytope of spin correlation matrices. For n ≤ 6, the facet description of such a polytope is provided through a minimal system of Bell-type inequalities. Full article
1087 KiB  
Article
Effect of Prey Refuge on the Spatiotemporal Dynamics of a Modified Leslie-Gower Predator-Prey System with Holling Type III Schemes
by Jianglin Zhao, Min Zhao and Hengguo Yu
Entropy 2013, 15(6), 2431-2447; https://doi.org/10.3390/e15062431 - 19 Jun 2013
Cited by 4 | Viewed by 5986
Abstract
In this paper, the spatiotemporal dynamics of a diffusive Leslie-Gower predator-prey model with prey refuge are investigated analytically and numerically. Mathematical theoretical works have considered the existence of global solutions, population permanence and the stability of equilibrium points, which depict the threshold expressions [...] Read more.
In this paper, the spatiotemporal dynamics of a diffusive Leslie-Gower predator-prey model with prey refuge are investigated analytically and numerically. Mathematical theoretical works have considered the existence of global solutions, population permanence and the stability of equilibrium points, which depict the threshold expressions of some critical parameters. Numerical simulations are performed to explore the pattern formation of species. These results show that the prey refuge has a profound effect on predator-prey interactions and they have the potential to be useful for the study of the entropy theory of bioinformatics. Full article
(This article belongs to the Special Issue Dynamical Systems)
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Figure 1
<p>Variation of dispersion relation of Equation (3) around the interior equilibrium point. The red line corresponds to <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.08</mn> </mrow> </semantics> </math>, the green is <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics> </math> and the blue is <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.35</mn> </mrow> </semantics> </math>.</p>
Full article ">Figure 2
<p>Spatial distributions of prey obtained with Equation (3) for (<b>a</b>) <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math>, (<b>b</b>) <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.08</mn> </mrow> </semantics> </math>, (<b>c</b>) <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.15</mn> </mrow> </semantics> </math>, (<b>d</b>) <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.22</mn> </mrow> </semantics> </math>, (<b>e</b>) <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.27</mn> </mrow> </semantics> </math>, (<b>f</b>) <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.35</mn> </mrow> </semantics> </math>. Other parameters are fixed as Equation (49).</p>
Full article ">Figure 3
<p>The spatiotemporal evolutions of prey obtained with Equation (3) at <span class="html-italic">x</span> = 100. (<b>a</b>) <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math>, (<b>b</b>) <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.08</mn> </mrow> </semantics> </math>, (<b>c</b>) <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.15</mn> </mrow> </semantics> </math>, (<b>d</b>) <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.22</mn> </mrow> </semantics> </math>, (<b>e</b>) <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.27</mn> </mrow> </semantics> </math>, (<b>f</b>) <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.35</mn> </mrow> </semantics> </math>. Other parameters are fixed as Equation (49).</p>
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1229 KiB  
Article
A Method for Choosing an Initial Time Eigenstate in Classical and Quantum Systems
by Gabino Torres-Vega and Mónica Noemí Jiménez-García
Entropy 2013, 15(6), 2415-2430; https://doi.org/10.3390/e15062415 - 17 Jun 2013
Cited by 2 | Viewed by 5665
Abstract
A subject of interest in classical and quantum mechanics is the development of the appropriate treatment of the time variable. In this paper we introduce a method of choosing the initial time eigensurface and how this method can be used to generate time-energy [...] Read more.
A subject of interest in classical and quantum mechanics is the development of the appropriate treatment of the time variable. In this paper we introduce a method of choosing the initial time eigensurface and how this method can be used to generate time-energy coordinates and, consequently, time-energy representations for classical and quantum systems. Full article
(This article belongs to the Special Issue Dynamical Systems)
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Graphical abstract

Graphical abstract
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<p>Normal curves to the energy surfaces for the nonlinear oscillator, with <math display="inline"> <mrow> <mi>ℓ</mi> <mo>=</mo> <mn>2</mn> </mrow> </math>, <math display="inline"> <mrow> <mi>k</mi> <mo>=</mo> <mn>9</mn> <mo>.</mo> <mn>8</mn> </mrow> </math> and <math display="inline"> <mrow> <mi>a</mi> <mo>=</mo> <mn>1</mn> </mrow> </math>. We only show the positive <span class="html-italic">q</span> axis. Any of these curves can be used as an initial time curve, but the curves that correspond to the extremal points of the potential function (<math display="inline"> <mrow> <mi>q</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mo>±</mo> <msqrt> <mrow> <msup> <mi>ℓ</mi> <mn>2</mn> </msup> <mo>−</mo> <msup> <mi>a</mi> <mn>2</mn> </msup> </mrow> </msqrt> </mrow> </math>) are the simplest ones: straight lines parallel to the coordinate axes.</p>
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<p>A time coordinate system for the nonlinear oscillator, generated with the initial curve <math display="inline"> <mrow> <mi>q</mi> <mo>=</mo> <msqrt> <mrow> <msup> <mi>ℓ</mi> <mn>2</mn> </msup> <mo>−</mo> <msup> <mi>a</mi> <mn>2</mn> </msup> </mrow> </msqrt> </mrow> </math>, with <math display="inline"> <mrow> <mi>ℓ</mi> <mo>=</mo> <mn>2</mn> </mrow> </math>, <math display="inline"> <mrow> <mi>k</mi> <mo>=</mo> <mn>9</mn> <mo>.</mo> <mn>8</mn> </mrow> </math> and <math display="inline"> <mrow> <mi>a</mi> <mo>=</mo> <mn>1</mn> </mrow> </math>. These curves will cover the phase space several times because the system is periodic.</p>
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<p>Density plots of the time-energy and phase-space representations of a time-energy Gaussian probability density for the nonlinear oscillator, with <math display="inline"> <mrow> <mi>ℓ</mi> <mo>=</mo> <mn>2</mn> </mrow> </math>, <math display="inline"> <mrow> <mi>k</mi> <mo>=</mo> <mn>9</mn> <mo>.</mo> <mn>8</mn> </mrow> </math> and <math display="inline"> <mrow> <mi>a</mi> <mo>=</mo> <mn>1</mn> </mrow> </math>. The widths are non-zero in both representations.</p>
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1002 KiB  
Article
On the Use of Information Theory to Quantify Parameter Uncertainty in Groundwater Modeling
by Alston Noronha and Jejung Lee
Entropy 2013, 15(6), 2398-2414; https://doi.org/10.3390/e15062398 - 13 Jun 2013
Cited by 3 | Viewed by 6308
Abstract
We applied information theory to quantify parameter uncertainty in a groundwater flow model. A number of parameters in groundwater modeling are often used with lack of knowledge of site conditions due to heterogeneity of hydrogeologic properties and limited access to complex geologic structures. [...] Read more.
We applied information theory to quantify parameter uncertainty in a groundwater flow model. A number of parameters in groundwater modeling are often used with lack of knowledge of site conditions due to heterogeneity of hydrogeologic properties and limited access to complex geologic structures. The present Information Theory-based (ITb) approach is to adopt entropy as a measure of uncertainty at the most probable state of hydrogeologic conditions. The most probable conditions are those at which the groundwater model is optimized with respect to the uncertain parameters. An analytical solution to estimate parameter uncertainty is derived by maximizing the entropy subject to constraints imposed by observation data. MODFLOW-2000 is implemented to simulate the groundwater system and to optimize the unknown parameters. The ITb approach is demonstrated with a three-dimensional synthetic model application and a case study of the Kansas City Plant. Hydraulic heads are the observations and hydraulic conductivities are assumed to be the unknown parameters. The applications show that ITb is capable of identifying which inputs of a groundwater model are the most uncertain and what statistical information can be used for site exploration. Full article
(This article belongs to the Special Issue Applications of Information Theory in the Geosciences)
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Figure 1
<p>Flowchart of ITb approach using MODFLOW-2000.</p>
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<p>The synthetic model setup. (<b>a</b>) The original schematic setup in Hill <span class="html-italic">et al.</span> [<a href="#B19-entropy-15-02398" class="html-bibr">19</a>]. (<b>b</b>) Plan view of the synthetic model setup.</p>
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<p>The bivariate probability density function (PDF) and univariate PDFs for HK1. and HK2.</p>
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<p>Parameter uncertainty of HK1 for the expected error E*. The bars indicate the standard deviation of HK1.</p>
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<p>The hydraulic conductivity distribution for the KCP site (modified from [<a href="#B23-entropy-15-02398" class="html-bibr">23</a>]). Each zone of hydraulic conductivity is defined with a closed boundary and different color. (<b>a</b>) The lower basal gravel layer. (<b>b</b>) The upper clayey silt layer.</p>
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2005 KiB  
Article
Entropy of Shortest Distance (ESD) as Pore Detector and Pore-Shape Classifier
by Gabor Korvin, Boris Sterligov, Klaudia Oleschko and Sergey Cherkasov
Entropy 2013, 15(6), 2384-2397; https://doi.org/10.3390/e15062384 - 10 Jun 2013
Cited by 2 | Viewed by 6327
Abstract
The entropy of shortest distance (ESD) between geographic elements (“elliptical intrusions”, “lineaments”, “points”) on a map, or between "vugs", "fractures" and "pores" in the macro- or microscopic images of triple porosity naturally fractured vuggy carbonates provides a powerful new tool for the digital [...] Read more.
The entropy of shortest distance (ESD) between geographic elements (“elliptical intrusions”, “lineaments”, “points”) on a map, or between "vugs", "fractures" and "pores" in the macro- or microscopic images of triple porosity naturally fractured vuggy carbonates provides a powerful new tool for the digital processing, analysis, classification and space/time distribution prognostic of mineral resources as well as the void space in carbonates, and in other rocks. The procedure is applicable at all scales, from outcrop photos, FMI, UBI, USI (geophysical imaging techniques) to micrographs, as we shall illustrate through some examples. Out of the possible applications of the ESD concept, we discuss in details the sliding window entropy filtering for nonlinear pore boundary enhancement, and propose this procedure as unbiased thresholding technique. Full article
(This article belongs to the Special Issue Applications of Information Theory in the Geosciences)
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Figure 1
<p>Ludwig Boltzmann’s grave in the Central Vienna Cemetery, with his famous equation, S=k log W.</p>
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<p>A model representing the case of strong correlation between the placement of the mineral occurrences (yellow dots), and lineaments.</p>
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<p>Spatial relation between three shapes ("granite outcrops" blue, "mineral occurrences" (red), and "lineaments", black). Scaled down by a factor <math display="inline"> <semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mn>5</mn> </msup> </mrow> </semantics> </math>, the model might represent an outcrop of a vuggy, fractured limestone (see <a href="#entropy-15-02384-f007" class="html-fig">Figure 7</a>), reducing it by <math display="inline"> <semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mn>8</mn> </msup> </mrow> </semantics> </math> it will resemble an optical micrograph of a triple porosity carbonate (<a href="#entropy-15-02384-f008" class="html-fig">Figure 8</a>, <a href="#entropy-15-02384-f009" class="html-fig">Figure 9</a>). Our entropy technique remains applicable through this enormous range of scales.</p>
Full article ">Figure 4
<p>Minkowski sum of a rectangle of sides a, b with a circle of radius r (<math display="inline"> <semantics> <mrow> <mi>r</mi> <mo>&lt;</mo> <mi>a</mi> <mo>,</mo> <mi>r</mi> <mo>&lt;</mo> <mi>b</mi> </mrow> </semantics> </math>).</p>
Full article ">Figure 5
<p>Illustration of the sliding window entropy technique for a better definition of the boundary of the pore <math display="inline"> <semantics> <mrow> <msub> <mi>A</mi> <mn>0</mn> </msub> </mrow> </semantics> </math>. The sliding window W, which moves out of <math display="inline"> <semantics> <mrow> <msub> <mi>A</mi> <mn>0</mn> </msub> </mrow> </semantics> </math>, has a size less than half the distance to the nearest pore. The sequence <math display="inline"> <semantics> <mrow> <msub> <mi>A</mi> <mn>0</mn> </msub> <mo>⊂</mo> <msub> <mi>A</mi> <mn>1</mn> </msub> <mo>⊂</mo> <mo>⋯</mo> <mo>⊂</mo> <msub> <mi>A</mi> <mi>N</mi> </msub> </mrow> </semantics> </math> is strictly increasing, the difference sets <math display="inline"> <semantics> <mrow> <msub> <mi>ρ</mi> <mi>k</mi> </msub> <mo>=</mo> <msub> <mi>A</mi> <mi>k</mi> </msub> <mo>\</mo> <msub> <mi>A</mi> <mrow> <mi>k</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> <math display="inline"> <semantics> <mrow> <mo stretchy="false">(</mo> <mi>k</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>⋯</mo> <mo>,</mo> <mi>N</mi> <mo stretchy="false">)</mo> </mrow> </semantics> </math> form one pixel wide “rings” or “halos” around <math display="inline"> <semantics> <mrow> <msub> <mi>A</mi> <mn>0</mn> </msub> </mrow> </semantics> </math>.</p>
Full article ">Figure 6
<p>Change of the Shannon entropy (Equation (17)) as W gradually moves out from the pore.</p>
Full article ">Figure 7
<p>Entropy of shortest distance (ESD) processing of a carbonate outcrop photo. The second image in the sequence shows the entropy map over the whole image, as discussed in the text, the cutoff <math display="inline"> <semantics> <mrow> <mi>H</mi> <mo>≤</mo> <mn>2</mn> </mrow> </semantics> </math> defines the pores (3rd image). The inset shows the histogram of distances from <span class="html-italic">randomly selected points</span> to the nearest pore.</p>
Full article ">Figure 8
<p>10 × magnification of a rock sample, taken from the outcrop in <a href="#entropy-15-02384-f006" class="html-fig">Figure 6</a>. The position of the section is perpendicular to the face of the rock wall.</p>
Full article ">Figure 9
<p>Entropy of shortest distance (ESD) isolines of the micrograph on <a href="#entropy-15-02384-f007" class="html-fig">Figure 7</a>. The ranges of entropy values are different for the various objects: Large vugs (H = 0.2–0.7), small vugs and pores (H = 1–1.7), solid matrix (H = 1.9–2.4).</p>
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156 KiB  
Article
Entropy Increase in Switching Systems
by José M. Amigó, Peter E. Kloeden and Ángel Giménez
Entropy 2013, 15(6), 2363-2383; https://doi.org/10.3390/e15062363 - 7 Jun 2013
Cited by 10 | Viewed by 5492
Abstract
The relation between the complexity of a time-switched dynamics and the complexity of its control sequence depends critically on the concept of a non-autonomous pullback attractor. For instance, the switched dynamics associated with scalar dissipative affine maps has a pullback attractor consisting of [...] Read more.
The relation between the complexity of a time-switched dynamics and the complexity of its control sequence depends critically on the concept of a non-autonomous pullback attractor. For instance, the switched dynamics associated with scalar dissipative affine maps has a pullback attractor consisting of singleton component sets. This entails that the complexity of the control sequence and switched dynamics, as quantified by the topological entropy, coincide. In this paper we extend the previous framework to pullback attractors with nontrivial components sets in order to gain further insights in that relation. This calls, in particular, for distinguishing two distinct contributions to the complexity of the switched dynamics. One proceeds from trajectory segments connecting different component sets of the attractor; the other contribution proceeds from trajectory segments within the component sets. We call them “macroscopic” and “microscopic” complexity, respectively, because only the first one can be measured by our analytical tools. As a result of this picture, we obtain sufficient conditions for a switching system to be more complex than its unswitched subsystems, i.e., a complexity analogue of Parrondo’s paradox. Full article
(This article belongs to the Special Issue Dynamical Systems)
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Figure 1
<p>This plot illustrates the extrapolation technique used to estimate the values of <math display="inline"> <msub> <mi>h</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>c</mi> <mi>r</mi> <mi>o</mi> </mrow> </msub> </math> in <a href="#entropy-15-02363-t001" class="html-table">Table 1</a>.</p>
Full article ">Figure 2
<p>This plot illustrates the extrapolation technique used to estimate the values of <math display="inline"> <msub> <mi>h</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>c</mi> <mi>r</mi> <mi>o</mi> </mrow> </msub> </math> in <a href="#entropy-15-02363-t002" class="html-table">Table 2</a>.</p>
Full article ">
468 KiB  
Article
Quantum Contextuality with Stabilizer States
by Mark Howard, Eoin Brennan and Jiri Vala
Entropy 2013, 15(6), 2340-2362; https://doi.org/10.3390/e15062340 - 7 Jun 2013
Cited by 19 | Viewed by 7902
Abstract
The Pauli groups are ubiquitous in quantum information theory because of their usefulness in describing quantum states and operations and their readily understood symmetry properties. In addition, the most well-understood quantum error correcting codes—stabilizer codes—are built using Pauli operators. The eigenstates of these [...] Read more.
The Pauli groups are ubiquitous in quantum information theory because of their usefulness in describing quantum states and operations and their readily understood symmetry properties. In addition, the most well-understood quantum error correcting codes—stabilizer codes—are built using Pauli operators. The eigenstates of these operators—stabilizer states—display a structure (e.g., mutual orthogonality relationships) that has made them useful in examples of multi-qubit non-locality and contextuality. Here, we apply the graph-theoretical contextuality formalism of Cabello, Severini and Winter to sets of stabilizer states, with particular attention to the effect of generalizing two-level qubit systems to odd prime d-level qudit systems. While state-independent contextuality using two-qubit states does not generalize to qudits, we show explicitly how state-dependent contextuality associated with a Bell inequality does generalize. Along the way we note various structural properties of stabilizer states, with respect to their orthogonality relationships, which may be of independent interest. Full article
(This article belongs to the Special Issue Quantum Information 2012)
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Figure 1
<p>The KCBS contextuality construction [<a href="#B16-entropy-15-02340" class="html-bibr">16</a>] involves five projectors of the form <math display="inline"> <mrow> <msub> <mo>Π</mo> <mi>i</mi> </msub> <mrow> <mo>=</mo> <mo>|</mo> </mrow> <msub> <mi>ψ</mi> <mi>i</mi> </msub> <mrow> <mo>〉</mo> <mspace width="-0.166667em"/> <mo>〈</mo> </mrow> <msub> <mi>ψ</mi> <mi>i</mi> </msub> <mrow> <mo>|</mo> <mo>∈</mo> </mrow> <msub> <mi mathvariant="script">H</mi> <mn>3</mn> </msub> </mrow> </math>, where the un-normalized versions of <math display="inline"> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mi>ψ</mi> <mi>i</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </math> and their mutual orthogonality relations are given in the graph depicted (connected vertices correspond to compatible and exclusive tests). The state <math display="inline"> <mrow> <mrow> <mo>|</mo> </mrow> <msubsup> <mi>ψ</mi> <mrow> <mo movablelimits="true" form="prefix">max</mo> </mrow> <mo>Γ</mo> </msubsup> <mrow> <mo>〉</mo> </mrow> </mrow> </math> is maximally contextual, insofar as it maximally violates the noncontextuality inequality <math display="inline"> <mrow> <msubsup> <mrow> <mo>〈</mo> <msub> <mo>Σ</mo> <mo>Γ</mo> </msub> <mo>〉</mo> </mrow> <mo movablelimits="true" form="prefix">max</mo> <mtext>NCHV</mtext> </msubsup> <mo>≤</mo> <mn>2</mn> </mrow> </math>.</p>
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<p>The graph <math display="inline"> <msub> <mo>Γ</mo> <mi>CHSH</mi> </msub> </math> that arises from the orthogonality relationship between the set of projectors <math display="inline"> <mrow> <mo>{</mo> <msub> <mo>Π</mo> <mn>1</mn> </msub> <mo>,</mo> <mi>…</mi> <mo>,</mo> <msub> <mo>Π</mo> <mn>8</mn> </msub> <mo>}</mo> </mrow> </math> as constructed in Equation (<a href="#FD61-entropy-15-02340" class="html-disp-formula">61</a>). The corresponding non-contextuality inequality <math display="inline"> <mrow> <mo>〈</mo> <msub> <mo>Σ</mo> <mo>Γ</mo> </msub> <mo>〉</mo> <mo>≤</mo> <mn>3</mn> </mrow> </math> is completely equivalent to the Bell inequality <math display="inline"> <mrow> <mo>〈</mo> <mi mathvariant="script">B</mi> <mo>〉</mo> <mo>≤</mo> <mn>2</mn> </mrow> </math>.</p>
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<p>All graphs that exhibit contextuality contain odd cycles <math display="inline"> <msub> <mi>C</mi> <mrow> <mn>2</mn> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> </math> or their complements <math display="inline"> <mover> <msub> <mi>C</mi> <mrow> <mn>2</mn> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>¯</mo> </mover> </math> [<a href="#B31-entropy-15-02340" class="html-bibr">31</a>]. For the case of the qubit CHSH graph, the only such subgraph that can be found is the pentagon <math display="inline"> <msub> <mi>C</mi> <mn>5</mn> </msub> </math>. There are 8 distinct induced pentagons within the qubit CHSH graph, as depicted.</p>
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<p>Two non-isomorphic orthogonality graphs, with vertices explicitly labelled, that are both equivalent to CHSH Bell inequality <math display="inline"> <mrow> <mo>〈</mo> <mi>X</mi> <mo>⊗</mo> <mi>X</mi> <mo>+</mo> <mi>X</mi> <mo>⊗</mo> <mi>Y</mi> <mo>+</mo> <mi>Y</mi> <mo>⊗</mo> <mi>X</mi> <mo>-</mo> <mi>Y</mi> <mo>⊗</mo> <mi>Y</mi> <mo>〉</mo> <mo>≤</mo> <mn>2</mn> </mrow> </math>. We showed in Equation (<a href="#FD59-entropy-15-02340" class="html-disp-formula">59</a>)–Equation (<a href="#FD65-entropy-15-02340" class="html-disp-formula">65</a>) how application of the CSW formalism [<a href="#B15-entropy-15-02340" class="html-bibr">15</a>] to <a href="#entropy-15-02340-f004" class="html-fig">Figure 4</a>a demonstrated this equivalence. A similar argument for <a href="#entropy-15-02340-f004" class="html-fig">Figure 4</a>b is provided in Equation (<a href="#FD73-entropy-15-02340" class="html-disp-formula">73</a>)–Equation (<a href="#FD76-entropy-15-02340" class="html-disp-formula">76</a>) below. Since our claim is that these graphs display contextuality, they must include an odd cycle or its complement; hence we highlight the pentagonal graph <math display="inline"> <msub> <mi>C</mi> <mn>5</mn> </msub> </math> contained within both graphs. (<b>a</b>) The graph <math display="inline"> <msub> <mo>Γ</mo> <mi>CHSH</mi> </msub> </math> comprised of 8 projectors; (<b>b</b>) The graph <math display="inline"> <msubsup> <mo>Γ</mo> <mi>CHSH</mi> <mo>′</mo> </msubsup> </math> comprised of 6 projectors.</p>
Full article ">Figure 5
<p>The structure of the alternate CHSH contextuality graph <math display="inline"> <msubsup> <mo>Γ</mo> <mi>CHSH</mi> <mo>′</mo> </msubsup> </math> of <a href="#entropy-15-02340-f004" class="html-fig">Figure 4</a>b can be recognized as the complement of the 5-pan graph. The family of <span class="html-italic">n</span>-pan graphs consist of an <span class="html-italic">n</span>-cycle in addition to a single vertex that is connected to the <span class="html-italic">n</span>-cycle by a single edge.</p>
Full article ">
358 KiB  
Article
Characterization of Ecological Exergy Based on Benthic Macroinvertebrates in Lotic Ecosystems
by Mi-Jung Bae, Fengqing Li, Piet F.M. Verdonschot and Young-Seuk Park
Entropy 2013, 15(6), 2319-2339; https://doi.org/10.3390/e15062319 - 7 Jun 2013
Cited by 6 | Viewed by 5895
Abstract
The evaluation of ecosystem health is a fundamental process for conducting effective ecosystem management. Ecological exergy is used primarily to summarize the complex dynamics of lotic ecosystems. In this study, we characterized the functional aspects of lotic ecosystems based on the exergy and [...] Read more.
The evaluation of ecosystem health is a fundamental process for conducting effective ecosystem management. Ecological exergy is used primarily to summarize the complex dynamics of lotic ecosystems. In this study, we characterized the functional aspects of lotic ecosystems based on the exergy and specific exergy from headwaters to downstream regions in the river’s dimensions (i.e., river width and depth) and in parallel with the nutrient gradient. Data were extracted from the Ecologische Karakterisering van Oppervlaktewateren in Overijssel (EKOO) database, consisting of 249 lotic study sites (including springs, upper, middle and lower courses) and 690 species. Exergy values were calculated based on trophic groups (carnivores, detritivores, detriti-herbivores, herbivores and omnivores) of benthic macroinvertebrate communities. A Self-Organizing Map (SOM) was applied to characterize the different benthic macroinvertebrate communities in the lotic ecosystem, and the Random Forest model was used to predict the exergy and specific exergy based on environmental variables. The SOM classified the sampling sites into four clusters representing differences in the longitudinal distribution along the river, as well as along nutrient gradients. Exergy tended to increase with stream size, and specific exergy was lowest at sites with a high nutrient load. The Random Forest model results indicated that river width was the most important predictor of exergy followed by dissolved oxygen, ammonium and river depth. Orthophosphate was the most significant predictor for estimating specific exergy followed by nitrate and total phosphate. Exergy and specific exergy exhibited different responses to various environmental conditions. This result suggests that the combination of exergy and specific exergy, as complementary indicators, can be used reliably to evaluate the health condition of a lotic ecosystem. Full article
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<p>Relative abundances (%) of the functional feeding groups (FFGs) of macroinvertebrates at four different river types.</p>
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<p>(<b>a</b>) Classification of sampling sites in SOM using macroinvertebrate abundance, (<b>b</b>) U-matrix and (<b>c</b>) dendrogram of a hierarchical cluster analysis of the SOM units using the Ward linkage method based on Euclidean distance. Acronyms in the SOM units refer to different river types (S: spring, U: upper course, M: middle course and L: lower course).</p>
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<p>Relative importance of environmental variables for predicting (<b>a</b>) exergy and (<b>b</b>) specific exergy using a Random Forest models based on the mean decrease Gini (MDG). MDG values were rescaled to a range of 0–100. Abbreviations of environmental variables are given in <a href="#entropy-15-02319-t001" class="html-table">Table 1</a>.</p>
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384 KiB  
Article
An Entropy-Based Weighted Concept Lattice for Merging Multi-Source Geo-Ontologies
by Junli Li, Zongyi He and Qiaoli Zhu
Entropy 2013, 15(6), 2303-2318; https://doi.org/10.3390/e15062303 - 7 Jun 2013
Cited by 35 | Viewed by 5786
Abstract
To deal with the complexities associated with the rapid growth in a merged concept lattice, a formal method based on an entropy-based weighted concept lattice (EWCL) is proposed as a mechanism for merging multi-source geographic ontologies (geo-ontologies). First, formal concept analysis (FCA) is [...] Read more.
To deal with the complexities associated with the rapid growth in a merged concept lattice, a formal method based on an entropy-based weighted concept lattice (EWCL) is proposed as a mechanism for merging multi-source geographic ontologies (geo-ontologies). First, formal concept analysis (FCA) is used to formalize different term-based representations in relation to the geographic domain, and to construct a merged formal context. Second, a weighted concept lattice (WCL) is applied to reduce the merged concept lattice, based on information entropy and a deviance analysis. The entropy of the attribute set is exploited to acquire the intent weight value, and the standard deviation contributes to computing the intent importance deviance value, according to the user preferences and interests. Some nodes of the merged concept lattice are then removed if their intent weights are lower than the intent importance thresholds specified by the user. Finally, experiments were conducted by combining fundamental geographic information data and spatial data in the hydraulic engineering domain from China. The results indicate that the proposed method is feasible and valid for reducing the complexities associated with the merging of geo-ontologies. Although there are still some problems in the application, the manuscript offers a new approach for the merging of geo-ontologies. Full article
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<p>The workflow of merging multi-source geo-ontologies based on EWCL.</p>
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<p>The merged concept lattice based on <a href="#entropy-15-02303-t003" class="html-table">Table 3</a>.</p>
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<p>The reduced weighted concept lattice when (<span class="html-italic">θ</span> = 0.40).</p>
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<p>The reduced weighted concept lattice when (<span class="html-italic">θ</span> = 0.52).</p>
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<p>The reduced weighted concept lattice when (<span class="html-italic">θ</span> = 0.40 and <span class="html-italic">δ</span> = 0.27).</p>
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248 KiB  
Article
Multi-Granulation Entropy and Its Applications
by Kai Zeng, Kun She and Xinzheng Niu
Entropy 2013, 15(6), 2288-2302; https://doi.org/10.3390/e15062288 - 6 Jun 2013
Cited by 18 | Viewed by 4664
Abstract
In the view of granular computing, some general uncertainty measures are proposed through single-granulation by generalizing Shannon’s entropy. However, in the practical environment we need to describe concurrently a target concept through multiple binary relations. In this paper, we extend the classical information [...] Read more.
In the view of granular computing, some general uncertainty measures are proposed through single-granulation by generalizing Shannon’s entropy. However, in the practical environment we need to describe concurrently a target concept through multiple binary relations. In this paper, we extend the classical information entropy model to a multi-granulation entropy model (MGE) by using a series of general binary relations. Two types of MGE are discussed. Moreover, a number of theorems are obtained. It can be concluded that the single-granulation entropy is the special instance of MGE. We employ the proposed model to evaluate the significance of the attributes for classification. A forward greedy search algorithm for feature selection is constructed. The experimental results show that the proposed method presents an effective solution for feature analysis. Full article
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<p>Significance and accuracy of single feature (<b>wine</b>). (<b>a</b>) Significance of a single feature computed with different evaluating. (<b>b</b>) Classification accuracies obtained for single features when using linear SVM and RBF SVM.</p>
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<p>Significance and accuracy of single feature (<b>wine</b>). (<b>a</b>) Significance of a single feature computed with different evaluating. (<b>b</b>) Classification accuracies obtained for single features when using linear SVM and RBF SVM.</p>
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<p>Significance and accuracy of single feature(<b>glass</b>). (<b>a</b>) Significance of a single feature computed with different evaluating. (<b>b</b>) Classification accuracies obtained for single features when using linear SVM and RBF SVM.</p>
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602 KiB  
Article
Entropy-Based Fast Largest Coding Unit Partition Algorithm in High-Efficiency Video Coding
by Mengmeng Zhang, Jianfeng Qu and Huihui Bai
Entropy 2013, 15(6), 2277-2287; https://doi.org/10.3390/e15062277 - 6 Jun 2013
Cited by 18 | Viewed by 6523
Abstract
High-efficiency video coding (HEVC) is a new video coding standard being developed by the Joint Collaborative Team on Video Coding. HEVC adopted numerous new tools, such as more flexible data structure representations, which include the coding unit (CU), prediction unit, and transform unit. [...] Read more.
High-efficiency video coding (HEVC) is a new video coding standard being developed by the Joint Collaborative Team on Video Coding. HEVC adopted numerous new tools, such as more flexible data structure representations, which include the coding unit (CU), prediction unit, and transform unit. In the partitioning of the largest coding unit (LCU) into CUs, rate distortion optimization (RDO) is applied. However, the computation complexity of RDO is too high for real-time application scenarios. Based on studies on the relationship between CUs and their entropy, this paper proposes a fast algorithm based on entropy to partition LCU as a substitute for RDO in HEVC. Experimental results show that the proposed entropy-based LCU partition algorithm can reduce coding time by 62.3% on average, with an acceptable loss of 3.82% using Bjøntegaard delta rate. Full article
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<p>Quadtree structure of HEVC.</p>
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<p>Example of the CU partitioning of HEVC: (<b>a</b>) Partitioned by H.264 and (<b>b</b>) Partitioned by HEVC.</p>
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<p>Flowchart of the proposed algorithm.</p>
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<p>Example of the anti-ground noise filter.</p>
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<p>(<b>a</b>) CU presentation of the sequence RaceHorsesC optimized by HM10.0 using the proposed algorithm. (<b>b</b>) CU presentation of the sequence RaceHorsesC optimized by HM10.0 with RDO.</p>
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<p>Experimental results of “RaceHorsesC” (832 × 480) under different QP settings (22,27,32,37). (<b>a</b>) Y PSNR <span class="html-italic">versus</span> Bitrate; (<b>b</b>) U PSNR <span class="html-italic">versus</span> Bitrate; (<b>c</b>) V PSNR <span class="html-italic">versus</span> Bitrate.</p>
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461 KiB  
Article
Bootstrap Methods for the Empirical Study of Decision-Making and Information Flows in Social Systems
by Simon DeDeo, Robert X. D. Hawkins, Sara Klingenstein and Tim Hitchcock
Entropy 2013, 15(6), 2246-2276; https://doi.org/10.3390/e15062246 - 5 Jun 2013
Cited by 35 | Viewed by 19146
Abstract
We characterize the statistical bootstrap for the estimation of informationtheoretic quantities from data, with particular reference to its use in the study of large-scale social phenomena. Our methods allow one to preserve, approximately, the underlying axiomatic relationships of information theory—in particular, consistency under [...] Read more.
We characterize the statistical bootstrap for the estimation of informationtheoretic quantities from data, with particular reference to its use in the study of large-scale social phenomena. Our methods allow one to preserve, approximately, the underlying axiomatic relationships of information theory—in particular, consistency under arbitrary coarse-graining—that motivate use of these quantities in the first place, while providing reliability comparable to the state of the art for Bayesian estimators. We show how information-theoretic quantities allow for rigorous empirical study of the decision-making capacities of rational agents, and the time-asymmetric flows of information in distributed systems. We provide illustrative examples by reference to ongoing collaborative work on the semantic structure of the British Criminal Court system and the conflict dynamics of the contemporary Afghanistan insurgency. Full article
(This article belongs to the Special Issue Estimating Information-Theoretic Quantities from Data)
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<p>A pedagogical example of the bootstrap in action.</p>
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<p>Prediction error curves and the existence of multiple classes. Solid curve: the Bhattacharyya bound for prediction of trial outcome for the period 1800 to 1820. Triangle symbols and solid line: actual prediction error, when drawing samples (words) from all trials within a class (guilty or not-guilty). As expected, the curve lies strictly below the Bhattacharyya bound. Diamond symbols and dashed line: actual prediction error, when drawing samples from a single trial. The prediction error actually rises (more samples lead to a less accurate prediction), suggesting that the underlying model (trials sample from one of two distributions) is incorrect. We restrict the set of trials here to those with at least one hundred (semantically-associated) words, so as to make the resampling process more accurate.</p>
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<p>Predictability of the Kandahar and Helmand time streams. Top: a dramatic asymmetry on short timescales provides strong suggestion of anticipatory, and potentially causal, effects transmitted <span class="html-italic">from</span> Kandahar <span class="html-italic">to</span> Helmand province on rapid (less than two-week) timescales. Bottom: the consistent, opposite asymmetry is seen in the reverse process. A rise in the predictability of Kandahar by Helmand on longer (one-month) timescales, mirrored in the top panel, suggests potentially longer-term seasonal or constraint-based information common to both systems.</p>
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<p>Left panel: bias for the 16-state entropy estimation case, under prior <math display="inline"> <msup> <mi>D</mi> <mo>′</mo> </msup> </math>. Dotted line: naive estimator; Dashed line, *-symbol: Wolpert and Wolf estimator; Dashed line, □-symbol: NSB estimator. Solid line: Bootstrap estimator. Right panel: one-sigma (solid line) and two-sigma (dashed line) error bar reliability; as the sampling factor increases, both rapidly approach their asymptotic values (thin horizontal lines). Average entropy for this prior is 2.4 bits.</p>
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<p>Left panel: bias for the estimation of mutual information on a <math display="inline"> <mrow> <mn>4</mn> <mo>×</mo> <mn>4</mn> </mrow> </math> joint probability distribution, under prior <math display="inline"> <msup> <mi>D</mi> <mo>′</mo> </msup> </math>. Dotted line: naive estimator; Dashed line, *-symbol: Wolpert and Wolf estimator; Dashed line, □-symbol: NSB estimator. Solid line: Bootstrap estimator. Right panel: one-sigma (solid line) and two-sigma (dashed line) error bar reliability; as the sampling factor increases, both rapidly approach their asymptotic values (thin horizontal lines). Average mutual information under this prior is 0.55 bits.</p>
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<p>Left panel: bias for the estimation of mutual information on a <math display="inline"> <mrow> <mn>16</mn> <mo>×</mo> <mn>16</mn> </mrow> </math> joint probability distribution, under prior <math display="inline"> <msup> <mi>D</mi> <mo>′</mo> </msup> </math>. Dotted line: naive estimator; Dashed line, *-symbol: Wolpert and Wolf estimator; Dashed line, □-symbol: NSB estimator. Solid line: Bootstrap estimator. Right panel: one-sigma (solid line) and two-sigma (dashed line) error bar reliability; as the sampling factor increases, both rapidly approach their asymptotic values (thin horizontal lines). Average mutual information for this prior is 1.33 bits.</p>
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<p>Estimator bias as a function of the entropy of the underlying distribution for the naive (dotted line), NSB (dashed line) and bootstrap (solid line) estimators. Distributions are over sixteen categories, drawn from <math display="inline"> <msup> <mi>D</mi> <mo>′</mo> </msup> </math>, and binned in 0.25 bit increments; the bias is for estimates made with sixteen samples (<span class="html-italic">i.e.</span>, <math display="inline"> <mrow> <mn>1</mn> <mo>×</mo> </mrow> </math> oversampling). Ranges shown are one-sigma error bars for the bias in the bin. As can be seen, all estimators tend to overestimate small entropies, and underestimate large entropies, with the cross-over point (and overall bias) depending on the method. As in the main text, <a href="#sec5dot3-entropy-15-02246" class="html-sec">Section 5.3</a>, we neglect cases where the empirical distribution has entropy zero; this is one source of the positive bias at the lowest entropy bins.</p>
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<p>The distribution of entropies for distributions sampled from the <math display="inline"> <msub> <mi>D</mi> <mn>1</mn> </msub> </math> (dotted line), <math display="inline"> <msub> <mi>D</mi> <mi>NSB</mi> </msub> </math> (dashed line) and <math display="inline"> <msup> <mi>D</mi> <mo>′</mo> </msup> </math> (solid line) priors. The <math display="inline"> <msub> <mi>D</mi> <mn>1</mn> </msub> </math> prior produces distributions very strongly skewed towards high entropies, while the <math display="inline"> <msub> <mi>D</mi> <mi>NSB</mi> </msub> </math> distribution is nearly flat for entropies larger than one bit.</p>
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3845 KiB  
Article
Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction
by Giuliana Pallotta, Michele Vespe and Karna Bryan
Entropy 2013, 15(6), 2218-2245; https://doi.org/10.3390/e15062218 - 4 Jun 2013
Cited by 550 | Viewed by 26564
Abstract
Understanding maritime traffic patterns is key to Maritime Situational Awareness applications, in particular, to classify and predict activities. Facilitated by the recent build-up of terrestrial networks and satellite constellations of Automatic Identification System (AIS) receivers, ship movement information is becoming increasingly available, both [...] Read more.
Understanding maritime traffic patterns is key to Maritime Situational Awareness applications, in particular, to classify and predict activities. Facilitated by the recent build-up of terrestrial networks and satellite constellations of Automatic Identification System (AIS) receivers, ship movement information is becoming increasingly available, both in coastal areas and open waters. The resulting amount of information is increasingly overwhelming to human operators, requiring the aid of automatic processing to synthesize the behaviors of interest in a clear and effective way. Although AIS data are only legally required for larger vessels, their use is growing, and they can be effectively used to infer different levels of contextual information, from the characterization of ports and off-shore platforms to spatial and temporal distributions of routes. An unsupervised and incremental learning approach to the extraction of maritime movement patterns is presented here to convert from raw data to information supporting decisions. This is a basis for automatically detecting anomalies and projecting current trajectories and patterns into the future. The proposed methodology, called TREAD (Traffic Route Extraction and Anomaly Detection) was developed for different levels of intermittency (i.e., sensor coverage and performance), persistence (i.e., time lag between subsequent observations) and data sources (i.e., ground-based and space-based receivers). Full article
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<p>Knowledge discovery functional architecture: historical database or real-time data stream of Automatic Identification System (AIS) messages is sequentially processed to incrementally learn maritime motion patterns through processes (“managers”) activated by relevant events. The knowledge discovery process is followed by on-line exploitation, such as route classification, prediction and anomaly detection.</p>
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<p>Stationary points (green dots) incrementally detected during a two-week period over the Strait of Gibraltar, an area characterized by intense traffic. Stationary points are then clustered using incremental Density-Based Spatial Clustering of Applications with Noise (DBSCAN) into port and offshore platform objects, whose concave hulls (right) consistently capture areas where vessels anchor outside ports.</p>
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<p>Waypoints detection and characterization over a <math display="inline"> <mrow> <mn>200</mn> <mo>×</mo> <mn>160</mn> </mrow> </math> km area in the North Adriatic Sea (<b>a</b>) from March 1 to May 15, 2012. The unsupervised analysis leads to the detection of entry (cyan), exit (magenta) and stationary areas (green) (<b>b</b>), one of them being an offshore regasification gateway as confirmed by the ship type distribution analysis (<b>c</b>), following the categorization in [<a href="#B39-entropy-15-02218" class="html-bibr">39</a>], performed on the Maritime Mobile Service Identity (MMSI) list of registered vessels.</p>
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<p>Set of highly dense routes into which the traffic in <a href="#entropy-15-02218-f003" class="html-fig">Figure 3</a> was decomposed.</p>
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<p>AIS traffic data in proximity of the Strait of Gibraltar (left) collected over two months, and (right) extracted routes between the learned port of Tarifa and the old port of Tangier, both highlighted in red.</p>
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<p>Daily patterns between northbound (left) and southbound (right) routes covered by four ferries whose schedule can be derived by the multiple peaks of the time histograms on the bottom.</p>
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<p>Color-coded routes (<b>a</b>) extracted over the area in <a href="#entropy-15-02218-f003" class="html-fig">Figure 3</a>, showing patterns not clearly visible by analyzing traffic density data (see <a href="#entropy-15-02218-f003" class="html-fig">Figure 3</a>b); one of them (<b>b</b>) is highlighted, showing in red the potential outliers detected and isolated using density-based clustering on the route points. The Kernel Density Estimation (KDE) distribution for the specific route is finally computed (<b>c</b>).</p>
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<p>Three-month satellite AIS positioning data over the Indian Ocean (<b>a</b>); superposition of detected routes (<b>b</b>). Two of them are further analyzed in terms of spatial (<b>c</b> and <b>e</b>) and travel times distribution (<b>d</b> and <b>f</b>).</p>
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<p>Portion of AIS messages captured by the learned system of routes over the reported areas of interest.</p>
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<p>Example of observed vessel track, <math display="inline"> <mrow> <mo>{</mo> <msub> <mi mathvariant="bold">v</mi> <mrow> <mi>t</mi> <mo>−</mo> <mn>2</mn> </mrow> </msub> <mo>,</mo> <msub> <mi mathvariant="bold">v</mi> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mi mathvariant="bold">v</mi> <mi>t</mi> </msub> <mo>}</mo> </mrow> </math> (red), associated temporal state sequence, <math display="inline"> <mrow> <mo>{</mo> <msub> <mi mathvariant="bold">s</mi> <mrow> <mi>t</mi> <mo>−</mo> <mn>2</mn> </mrow> </msub> <mo>,</mo> <msub> <mi mathvariant="bold">s</mi> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mi mathvariant="bold">s</mi> <mi>t</mi> </msub> <mo>}</mo> </mrow> </math> (circles) and points (blue) of a compatible route, as resulting from the traffic knowledge discovery process. If the selected radius is too large (e.g., <math display="inline"> <mrow> <msup> <mi>d</mi> <mo>′</mo> </msup> <mo>&gt;</mo> <mi>d</mi> </mrow> </math>), distinct local directional distributions can be included into the same state, biasing the motion characterization of the relevant observation neighborhood and, thus, the route classification process.</p>
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<p>Estimation of transition probabilities: Empirical (solid blue line) and fitted Weibull-like (dashed red line) distributions of the distances, <math display="inline"> <msub> <mo>Δ</mo> <mi>p</mi> </msub> </math>, in nautical miles between the predicted and actual positions of vessels in the North Adriatic Sea Area analyzed in <a href="#entropy-15-02218-f003" class="html-fig">Figure 3</a>. The time lag, <math display="inline"> <msub> <mo>Δ</mo> <mi>t</mi> </msub> </math>, ranges from five to 60 minutes, with an increment of five minutes. The figure shows how to derive the transition probability, <math display="inline"> <mrow> <mi>P</mi> <mo stretchy="false">(</mo> <msub> <mi mathvariant="bold">s</mi> <mi>t</mi> </msub> <mo>|</mo> <msub> <mi mathvariant="bold">s</mi> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msubsup> <mi>R</mi> <mrow> <mi>c</mi> </mrow> <mi>k</mi> </msubsup> <mo stretchy="false">)</mo> </mrow> </math>, from the distance between the new observation, <math display="inline"> <mrow> <mo>[</mo> <msub> <mi>x</mi> <mi>t</mi> </msub> <mo>,</mo> <msub> <mi>y</mi> <mi>t</mi> </msub> <mo>]</mo> </mrow> </math>, and the predicted position, <math display="inline"> <mrow> <mo>[</mo> <msub> <mover accent="true"> <mi>x</mi> <mo>^</mo> </mover> <mi>t</mi> </msub> <mo>,</mo> <msub> <mover accent="true"> <mi>y</mi> <mo>^</mo> </mover> <mi>t</mi> </msub> <mo>]</mo> </mrow> </math>, given the <math display="inline"> <msubsup> <mi>R</mi> <mrow> <mi>c</mi> </mrow> <mi>k</mi> </msubsup> </math> and the previous observation, <math display="inline"> <mrow> <mo>[</mo> <msub> <mi>x</mi> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mi>y</mi> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> <mo>]</mo> </mrow> </math>. This gives a measure of match between the route and the observed state sequence.</p>
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<p>Vessel destination prediction given the set of compatible routes (<b>a</b>) at three different time-frames (<b>b</b>, <b>c</b> and <b>d</b>). The probability of vessel location is computed based on Equation (<a href="#FD6-entropy-15-02218" class="html-disp-formula">6</a>) and conditioned to the distribution of vessel types within each route. It can be seen that the extracted routes provide enough information to consistently predict the vessel position hours ahead, even in relatively complex routing systems.</p>
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<p>Posterior probability of the observed track for the monitored vessel of interest. The vessel starts from Port of Livorno (green dots) and exits the area in the exit point (magenta), after making an anomalous double U-turn.</p>
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253 KiB  
Article
Entropy Harvesting
by Edward Bormashenko
Entropy 2013, 15(6), 2210-2217; https://doi.org/10.3390/e15062210 - 4 Jun 2013
Cited by 1 | Viewed by 7252
Abstract
The paper introduces the notion of “entropy harvesting” in physical and biological systems. Various physical and natural systems demonstrate the ability to decrease entropy under external stimuli. These systems, including stretched synthetic polymers, muscles, osmotic membranes and suspensions containing small hydrophobic particles, are [...] Read more.
The paper introduces the notion of “entropy harvesting” in physical and biological systems. Various physical and natural systems demonstrate the ability to decrease entropy under external stimuli. These systems, including stretched synthetic polymers, muscles, osmotic membranes and suspensions containing small hydrophobic particles, are called “entropic harvesters”. Entropic force acting in these systems increases with temperature. Harvested entropy may be released as mechanical work. The efficiency of entropy harvesting increases when the temperature is decreased. Natural and artificial energy harvesters are presented. Gravity as an entropic effect is discussed. Full article
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<p>(<b>A</b>) The deformed with force <math display="inline"> <semantics> <mover accent="true"> <mi>F</mi> <mo>→</mo> </mover> </semantics> </math> polymer strip—the simplest example of the “entropy harvester”.</p>
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<p>The model of a polymer chain. <math display="inline"> <semantics> <mrow> <mover accent="true"> <mi>R</mi> <mo>→</mo> </mover> <mo>=</mo> <mstyle displaystyle="true"> <munderover> <mo>∑</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mrow> <msub> <mover accent="true"> <mi>r</mi> <mo>→</mo> </mover> <mi>i</mi> </msub> </mrow> </mstyle> </mrow> </semantics> </math> is the end-to-end vector.</p>
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<p>To prevent osmotic flow, <span class="html-italic">P<sub>2</sub></span> must exceed <span class="html-italic">P<sub>1</sub></span>. The system represents an example of an “entropy harvester”.</p>
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<p>Clustering of hydrophobic particles dispersed in water, due to the hydrophobic effect.</p>
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<p>A particle with mass <span class="html-italic">m</span> approaches the holographic screen. The screen bounds the emerged part of space which contains the particle, and stores data that describe the part of space that has not yet emerged.</p>
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1974 KiB  
Article
An Automatic Multilevel Image Thresholding Using Relative Entropy and Meta-Heuristic Algorithms
by Yun-Chia Liang and Josue R. Cuevas
Entropy 2013, 15(6), 2181-2209; https://doi.org/10.3390/e15062181 - 3 Jun 2013
Cited by 13 | Viewed by 6554
Abstract
Multilevel thresholding has been long considered as one of the most popular techniques for image segmentation. Multilevel thresholding outputs a gray scale image in which more details from the original picture can be kept, while binary thresholding can only analyze the image in [...] Read more.
Multilevel thresholding has been long considered as one of the most popular techniques for image segmentation. Multilevel thresholding outputs a gray scale image in which more details from the original picture can be kept, while binary thresholding can only analyze the image in two colors, usually black and white. However, two major existing problems with the multilevel thresholding technique are: it is a time consuming approach, i.e., finding appropriate threshold values could take an exceptionally long computation time; and defining a proper number of thresholds or levels that will keep most of the relevant details from the original image is a difficult task. In this study a new evaluation function based on the Kullback-Leibler information distance, also known as relative entropy, is proposed. The property of this new function can help determine the number of thresholds automatically. To offset the expensive computational effort by traditional exhaustive search methods, this study establishes a procedure that combines the relative entropy and meta-heuristics. From the experiments performed in this study, the proposed procedure not only provides good segmentation results when compared with a well known technique such as Otsu’s method, but also constitutes a very efficient approach. Full article
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Graphical abstract

Graphical abstract
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<p>Flowchart of the proposed optimization procedure.</p>
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<p>Genetic Algorithm (GA) overview.</p>
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<p>Overview of the Particle Swarm Optimization (PSO) algorithm.</p>
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<p>Test image 1: (<b>a</b>) Original image; Thresholded image implementing (<b>b</b>) VOA, (<b>c</b>) GA, (<b>d</b>) PSO, and (<b>e</b>) Otsu’s method using three thresholds.</p>
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<p>Test image 2: (<b>a</b>) Original image; Thresholded image implementing (<b>b</b>) VOA, (<b>c</b>) GA, (<b>d</b>) PSO, and (<b>e</b>) Otsu’s method with two thresholds.</p>
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<p>Test image 3: (<b>a</b>) Original image; Thresholded image implementing (<b>b</b>) VOA, (<b>c</b>) GA, (<b>d</b>) PSO, and (<b>e</b>) Otsu’s method with four thresholds. (taken from [<a href="#B1-entropy-15-02181" class="html-bibr">1</a>] )</p>
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<p>Behavior of (<b>a</b>) Relative Entropy function <span class="html-italic">J</span>(<span class="html-italic">d</span>), (<b>b</b>) <span class="html-italic">P</span>(<span class="html-italic">d</span>), and (<b>c</b>) Objective function Θ(<span class="html-italic">d</span>) over different numbers of thresholds with different meta-heuristics VOA, GA and PSO on test image 1.</p>
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<p>Fitting of the histogram of the test image 1 implementing (<b>a</b>) VOA, (<b>b</b>) GA, and (<b>c</b>) PSO.</p>
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<p>Behavior of (<b>a</b>) Relative Entropy function <span class="html-italic">J</span>(<span class="html-italic">d</span>), (<b>b</b>) <span class="html-italic">P</span>(<span class="html-italic">d</span>), and (<b>c</b>) Objective function Θ(<span class="html-italic">d</span>) over different numbers of thresholds with different meta-heuristics VOA, GA and PSO on test image 2.</p>
Full article ">Figure 9 Cont.
<p>Behavior of (<b>a</b>) Relative Entropy function <span class="html-italic">J</span>(<span class="html-italic">d</span>), (<b>b</b>) <span class="html-italic">P</span>(<span class="html-italic">d</span>), and (<b>c</b>) Objective function Θ(<span class="html-italic">d</span>) over different numbers of thresholds with different meta-heuristics VOA, GA and PSO on test image 2.</p>
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<p>Fitting of the histogram of the test image 2 implementing (<b>a</b>) VOA, (<b>b</b>) GA, and (<b>c</b>) PSO.</p>
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<p>Behavior of (<b>a</b>) Relative Entropy function <span class="html-italic">J</span>(<span class="html-italic">d</span>), (<b>b</b>) <span class="html-italic">P</span>(<span class="html-italic">d</span>), and (<b>c</b>) Objective function Θ(<span class="html-italic">d</span>) over different numbers of thresholds with different meta-heuristics VOA, GA and PSO on test image 3.</p>
Full article ">Figure 11 Cont.
<p>Behavior of (<b>a</b>) Relative Entropy function <span class="html-italic">J</span>(<span class="html-italic">d</span>), (<b>b</b>) <span class="html-italic">P</span>(<span class="html-italic">d</span>), and (<b>c</b>) Objective function Θ(<span class="html-italic">d</span>) over different numbers of thresholds with different meta-heuristics VOA, GA and PSO on test image 3.</p>
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<p>Fitting of the histogram of the test image 3 implementing (<b>a</b>) VOA, (<b>b</b>) GA, and (<b>c</b>) PSO.</p>
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<p>Test image 4: (<b>a</b>) Original image; Thresholded image implementing (<b>b</b>) VOA, (<b>c</b>) GA, (<b>d</b>) PSO, and (<b>e</b>) Otsu’s method.</p>
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<p>Behavior of (<b>a</b>) Relative Entropy function <span class="html-italic">J</span>(<span class="html-italic">d</span>), (<b>b</b>) <span class="html-italic">P</span>(<span class="html-italic">d</span>), and (<b>c</b>) Objective function Θ(<span class="html-italic">d</span>) over different numbers of thresholds with different meta-heuristics VOA, GA, and PSO on test image 4.</p>
Full article ">Figure 14 Cont.
<p>Behavior of (<b>a</b>) Relative Entropy function <span class="html-italic">J</span>(<span class="html-italic">d</span>), (<b>b</b>) <span class="html-italic">P</span>(<span class="html-italic">d</span>), and (<b>c</b>) Objective function Θ(<span class="html-italic">d</span>) over different numbers of thresholds with different meta-heuristics VOA, GA, and PSO on test image 4.</p>
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<p>Fitting of the histogram of the test image 4 implementing (<b>a</b>) VOA, (<b>b</b>) GA, and (<b>c</b>) PSO.</p>
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<p>Test image 5: (<b>a</b>) Original image; Thresholded image implementing (<b>b</b>) VOA, (<b>c</b>) GA, (<b>d</b>) PSO, and (<b>e</b>) Otsu’s method with 4 thresholds.</p>
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<p>Behavior of (<b>a</b>) Relative Entropy function <span class="html-italic">J</span>(<span class="html-italic">d</span>), (<b>b</b>) <span class="html-italic">P</span>(<span class="html-italic">d</span>), and (<b>c</b>) Objective function Θ(<span class="html-italic">d</span>) over different numbers of thresholds with different meta-heuristics VOA, GA and PSO on test image 5.</p>
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<p>Fitting of the histogram of the test image 5 implementing (a) VOA, (b) GA, and (c) PSO.</p>
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1156 KiB  
Article
Thermoelectric System in Different Thermal and Electrical Configurations: Its Impact in the Figure of Merit
by Alexander Vargas-Almeida, Miguel Angel Olivares-Robles and Pablo Camacho-Medina
Entropy 2013, 15(6), 2162-2180; https://doi.org/10.3390/e15062162 - 31 May 2013
Cited by 17 | Viewed by 10947
Abstract
In this work, we analyze different configurations of a thermoelectric system (TES) composed of three thermoelectric generators (TEGs). We present the following considerations: (a) TES thermally and electrically connected in series (SC); (b) TES thermally and electrically connected in parallel (PSC); and (c) [...] Read more.
In this work, we analyze different configurations of a thermoelectric system (TES) composed of three thermoelectric generators (TEGs). We present the following considerations: (a) TES thermally and electrically connected in series (SC); (b) TES thermally and electrically connected in parallel (PSC); and (c) parallel thermally and series electrical connection (SSC). We assume that the parameters of the TEGs are temperature-independent. The systems are characterized by three parameters, as it has been showed in recent investigations, namely, its internal electrical resistance, R, thermal conductance under open electrical circuit condition, K, and Seebeck coefficient α. We derive the equivalent parameters for each of the configurations considered here and calculate the Figure of Merit Z for the equivalent system. We show the impact of the configuration of the system on Z, and we suggest optimum configuration. In order to justify the effectiveness of the equivalent Figure of Merit, the corresponding efficiency has been calculated for each configuration. Full article
(This article belongs to the Special Issue Entropy and Energy Extraction)
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Graphical abstract

Graphical abstract
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<p>Thermoelectric generator segmented, each P- or N-type branch consists of two different materials; figure from ref. [<a href="#B16-entropy-15-02162" class="html-bibr">16</a>].</p>
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<p>Schematic representation of a thermally and electrically connected in series (SC-TES) thermoelectric system composed of two stages thermally and electrically connected in series. (<b>a</b>) SC-TES; (<b>b</b>) practical device related to SC-TES.</p>
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<p>Schematic representation of a segmented-conventional TES thermally and electrically connected in parallel thermoelectric system (PSC-TES) composed by a segmented TEM and conventional TEM thermally and electrically connected in parallel. (<b>a</b>) PSC-TES; (<b>b</b>) practical device related to PSC-TES.</p>
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<p>Schematic representation of a mixed segmented-conventional thermoelectric system (SSC-TES) composed by segmented TEM and conventional TEM thermally connected in parallel and electrically connected in series. (<b>a</b>) SSC-TES; (<b>b</b>) practical device related to SSC-TES.</p>
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<p><math display="inline"> <msub> <mi>Z</mi> <mrow> <mi>e</mi> <mi>q</mi> <mo>-</mo> <mi>S</mi> <mi>C</mi> </mrow> </msub> </math> <span class="html-italic">vs</span>. the ratio <math display="inline"> <mrow> <msub> <mi>α</mi> <mn>3</mn> </msub> <mo>/</mo> <msub> <mi>α</mi> <mn>2</mn> </msub> </mrow> </math>, maintaining <math display="inline"> <msub> <mi>α</mi> <mn>1</mn> </msub> </math> y <math display="inline"> <msub> <mi>α</mi> <mn>2</mn> </msub> </math> constant.</p>
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<p><math display="inline"> <msub> <mi>Z</mi> <mrow> <mi>e</mi> <mi>q</mi> <mo>-</mo> <mi>P</mi> <mi>S</mi> <mi>C</mi> </mrow> </msub> </math> <span class="html-italic">vs</span>. the ratio, <math display="inline"> <mrow> <msub> <mi>α</mi> <mn>2</mn> </msub> <mo>/</mo> <msub> <mi>α</mi> <mn>1</mn> </msub> </mrow> </math>, maintaining <math display="inline"> <msub> <mi>α</mi> <mn>1</mn> </msub> </math> and <math display="inline"> <msub> <mi>α</mi> <mn>3</mn> </msub> </math> constant.</p>
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<p><math display="inline"> <msub> <mi>Z</mi> <mrow> <mi>e</mi> <mi>q</mi> <mo>-</mo> <mi>S</mi> <mi>C</mi> </mrow> </msub> </math> <span class="html-italic">vs</span>. the ratio, <math display="inline"> <mrow> <msub> <mi>α</mi> <mn>2</mn> </msub> <mo>/</mo> <msub> <mi>α</mi> <mn>1</mn> </msub> </mrow> </math>, maintaining <math display="inline"> <msub> <mi>α</mi> <mn>1</mn> </msub> </math>, y, <math display="inline"> <msub> <mi>α</mi> <mn>3</mn> </msub> </math> constant.</p>
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<p><math display="inline"> <msub> <mi>η</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mo>-</mo> <mi>S</mi> <mi>C</mi> </mrow> </msub> </math> <span class="html-italic">vs</span>. the ratio, <math display="inline"> <mrow> <msub> <mi>α</mi> <mn>3</mn> </msub> <mo>/</mo> <msub> <mi>α</mi> <mn>2</mn> </msub> </mrow> </math>.</p>
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<p><math display="inline"> <msub> <mi>η</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mo>-</mo> <mi>P</mi> <mi>S</mi> <mi>C</mi> </mrow> </msub> </math> <span class="html-italic">vs</span>. the ratio <math display="inline"> <mrow> <msub> <mi>α</mi> <mn>2</mn> </msub> <mo>/</mo> <msub> <mi>α</mi> <mn>1</mn> </msub> </mrow> </math>.</p>
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<p><math display="inline"> <msub> <mi>η</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mo>-</mo> <mi>S</mi> <mi>S</mi> <mi>C</mi> </mrow> </msub> </math> <span class="html-italic">vs</span>. the ratio, <math display="inline"> <mrow> <msub> <mi>α</mi> <mn>2</mn> </msub> <mo>/</mo> <msub> <mi>α</mi> <mn>1</mn> </msub> </mrow> </math>.</p>
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<p>According to the <a href="#entropy-15-02162-t001" class="html-table">Table 1</a>, the numeric values obtained for the Figure of Merit equivalents in each connection are displayed. The most optimum value corresponds to the thermal and electrical configuration in parallel.</p>
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1615 KiB  
Article
Zero Delay Joint Source Channel Coding for Multivariate Gaussian Sources over Orthogonal Gaussian Channels
by Pål Anders Floor, Anna N. Kim, Tor A. Ramstad and Ilangko Balasingham
Entropy 2013, 15(6), 2129-2161; https://doi.org/10.3390/e15062129 - 31 May 2013
Cited by 10 | Viewed by 7031
Abstract
Communication of a multivariate Gaussian source transmitted over orthogonal additive white Gaussian noise channels using delay-free joint source channel codes (JSCC) is studied in this paper. Two scenarios are considered: (1) all components of the multivariate Gaussian are transmitted by one encoder as [...] Read more.
Communication of a multivariate Gaussian source transmitted over orthogonal additive white Gaussian noise channels using delay-free joint source channel codes (JSCC) is studied in this paper. Two scenarios are considered: (1) all components of the multivariate Gaussian are transmitted by one encoder as a vector or several ideally collaborating nodes in a network; (2) the multivariate Gaussian is transmitted through distributed nodes in a sensor network. In both scenarios, the goal is to recover all components of the multivariate Gaussian at the receiver. The paper investigates a subset of JSCC consisting of direct source-to-channel mappings that operate on a symbol-by-symbol basis to ensure zero coding delay. A theoretical analysis that helps explain and quantify distortion behavior for such JSCC is given. Relevant performance bounds for the network are also derived with no constraints on complexity and delay. Optimal linear schemes for both scenarios are presented. Results for Scenario 1 show that linear mappings perform well, except when correlation is high. In Scenario 2, linear mappings provide no gain from correlation when the channel signal-to-noise ratio (SNR) gets large. The gap to the performance upper bound is large for both scenarios, regardless of SNR, when the correlation is high. The main contribution of this paper is the investigation of nonlinear mappings for both scenarios. It is shown that nonlinear mappings can provide substantial gain compared to optimal linear schemes when correlation is high. Contrary to linear mappings for Scenario 2, carefully chosen nonlinear mappings provide a gain for all SNR, as long as the correlation is close to one. Both linear and nonlinear mappings are robust against variations in SNR. Full article
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Figure 1

Figure 1
<p>Block diagram for networks under consideration. (<b>a</b>) Scenario 1: cooperative encoders; (<b>b</b>) Scenario 2: distributed encoders.</p>
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<p>Comparison between distributed and cooperative linear scheme and OPTA. (<b>a</b>) M = 2 and <math display="inline"> <mrow> <msub> <mi>ρ</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>9</mn> </mrow> </math>; (<b>b</b>) M = 4,10 and <math display="inline"> <mrow> <msub> <mi>ρ</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>99</mn> </mrow> </math>.</p>
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<p>Shannon-Kotelnikov (S-K) mappings. The curves represent a scalar source mapped through <math display="inline"> <mi mathvariant="bold">f</mi> </math> in the channel space. Positive source values reside on the blue curves, while negative reside on the red. (<b>a</b>) <math display="inline"> <mrow> <mi>M</mi> <mo>=</mo> <mn>2</mn> </mrow> </math>: Archimedes spiral; (<b>b</b>) <math display="inline"> <mrow> <mi>M</mi> <mo>=</mo> <mn>3</mn> </mrow> </math>: “Ball of Yarn”.</p>
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<p>Performance of S-K mappings when <math display="inline"> <mrow> <mi>M</mi> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> </mrow> </math> for several values of Δ.</p>
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<p>1:2 S-K mappings. (<b>a</b>) Linear and nonlinear mappings; (<b>b</b>) when spiral arms come too close, noise may take the transmitted vector, <math display="inline"> <mrow> <mi mathvariant="bold">f</mi> <mo>(</mo> <msub> <mi>y</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> </math>, closer to another fold of the curve, leading to large decoding errors.</p>
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<p>Channel space structures when <math display="inline"> <mrow> <mi>M</mi> <mo>=</mo> <mn>2</mn> </mrow> </math> and <math display="inline"> <mrow> <msub> <mi>ρ</mi> <mi>x</mi> </msub> <mo>&lt;</mo> <mn>1</mn> </mrow> </math>. (<b>a</b>) 5 bit distributed quantizer (DQ), <math display="inline"> <mrow> <msub> <mi>ρ</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>95</mn> </mrow> </math>; (<b>b</b>) 5 bit DQ, <math display="inline"> <mrow> <msub> <mi>ρ</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>99</mn> </mrow> </math>; (<b>c</b>) sawtooth mapping, <math display="inline"> <mrow> <msub> <mi>ρ</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>99</mn> </mrow> </math>; (<b>d</b>) Archimedes spiral, <math display="inline"> <mrow> <msub> <mi>ρ</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>999</mn> </mrow> </math>.</p>
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<p>How the three lines, <math display="inline"> <mrow> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>=</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo>-</mo> <mi>κ</mi> </mrow> </math> (red), <math display="inline"> <mrow> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>=</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> </mrow> </math> (blue) and <math display="inline"> <mrow> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>=</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo>+</mo> <mi>κ</mi> </mrow> </math> (green), are mapped by selected nonlinear mappings. (<b>a</b>) Collaborative mapping in Equation (<a href="#FD25-entropy-15-02129" class="html-disp-formula">25</a>); (<b>b</b>) distributed mapping in Equation (<a href="#FD21-entropy-15-02129" class="html-disp-formula">21</a>); (<b>c</b>) sawtooth mapping in Equation (<a href="#FD27-entropy-15-02129" class="html-disp-formula">27</a>); (<b>d</b>) DQ from <a href="#entropy-15-02129-f006" class="html-fig">Figure 6</a>b.</p>
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<p>Illustration on how to approximately calculate anomalous errors when only common information is to be reconstructed.</p>
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<p>Performance of cooperative S-K mappings (simulated) and Block Pulse Amplitude Modulation (BPAM) for <math display="inline"> <mrow> <mi>M</mi> <mo>=</mo> <mn>2</mn> </mrow> </math> sources when (<b>a</b>) <math display="inline"> <mrow> <msub> <mi>ρ</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>99</mn> </mrow> </math>; (<b>b</b>) <math display="inline"> <mrow> <msub> <mi>ρ</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>999</mn> </mrow> </math>.</p>
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<p>Geometrical illustration of Sawtooth mapping used for calculation of distortion. Only <math display="inline"> <mrow> <mi mathvariant="bold">f</mi> <mo>(</mo> <mi>y</mi> <mo>)</mo> </mrow> </math>, <span class="html-italic">i.e.,</span> the transformation of common information, is displayed here.</p>
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<p>Performance of distributed S-K mappings (simulated) and distributed linear scheme for <math display="inline"> <mrow> <mi>M</mi> <mo>=</mo> <mn>2</mn> </mrow> </math> sources when (<b>a</b>) <math display="inline"> <mrow> <msub> <mi>ρ</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>99</mn> </mrow> </math>; (<b>b</b>) <math display="inline"> <mrow> <msub> <mi>ρ</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>999</mn> </mrow> </math>.</p>
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<p>Comparison of cooperative S-K mappings, distributed S-K mappings and 5-bit DQ for <math display="inline"> <mrow> <mi>M</mi> <mo>=</mo> <mn>2</mn> </mrow> </math> when <math display="inline"> <mrow> <msub> <mi>ρ</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>999</mn> </mrow> </math>. DQ is optimized for 18 dB SNR.</p>
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755 KiB  
Review
Quantum Thermodynamics: A Dynamical Viewpoint
by Ronnie Kosloff
Entropy 2013, 15(6), 2100-2128; https://doi.org/10.3390/e15062100 - 29 May 2013
Cited by 575 | Viewed by 31941
Abstract
Quantum thermodynamics addresses the emergence of thermodynamic laws from quantum mechanics. The viewpoint advocated is based on the intimate connection of quantum thermodynamics with the theory of open quantum systems. Quantum mechanics inserts dynamics into thermodynamics, giving a sound foundation to finite-time-thermodynamics. The [...] Read more.
Quantum thermodynamics addresses the emergence of thermodynamic laws from quantum mechanics. The viewpoint advocated is based on the intimate connection of quantum thermodynamics with the theory of open quantum systems. Quantum mechanics inserts dynamics into thermodynamics, giving a sound foundation to finite-time-thermodynamics. The emergence of the 0-law, I-law, II-law and III-law of thermodynamics from quantum considerations is presented. The emphasis is on consistency between the two theories, which address the same subject from different foundations. We claim that inconsistency is the result of faulty analysis, pointing to flaws in approximations. Full article
(This article belongs to the Special Issue Quantum Information 2012)
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Figure 1
<p>The tricycle on the left and the wire on the right; elementary components in a quantum network. The tricycle combines three energy currents. The tricycle in the figure is connected to three heat baths, demonstrating a heat-driven refrigerator. The wire combines two energy currents. The wire in the figure is connected to a hot and cold bath. The I-law and II-law are indicated.</p>
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<p>An example of a quantum thermodynamic network composed of wires and tricycles.</p>
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<p>A demonstration of the III-law shown as the vanishing of the cooling current and the rate of temperature decrease as <math display="inline"> <mrow> <msub> <mi>T</mi> <mi>c</mi> </msub> <mo>→</mo> <mn>0</mn> </mrow> </math>. The harmonic bath in 3-d is indicated in blue, and the Bose gas in 3-d in red. The Bose gas cools faster when <math display="inline"> <mrow> <msub> <mi>T</mi> <mi>c</mi> </msub> <mo>→</mo> <mn>0</mn> </mrow> </math>, but its rate of temperature decrease is slower than the harmonic bath.</p>
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698 KiB  
Article
Analysis of Entropy Generation Rate in an Unsteady Porous Channel Flow with Navier Slip and Convective Cooling
by Tirivanhu Chinyoka and Oluwole Daniel Makinde
Entropy 2013, 15(6), 2081-2099; https://doi.org/10.3390/e15062081 - 28 May 2013
Cited by 36 | Viewed by 5718
Abstract
This study deals with the combined effects of Navier Slip, Convective cooling, variable viscosity, and suction/injection on the entropy generation rate in an unsteady flow of an incompressible viscous fluid flowing through a channel with permeable walls. The model equations for momentum and [...] Read more.
This study deals with the combined effects of Navier Slip, Convective cooling, variable viscosity, and suction/injection on the entropy generation rate in an unsteady flow of an incompressible viscous fluid flowing through a channel with permeable walls. The model equations for momentum and energy balance are solved numerically using semi-discretization finite difference techniques. Both the velocity and temperature profiles are obtained and utilized to compute the entropy generation number. The effects of key parameters on the fluid velocity, temperature, entropy generation rate and Bejan number are depicted graphically and analyzed in detail. Full article
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Figure 1
<p>Schematic diagram of the problem.</p>
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<p>Transient and steady state profiles.</p>
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<p>Analytic and numerical solution: (<b>a</b>) default values, (<b>b</b>) <math display="inline"> <mrow> <msub> <mi>λ</mi> <mn>1</mn> </msub> <mo>=</mo> <msub> <mi>λ</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0</mn> </mrow> </math>.</p>
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<p>Effects of the Prandtl number, <math display="inline"> <mi>Pr</mi> </math>.</p>
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<p>Effects of the Reynolds number, <math display="inline"> <mi>Re</mi> </math>.</p>
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<p>Effects of the viscosity parameter, <span class="html-italic">m</span>.</p>
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<p>Effects of the lower wall Biot number <math display="inline"> <mrow> <mo>(</mo> <msub> <mi>Bi</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> </math>.</p>
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<p>Effects of the upper wall Biot number <math display="inline"> <mrow> <mo>(</mo> <msub> <mi>Bi</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> </math>.</p>
Full article ">Figure 9
<p>Effects of the Eckert number, (<math display="inline"> <mi>Ec</mi> </math>).</p>
Full article ">Figure 10
<p>Effects of the lower wall slip parameter, <math display="inline"> <mrow> <mo>(</mo> <msub> <mi>λ</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> </math>.</p>
Full article ">Figure 11
<p>Effects of the upper wall slip parameter, <math display="inline"> <mrow> <mo>(</mo> <msub> <mi>λ</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> </math>.</p>
Full article ">Figure 12
<p>Variation of entropy generation rate with <span class="html-italic">η</span> and <span class="html-italic">t</span>.</p>
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<p>Variation of Bejan number with <span class="html-italic">η</span> and <span class="html-italic">t</span>.</p>
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<p>Variation of entropy generation rate with <span class="html-italic">η</span> and <math display="inline"> <mi>Re</mi> </math>.</p>
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<p>Variation of entropy generation rate with <span class="html-italic">η</span> and <math display="inline"> <mi>Pr</mi> </math>.</p>
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<p>Variation of entropy generation rate with <span class="html-italic">η</span> and <span class="html-italic">m</span>.</p>
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<p>Variation of entropy generation rate with <span class="html-italic">η</span> and <math display="inline"> <mrow> <mi>Br</mi> <mspace width="0.166667em"/> <msup> <mo>Ω</mo> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> </mrow> </math>.</p>
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<p>Variation of entropy generation rate with <span class="html-italic">η</span> and <math display="inline"> <msub> <mi>λ</mi> <mn>1</mn> </msub> </math>.</p>
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<p>Variation of entropy generation rate with <span class="html-italic">η</span> and <math display="inline"> <msub> <mi>λ</mi> <mn>2</mn> </msub> </math>.</p>
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<p>Variation of entropy generation rate with <span class="html-italic">η</span> and <math display="inline"> <msub> <mi>Bi</mi> <mn>1</mn> </msub> </math>.</p>
Full article ">Figure 21
<p>Variation of entropy generation rate with <span class="html-italic">η</span> and <math display="inline"> <msub> <mi>Bi</mi> <mn>2</mn> </msub> </math>.</p>
Full article ">Figure 22
<p>Variation of Bejan number with <span class="html-italic">η</span> and <math display="inline"> <mi>Re</mi> </math>.</p>
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<p>Variation of Bejan number with <span class="html-italic">η</span> and <math display="inline"> <mi>Pr</mi> </math>.</p>
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<p>Variation of Bejan number with <span class="html-italic">η</span> and <span class="html-italic">m</span>.</p>
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<p>Variation of Bejan number with <span class="html-italic">η</span> and <math display="inline"> <mrow> <mi>Br</mi> <mspace width="0.166667em"/> <msup> <mo>Ω</mo> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> </mrow> </math>.</p>
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<p>Variation of Bejan number with <span class="html-italic">η</span> and <math display="inline"> <msub> <mi>λ</mi> <mn>1</mn> </msub> </math>.</p>
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<p>Variation of Bejan number with <span class="html-italic">η</span> and <math display="inline"> <msub> <mi>λ</mi> <mn>2</mn> </msub> </math>.</p>
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<p>Variation of Bejan number with <span class="html-italic">η</span> and <math display="inline"> <msub> <mi>Bi</mi> <mn>1</mn> </msub> </math>.</p>
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<p>Variation of Bejan number with <span class="html-italic">η</span> and <math display="inline"> <msub> <mi>Bi</mi> <mn>2</mn> </msub> </math>.</p>
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705 KiB  
Article
Generalized Least Energy of Separation for Desalination and Other Chemical Separation Processes
by Karan H. Mistry and John H. Lienhard V
Entropy 2013, 15(6), 2046-2080; https://doi.org/10.3390/e15062046 - 27 May 2013
Cited by 96 | Viewed by 9728
Abstract
Increasing global demand for fresh water is driving the development and implementation of a wide variety of seawater desalination technologies driven by different combinations of heat, work, and chemical energy. This paper develops a consistent basis for comparing the energy consumption of such [...] Read more.
Increasing global demand for fresh water is driving the development and implementation of a wide variety of seawater desalination technologies driven by different combinations of heat, work, and chemical energy. This paper develops a consistent basis for comparing the energy consumption of such technologies using Second Law efficiency. The Second Law efficiency for a chemical separation process is defined in terms of the useful exergy output, which is the minimum least work of separation required to extract a unit of product from a feed stream of a given composition. For a desalination process, this is the minimum least work of separation for producing one kilogram of product water from feed of a given salinity. While definitions in terms of work and heat input have been proposed before, this work generalizes the Second Law efficiency to allow for systems that operate on a combination of energy inputs, including fuel. The generalized equation is then evaluated through a parametric study considering work input, heat inputs at various temperatures, and various chemical fuel inputs. Further, since most modern, large-scale desalination plants operate in cogeneration schemes, a methodology for correctly evaluating Second Law efficiency for the desalination plant based on primary energy inputs is demonstrated. It is shown that, from a strictly energetic point of view and based on currently available technology, cogeneration using electricity to power a reverse osmosis system is energetically superior to thermal systems such as multiple effect distillation and multistage flash distillation, despite the very low grade heat input normally applied in those systems. Full article
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<p>A generalized control volume with <span class="html-italic">q</span> inlets, <span class="html-italic">r</span> outlets, <span class="html-italic">p</span> heat transfer inputs, and work input. The control volume can exchange work (<math display="inline"> <mrow> <msub> <mi>p</mi> <mn>0</mn> </msub> <mspace width="0.166667em"/> <mi mathvariant="normal">d</mi> <mi>V</mi> <mo>/</mo> <mi mathvariant="normal">d</mi> <mi>t</mi> </mrow> </math>), heat (<math display="inline"> <msub> <mover accent="true"> <mi>Q</mi> <mo>˙</mo> </mover> <mn>0</mn> </msub> </math>), and mass (<math display="inline"> <msub> <mover accent="true"> <mi>N</mi> <mo>˙</mo> </mover> <mrow> <mn>0</mn> <mo>,</mo> <mi>i</mi> </mrow> </msub> </math>) with the environment through the system boundary.</p>
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<p>A control volume for an arbitrary black box chemical separator powered by work only.</p>
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<p>Least work of separation as a function of feed salinity and recovery ratio. Feed water is at <math display="inline"> <mrow> <msub> <mi>T</mi> <mn>0</mn> </msub> <mo>=</mo> <msup> <mn>25</mn> <mo>∘</mo> </msup> <mi mathvariant="normal">C</mi> </mrow> </math>.</p>
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<p>A control volume for an arbitrary black box chemical separator when heat is the only form of energy input.</p>
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<p>Least heat of separation as a function of feed salinity and recovery ratio. Feed water is at <math display="inline"> <mrow> <msub> <mi>T</mi> <mn>0</mn> </msub> <mo>=</mo> <msup> <mn>25</mn> <mo>∘</mo> </msup> <mi mathvariant="normal">C</mi> </mrow> </math> and <math display="inline"> <msub> <mi>y</mi> <mi>f</mi> </msub> </math> = 35 g/kg.</p>
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<p>Maximum gained output ratio (GOR) for desalination processes with various temperatures of heat input. Feed water is at <math display="inline"> <mrow> <msub> <mi>T</mi> <mn>0</mn> </msub> <mo>=</mo> <msup> <mn>25</mn> <mo>∘</mo> </msup> <mi mathvariant="normal">C</mi> </mrow> </math> and <math display="inline"> <mrow> <msub> <mi>y</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>35</mn> <mi mathvariant="normal">g</mi> <mo>/</mo> <mi>kg</mi> </mrow> </math>.</p>
Full article ">Figure 7
<p>A control volume for an arbitrary black box chemical separator when fuel is the only form of energy input.</p>
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<p>Least amount of fuel needed when using combustion as a function of fuel type and recovery ratio. Feed water is at <math display="inline"> <mrow> <msub> <mi>T</mi> <mn>0</mn> </msub> <mo>=</mo> <msup> <mn>25</mn> <mo>∘</mo> </msup> <mi mathvariant="normal">C</mi> </mrow> </math> and <math display="inline"> <mrow> <msub> <mi>y</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>35</mn> <mi mathvariant="normal">g</mi> <mo>/</mo> <mi>kg</mi> </mrow> </math>.</p>
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<p>Least amount of salt needed when letting a salt dissociate to equilibrium as a function of recovery ratio for CaSO<sub>4</sub> and AgSO<sub>4</sub>. Feed water is at <math display="inline"> <mrow> <msub> <mi>T</mi> <mn>0</mn> </msub> <mo>=</mo> <msup> <mn>25</mn> <mo>∘</mo> </msup> <mi mathvariant="normal">C</mi> </mrow> </math> and <math display="inline"> <mrow> <msub> <mi>y</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>35</mn> <mi mathvariant="normal">g</mi> <mo>/</mo> <mi>kg</mi> </mrow> </math>.</p>
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<p>Schematic diagram of a typical single stage reverse osmosis system with a forward osmosis and pressure exchanger energy recovery devices.</p>
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<p>A control volume for an arbitrary black box chemical separator powered by work and a salinity gradient engine.</p>
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<p>Least work of separation for a black box separator with a salinity gradient engine as a function of feed salinity, recovery ratio, and ratio of the energy recovery stream to the feed. Feed water is at <math display="inline"> <mrow> <msub> <mi>T</mi> <mn>0</mn> </msub> <mo>=</mo> <msup> <mn>25</mn> <mo>∘</mo> </msup> <mi mathvariant="normal">C</mi> </mrow> </math> and <math display="inline"> <mrow> <msub> <mi>y</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>35</mn> <mi mathvariant="normal">g</mi> <mo>/</mo> <mi>kg</mi> </mrow> </math>.</p>
Full article ">Figure 13
<p>Least work of separation for a black box separator with a salinity gradient engine as a function of feed salinity, recovery ratio, and salinity of the energy recovery stream. Feed water is at <math display="inline"> <mrow> <msub> <mi>T</mi> <mn>0</mn> </msub> <mo>=</mo> <msup> <mn>25</mn> <mo>∘</mo> </msup> <mi mathvariant="normal">C</mi> </mrow> </math> and <math display="inline"> <mrow> <msub> <mi>y</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>35</mn> <mi mathvariant="normal">g</mi> <mo>/</mo> <mi>kg</mi> </mrow> </math>.</p>
Full article ">Figure 14
<p>The power plant converts heat input into work output, work for the desalination plant, and heat for the desalination plant. It is assumed that the power plant operates at a Second Law efficiency of <math display="inline"> <msub> <mi>η</mi> <mrow> <mi>p</mi> <mi>p</mi> </mrow> </msub> </math>.</p>
Full article ">Figure 15
<p>The Second Law efficiency of a work-driven desalination system operating in a cogeneration scheme can never reach 100% unless the power plant operates reversibly. Typical values for current reverse osmosis systems are highlighted. Feed water is at <math display="inline"> <mrow> <msub> <mi>T</mi> <mn>0</mn> </msub> <mo>=</mo> <msup> <mn>25</mn> <mo>∘</mo> </msup> <mi mathvariant="normal">C</mi> </mrow> </math> and <math display="inline"> <mrow> <msub> <mi>y</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>35</mn> <mi mathvariant="normal">g</mi> <mo>/</mo> <mi>kg</mi> </mrow> </math>.</p>
Full article ">Figure 16
<p>The power plant converts heat input into work output, work for the desalination plant, and heat for the desalination plant. Feed water is at <math display="inline"> <mrow> <msub> <mi>T</mi> <mn>0</mn> </msub> <mo>=</mo> <msup> <mn>25</mn> <mo>∘</mo> </msup> <mi mathvariant="normal">C</mi> </mrow> </math> and <math display="inline"> <mrow> <msub> <mi>y</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>35</mn> </mrow> </math> g/kg.</p>
Full article ">Figure 17
<p>Second Law efficiency for a thermal desalination plant requiring work for pumping. Lines for pump work are in increments of 0.5 kWh. As the pump work increases, the Second Law efficiency decreases. Feed water is at <math display="inline"> <mrow> <msub> <mi>T</mi> <mn>0</mn> </msub> <mo>=</mo> <msup> <mn>25</mn> <mo>∘</mo> </msup> <mi mathvariant="normal">C</mi> </mrow> </math> and <math display="inline"> <mrow> <msub> <mi>y</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>35</mn> <mi mathvariant="normal">g</mi> <mo>/</mo> <mi>kg</mi> </mrow> </math>.</p>
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746 KiB  
Article
Reliability of Inference of Directed Climate Networks Using Conditional Mutual Information
by Jaroslav Hlinka, David Hartman, Martin Vejmelka, Jakob Runge, Norbert Marwan, Jürgen Kurths and Milan Paluš
Entropy 2013, 15(6), 2023-2045; https://doi.org/10.3390/e15062023 - 24 May 2013
Cited by 92 | Viewed by 11699
Abstract
Across geosciences, many investigated phenomena relate to specific complex systems consisting of intricately intertwined interacting subsystems. Such dynamical complex systems can be represented by a directed graph, where each link denotes an existence of a causal relation, or information exchange between the nodes. [...] Read more.
Across geosciences, many investigated phenomena relate to specific complex systems consisting of intricately intertwined interacting subsystems. Such dynamical complex systems can be represented by a directed graph, where each link denotes an existence of a causal relation, or information exchange between the nodes. For geophysical systems such as global climate, these relations are commonly not theoretically known but estimated from recorded data using causality analysis methods. These include bivariate nonlinear methods based on information theory and their linear counterpart. The trade-off between the valuable sensitivity of nonlinear methods to more general interactions and the potentially higher numerical reliability of linear methods may affect inference regarding structure and variability of climate networks. We investigate the reliability of directed climate networks detected by selected methods and parameter settings, using a stationarized model of dimensionality-reduced surface air temperature data from reanalysis of 60-year global climate records. Overall, all studied bivariate causality methods provided reproducible estimates of climate causality networks, with the linear approximation showing higher reliability than the investigated nonlinear methods. On the example dataset, optimizing the investigated nonlinear methods with respect to reliability increased the similarity of the detected networks to their linear counterparts, supporting the particular hypothesis of the near-linearity of the surface air temperature reanalysis data. Full article
(This article belongs to the Special Issue Applications of Information Theory in the Geosciences)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Location of areas dominated by specific components of the surface air temperature data using VARIMAX-rotated PCA decomposition. For each location, the color corresponding to the component with maximal intensity was used. White dots represent approximate centers of mass of the components, used in subsequent figures for visualization of the nodes of the networks.</p>
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<p>Reliability of causality network detection using different causality estimators, and the similarity to linear causality network estimates using the Fourier surrogate model. For each estimator, six causality networks are estimated, one for each decade-long section of model stationary data (a Fourier surrogate realization of the original data). Black: the height of the bar corresponds to the average Spearman’s correlation across all 15 pairs of decades. White: the height of the bar corresponds to the average Spearman’s correlation of nonlinear causality network and linear causality network across 6 decades.</p>
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<p>The variability of causality network detection using different causality estimators and the similarity to linear causality network estimates for the original data. For each estimator, six causality networks are estimated, one for each decade of the data. Black: the height of the bar corresponds to the average Spearman’s correlation across all 15 pairs of decades. White: the height of the bar corresponds to the average Spearman’s correlation of nonlinear causality network and linear causality network across 6 decades.</p>
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<p>The reliability of causality network detection using different causality estimators and the similarity to linear causality network estimates for the stationary model constructed as multivariate AR(1) surrogate of the original data. For each estimator, six causality networks are estimated, one for each decade of modeled stationary data. Black: the height of the bar corresponds to the average Spearman’s correlation across all 15 pairs of decades. White: the height of the bar corresponds to the average Spearman’s correlation of nonlinear causality network and linear causality network across 6 decades.</p>
Full article ">Figure 5
<p>The reliability of causality network detection using different causality estimators and the similarity to linear causality network estimates for the stationary model constructed as multivariate AR(1) surrogate of the original data. For each estimator, six causality networks are estimated, each for a separate realization of the multivariate AR(1) process fitted to the original data. Black: the height of the bar corresponds to the average Spearman’s correlation across all 15 pairs of decades. White: the height of the bar corresponds to the average Spearman’s correlation of nonlinear causality network and linear causality network across 6 decades.</p>
Full article ">Figure 6
<p>The reliability of causality network detection using different causality estimators and the similarity to linear causality network estimates. For each estimator, six causality networks are estimated, one for each decade of modeled stationary data. Black: the height of the bar corresponds to the average Jaccard similarity coefficient across all 15 pairs of decades. White: the height of the bar corresponds to the average Jaccard similarity coefficient of nonlinear causality network and linear causality network across 6 decades.</p>
Full article ">Figure 7
<p>Causality network obtained by averaging the results for the six decades (total time span 1948–2007) for decomposed data (67 components represented by center of mass). Only the 100 strongest links are shown. For each decade, the network was estimated by linear Granger causality.</p>
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<p>Causality network obtained by averaging the results for the six decades (total time span 1948–2007) for decomposed data (67 components represented by center of mass). Only the 100 strongest links are shown. For each decade, the network was estimated by (nonlinear) transfer entropy using the equiqantal binning method with <math display="inline"> <mrow> <mi>Q</mi> <mo>=</mo> <mn>2</mn> </mrow> </math>.</p>
Full article ">Figure 9
<p>Causality network obtained by averaging the results for the six decades (total time span 1948–2007) for decomposed data (67 components represented by center of mass). Only the 100 strongest links are shown. For each decade, the network was detected by the fully multivariate linear Granger causality.</p>
Full article ">Figure 10
<p>The reliability of causality network detection using different causality estimators and the similarity to linear causality network estimates for the Fourier surrogates model. For each estimator, six causality networks are estimated, one for each decade-long section of the model stationary data (a Fourier surrogate realization of the original data). Black: the height of the bar corresponds to the average Spearman’s correlation across all 15 pairs of decades. White: the height of the bar corresponds to the average Spearman’s correlation of nonlinear causality network and linear causality network across 6 decades.</p>
Full article ">Figure 11
<p>Causality network obtained by averaging the results for the six decades (total time span 1948–2007) for gridded data (162 spatial locations). Only the 200 strongest links are shown. For each decade, the network was estimated by linear Granger causality.</p>
Full article ">Figure 12
<p>Causality network obtained by averaging the results for the six decades (total time span 1948–2007) for gridded data (162 spatial locations). Only the 200 strongest links are shown. For each decade, the network was estimated by (nonlinear) conditional mutual information, using the equiqantal binning method with <math display="inline"> <mrow> <mi>Q</mi> <mo>=</mo> <mn>2</mn> </mrow> </math>.</p>
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289 KiB  
Article
The Dynamics of Concepts in a Homogeneous Community
by Eugene Khmelnitsky and Eugene Kagan
Entropy 2013, 15(6), 2012-2022; https://doi.org/10.3390/e15062012 - 23 May 2013
Cited by 3 | Viewed by 4886
Abstract
The paper addresses informational interactions in a community and considers the dynamics of concepts that represent distribution of knowledge among the individuals. The evolution of a set of concepts maintained by a community is derived by the use of the concepts’ significance in [...] Read more.
The paper addresses informational interactions in a community and considers the dynamics of concepts that represent distribution of knowledge among the individuals. The evolution of a set of concepts maintained by a community is derived by the use of the concepts’ significance in the communication between “cognoscenti” and “dilettanti” and of birth-death processes. The dynamics of concepts depend on the allocation of communication resources and can be governed by an informational principle that requires minimum self-information of the set of concepts over a time horizon. With respect to that principle, the introduction of a new concept into a community’s disposal is shown to lead to a steady-state self-information, which is smaller than that before the introduction of the new concept. Full article
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<p>The scheme of the concept’s and community dynamics.</p>
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<p>Probability <math display="inline"> <semantics> <mrow> <msub> <mi>p</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics> </math>for different values of death rate <math display="inline"> <semantics> <mi>γ</mi> </semantics> </math> and significance <math display="inline"> <semantics> <mrow> <msub> <mi>ψ</mi> <mi>i</mi> </msub> </mrow> </semantics> </math>.</p>
Full article ">Figure 3
<p>The self-information <math display="inline"> <semantics> <mrow> <msub> <mi>I</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mo>−</mo> <msubsup> <mstyle displaystyle="true"> <mo>∑</mo> </mstyle> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>N</mi> <mo>+</mo> <mn>1</mn> </mrow> </msubsup> <mi>ln</mi> <msub> <mi>p</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics> </math> over the transition period.</p>
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409 KiB  
Article
Bias Adjustment for a Nonparametric Entropy Estimator
by Zhiyi Zhang and Michael Grabchak
Entropy 2013, 15(6), 1999-2011; https://doi.org/10.3390/e15061999 - 23 May 2013
Cited by 11 | Viewed by 8297
Abstract
Zhang in 2012 introduced a nonparametric estimator of Shannon’s entropy, whose bias decays exponentially fast when the alphabet is finite. We propose a methodology to estimate the bias of this estimator. We then use it to construct a new estimator of entropy. Simulation [...] Read more.
Zhang in 2012 introduced a nonparametric estimator of Shannon’s entropy, whose bias decays exponentially fast when the alphabet is finite. We propose a methodology to estimate the bias of this estimator. We then use it to construct a new estimator of entropy. Simulation results suggest that this bias adjusted estimator has a significantly lower bias than many other commonly used estimators. We consider both the case when the alphabet is finite and when it is countably infinite. Full article
(This article belongs to the Special Issue Estimating Information-Theoretic Quantities from Data)
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<p>(<b>a</b>) Plot of <span class="html-italic">v</span> on the <span class="html-italic">x</span>-axis and <math display="inline"> <mrow> <mo form="prefix">ln</mo> <mo stretchy="false">(</mo> <msub> <mover accent="true"> <mo>Δ</mo> <mo stretchy="false">^</mo> </mover> <mi>v</mi> </msub> <mo stretchy="false">)</mo> </mrow> </math> on the <span class="html-italic">y</span>-axis. This is based on a random sample of size 200 from a Zipf distribution. The overlaid line is the estimated <math display="inline"> <mrow> <mo form="prefix">ln</mo> <mi>δ</mi> <mo>(</mo> <mi>v</mi> <mo>)</mo> </mrow> </math>; (<b>b</b>) Plot of <math display="inline"> <mrow> <mo form="prefix">ln</mo> <mo>(</mo> <mi>v</mi> <mo>)</mo> </mrow> </math> on the <span class="html-italic">x</span>-axis and <math display="inline"> <mrow> <mo form="prefix">ln</mo> <mo stretchy="false">(</mo> <msub> <mover accent="true"> <mo>Δ</mo> <mo stretchy="false">^</mo> </mover> <mi>v</mi> </msub> <mo stretchy="false">)</mo> </mrow> </math> on the <span class="html-italic">y</span>-axis. This is based on a random sample of size 200 from a Poisson distribution. The overlaid line is the estimated <math display="inline"> <mrow> <mo form="prefix">ln</mo> <mi>δ</mi> <mo>(</mo> <mi>v</mi> <mo>)</mo> </mrow> </math>.</p>
Full article ">Figure 1 Cont.
<p>(<b>a</b>) Plot of <span class="html-italic">v</span> on the <span class="html-italic">x</span>-axis and <math display="inline"> <mrow> <mo form="prefix">ln</mo> <mo stretchy="false">(</mo> <msub> <mover accent="true"> <mo>Δ</mo> <mo stretchy="false">^</mo> </mover> <mi>v</mi> </msub> <mo stretchy="false">)</mo> </mrow> </math> on the <span class="html-italic">y</span>-axis. This is based on a random sample of size 200 from a Zipf distribution. The overlaid line is the estimated <math display="inline"> <mrow> <mo form="prefix">ln</mo> <mi>δ</mi> <mo>(</mo> <mi>v</mi> <mo>)</mo> </mrow> </math>; (<b>b</b>) Plot of <math display="inline"> <mrow> <mo form="prefix">ln</mo> <mo>(</mo> <mi>v</mi> <mo>)</mo> </mrow> </math> on the <span class="html-italic">x</span>-axis and <math display="inline"> <mrow> <mo form="prefix">ln</mo> <mo stretchy="false">(</mo> <msub> <mover accent="true"> <mo>Δ</mo> <mo stretchy="false">^</mo> </mover> <mi>v</mi> </msub> <mo stretchy="false">)</mo> </mrow> </math> on the <span class="html-italic">y</span>-axis. This is based on a random sample of size 200 from a Poisson distribution. The overlaid line is the estimated <math display="inline"> <mrow> <mo form="prefix">ln</mo> <mi>δ</mi> <mo>(</mo> <mi>v</mi> <mo>)</mo> </mrow> </math>.</p>
Full article ">Figure 2
<p>We compare the absolute value of the bias of our estimator (New Sharp) with that of the plug-in (MLE), the Miller–Madow (MM), and the one given in Equation (<a href="#FD4-entropy-15-01999" class="html-disp-formula">4</a>) (New). The <span class="html-italic">x</span>-axis is the sample size and the <span class="html-italic">y</span>-axis is the absolute value of the bias. The plots correspond to the distributions: (<b>a</b>) Triangular distribution and (<b>b</b>) Zipf distribution.</p>
Full article ">Figure 3
<p>We compare the absolute value of the bias of our estimator with that of the NSB estimator. The <span class="html-italic">x</span>-axis is the sample size and the <span class="html-italic">y</span>-axis is the absolute value of the bias. The plots correspond to the distributions: (<b>a</b>) Triangular distribution and (<b>b</b>) Zipf distribution.</p>
Full article ">Figure 4
<p>We compare the absolute value of the bias of our estimator (New Sharp) with that of the plug-in (MLE), the Miller–Madow (MM), and the one given in Equation (<a href="#FD4-entropy-15-01999" class="html-disp-formula">4</a>) (New). The <span class="html-italic">x</span>-axis is the sample size and the <span class="html-italic">y</span>-axis is the absolute value of the bias. The plots correspond to the distributions: (<b>a</b>) Power; (<b>b</b>) Geometric; and (<b>c</b>) Poisson.</p>
Full article ">Figure 5
<p>We compare the absolute value of the bias of our estimator with that of the NSB estimator. The <span class="html-italic">x</span>-axis is the sample size and the <span class="html-italic">y</span>-axis is the absolute value of the bias. The plots correspond to the distributions: (<b>a</b>) Power; (<b>b</b>) Geometric; and (<b>c</b>) Poisson.</p>
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360 KiB  
Article
Inequality of Chances as a Symmetry Phase Transition
by Jorge Rosenblatt
Entropy 2013, 15(6), 1985-1998; https://doi.org/10.3390/e15061985 - 23 May 2013
Cited by 2 | Viewed by 5540
Abstract
We propose a model for Lorenz curves. It provides two-parameter fits to data on incomes, electric consumption, life expectation and rate of survival after cancer. Graphs result from the condition of maximum entropy and from the symmetry of statistical distributions. Differences in populations [...] Read more.
We propose a model for Lorenz curves. It provides two-parameter fits to data on incomes, electric consumption, life expectation and rate of survival after cancer. Graphs result from the condition of maximum entropy and from the symmetry of statistical distributions. Differences in populations composing a binary system (poor and rich, young and old, etc.) bring about chance inequality. Symmetrical distributions insure equality of chances, generate Gini coefficients Gi £ ⅓, and imply that nobody gets more than twice the per capita benefit. Graphs generated by different symmetric distributions, but having the same Gini coefficient, intersect an even number of times. The change toward asymmetric distributions follows the pattern set by second-order phase transitions in physics, in particular universality: Lorenz plots reduce to a single universal curve after normalisation and scaling. The order parameter is the difference between cumulated benefit fractions for equal and unequal chances. The model also introduces new parameters: a cohesion range describing the extent of apparent equality in an unequal society, a poor-rich asymmetry parameter, and a new Gini-like indicator that measures unequal-chance inequality and admits a theoretical expression in closed form. Full article
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>(<b>a</b>) Concave and convex L-graphs resulting from equal- and unequal-chance inequality, respectively. <span style="color:red">─∙─</span> Perfect equality, <math display="inline"> <semantics> <mrow> <mi>L</mi> <mrow> <mo>(</mo> <mi>F</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>F</mi> </mrow> </semantics> </math>. <math display="inline"> <semantics> <mrow> <mstyle mathcolor="#00B050"> <mo>——</mo> </mstyle> </mrow> </semantics> </math> Gaussian and its Gini-equivalent uniform density, both having <math display="inline"> <semantics> <mrow> <mi>G</mi> <mi>i</mi> <mo>=</mo> <mn>0.17</mn> </mrow> </semantics> </math><b>—</b>Equal-chance line, <math display="inline"> <semantics> <mrow> <mi>L</mi> <mrow> <mo>(</mo> <mi>F</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>F</mi> <mo>²</mo> </mrow> </semantics> </math>. <math display="inline"> <semantics> <mrow> <mstyle mathcolor="#00B050"> <mo>▲▲▲</mo> </mstyle> </mrow> </semantics> </math> USA incomes. <math display="inline"> <semantics> <mrow> <mstyle mathcolor="red"> <mo>◦◦◦</mo> </mstyle> </mrow> </semantics> </math> World electricity consumption. <math display="inline"> <semantics> <mrow> <mstyle mathcolor="#0070C0"> <mo>▢▢▢</mo> </mstyle> </mrow> </semantics> </math> Life expectation. ♦♦♦ Cancer rate of survival. Class boundaries, <math display="inline"> <semantics> <mrow> <mi>F</mi> <mo>²</mo> <mo>=</mo> <mfrac> <mn>1</mn> <mn>4</mn> </mfrac> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>L</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mrow> <mi>F</mi> <mo>²</mo> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <mn>1</mn> <mo>−</mo> <mi>F</mi> <mo>²</mo> </mrow> </semantics> </math>. <b>––</b> Unit-slope tangent. X──X Distance <span class="html-italic">d</span> from equal-chance line. (<b>b</b>) Corresponding statistical distributions. Symbols apply as in (<b>a</b>).</p>
Full article ">Figure 2
<p>Symmetry-dependent universal behaviours. (<b>a</b>) <span class="html-italic">ECI concave L-curves</span>: ––Perfect equality, <math display="inline"> <semantics> <mrow> <mi>G</mi> <mi>i</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math>. <b>∙∙∙∙</b> Uniform distribution, <math display="inline"> <semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>0.33</mn> <mo>,</mo> <mtext>  </mtext> <mi>G</mi> <mi>i</mi> <mo>=</mo> <mn>0.17</mn> </mrow> </semantics> </math>. <math display="inline"> <semantics> <mrow> <mstyle mathcolor="red"> <mo>– – –</mo> </mstyle> </mrow> </semantics> </math> Gini-equivalent, Gaussian-like distribution, <math display="inline"> <semantics> <mrow> <mi>G</mi> <mi>i</mi> <mo>=</mo> <mn>0.17.</mn> </mrow> </semantics> </math> (<b>b</b>) <span class="html-italic">UCI convex L-curves</span>: <math display="inline"> <semantics> <mrow> <mstyle mathcolor="#00B050"> <mo>▲▲▲</mo> </mstyle> </mrow> </semantics> </math> Incomes. <math display="inline"> <semantics> <mrow> <mstyle mathcolor="red"> <mo>◦◦◦</mo> </mstyle> </mrow> </semantics> </math> World electricity consumption. <math display="inline"> <semantics> <mrow> <mstyle mathcolor="#0070C0"> <mo>▢▢▢</mo> </mstyle> </mrow> </semantics> </math> Life expectation. ♦♦♦ Rate of survival after cancer. <b>––</b> Theoretical curve. <b>∙∙∙∙∙</b> Absolute inequality. <math display="inline"> <semantics> <mrow> <mstyle mathcolor="#00B050"> <mo>- - -</mo> </mstyle> </mrow> </semantics> </math> Fictitious data before normalisation and symmetrisation (right-hand ordinates, with <math display="inline"> <semantics> <mrow> <mi>d</mi> <msqrt> <mn>2</mn> </msqrt> <mo>=</mo> <mn>0.7</mn> <mo>,</mo> <mtext>  </mtext> <msub> <mi>X</mi> <mi>M</mi> </msub> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics> </math>).</p>
Full article ">Figure 3
<p>Conventional <math display="inline"> <semantics> <mrow> <mi>L</mi> <mrow> <mo>(</mo> <mi>F</mi> <mo>)</mo> </mrow> </mrow> </semantics> </math> Lorenz plots. Symbols for data and for their fits by model predictions are shown in the insert. The model should not apply for values <math display="inline"> <semantics> <mrow> <mi>k</mi> <mo>²</mo> <mo>&gt;</mo> <mn>0.22</mn> </mrow> </semantics> </math>.</p>
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1013 KiB  
Article
Metric Structure of the Space of Two-Qubit Gates, Perfect Entanglers and Quantum Control
by Paul Watts, Maurice O'Connor and Jiří Vala
Entropy 2013, 15(6), 1963-1984; https://doi.org/10.3390/e15061963 - 23 May 2013
Cited by 19 | Viewed by 6847
Abstract
We derive expressions for the invariant length element and measure for the simple compact Lie group SU(4) in a coordinate system particularly suitable for treating entanglement in quantum information processing. Using this metric, we compute the invariant volume of the space of two-qubit [...] Read more.
We derive expressions for the invariant length element and measure for the simple compact Lie group SU(4) in a coordinate system particularly suitable for treating entanglement in quantum information processing. Using this metric, we compute the invariant volume of the space of two-qubit perfect entanglers. We find that this volume corresponds to more than 84% of the total invariant volume of the space of two-qubit gates. This same metric is also used to determine the effective target sizes that selected gates will present in any quantum-control procedure designed to implement them. Full article
(This article belongs to the Special Issue Quantum Information 2012)
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Figure 1

Figure 1
<p>(Colour online) The Weyl chamber in <math display="inline"> <mrow> <msub> <mi>c</mi> <mn>1</mn> </msub> <msub> <mi>c</mi> <mn>2</mn> </msub> <msub> <mi>c</mi> <mn>3</mn> </msub> </mrow> </math>-space. The perfect entanglers make up the region highlighted in red.</p>
Full article ">Figure 2
<p>(Colour online) The Weyl chamber in <math display="inline"> <mrow> <msub> <mi>g</mi> <mn>1</mn> </msub> <msub> <mi>g</mi> <mn>2</mn> </msub> <msub> <mi>g</mi> <mn>3</mn> </msub> </mrow> </math>-space, with the region of perfect entanglers highlighted in red.</p>
Full article ">Figure 3
<p>(Colour online) Cube volumes within the Weyl chamber. The volume factor <math display="inline"> <msub> <mi>M</mi> <mi mathvariant="script">A</mi> </msub> </math> as a function of <math display="inline"> <mrow> <mo>(</mo> <msubsup> <mi>c</mi> <mn>1</mn> <mo>*</mo> </msubsup> <mo>,</mo> <msubsup> <mi>c</mi> <mn>2</mn> <mo>*</mo> </msubsup> <mo>)</mo> </mrow> </math> on horizontal slices with, from left to right, <math display="inline"> <mrow> <msubsup> <mi>c</mi> <mn>3</mn> <mo>*</mo> </msubsup> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>12</mn> </mrow> </math>, <math display="inline"> <mrow> <msubsup> <mi>c</mi> <mn>3</mn> <mo>*</mo> </msubsup> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>6</mn> </mrow> </math> and <math display="inline"> <mrow> <msubsup> <mi>c</mi> <mn>3</mn> <mo>*</mo> </msubsup> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>4</mn> </mrow> </math>.</p>
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