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Entropy, Volume 12, Issue 6 (June 2010) – 14 articles , Pages 1325-1652

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293 KiB  
Review
General Framework of Pressure Effects on Structures Formed by Entropically Driven Self-Assembly
by Takashi Yoshidome
Entropy 2010, 12(6), 1632-1652; https://doi.org/10.3390/e12061632 - 23 Jun 2010
Cited by 4 | Viewed by 6896
Abstract
We review a general framework of pressure effects on the structures formed by entropically driven self-assembly (for example, denaturation of proteins from their native structure and dissociation of ordered structure of the amyloid fibril occur at high pressures). In the framework, the translational [...] Read more.
We review a general framework of pressure effects on the structures formed by entropically driven self-assembly (for example, denaturation of proteins from their native structure and dissociation of ordered structure of the amyloid fibril occur at high pressures). In the framework, the translational entropy of water is an essential factor. Our findings are as follows: at low pressures, the structures almost minimizing the excluded volume (EV) generated for water molecules are stable. On the other hand, at high pressures, the structures possessing the largest possible water-accessible surface area together with sufficiently small EV become more stable. These characteristics are consistent with experimental observations. Full article
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Figure 1
<p>Schematic representation of three side chains. The excluded volume generated by a side chain is the volume occupied by the side chain itself plus the volume shown in gray. When side chains are closely packed, the excluded volumes overlap, leading to a gain of the water entropy.</p>
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<p>Reduced density profiles of hard-sphere solvent near a hard-sphere solute <span class="html-italic">g</span><sub>US</sub>(<span class="html-italic">r</span>) at <span class="html-italic">ρ</span><sub>S</sub><span class="html-italic">d</span><sub>S</sub><sup>3</sup> = 0.2 (dotted line), <span class="html-italic">ρ</span><sub>S</sub><span class="html-italic">d</span><sub>S</sub><sup>3</sup> = 0.5 (2-dot dashed line), and <span class="html-italic">ρ</span><sub>S</sub><span class="html-italic">d</span><sub>S</sub><sup>3</sup> = 0.7 (solid line).</p>
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<p>(a) <span class="html-italic">C</span><sub>1</sub> (Å<sup>−3</sup>), <span class="html-italic">C</span><sub>2</sub> (Å<sup>−2</sup>), and <span class="html-italic">C</span><sub>2</sub>/<span class="html-italic">C</span><sub>1</sub> (Å) of solvation entropy, −<span class="html-italic">S</span>/k<sub>B</sub>, plotted against solvent density corresponding to the pressure. (b) <span class="html-italic">C</span><sub>1</sub> and <span class="html-italic">C</span><sub>2</sub> (Å) of partial molar volume plotted against solvent density.</p>
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<p>Negative of the entropy change of solvent scaled by k<sub>B</sub> upon the transition from the native structure to (a) the swelling structure and (b) the random-coil structures of protein G plotted against the bulk solvent density corresponding to the pressure <span class="html-italic">P</span>. −Δ<span class="html-italic">S</span>/k<sub>B</sub> = (−<span class="html-italic">S</span>/k<sub>B</sub>)<sup>Unfold</sup>−{(−<span class="html-italic">S</span>/k<sub>B</sub>)<sup>Native</sup>} where the superscripts “Native” and “Unfold” represent the values for the native structure and for the unfolded structure, respectively. (c) Decomposition of −Δ<span class="html-italic">S</span>/k<sub>B</sub> for swelling structure of protein G [<a href="#entropy-12-01632-f004" class="html-fig">Figure 4</a>(a)] into <span class="html-italic">C</span><sub>1</sub>Δ<span class="html-italic">V</span><sub>ex</sub>, <span class="html-italic">C</span><sub>2</sub>Δ<span class="html-italic">A</span>, and <span class="html-italic">C</span><sub>3</sub>Δ<span class="html-italic">X</span>+<span class="html-italic">C</span><sub>4</sub>Δ<span class="html-italic">Y</span> at each bulk solvent density.</p>
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<p>Δ<span class="html-italic">S</span> and |Δ<span class="html-italic">S</span><sub>C</sub>| plotted against the bulk solvent density corresponding to the pressure. |Δ<span class="html-italic">S</span><sub>C</sub>| lies between the two dashed lines. The SE gain and the CE loss upon the transition from the coil state (an ensemble of random coils) to the complete α-helix structure are compared.</p>
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676 KiB  
Article
Eigenvalue and Entropy Statistics for Products of Conjugate Random Quantum Channels
by Benoît Collins and Ion Nechita
Entropy 2010, 12(6), 1612-1631; https://doi.org/10.3390/e12061612 - 23 Jun 2010
Cited by 9 | Viewed by 7510
Abstract
Using the graphical calculus and integration techniques introduced by the authors, we study the statistical properties of outputs of products of random quantum channels for entangled inputs. In particular, we revisit and generalize models of relevance for the recent counterexamples to the minimum [...] Read more.
Using the graphical calculus and integration techniques introduced by the authors, we study the statistical properties of outputs of products of random quantum channels for entangled inputs. In particular, we revisit and generalize models of relevance for the recent counterexamples to the minimum output entropy additivity problems. Our main result is a classification of regimes for which the von Neumann entropy is lower on average than the elementary bounds that can be obtained with linear algebra techniques. Full article
(This article belongs to the Collection Quantum Information)
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Graphical abstract

Graphical abstract
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<p>Basic diagrams and axioms: (a) diagram for a general tensor <span class="html-italic">M</span>; (b) trace of a <math display="inline"> <mrow> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> </math>-tensor (matrix) <span class="html-italic">M</span>; (c) Scalar product <math display="inline"> <mrow> <mo>〈</mo> <mi>y</mi> <mspace width="0.277778em"/> <mo>|</mo> <mspace width="0.277778em"/> <mi>M</mi> <mspace width="0.277778em"/> <mo>|</mo> <mspace width="0.277778em"/> <mi>x</mi> <mo>〉</mo> </mrow> </math>; (d) tensor product of two diagrams. The round, square and diamond-shaped labels correspond to pairs of dual finite dimensional complex Hilbert spaces.</p>
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<p>Densities for the Marchenko-Pastur measures of parameters <math display="inline"> <mrow> <mi>c</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>5</mn> </mrow> </math>, <math display="inline"> <mrow> <mi>c</mi> <mo>=</mo> <mn>1</mn> </mrow> </math> and <math display="inline"> <mrow> <mi>c</mi> <mo>=</mo> <mn>5</mn> </mrow> </math>. For <math display="inline"> <mrow> <mi>c</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>5</mn> </mrow> </math>, only the absolutely continuous part of the measure was plotted; <math display="inline"> <msub> <mi>π</mi> <mrow> <mn>1</mn> <mo>/</mo> <mn>5</mn> </mrow> </msub> </math> has a Dirac mass of <math display="inline"> <mrow> <mn>4</mn> <mo>/</mo> <mn>5</mn> </mrow> </math> at <math display="inline"> <mrow> <mi>x</mi> <mo>=</mo> <mn>0</mn> </mrow> </math> which is not represented.</p>
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<p>Diagram for a quantum channel with equal input and output spaces. The state <math display="inline"> <msub> <mi>P</mi> <mi>k</mi> </msub> </math> of the ancilla space is omitted, since it has no role to play in the computations. Round labels attached to boxes correspond to input/output spaces <sup><span class="html-italic">n</span></sup> and square symbols correspond to ancilla spaces <sup><span class="html-italic">k</span></sup>.</p>
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<p>Diagram for a quantum channel with different input and output spaces. Round labels attached to boxes correspond to output spaces <sup><span class="html-italic">n</span></sup>, square symbols correspond to ancilla spaces <sup><span class="html-italic">k</span></sup> and diamonds correspond to input spaces <sup><span class="html-italic">m</span></sup>. The rank-one projector <math display="inline"> <msub> <mi>P</mi> <mi>l</mi> </msub> </math> is omitted.</p>
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<p>Diagram for the output of a product of two conjugate channels, when the input is the maximally entangled state. The complex Hilbert spaces associated to labels are as follows: <math display="inline"> <mrow> <mrow> <mo>◦</mo> <mo> </mo> <mo>•</mo> </mrow> <msup> <mo>⇝</mo> <mi>n</mi> </msup> </mrow> </math>, <math display="inline"> <mrow> <mrow> <mo>▫</mo> <mo> </mo> <mo>▪</mo> </mrow> <msup> <mo>⇝</mo> <mi>k</mi> </msup> </mrow> </math>, <math display="inline"> <mrow> <mrow> <mo>◇</mo> <mo> </mo> <mo>◆</mo> </mrow> <msup> <mo>⇝</mo> <mi>m</mi> </msup> </mrow> </math> and <math display="inline"> <mrow> <mrow> <mo>▵</mo> <mo> </mo> <mo>▴</mo> </mrow> <msup> <mo>⇝</mo> <mi>l</mi> </msup> </mrow> </math>.</p>
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877 KiB  
Article
Projection Pursuit Through ϕ-Divergence Minimisation
by Jacques Touboul
Entropy 2010, 12(6), 1581-1611; https://doi.org/10.3390/e12061581 - 14 Jun 2010
Cited by 3 | Viewed by 8246
Abstract
In his 1985 article (“Projection pursuit”), Huber demonstrates the interest of his method to estimate a density from a data set in a simple given case. He considers the factorization of density through a Gaussian component and some residual density. Huber’s work is [...] Read more.
In his 1985 article (“Projection pursuit”), Huber demonstrates the interest of his method to estimate a density from a data set in a simple given case. He considers the factorization of density through a Gaussian component and some residual density. Huber’s work is based on maximizing Kullback–Leibler divergence. Our proposal leads to a new algorithm. Furthermore, we will also consider the case when the density to be factorized is estimated from an i.i.d. sample. We will then propose a test for the factorization of the estimated density. Applications include a new test of fit pertaining to the elliptical copulas. Full article
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<p>Graph of the distribution to estimate (red) and of our own estimate (green).</p>
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<p>Graph of the distribution to estimate (red) and of Huber’s estimate (green).</p>
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<p>Graph of the regression of <math display="inline"> <msub> <mi>X</mi> <mn>1</mn> </msub> </math> on <math display="inline"> <msub> <mi>X</mi> <mn>0</mn> </msub> </math> based on the least squares method (red) and based on our theory (green).</p>
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<p>Graph of the regression of <math display="inline"> <msub> <mi>X</mi> <mn>1</mn> </msub> </math> on <math display="inline"> <msub> <mi>X</mi> <mn>0</mn> </msub> </math> based on our theory (green).</p>
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<p>Graph of the estimate of <math display="inline"> <mrow> <mrow> <mo stretchy="false">(</mo> <msub> <mi>x</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo stretchy="false">)</mo> </mrow> <mo>↦</mo> <msub> <mi>c</mi> <mi>ρ</mi> </msub> <mrow> <mo stretchy="false">(</mo> <msub> <mi>F</mi> <mrow> <mi>G</mi> <mi>u</mi> <mi>m</mi> <mi>b</mi> <mi>e</mi> <mi>l</mi> </mrow> </msub> <mrow> <mo stretchy="false">(</mo> <msub> <mi>x</mi> <mn>0</mn> </msub> <mo stretchy="false">)</mo> </mrow> <mo>,</mo> <msub> <mi>F</mi> <mrow> <mi>E</mi> <mi>x</mi> <mi>p</mi> <mi>o</mi> <mi>n</mi> <mi>e</mi> <mi>n</mi> <mi>t</mi> <mi>i</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> <mrow> <mo stretchy="false">(</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo stretchy="false">)</mo> </mrow> <mo stretchy="false">)</mo> </mrow> </mrow> </math>.</p>
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<p>Graph of the regression of log of Nokia on Sanofi based on the least squares method (red) and based on our theory (green).</p>
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172 KiB  
Article
A Concentrated, Nonlinear Information-Theoretic Estimator for the Sample Selection Model
by Amos Golan and Henryk Gzyl
Entropy 2010, 12(6), 1569-1580; https://doi.org/10.3390/e12061569 - 14 Jun 2010
Viewed by 6734
Abstract
This paper develops a semi-parametric, Information-Theoretic method for estimating parameters for nonlinear data generated under a sample selection process. Considering the sample selection as a set of inequalities makes this model inherently nonlinear. This estimator (i) allows for a whole class of different [...] Read more.
This paper develops a semi-parametric, Information-Theoretic method for estimating parameters for nonlinear data generated under a sample selection process. Considering the sample selection as a set of inequalities makes this model inherently nonlinear. This estimator (i) allows for a whole class of different priors, and (ii) is constructed as an unconstrained, concentrated model. This estimator is easy to apply and works well with small or complex data. We provide a number of explicit analytical examples for different priors’ structures and an empirical example. Full article
523 KiB  
Article
Families of Alpha- Beta- and Gamma- Divergences: Flexible and Robust Measures of Similarities
by Andrzej Cichocki and Shun-ichi Amari
Entropy 2010, 12(6), 1532-1568; https://doi.org/10.3390/e12061532 - 14 Jun 2010
Cited by 315 | Viewed by 18003
Abstract
In this paper, we extend and overview wide families of Alpha-, Beta- and Gamma-divergences and discuss their fundamental properties. In literature usually only one single asymmetric (Alpha, Beta or Gamma) divergence is considered. We show in this paper that there exist families of [...] Read more.
In this paper, we extend and overview wide families of Alpha-, Beta- and Gamma-divergences and discuss their fundamental properties. In literature usually only one single asymmetric (Alpha, Beta or Gamma) divergence is considered. We show in this paper that there exist families of such divergences with the same consistent properties. Moreover, we establish links and correspondences among these divergences by applying suitable nonlinear transformations. For example, we can generate the Beta-divergences directly from Alpha-divergences and vice versa. Furthermore, we show that a new wide class of Gamma-divergences can be generated not only from the family of Beta-divergences but also from a family of Alpha-divergences. The paper bridges these divergences and shows also their links to Tsallis and Rényi entropies. Most of these divergences have a natural information theoretic interpretation. Full article
718 KiB  
Communication
Fairness Is an Emergent Self-Organized Property of the Free Market for Labor
by Venkat Venkatasubramanian
Entropy 2010, 12(6), 1514-1531; https://doi.org/10.3390/e12061514 - 14 Jun 2010
Cited by 12 | Viewed by 16740
Abstract
The excessive compensation packages of CEOs of U.S. corporations in recent years have brought to the foreground the issue of fairness in economics. The conventional wisdom is that the free market for labor, which determines the pay packages, cares only about efficiency and [...] Read more.
The excessive compensation packages of CEOs of U.S. corporations in recent years have brought to the foreground the issue of fairness in economics. The conventional wisdom is that the free market for labor, which determines the pay packages, cares only about efficiency and not fairness. We present an alternative theory that shows that an ideal free market environment also promotes fairness, as an emergent property resulting from the self-organizing market dynamics. Even though an individual employee may care only about his or her salary and no one else’s, the collective actions of all the employees, combined with the profit maximizing actions of all the companies, in a free market environment under budgetary constraints, lead towards a more fair allocation of wages, guided by Adam Smith’s invisible hand of self-organization. By exploring deep connections with statistical thermodynamics, we show that entropy is the appropriate measure of fairness in a free market environment which is maximized at equilibrium to yield the lognormal distribution of salaries as the fairest inequality of pay in an organization under ideal conditions. Full article
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<p>Spreading of the salary distribution under competition.</p>
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<p>Free market interaction between A and B: Initial macrostate.</p>
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<p>Free market interaction between A and B: Evolution of a new macrostate.</p>
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<p>Free market interaction between A and B: New equilibrium macrostate.</p>
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<p>Free market interaction between A and C: Initial macrostate.</p>
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<p>Free market interaction between A and C: New equilibrium macrostate.</p>
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392 KiB  
Article
Entropy-Based Method of Choosing the Decomposition Level in Wavelet Threshold De-noising
by Yan-Fang Sang, Dong Wang and Ji-Chun Wu
Entropy 2010, 12(6), 1499-1513; https://doi.org/10.3390/e12061499 - 10 Jun 2010
Cited by 45 | Viewed by 8812
Abstract
In this paper, the energy distributions of various noises following normal, log-normal and Pearson-III distributions are first described quantitatively using the wavelet energy entropy (WEE), and the results are compared and discussed. Then, on the basis of these analytic results, a method for [...] Read more.
In this paper, the energy distributions of various noises following normal, log-normal and Pearson-III distributions are first described quantitatively using the wavelet energy entropy (WEE), and the results are compared and discussed. Then, on the basis of these analytic results, a method for use in choosing the decomposition level (DL) in wavelet threshold de-noising (WTD) is put forward. Finally, the performance of the proposed method is verified by analysis of both synthetic and observed series. Analytic results indicate that the proposed method is easy to operate and suitable for various signals. Moreover, contrary to traditional white noise testing which depends on “autocorrelations”, the proposed method uses energy distributions to distinguish real signals and noise in noisy series, therefore the chosen DL is reliable, and the WTD results of time series can be improved. Full article
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<p>The lag-1 autocorrelation coefficient <span class="html-italic">R<sub>1</sub></span> and energy <span class="html-italic">E</span> of sub-signals of various noises under different decomposition levels (DLs).</p>
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<p>Values of wavelet energy entropy (WEE) of various noises when analyzed by using different decomposition levels (DLs).</p>
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<p>Steps of choosing suitable decomposition level (DL) in the process of wavelet threshold de-noising by using the proposed method.</p>
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<p>Three synthetic series data used in this paper.</p>
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<p>Values of WEE of three synthetic series and the corresponding derivation coefficients when analyzed by using different decomposition levels (DLs).</p>
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<p>Wavelet variance curves of the de-noised synthetic series obtained by using different decomposition levels (DLs).</p>
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<p>De-noising results of the three synthetic series by using the chosen decomposition levels (DLs).</p>
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<p>Values of WEE of the two observed series and the corresponding derivation coefficients when analyzed by using different decomposition levels (DLs).</p>
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<p>De-noising results of the two observed series by using the chosen decomposition levels (DLs) (upper), histograms of the separated noise (mid) and the wavelet variance curves of the de-noised series and observed series data (lower).</p>
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480 KiB  
Article
Prediction of Droplet Size and Velocity Distribution in Droplet Formation Region of Liquid Spray
by Ehsan Movahednejad, Fathollah Ommi and S. Mostafa Hosseinalipour
Entropy 2010, 12(6), 1484-1498; https://doi.org/10.3390/e12061484 - 10 Jun 2010
Cited by 43 | Viewed by 11495
Abstract
Determining the distributions of size and velocity of droplets formed at the end of primary breakup region is followed in this paper. The droplet formation stage at the end of primary breakup is random and stochastic and it can be modeled by statistical [...] Read more.
Determining the distributions of size and velocity of droplets formed at the end of primary breakup region is followed in this paper. The droplet formation stage at the end of primary breakup is random and stochastic and it can be modeled by statistical means based on the maximum entropy principle (MEP). The MEP formulation predicts the atomization process while satisfying constraint equations based on conservations of mass, momentum and energy. This model is capable of considering drag force on produced droplets through gas-liquid interaction using new approach. The model prediction is compared favorably with the experimentally measured size and velocity distributions of droplets for sprays produced by the two nozzles of considerably different geometries and shows satisfactory agreement. Full article
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<p>Comparison of theoretical and experimental contour.</p>
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<p>Comparison of theoretical (solid line) and experimental (dashed line) [<a href="#B12-entropy-12-01484" class="html-bibr">12</a>] droplet size distribution.</p>
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<p>Comparison between the theoretical (MEP) and experimental (Mitra [<a href="#B18-entropy-12-01484" class="html-bibr">18</a>]) droplet size distribution for PWC nozzle.</p>
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<p>Atomization regions of the spray from the hollow cone nozzle [<a href="#B22-entropy-12-01484" class="html-bibr">22</a>].</p>
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<p>Number density of droplets <span class="html-italic">versus</span> dimensionless droplet size.</p>
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<p>Number density of droplets <span class="html-italic">versus</span> dimensionless droplet velocity.</p>
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<p>Droplet size distribution of PWC nozzle for two different positions in downstream direction [<a href="#B18-entropy-12-01484" class="html-bibr">18</a>].</p>
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<p>Prediction of droplet size distribution of PWC nozzle using MEP model for two different positions in downstream direction.</p>
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2596 KiB  
Article
A Network Model of Interpersonal Alignment in Dialog
by Alexander Mehler, Andy Lücking and Petra Weiß
Entropy 2010, 12(6), 1440-1483; https://doi.org/10.3390/e12061440 - 9 Jun 2010
Cited by 49 | Viewed by 10202
Abstract
In dyadic communication, both interlocutors adapt to each other linguistically, that is, they align interpersonally. In this article, we develop a framework for modeling interpersonal alignment in terms of the structural similarity of the interlocutors’ dialog lexica. This is done by means [...] Read more.
In dyadic communication, both interlocutors adapt to each other linguistically, that is, they align interpersonally. In this article, we develop a framework for modeling interpersonal alignment in terms of the structural similarity of the interlocutors’ dialog lexica. This is done by means of so-called two-layer time-aligned network series, that is, a time-adjusted graph model. The graph model is partitioned into two layers, so that the interlocutors’ lexica are captured as subgraphs of an encompassing dialog graph. Each constituent network of the series is updated utterance-wise. Thus, both the inherent bipartition of dyadic conversations and their gradual development are modeled. The notion of alignment is then operationalized within a quantitative model of structure formation based on the mutual information of the subgraphs that represent the interlocutor’s dialog lexica. By adapting and further developing several models of complex network theory, we show that dialog lexica evolve as a novel class of graphs that have not been considered before in the area of complex (linguistic) networks. Additionally, we show that our framework allows for classifying dialogs according to their alignment status. To the best of our knowledge, this is the first approach to measuring alignment in communication that explores the similarities of graph-like cognitive representations. Full article
(This article belongs to the Special Issue Complexity of Human Language and Cognition)
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<p>Priming of representations within two networks of mental representations of an interlocutor <span class="html-italic">A</span> and <span class="html-italic">B</span>, respectively.</p>
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<p>Critical (upper row) and uncritical (lower row) objects and their naming (/in diagonal slashes/) in the JMG.</p>
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<p>Schematic depiction of an object arrangement in the JMG: agent <span class="html-italic">A</span> (left side) plays two instruction cards as does agent <span class="html-italic">B</span> (right side). Numbers indicate the order of the cards being played. The map in the middle shows the object arrangement after these for cards have been processed.</p>
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<p>Schematic representation of a <span class="html-italic">Two-layer Time-Aligned Network</span> (TiTAN) series starting with two initially unlinked lexica of interlocutor <span class="html-italic">A</span> and <span class="html-italic">B</span> (upper left). Both interlocutor lexica are networked step by step (upper right) till, finally, a dialog lexicon emerges that is spanned by intra- and interpersonal links across the alignment channel (lower left). The lower right part of the figure highlights the role of turn-taking as the means by which dialog lexica (represented as TiTAN series) gradually evolve.</p>
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<p>A graph-theoretical model of turning points of lexical alignment (cf. [<a href="#B42-entropy-12-01440" class="html-bibr">42</a>]). On the left side, a two-layer dialog lexicon is shown whose layers are completely separated as there are no links crossing the alignment channel. The right side depicts the opposite case where both interlocutor lexica are identical and where each item is linked across the alignment channel with its correspondent in the lexicon of the opposite interlocutor.</p>
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<p>The final two-layer network of a TiTAN series that represents a gradually evolving dialog lexicon of two interlocutors. Initially, no items are interlinked. From turn to turn, more and more associations are established intra- and interpersonally so that the dialog lexicon is finally structured as depicted by the network. Edge weights are represented by the thickness of lines.</p>
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<p>Two labeled graphs <math display="inline"> <semantics> <mrow> <msub> <mi>G</mi> <mn>1</mn> </msub> <mo>=</mo> <mrow> <mo>(</mo> <msub> <mi>V</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>E</mi> <mn>1</mn> </msub> <mrow> <mo>,</mo> </mrow> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> </mrow> <mo>)</mo> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>G</mi> <mn>2</mn> </msub> <mo>=</mo> <mrow> <mo>(</mo> <msub> <mi>V</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>E</mi> <mn>2</mn> </msub> <mrow> <mo>,</mo> </mrow> <msub> <mi mathvariant="script">L</mi> <mn>2</mn> </msub> </mrow> <mo>)</mo> </mrow> </semantics> </math> as the projections <math display="inline"> <semantics> <mrow> <msub> <mi>π</mi> <mi>A</mi> </msub> <mrow> <mo>(</mo> <mi>G</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>G</mi> <mn>1</mn> </msub> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>π</mi> <mi>B</mi> </msub> <mrow> <mo>(</mo> <mi>G</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>G</mi> <mn>2</mn> </msub> </mrow> </semantics> </math> of a graph <math display="inline"> <semantics> <mrow> <mi>G</mi> <mo>=</mo> <mo>(</mo> <msub> <mi>V</mi> <mn>1</mn> </msub> <mo>∪</mo> <msub> <mi>V</mi> <mn>2</mn> </msub> <mo>,</mo> <mi>E</mi> <mo>,</mo> <mi mathvariant="script">L</mi> <mo>)</mo> </mrow> </semantics> </math> such that <math display="inline"> <semantics> <mrow> <msub> <mi>E</mi> <mn>1</mn> </msub> <mo>⊂</mo> <mi>E</mi> <mo>⊃</mo> <msub> <mi>E</mi> <mn>2</mn> </msub> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi mathvariant="script">L</mi> <mo>=</mo> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> <mo>∪</mo> <msub> <mi mathvariant="script">L</mi> <mn>2</mn> </msub> </mrow> </semantics> </math>. <math display="inline"> <semantics> <msub> <mi>G</mi> <mn>1</mn> </msub> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>G</mi> <mn>2</mn> </msub> </semantics> </math> share, for example, an equally labeled vertex in the 1-sphere of <math display="inline"> <semantics> <mrow> <mi>v</mi> <mo>∈</mo> <msub> <mi>V</mi> <mn>1</mn> </msub> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>x</mi> <mo>∈</mo> <msub> <mi>V</mi> <mn>2</mn> </msub> </mrow> </semantics> </math>, respectively.</p>
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<p>The temporal dynamics of the cluster coefficient <span class="html-italic">C</span><sub>1</sub> [<a href="#B61-entropy-12-01440" class="html-bibr">61</a>] (<span class="html-italic">y</span>-axis) as a function of time (<span class="html-italic">x</span>-axis) by example of the TiTAN series induced from Dialog 19<math display="inline"> <semantics> <msup> <mrow/> <mo>*</mo> </msup> </semantics> </math> (•) in relation to the corresponding BM-I (+), BM-II (×), BM-III (Δ), SM-I (□), and SM-II (∘) model.</p>
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<p>The temporal dynamics of the weighted cluster coefficient <math display="inline"> <semantics> <mrow> <mo>〈</mo> <msub> <mi>C</mi> <mi>w</mi> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>〉</mo> </mrow> </semantics> </math> [<a href="#B62-entropy-12-01440" class="html-bibr">62</a>] (<span class="html-italic">y</span>-axis) as a function of time (<span class="html-italic">x</span>-axis) by example of the TiTAN series induced from Dialog 19<math display="inline"> <semantics> <msup> <mrow/> <mo>*</mo> </msup> </semantics> </math> (•) in relation to the corresponding BM-I (+), BM-II (×), BM-III (Δ), SM-I (□), and SM-II (∘) model.</p>
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<p>The temporal dynamics of the average cluster coefficient <span class="html-italic">C</span><sub>1</sub> [<a href="#B61-entropy-12-01440" class="html-bibr">61</a>] (<span class="html-italic">y</span>-axis) as a function of time (<span class="html-italic">x</span>-axis) by example of both layers of the TiTAN series induced from Dialog 19<math display="inline"> <semantics> <msup> <mrow/> <mo>*</mo> </msup> </semantics> </math> (•) in relation to the corresponding BM-I (+), BM-II (×), BM-III (Δ), SM-I (□), and SM-II (∘) model.</p>
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<p>The temporal dynamics of the cluster coefficient [<a href="#B61-entropy-12-01440" class="html-bibr">61</a>] (<span class="html-italic">y</span>-axis) as a function of time (<span class="html-italic">x</span>-axis) by example of 11 dialog lexica (green) in relation to the corresponding BM-I (red), BM-III (cyan), and SM-I (blue) models.</p>
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<p>The temporal dynamics of the normalized average geodesic distance (upper left), its average over both interlocutor lexica (upper right), the standardized closeness centrality [<a href="#B63-entropy-12-01440" class="html-bibr">63</a>] (lower left) and its corresponding average (<span class="html-italic">y</span>-axis) by example of the TiTAN series induced from Dialog 19<math display="inline"> <semantics> <msup> <mrow/> <mo>*</mo> </msup> </semantics> </math> (•) in relation to the corresponding BM-I (+), BM-II (×), BM-III (Δ), SM-I (□), and SM-II (∘) models.</p>
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<p>The temporal dynamics of the normalized average geodesic distance and the standardized closeness centrality (<span class="html-italic">y</span>-axis) both averaged over the layers of two-layer networks by example of 11 dialog lexica (green) in relation to the corresponding BM-I (red), BM-III (cyan), and SM-I (blue) models.</p>
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<p>The temporal dynamics of the index of modularity [<a href="#B68-entropy-12-01440" class="html-bibr">68</a>] (<span class="html-italic">y</span>-axis) averaged over the layers of two-layer networks by example of 11 dialog lexica (green) in relation to the corresponding BM-I (red), and SM-I (blue) models.</p>
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<p>The temporal dynamics of <math display="inline"> <semantics> <mrow> <mi>S</mi> <mrow> <mo>(</mo> <msub> <mi>G</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>G</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <msub> <mrow> <mo>|</mo> </mrow> <mrow> <mo>(</mo> <mi>v</mi> <mo>,</mo> <mi>w</mi> <mo>)</mo> </mrow> </msub> </mrow> </semantics> </math> (left) and <math display="inline"> <semantics> <msub> <mi>d</mi> <mi>W</mi> </msub> </semantics> </math> (right) (<span class="html-italic">y</span>-axis) by example of 11 dialog lexica (green) in relation to the corresponding BM-I (red), BM-III (cyan), and SM-I (blue) models.</p>
Full article ">Figure 16
<p>Distribution of <span class="html-italic">F</span>-scores (<span class="html-italic">y</span>-axis) of 50 classifications (<span class="html-italic">x</span>-axis) each of which operates on 24 dialogs as represented by two-layer networks. The classification starts with the endpoints of the 24 dialogs (<span class="html-italic">i.e.</span>, from the left on the <span class="html-italic">x</span>-axis) and then regresses event by event. The ordinate plots the corresponding <span class="html-italic">F</span>-scores. Bullets (•) denote the values of the best performing classification as a result of a genetic search for the best performing subset of 50 topological features. Circles (∘) denote the values of a genetic search for the best performing subset of 12 features that give an <span class="html-italic">F</span>-score of <math display="inline"> <semantics> <mrow> <mi>F</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> for the first classification (<span class="html-italic">i.e.</span>, the endpoints of the dialogs). Diamonds are the corresponding <span class="html-italic">F</span>-scores that are produced by using all these 12 features without any additional optimization. Squares (□) denote the <span class="html-italic">F</span>-scores of only two features, <math display="inline"> <semantics> <mrow> <mi>S</mi> <mrow> <mo>(</mo> <msub> <mi>G</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>G</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <msub> <mrow> <mo>|</mo> </mrow> <mrow> <mo>(</mo> <mi>v</mi> <mo>,</mo> <mi>w</mi> <mo>)</mo> </mrow> </msub> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>d</mi> <mi>W</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>G</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>G</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> </mrow> </semantics> </math>. Finally, the straight lines denote the <span class="html-italic">F</span>-scores of two baselines.</p>
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<p>Three scenarios of dialog network formation: (1) schematic depiction of a time point of a TiTAN series. (2) Formation of clusters at a certain time point of a TiTAN series. The vertices labeled by <span class="html-italic">A</span> denote a general noun that is used by both interlocutors in different thematic contexts. (3) Cluster formation with interlocutor lexica as a result of sudden topic changes.</p>
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159 KiB  
Article
Thermodynamical Description of Running Discontinuities: Application to Friction and Wear
by Claude Stolz
Entropy 2010, 12(6), 1418-1439; https://doi.org/10.3390/e12061418 - 1 Jun 2010
Cited by 13 | Viewed by 7920
Abstract
The friction and wear phenomena appear due to contact and relative motion between two solids. The evolution of contact conditions depends on loading conditions and mechanical behaviours. The wear phenomena are essentially characterized by a matter loss. Wear and friction are in interaction [...] Read more.
The friction and wear phenomena appear due to contact and relative motion between two solids. The evolution of contact conditions depends on loading conditions and mechanical behaviours. The wear phenomena are essentially characterized by a matter loss. Wear and friction are in interaction due to the fact that particles are detached from the solids. A complex medium appears as an interface having a strong effect on the friction condition. The purpose of this paper is to describe such phenomena taking account of different scales of modelization in order to derive some macroscopic laws. A thermodynamical approach is proposed and models of wear are analysed in this framework where the separation between the dissipation due to friction and that due to wear is made. Applications on different cases are presented. Full article
(This article belongs to the Special Issue Entropy and Friction Volume 2)
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Figure 1
<p>Stribeck’s curve.</p>
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<p>The microscopic description of wear process.</p>
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<p>The transition from microscopic to mesoscopic description of wear process.</p>
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<p>The transition from mesoscopic description to macroscopic description of wear process.</p>
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3829 KiB  
Article
Effect of an External Oriented Magnetic Field on Entropy Generation in Natural Convection
by Atef El Jery, Nejib Hidouri, Mourad Magherbi and Ammar Ben Brahim
Entropy 2010, 12(6), 1391-1417; https://doi.org/10.3390/e12061391 - 28 May 2010
Cited by 59 | Viewed by 9374
Abstract
The influence of an external oriented magnetic field on entropy generation in natural convection for air and liquid gallium is numerically studied in steady-unsteady states by solving the mass, the momentum and the energy conservation equations. Entropy generation depends on five parameters which [...] Read more.
The influence of an external oriented magnetic field on entropy generation in natural convection for air and liquid gallium is numerically studied in steady-unsteady states by solving the mass, the momentum and the energy conservation equations. Entropy generation depends on five parameters which are: the Prandtl number, the irreversibility coefficients, the inclination angle of the magnetic field, the thermal Grashof and the Hartmann numbers. Effects of these parameters on total and local irreversibilities as well as on heat transfer and fluid flow are studied. It was found that the magnetic field tends to decrease the convection currents, the heat transfer and entropy generation inside the enclosure. Influence of inclination angle of the magnetic field on local irreversibility is then studied. Full article
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Figure 1
<p>Cavity configuration in presence of an oriented magnetic field.</p>
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<p>Nusselt number <span class="html-italic">versus</span> Hartmann number for <span class="html-italic">α</span> = 0°.</p>
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<p>Total dimensionless entropy generation (σ<sub>t</sub>) <span class="html-italic">versus</span> dimensionless time for <span class="html-italic">Pr</span> = 0.71, <span class="html-italic">α</span> = 0° and <span class="html-italic">χ</span><sub>1</sub> = 10<sup>−</sup><sup>3</sup> at different Hartmann number values: (a) <span class="html-italic">Gr<sub>T</sub></span> = 10<sup>3</sup>, (b) <span class="html-italic">Gr<sub>T</sub></span> = 10<sup>4</sup>, (c) <span class="html-italic">Gr<sub>T</sub></span> = 10<sup>5</sup>.</p>
Full article ">Figure 3 Cont.
<p>Total dimensionless entropy generation (σ<sub>t</sub>) <span class="html-italic">versus</span> dimensionless time for <span class="html-italic">Pr</span> = 0.71, <span class="html-italic">α</span> = 0° and <span class="html-italic">χ</span><sub>1</sub> = 10<sup>−</sup><sup>3</sup> at different Hartmann number values: (a) <span class="html-italic">Gr<sub>T</sub></span> = 10<sup>3</sup>, (b) <span class="html-italic">Gr<sub>T</sub></span> = 10<sup>4</sup>, (c) <span class="html-italic">Gr<sub>T</sub></span> = 10<sup>5</sup>.</p>
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<p>Total dimensionless entropy generation (σ<sub>t</sub>) <span class="html-italic">versus</span> dimensionless time for <span class="html-italic">Pr</span> = 0.02, <span class="html-italic">α</span> = 0°, and <span class="html-italic">χ</span><sub>1</sub> = 10<sup>−</sup><sup>3</sup> at different Hartmann number values: (a) <span class="html-italic">Gr<sub>T</sub></span> = 10<sup>4</sup>, (b) <span class="html-italic">Gr<sub>T</sub></span> = 10<sup>5</sup>.</p>
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<p>Nusselt number <span class="html-italic">versus</span> time at <span class="html-italic">Gr<sub>T</sub></span> = 10<sup>5</sup>, <span class="html-italic">χ</span><sub>1</sub> = 10<sup>−</sup><sup>3</sup> and <span class="html-italic">Ha</span> = 0 for <span class="html-italic">Pr</span> = 0.02 and 0.71.</p>
Full article ">Figure 6
<p>Hartmann number <span class="html-italic">versus</span> inclination angle of the magnetic field for <span class="html-italic">Pr</span> = 0.71, <span class="html-italic">Gr<sub>T</sub></span> = 10<sup>5</sup> and <span class="html-italic">χ</span><sub>1</sub> = 10<sup>−</sup><sup>3</sup>.</p>
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<p>Transient entropy generation for <span class="html-italic">Gr<sub>T</sub></span> = 10<sup>5</sup>, <span class="html-italic">χ</span><sub>1</sub> = 10<sup>−</sup><sup>3</sup> and <span class="html-italic">Ha</span> = 0: (a) viscous irreversibility, (b) thermal irreversibility.</p>
Full article ">Figure 7 Cont.
<p>Transient entropy generation for <span class="html-italic">Gr<sub>T</sub></span> = 10<sup>5</sup>, <span class="html-italic">χ</span><sub>1</sub> = 10<sup>−</sup><sup>3</sup> and <span class="html-italic">Ha</span> = 0: (a) viscous irreversibility, (b) thermal irreversibility.</p>
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<p>Critical thermal Grashof number <span class="html-italic">versus</span> Hartmann number for <span class="html-italic">Pr</span> = 0.71, <span class="html-italic">α</span> = 0° and <span class="html-italic">χ</span><sub>1</sub> = 10<sup>−</sup><sup>3</sup>.</p>
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<p>Nusselt number <span class="html-italic">versus</span> Hartmann at different thermal Grashof numbers for <span class="html-italic">Pr</span> = 0.71 and 0.02 at <span class="html-italic">α</span> = 0°.</p>
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<p>Total dimensionless entropy generation <span class="html-italic">versus</span> Hartmann number at <span class="html-italic">α</span> = 0°, <span class="html-italic">Gr<sub>T</sub></span> = 10<sup>4</sup> at <span class="html-italic">χ</span><sub>1</sub> = 10<sup>−2</sup>, 10<sup>−3</sup>, 10<sup>−4</sup>: (a) <span class="html-italic">Pr</span> = 0.71, (b) <span class="html-italic">Pr</span> = 0.02.</p>
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<p>Total and local irreversibilities <span class="html-italic">versus α</span> at <span class="html-italic">Gr<sub>T</sub></span> = 10<sup>5</sup> and <span class="html-italic">χ</span><sub>1</sub> = 10<sup>−</sup><sup>2</sup>: (a) <span class="html-italic">Pr</span> = 0.71 and <span class="html-italic">Ha</span> = 10, (b) <span class="html-italic">Pr</span> = 0.71 and <span class="html-italic">Ha</span> = 100, (c) <span class="html-italic">Pr</span> = 0.02 and <span class="html-italic">Ha</span> = 10, (d) <span class="html-italic">Pr</span> = 0.02 and <span class="html-italic">Ha</span> = 100.</p>
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<p>Stream lines at <span class="html-italic">Gr<sub>T</sub></span> = 10<sup>5</sup> , <span class="html-italic">Ha</span> = 50 for: (a) <span class="html-italic">Pr</span> = 0.02, (b) <span class="html-italic">Pr</span> = 0.71.</p>
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<p>Isothermal lines at <span class="html-italic">Gr<sub>T</sub></span> = 10<sup>5</sup> , <span class="html-italic">Ha</span> = 50 for: (a) <span class="html-italic">Pr</span> = 0.02, (b) <span class="html-italic">Pr</span> = 0.71.</p>
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<p>Entropy generation maps at <span class="html-italic">Ha</span> = 0 and <span class="html-italic">χ</span><sub>1</sub> = 10<sup>−</sup><sup>3</sup>: (a) <span class="html-italic">Pr</span> = 0.71, (b) <span class="html-italic">Pr</span> = 0.02, (1) <span class="html-italic">Gr<sub>T</sub></span> = 10<sup>3</sup>, (2) <span class="html-italic">Gr<sub>T</sub></span> = 10<sup>4</sup>, (3) <span class="html-italic">Gr<sub>T</sub></span> = 10<sup>5</sup>.</p>
Full article ">Figure 15
<p>Entropy generation maps at <span class="html-italic">Gr<sub>T</sub></span> = 10<sup>5</sup>, <span class="html-italic">χ</span><sub>1</sub> = 10<sup>−</sup><sup>3</sup>, <span class="html-italic">Ha</span> = 50: (a) <span class="html-italic">Pr</span> = 0.71, (b) <span class="html-italic">Pr</span> = 0.02.</p>
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964 KiB  
Article
Entropy in Tribology: in the Search for Applications
by Michael Nosonovsky
Entropy 2010, 12(6), 1345-1390; https://doi.org/10.3390/e12061345 - 28 May 2010
Cited by 83 | Viewed by 10318
Abstract
The paper discusses the concept of entropy as applied to friction and wear. Friction and wear are classical examples of irreversible dissipative processes, and it is widely recognized that entropy generation is their important quantitative measure. On the other hand, the use of [...] Read more.
The paper discusses the concept of entropy as applied to friction and wear. Friction and wear are classical examples of irreversible dissipative processes, and it is widely recognized that entropy generation is their important quantitative measure. On the other hand, the use of thermodynamic methods in tribology remains controversial and questions about the practical usefulness of these methods are often asked. A significant part of entropic tribological research was conducted in Russia since the 1970s. Surprisingly, many of these studies are not available in English and still not well known in the West. The paper reviews various views on the role of entropy and self-organization in tribology and it discusses modern approaches to wear and friction, which use the thermodynamic entropic method as well as the application of the mathematical concept of entropy to the dynamic friction effects (e.g., the running-in transient process, stick-slip motion, etc.) and a possible connection between the thermodynamic and information approach. The paper also discusses non-equilibrium thermodynamic approach to friction, wear, and self-healing. In general, the objective of this paper is to answer the frequently asked question “is there any practical application of the thermodynamics in the study of friction and wear?” and to show that the thermodynamic methods have potential for both fundamental study of friction and wear and for the development of new (e.g., self-lubricating) materials. Full article
(This article belongs to the Special Issue Entropy and Friction Volume 2)
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Figure 1
<p>Heat flow away from the frictional interface.</p>
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<p>Hidden energy density <span class="html-italic">vs</span>. the number of cycles [<a href="#B29-entropy-12-01345" class="html-bibr">29</a>].</p>
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<p>(a) Self-organized protective film at the interface of a composite material (b) The coefficient of friction as a function of film thickness for various values of the microstructure parameter <span class="html-italic">ψ</span>. Sub-critical values of <span class="html-italic">ψ</span> &lt; <span class="html-italic">ψ<sub>cr</sub></span> result in the positive slope (no layer formed), whereas <span class="html-italic">ψ</span> &lt; <span class="html-italic">ψ<sub>cr</sub></span> results in the instability and self-organization of the protective layer. The slope depends on the ratio of the bulk and layer values of μ, which allows finding composite microstructure that provides the self-organization of the layer.</p>
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<p>A significant wear and friction reduction with decreasing particle size in Al-Al<sub>2</sub>O<sub>3</sub> nanocomposite (based on [<a href="#B31-entropy-12-01345" class="html-bibr">31</a>]) can be attributed to surface self-organization</p>
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<p>(a) Variation of the steady state and stick-slip friction with sliding distance [<a href="#B35-entropy-12-01345" class="html-bibr">35</a>]; (b) a typical decrease of friction during the running-in.</p>
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<p>A feedback loop (a) model and (b) its presentation in Simulink. Two simultaneous processes (adhesion and deformation) affect surface roughness in different manners. (c) Total friction is the sum of the deformational and adhesional components and the equilibrium value of roughness R corresponds to the minimum value of friction. Consequently, an equilibrium value of roughness exists, which corresponds to minimum friction [<a href="#B36-entropy-12-01345" class="html-bibr">36</a>].</p>
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<p>The time-dependence of the coefficient of friction and roughness parameter during the running-in simulated with Simulink for A = B and A ≠ B. For A = B, while roughness reaches its equilibrium value, the coefficient of friction always decreases. Therefore, self-organization of the rough interface results in the decrease of friction and wear. For A ≠ B the coefficient of can decrease or increase depending on the initial value of roughness [<a href="#B36-entropy-12-01345" class="html-bibr">36</a>].</p>
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<p>The change of the coefficient of friction and the surface roughness in Cu substrate with the number of cycles during a ball-on-disk test with a tungsten carbide (WC) ball [<a href="#B36-entropy-12-01345" class="html-bibr">36</a>].</p>
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<p>Elastic waves radiated from the frictional interface between two elastic half-spaces. The bodies are shown pre-stressed with the pressure <span class="html-italic">p</span><sup>*</sup> and shear <span class="html-italic">q</span><sup>*</sup> applied at infinity [<a href="#B45-entropy-12-01345" class="html-bibr">45</a>].</p>
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<p>Positive feedback leading to the friction-induced instabilities [<a href="#B22-entropy-12-01345" class="html-bibr">22</a>]</p>
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<p>Friction reduction due to propagating stick-slip zones. The shear force <span class="html-italic">F</span> is smaller than the force needed to initiate friction μ<span class="html-italic">W</span>; however, due to many propagating slip regions the two contacting bodies shift relative to each other in what is observed as friction at the reduced apparent coefficient of friction μ<sub>ap</sub> = <span class="html-italic">F/W</span>&lt;μ.</p>
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<p>Normal (<span class="html-italic">y</span>) and tangential (<span class="html-italic">x</span>) degrees of freedom during friction.</p>
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<p>Schematics of self-healing using (a) precipitation (figure provided by Mr. J. M. Lucci, from the UWM) (b) reinforcement with shape-memory alloy (c) embedding of a healing agent (e.g., low melting point solder).</p>
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<p>Block diagram of the healing process. Deterioration is caused by an external force. The deteriorated system is brought out of equilibrium so that the restoring (“healing”) force is created, which is coupled with the degradation flow through the parameter <span class="html-italic">M</span>.</p>
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<p>Self-healing observed at the macroscale (healed cracks and increased orderliness) and microscale (ruptured microcapsules and decreased orderliness) [<a href="#B23-entropy-12-01345" class="html-bibr">23</a>].</p>
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<p>The paradigm of Green Tribology: renewable energy, biomimetic surfaces, and biodegradable lubrication [<a href="#B61-entropy-12-01345" class="html-bibr">61</a>,<a href="#B62-entropy-12-01345" class="html-bibr">62</a>,<a href="#B63-entropy-12-01345" class="html-bibr">63</a>].</p>
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64 KiB  
Correction
Misleading Reference
by Peter Harremoës
Entropy 2010, 12(6), 1344; https://doi.org/10.3390/e12061344 - 27 May 2010
Viewed by 5458
Abstract
We just became aware that the article [1] published in Entropy 2009 contains a false reference. [...] Full article
281 KiB  
Article
Cultural Naturalism
by Arto Annila and Stanley Salthe
Entropy 2010, 12(6), 1325-1343; https://doi.org/10.3390/e12061325 - 26 May 2010
Cited by 24 | Viewed by 10389
Abstract
Culture can be viewed as the means by which a society can live in its surroundings by acquiring and consuming free energy. This naturalistic notion assumes that everything can be valued in terms of energy, hence also social changes can be described as [...] Read more.
Culture can be viewed as the means by which a society can live in its surroundings by acquiring and consuming free energy. This naturalistic notion assumes that everything can be valued in terms of energy, hence also social changes can be described as natural processes that are influenced by the 2nd Law of Thermodynamics. This universal law, when formulated as an equation of motion, reveals that societies emerge, evolve and eventually extinguish after tapping, exploiting and finally depleting their resources, which we can say are ultimately valued in energetic terms. The analysis reveals that trajectories of societies are, however, inherently non-integrable, i.e., unpredictable in detail because free energy as the driving force, being finite, is inseparable from the flows of energy. Nonetheless, the universal tendency to diminish energy differences within a system and with respect to its surroundings in the least possible time gives rise to highly economical but seemingly immaterial means of energy transduction that associate with cultural codes, habits, traditions, taboos and values. Moreover, cultural naturalism clarifies that identities develop and mature in interactions, and that class structure results from the quest for maximum entropy partition. While social changes in complex societies are inherently intractable, the profound principle allows us to recognize universal tendencies in diverse cultural characteristics, and to rationalize prospects for the future. Full article
(This article belongs to the Special Issue Nonequilibrium Thermodynamics)
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Figure 1

Figure 1
<p>This self-similar diagram depicts energy transduction systems in a nested hierarchy of systems within systems. Each system (loop) is immersed in a surrounding system (larger loop). Interactions (color-coded wavy arrows) hold the system’s constituents together. A stationary system that is not subject to forces moves along closed trajectories. The mutual density differences, Δ<span class="html-italic">μ</span><span class="html-italic"><sub>jk</sub></span>, and those relative to the surroundings, Δ<span class="html-italic">Q<sub>jk</sub></span>, drive the <span class="html-italic">j</span> and <span class="html-italic">k</span> systems (open loops) in evolution along open, inherently unpredictable trajectories toward more probable states. Social systems are here regarded as energy transduction systems within systems that couple to each other and to their respective surroundings via cultural interactions that diminish differences in energy.</p>
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