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148 results for “Entropy”
Entropy-based Probabilistic Fatigue Damage Prognosis and Algorithmic Performance Comparison
In this paper, a maximum entropy-based general framework for probabilistic fatigue damage prognosis is investigated. The proposed methodology is based on an underlying physics-based crack growth model. V arious uncertainties from measurements, modeling, and parameter estimations are considered to describe the stochastic process of fatigue damage accumulation. A probabilistic prognosis updating procedure based on the maximum relative entropy concept is proposed to incorporate measurement data. Markov Chain Monte Carlo (MCMC) technique is used to provide the posterior samples for model updating in the maximum entropy approach. Experimental data are used to demonstrate the operation of the proposed probabilistic prognosis methodology. A set of prognostics-based metrics are employed to quantitatively evaluate the prognosis performance and compare the proposed method with the classical Bayesian updating algorithm. In particular, model accuracy, precision and convergence are rigorously evaluated in* addition to the qualitative visual comparison. It is shown that the proposed maximum relative entropy methodology has narrower confidence bounds of the remaining life prediction than classical Bayesian updating algorithm.
Archive of Chandra Cluster Entropy Profile Tables (ACCEPT) Catalog
This table, the Archive of Chandra Cluster Entropy Profile Tables (ACCEPT) Catalog, contains the radial entropy profiles of the intracluster medium (ICM) for a collection of 239 clusters taken from the Chandra X-ray Observatory's Data Archive. Entropy is of great interest because it controls ICM global properties and records the thermal history of a cluster. The authors find that most ICM entropy profiles are well fitted by a model which is a power law at large radii and approaches a constant value at small radii: K(r) = K<sub>0</sub> + K<sub>100</sub> (r/100 kpc)<sup>alpha</sup>, where K<sub>0</sub> quantifies the typical excess of core entropy above the best-fitting power law found at larger radii. The authors also show that the K<sub>0</sub> distributions of both the full archival sample and the primary Highest X-Ray Flux Galaxy Cluster Sample of Reiprich (2001, Ph.D. thesis) are bimodal with a distinct gap between K<sub>0</sub> ~ 30 - 50 keV cm<sup>2</sup> and population peaks at K<sub>0</sub> ~ 15 keV cm<sup>2</sup> and K<sub>0</sub> ~ 150 keV cm<sup>2</sup>. The effects of point-spread function smearing and angular resolution on best-fit K<sub>0</sub> values are investigated using mock Chandra observations and degraded entropy profiles, respectively. The authors find that neither of these effects is sufficient to explain the entropy-profile flattening they measure at small radii. The influence of profile curvature and the number of radial bins on the best-fit K<sub>0</sub> is also considered, and they find no indication that K<sub>0</sub> is significantly impacted by either. All data and results associated with this work are publicly available via the project web site <a href="http://www.pa.msu.edu/astro/MC2/accept/">http://www.pa.msu.edu/astro/MC2/accept/</a>. The sample is collected from observations taken with the Chandra X-ray Observatory and which were publicly available in the CDA (Chandra Data Archive) as of 2008 August. This table was created by the HEASARC in January 2012 based on <a href="https://cdsarc.cds.unistra.fr/ftp/cats/J/ApJS/182/12">CDS Catalog J/ApJS/182/12</a> files table1.dat and table5.dat. This is a service provided by NASA HEASARC .
Dataset related to article: "Are Strategies Favoring Pattern Matching a Viable Way to Improve Complexity Estimation Based on Sample Entropy?"
<p>This record contains data related to article "Are Strategies Favoring Pattern Matching a Viable Way to Improve Complexity Estimation Based on Sample Entropy?"</p> <p>It has been suggested that a viable strategy to improve complexity estimation based on</p> <p>the assessment of pattern similarity is to increase the pattern matching rate without enlarging the</p> <p>series length. We tested this hypothesis over short simulations of nonlinear deterministic and linear</p> <p>stochastic dynamics affected by various noise amounts. Several transformations featuring a</p> <p>different ability to increase the pattern matching rate were tested and compared to the usual strategy</p> <p>adopted in sample entropy (SampEn) computation. The approaches were applied to evaluate the</p> <p>complexity of short-term cardiac and vascular controls from the beat-to-beat variability of heart</p> <p>period (HP) and systolic arterial pressure (SAP) in 12 Parkinson disease patients and 12 age- and</p> <p>gender-matched healthy subjects at supine resting and during head-up tilt. Over simulations, the</p> <p>strategies estimated a larger complexity over nonlinear deterministic signals and a greater</p> <p>regularity over linear stochastic series or deterministic dynamics importantly contaminated by</p> <p>noise. Over short HP and SAP series the techniques did not produce any practical advantage, with</p> <p>an unvaried ability to discriminate groups and experimental conditions compared to the traditional</p> <p>SampEn. Procedures designed to artificially increase the number of matches are of no</p> <p>methodological and practical value when applied to assess complexity indexes.</p>
Supplementary data: "Challenges in automated high-throughput ab initio thermodynamics of magnetic high-entropy alloys"
<p>These supplementary data contain input and output files of EMTO density-functional theory (DFT) calculations and IPython/Jupyter notebooks that were used in analyzing the data for the paper "Challenges in automated high-throughput ab initio thermodynamics of magnetic high-entropy alloys".</p> <p>The data are organized as follows:</p> <ul> <li><em>alloy_discovery</em> folder contains the analysis notebooks and the processed EMTO DFT output data in a h5 database. <ul> <li>An installation of Python and Jupyter notebooks is required to run the notebooks. A recommended way of installing them is the Anaconda Python distribution: https://www.anaconda.com/download/</li> <li>It is recommended to first run the Requirements notebook, which checks what Python dependencies are needed to be downloaded.</li> <li>DFT output data is stored as an HDF5 database is located in the <em>results</em> subfolder.</li> <li>Figures generated with the notebooks are located in the <em>figures </em>subfolder.</li> </ul> </li> <li><em>output_files </em>folder contains raw output files of EMTO DFT calculations.</li> <li><em>input_files </em>folder contains raw input files that were used in the EMTO DFT calculations.</li> </ul>
Monitoring the Evolution of Asynchrony between Mean Arterial Pressure and Mean Cerebral Blood Flow via Cross-Entropy Methods
<p>Porta A, Gelpi F, Bari V, Cairo B, De Maria B, Panzetti CM, Cornara N, Bertoldo EG, Fiolo V, Callus E, De Vincentiis C, Volpe M, Molfetta R, Pistuddi V, Ranucci M. Monitoring the Evolution of Asynchrony between Mean Arterial Pressure and Mean Cerebral Blood Flow via Cross-Entropy Methods. Entropy (Basel). 2022 Jan 2;24(1):80. doi: 10.3390/e24010080. PMID: 35052106; PMCID: PMC8774596.</p> <p>Abstract</p> <p>Cerebrovascular control is carried out by multiple nonlinear mechanisms imposing a certain degree of coupling between mean arterial pressure (MAP) and mean cerebral blood flow (MCBF). We explored the ability of two nonlinear tools in the information domain, namely cross-approximate entropy (CApEn) and cross-sample entropy (CSampEn), to assess the degree of asynchrony between the spontaneous fluctuations of MAP and MCBF. CApEn and CSampEn were computed as a function of the translation time. The analysis was carried out in 23 subjects undergoing recordings at rest in supine position (REST) and during active standing (STAND), before and after surgical aortic valve replacement (SAVR). We found that at REST the degree of asynchrony raised, and the rate of increase in asynchrony with the translation time decreased after SAVR. These results are likely the consequence of the limited variability of MAP observed after surgery at REST, more than the consequence of a modified cerebrovascular control, given that the observed differences disappeared during STAND. CApEn and CSampEn can be utilized fruitfully in the context of the evaluation of cerebrovascular control via the noninvasive acquisition of the spontaneous MAP and MCBF variability.</p>
Dataset related to article: " ENTROPY METRICS CORRELATING WITH HIGHER RESIDUAL FUNCTIONING IN PATIENTS WITH CHRONIC DISORDERS OF CONSCIOUSNESS "
<p>the dataset contains data ENTROPY MEASURES</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Alterations of Cardiovascular Complexity during Acute Exposure to High Altitude: A Multiscale Entropy Approach
<p>The raw data for reproducing figure 2 of the paper DOI: <a href="https://doi.org/10.3390/e21121224">10.3390/e21121224</a></p>
Information-Domain Analysis of Cardiovascular Complexity: Night and Day Modulations of Entropy and the Effects of Hypertension
<p>The raw data for reproducing figures 6, 7 and 8 of the paper DOI: 10.3390/e21060550</p>
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