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148 results for “entropy”
Data and graphics from: "Three invariants of strange attractors derived through hypergeometric entropy"
<p>This package contains data and graphics related to the publication:</p><p>Keisuke Okamura, "Three invariants of chaotic attractors derived through hypergeometric entropy", Chaos, Solitons & Fractals 170 (2023) 113392; <a href="https://doi.org/10.1016/j.chaos.2023.113392">https://doi.org/10.1016/j.chaos.2023.113392</a></p><p> </p><p><strong>Abstract of the original article</strong></p><p>A new description of strange attractor systems through three geometrical and dynamical invariants is provided. They are the correlation dimension (\(\mathcal{D}\)) and the correlation entropy (\(\mathcal{K}\)), both having attracted attention over the past decades, and a new invariant called the correlation concentration (\(\mathcal{A}\)) introduced in the present study. The correlation concentration is defined as the normalised mean distance between the reconstruction vectors, evaluated by the underlying probability measure on the infinite-dimensional embedding space. These three invariants determine the scaling behaviour of the system's Rényi-type extended entropy, modelled by Kummer's confluent hypergeometric function, with respect to the gauge parameter (\(\rho\)) coupled to the distance between the reconstruction vectors. The entropy function reproduces the known scaling behaviours of \(\mathcal{D}\) and \(\mathcal{K}\) in the 'microscopic' limit \(\rho\to\infty\) while exhibiting a new scaling behaviour of \(\mathcal{A}\) in the other, 'macroscopic' limit \(\rho\to 0\). The three invariants are estimated simultaneously via nonlinear regression analysis without needing separate estimations for each invariant. The proposed method is verified through simulations in both discrete and continuous systems.</p><p> </p><p><strong>Data files</strong></p><p>The following files are provided (see the original article for the notation):</p><ul><li>The comma-separated values (CSV) file named 'est_sum_all.csv' summarises the results of the correlation dimension (\(\mathcal{D}\)), correlation entropy (\(\mathcal{K}\)) and correlation concentration (\(\mathcal{A}\)) estimates for two discrete (logistic and Hénon maps) and four continuous (Lorenz, Rössler, Duffing-Ueda and Langford attractors) chaotic systems in tabular form.</li><li>The six CSV files whose file names begin with 'H_' record the extended Rényi entropy values calculated for each \(\sigma\) value (in increments of 0.1) for the chaotic system whose name follows the prefix, for each embedding dimension (\(m\)). The column 'mN' shows the results for the embedding dimension \(N\).</li><li>The six PDF files whose file names begin with 'gr_' illustrate, for the named chaotic systems following their prefixes, their i) appearance (left), ii) graph of the extended Rényi entropy as a function of \(\sigma\) (centre) and iii) the chaotic invariants (\(\mathcal{D}, \mathcal{K}, \mathcal{A}\)) estimation results (right).</li></ul>
Supplementary materials for "Relative Information Gain: Shannon entropy-based measure of the relative structural conservation in RNA alignments"
<p>Supplementary materials for "Relative Information Gain: Shannon entropy-based measure of the relative structural conservation in RNA alignments". These include precalculated RNA Blocks, MBRs (Matrix of Bear encoded RNA), sPSSMs (structural Position Specific Scoring Matrix), RIG (Relative Information Gain) scores, and plots calculated for 3016 Rfam 14.1 families. In particular:</p> <ul> <li><strong>alignments.zip:</strong> zipped file containing the structural alignments for each Rfam family.</li> <li><strong>RNA_Blocks.zip</strong>: zipped file containing the RNA blocks used to derive different substitution matrices.</li> <li><strong>MBRs.zip</strong>: zipped file containing the substitution matrices.</li> <li><strong>sPSSMs.zip</strong>: zipped file containing the structural Position Specific Scoring Matrices.</li> <li><strong>RIGs.zip</strong>: zipped file containing the RIG scores.</li> <li><strong>entropy.zip</strong>: zipped file containing the (rescaled) entropy.</li> <li><strong>plots.zip</strong>: zipped file containing the plots. </li> </ul> <p>All the scripts to build all these files are available at <a href="https://github.com/helmercitterich-lab/RIG">https://github.com/helmercitterich-lab/RIG</a>.</p>
Data from: Variance sum rule for entropy production
<p>Entropy production is the hallmark of nonequilibrium physics, quantifying irreversibility, dissipation, and the efficiency of energy transduction processes. Despite many efforts, its measurement at the nanoscale remains challenging. We introduce a variance sum rule for displacement and force variances that permits us to measure the entropy production rate in nonequilibrium steady states. We first illustrate it for directly measurable forces, such as an active Brownian particle in an optical trap. Data for this analysis can be found in the repository (1) described below. We then apply the variance sum rule to flickering experiments in human red blood cells (repositories (2-4)). We find that the entropy production rate is spatially heterogeneous with a finite correlation length (in particular, data in the repository (4)) and its average value agrees with calorimetry measurements. </p> <p>The dataset is composed of 4 repositories:</p> <p>1) SwitchingTrap.zip, containing data from Optical-tweezer experiments and used in Fig. 2 and 3 in the main paper, all data are three-column files featuring time (s), position (nm), and force (pN);</p> <p>2) OpticalStretching.zip, containing data from Optical-tweezer experiments shown in Fig. 4a in the main paper, all data are two-column files featuring time (s) and position (nm) traces;</p> <p>3) OpticalSensing.zip, containing data from Optical-tweezer experiments shown in Fig. 4b in the main paper, all data are one-column files featuring position (m) traces, sampling frequency 25kHz;</p> <p>4) OpticalMicroscopy.rar, containing data from Optical-microscopy experiments shown in Fig. 4c in the main paper, all data are one column files featuring position (nm) traces, sampling frequency 2kHz.</p>
Data from: Multiscale chromatin dynamics and high entropy in plant iPSC ancestors
<p>Plant protoplasts constitute the starting material to induce pluripotent cell masses <em>in vitro</em> competent for tissue regeneration. Dedifferentiation is associated with large-scale chromatin reorganisation and massive transcriptome reprogramming, characterized by stochastic gene expression. How this cellular variability reflects on chromatin organisation in individual cells and what are the factors influencing chromatin transitions during culturing is largely unknown. High-throughput imaging and a custom, supervised image analysis protocol extracting over 100 chromatin features unravelled a rapid, multiscale dynamics of chromatin patterns which trajectory strongly depends on nutrients availability. Decreased abundance in H1 (linker histones) is hallmark of chromatin transitions. We measured a high heterogeneity of chromatin patterns indicating an intrinsic entropy as hallmark of the initial cultures. We further measured an entropy decline over time, and an antagonistic influence by external and intrinsic factors, such as phytohormones and epigenetic modifiers, respectively. Collectively, our study benchmarks an approach to understand the variability and evolution of chromatin patterns underlying plant cell reprogramming <em>in vitro</em>.</p>
Small dataset machine-learning approach for efficient design space exploration: engineering ZnTe-based high-entropy alloys for water splitting
<p>Atomic structure data used in the research article entitled "Small Dataset Machine-Learning Approaches to Explore the Design Space of High-Entropy Alloys: Engineering ZnTe-based Multicomponent Alloys for the Photo-Splitting of Water"</p>
Project files provided as supporting information to the manuscript "Making sense of complex systems through resolution, relevance, and mapping entropy"
<p>README file to the project files provided as supporting information to the manuscript “Making sense of complex systems through resolution, relevance, and mapping entropy”</p> <p>Feb. 25, 2022</p> <p>Authors: Roi Holtzman, Marco Giulini and Raffaello Potestio</p> <p>==================================</p> <p>The dataset contains the following files:</p> <p>- A README file with the description of the pymap program for describing how different selections of *N* out of *n* degrees of freedom (mappings) affect the amount of information retained about a full data set.<br> - The pymap.py program<br> - The pymap.yml support file<br> - The data.tar tarball with the setup data<br> - The results.tar tarball with the output data<br> ===</p>
Entropy driven order in an array of nanomagnets
<p>Long-range ordering, while typically understood as a decrease in entropy, can also be driven by increasing system entropy in certain special cases. We demonstrate that artificial spin ice arrays of single-domain nanomagnets can be designed to produce entropy-driven order. We probe thermally active tetris artificial spin ice, known to have a zero point Pauli entropy, both experimentally and through simulations. We find two-dimensional magnetic ordering in one subset of the nanomagnet moments, which we demonstrate to be induced by disorder (i.e., increased entropy) in another subset of moments. Contrasting with other entropy-driven systems, the degrees of freedom in artificial spin ice are both designable and directly observable at the microscale, and the entropy of the system is precisely calculable in simulations. This robust example, in which the system's interactions and ground state entropy are well-defined, significantly expands the experimental landscape for the study of entropy-driven ordering.</p>
Data for a publication "The role of the preparation route on microstructure and mechanical properties of AlCoCrFeNi high entropy alloy"
<p>A dataset containing data for the published article "The role of the preparation route on microstructure and mechanical properties of AlCoCrFeNi high entropy alloy".</p> <p> </p> <p>For more details, please read the <strong>README Description of data and analysis.txt</strong> file.</p> <p> </p> <p> </p>
Alloy Sustainability Database - Evaluating the Economic, Environmental, and Societal Impacts of High-Entropy Alloys
<p>This database encompasses 18 elements and details over 400 high entropy alloys (HEAs), Ni-based superalloys, and steels, providing an extensive review of their properties and capabilities. To assess the broader implications of these materials, we have developed nine indicators that evaluate their economic, environmental, and human health impacts. Each indicator is meticulously described, including the methods used for their calculation. This data has been compiled with the goal of integrating considerations of societal impact into the alloy design process, thereby promoting the development of materials that are not only innovative but also socially responsible.</p>
FLAME: a novel approach for modelling burned area in the Brazilian biomes using the Maximum Entropy concept - Input Data
<p>This repository contains driving data used by training and evaluation of FLAME in the "FLAME: a novel approach for modelling burned area in the Brazilian biomes using the Maximum Entropy concept" paper. All NetCDF files are on regular, 0.5-degree grids on a monthly timestep over Brazil. </p> <div>Not all variables were used in the final analysis<br> <table> <tbody> <tr> <td><strong> NetCDF File</strong></td> <td> <p><strong> Variable</strong></p> </td> <td> <p><strong>Used/not Used</strong></p> </td> <td> <p><strong>Source/Reference</strong></p> </td> </tr> <tr> <td> <p>burned_area.nc</p> </td> <td> <p>Burned area</p> </td> <td> <p>As training data</p> </td> <td>MCD64A1/ Giglio et al. (2018)</td> </tr> <tr> <td> <p>burned_area_nat_veg.nc</p> </td> <td> <p>Burned area in natural vegetation</p> </td> <td> <p>As training data</p> </td> <td>MCD64A1/ Giglio et al. (2018) and Mapbiomas, 2022</td> </tr> <tr> <td> <p>burned_area_non_nat_veg.nc</p> </td> <td> <p>Burned area in non natural vegetation</p> </td> <td> <p>As training data</p> </td> <td>MCD64A1/ Giglio et al. (2018) and Mapbiomas, 2022</td> </tr> <tr> <td> <p> tas_max.nc</p> </td> <td> <p> Maximum Temperature</p> </td> <td> <p>Used</p> </td> <td><br><br> <p>ISIMIP3a</p> <p>FRIELER et al. (2023)</p> </td> </tr> <tr> <td> <p>precip.nc</p> </td> <td> <p>Precipitation</p> </td> <td> <p>Used</p> </td> </tr> <tr> <td> <p>vpd.nc</p> </td> <td> <p>Vapor pressure deficit</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>rhumid.nc</p> </td> <td> <p> Relative Humidity </p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td><br>consec_dry_days.nc</td> <td><br> <p>Consecutive number of dry days </p> </td> <td>Not Used</td> </tr> <tr> <td> <p>soilM.nc</p> </td> <td> <p>Soil Moisture</p> </td> <td> <p>Not Used</p> </td> <td> <p>JULES-ES</p> </td> </tr> <tr> <td> <p>lightn.nc </p> </td> <td> <p> Lightning</p> </td> <td> <p>Not Used</p> </td> <td><br> <p> ISIMIP3a</p> <p>FRIELER et al. (2023)</p> </td> </tr> <tr> <td> <p>popDen.nc</p> </td> <td> <p> Population density</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>road_density.nc</p> </td> <td> <p>Road density</p> </td> <td>Used</td> <td> <p> GRIP global</p> <p>(MEIJER et al., 2018)</p> </td> </tr> <tr> <td> <p>cveg.nc</p> </td> <td> <p>Vegetation carbon</p> </td> <td> <p>Not Used</p> </td> <td><br> <p>JULES-ES</p> </td> </tr> <tr> <td> <p>csoil.nc</p> </td> <td> <p>Carbon in dead vegetation</p> </td> <td>Used</td> <td><br> <p>JULES-ES</p> </td> </tr> <tr> <td> <p>forest.nc</p> </td> <td> <p> Forest</p> </td> <td> <p>Used</p> </td> <td><br><br><br> <p> MAPBIOMAS, 2022</p> </td> </tr> <tr> <td> <p>grassland.nc</p> </td> <td> <p> Grassland</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>savanna.nc</p> </td> <td> <p> Savanna</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>cropland.nc</p> </td> <td> <p> Cropland</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>pasture.nc</p> </td> <td> <p> Pasture</p> </td> <td> <p>Used</p> </td> </tr> <tr> <td> <p>np.nc</p> </td> <td> <p>Number of patches </p> </td> <td> <p>Not Used</p> </td> <td><br><br> <p>Calculated from MAPBIOMAS,<br>2022</p> <br><br></td> </tr> <tr> <td> <p>ed.nc </p> </td> <td>Edge density</td> <td>Used</td> </tr> </tbody> </table> </div> <p> </p>
Fig. 1 in Maximum entropy niche-based modeling (Maxent) of potential geographical distribution of Coreura albicosta (Lepidoptera: Erebidae: Ctenuchina) in Mexico
Fig. 1. Model of potential distribution of Coreura albicosta with enhancement of the favorable climatic regions for this species, and superposition with the network of protected areas of México. Gray: lower probability of appropriate environmental conditions for distribution of the species. Light gray sections represent the decision threshold (0.2426) in which the grids are favorable for the distribution of the species. Darker sections inside light gray: areas with high probability of presence of the species. Black dots indicate the known distribution of the species. The protected areas are represented with a black line.
Fig. 1 in Actual and potential distribution of five regulated avocado pests across Mexico, using the maximum entropy algorithm
Fig. 1. Potential distribution of 5 insect pests of quarantine importance in Mexican avocados, based on ecological niche modeling. A) Conotrachelus aguacatae; B) Conotrachelus perseae; C) Copturus aguacatae; D) Heilipus lauri; and E) Stenoma catenifer. Current and potential distribution in Mexico was projected according to biogeographic provinces (Morrone 2005, 2014a), 1 = Baja California, 2 = California, 3 = Sonora, 4 = Sierra Madre Occidental, 5 = Mexican Plateau, 6 = Tamaulipeca, 7 = Mexican Pacific Coast, 8 = Trans-Mexican Volcanic Belt, 9 = Sierra Madre Oriental, 10 = Veracruzana, 11 = Balsas Basin, 12 = Sierra Madre del Sur, 13 = Chiapas, 14 = Yucatan. Scale values: 0 = absence, 1 = presence.
Fig. 2 in Actual and potential distribution of five regulated avocado pests across Mexico, using the maximum entropy algorithm
Fig. 2. Geographic areas in Mexico where both the insect pest and avocados are found. Shading indicates hectares affected. A) Conotrachelus aguacatae; B) Conotrachelus perseae; C) Copturus aguacatae; D) Heilipus lauri; and E) Stenoma catenifer.
BRAIN Journal-Brain signal analysis using EEG and Entropy to study the effect of physical and mental tasks on cognitive performance-Figure 2. Energy-VAS subjective measures for the participants (n=12) under two conditions (control and exercise involved cognitive task).
<p>As shown in Figure 2, it was found that there was a statistically significant interaction in the<br> percentage of mental fatigue between the condition type and time-on-task factor times (F(6, 22) =<br> 492.19, p < 0.001) as well as there was a significant main effect of time-on-task (time5 to time30)<br> (F(5, 22) = 463.794, p < 0.001). In addition, there was also a significant main effect in the condition type (F (1, 22) = 713.133, p < 0.001) which represented a large effect size. For the physical fatigue<br> subjective measure, there was a significant difference between the two experimental conditions (p <<br> 0.001), and within the subject test times (p < 0.001). However, there was no significant difference<br> between the means of the concentration visual analogue scale for these two experimental conditions<br> (p = 0.057) despite a significant difference (p < 0.001) in the time-on-task repeated measures.</p>
BRAIN Journal-Brain signal analysis using EEG and Entropy to study the effect of physical and mental tasks on cognitive performance-Figure 1. The 10-20 international system electrode placement showing the EEG electrode placement
<p>For the EEG analysis, the average power in the theta band (4 – 8 Hz), and alpha band (8 – 12<br> Hz) were computed at the frontal and parietal electrodes Fz and Pz respectively. Next, the ratio of<br> these two powers was determined, and named the ‘cognitive ratio’ as several researchers found that<br> the fronto-parietal network play important roles in cognitive activities.</p>
Spatial Autocorrelation and Entropy for Renewable Energy Forecasting
<p>Additional resources of the paper "Spatial Autocorrelation and Entropy for Renewable Energy Forecasting".</p> <p>The repository includes:</p> <p>- Datasets;</p> <p>- Prediction system and instructions;</p> <p>- Additional experimental results. </p>
Excess Entropy Scaling in Supercooled Ternary Metallic Mixtures
<p>The folder contains the results from the simulations;<br> for some of the mixtures the python start file is included.</p> <p>The Jupyter notebook files were used to make the plots in our report.</p> <p>The csv files in the main folder contain the most important results.</p> <p>The files "binary_results.csv" and "single_results.csv" were taken from the research paper:<br> "Excess-entropy scaling in supercooled binary mixtures" I. H. Bell, J. C. Dyre, and T. S. Ingebrigtsen <br> (Nature communications)</p> <p>The dataset includes the results organized the following way:<br> Mixture -> density -> "simfiles" -> temperature -> run-number -> results of the simulation</p> <p>In order to use the Jupyter notebooks, the file paths may have to be updated.</p> <p>Let us know if you find the results useful or have suggestions for us.</p>
Forecasting and Tracking Volcanic Explosions using Shannon Entropy at Volcán de Colima
<pre><strong>Forecasting and Tracking Volcanic Explosions using Shannon Entropy at Volcán de Colima</strong>. by: Pablo Rey-Devesa (1,2),*, Janire Prudencio (1,2), Carmen Benítez (3), Mauricio Bretón (4), Imelda Plasencia (4), Zoraida León (4), Félix Ortigosa (4), Ligdamis Gutiérrez (1,2), Raúl Arámbula (4) and Jesús M. Ibáñez (1,2). <strong>Institutions associated</strong>: (1) Department of Theoretical Physics and Cosmos. Science Faculty. Avd. Fuentenueva s/n. University of Granada. 18071. Granada. Spain. (2) Andalusian Institute of Geophysiscs. Campus de Cartuja. University of Granada. C/Profesor Clavera 12. 18071. Granada. Spain. (3) Department of Signal Theory, Telematics and Communication. University of Granada. Informatics and Telecommunication School. 18071. Granada. Spain. (4) Centro Universitario de Estudios Vulcanológicos (CUEV), Observatorio Vulcanológico, Universidad de Colima, Colima, México <strong>Acknowledgment</strong>: a) This study was partially supported by the Spanish FEMALE (PID2019-106260GB-I00) and PROOF-FOREVER (EUR2022.134044) projects. P. Rey-Devesa was funded by the Ministerio de Ciencia e Innovación del Gobierno de España (MCIN), Agencia Estatal de Investigación (AEI), Fondo Social Europeo (FSE) and Programa Estatal de Promoción del Talento y su Empleabilidad en I+D+I Ayudas para contratos predoctorales para la formación de doctores 2020 (PRE2020-092719). b) To the Visual Monitoring and Seismicity Monitoring projects, both from the Center for Volcanological Studies of the Colima University. <strong>Data availability statemen</strong>t: Seismic data from Volcán de Fuego de Colima. <strong>Contents</strong>: Seismic Data from Volcán de Fuego de Colima recorded at stations INCA and SOMA. The data represent the vertical component of the seismic signal, associated to the period analyzed in the study: "<em>Forecasting and Tracking Volcanic Explosions using Shannon Entropy at Volcán de Colima</em>" Data available between January 2015 and May 2017.</pre>
Job Shop Instances with Varying Job and Operation Entropy
<p>This dataset contains multiple sets of job shop problem instances with varying characteristics. Instances have been generated for various target inter-instance job entropies, intra-instance job entropies, inter-instance operation entropies, and intra-instance operation entropies. For reference, the dataset also contains randomly generated job shop instances. More information about the data generation and the used terminology can be found in the accompanying data publication.</p> <p>Each problem instance contains the following information in json-format:</p> <ul> <li>id: a unique id for each generated problem instance</li> <li>opt_time: the optimal makespan for the problem instance determined by Google ORTools</li> <li>intra_instance_operation_entropy: the intra-instance operation entropy of the instance</li> <li>num_ops_per_job: the number of operations of each job max_op_time: the maximum processing time of all operations</li> <li>machine_types: the type of machine required for each operation in each job</li> <li>durations: the processing time of each operation in each job</li> </ul>
Data from: Variance sum rule for entropy production
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