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148 results for “entropy”
Data from: Multiscale chromatin dynamics and high entropy in plant iPSC ancestors
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Entropy driven order in an array of nanomagnets
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Entropy Poduction in a Box: Analysis of Instabilities in Confined Hydrothermal Systems
<p>This data set was generated using the numerical tool SHEMAT suite. All relevant input and output files are given here for the scenarios discussed in our manuscript: (1) onset of convection, varying box geometry for (2) homogeneous models, and (3) heterogeneous models.</p> <p>Additionally, some Matlab files show how the simulation results are analyses d in terms of the entropy production.</p>
Kinoson data for FCC random and high entropy alloys
<p>This database contains the single KMC-step datasets for the "Ordered" and "Random" rate lattice gas systems, as well the "Complex" high-entropy alloy to accompany our main publication along with our code repository mentioned therein. The datasets are given as HDF5 files, readable with the `h5py` module in python 3. These files are listed below:</p><p>* CrystalData: `CrystData.h5` and `CrystData_ortho_5_cube.h5` contain all the necessary FCC crystal structure data for primitive 8x8x8 and orthogonal 5x5x5 FCC supercells.</p><p>* Lattice_gas/2-component/Ordered_rate: `singleStep_FCC_SR2_c0_X_Run2.h5` are the datasets for the 2-component "Ordered" rate lattice gas systems</p><p>* Lattice_gas/2-component/Random_rate: `singleStepFCC_CR2_c0_X_Run_3.h5` are the datasets for the 2-component "Random" rate lattice gas systems, with X = 60, 70, 75, 80, 85 for the slow species concentration. In these datasets, the slow and fast species have integer labels of 0 and 1, and the vacancy has an integer label of 2.</p><p>* Lattice_gas/5-component/Ordered_rate: `singleStep_FCC_5comp_LG_EquiComp_T.h5` are the datasets for the 5-component "Ordered" rate lattice gases</p><p>* Lattice_gas/5-component/Random_rate: `singleStep_HEA_dist_EquiComp_T.h5` are the datasets for the 5-component "Random" rate lattice gases, with T = 1073, 1173, 1273, 1373 for the simulated temperatures. In these datasets, the four slow species have integer labels 0, 1, 2 and 3, while the fast species has an integer label of 4, and the vacancy has an integer label of 5.</p><p>* HEA_MEAM: `singleStep_HEA_MEAM_T_ftol_1e-3.h5` (T = 773, 1073, 1173, 1273, 1373) contain the single KMC step samples for the complex high-entropy alloy simulated with the MEAM potential. In these datasets, the vacancy has an integer label of 0, while Co, Ni, Cr, Fe and Mn have integer labels 1, 2, 3, 4 and 5.</p><p>Along with the datasets, in each directory, the optimal neural network models for each system have also been provided as PyTorch "state dictionaries", along with the optimal neural network-predicted relaxation vectors. To illustrate the use of these datasets, neural networks and their relaxation vectors, python 3 Jupyter notebooks have also been provided to show the calculation of the transport coefficients using the Scaled Bias Basis (SBB) Method for all systems, as well as the NN+SRBC method for the "Complex" high-entropy alloy. In each directory, these notebooks have been numbered in their file names so as to make it easier to navigate through them. Along with such examples and the datasets, source codes for our neural network models and cluster expansion models are available in the code repository mentioned in our main text. Modules in this repository are also required to run these example notebooks.</p><p> </p>
Data for "Competition between phase ordering and phase segregation in the Ti$_x$NbMoTaW and Ti$_x$VNbMoTaW refractory high-entropy alloys"
<p>Data associated with the arXiv preprint: "Competition between phase ordering and phase segregation in the Ti$_x$NbMoTaW and Ti$_x$VNbMoTaW refractory high-entropy alloys". Version 2 corrects an error in the files associated with fitted atom-atom interaction energies.</p>
LIGO_Entropy_Dataset
<p>.h5 files with both LVK data and BRAHMA corrected data</p>
Considering Time and Feature Entropy in Calibrated Recommendations
<p>Dataset and results from the paper "Considering Time and Feature Entropy in Calibrated Recommendations" authored by Diego Corrêa da Silva, Dietmar Jannach and Frederico Araújo Durão.</p>
Data for Theory, Observations, and Simulations of Kinetic Entropy in a Magnetotail Electron Diffusion Region
<p>This .rar file contains the simulation data used in the paper titled "Theory, Observations, and Simulations of Kinetic Entropy in a Magnetotail ElectronDiffusion Region" along with needed instructions for reproducing the simulation plots shown in the paper. </p>
Approximate Entropy of Spiking Series Reveals Different Dynamical States in Cortical Assemblies
<p>Self-organized criticality theory proved that information transmission and computational performances of neural networks are optimal in critical state. By using recordings of the spontaneous activity originated by dissociated neuronal assemblies coupled to Micro-Electrode Arrays (MEAs), we tested this hypothesis using Approximate Entropy (ApEn) as a measure of complexity and information transfer. We analysed 60 min of electrophysiological activity of three neuronal cultures exhibiting either sub-critical, critical or super-critical behaviour. The firing patterns on each electrode was studied in terms of the inter-spike interval (ISI), whose complexity was quantified using ApEn. We assessed that in critical state the local complexity (measured in terms of ApEn) is larger than in sub- and super-critical conditions. Our estimations were stable when considering epochs as short as 5 min. These preliminary results indicate that ApEn has the potential of being a reliable and stable index to monitor local information transmission in a neuronal network during maturation. Thus, ApEn applied on ISI time series appears to be potentially useful to reflect the overall complex behaviour of the neural network, even monitoring a single specific location.</p>
Data of "Ductile fracture of high entropy alloys: from the design of an experimental campaign to the development of a micromechanics-based modeling framework"
<p>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data):</p> <p>title = "Ductile fracture of high entropy alloys: from the design of an experimental campaign to the development of a micromechanics-based modeling framework",<br> journal = "Engineering Fracture Mechanics",<br> year = "2022",<br> volume = "275",<br> pages = "108844 ",<br> doi = "https://doi.org/10.1016/j.engfracmech.2022.108844",<br> author = "Antoine Hilhorst, Julien Leclerc, Thomas Pardoen, Pascal J. Jacques, Ludovic Noels, Van-Dung Nguyen"</p> <p>New version following review.</p> <p> </p> <p> </p>
Data for "Structure, short-range order, and phase stability of the Al$_x$CrFeCoNi high-entropy alloy: Insights from a perturbative, DFT-based analysis"
<p>Data associated with "Structure, short-range order, and phase stability of the AlxCrFeCoNi high-entropy alloy: Insights from a perturbative, DFT-based analysis", published in npj Comput. Mater. <strong>10</strong>, 271 (2024).</p>
The evaluation of climate change competitiveness via DEA models and Shannon's entropy: EU regions case
<p>Supplementary materials for the article titled “The evaluation of climate change competitiveness via DEA models and Shannon’s entropy: EU regions case”.</p> <p><span>The data was collected under project provided by the National Science Centre, Poland; Grant No. 2019/35/B/HS5/01548.</span></p> <div> <div> <div> <p> </p> </div> </div> </div> <p> </p>
From Movement to Infection: Deciphering COVID-19 Transmission via Transfer Entropy
<p>We retrieved daily cases for all of Spain reported at the level of provinces and the level of Basic Health Areas (BHA) for Catalunya and Madrid (<a href="https://doi.org/10.5281/zenodo.4634868">https://doi.org/10.5281/zenodo.4634868</a>). Each record corresponds to a geo-referenced time series where each record has an associated date, the corresponding identifier of the layer, a code of the zone i.e. a province or BHA) and the number of cases reported on that date (daily incidence).</p> <p>Mobility and population data records come from a study conducted by the Spanish Ministry of Transport, Mobility and Urban Agenda (MITMA, \emph{Ministerio de Transportes, Movilidad y Agenda Urbana}) which analyses the mobility and distribution of the population in Spain from February 14th 2020 to May 9th 2021 <a href="https://www.mitma.gob.es/ministerio/covid-19/evolucion-movilidad-big-data">https://www.mitma.gob.es/ministerio/covid-19/evolucion-movilidad-big-data</a> retrieved from <a href="https://doi.org/10.5281/zenodo.4634895">https://doi.org/10.5281/zenodo.4634895</a>. The data records are based on a sample of more than 13 million anonymized mobile-phone lines provided by a single mobile operator whose subscribers are evenly distributed and include two different mobility indicators. Daily origin-destination matrices (ODM) account for the number of trips between 2850 mobility areas that cover almost the entire territory of Spain, reconstructed by combining cell phone antenna coverage areas with districts and municipalities.</p> <p>The second mobility indicator reports for each mobility areas the total number of persons that have performed 0, 1, 2 or more than 2 trips in a given date. The indicator accounts for the fractions of people performing at least one trip or none, as well as the estimated total population in that zone for the given date. In this work, we used the mobility indicators \cite{data-mobility} and population data \cite{data-population}, projected into provinces and BHA. </p> <p>The data set also include the zonifications in geoJSON for zon_bas_13, abs_09 and cnig_provincias retrieved from <a href="https://doi.org/10.5281/zenodo.4634663">https://doi.org/10.5281/zenodo.4634663</a>.</p>
High Entropy 2D Metals Sulfides: Fast Synthesis, Exfoliation and Electrochemical Activity in Overall Water Splitting at Alkaline pH
<p>Dataset for the article "High Entropy 2D Metals Sulfides: Fast Synthesis, Exfoliation and Electrochemical Activity in Overall Water Splitting at Alkaline pH"</p>
Supplementary Material v1: Multivariate entropy characterizes the gene expression and protein -protein networks in four types of cancer
<p>Supplementary Material PDF</p> <p>Multivariate entropy characterizes the gene expression and protein -protein networks in four types of cancer</p>
Propagation delays in carry-chains and calculation of estimated min-entropy
<p>Measurements of the propagation delays in the carry-chains of Spartan 6 Xilinx and Cyclone IV Intel FPGAs and calculation of estimated min-entropy. </p> <p>Hardware used: Cyclone IV (EP4CGX150DF31C7) & Spartan 6 (XC6SLX16) FPGAs</p>
The Evolution of Gas Giant Entropy During Formation by Runaway Accretion
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2017ApJ...834..149B/abstract">Berardo et al. (2017)</a>. MESA version 7624.</p> <p>Publication DOI: <a href="https://doi.org/10.3847/1538-4357/834/2/149">10.3847/1538-4357/834/2/149</a></p>
2D Ising model Monte Carlo configurations for the paper Entropy from Machine Learning
<p>Monte Carlo configurations for the 2D Ising model on a periodic 20x20 lattice used in the paper <em>Entropy from Machine Learning.</em></p> <p>Contains:</p> <ul> <li>20000 configurations for each temperature in the range T=1.0 to T=4.0 in 0.1 intervals</li> <li>40000 configurations at T=Tc</li> <li>README.md file with information about reading into Python</li> </ul> <p>See repository <a href="https://github.com/rmldj/ml-entropy">github.com/rmldj/ml-entropy.</a></p>
Data for a publication "Microstructure and mechanical properties of in-situ SiO2-reinforced mechanically alloyed CoCrFeNiMnX (X= 5, 20, 35 at.%) high-entropy alloys"
<p>Dataset contains data that has been used within the manuscript entitled: "Microstructure and mechanical properties of in-situ SiO2-reinforced mechanically alloyed CoCrFeNiMnX (X= 5, 20, 35 at.%) high-entropy alloys". For more information, please read the README.txt file.</p>
Graphical Abstract for "Affinity-based extension of non-extensive entropy and statistical mechanics"
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