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199 results for “void”
Detecting cosmic voids via maps of geometric optics parameters
<ul> <li>lensing-ddbb4ac.pdf - research data in pdf format</li> <li>void_matches*.dat - plain text results files corresponding to Table 3 and Figures 2, 4, 6, 8.</li> <li>lensing-ddbb4ac-journal.tar.gz - source package for producing the article pdf, together with the reproducibility package, but without the git history; appropriate for ArXiv</li> <li>lensing-ddbb4ac-git.bundle - git source package that can be unbundled with 'git clone lensing-e4f7af0-git.bundle' and used for reproducibility: to download data, do calculations, analyse them, plot them and produce the research data pdf</li> <li>software-ddbb4ac.tar.gz - this should contain all the software, apart from a minimal POSIX-compatible system and LaTeX packages, needed for compiling and installing the software used in producing this work</li> <li>lensing-ddbb4ac-snapshot.tar.gz - source files of the project; these should be enough, provided that external software packages can be downloaded, to reproduce the full project</li> </ul> <p>The authors grant a perpetual, non-exclusive licence to distribute this pdf preprint.</p> <p>All the other materials here are free-licensed, as stated in the individual files and packages.</p>
VAST void catalogs for SDSS DR7
<p><em>Version 1.3.1 of the void catalogs; the NSAID column in the V2*galzones.dat have been corrected (previous versions incorrectly listed the row number of the galaxy into the NSA catalog as the NSAID). The peer-reviewed publication for these catalogs is published in the Astrophysical Journal Supplemental Series, an American Astronomical Society journal. The paper is available at <a href="https://iopscience.iop.org/article/10.3847/1538-4365/acabcf" target="_blank" rel="noopener">https://iopscience.iop.org/article/10.3847/1538-4365/acabcf</a>; please cite this article when using any of these catalogs.</em></p> <p>We provide three void catalogs using a volume-limited subsample of the SDSS DR7 for two cosmologies (Planck 2018 and WMAP5) using new implementations of two different void-finding algorithms from the Void Analysis Software Toolkit (<a href="https://vast.readthedocs.io/en/latest/">VAST</a>): VoidFinder and Voronoi Voids (V<sup>2</sup>). We identify 1163 cosmic voids with VoidFinder, 531 with V<sup>2</sup> using VIDE pruning, and 518 with V<sup>2</sup> using REVOLVER pruning, with the Planck 2018 cosmology, and 1184, 535, and 519 voids with the WMAP5 cosmology, respectively. These catalogs exist within the SDSS DR7 main survey volume, out to a maximum redshift of 0.114. Effective radii and centers for all voids are computed and included with the catalogs; the median void effective radius is 15-19 <em>h</em><sup>-1</sup> Mpc for all three catalogs, and none have an effective radius larger than 54 <em>h</em><sup>-1</sup> Mpc.</p> <p>All catalog files are in ASCII format with commented headers. An example of each file is shown in Tables 1-5 in the associated journal article. For each galaxy in the _galzones.dat files, the NSAID column corresponds to that galaxy's ID number in the NASA-Sloan Atlas. Guidance on how to use these catalogs can be found at <a href="https://vast.readthedocs.io/en/latest/">https://vast.readthedocs.io/en/latest/</a>. We also provide the mask file used in generating the VoidFinder void catalog with Planck 2018 cosmology.</p> <p><strong>Planck 2018 cosmology</strong></p> <ul> <li><strong>VoidFinder</strong> <ul> <li>VoidFinder-nsa_v1_0_1_Planck2018_comoving_holes.txt</li> <li>VoidFinder-nsa_v1_0_1_Planck2018_comoving_maximals.txt</li> <li>NSA_main_mask.pickle</li> </ul> </li> <li><strong>V<sup>2</sup></strong> <ul> <li><strong>VIDE pruning</strong> <ul> <li>V2_VIDE-nsa_v1_0_1_Planck2018_galzones.dat</li> <li>V2_VIDE-nsa_v1_0_1_Planck2018_zonevoids.dat</li> <li>V2_VIDE-nsa_v1_0_1_Planck2018_zobovoids.dat</li> </ul> </li> <li><strong>REVOLVER pruning</strong> <ul> <li>V2_REVOLVER-nsa_v1_0_1_Planck2018_galzones.dat</li> <li>V2_REVOLVER-nsa_v1_0_1_Planck2018_zonevoids.dat</li> <li>V2_REVOLVER-nsa_v1_0_1_Planck2018_zobovoids.dat</li> </ul> </li> </ul> </li> </ul> <p><strong>WMAP5 cosmology</strong></p> <ul> <li><strong>VoidFinder</strong> <ul> <li>VoidFinder-nsa_v1_0_1_WMAP5_comoving_holes.txt</li> <li>VoidFinder-nsa_v1_0_1_WMAP5_comoving_maximals.txt</li> </ul> </li> <li><strong>V<sup>2</sup></strong> <ul> <li><strong>VIDE pruning</strong> <ul> <li>V2_VIDE-nsa_v1_0_1_WMAP5_galzones.dat</li> <li>V2_VIDE-nsa_v1_0_1_WMAP5_zonevoids.dat</li> <li>V2_VIDE-nsa_v1_0_1_WMAP5_zobovoids.dat</li> </ul> </li> <li><strong>REVOLVER pruning</strong> <ul> <li>V2_REVOLVER-nsa_v1_0_1_WMAP5_galzones.dat</li> <li>V2_REVOLVER-nsa_v1_0_1_WMAP5_zonevoids.dat</li> <li>V2_REVOLVER-nsa_v1_0_1_WMAP5_zobovoids.dat</li> </ul> </li> </ul> </li> </ul>
Indicative distribution map for Ecosystem Functional Group SF2.2 Flooded mines and other voids
<p>This archive contains indicative distribution maps and profiles for <strong>SF2.2 Flooded mines and other voids</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Indicative distribution map for Ecosystem Functional Group S2.1 Anthropogenic subterranean voids
<p>This archive contains indicative distribution maps and profiles for <strong>S2.1 Anthropogenic subterranean voids</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Porous single crystal unit-cell simulation database for ductile fracture by void growth and coalescence
<p>Ductile fracture through void growth to coalescence occurs at the grain scale in numerous metallic alloys encountered in engineering applications. In order to perform mechanical homogenization of porous single crystals, a database of porous single crystal unit-cell simulation results has been gathered through Finite Element Modeling and Fast-Fourrier Transform simulations, respectively performed on Z-set and Amitex_FFTP. In these simulations, a cubic unit-cell with a unique central spherical void undergo axisymmetric mechanical loading. Mechanical simulations are performed within finite strain theory. Input parameters of interest are stress triaxiality, crystallographic orientation, initial porosity and strain hardening law type; results include macroscopic stress, macroscopic deformation gradient, porosity, void aspect ratio, ligament size and cell aspect ratio.</p>
Casting voids in nickel superalloy and the mechanical behaviour under room temperature tensile deformation
<p>This repository contains the original X-ray tomography data presented in the publication 'Casting voids in nickel superalloy and the mechanical behaviour under room temperature tensile deformation'.</p> <p>The XCT data were collected using Zeiss Xradia Versa 520 instrument from Henry Moseley X-ray Imaging facility at the University of Manchester. Access to the instrument was granted by Henry Royce Institute through PhD access funding scheme for Zhuocheng Xu. Data to produce Figure8 and Figure9 are also uploaded.</p>
Micromechanics of Void Nucleation and Early Growth at Incoherent Precipitates: Lattice-trapped and Dislocation-mediated Delamination Modes
<p>This repository contains raw data analyzed in the referenced manuscript published in Crystals (<a href="https://doi.org/10.3390/cryst11010045">10.3390/cryst11010045</a>). See the included README file for detailed information on the contents.</p>
Probing Patchy Reionization with the Void Probability Function in the era of RST
<p>Neutral hydrogen attenuates the Lyman-alpha emission of galaxies, dimming and increasing the apparent clustering of Lyman-Alpha Emitters (LAEs). LAEs are more likely to be observed in the regions that have already been ionized, so LAEs within more neutral intergalactic medium appear more clustered. Using the Jensen et al 2014 simulations of LAEs at various ionization fractions, we show how the Void Probability Function (VPF) measures the clustering signals caused by an increasingly neutral intergalactic medium. The VPF is sensitive to voids and can be easily compared for samples of the same number density, and therefore might be the best option for teasing out the particular clustering caused by 'inside-out' patchy reionization. We simulate what ionization fractions constraints the Lyman Alpha Galaxies in the Epoch of Reionization (LAGER) survey can give with the VPF, and motivate the use of the VPF for reionization surveys with WFIRST.</p>
An Anti-halo Void Catalogue of the Local Super-Volume
<p>Data from the construction of an anti-halo void catalogue of the Local Super-Volume. This upload contains the full anti-halo catalogue (also included in the associated publication) as well as the individual anti-halo catalogues for each resimulated posterior sample. We also include anti-halo catalogues from the 20 independent random simulations used in the associated paper for constructing the filters and comparing to Lambda-CDM.</p><p>The catalogue is provided in both comma separated values (csv) and numpy (npz) formats. For high precision work, npz should be preferred.</p>
3D and multimodal X-ray microscopy reveals the impact of voids in CIGS solar cells
<p>Data repository for the article "3D and multimodal X-ray microscopy reveals the impact of voids in CIGS solar cells" <strong>DOI: </strong>10.1002/advs.202301873</p>
3D representation of meso-voids location and morphology within the fibrous pore space of three carbon fabrics
<p>Segmented meso-voids from a tomography scan of preforms impregnated under capillary dominated conditions, combined to 3D scans of the initial prefrom dry state.</p>
Data Augmentation for learning mechanical digital twins of voids in welding joints
<p>In Source-2_Data_Augmentation:</p> <p>Exercice1_augmentation.ipynb Jupyter Notebook for data warpping of defect images.</p> <p>Exercice2_augmentation_multimodale.ipynb Jupyter Notebook for multimodal data augmentaion (defect images and mechanical fields) via oversampling</p> <p>Exercice3_clustering.ipynb Data clustering using the k-medoids algorithm applied to mechanical dissimilarity of the defects.</p> <p>k_medoids.py is a python code of a kmedoids algorithm.</p> <p>in Data:</p> <p>All_images.npy (numpy file) contains the defect images.</p> <p>All_Stresses.npy (numpy) contains mechanical fields, All_Stresses[k,i,j,ic,it] is the instance number k of the component ic of the Cauchy stress tensor at time it. The mechanical problem is decribed in <a href="https://dx.doi.org/10.5802/crmeca.51">⟨10.5802/crmeca.51⟩</a>. <a href="https://hal.archives-ouvertes.fr/hal-03113503">⟨hal-03113503⟩.</a></p> <p>New_images_1.npy and New_Stresses_1.npy are augmented data for k=1.</p> <p>New_images_87.npy and New_Stresses_87.npy are augmented data for k=87.</p> <p>Dissimilarity_Stress.npy is the Frobenius norm of the distances between stress tensors (All_Stresses.npy).</p> <p> </p>
Supplemental Data for the Journal Article Entitled The mechanistic origins of heterogeneous void growth during ductile failure accepted for publication by Acta Materialia
<p>This repository contains the Supplemental Data for the Journal Article Entitled The mechanistic origins of heterogeneous void growth during ductile failure accepted for publication by Acta Materialia.</p> <p>Authors: M.W. Vaughan<sup>a</sup>, H. Lim<sup>a</sup>, B. Pham<sup>a</sup>, R. Seede<sup>a</sup>, A. T. Polonsky<sup> a</sup>, K. Johnson<sup> a</sup>, P. J. Noell<sup>a,*</sup></p> <p>a: Sandia National Laboratories, Albuquerque, NM 87123</p> <p>Each folder contains a ReadMe.txt describing the files and their organization within each folder. </p> <p><br>Acknowledgements:<br><span>This work was supported by the Laboratory Directed Research and Development program at Sandia National Laboratories, a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc. for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. The views expressed in the article do not necessarily represent the views of the U.S. DOE or the United States Government. </span></p>
Predicting the Onset of Void Swelling in Irradiated Metals with Machine Learning
<p>Dataset to support the manuscript, "Predicting the Onset of Void Swelling in Irradiated Metals with Machine Learning", which contains all variables for predicting the target variable.</p>
Noise to void model and results of denoising + segmentation using VollSeg
<p>Noise to void model and results of denoising + segmentation using VollSeg</p>
Catalogues of voids as antihalos in the local Universe
<p>These archives contain the data products described in "Catalogues of voids as antihalos in the local Universe" by H. Desmond, M. Hutt, J. Devriendt and A. Slyz (2021). See the readme for further details.</p>
Critical Resolved Shear Stresses for Dislocation Motion in FeCrNi with and without voids.
<p>Simulation results, Atomistic Simulations of Dislocation-Void Interactions in Concentrated Solid Solution Alloys, Published in MDPI Metals</p>
The Effect of Oral PhenazopyrIdine on Perioperative Voiding After Mid-urethral sliNg (EPIPhANy Study)
ClinicalTrials.gov study NCT02806713. IPD Sharing: NO. Countries: 1. Publications: 2.
Urinary Retention After Total Laparoscopic Hysterectomy With Immediate Foley Catheter Removal Versus Backfill Void Trial
ClinicalTrials.gov study NCT03141372. IPD Sharing: NO. Countries: 1. Publications: 22.
Pre-operative Tamsulosin and Time to Spontaneous Void After Hysterectomy
ClinicalTrials.gov study NCT04859660. IPD Sharing: NO. Countries: 1. Publications: 1.
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