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472 results for “Geometry”
Evaluating the predictive character of the method of Constrained Geometries Simulate External Force with Density Functional Theory.
<p>## Abstract</p> <p>from [1]:</p> <p>Mechanochemistry is a fast-developing field of interdisciplinary research with a growing number of applications. Therefore, many theoretical methods have been developed to quickly predict the outcome of mechanically induced reactions. Constrained geometries simulate External Force (CoGEF) is one of the earlier methods in this field. It is easily implemented and can be conducted with most DFT codes. However, recently, we observed totally different predictions for model systems of epoxy resins in different conformations and with different density functionals. To better understand the conformational and functional dependence in typical CoGEF calculations we present a systematic evaluation of the CoGEF method for different model systems covering homolytic and heterolytic bond cleavage reactions, electrocyclic ring opening reactions and scission of non-covalent interactions in hydrogen-bond complexes. From our calculations we observe that many mechanochemical descriptors strongly depend on the functional used, however, a systematic trend exists for the relative maximum Force. In general, we observe that the CoGEF procedure is forcing the system to high energetic regions on the molecular potential energy profiles, which can lead to unexpected and uncorrelated predictions of mechanochemical reactions. This is questioning the true predictive character of the method.</p> <p> </p> <p>## Contact</p> <p>Christian R. Wick</p> <p>Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Faculty of Science, Department of Physics, PULS Group, Interdisciplinary Center for Nanostructured Films (IZNF), Cauerstrasse 3, 91058, Germany</p> <p> </p> <p>## License</p> <p>Creative Commons Attribution 4.0 International</p> <p> </p> <p>## Context</p> <p>Dataset to paper [1]</p> <p> </p> <p>## Contents</p> <ul> <li>All COGEF trajectories in xyz format.</li> <li>All CoGEF distances and DFT Energies in csv format.</li> </ul> <p>The following DFT levels of theory were investigated:</p> <ul> <li>B3LYP/6-31G(d)</li> <li>B3LYP-D3BJ/def2-SVP</li> <li>BP86-D3/def2-SVP</li> <li>PBE1PBE/def2-SVP</li> <li>M06-D3/def2-SVP</li> </ul> <p> </p> <p>## Folder structure</p> <ul> <li>- compound_X : data set for compound number X (numbering corresponds to the numbering scheme in [1]) <ul> <li>the xyz trajectories follow the following naming convention: "DFT_method"_"unrestricted/restricted".xyz</li> <li>the csv files follow the naming convention: "DFT_method"_"unrestricted/restricted".xyz.csv</li> </ul> </li> </ul> <p>## Software</p> <p>### COGEFF calculations: COGEF.py v1.8.0</p> <p>Zenodo release:</p> <p>https://doi.org/10.5281/zenodo.7079733</p> <p>### DFT calculations:</p> <p>Gaussian 16 Rev B [2]</p> <p> </p> <p>## Funding</p> <p>This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 377472739/GRK 2423/1-2019 FRASCAL.</p> <p><br> ## References</p> <p>[1] C. R. Wick, E. Topraksal, D. M. Smith, A.-S. Smith, "Evaluating the predictive character of the method of Constrained Geometries Simulate External Force with Density Functional Theory.", Forces in Mechanics, 9, 100143; doi:10.1016/j.finmec.2022.100143</p> <p>[2] Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Petersson, G. A.; Nakatsuji, H.; et al. Gaussian 16 Rev. B.01, 2016.</p>
DocumeNDT stone masonry walls geometry: Photogrammetric models dataset
<p>This repository contains detailed geometric data from six stone masonry walls constructed for an experimental campaign carried out at the laboratory of the Institute for Physical and Information Technologies (ITEFI), from the Spanish National Research Council (CSIC).</p> <p>The data set is structured in 2 levels of folders:<br> - At first level, the 6 folders correspond to the 6 tested stone masonry walls (Wall 1-6).<br> - At second level, for each wall, there are two folders. The first folder contains the photogrammetric model of each wall, e.g., "W1", and a photograph showing the wall. The second folders contains individual photogrammetric models of each stone composing the walls and their exact location within the walls, e.g., "Stone 101".</p> <p>The global coordinates of the general photogrammetry of the wall and the individual photogrammetries of the stones are the same. They can be imported in any viewer and will be correctly located.</p> <p>The geometric data is presented in .obj files that can be imported in any 3D modeling software.</p> <p>Please cite the following related publication:</p> <p>Ortega J, Meersman MFL, Aparicio S, Liébana JC, Martin R, Anaya JJ, Gonzalez M. An automated sonic tomography system for the inspection of historical masonry walls (2023)</p>
HEPES and MES geometry analysis dataset
<p>Structure dataset used to analyse HEPES and MES geometry, as described in 'Analysis of protein structures containing HEPES and MES molecules' by Macnar JM, et al.</p>
LungVis1.0: Active learning AI-powered 3D imaging ecosystem for spatial profiling of lung geometry and pulmonary nanoparticle delivery
<p>The imaging dataset was obtained by light sheet fluorescence microscopy on tissue cleared murine lungs. It includes whole lung autofluorence image, particle fluorescence image, and artifical intelligence nnU-Net generated lung airway segments. The dataset provides 78 healthy murine lung strucutre and airway geometry for C57BL/6 mice and offers comprehensive delivery features including qualitative and quantitative analysis on the temporal and spatial inter- and intra-acinar deposition patterns and NP regional dosimetry for four commonly-used routes of pulmonary delivery,namely intranasal liquid aspiration, intratracheal liquid instillation, ventilator-assisted and nose-only aerosol inhalation.</p> <p>Raw LSFM imaging data collection was carried out between 2017-2021, the AI code and generated airway segmention were performed in 2021-2022, the whole datasets were then compiled in 2023. </p> <p>Please ensure to cite our paper for any reuse or reanalysis. Yang, L., Liu, Q., Kumar, P. <em>et al.</em> LungVis 1.0: an automatic AI-powered 3D imaging ecosystem unveils spatial profiling of nanoparticle delivery and acinar migration of lung macrophages. <em>Nat Commun</em> <strong>15</strong>, 10138 (2024). https://doi.org/10.1038/s41467-024-54267-1</p> <p>For any inquiries, please feel free to contact us at lin.yang@helmholtz-munich.de </p>
Supplementary data: Geometry-preserving Expansion Microscopy microplates enable high fidelity nanoscale distortion mapping
<p>--- Data supplement---</p> <p>--- Title: Geometry-preserving Expansion Microscopy microplates enable high fidelity nanoscale distortion mapping</p> <p>The zip file contains the following directories and subdirectories. The files, recommended software, calling code and relevance to the main manuscript (preprint found at https://doi.org/10.1101/2023.02.20.529230) are included in the notes below:</p> <p>(i) “STL files” – Includes .stl file formats of the Expansion Microscopy microplate components. These files can be opened with any CAD software (e.g. Fusion 360) or any opensource 3D printer slicer programme (e.g. Chitubox v1.9.5)</p> <p>(ii) “Image alignment & distortion analysis code” – Directory containing the ImageJ macros and custom-written Python scripts for the analysis pipeline described in the main manuscript, from image scaling and alignment to for distortion plotting and RMS Error analysis and plotting. Subdirectories include:</p> <ul> <li>a.“Distortion analysis and RMSE plotting code” – Directory contains custom-written Python scripts for updated distortion analysis, plotting, RMS error calculation, and code for combining the plots. Each .py file can be run with any Python distribution. Dependency libraries and modules (all opensource) include numpy, os, cv2, skimage, and tifffile</li> <li>b.“Basic Align Macro.ijm” – ImageJ macro for basic image alignment of pre- and post-Expansion Microscopy images. Refer to <a href="https://imagej.nih.gov/ij/developer/macro/macros.html#tools">https://imagej.nih.gov/ij/developer/macro/macros.html#tools</a> on how to install and run ImageJ macros.</li> </ul> <p> </p> <p>(iii)“Example data and analysis scripts” – Directory containing example datasets and worked examples of analysis. Subdirectories include:</p> <ul> <li>a.“Single-channel_HeLa_cells”. Worked example of single-colour dataset of a cluster of HeLa cells stained with NHS-AZ488. Folder includes pre-Expansion and post-Expansion images (.tif format), overlays of the pre- and post-Expansion images along with distortion vector maps, and plots of normalised RMS error (in % values) against measurement length scale (in micrometers) saved as numpy arrays (.npy format). This can be called in using a Python code similar to: numpy.load("file name")</li> <li>b.“Multiplexed_Drosophila_wing”. Worked example of two-colour dataset of Drosophila fly wing tissue images stained with NHS-Alexa647 and anti-E-cad-GFP/Alexa488. Folder includes pre-Expansion and post-Expansion images (.tif format), overlays of the pre- and post-Expansion images along with distortion vector maps, and plots of normalised RMS error (in % values) against measurement length scale (in micrometers) saved as numpy arrays (.npy format).</li> <li>c.“Multiplexed_HeLa_cells”. Worked example of two-colour dataset of cultured HeLa cell images stained with NHS-AZ488 and antibodies. Each folder includes pre-Expansion and post-Expansion images (.tif format), overlays of the pre- and post-Expansion images along with distortion vector maps, and plots of normalised RMS error (in % values) against measurement length scale (in micrometers) saved as numpy arrays (.npy format): <ul> <li>I.“KDELAlexa594_NHSAZ488” – antibody staining against KDEL</li> <li>II.“NUP98Alexa594_NHSAZ488” – antibody staining against Nups98</li> </ul> </li> </ul> <p> </p> <ul> <li>d.“Distortion Correction”. Folder contains two subfolders of output images from distortion corrections using the Linear Stack Alignment with SIFT plugin in ImageJ v1.54f. Each example consists of a pre- and post-Expansion image file, and the ‘corrected’ image file generated with 5, 10, and 50 voxel B-spline sampling.</li> </ul> <p> </p> <p>These files are placed in public domain under Creative Commons license CC BY-ND 4.0</p>
Supplemental Data for "Energy minimization of paired composite fermion wave functions in the spherical geometry"
<p>Includes extra data for "Energy minimization of paired composite fermion wave functions in the spherical geometry".</p>
Influence of He$^{++}$ and shock geometry on interplanetary shocks in the solar wind: 2D Hybrid simulations
<p>After protons, alpha particles (He$^{++}$) are the most important ion species in the solar wind, constituting typically about 5\% of the total ion number density. Due to their different charge-to-mass ratio protons and He$^{++}$ particles are accelerated differently when they cross the electrostatic potential in a collisionless shock. This behavior can produce changes in the velocity distribution function (VDF) for both species generating anisotropy in the temperature which is considered to be the energy source for various phenomena such as ion cyclotron and mirror mode waves. How these changes in temperature anisotropy and shock structure depend on the percentage of He$^{++}$ particles and the geometry of the shock is not completely understood. In this paper we have performed various 2D local hybrid simulations (particle ions, massless fluid electrons) with similar characteristics (e.g., Mach number) to interplanetary shocks for both quasi-parallel and quasi-perpendicular geometries self-consistently including different percentages of He$^{++}$ particles. We have found changes in the shock transition behavior as well as in the temperature anisotropy as functions of both the shock geometry and He$^{++}$ particle abundance: The change of the initial $\theta_{Bn}$ leads to variations of the efficiency with which particles can escape to the upstream region facilitating or not the formation of compressive structures in the magnetic field that will produce increments in perpendicular temperature. The regions where both temperature anisotropy and compressive fluctuations appear tend to be more extended and reach higher values as the He$^{++}$ content in the simulations increases.</p> <p> </p>
Computational modelling of metal soap formation in historical oil paintings: the influence of fatty acid concentration and nucleus geometry on the induced chemo-mechanical damage.
<p>Metal soap formation is one of the most wide-spread degradation mechanisms observed in historical oil paintings, affecting works of art from museum collections worldwide. Metal soaps develop from a chemical reaction between metal ions present in the pigments and saturated fatty acids, which are released by the oil binder. The presence of large metal soap crystals inside paint layers or at the paint surface can be detrimental for the visual appearance of artworks. Moreover, metal soaps can possibly trigger mechanical damage, ultimately resulting in flaking of the paint. This paper departs from a recently proposed computational model to predict chemo-mechanical degradation in historical oil paintings, as presented in Eumelen et al. (J Mech Phys Solids 132:103683, 2019). The model describes metal soap formation and growth, which are phenomena that are driven by the diffusion of saturated fatty acids and proceed by a nucleation process from a crystalline nucleus of small size. This results into a chemically-induced strain in the paint, which may promote crack nucleation and propagation. The proposed model is here used to investigate the effects of saturated fatty acid concentration and initial nucleus geometry on the amount of chemo-mechanical damage generated. Numerical simulations show that both factors have a marginal influence on the growth rate of the metal soap crystal, but play a significant role on the extent of fracture induced in the paint.</p>
The Geometry and Genetics of Hybridization
<p>When divergent populations form hybrids, hybrid fitness can vary with genome composition, current environmental conditions, and the divergence history of the populations. We develop analytical predictions for hybrid fitness, which incorporate all three factors. The predictions are based on Fisher's geometric model, and apply to a wide range of population genetic parameter regimes and divergence conditions, including allopatry and parapatry, local adaptation and drift. Results show that hybrid fitness can be decomposed into intrinsic effects of admixture and heterozygosity, and extrinsic effects of the (local) adaptedness of the parental lines. Effect sizes are determined by a handful of geometric distances, which have a simple biological interpretation. These distances also reflect the mode and amount of divergence, such that there is convergence towards a characteristic pattern of intrinsic isolation. We next connect our results to the quantitative genetics of line crosses in variable or patchy environments. This means that the geometrical distances can be estimated from cross data, and provides a simple interpretation of the ``composite effects''. Finally, we develop extensions to the model, involving selectively-induced disequilibria, and variable phenotypic dominance. The geometry of fitness landscapes provides a unifying framework for understanding speciation, and wider patterns of hybrid fitness.</p>
VR-Together Pilot 3: Geometry of living room, bedroom. Materials, textures and illumination
<p>VR-Together Pilot 3 geometry of living room and bedroom, materials, textures and illumination.</p> <p>This dataset contains de material created for the <a href="https://vrtogether.eu/about-vr-together/pilots/pilot3/">Pilot 3 of the VR-Together project</a>. In particular, it contains the following material:</p> <ul> <li>Geometry of the living room</li> <li>Geometry of the bedroom</li> <li>Objects</li> <li>Textures</li> <li>Illumination scheme</li> </ul> <p>All the previous material has been created in Unity.</p> <p>VR-Together has been funded by the European Commission as part of the H2020 program, under the grant agreement 762111.</p>
The Complex Geometry and Dynamical Role of Stellar Wind Bubbles in Turbulent Molecular Clouds
<p>Research Data Management Package for paper in Monthly Notices of the Royal Astronomical Society with same title and author list</p>
ISL2014BASELINE - An eyetracking dataset from facilitating secondary geometry lessons
<p>This dataset contains eye-tracking data from a single subject (a researcher), facilitating two geometry lessons in a secondary school classroom, with 11-12 year old students using laptops and a projector. These sessions were recorded in the frame of the MIOCTI project (http://chili.epfl.ch/miocti).</p> <p>This dataset has been used in several scientific works, such as the ECTEL 2015 (http://ectel2015.httc.de/) conference paper "Studying Teacher Orchestration Load in Technology-Enhanced Classrooms: A Mixed-method Approach and Case Study", by Luis P. Prieto, Kshitij Sharma, Yun Wen & Pierre Dillenbourg (the analysis and usage of this dataset is available publicly at https://github.com/chili-epfl/ectel2015-orchestration-school)</p>
Optimized geometries for selected ions of ionic liquids and small molecules
<p>The geometries of these selected chemical entities were optimized at the ab initio or semiempirical levels of theory. They can be conveniently used to create more complicated systems through combining species like the free PACKMOL software offers. </p>
A geometry preserving, conservative, mesh-to-mesh isogeometric interpolation algorithm for spatial adaptivity of the multigroup, second-order even-parity form of the neutron transport equation
<p>In this paper a method is presented for the application of energy-dependent spatial meshes applied to the multigroup, second-order, even-parity form of the neutron transport equation using Isogeometric Analysis (IGA). The computation of the inter-group regenerative source terms is based on conservative interpolation by Galerkin projection. The use of Non-Uniform Rational B-splines (NURBS) from the original computer-aided design (CAD) model allows for efficient implementation and calculation of the spatial projection operations while avoiding the complications of matching different geometric approximations faced by traditional finite element methods (FEM). The rate-of-convergence was verified using the method of manufactured solutions (MMS) and found to preserve the theoretical rates when interpolating between spatial meshes of different refinements. The scheme’s numerical efficiency was then studied using a series of two-energy group pincell test cases where a significant saving in the number of degrees-of-freedom can be found if the energy group with a complex variation in the solution is refined more than an energy group with a simpler solution function. Finally, the method was applied to a heterogeneous, seven-group reactor pincell where the spatial meshes for each energy group were adaptively selected for refinement. It was observed that by refining selected energy groups a reduction in the total number of degrees-of-freedom for the same total L2 error can be obtained.</p>
Supplementary materials for "Improving diffusion-based protein backbone generation with global-geometry-aware latent encoding"
<h1>Info</h1> <p>This dataset contains the supplementary materials for "Improving diffusion-based protein backbone generation with global-geometry-aware latent encoding". </p> <p>For <strong>source code </strong>and <strong>detailed instructions on usage, </strong>please refer to our <a href="https://github.com/meneshail/TopoDiff/tree/main" target="_blank" rel="noopener">github</a> .</p> <h1>Supplementary data</h1> <h2>weights.tar.gz</h2> <p>The trained model weights used in the paper.</p> <h2>dataset.zip</h2> <p>CATH-60 Dataset used in the paper. In the notebook directory of our <a href="https://github.com/meneshail/TopoDiff/tree/main" target="_blank" rel="noopener">github</a> , we provide an example on encoding and visualize it with our trained encoder.</p> <h2>design.zip</h2> <p>The 21 novel mainly-beta designs selected for experiment validation. Along with the generated backbone, we also provide the prediction results from AlphaFold and ESMFold.</p> <h2>benchmark_sample.zip</h2> <p>Sampled backbones used for all benchmark experiment (All methods and variants included).</p> <h2>evaluation.tar.gz</h2> <p>Precomputed CATH reference data for coverage metric computation. Need to be downloaded for using evaluation scripts. </p>
Efficient excitation transfer in an LH2-inspired nanoscale stacked ring geometry
<p>The data supporting the findings in the manuscript "Efficient excitation transfer in an LH2-inspired nanoscale stacked ring geometry" are available here.</p>
Input geophysical and geological data for "Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees"
<p>This is a companion dataset to the manuscript: <br><br>Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees,</p><p>by: Jeremie Giraud , Mary Ford, Guillaume Caumon, Lachlan Grose, Vitaliy Ogarko, Roland Martin, and Paul Cupillard.<br><br>This dataset contains the input data used in the inversion, in terms of the gravity data and the geological data used in the inversion.<br><br>The *.txt file contains the gravity data as inverted in the manuscript: X, Y, Z, Value.<br>The *.csv file contains the geological data: location of the contacts and orientation data.</p>
Dataset for "Complexity of crack front geometry enhances toughness of brittle solids"
<div>This data package supports the publication</div> <div>'Complexity of crack front geometry enhances toughness of brittle solids'</div> <div>by Xinyue Wei, Chenzhuo Li, Cían McCarthy, and John M. Kolinski</div> <div>Nature physics (2024) - <span><a href="https://doi.org/10.1038/s41567-024-02435-x">https://doi.org/10.1038/s41567-024-02435-x</a></span></div> <div> </div> <div>DOI: 10.5281/zenodo.10604552</div> <div> </div> <div> </div> <div>The data package includes </div> <div>- data for material characterization in 'material_test.xlsx';</div> <div>- segmented 3D crack data in "segmented_3D_crack_stacks" folder.</div> <div> </div> <div> </div> <div>'material_test.xlsx' includes</div> <div>- shear modulus for Gel1, Gel2, Gel3, Gel4, and PDMS;</div> <div>- stretch v.s. engineering stress for the five materials.</div> <div> </div> <div> </div> <div>'segmented_3D_crack_stacks' includes</div> <div>- 'sample_info.xlsx': metadata for all the cracks including the critically loaded cracks and the cracks with local </div> <div> propagation in two sheets, with the following information:</div> <div>- 'crack_name' corresponds to the title of each tif file in the subfolders;</div> <div>- 'sample' indicates the material and thickness of the samples;</div> <div>- 'xy_scale_um' and 'z_scale_um' are the resolutions in microns in xyz direction ;</div> <div>- 's_start' and 's_end' provide the range of the valid slices, with s_end excluded. The first slice is s=0;</div> <div>- 'tile_s_start' and 'tile_s_end' provide the range of the valid slices of the tile scans. The tile scans </div> <div> are taken for the measurement of strain energy release rate from the far field CTOD. Since the field of </div> <div> view is large enough to cover the K-dominant region of Gel1, tile scan is not taken for Gel1;</div> <div>- 'remarks' are the comments for the data points (if applicable).</div> <div> </div> <div>- 'critically_loaded_cracks': tif stacks for the binarized 3D cracks that are loaded to critical state. </div> <div> Different materials and sample thicknesses are in separate folders. </div> <div>- 'local_propagation': tif stacks for the binarized 3D cracks before critically loaded. </div> <div> </div> <div> </div>
MODEX: Ripple Geometry Data
<p>Processed ripple geometry data from the MODEX (Morphological Diffusivity EXperiment - PI: Dr. Matthieu A. de Schipper) experiment. For more detail, refer to the README (<a href="../api/records/10864977/draft/files/MODEX_RippleGeometryData_README.pdf/content" target="_blank" rel="noopener noreferrer">MODEX_RippleGeometryData_README.pdf</a>).</p> <p>This data serves as a reference for the following paper:</p> <p>Lee, S.B., Wengrove, M.E., de Schipper, M.A., Kleinhans, M.G., Ruessink, G., and Hopkins, J. Observation and Prediction of Sand Ripple Geometry on a Sloped Bed Under Varying Combined Wave-Current Flows.</p>
Plate interface geometry complexity and persistent heterogenous coupling revealed by a high-resolution earthquake focal mechanism catalog in Mentawai, Sumatra
<p>This website contains all the outputs from the study entitled “Plate interface geometry complexity and persistent heterogenous coupling revealed by a high-resolution earthquake focal mechanism catalog in Mentawai, Sumatra”. The contents include the seismic stations used in this study, obtained focal mechanism solutions, corresponding waveform fits, relocation results, and depth-phase modeling results. Each figure (started with ${ID}) is corresponding to the Earthquake ID as shown in Table S1.txt.</p>
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International Brain Laboratory public data
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OpenNeuro
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