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12,393 results for “Materiality”
Limb work and joint work minimisation reveal an energetic benefit to the elbows-back, knees-forward limb design in parasagittal quadrupeds. Supplementary material including data, simulation code and simulation results
<p>Quadrupedal animal locomotion is energetically costly. We explore two forms of mechanical work that may be relevant in imposing these physiological demands. Limb work, due to the forces and velocities between the stance foot and the centre of mass, could theoretically be zero given vertical limb forces and horizontal centre of mass path. To prevent pitching, skewed vertical force profiles would then be required, with forelimb forces high in late stance and hindlimb forces high in early stance. By contrast, joint work – the positive mechanical work performed by the limb joints – would be reduced with forces directed through the hip or shoulder joints. Measured quadruped kinetics show features consistent with compromised reduction of both forms of work, suggesting some degree of, but not perfect, inter-joint energy transfer. The elbows-back, knees-forward design reduces the joint work demand of a low limb-work, skewed, vertical force profile. This geometry allows periods of high force to be supported when the distal segment is near vertical, imposing low moments about the elbow or knee, while the shoulder or hip avoids high joint power despite high moments because the proximal segment barely rotates – translation over this period is due to rotation of the distal segment.</p>
Polish is quantitatively different on quartzite flakes used on different worked materials [Python analysis]
<p>This upload includes the following files related to the Python analysis:<br> 1. Raw data as a XLSX table (processing-quartzite-final-2020-04-29.xlsx) is the output from R Script #1 (see <a href="https://doi.org/10.5281/zenodo.3979139">https://doi.org/10.5281/zenodo.3979139</a>), even though the filename is slightly different.</p> <p>Plus, for each analysis (full and restricted datasets), included in the corresponding ZIP archive:<br> 2. Jupyter notebooks of the analysis (Classification_RandSplitFeature_Revision_VXX.ipynb) rendered to HTML file (Classification_RandSplitFeature_Revision_VXX.html)<br> 3. Dataframe including the artificially filled datapoints<br> 4. Output of the analysis as PDF:<br> • Confusion matrices ("CM")<br> • Decision trees on selected features ("DecisionTreeSel")<br> • Balanced accuracy vs. maximum depth ("depth")<br> • Mutual information ("MI")<br> • Pairplots of selected features ("pairplot")<br> • Performance on training sets ("performance")</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p>
Supplementary material for "A Partitioned Finite Element Method for power-preserving discretization of open systems of conservation laws"
<p>This archive contains supplementary material for the paper "A Partitioned Finite Element Method for power-preserving discretization of open systems of conservation laws", containing the source codes for the numerial results presented in the paper. An arXiv pre-print version of the paper is available <a href="https://arxiv.org/abs/1906.05965">here</a>.</p> <p>The following codes are provided:</p> <ul> <li> <p><code>codes/simulation1D_small.jl</code>: small amplitudes (linear) 1D simulation</p> </li> <li> <p><code>codes/simulation1D_large.jl</code>: large amplitudes (nonlinear) 1D simulation</p> </li> <li> <p><code>codes/simulation1D_analytical_gradient</code>: large amplitudes 1D simulation, but using an analytical nonlinear Hamiltonian gradient expression</p> </li> <li> <p><code>codes/simulation2D.jl</code>: large amplitudes (nonlinear) 2D simulation</p> </li> <li> <p><code>codes/convergence1D.jl</code>: convergence analysis of the 1D linear case</p> </li> <li> <p><code>codes/convergence2D.m</code>: convergence analysis of the 2D linear case</p> </li> </ul> <p>A GitHub with the codes and a few instructions on usage is available <a href="http://github.com/flavioluiz/PFEM-article-supplementary-material">here</a>.</p> <p><strong>Acknowledgements</strong></p> <p>This work has been performed in the frame of the Collaborative Research DFG and ANR project INFIDHEM, entitled "Interconnected of Infinite-Dimensional systems for Heterogeneous Media", nº ANR-16-CE92-0028. Further information is available <a href="http://websites.isae-supaero.fr/infidhem/the-project">here</a>.</p>
Supplementary Materials for "The Effects of Chromosome Doubling on Morphology, Salinity Tolerance, Essential Oil Composition, and Gene Expression of Biosynthesis Pathway in Peppermint (Mentha piperita L.)"
<p>Shandong Province Key Laboratory of Applied Microbiology, Ecology Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250103, China; zhaozjfrances@163.com (Z.Z.); yanli_wei@163.com (Y.W.); menshenlai@163.com (K.Y.); sdkinghills@sina.com (B.L.); liling33802400@163.com (L.L.); yanght@sdas.org (H.Y)</p>
Supplementary materials for the article on 'How to express evolution in English Pokémon names', published in Linguistics
<p>The zip-archive contains two folders, "Graphics" and "statistics". For the statistical analyses, cf. the Rdm-file in the "statistics" folder. The data is stored in csv-files. For a description of the data, cf. the publication itself.</p>
Supplementary materials for article focused on traffic information enrichment: Animation and complete overview of all road segments on which basis summary test results were calculated
<p><strong>Supplementary materials </strong>(Appendix A and B) for the article:</p> <p>Traffic Information Enrichment: Creating Long-Term Traffic Speed Prediction Ensemble Model for Better Navigation through Waypoints</p> <p><em>Abstract: </em>Traffic speed prediction for a selected road segment from a short-term and long-term perspective is among the fundamental issues of intelligent transportation systems (ITS). During the course of the past two decades, many artefacts (e.g., models) have been designed dealing with traffic speed prediction. However, no satisfactory solution has been found for the issue of a long-term prediction for days and weeks using the vast spatial and temporal data. This article aims to introduce a long-term traffic speed prediction ensemble model using country-scale historic traffic data from 37,002 km of roads, which constitutes 66% of all roads in the Czech Republic. The designed model comprises three submodels and combines parametric and nonparametric approaches in order to acquire a good-quality prediction that can enrich available real-time traffic information. Furthermore, the model is set into a conceptual design which expects its usage for the improvement of navigation through waypoints (e.g., delivery service, goods distribution, police patrol) and the estimated arrival time. The model validation is carried out using the same network of roads, and the model predicts traffic speed in the period of 1 week. According to the performed validation of average speed prediction at a given hour, it can be stated that the designed model achieves good results, with mean absolute error of 4.67 km/h. The achieved results indicate that the designed solution can effectively predict the long-term speed information using large-scale spatial and temporal data, and that this solution is suitable for use in ITS.</p> <p>Simunek, M., & Smutny, Z. (2021). Traffic Information Enrichment: Creating Long-Term Traffic Speed Prediction Ensemble Model for Better Navigation through Waypoints. <em>Applied Sciences</em>, 11(1), 315. <a href="https://doi.org/10.3390/app11010315">https://doi.org/10.3390/app11010315</a></p> <p> </p> <p><strong>Appendix A</strong><br> Examples of the deviation between the average speed and the FreeFlowSpeed for selected hours.</p> <p> </p> <p><strong>Appendix B</strong><br> The text file provides a complete overview of all road segments on which basis summary test results were calculated in Section 6 of the article.</p>
Neutrino emission by plasmon decay in a strong magnetic field: Data material
<p>This data reference paper summarizes the fundamental calculated results obtained analytically for the case of a neutron star (NS) crust: it’s cooling rate, time-scale evolution and neutrino luminosity, based on two different theories- Photo-Neutrino (PN) interaction and conventional weak interaction. Considering the NS environment as a degenerate plasmon composition the calculation tables are given. </p>
Supplemental Material for "Biomechanical analyses of Cambrian euarthropod limbs reveal their effectiveness in mastication and durophagy"
<p>Durophagy arose in the Cambrian and greatly influenced the diversification of biomineralised defensive structures throughout the Phanerozoic. Spinose gnathobases on protopodites of Cambrian euarthropod limbs are considered key innovations for shell-crushing, yet few studies have demonstrated their effectiveness with biomechanical models. Here we present finite element analysis models of two Cambrian trilobites with prominent gnathobases—<i>Redlichia rex</i> and <i>Olenoides serratus</i>—and compare these to the protopodites of the Cambrian euarthropod <i>Sidneyia inexpectans</i> and the modern American horseshoe crab, <i>Limulus polyphemus</i>. Results show that <i>L. polyphemus</i>, <i>S. inexpectans</i> and <i>R. rex</i> have broadly similar microstrain patterns, reflecting effective durophagous abilities. Conversely, low microstrain values across the <i>O. serratus</i> protopodite suggest that the elongate gnathobasic spines transferred minimal strain, implying that this species was less well-adapted to masticate hard prey. These results confirm that Cambrian euarthropods with transversely elongate protopodites bearing short, robust gnathobasic spines were likely durophages. Comparatively, taxa with shorter protopodites armed with long spines, such as <i>O. serratus</i>, were more likely restricted to a soft food diet. The prevalence of Cambrian gnathobase-bearing euarthropods and their various feeding specialisations may have accelerated the development of complex trophic relationships within early animal ecosystems, especially the 'arms race' between predators and biomineralised prey.</p>
Raw data for manuscript "Simple and robust method for determination of laser fluence thresholds for material modifications: an extension of Liu's approach to imperfect beams"
<p>Raw data for section 4 ("Extension of Liu’s method for Gaussian beams truncated using circular apertures") and section 5 (" Experimental validation and practical relevance") of the manuscript : Simple and robust method for determination of laser fluence thresholds for material modifications: an extension of Liu’s approach to imperfect beams.</p> <p>Documents with names "PSFlab_raw..... .txt" and "ForLiu.... .dat" correspond to section 4. The other documents correspond to section 5.</p>
BeMAGIC_Suitable ME materials_CNR
<p>BeMAGIC ITN (GA 861145) - Suitable ME materials/designs, results from CNR.</p>
Determinants of Airbnb prices in European cities: A spatial econometrics approach (Supplementary Material)
<p>This repository contains supplementary materials for the article:</p> <p><strong>Determinants of Airbnb prices in European cities: A spatial econometrics approach</strong></p> <p><strong>(</strong>DOI<strong>: </strong><a href="https://doi.org/10.1016/j.tourman.2021.104319">https://doi.org/10.1016/j.tourman.2021.104319</a>)</p> <p>The materials include the used datasets and Python scripts for spatial regression models.</p> <p><strong>Datasets</strong></p> <p>For each city two files are provided: data for weekday and weekend offers</p> <p>The columns are as following:</p> <ul> <li>realSum: the full price of accommodation for two people and two nights in EUR</li> <li>room_type: the type of the accommodation </li> <li>room_shared: dummy variable for shared rooms</li> <li>room_private: dummy variable for private rooms</li> <li>person_capacity: the maximum number of guests </li> <li>host_is_superhost: dummy variable for superhost status</li> <li>multi: dummy variable if the listing belongs to hosts with 2-4 offers</li> <li>biz: dummy variable if the listing belongs to hosts with more than 4 offers</li> <li>cleanliness_rating: cleanliness rating</li> <li>guest_satisfaction_overall: overall rating of the listing</li> <li>bedrooms: number of bedrooms (0 for studios)</li> <li>dist: distance from city centre in km</li> <li>metro_dist: distance from nearest metro station in km</li> <li>attr_index: attraction index of the listing location</li> <li>attr_index_norm: normalised attraction index (0-100)</li> <li>rest_index: restaurant index of the listing location</li> <li>attr_index_norm: normalised restaurant index (0-100)</li> <li>lng: longitude of the listing location</li> <li>lat: latitude of the listing location</li> </ul> <p><strong>Programming Scripts</strong></p> <p>In this repository you will find a script for spatial regressions in Python using PySAL (models_robust.py).</p> <p>The codes cover the following regression models:</p> <ul> <li>OLS</li> <li>SLX (lagged_x)</li> <li>SAR (lagged_y)</li> <li>SDM (lagged_x_y)</li> <li>SEM (lagged_e)</li> <li>SDEM (lagged_e_x)</li> </ul> <p>Main parameters:</p> <ul> <li>cities - list of cities from the dataset to be included in the analysis</li> <li>Robust=False: calculate the OLS, SLX, SAR and SDM regressions with W (weight matrix) based on 10 closest neighbours</li> <li>Robust=True: calculate all regression models with different specifications of W</li> <li>direct_indirect=True: calculate the direct and indirect effects (based on Golgher, A. B., & Voss, P. R. (2016). How to Interpret the Coefficients of Spatial Models: Spillovers, Direct and Indirect Effects. Spatial Demography (Vol. 4). https://doi.org/10.1007/s40980-015-0016-y)</li> </ul> <p>Key functions:</p> <ul> <li>create_weights - defines the W specification</li> <li>write_stats - calculates's Moran's I and Geary's C</li> <li>direct - calculates the direct effect of the variable</li> <li>indirect - calculates the indirect effect</li> <li>coord - sets the coordinate refence system (CRS) appropriate to the analysed city</li> <li>total_results calculates the regressions</li> <li>the coordinates are projected from GPS (epsg:4326) to the local CRS (km_lat, km_lon)</li> <li>all regressions are saved as formatted txt table</li> <li>the results can be also saved as csv table</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Supplementary Material and Code of Evidence for immunity to SARS-CoV-2 from epidemiological data series
<p>This Supplementary Material file, Code and Data correspond to the manuscript:</p> <p>Evidence for immunity to SARS-CoV-2 from epidemiological data series<br> Pablo Yubero, Alvar A. Lavin, Juan F Poyatos<br> medRxiv 2020.07.22.20160028; doi: https://doi.org/10.1101/2020.07.22.20160028</p>
Supplementary Materials for "Transcriptome responses to heat and cold stress in prepupae of Trichogramma chilonis"
<p>Prepupae of <em>T. chilonis</em> were exposed to 10 (T1), 25 (T2), and 40°C (T3) for 4 h. Transcriptome of each group was sequenced. Comparison analysis was further performed to explore the molecular response to temperature stresses. These supplementary materials could provide us with a comprehensive view of the molecular mechanisms of <em>T. chilonis</em> in response to temperature stresses.</p>
Data from Understanding X-ray spectroscopy of carbonaceous materials by combining experiments, density functional theory and machine learning. Parts I and II.
<p>Understanding X-ray spectroscopy of carbonaceous materials by combining experiments, density functional theory, and machine learning; Parts I and II.</p> <p>This data-set is published in Refs. [1-2] and it is now made openly accessible. The data-set consists of computational X-ray spectroscopy fingerprints of plain and functionalized amorphous carbon. This data can be used in interpretation of experimental spectroscopy data (XAS and XPS). The spectra are averages of certain atomic environments, that are described in the publications. Standard deviation is included in the third column. Please, feel free to use the data-set, and if you do so, remember to cite Refs. [1-2] and this source. If there are any questions, please contact the corresponding author. </p> <p> </p> <p>[1] A. Aarva, V. L. Deringer, S. Sainio, T. Laurila, andM. A. Caro, “Understanding X-ray spectroscopy of carbonaceous materials by combining experiments, density functional theory, and machine learning. Part I: Fingerprint spectra,” Chem. Mater. 31, 9243–9255 (2019).</p> <p>[2] A. Aarva, V. L. Deringer, S. Sainio, T. Laurila, andM. A. Caro, “Understanding X-ray spectroscopy of carbonaceous materials by combining experiments, density functional theory, and machine learning. Part II: Quantitative fitting of spectra,” Chem. Mater. 31, 9256–9267(2019).</p> <p> </p> <p>Funding and resources for the work are acknowledged as follows:</p> <p>Funding from the Academy of Finland (project no.285526) and the computational resources provided for this project by CSC – IT Center for Science are gratefully acknowledged. M. A. C. acknowledges personal funding from the Academy of Finland under project no. 310574.V. L. D. acknowledges a Leverhulme Early Career Fellowship and support from the Isaac Newton Trust. M. A. C.and V. L. D. are grateful for travelling support from the HPC-Europa3 program under the auspices of the European Union’s Horizon 2020 framework (grant agreement no. 730897). Use of the Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under contract no. DE-AC02-76SF00515. S. S. acknowledges personal funding from Instrumentarium Science Foundation and the Walter Ahlström Foundation.</p> <p> </p>
Supplementary Material - Table 1: MAG summary
<p><strong>Supplementary Table 1. Details on taxonomic classification, genes of interest, completeness, redundancy, and abundance of MAGs obtained in this study. </strong>This file relates to the preprint https://www.biorxiv.org/content/10.1101/2020.09.22.307553v1, "Enrichment of novel <em>Verrucomicrobia, Bacteroidetes</em> and <em>Krumholzibacteria</em> in an oxygen-limited, methane- and iron-fed bioreactor inoculated with Bothnian Sea sediments".</p>
Official video of the MASTRO project focusing on Intelligent bulk MAterials for Smart TRanspOrt industries
<p>Take a look at our video presenting the concept and objectives of the MASTRO project!</p> <p> For more information visit our website: <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbTc3WEE1aEdrUHc5ek1HRmEwSWdWcXZrcTlRUXxBQ3Jtc0ttZzJqYVRWTjZqbFZvTnRuNkFKdDhXNlc0T3hDMVQ2VFZnQjNucVZHVm8xZWtWa21iNWRldmxqSFhNYWphZDRVdndsTVhFTmVxR3dzXzhRbGZTMjdsRmF2NzdwakJOLXJfSlV4dVlVbHlHU3JITzB6TQ&q=https%3A%2F%2Fwww.mastro-h2020.eu%2F">https://www.mastro-h2020.eu/</a> </p>
Data from: DNA barcoding identifies cryptic animal tool materials
<p>Some animals fashion tools and other constructions out of plant materials to aid foraging, reproduction, self-maintenance, and protection. The choice of raw materials can affect the structure and mechanical properties of the resulting artefacts, with significant fitness consequences. Documenting animals' material preferences is challenging, however, as manufacture behaviour is often difficult to observe directly, and materials may be processed so heavily that they lack identifying features. Here, we use DNA barcoding techniques to identify, from just a few recovered tool specimens, the plant species New Caledonian crows (<em>Corvus moneduloides</em>) use for crafting elaborate hooked stick tools in one of our long-term study populations. The method succeeded where extensive fieldwork using conventional approaches had failed, including targeted observations, radio-tracking, bird-mounted video-cameras, and behavioural experiments with wild and temporarily captive subjects. We believe that DNA barcoding will prove useful for investigating many other tool and construction behaviours, helping to unlock significant research potential across a wide range of study systems.</p>
Brick : Tiled material
4k textures.these can be used on a flat geometry.not just the high poly model uploaded here. Original scan made out of 120 photos, Agisoft. Zbrush for the detail projection-> subd plane Substance Painter for the editing and tileing. Source: Objaverse 1.0 / Sketchfab
Supplementary Material for Frontiers Plant Genetics and Genomics 'Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality'
<p>Authors</p> <p>Zhian N. Kamvar, Jonah C. Brooks, and Niklaus J. Grünwald</p>
Mouse and Human Co-expression maps and supplementary material for: "A comparison of human and mouse gene co-expression networks reveals conservation and divergence at the tissue, pathway and disease levels"
<p>Co-expression maps of the human and mouse species derived from microarray data for the first release of the GeneFriend tool.</p> <p>The two co-expression maps have been compared in order to discern similarities and differences between the two species. The results have been described in the manuscript titled: "A comparison of human and mouse gene co-expression networks reveals conservation and divergence at the tissue, pathway and disease levels".</p> <p>The supplementary material of the manuscript have also been included in this repository.</p> <p> </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.