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11 results for “particle physics”
Simulation data for "Characteristics of Wave-Particle Power Transfer as a Function of Electron Pitch Angle in Nonlinear Frequency Chirping" which will be submitted to Journal of Geophysical Research: Space Physics
<p>Simulation data for "Characteristics of Wave-Particle Power Transfer as a Function of Electron Pitch Angle in Nonlinear Frequency Chirping" which will be submitted to Journal of Geophysical Research: Space Physics.</p> <p>Including the simulation input parameter file and the necessary output data to plot each figure in the article. </p>
The effect of particle board industry waste tar on the physical and biological durability of wood
<p>This manuscript investigated the effect of waste tar from particle board factories on some physical and biological resistance properties of Scots pine (<em>Pinus sylvestris </em>L.) and beech (<em>Fagus orientalis </em>L.) woods. Solutions were prepared by dissolving waste tar in ethanol:toluene (1v:1v) in concentrations of 5%, 10%, 15% and 20% and treated under vacuum and pressure. In addition, surface coating (SC) was applied by spreading 96% waste tar on the wood surfaces after treatment. Deep-treated and surface-coated (DT+SC) wood samples were exposed to the wood-decay fungi (<em>Corillous versicolor</em> L. and <em>Neolentinus lepideus</em> Fr.) and wood destroying house borer (<em>Hylotrupes bajulus </em>L.) larvaes. Total phenolic content, water uptake, water-repellent efficiency and surface contact angle were tested. The highest mean weight loss (45.23%) was found in the beech wood control samples exposed to <em>C. versicolor</em>. Beech samples deep-treated with a 20% concentration and surface treatment (DT+SC) yielded a mass loss of 14.03%. The <em>H. bajulus</em> larvae mortality rate was found to be 80% in the Scots pine wood samples deep-treated with 20% waste tar. The deep treatment with the waste tar solution significantly increased the surface contact angle values of the Scots pine wood, thereby significantly reducing the wettability of the wood compared to the untreated control samples. </p>
WASHTREET. Runoff velocity data using different Particle Image Velocimetry (PIV) techniques in a full scale urban drainage physical model
<p><strong>WASHTREET - Runoff velocity data using different Particle Image Velocimetry (PIV) techniques in a full scale urban drainage physical model.</strong></p> <p>This dataset contains raw data and runoff velocities results obtained using seeded and unseeded Particle Image Velocimetry (PIV) techniques in an urban drainage physical model, which is placed in the Hydraulic Laboratory of the Centre for Technological Innovation in Construction and Civil Engineering (CITEEC) at the University of A Coruña (Spain). The objective of this work is to obtain an accurate representation of the surface velocity distribution as part of the <a href="https://zenodo.org/communities/washtreet">WASHTREET project</a>, where a series of high-resolution experiments were performed measuring urban surface wash-off and sediment transport through gully pots and pipes under laboratory-controlled conditions. The experimental facility is a 36 m<sup>2</sup> full-scale street section and consists of a rainfall simulator placed over a concrete street surface with two gully pots that drain runoff into an underground pipe system. The dataset was used in the work developed in Naves et al. (2019) (DOI: <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a>).</p> <p>A detailed description of experimental setup, procedure, postprocessing and results can be consulted in ‘<em>1_TestsDescription.pdf’. </em>4K resolution and 25 fps raw videos from which frames are extracted for the PIV analysis are provided for each experiment performed in separated zip files (named as <em>‘2.</em>(test ID)<em>_RawVideos_</em>(configuration)<em>.zip’</em>). Experiments includes three different steady rainfalls of 30, 50 and 80 mm/h of rain intensity and were recorded with and without added fluorescent traces. Data to orthorectify frames from videos are provided in ‘<em>3_SpatialCalibration.zip</em>’. In addition, 60 seconds of steady conditions are extracted for each test and the frames are processed to obtain velocities from a PIV analysis. ‘<em>4_ProcessedFrames_SteadyFlow.zip’ </em>includes the 1500 rectified and processed frames for each experiment to perform the PIV analysis. Results of runoff velocity distributions are included in ‘<em>5_VelocityResults.zip’</em>.</p> <p>Further details of the rainfall simulator, physical model geometry and more hydraulic and sediment transport results can be consulted in <a href="http://doi.org/10.5281/zenodo.3233918"><em>WASHTREET hydraulic, wash-off and sediment transport experimental data</em></a>. In addition, data regarding the use of photogrammetry to obtain the elevation map of this physical model is included in <a href="http://www.doi.org/10.5281/zenodo.3241337">WASHTREET Structure from Motion data</a>.</p> <p>The WASHTREET project is being developed in the scope of the PhD thesis of the first author, which is in receipt of a Spanish Ministry of Science, Innovation and Universities predoctoral grant [FPU14/01778]. The project also receive funding from the Spanish Ministry of Science, Innovation and Universities under POREDRAIN project RTI2018-094217-B-C33 (MINECO/FEDER-EU)</p> <p>Derived publications:</p> <ul> <li>Naves, J., Anta, J., Puertas, J., Regueiro-Picallo, M., & Suárez, J. (2019). Using a 2D shallow water model to assess Large-Scale Particle Image Velocimetry (LSPIV) and Structure from Motion (SfM) techniques in a street-scale urban drainage physical model. <em>Journal of Hydrology</em>, <em>575</em>, 54-65. <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a></li> <li>Naves, J., Anta, J., Suárez, J., & Puertas, J. (2020). Hydraulic, wash-off and sediment transport experiments in a full-scale urban drainage physical model. <em>Scientific Data</em>, <em>7</em>(1), 1-13. <a href="https://doi.org/10.1038/s41597-020-0384-z">https://doi.org/10.1038/s41597-020-0384-z</a></li> <li>Naves, J., García, J. T., Puertas, J., & Anta, J. (2021). Assessing different imaging velocimetry techniques to measure shallow runoff velocities during rain events using an urban drainage physical model. <em>Hydrology and Earth System Sciences</em>, <em>25</em>(2), 885-900. <a href="https://doi.org/10.5194/hess-25-885-2021">https://doi.org/10.5194/hess-25-885-2021</a> </li> </ul>
Individual Particle Dataset: Physical-chemical properties of non-soluble particles in a hailstone collected in Argentina
<p>This is the dataset of physical/chemical properties of individual particles described in the EGU AMT manuscript submission: Bernal Ayala et al. (2024). Exploring non-soluble particles in hailstones through innovative confocal laser and scanning electron microscopy techniques. </p>
Data for: Fungal parasitism on diatoms alters formation and bio–physical properties of sinking aggregates: Particle analyses
<p>Phytoplankton forms the base of aquatic food webs and element cycling in diverse aquatic systems. The fate of phytoplankton-derived organic matter, however, often remains unresolved as it is controlled by complex, interlinked remineralization and sedimentation processes. We here investigate a rarely considered control mechanism on sinking organic matter fluxes: fungal parasites infecting phytoplankton. We demonstrate that bacterial colonization was promoted 3.5-fold on fungal-infected phytoplankton cells in comparison to non-infected cells in a cultured model pathosystem (diatom <em>Synedra</em>, fungal microparasite <em>Zygophlyctis</em>, and co-growing bacteria), and even ≥17-fold in field-sampled populations (<em>Planktothrix</em>, <em>Synedra</em>, and <em>Fragilaria</em>). The <em>Synedra</em>–<em>Zygophlyctis</em> model system further revealed that fungal infections reduced the formation of aggregates. Moreover, carbon respiration was 2-fold higher and settling velocities 11–48% lower for similar-sized fungal-infected <em>vs</em> non-infected aggregates. Our data imply that parasites can effectively control the fate of phytoplankton-derived organic matter on a single-cell to single-aggregate scale, potentially enhancing remineralization and reducing sedimentation in freshwater and coastal systems.</p>
Data for: Fungal parasitism on diatoms alters formation and bio–physical properties of sinking aggregates: Particle analyses
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Influence of calcination temperature and particle size distribution on the physical properties of SrFe12O19 and BaFe12O19 hexaferrite powder - part 2
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Dataset used for "End-to-end simulation of particle physics events with Flow Matching and generator Oversampling" , https://arxiv.org/abs/2402.13684
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Appendix and Dataset: Exploring the History of Negatively Charged Particles through Physics Comics: A Study of Thomson and His Hypothesis
<p>Appendix and Dataset: Exploring the History of Negatively Charged Particles through Physics Comics: A Study of Thomson and His Hypothesis</p> <ol> <li>Appendix A English Translation of Text in the Comic Story</li> <li>Appendix B Distribution of the results of the material expert questionnaire</li> <li>Appendix C Distribution of the results of the media expert questionnaire</li> <li>Appendix D Distribution of the results of the user ratings</li> </ol>
A new physical mechanism of rainfall facilitation to new particle formation
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Data from: Collector motion affects particle capture in physical models and in wind pollination
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.