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391 results for “Roughness”
Hillslope Roughness Reveals Forest Sensitivity to Extreme Winds (Codes and Data)
<p>These are data are python scripts for creating figures in Doane et al., 2022 (Hillslope Roughness Reveals Forest Sensitivity to Extreme Winds). </p>
CHOP Waterworks rough scan, Cal Anderson Park
This is the Waterworks at Cal Anderson Park during the heart of protests against racism in America and systemic police brutality. I visited it on Sunday, June 14, 2020, with my wife and kids. The energy was very powerful and elecrifying. This part of the park was covered in protest grafitti. As of this posting, no Seattle police officers have faced any charges for the well documented police brutality of 2020 protests, which included the use of chemical weapons banned by the Geneva Convention, on children and in residential neighborhoods. As of this posting, none of the demands outlined by Seattle protesters have been met. Black Lives Matter. Google Doc of Anti-Racism Resources - https://docs.google.com/document/d/1BRlF2_zhNe86SGgHa6-VlBO-QgirITwCTugSfKie5Fs/preview?pru=AAABcoDeukY*npk2DCbWQ_ymtZB_7xBMNA Source: Objaverse 1.0 / Sketchfab
MFMET webinar - 03. Wettability and surface roughness
<p>This video presents measurements of surface properties in microfluidics. The measured quantities are wettability, surface energy, and surface roughness.</p> <p>Further reading can be found:<br> <br>Reference:<br>Deliverable 5 - Guidelines for the measurement of key performance parameters of microfluidic connections including the identification of key properties in an interface. <a href="https://doi.org/10.5281/zenodo.10731759">Link</a></p> <p><br>The project (20NRM02 MFMET) have received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme.</p>
Dataset accompanying the publication "Towards 3D determination of the surface roughness of core-shell microparticles as a routine quality control procedure by scanning electron microscopy"
<p>This dataset accompanies the following publication:</p> <p>Hülagü, D., Tobias, C., Dao, R., Komarov, P., Rurack, K., Hodoroaba, V.-D., Towards 3D determination of the surface roughness of core-shell microparticles as a routine quality control procedure by scanning electron microscopy. Sci.Rep, <span>14<span>, 17936 (2024), https://doi.org/10.1038/s41598-024-68797-7.</span></span></p> <p>It contains SEM and AFM-in-SEM images of polystyrene (PS) core particles, polystyrene-iron oxide (PS/Fe3O4) core-shell particles, and polystyrene-iron oxide-silica (PS/Fe3O4/SiO2) core-shell-shell particles. Please refer to the publication and its supporting information for more details on the acquisition and contents of the dataset, as well as the GitHub repository at https://github.Com/denizhulagu/roughness-analysis-by-electron-microscopy.</p> <p> </p> <p>The investigated particles were produced at BAM laboratories as previously described in:</p> <p>Hülagü, D. et al. Generalized analysis approach of the profile roughness by electron microscopy with the example of hierarchically grown polystyrene–iron oxide–silica core–shell–shell particles. Adv. Eng. Mater. 24, 2101344, https://doi.org/10.1002/adem.202101344 (2022).</p> <p>Tobias, C., Climent, E., Gawlitza, K. & Rurack, K. Polystyrene microparticles with convergently grown mesoporous silica shells as a promising tool for multiplexed bioanalytical assays. ACS Appl. Mater. Interfaces 13, 207, https://dx.doi.org/10.1021/acsami.0c17940 (2020).</p>
SmartRoadSense: aggregated road surface roughness dataset
<p>Dataset of aggregated road surface quality data points, collected through the mobile crowdsensing application SmartRoadSense.</p>
Effects of roughness Reynolds number on scalar transfer mechanisms at the sediment-water interface
<p>The data are used in the paper "Effects of roughness Reynolds number on scalar transfer mechanisms at the sediment-water interface" which was submitted to the journal-Water Resources Research. The paper used particles with different sizes, experimental bed is reproduced and the scalar transfer from water to sediment is modelled. We found that The transfer factor changes to turbulent diffusion at the sediment-water interface as the roughness Reynolds number increases. And the turbulent Schmidt number is higher near the SWI and decrease to 0.5 at the SWI, ranging from 0.5 to 1 within the water depth.</p>
Fig. 42 in Diamonds in the rough: Ibotyporanga (Araneae, Pholcidae) spiders in semi-arid Neotropical environments
Fig. 42. Known geographic distribution of Brazilian species with plesiomorphic short procursus.
Response of flow and saltating particle characteristics to bed roughness and particle spatial density
<p>The data were used in the paper "Response of flow and saltating particle characteristics to bed roughness and particle spatial density" which was submitted to "<em>Water Resources Research</em>". In this paper, The numerical model combining LES method and point-particle method is applied for tracking particle trajectories. The effects of bed roughness and particle spatial density on the bedload transport are investigated by numerical simulations. The distributions of key parameters for saltation, including their changes, are assessed using the PDF curves. </p>
Data from: Lipidome modulation by dietary omega-3 polyunsaturated fatty acid supplementation or selective soluble epoxide hydrolase inhibition suppresses rough LPS-accelerated glomerulonephritis in lupus-prone mice
<p>Lipopolysaccharide (LPS)-accelerated autoimmune glomerulonephritis (GN) in lupus-prone NZBWF1 mice is a preclinical model that is potentially applicable for investigating lipidome-modulating interventions. LPS can be expressed as one of two chemotypes: smooth LPS (S-LPS) and rough LPS (R-LPS) which is devoid of O-antigen polysaccharide sidechain. Since these chemotypes differentially affect TLR4-mediated immune cell responses, these differences may influence GN induction. Therefore, we initially compared the effects of subchronic i.p. injection for 5 wk with 1) <em>Salmonella</em> S-LPS, 2) <em>Salmonella</em> R-LPS, or 3) saline vehicle (VEH) (Study 1) in female NZBWF1 mice. R-LPS induced robust elevations in blood urea nitrogen, proteinuria, and hematuria that were not evident in VEH- or S-LPS-treated mice. Histopathologic examination of R-LPS-treated mice one week after final injection further revealed more robust hypertrophy, hyperplasia, thickened membranes, lymphocytic accumulation containing B and T cells, and glomerular IgG deposition consistent with GN but not in VEH- or S-LPS-treated groups. R-LPS but not S-LPS induced spleen enlargement with lymphoid hyperplasia as well as modest inflammatory cell recruitment in the liver. We next employed our optimized R-LPS model to discern the impact of two lipidome-modulating interventions, omega-3 polyunsaturated fatty acid (PUFA) supplementation and soluble epoxide hydrolase (sEH) inhibition, on GN (Study 2). Specifically, the effects of consuming the omega-3 PUFA docosahexaenoic acid (DHA) (10 g/kg diet) and/or the sEH inhibitor TPPU (22.5 mg/kg diet) on R-LPS triggering were compared. Resultant blood fatty acid profiles and epoxy fatty acid concentrations reflected the anticipated DHA- and TPPU-mediated lipidome changes. The relative rank order of R-LPS-induced GN severity among groups fed experimental diets based on proteinuria, hematuria, histopathologic scoring, and glomerular IgG deposition was: VEH/CON < R-LPS/DHA ≈ R-LPS/TPPU <<< R-LPS/ TPPU+DHA ≈ R-LPS/CON. These interventions had modest to negligible effects on R-LPS-induced splenomegaly, plasma antibody responses, liver inflammation, and inflammation-associated kidney gene expression. Collectively, our results show for the first time that absence of O-antigenic polysaccharide in R-LPS is critical to accelerated GN in lupus-prone mice. Furthermore, intervention by lipidome modulation through DHA feeding or sEH inhibition suppressed R-LPS-induced GN; however, these ameliorative effects were greatly diminished upon combining the treatments.</p>
An experimental and numerical study of turbulent oscillatory flow over an irregular rough wall
<p>Phase-averaged experimental data, phase- and spatial-averaged numerical data, and digitized roughness as described in: Dunbar, D., van der A, D.A., Scandura, T., O'Donoghue, T. (2023). An experimental and numerical study of turbulent oscillatory flow over an irregular rough wall. Journal of Fluid Mechanics, 955, A33, doi:10.1017/jfm.2022.1090.</p> <p>If interested in the time-series data and/or the spatially varying numerical data please contact the corresponding author of the paper. </p>
On the influence of bed roughness on saltation in inertial regime
<p>Matlab files containing jumps (hops) features (measurement done via Tracker software, image analysis) by columns: 1/ hop length, 2/ hop height, 3/ horizontal rebund velocity, 4/ vertical rebund velocity, 5/ horizontal impact velocity, 6/ vertical impact velocity, 7/ horizontal impact velocity at the end of the jump, 8/ vertical impact velocity at the end of the jump, 9/ rebund angle, 10/ impact angle, 11/ impact angle at the end of the jump. Original videos available on request (very large files).</p>
The Effect of Bristle Brush, Rubber Cup, and Air Polishing on Tooth Surface Roughness of Scaled Teeth
ClinicalTrials.gov study NCT06857474. IPD Sharing: YES. Countries: 1. Publications: 5.
Data from: Winners and losers from climate change: An analysis of tree growth and survival responses to temperature and precipitation for roughly 150 species across the contiguous U.S.
Open the record for dataset details and reuse information.
Dynamic snow surface aerodynamic roughness lengths (z0) characterized by snow depths using LIDAR
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Data from: Navigating nature's terrain: Jumping performance robust to substrate moisture and roughness by blackspotted rockskippers (Entomacrodus striatus)
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Data from: Lipidome modulation by dietary omega-3 polyunsaturated fatty acid supplementation or selective soluble epoxide hydrolase inhibition suppresses rough LPS-accelerated glomerulonephritis in lupus-prone mice
Open the record for dataset details and reuse information.
Fresh snow, peak accumulation, and ablation-Sun Cup snow surface datasets for evaluation of geometry aerodynamic roughness code
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Data from: Spectral wear modelling of rubber friction on a hard substrate with large surface roughness
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H. J. Andrews Experimental Forest site, station Andrews Watershed 1, study of plant cover of Agrostis scabra (rough bentgrass) in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from H. J. Andrews Experimental Forest (AND) contains plant cover of Agrostis scabra (rough bentgrass) measurements in percent units and were aggregated to a yearly timescale.
Kellogg Biological Station site, station Kellogg Biological Station, study of aboveground net primary productivity of Geum laciniatum (rough avens) in units of gramsPerMeterSquaredPerYear on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Kellogg Biological Station (KBS) contains aboveground net primary productivity of Geum laciniatum (rough avens) measurements in gramsPerMeterSquaredPerYear units and were aggregated to a yearly timescale.
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International Brain Laboratory public data
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