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293 results for “suction”
Soil water content (volumetric %) for 33kPa and 1500kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Soil water content (volumetric) in percent for 33 kPa and 1500 kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Training points are based on a global compilation of soil profiles (<a href="https://ncsslabdatamart.sc.egov.usda.gov/">USDA NCSS</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, <a href="https://data.isric.org/geonetwork/srv/eng/catalog.search#/metadata/a351682c-330a-4995-a5a1-57ad160e621c">ISRIC WISE</a>, <a href="http://egrpr.esoil.ru/">EGRPR</a>, <a href="https://esdac.jrc.ec.europa.eu/content/soil-profile-analytical-database-2">SPADE</a>, <a href="https://open.canada.ca/data/en/dataset/6457fad6-b6f5-47a3-9bd1-ad14aea4b9e0">CanNPDB</a>, <a href="https://data.nal.usda.gov/dataset/unsoda-20-unsaturated-soil-hydraulic-database-database-and-program-indirect-methods-estimating-unsaturated-hydraulic-properties">UNSODA</a>, <a href="https://doi.pangaea.de/10.1594/PANGAEA.885492">SWIG</a>, <a href="http://www.cprm.gov.br/en/Hydrology/Research-and-Innovation/HYBRAS-4208.html">HYBRAS</a> and <a href="http://dx.doi.org/10.4228/ZALF.2003.273">HydroS</a>). Data import steps are available <a href="https://gitlab.com/openlandmap/compiled-ess-point-data-sets/-/tree/master/themes/sol/SoilHydroDB"><strong>here</strong></a>. Spatial prediction steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil/soil_water">here</a></strong>. Note: these are actually measured and mapped soil content values; no Pedo-Transfer-Functions have been used (except to fill-in the missing NCSS bulk densities). Available water capacity in mm (derived as a difference between field capacity and wilting point multiplied by layer thickness) per layer is available <strong><a href="https://doi.org/10.5281/zenodo.2629148">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize some of the maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>watercontent.33kPa = water content (volumetric percent) under field capacity (33 kPa suction),</li> <li>usda.4b1c = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>
Real-world grasp data of a dual-arm Yumi robot with a parallel gripper and suction cup end-effectors
<p>The attached txt file contains indexes to a cleaner subset of the data issued in the first version.</p> <p>Note: <br>Version 1 contains samples with failure cases due to environment constraints, which work well for the platform used in GraspAgent 1.0 (https://doi.org/10.1109/LRA.2024.3502066). However, this can degrade the performance if used on another platform with different constraints. To solve this, version 2 reports a subset of the raw data, excluding the failure modes due to environmental causes. </p>
Analysis of bacteria, inflammation, and exudation in epidermal suction blister wounds reveals dynamic changes during wound healing - Dataset
<p>Dataset for "<strong>Analysis of bacteria, inflammation, and exudation in epidermal suction blister wounds reveals dynamic changes during wound healing</strong>". Data were generated from samples collected to a biobank from suction blister wounds at various timepoints during a clinical trial (<a href="https://clinicaltrials.gov/study/NCT05378997?term=NCT05378997&rank=1"><strong>NCT05378997</strong></a>). Data include concentration of bacteria in swab and dressing fluid samples, neutrophil proteins (HNE, MPO, and HBP), cytokines (IFN-gamma, interleukin IL-1beta, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13, TNF-alpha) and total protein measured in dressing fluid samples, and bacterial species identified by MALDI-TOF in swab and dressing fluid samples.</p>
Figure 1. Diagnostic characteristics identifying NMV P252567 in Suction feeding preceded filtering in baleen whale evolution
Figure 1. Diagnostic characteristics identifying NMV P252567 as an aetiocetid. A, explanatory line drawing of the skull; B, photograph of the skull (left) and mandible (right), both in dorsal view.
Figure 3 in Suction feeding preceded filtering in baleen whale evolution
Figure 3. Additional teeth of NMV P252567. A, left upper incisor; B, right upper incisor; C, double-rooted postcanine 4. All teeth are shown in lingual (left) and labial (right) views. The lack or comparatively small degree of wear on the incisors suggests they may have been largely (left upper incisor) or partially (right upper incisor) enclosed within the gingiva, protecting them from the abrasive wear that affected the other teeth. A fifth double-rooted postcanine closely resembles postcanines 2 and 4 in terms of its wear, but is still partially encased in sediment and hence not shown here.
Figure 5 in Suction feeding preceded filtering in baleen whale evolution
Figure 5. Suction feeding precedes baleen filtering in mysticete evolution. A, consensus tree of aetiocetid evolutionary relationships, based on all cladistic studies published to date (e.g. Deméré and Berta, 2008; Deméré et al., 2008; Fitzgerald, 2010; Geisler and Sanders, 2003; Marx and Fordyce, 2015; Steeman, 2007), showing major feeding-related synapomorphies; B, life reconstructions (top) and skulls (in lateral view) of a representative archaeocete (Dorudon atrox), aetiocetid (NMV P252567), eomysticetid (Yamatocetus canaliculatus) and extant suction feeding mysticete (grey whale, Eschrichtius robustus); C, inferred behaviours and feeding strategies. Life reconstructions by Carl Buell.
Figure 2 in Suction feeding preceded filtering in baleen whale evolution
Figure 2. Wear patterns on representative teeth of NMV P252567, suggesting suction feeding in an aetiocetid. A, left upper canine or first premolar; B, double-rooted postcanine 1; C,?lower double-rooted postcanine 2; D,?lower double-rooted postcanine 3; E, micro-computed tomography cross section of postcanine 1, showing the depth and rounded edges of the horizontal striations (marked by large black arrows). A–C are shown in lingual, labial and anterior/posterior view, D in lingual view only.
Figure 4 in Suction feeding preceded filtering in baleen whale evolution
Figure 4. Cross section of the rostrum and lower jaws of A, a balaenid, B, a balaenopterid, and C, an aetiocetid, illustrating the relative movement of the mandible during jaw closure (red arrows). All drawings show the mouth slightly open. In right whales (A) and rorquals (B), the laterally bowed mandibles and/or tall lower lips rotate inwards on to the labial surface of the baleen plates, thereby leaving the rack intact. In aetiocetids (C), the movement of the mandible is mostly vertical and the upper and lower jaws need to approach each other enough to allow the teeth to occlude, thereby risking interference with any baleen present. A and B are adapted from Pivorunas (1979: fig. 3).
Figure 9 in Drag of suction cup tags on swimming animals: Modeling and measurement
Figure 9. Simulation results for the lift forces acting on the Model A and B tags (top panel) in constant flow (5.6 m/s) as a function of orientation (–20º to 180º). Measured results as a function of orientation (–20º to 90º) in constant 5.6 m/s flow are compared to simulations for the Model A tag (bottom left panel) and Model B tag (bottom right panel).
Figure 8 in Drag of suction cup tags on swimming animals: Modeling and measurement
Figure 8. Simulation results for the drag forces acting on the Model A and B tags (top panel) in constant flow (5.6 m/s) as a function of orientation (–20º to 180º). Measured results as a function of orientation (–20º to 90º) in constant 5.6 m/s flow are compared to simulations for the Model A tag (bottom left panel) and Model B tag (bottom right panel).
Figure 4. CFD simulation results for Models A in Drag of suction cup tags on swimming animals: Modeling and measurement
Figure 4. CFD simulation results for Models A (panels A and C) and B (panels B and D) in steady 5.6 m/s flow. The blue, yellow, and green regions are areas of reduced flow speed that generate forces on the tags. The upper panels show the flow speed over a horizontal cross-section at the tag midline. The lower panels show flow speed over a vertical cross-section at the centerline of the tag. The improved flow around Model B is evident in the smaller magnitude of blue coloration in the wake behind the tag.
Figure 2 in Drag of suction cup tags on swimming animals: Modeling and measurement
Figure 2. An illustration of the Model A tag in the computational domain used for the simulations of all tag designs. The fluid flow is from left to right and representative orientations of the tag to the flow are shown at the bottom of the figure.
Figure 7 in Drag of suction cup tags on swimming animals: Modeling and measurement
Figure 7. Simulation results for the lift forces acting on the Model A and B tags (top panel) in variable flow (0.25–10 m/s). Measured results in variable flow speed (1–5.6 m/s) are compared to simulations for the Model A tag (bottom left panel) and Model B tag (bottom right panel).
Figure 10 in Drag of suction cup tags on swimming animals: Modeling and measurement
Figure 10. Experimental load testing of a Model A tag attached at three test sites on a common dolphin cadaver using four silicone suction cups. Applied force vs. calculated total cup attachment force at the four silicone suction cups are shown for lift (bottom panel) and drag (top panel) loading. The average pressure difference for the four cups is also shown on the right hand axis, where 1 atmosphere is approximately 100 kPa. Forces were applied via a line attached to the tag by pulling either perpendicular to the body (lift) or parallel (drag). The curves end where the cup attachment failed or the cups began to slide. The average of two trials at each site is shown in each panel.
Figure 6 in Drag of suction cup tags on swimming animals: Modeling and measurement
Figure 6. Simulation results for the drag forces acting on the Model A and B tags (top panel) in variable flow (0.25–10 m/s) with fixed orientation (0º). Measured results in variable flow speed (1–5.6 m/s) are compared to simulations for the Model A tag (bottom left panel) and Model B tag (bottom right panel).
Predicted soil water content (volumetric %) for 33kPa and 1500kPa suctions at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Migrated to: <a href="https://doi.org/10.5281/zenodo.2629589">https://doi.org/10.5281/zenodo.2629589</a></p>
Fig. 1 in Dispersal records of the sugarcane aphid, Melanaphis sacchari (Zehntner) (Hemiptera: Aphididae), through the Midwest Suction Trap Network
Fig. 1. Seasonal population dynamics of the sugarcane aphid, Melanaphis sacchari, collected between 2015 and 2017 from selected states in the Midwest Suction Trap Network.
Data for: Research and application of bag filter system for railway ballast bed coal suction vehicles
<p>The current bag filter system used by railway ballast bed coal suction vehicles for cleaning coal dust from railway tunnels has low operational efficiency and generates significant volumes of dust. This paper describes a simulation test unit designed to enhance the dust removal performance in railway tunnels. The flow field inside the simulation test unit is investigated under different operating conditions through numerical simulations, and the variations in air volume and working resistance, total dust collection efficiency, and optimal operating parameters of a pulse cleaning system are identified through a series of experiments. The numerical results show that the pulse cleaning system does not significantly affect the uniformity of the flow field distribution at the bottom of the filter cartridge during the process of operation. The experimental research indicates that the simulation test unit satisfies the design requirements, achieving an average total dust removal efficiency of 99.93%. A field application shows that the total dust mass concentration at the operator position can be reduced from 335.8 mg.m<sup>−3</sup> to 4.2 mg.m<sup>−3</sup>, effectively improving the operating environment within the tunnel.</p>
Coupling Novel Soil Moisture-Suction Sensors and UAV Photogrammetry Technology to the Performance of Highway Embankments
<p>The movement of water plays a critical role in the mechanical performance and service life of transportation infrastructure, especially for pavement subgrades and highway embankments consisting of high-plasticity, expansive soils that saturate and ultimately lead to infrastructure distress. Shallow slides along highway embankments are ubiquitous across Region 6 because long-term wetting and drying cycles considerably weaken these compacted soils. In the aftermath of heavy rains, pore-water pressures increase to a critical threshold such that a failure occurs. The implications of embankment failures range from repeated maintenance repairs to long-term road closures. A comprehensive approach to model highway embankments comprising of laboratory testing, setup, and field data collection using unmanned aerial vehicles has been proposed in this study.</p>
A new theoretical performance landscape for suction feeding reveals adaptive kinematics in a natural population of reef damselfish
<p><span>Understanding how organismal traits determine performance and</span><span>, </span><span>ultimately</span><span>, </span><span>fitness is a fundamental goal of evolutionary ecomorphology. However, multiple traits can interact in non-linear and context-dependent ways to affect performance, hindering efforts to place natural populations with respect to performance peaks or valleys. Here, we used an established mechanistic model of suction-feeding performance (SIFF) derived from hydrodynamic principles to estimate a theoretical performance landscape for zooplankton prey capture. This performance space can be used to predict prey capture performance for any combination of six morphological and kinematic trait values. We then mapped in situ high-speed video observations of suction feeding in a natural population of a coral reef zooplanktivore, Chromis viridis, onto the performance space to estimate the population's location with respect to the topography of the performance landscape. Although the kinematics of the natural population closely matched regions of high performance in the landscape, the population was not located on a performance peak. Individuals were furthest from performance peaks on the peak gape, ram speed and mouth opening speed trait axes. Moreover, we found that the trait combination</span><span>s </span><span>in the observed population were associated with higher performance than expected by chance, suggesting that these combinations are under selection. Our results provide a framework for assessing whether natural populations occupy performance optima.</span></p>
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