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6,498 results for “Physicality”
A physical corrosion model for bioabsorbable metal stents: Supporting Data
<p>Data including UMATs and Abaqus input files related to the paper 'A physical corrosion model for bioabsorbable metal stents' <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.actbio.2013.12.059" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.actbio.2013.12.059</span></a></p>
Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
<p>Information about the spatial distribution of soil hydraulic parameters is necessary for the accurate prediction of soil water flow and coupled movement of chemicals and heat at the field scale using a process-based model. Physics-informed neural networks (PINNs), which can provide physical constraints in deep learning to obtain a mesh-free solution, can be used to inversely estimate the soil hydraulic parameters from less and noisy training data. Previous studies using PINNs have successfully estimated soil hydraulic parameters for homogeneous soil but estimating such parameters of layered soil profiles where the interface depth and the parameters are unknown still has some difficulties. The objective of this study was to develop PINNs to inversely estimate the distribution of soil hydraulic parameters, such as saturated hydraulic conductivity and <em>α</em> and <em>n</em>, of the Mualem-van Genuchten model directly within layered soil profiles by predicting changes in pressure head from training data based on simulation results at given depths during infiltration. The impact of factors affecting PINNs performance, such as the weights assigned to each component of the loss function, the time range used in error computations, and the number of samples used to assess physical constraint was investigated. By assigning a larger weight to the physical constraint and excluding the earlier stage of infiltration in the loss function, the changes in pressure head and the three soil hydraulic parameter distributions within the layered soil profiles were successfully estimated. The developed PINNs can be further applied to more complex soils and can be improved.</p>
Data for "The Role of Mood and Stress in Physical Activity Engagement: Insights from Ecological Momentary Assessment
<p>Data collected in the SmartPA study in a joint effort from the Department of Psychology at the University of Salzburg (Jens Blechert) and the Ludwig Boltzmann Institute for Digital Health and Prevention Salzburg.</p>
Physical seed damage, not rodent's saliva, accelerates seed germination of trees in a subtropical forest
<p>Many tree species adopt fast seed germination to escape the predation risk by rodents. Physical seed damage and the saliva of rodents on partially consumed seeds may also act as cues for the seed to accelerate the germination process. However, the impacts of these factors on seed germination rate and speed remain unclear. In this study, we investigated such impacts on the germination rate and speed (reversal of germination time) of four tree species (<em>Quercus variabilis</em>, <em>Q. serrata</em>, <em>Q. acutissima</em>, and <em>Q. glauca</em>) after partial consumption by four rodent species, through a series of experiments. We also examined how seed traits may affect the damage degree by rodents by analyzing the relationship between the germination rate and time of rodent-damaged seeds and the traits. We found that artificially and rodent-damaged seeds exhibited a significantly higher seed germination rate and speed, compared to intact seeds. Also, the rodent saliva on seeds showed no significant effect on seed germination rate and speed. Furthermore, We observed significant positive correlations between several seed traits (including seed mass, coat thickness, and protein content) and seed germination rate, but these seed traits had a positive correlation with the germination rate and speed. These correlations are likely due to the beneficial traits countering seed damage by rodents. Overall, our results highlight the significant role of physical seed damage by rodents (rather than their saliva) in facilitating seed germination of tree species and potential mutualism between rodents and trees. Additionally, our results may have some implications in forest restoration, such that intentionally sowing or dispersing slightly damaged seeds by humans or drones may increase the likelihood of successful seed regeneration.</p>
Physics-based Simulations of 3D Wave Propagation - Case study deriving from the Le Teil earthquake
<p>This dataset contains 4,000 simulation results of the 3D elastic wave equation in a setting deriving from the Le Teil earthquake (France, 2019). The elastic wave equation governs the propagation of waves in a 3D propagation medium. Two types of data are given in this dataset: a materials dataset and a velocity dataset.</p> <h2>Materials dataset</h2> <p>Each material describes the propagation domain used for one numerical simulation. It is built from non-stationary random fields added to the reference 1D velocity profile and corresponds to the velocity of shear waves. The minimum value is 1500m/s and the maximum is 4500m/s. All materials contain a 1800m-thick bottom layer with a constant velocity of 4500m/s. </p> <p>All materials are 3D arrays of shape 32 x 32 x 32.They correspond to a physical size of 9.6 x 9.6 x 9.6km³. </p> <h3>Practical use</h3> <p>Materials are provided as `.npy` arrays, readable with python: `a = np.load(‘materials0-1999.npy’)`<br>Each file contains 2000 materials. Therefore, `a` is of shape (2000, 32, 32, 32). Indices correspond to the material index, the x coordinate (from West to East), the y coordinate (from South to North), and the z coordinate (from bottom to top). </p> <h2>Velocity dataset</h2> <p>The velocity dataset contains the velocity wavefields simulated at the surface of each propagation domain. They have been generated by solving the 3D elastic wave equation with the high-performance computing code SEM3D based on the Spectral Element Method (https://github.com/sem3d/SEM). To each material described above corresponds one velocity field, obtained by the propagation of waves through this material.</p> <p>Velocity fields were recorded by a grid of 16 x 16 virtual sensors located at the surface of the propagation domain between 150m and 450m (600m between consecutive sensors). Each sensor records the 3-component velocity with a 100Hz sampling between 0s and 20s. </p> <p>Computational details: The computational mesh was designed with elements of size 300m and 7 Gauss-Lobato-Legendre quadrature points. It can accurately represent the propagation of waves up to 5Hz frequency. Waves were generated by a point-wise source placed at the bottom of the domain, inside the constant layer (the position of the source is 4800, 4800, -8400m). The seismic source derives from the Le Teil earthquake [Delouis et al., 2021, doi:10.5802/crgeos.78]. The seismic source is described by a moment tensor with fixed orientation (strike = 48°, dip = 45°, and rake = 88°) and amplitude (moment magnitude M0=2.47 · 10^16 N.m).</p> <h3>Practical use</h3> <p>Results are given in .feather dataframes, readable with pandas library in Python: v = pd.read_feather(‘velocity0-99.feather’). Each dataframe contains 100 simulation results. Each row of the dataframe has the following format: </p> <table> <tbody> <tr> <td>run</td> <td>field</td> <td>x</td> <td>y</td> <td>z</td> <td>0.0</td> <td>0.01</td> <td>0.02</td> <td>...</td> <td>19.98</td> <td>19.99</td> </tr> <tr> <td>12</td> <td>Veloc E</td> <td> <p>150.0</p> </td> <td>770.0</td> <td>-1.0</td> <td>0</td> <td>0</td> <td>0</td> <td>...</td> <td>1.1e-5</td> <td>1.0e-5</td> </tr> <tr> <td>12</td> <td>Veloc N</td> <td> <p>150.0</p> </td> <td>770.0</td> <td>-1.0</td> <td>0</td> <td>0</td> <td>0</td> <td>...</td> <td>3e-6</td> <td>3e-6</td> </tr> <tr> <td>12</td> <td>Veloc Z</td> <td> <p>150.0</p> </td> <td>770.0</td> <td>-1.0</td> <td>0</td> <td>0</td> <td>0</td> <td>...</td> <td>-2.6e-5</td> <td>-2.7e-5</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> <p>where `run` indicates the index of the material used in this simulation, `field` indicates the component of the velocity field (`Veloc E` for East-West, `Veloc N` for North-South, `Veloc Z` for Vertical). `x`, `y`, `z` are the coordinates of the sensor (in meters). The next 2000 columns contain the velocity field for times 0, 0.01, …, 19.99.</p> <h1>Related work</h1> <p>This dataset was used to fine-tune a Factorized Fourier Neural Operator (F-FNO, Lehmann et al. 2024, doi:10.1016/j.cma.2023.116718) to predict ground motion wavefields from 3D geologies. The code to train the F-FNO is available at https://github.com/lehmannfa/HEMEW3D</p>
Figure 2 in Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics
Figure 2. Maxent predictions of suitable zebra mussel (Dreissena polymorpha) habitat in Texas. Shading indicates the logistic output of the Maxent model. Polygons represent state and national borders as well as major river basins within Texas.
Figure 1 in Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics
Figure 1. Physicochemical data survey lakes. Sites categorized by TPWD (at the time of this study in 2016) as "infested" (the water body has an established, reproducing population) or "positive" (zebra mussels or their larvae have been detected on more than one occasion despite lack of evidence of a fully established, reproducing population) are indicated by red triangles and included: Lakes Austin, Belton, Bridgeport, Dean Gilbert, Lavon, Lewisville, Ray Roberts, Stillhouse Hollow, Texoma, Travis, and Waco. Sites categorized by TPWD as zebra mussel "negative" are indicated by green circles and included: Lakes Aquilla, Buchanan, Georgetown, Granbury, Granger, Hubbard Creek, Inks, Lady Bird, LBJ, Limestone, Marble Falls, Palo Pinto, Pflugerville, Possum Kingdom, Proctor, and Whitney.
Figure 4 in Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics
Figure 4. Biplot of components 1 and 2 (top) and 1 and 3 (bottom) from Principal Component Analysis of water quality variables in 27 study lakes. Variables that predominated in each component (|factor loading| ≥ 0.50) are shown on the appropriate axes. Individual lake data are represented by symbols, with open circles representing lakes without previously reported incidences of zebra mussels (absent, 16 lakes), and solid circles those known to harbor the invasive species (present, 11 lakes) at the time of sampling (October 2016). No separation between the two lake groups is evident in either of the biplots. Ca, calcium, N, nitrogen; P, phosphorous.
Figure 3 in Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics
Figure 3. Maxent predictions of suitable quagga mussel (Dreissena bugensis) habitat in Texas. Shading indicates the logistic output of the Maxent model. Polygons represent state and national borders as well as major river basins within Texas.
On the Role of Physical Processes in Controlling Equatorial Plasma Bubble Morphology
<p>Dataset for the paper entitled "On the Role of Physical Processes in Controlling Equatorial Plasma Bubble Morphology" submitted to the Journal of Geophysical Research: Space Physics.</p>
The interaction of physical structure and nutrient loading drives ecosystem change in a large tropical lake over 40 years (DATA)
<p><strong><span>Datasets for manuscript entitled "The interaction of physical structure and nutrient loading drives ecosystem change in a large tropical lake over 40 years"</span></strong></p> <p><em><span>Includes data collected by Fadum and Hall as well as unplublished data from Vaux and Goldman 1984.</span></em></p> <p><span><strong>Abstract: </strong>Many lakes across the world are entering novel states and experiencing altered biogeochemical cycling due to local anthropogenic stressors. In the tropics, understanding the drivers of these changes can be difficult due to a lack of documented historic conditions or an absence of continuous monitoring that can distinguish between intra- and inter-annual variation. Over the last forty years (1980–2020), Lake Yojoa (Honduras) has experienced increased watershed development as well as the introduction of a large net-pen Tilapia farm, resulting in a dramatic reduction in seasonal water clarity, increased trophic state and altered nutrient dynamics, shifting Lake Yojoa from an oligotrophic (low productivity) to mesotrophic (moderate productivity) ecosystem. To assess the changes that have occurred in Lake Yojoa as well as putative drivers for those changes, we compared Secchi depth (water clarity), dissolved inorganic nitrogen (DIN), and total phosphorus (TP) concentrations at continuous semi-monthly intervals for the three years between 1979 and 1983 and again at continuous 16-day intervals for 2018–2020. Between those two periods we observed the loss of a clear water phase that previously occurred in the months when the water column was fully mixed. Seasonal peaks in DIN coincident with mixing suggest that an enhanced accumulation of ammonium in the hypolimnion (the bottom layer of a stratified lake) during stratification, and release to the epilimnion (the top layer of a stratified lake) with mixing maintains high algal abundance and subsequently low Secchi depth during what was previously the clear water phase. This interaction of nutrient loading and Lake Yojoa's monomictic stratification regime illustrates a key phenomenon in how physical water column structure and nutrients interact in tropical monomictic lakes. This work highlights the need to consider nutrient dynamics of warm anoxic hypolimnions, not just surface water nutrient concentrations, to understand environmental change in these societally important but understudied ecosystems.</span></p> <p> </p> <p><em><strong><span>(for more recent years of data collection see additional zenodo repositories by Fadum and/or Hall) </span></strong></em></p>
Physical connection between the tropical Indian Ocean tripole and western Tibetan Plateau surface air temperature during boreal summer
<p><em>These experiments are used to study atmospheric circulation responses to SST forcing related to Indian Ocean tripole mode, including the precipitation, omega, geopotential height, zonal and meridional winds.</em></p>
Figure 3 in Management of invasive, plague-carrying signal crayfish by physical exclusion barriers
Figure 3. Discharge of the Bottwar stream (blue line) since construction of the barriers (daily means measured in the lower course of the stream, obtained from the "Umweltinformationssystem (UIS) der LUBW Landesanstalt für Umwelt Baden-Württemberg"). The horizontal hatched line corresponds to the stream discharge at which the 1st, flow-based barrier (B1) was effective to exclude signal crayfish during the in-situ assessment of barrier efficacy (date highlighted by vertical line). The red ticks beside the time axis indicate days with a stream discharge lower than 0.95 x this threshold, i.e., conditions at which barrier functionality has been presumably compromised by low stream flow. Please note the marked increase in duration and intensity of extreme low-flow conditions since 2018.
Figure 2 in Management of invasive, plague-carrying signal crayfish by physical exclusion barriers
Figure 2. Distribution of native and invasive crayfish species in the study area from 2011 to 2020, as indicated by monitoring data of the Fisheries Research Station (2011–2014) and the intensive crayfish surveys in 2017 and 2020 (this study; Table S2 and Figure S2). The signal crayfish distribution prior to 2013 remains unknown, as indicated by the question mark in the top left panel. Dots represent the exclusion barriers implemented in 2014 (numbered in direction of upstream signal crayfish spread; fill color indicates barrier functionality with pink = flow-based and yellow = waterfall-based). Detail maps in the lower panels show the fine-scale signal crayfish distribution at B1.
Figure 1 in Management of invasive, plague-carrying signal crayfish by physical exclusion barriers
Figure 1. Overview of the study area and the known crayfish distribution in 2014 (A, B), and location and pictures of the three exclusion barriers (modified pipe culverts; panel C; please note the rotated map); fill color indicates barrier functionality with pink = flow-based and yellow = waterfall-based barriers. Blue arrows indicate the direction of stream flow. The red arrow in A denotes the study area.
FIGURE 3 in Physical habitat as predictor of fish trophic structure in Brazilian Atlantic rainforest streams
FIGURE 3 | Biplot of the first two canonical functions showing how substrate composition (SC), meso-habitat variability (MH), and bank stability (BS) correlate with richness of carnivores (SCar), invertivores (SInv), omnivores (SOmn), and herbivorous-detritivores (SHD).
FIGURE 1 in Physical habitat as predictor of fish trophic structure in Brazilian Atlantic rainforest streams
FIGURE 1 | Sampling sites located in the Paranapanema river basin, São Paulo state, Brazil. The Paranapanema river is highlighted in blue.
Fig. 5 in Application of the physical habitat simulation for fish species to assess environmental flows in an Atlantic Forest Stream in South-eastern Brazil
Fig. 5. Average habitat suitability index –HSI– in the upper and lower reach of the riacho São Pedro, for the three fish species.
Fig. 6 in Application of the physical habitat simulation for fish species to assess environmental flows in an Atlantic Forest Stream in South-eastern Brazil
Fig. 6. Weighted usable area (WUA) in the upper and lower reach of the riacho São Pedro, for the three fish species.
Fig. 4 in Application of the physical habitat simulation for fish species to assess environmental flows in an Atlantic Forest Stream in South-eastern Brazil
Fig. 4. Habitat suitability curves for depth (m), mean water column velocity (m/s) and type of substrate for the three fish species in the riacho São Pedro. Substrate types are; 1- large boulder, 2- boulder, 3- cobble, 4- gravel, 5- fine gravel, 6- sand and 7- vegetation.
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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.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.