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5,371 results for “Flow”
Conservative Interpolation Between Control-Volume Meshes: Application to Advection & Multiphase Flow
<p>Numerical Raw Data pertaining to the Publication linked to the DOI above. Uploaded as per Open Access requirements.</p> <p>The paper itself presents a general, higher order conservative and bounded interpolation for the dynamic and adaptive meshing of continuous and discontinuous control-volume fields. The method is discussed in detail and its robustness and applicability demonstrated through several examples, including advection dominated inertial flow as well multiphase porous media flow.</p> <p> </p>
Flume data on flow resistance of flexible foliated woody vegetation
<p>This dataset contains flume data on flow resistance of flexible foliated woody vegetation. The data have been previously reported and analyzed by Västilä et al. (2013), Västilä & Järvelä (2014), and Box et al. (2021), respectively.</p> <p>The data were used in developing and validating the modeling of flexible woody vegetation in D-FLOW FM module of the Delft3D Flexible Mesh (Delft3D FM) Suite, as reported in Västilä et al. (submitted). Two leaf-aread-index based flow resistance formulas (Järvelä 2004, Eq. 16, JAR; and Västilä & Järvelä 2014, Eq. 17, VAS) were implemented into D-FLOW FM by Västilä et al. (submitted). </p> <p>This contribution consists of three datasets, in all of which the plants were approximately homogeneously distributed across the entire flume bed: 1) artificial nature-like shrubs with 2 cm tall understory grasses under low relative submergence (h/hd=0.9-3.1) conditions (Box et al., 2021), 2) Black Poplars at three plant densities under just submerged conditions (Västilä et al., 2013), and 3) four natural deciduous riparian species at different leaf-area-to-stem-area ratios (AL/AS) under just submerged conditions (Västilä & Järvelä, 2014). </p> <p>The measured data are compiled in the file "Vastila_etal_2024_Flume data on flow resistance of flexible foliated woody vegetation.xlsx". Dataset 1 is located in sheet "Box_etal_2021_data.xlsx", while Datasets 2 and 3 are combined in sheet "Västilä&Järvelä_2013+2014_data". The corresponding metadata descriptions are included in separate sheets. The sheet "VAS+JAR_parameters" includes tables reporting the recommended parameter values of the JAR (Table 1) and VAS (Table 2) formulas as well as the parameter values of both formulas as derived at low and high levels of AL/AS (Table 3) to analyze the uncertainty of the formulas related to the parameter dependency on AL/AS (see Västilä et al. submitted). The datasets include a specific model ID code for each case in column entitled "Model ID".</p> <p>References<br>- Box, W., Järvelä, J. & Västilä, K. 2021. Flow resistance of floodplain vegetation mixtures for modelling river flows. Journal of Hydrology 601: 126593. https://doi.org/10.1016/j.jhydrol.2021.126593</p> <p>- Järvelä, J. 2004. Determination of flow resistance caused by non-submerged woody vegetation. International Journal of River Basin Management 2(1): 61–70. https://doi.org/10.1080/15715124.2004.9635222</p> <p>- Västilä, K., Järvelä, J., & Aberle, J. 2013. Characteristic reference areas for estimating flow resistance of natural foliated vegetation. Journal of Hydrology 492: 49-60. DOI: http://dx.doi.org/10.1016/j.jhydrol.2013.04.015</p> <p>- Västilä, K. & Järvelä, J. 2014. Modeling flow resistance of woody vegetation using physically-based parameters for foliage and stem. Water Resources Research 50(1): 229-245. DOI: 10.1002/2013WR013819</p> <p>- Västilä, K., Berends, K.D., Järvelä, J., Penning, E.W.E. (submitted) Development of flow resistance modeling using state-of-the-art formulas for flexible woody vegetation in Delft3D FM. </p>
PRISMA Flow Diagram
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Supplementary material 1 from: Satmari A, Miok K, Ion MC, Zaharia C, Schrimpf A, Pârvulescu L (2023) Headwater refuges: Flow protects Austropotamobius crayfish from Faxonius limosus invasion. NeoBiota 89: 71-94. https://doi.org/10.3897/neobiota.89.110085
Hyperparameter tuning using Grid Search
Pulses of RhoA Signaling Stimulate Actin Polymerization and Flow in Protrusions to Drive Collective Cell Migration
<p>In animals, cells often move as collectives to shape organs, close wounds, or—in the case of disease—metastasize. To accomplish this, cells need to generate force to propel themselves forward. The motility of singly migrating cells is driven largely by an interplay between Rho GTPase signaling and the actin network. Whether cells migrating as collectives use the same machinery for motility is unclear. Using the zebrafish posterior lateral line primordium as a model for collective cell migration, we find that active RhoA and myosin II cluster on the basal sides of the primordium cells and are required for primordium motility. Positive and negative feedbacks cause RhoA and myosin II activities to pulse. These pulses of RhoA signaling stimulate actin polymerization at the tip of the protrusions and myosin II-dependent actin flow and protrusion retraction at the base of the protrusions, and deform the basement membrane underneath the migrating primordium. This suggests that RhoA-induced actin flow on the basal sides of the cells constitutes the motor that pulls the primordium forward, a scenario that likely underlies collective migration in other—but not all—contexts.</p>
Assotiation between patent ductus arteriosus FLOW and home oxygen therapy in infants <26 weeks of gestation
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Flow-induced periodic chiral structures in an achiral nematic liquid crystal
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Experimental Study of the Drag Force Applied by Supercritical Flows on Emerging Obstacles
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Supplementary Information for "Deciphering controls of pore-pressure evolution on sediment bed erosion by debris flows"
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Heat flow at present-day Mars and evolutionary frame of the Martian heat flows
<p><strong>Citation of global grids and maps: </strong>For more information about the methodology of the generation of SHF and CHF models of Mars please check and cite <strong>Parro, L., Jiménez-Díaz, A., Mansilla, F. <em>et al.</em> Present-day heat flow model of Mars. <em>Sci Rep</em> 7, 45629 (2017). https://doi.org/10.1038/srep45629</strong></p> <p>-------------------------------------------------------------------</p> <p>In the frame of the UPWARDS project, researchers of the Universidad Complutense de Madrid (UCM, Spain) have developed <strong>crustal thickness (CT), crustal heat flow (CHF) and surface heat flow (SHF) models for Mars at different ages </strong>(present-day, 1.5 Gyr, 3.0 Gyr, 3.5 Gyr), which are freely available for science and education.</p> <p>This work has received funding from the European Union's Horizon 2020 Programme (H2020-Compet-08-2014) under grant agreement UPWARDS-633127.</p> <p>For a interactive view, please check: https://ucmadrid.maps.arcgis.com/apps/webappviewer/index.html?id=1ac196e992bd4b7393aa288d20801f4c</p> <p>-------------------------------------------------------------------</p> <p>Other references:</p> <p>Ruiz, J., McGovern, P.J., Jiménez-Díaz, A., López, V., Williams, J-P., Hahn, B.C., Tejero, R., 2011. The thermal evolution of Mars as constrained by paleo-heat flows. Icarus 215, 508-517.</p> <p>Ruiz, J., 2014. The early heat loss evolution of Mars and their implications for internal and environmental history. Sci. Rep. 4, 4338. doi:10.1038/srep04338.</p> <p>Egea-González, I., Jiménez-Díaz, A., Parro, L.M., López, V., Williams, J.-P., Ruiz, J., 2017. Thrust fault modeling and Late-Noachian lithospheric structure of the circum-Hellas region, Mars. Icarus 288, 53-68.</p> <p>Jiménez-Díaz, A., Egea-Gonzalez, I., Parro, L. M., Tasaka, M., & Ruiz, J. (2020). The thermal structure and mechanical behavior of the martian lithosphere. <em>Icarus</em>, <em>353</em>, 113635. https://doi.org/10.1016/j.icarus.2020.113635</p> <p> </p>
Figure 3 from: Mesa-Varona O, Plaza-Rodríguez C, Valentin L, Filter M (2024) WarenstromInfo: a tool for the easy extraction and visualisation of trade flow data. Research Ideas and Outcomes 10: e112227. https://doi.org/10.3897/rio.10.e112227
Figure 3 WI User Interfaces: UI3. In the UI3, countries involved in the query can be selected (I and J). Input data from UI3 are included in the query by clicking "Next" (K).
Figure 5 from: Mesa-Varona O, Plaza-Rodríguez C, Valentin L, Filter M (2024) WarenstromInfo: a tool for the easy extraction and visualisation of trade flow data. Research Ideas and Outcomes 10: e112227. https://doi.org/10.3897/rio.10.e112227
Figure 5 A screenshot of the BACI SQLite database builder workflow. The workflow presented here is organised into two main sections, with interconnected KNIME nodes displayed in each section. The yellow-boxed nodes are responsible for creating and loading the database where the data are stored. The green-boxed section shows: Locate BACI csv files (BACI files must have been previously downloaded and stored in a folder that is pointed in the "List Files/Folders" KNIME node). Split the files according to the HS data provided and Load csv files, filtering and storage in SQLite database (carried out in each consecutive metanode). In this last section, the user can decide not to filter the data or customise the filter criteria of the BACI database, including more types of data apart from those agrifood data that are included in the default configuration of the workflow. Variable flow connections (red lines) can be removed in the second green-boxed section, if an HPC (High Performance Computing) service is available. This will allow the workflow to run much faster as the nodes will not be executed one after the other, but all at the same time.
Figure 4 from: Mesa-Varona O, Plaza-Rodríguez C, Valentin L, Filter M (2024) WarenstromInfo: a tool for the easy extraction and visualisation of trade flow data. Research Ideas and Outcomes 10: e112227. https://doi.org/10.3897/rio.10.e112227
Figure 4 WI User Interfaces: UI4. The UI4 provides an overview of the final WI outputs, displaying a list with the initial input variables (L), three maps displaying trade flows (a world map, a world map focused on Europe and an European map) (M), download options (N), slide filter bars for value and weight preview (O) and trade flow data in the preview table (P).
Figure 1 from: Mesa-Varona O, Plaza-Rodríguez C, Valentin L, Filter M (2024) WarenstromInfo: a tool for the easy extraction and visualisation of trade flow data. Research Ideas and Outcomes 10: e112227. https://doi.org/10.3897/rio.10.e112227
Figure 1 WI User Interfaces: UI1. In the UI1, users can select the desired database (EUROSTAT/BACI) (A). The database selection is concluded by clicking "Next" (B), which will lead the user to the following UI.
Figure 2 from: Mesa-Varona O, Plaza-Rodríguez C, Valentin L, Filter M (2024) WarenstromInfo: a tool for the easy extraction and visualisation of trade flow data. Research Ideas and Outcomes 10: e112227. https://doi.org/10.3897/rio.10.e112227
Figure 2 User Interfaces: UI2. In the UI2, the following input options can be selected: standard selection (C), time range of the search (D), the email option (E), the pre-filter option (F) and the table with the embedded live filter (G). Input data from UI2 are processed by clicking "Next" (H).
Original data and code for Kang, Mooshagian, and Snyder (2024) Functional organization of posterior parietal cortex circuitry based on inferred information flow
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The Effect of Blood-Flow Restriction Walk Training on Insulin Sensitivity and Aerobic Capacity among Type 2 Diabetes: the raw data and IRB
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Experimental flume dataset for debris flow study
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Fig. 1 in A phylogenetic investigation of the taxonomically problematic Eucalyptus odorata complex (E. section Adnataria series Subbuxeales): evidence for extensive interspecific gene flow and reticulate evolution
Fig. 1. (Caption on next page)
Efficiently characterizing quantum information flow, loss and recovery in the central spin system
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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
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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.