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5,371 results for “Flow”
BRAIN Journal-Two new software behavioral design patterns: Obligation Link and History Reminder-Figure 4. The Flow chart of the "History Reminder" design pattern
<p>The “History Reminder” design pattern has two main classes: HistoryReminder and HistoryReminderOriginator. The flow chart of the “History Reminder” design pattern is given in Figure 4.</p>
BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 5. Emotional flow after 3 hours of graphical editing
<p>To resume, we started with a product interface, found a way to determine two opposite states, than used that way to map user interaction with the product and determine the emotional answer to that interface. The results can then be used to improve product design, to elicit certain emotional responses, etc. For example, in this case, due to the rapid movement (anger) patterns in the aligning phase, a layout that minimizes this can be developed, using a shortcut menu or dynamically appearing guidelines. Furthermore, the user interface can be imagined to be able to learn working patterns and shift the shortcut menu from an aligning menu, if it detects anger patterns, into a color/shape picking menu, if it detects relaxation patterns, therefore assessing the emotional impact and improving the design of the interface in the same time.</p>
STRATIFICATION EFFECTS ON FLOW HYDRODYNAMICS AND MIXING AT A CONFLUENCE WITH A HIGHLY DISCORDANT BED AND A RELATIVELY LOW VELOCITY RATIO
<p>The effects of temperature induced stratification on flow hydrodynamics, thermal mixing and the capacity of the flow to entrain sediment at a medium-size stream confluence with a highly discordant bed are investigated. To isolate the effects due to differences in the temperature/density of the incoming streams, two simulations were conducted with identical flow conditions (mean velocity ratio VR=2.44, temperature difference between the two streams ΔT=4.7<sup>0 </sup>C). In the first case the Richardson number was Ri=0 (no coupling between the temperature and the momentum equations via the Boussinesq approximation), while in the second simulation Ri=0.67. Even in the Ri=0 case the structure of the mixing interface (MI) was different from the one expected for concordant bed confluences with a similar confluence angle and VR. The MI contained only co-rotating eddies shed in the shear layer forming on the fast speed side of the confluence apex. In the Ri = 0.67 case no wake region was present but a large recirculation eddy formed not far from the confluence apex. In both cases, the flow near the upstream part of the MI was found to be highly 3D and to allow the passage of particles from one side of the confluence to the other. While in the Ri = 0 case mixing was driven by the MI eddies, in the Ri = 0.67 case mixing was controlled by large near-bed intrusions of heavier fluid from the tributary containing colder water and also by the fluid advected in and out of the recirculation eddy.</p>
On the Rayleigh–Kuo criterion for the tertiary instability of zonal flows
<p>These are the supplementary Matlab codes that can be used to generate the data in figure 3, 4, and 6 in the manuscript "On the Rayleigh--Kuo criterion for the tertiary instability of zonal flows". The abstract of this manuscript reads as follows:</p> <p>This paper reports the stability conditions for intense zonal flows (ZFs) and the growth rate $\gamma_{\rm TI}$ of the corresponding "tertiary" instability (TI) within the generalized Hasegawa--Mima plasma model. The analytic calculation extends and revises Kuo's analysis of the mathematically similar barotropic vorticity equation for incompressible neutral fluids on a rotating sphere [H.-L. Kuo, J. Meteor. 6, 105 (1949)]; then, the results are applied to the plasma case. An error in Kuo's original result is pointed out. An explicit analytic formula for $\gamma_{\rm TI}$ is derived and compared with numerical calculations. It is shown that a ZF is TI-unstable under the Rayleigh--Kuo criterion known from geophysics plus the condition that the ZF wave number must exceed the inverse ion sound radius. For a sinusoidal ZF, these two necessary conditions combined together are also sufficient for the TI. For non-sinusoidal ZFs, the results are qualitatively consistent. As a corollary, there is no TI in the geometrical-optics limit, i.e., when the DW wavelength is small compared to the ZF scale. This also means that the traditional wave kinetic equation under the geometrical-optics assumption cannot adequately describe the ZF stability.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 3. The flow diagram of the SBRP solution by using GA
<p>The distance optimization needed for the formation of the objective function that can be seen in equation number 1 was done using GA operators and parameters. The flowchart that can be seen in Figure 3 shows how the school bus routes are formed using GA.</p>
Turbulence statistics in smooth wall oscillatory boundary layer flow
<p>Experimental and numerical datasets belonging to Van der A, D.A., Scandura, P., O’Donoghue, T. (2018). Turbulence statistics in smooth wall oscillatory boundary layer flow, Journal of Fluid Mechanics, 849, 192-230. </p> <p> </p>
Time-averaged simulation results and in vivo measurements to show the impact of red blood cells on the flow field in the cortical microvasculature
<p>The dataset contains 5 files. 4 of them are time-averaged results of blood flow simulations with discrete red blood cell (RBC) tracking in realistic microvascular networks. The 5th file contains median values of RBC velocity measurements at capillary bifurcations in the somatosensory cortex of the mouse.</p> <p>Further notes on the simulation results:<br> - The realistic microvascular networks are from the mouse parietal cortex and have first been published in Blinder et al., 2013, Nature Neuroscience (<a href="https://doi.org/10.1038/nn.3426">https://doi.org/10.1038/nn.3426</a>).<br> - The numerical model to simulate blood flow in realistic microvascular networks has been described in Schmid et al., 2017, PLOS Computational Biology (<a href="https://doi.org/10.1371/journal.pcbi.1005392">https://doi.org/10.1371/journal.pcbi.1005392</a>).<br> - MVN1 and MVN2 stands for microvascular network 1 and 2, respectively.<br> - wRBCs and wpPs stands for 'with red blood cells' and 'with passive particles'. These terms describe two different numerical models: The wRBC-model accounts for all RBC related flow phenomena. The wpP neglects the phase-separation and the Fahraeus-Lindqvist effect, i.e. RBCs and flow are decoupled. Further details are available from Schmid et al. (2019, <a href="https://doi.org/10.1371/journal.pcbi.1007231">https://doi.org/10.1371/journal.pcbi.1007231</a>)</p> <p><strong>File format: </strong>pickle (Python)</p> <p><strong>Files 1 - 4 </strong>(Time-averaged simulation results):<br> Filenames: MVN1_wpPs.tar.bz2, MVN2_wpPs.tar.bz2, MVN1_wRBCs.tar.bz2, MVN2_wRBCs.tar.bz2</p> <p>Each compressed folder contains two files:<br> <br> edgesDict.pkl: dictionary with edge/vessel related data: </p> <ul> <li>flow: Flow rate in vessel [um^3/ms]</li> <li>length: Vessel length [um] (Tortuosity is considered)</li> <li>htt: Tube hematocrit in vessel [-]</li> <li>diameter: Effective vessel diameter [um]</li> <li>connectivity: Vertex indices, e.g. start and end vertex of the corresponding vessel</li> </ul> <p>verticesDict.pkl: dictionary with vertex/bifurcation related data:</p> <ul> <li>index: Index of the current vertex </li> <li>coords: Coordinates to describe the position of the vertex [um]</li> <li>pressure: Pressure at the vertex [mmHg]</li> </ul> <p> </p> <p><strong>File 5</strong> (in vivo RBC velocity measurements):<br> Filename: measurementDict.pkl</p> <p>keys:</p> <ul> <li>divergent_d1: divergent bifurcation, RBC velocity measurement in daughter vessel 1</li> <li>divergent_d2: divergent bifurcation, RBC velocity measurement in daughter vessel 2</li> <li>convergent_m1: convergent bifurcation, RBC velocity measurement in mother vessel 1</li> <li>convergent_m2: convergent bifurcation, RBC velocity measurement in mother vessel 2</li> </ul> <p><br> Data structure: list of list,<br> e.g. daughter vessel 1:<br> [[bif.1 - measure.1, bif.1 - measure.2, bif.1 - measure.3], [bif.2 - measure.1, bif.2 - measure.2, bif.2 - measure.3],...]<br> bif.: bifurcation, measure.: measurement.<br> The order of bifurcations is the same for 'divergent_d1' and 'divergent_d2' (and for 'convergent_m1' and 'convergent_m2'). </p> <p> </p>
Process Flow Diagram of Cardiac Surgery Pathway
<p><em><strong>This image is supplementary material for:</strong></em></p> <p>Currie, C. S.M. Monks, T. (2018) <em>Modelling Diseases: Prevention, Cure, and Management.</em> In M. Rabe, A. Juan, N. Mustafee, A. Skoogh, & B. Johansson (Eds.). Proceedings of the Winter Simulation Conference 2018. Gothenburg, Sweden.</p> <p><em>Relevant to:</em></p> <p><strong>Section 4.1.4. Spend some Time Observing How the Operations of the Disease Pathway are Managed</strong></p> <p>The description of the disease pathway obtained in the initial meeting may be idealized. It is advised that modelers spend at least a few hours in the one or more services in the real pathway to observe how it operates and to interview on-the-ground workforce. Observations of the system under pressure are useful to understand the complexities of the pathway and will reveal aspects that have been missed or that operate differently from described. This Figure illustrates a cardiac surgery pathway at a large hospital that operated on patients from a network of smaller hospitals in the UK. The diagram itself was primarily constructed in an initial meeting with a group of cardiac surgeons. Observation of the system and shadowing of specialist cardiac nurses revealed two differences from the idealized model. Firstly, patients could deteriorate and ‘bounce back’ from post-operative care to higher levels of care. Secondly, in times of pressure, the post operative beds were used flexibly to accommodate patients waiting for surgery and those who had already undergone surgery.</p>
Dataset: Enabling SSH Protocol Visibility in Flow Monitoring
<p>Dataset contains SSH flows from Masaryk University campus network. Captured during August 2018. IP addresses are hashed using salted SHA-256.</p> <p>The header for both data files is as follows:</p> <pre><code>Date flow start|Date flow end|Src IP|sPort|Dst IP|dPort|Proto|Packets|Bytes|Flags|SSH Client Version|SSH Client Application|SSH Server Version|SSH Server Application|SSH Login Attempts|SSH Login|SSH Kex|SSH Host Key|SSH Client Encryption|SSH Server Encryption|SSH Client MAC|SSH Server MAC|SSH Client Compression|SSH Server Compression</code></pre> <p> </p> <p>When using this dataset, please cite the original work as follows:</p> <pre><code>@inproceedings{1519096, author = {Celeda, Pavel and Velan, Petr and Kral, Benjamin and Kozak, Ondrej}, address = {Washington DC, USA}, booktitle = {IFIP/IEEE International Symposium on Integrated Network Management (IM 2019)}, keywords = {SSH; flow; monitoring; network; dataset}, howpublished = {online}, language = {eng}, location = {Washington DC, USA}, isbn = {978-3-903176-15-7}, pages = {569-574}, publisher = {IFIP Open Digital Library}, title = {Enabling SSH Protocol Visibility in Flow Monitoring}, url = {http://dl.ifip.org/db/conf/im/im2019exp/189410.pdf}, year = {2019} }</code></pre> <p> </p>
Data underpinning "Coupling between cerebral blood flow and cerebral blood volume: Contributions of different vascular compartments"
<p>The data in this archive was acquired to investigate the coupling between cerebral blood flow and cerebral blood volume across different vascular compartments. These results will form the basis of a forthcoming publication. Please reference this dataset (version v1.1.0) if you use it in your work. Wesolowski R, Blockley NP, Driver ID, Francis ST, Gowland PA. Data underpinning "Coupling between cerebral blood flow and cerebral blood volume: Contributions of different vascular compartments". Zenodo 2018. doi: 10.5281/zenodo.1411018. </p> <p>This dataset contains measurements of the haemodynamic responses of different compartments to a visual stimulus (8Hz red LED goggles, 19.2s ON, 40.8s OFF). Time course data are cycle averaged for the following haemodynamic properties;</p> <ol> <li>Arterial cerebral blood volume (CBVa) measured using Look-Locker Flow-sensitive Alternating Inversion Recovery (LL-FAIR) sensitised to CBVa.</li> <li>Cerebral blood flow (CBF) measured using Look-Locker Flow-sensitive Alternating Inversion Recovery (LL-FAIR) sensitised to CBF.</li> <li>Total cerebral blood volume (CBVtot) measured using bolus injections of a Gadolinium based contrast agent combined with T2* weighed gradient echo EPI.</li> </ol> <p>In addition, weighted mean and standard deviation of the changes in these parameters are presented using time windows of 9.6–19.2s and 40.8–60s for ON and OFF, respectively. Weighting is performed with respect to the number of voxels present in each of the subjects regions of interest.</p> <p>Time course data were extracted from two different regions of interest;</p> <ol> <li>ROI<sub>CBF</sub>: defined using a CBF localiser</li> <li>ROI<sub>COMMON</sub>: defined using the overlap of CBF, CBV<sub>a</sub> and CBV<sub>tot</sub> localisers</li> </ol> <p>Furthermore, the transit times for CBF and CBVa were estimated for an average stimulus cycle and mean values extracted using time windows of 9.6–19.2s and 40.8–60s for ON and OFF, respectively. These data were extracted from ROI<sub>CBF</sub>.</p> <p>Changes</p> <p>- Addition of propagation of uncertainty for CBVv and the Grubb constants alpha_tot and alpha_a.</p> <p> </p>
Dataset of Paper "Novel procedure for the numerical simulation of solar water disinfection processes in flow reactors" (DOI: 10.1016/j.cej.2018.10.131)
<p>Datasets of Paper "Novel procedure for the numerical simulation of solar water disinfection processes in flow reactors".</p> <p>DOI: 10.1016/j.cej.2018.10.131</p> <p>Data of the velocity profiles at different distances from the inlet of a solar rainwater reactor.</p> <p>Data of the simulated radiation field inside of a solar rainwater reactor as a function of the location, date, time and CPC inclination.</p> <p>Data of the disinfection efficiency versus illumination time in a solar reactor under simulated and natural sunlight.</p>
District heating modelling data for the publication "Integration of feed flow temperatures in unit commitment models of future district heating systems"
<p>Modelling data for a district heating system model which has been used for the publication "Integration of feed flow temperatures in unit commitment models of future district heating systems" on the 4th Generation District Heating (4GDH) conference 2018.</p>
Yale stocks and flows database (YSTAFDB)
<p>This Zenodo upload accompanies a Data Descriptor in <em>Scientific Data</em>, titled "YSTAFDB, a unified database of material stocks and flows for sustainability science". We refer to this database as the Yale stocks and flows database (YSTAFDB).</p> <p>Here, we provide core and supplementary ('hierarchy') tables for YSTAFDB in comma separated value file format. We additionally provide an example of a template that we used to manually prepare data for entry into YSTAFDB.</p> <p>A complete description of YSTAFDB can be found in the Data Descriptor mentioned above. Key properties of YSTAFDB include:</p> <ul> <li>100,000+ material cycles, criticality, and recycling data records</li> <li>quantitative stocks and flows data for 62 elemental cycles and various engineering material cycles</li> <li>data records describing stocks and flows at spatial scales from cities to global, and from the early 1800s to ca. 2013</li> </ul>
The impact of biological bedforms on near-bed and subsurface flow: a laboratory validated numerical study of flow in the vicinity of pits and mounds
<p>The data were used in the paper "The impact of biological bedforms on near-bed and subsurface flow: a laboratory validated numerical study of flow in the vicinity of pits and mounds" which was submitted to "Journal of Geophysical Research-Earth Surface". In this paper, a novel, unified water-sediment three-dimensional model is developed to investigate the impact of simulated biogenic bedforms. The impact of biogenic bedforms on near-bed turbulence and sediment entrainment is discussed in gravelly substrates. Biogenic bedform morphology has an important role in determining the spatial extent of up- and down-welling flow.</p>
Production Line energy and material flow dataset
<p>File dumpXXXXXX-1122 recounts production of 16 pieces, with<br> workplan "production with no fuse".<br> File dumpXXXXXX-1302 recounts production of 16 pieces, with workplan :production with left<br> fuse"</p>
Lac Croche V-notch weir water flow data collected at the Station de biologie des Laurentides (SBL) de l'Université de Montréal, St-Hippolyte QC
<p>These datasets comprise hourly and daily water flow data collected at the Lac Croche v-notch weir at the Station de biologie des Laurentides (SBL) between 2014/04/01 and 2019/05/01.</p>
Doctoral Thesis Artifact "User-Centered Tool Design for Data-Flow Analysis"
<p>This artifact contains the evaluation data and source code accompanying the doctoral thesis "User-Centered Tool Design for Data-Flow Analysis" by Lisa Nguyen Quang Do. The artifact contains (1) the survey questions and anonymized answers of the surveys conducted during the thesis, (2) the user study questionnaires, results, and test applications of the user studies conducted for the thesis, (3) the source code of the research prototypes and video demonstrations of their interfaces, and (4) the benchmark suites used for the empirical evaluation of those prototypes.</p>
Dataset for "Tidal control of the flow through long, narrow straits: a modeling study for the Seto Inland Sea"
<p>This dataset contains simulated results used to draw figures in the following paper.</p> <p>Kurogi, M. & Hasumi, H. Tidal control of the flow through long, narrow straits: a modeling study for the Seto Inland Sea, Sci. Rep. 9, 11077 (2019).</p> <p>==== List of data</p> <p> dep_T.nc: Depth of tracer point<br> dep_V.nc: Depth of velocity point<br> ssh.nc: Sea Surface Height for TIDE (hourly snapshots of July 2012)<br> ubt_TIDE.nc: Eastward barotropic velocity for TIDE (monthly mean)<br> vbt_TIDE.nc: Northward barotropic velocity for TIDE (monthly mean)<br> ubt_NTIDE.nc: Eastward barotropic velocity for NTIDE (monthly mean)<br> vbt_NTIDE.nc: Northward barotropic velocity for NTIDE (monthly mean)<br> ubt_NTIDE_DV0.nc: Eastward barotropic velocity for NTIDE_DV0 (monthly mean)<br> vbt_NTIDE_DV0.nc: Northward barotropic velocity for NTIDE_DV0 (monthly mean)<br> ubt_NTIDE_D.nc: Eastward barotropic velocity for NTIDE_D (monthly mean)<br> vbt_NTIDE_D.nc: Northward barotropic velocity for NTIDE_D (monthly mean)<br> ubt_NTIDE_DV.nc: Eastward barotropic velocity for NTIDE_DV (monthly mean)<br> vbt_NTIDE_DV.nc: Northward barotropic velocity for NTIDE_DV (monthly mean)<br> ubt_NTIDE_DVA.nc: Eastward barotropic velocity for NTIDE_DVA (monthly mean)<br> vbt_NTIDE_DVA.nc: Northward barotropic velocity for NTIDE_DVA (monthly mean)<br> ubt_inout.nc: Eastward barotropic velocity for the inflow-outflow model (monthly mean)<br> vbt_inout.nc: Northward barotropic velocity for the inflow-outflow model (monthly mean)<br> amv_TIDE.nc: Vertical average of vertical viscosity for TIDE (monthly mean)<br> ahv_TIDE.nc: Vertical average of vertical diffusivity for TIDE (monthly mean)<br> amv_NTIDE.nc: Vertical average of vertical viscosity for NTIDE (monthly mean)<br> ahv_NTIDE.nc: Vertical average of vertical diffusivity for NTIDE (monthly mean)<br> ke.nc: Vertical average of kinetic energy per unit mass for TIDE (monthly mean)<br> ke_bt.nc: Vertical average of barotropic kinetic energy per unit mass for TIDE (monthly mean)</p> <p> </p>
Compilation of hydraulic models for the study of the spatial averaging on flow laws
<p><strong>1.Summary</strong></p> <p>Datasets used in the article written by Ernesto Rodríguez, Michael Durand and Renato Prata de Moraes Frasson entitled “Observing rivers with varying spatial scales”.</p> <p><strong>2.File description</strong></p> <p>Data will be contained in one NetCDF file per river. The file contains the following groups and variables:</p> <p><strong>/River_Info/</strong></p> <p>Name: River name, data type: char</p> <p>QWBM: Mean annual discharge from the water balance model WBMsed (Cohen et al., 2014)</p> <p>rch_bnd: Reach boundaries measured in meters from the upstream end of the model</p> <p>gdrch: Reaches used in the study. Used to exclude small reaches defined around low-head dams and other obstacles where Manning’s equation should not be applied.</p> <p><strong>/XS_Timeseries/</strong></p> <p>t: Time measured in days since the first day or “0-January-0000” for cases when specific dates were available. Dimension: 1,time step.</p> <p>Z: Bed elevation in meters. Dimension: Cross-section, time step.</p> <p>xs_rch: Reach number for each cross-section. Dimension: Cross-section,1.</p> <p>X: Flow distance measured from the most upstream end of the model to the cross-section (meters). Dimension: Cross-section, 1.</p> <p>W: River width in meters. Dimension: Cross-section, time step.</p> <p>Q: Discharge (m<sup>3</sup>/s). Dimension: Cross-section, time step.</p> <p>H: Water surface elevation in meters. Dimension: Cross-section, time step.</p> <p>A: Cross-sectional area of flow in m<sup>2</sup>. Dimension: Cross-section, time step.</p> <p>P: Wetted perimeter in meters. Dimension: Cross-section, time step.</p> <p>n: Manning’s roughness. Dimension: Cross-section, time step.</p> <p><strong>/Reach_Timeseries/</strong></p> <p>t: Time measured in days since the first day or “0-January-0000” for cases when specific dates were available. Dimension: 1,time step.</p> <p>W: Reach averaged river width in meters. Dimension: Reach, time step.</p> <p>Q: Reach averaged discharge (m<sup>3</sup>/s). Dimension: Reach, time step.</p> <p>H: Reach averaged water surface elevation in meters. Dimension: Reach, time step.</p> <p>S: Reach averaged water surface slope in meters per meter. Reach, time step.</p> <p>A: Reach averaged area of flow in m<sup>2</sup>. Dimension: Reach, time step.</p> <p>P: Reach averaged wetted perimeter in meters. Not available for all rivers. Fill value: NaN. Dimension: Reach, time step.</p> <p><strong>References</strong></p> <p>Cohen, S., A. J. Kettner, and J. P. M. Syvitski (2014), Global suspended sediment and water discharge dynamics between 1960 and 2010: Continental trends and intra-basin sensitivity, <em>Glob. Planet. Change</em>, <em>115</em>, 44-58, doi: <a href="https://doi.org/10.1016/j.gloplacha.2014.01.011">10.1016/j.gloplacha.2014.01.011</a>.</p> <p>Rodríguez, E., Durand, M. T., & Frasson, R. P. d. M. (2020). Observing rivers with varying spatial scales. Water Resources Research. doi: <a href="https://doi.org/10.1029/2019WR026476">10.1029/2019WR026476 </a></p>
Figure 1 in Gene Flow Patterns of the Aedes aegypti (Diptera: Culicidae) Mosquito in Colombia: a Continental Comparison Suggests Multiple Invasion Routes and Gene Exchange
Figure 1 Geographic location of the A. aegypti populations included in this study and gene-flow models evaluated. In both graphics, circles indicate the populations and arrows represent the gene flow between populations. A) Scale at American continent level (N = 2,996 specimens from six locations: Mexico – North America (M-NA), Venezuela (VZ), Peru (PE), Brazilian Amazon (BrAm), southeastern Brazil (SEBr), and Colombia (CO). B) Scale at South America level (N = 1,083 specimens from six locations: Venezuela, Peru, Brazilian Amazon (5 locations; Brazilian Amazon (BrAM), Manaus (MAO), Belém (BL). Boa Vista (BV), Rio Branco (RB), Porto Velho (PV)), Southeastern Brazil (SEBr) and Colombia (2 locations; Sucre (S), Quindio (Q)).
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.