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5,371 results for “Flow”
Figure 4. Phylogeny constructed through Bayesian inference estimated from the 35H in Gene Flow Patterns of the Aedes aegypti (Diptera: Culicidae) Mosquito in Colombia: a Continental Comparison Suggests Multiple Invasion Routes and Gene Exchange
Figure 4. Phylogeny constructed through Bayesian inference estimated from the 35H found of the ND4 gene for the A. aegypti populations in the American continent. The blue horizontal bars above the branches reflect the 95% CI for the branch supports. The color bars (blue, green, and red) on the tree terminals indicate which haplotypes are exclusive for a specific population. The dotted lines on the right side of the tree and numbers I or II indicate to what clade each of the terminals belong. H1-Col (Colombia (Sucre and Quindio), Venezuela, Peru, M-NA, Brasil (MA-O, RBPV, SEBr, BE-L)), H4 (Venezuela, M-NA, Brasil (MA-O, RBPV, BEL)), H3 (Venezuela, M-NA, Brazil (RBPV, SEBr, BE-L)), H2-Col (Colombia (Sucre), Venezuela, Peru, M-NA, Brazil (RBPV, SEBr, BE-L)), H13 (M-NA, Brazil (SEBr)), H8 (Venezuela, Brazil (SEBr)).
Figure 3 in Gene Flow Patterns of the Aedes aegypti (Diptera: Culicidae) Mosquito in Colombia: a Continental Comparison Suggests Multiple Invasion Routes and Gene Exchange
Figure 3. Representation of the gene-flow model with greater subsequent probability in the scenarios of the Americas and South America.A. Scale at American continent level (N = 2,996 specimens from six locations: Mexico–North America, Venezuela, Peru, Brazilian Amazon, southeastern Brazil, and Colombia. Within the continent context, the Stepping Stone 1 model shows through the green vectors the way the gene flow occurs among the different populations evaluated. B. Scale at South America level; the Full-migration model indicates migration among all the populations, hence, plotting of the potential migration routes through passive land transport (Pan-American highway) and riverine paths (principal rivers).
Text-fig. 1. A. Location of the sites of Capo di Fiume, Palena and Pollenzo near Alba. B. Capo di Fiume stratigraphic section. Facies of coastal-transitional marine associations – a. Freshwater marsh and tidal creeks interval, b. Swamp interval, c1–c4. Facies of eustarine bay associations, d1–d6. Facies of open shelf marine associations. Symbols: "black star" – fossiliferous horizon with plant material studied here, 1. mottled grey to dark-brown marls and clayey marls, 2. fissile dark-grey marls and shaly marls, 3. limestones, 4. marly limestones and limey marls, 5. bio-lithoclastic calcarenites, 6. lime conglomerate, 7. massive muddy deposit produced by mass-flow mechanism, 8. diatomitic marls, 9. "terra rossa" soil (modified after Carnevale et al. 2011). in Feather Palm Foliage From The Messinian Of Italy (Capo Di Fiume, Palena And Pollenzo Near Alba) Within The Framework Of Northern Mediterranean Late Miocene Flora
Text-fig. 1. A. Location of the sites of Capo di Fiume, Palena and Pollenzo near Alba. B. Capo di Fiume stratigraphic section. Facies of coastal-transitional marine associations – a. Freshwater marsh and tidal creeks interval, b. Swamp interval, c1–c4. Facies of eustarine bay associations, d1–d6. Facies of open shelf marine associations. Symbols: "black star" – fossiliferous horizon with plant material studied here, 1. mottled grey to dark-brown marls and clayey marls, 2. fissile dark-grey marls and shaly marls, 3. limestones, 4. marly limestones and limey marls, 5. bio-lithoclastic calcarenites, 6. lime conglomerate, 7. massive muddy deposit produced by mass-flow mechanism, 8. diatomitic marls, 9. "terra rossa" soil (modified after Carnevale et al. 2011).
Text-fig. 3. Distribution of main types of volcanoes in the NearShore Volcanic Belt of Eastern Sikhote-Alin' (Eocene–Neogene). 1 – Central volcanoes (partly preserved); 2 – Central volcanoes (destructed); 3 – Shield and gentle sloping volcanoes with a dolerite or trachy-basaltic neck on the top; 4 – Lava and scoria cones; 5 – Pyroclastic, tuffaceous coarse- and fine-grained terrigenous sedimentary rocks, partly with plant-bearing levels; 6 – Eruption centers of plateau-basalts and the direction of lava flows; 7 – Main Late Cenozoic basaltic plateaus; 8 – Fumarol fields; 9 – Hot springs. in Mid-Latitude Palaeogene Floras Of Eurasia Bound To Volcanic Settings And Palaeoclimatic Events - Experience Obtained From The Far East Of Russia (Sikhote-Alin') And Central Europe (Bohemian Massif)
Text-fig. 3. Distribution of main types of volcanoes in the NearShore Volcanic Belt of Eastern Sikhote-Alin' (Eocene–Neogene). 1 – Central volcanoes (partly preserved); 2 – Central volcanoes (destructed); 3 – Shield and gentle sloping volcanoes with a dolerite or trachy-basaltic neck on the top; 4 – Lava and scoria cones; 5 – Pyroclastic, tuffaceous coarse- and fine-grained terrigenous sedimentary rocks, partly with plant-bearing levels; 6 – Eruption centers of plateau-basalts and the direction of lava flows; 7 – Main Late Cenozoic basaltic plateaus; 8 – Fumarol fields; 9 – Hot springs.
Text-fig. 12. Geological plan of the Velikaya Kema plant-bearing locality (4 km north of Velikaya Kema village). 1 – basalt with flaggy flows; 2 – massive basalt; 3 – andesite with flaggy flows; 4 – andesite-basalt; 5 – trachyte, 6 – felsite; 7 – agglomerate, basalt and andesite; 8 – tuff coarse-grained; 9 – conglomerate; 10 – thin layers of andesitic tuff; 11 – tuffite, tuffaceous argillite, diatomite; 12 – plant bearing levels. in Mid-Latitude Palaeogene Floras Of Eurasia Bound To Volcanic Settings And Palaeoclimatic Events - Experience Obtained From The Far East Of Russia (Sikhote-Alin') And Central Europe (Bohemian Massif)
Text-fig. 12. Geological plan of the Velikaya Kema plant-bearing locality (4 km north of Velikaya Kema village). 1 – basalt with flaggy flows; 2 – massive basalt; 3 – andesite with flaggy flows; 4 – andesite-basalt; 5 – trachyte, 6 – felsite; 7 – agglomerate, basalt and andesite; 8 – tuff coarse-grained; 9 – conglomerate; 10 – thin layers of andesitic tuff; 11 – tuffite, tuffaceous argillite, diatomite; 12 – plant bearing levels.
Fig. 2. Flow diagrams showing a in "Weight of evidence" as a tool for evaluating disease in wildlife: An example assessing parasitic infection in Northern bobwhite (Colinus virginianus)
Fig. 2. Flow diagrams showing a weight of evidence framework using the (A) 7 questions proposed by Burkhardt-Holm and Scheurer (2007) and the (B) modified questions for addressing disease(s) in wildlife.
Accompanying data for publication: "Learning the Optimal Power Flow: Environment Design Matters"
<p>All the data created for the publication "Learning the Optimal Power Flow: Environment Design Matters" by Wolgast and Nieße. The dataset contains all training runs performed, including the final neural network weights, meta-data about the training run, and various metrics during the course of training, which were used to generate the results and plots. The source code to re-produce the plots for the publication (and everything else) can be found on GitHub: https://github.com/Digitalized-Energy-Systems/rl-opf-env-design</p>
Data basis of "Investigation of Railway Network Capacity by Means of Dynamic Flows"
<p>Input data for the article <strong>Investigation of Railway Network Capacity by Means of Dynamic Flows (Nikolayzik, Maus and Nießen).</strong></p> <p>The dataset contains two files for each analysed scenario (complete network, upper subnetwork, lower subnetwork).</p> <p>The first file ("input_data_infrastructure_{scenario}.csv") contains information on the investigated infrastructure:<br>For each station the number of available tracks is listed and for the lines information on whether it is a single- or double-track line, the average minimum headway time, hourly capacity limits and travel times for the different train types are included. The information is thereby split into two parts, depending on whether the core network or the linking lines are described.</p> <p>The second file ("input_data_trains_{scenario}.csv") contains the trains that can generally be scheduled in the considered network, including information on the corresponding train type, departure frequencies, their routes and a minimally allowed dwell time.</p> <p> </p> <p>Further, the file "input_data_route_conflicts_nodes.py" contains the information on which routes inside a station exclude each other as is described in the article.</p> <p> </p> <p> </p>
FIGURE 4 in Visualizing the fluid flow through the complex skeletonized respiratory structures of a blastoid echinoderm
FIGURE 4. Visualization of the flow within the 3D printed model (Re = 0.376, see Table 1). Flow in the folds consists of horizontal bands of distinct red and blue color, indicating no adoral component to flow and no mixing within the folds, consistent with Hypothesis 2 (see text, Figure 2.2). The still used in the print version of this paper is a single frame from the flow pattern observed, showing the steady-state flow pattern after nine minutes of flow. The animation is sped up 16x (for video see palaeo-electronica.org/content/2015/1073-blastoid-hydrospire-fluid-flow).
FIGURE 2. Schematic showing hypothesized flow patterns within the hydrospire folds. 2.1 in Visualizing the fluid flow through the complex skeletonized respiratory structures of a blastoid echinoderm
FIGURE 2. Schematic showing hypothesized flow patterns within the hydrospire folds. 2.1, In Hypothesis 1, the flow has an adoral component representing respiratory leakage. 2.2, In Hypothesis 2, the flow is entirely radial, without leakage. See text for further discussion.
FIGURE 3 in Visualizing the fluid flow through the complex skeletonized respiratory structures of a blastoid echinoderm
FIGURE 3. Digital and physical models use to visualize fluid flow. 3.1, Digital solid model of approximately the lower quarter of a hydrospire of Pentremites rusticus, using Blender (see text). 3.2, 3D-printed rendering of the digital model, shown with inlet headers connected.
FIGURE 1 in Visualizing the fluid flow through the complex skeletonized respiratory structures of a blastoid echinoderm
FIGURE 1. Anatomy of the hydrospires of the blastoid Pentremites rusticus. 1.1, Location of one of the five radially distributed hydrospires within the calyx, showing incurrent hydrospire pores, and excurrent spiracle (inferred direction of water flow indicated by the arrows). 1.2, Oblique view of a section of a hydrospire and associated structures. Modified from Schmidtling and Marshall (2010).
Finding Your Flow: Troubleshooting Options
<p>The purpose of this decision-making flowchart is to help researchers or other project stakeholders navigate to finding help with project-related challenges generally, and project-related challenges with data and cyberinfrastructure in particular. Figuring out how to troubleshoot a problem may be perceived as overwhelming. The flowchart seeks to parse the problem and guide the user to possible solutions. In addition to problem type, the steps of the flow chart also include a time dimension, i.e. urgency.</p> <p>The graphic was developed as part of a project funded by the National Science Foundation to promote water quality research resilience in the wake of a natural disaster such as a hurricane. The graphic was used as part of a multi-session training workshop on integrating cyberinfrastructure tools for water quality research. The workshop included participants with different levels of familiarity with cyberinfrastructure tools.</p>
FIGURE 2 in Different stocks of Prochilodus lineatus along the Cinzas River basin: contributions from a free-flowing tributary in the Capivara Reservoir region, Paranapanema River basin (upper Paraná River)
FIGURE 2 | Results of Bayesian clustering analysis (STRUCTURE) forProchilodus lineatus samples obtained along in Laranjinha River (L1–L4), Cinzas River main channel (CI), Capivara Reservoir (CA), Tibagi River (TI) and a restocking event (ST). Estimates of the number of K groups based on mean likelihood Ln(K) (A) and ∆K statistic (B). Graphical representation based on K = 3 (C). Each column represents a different individual and the colors represent the probability membership coefficient of that individual for each genetic cluster.
FIGURE 1 in Different stocks of Prochilodus lineatus along the Cinzas River basin: contributions from a free-flowing tributary in the Capivara Reservoir region, Paranapanema River basin (upper Paraná River)
FIGURE 1 | Sampling locations for Prochilodus lineatus along the Capivara Reservoir region (Paranapanema River basin), including the Laranjinha River (L1–L4), Cinzas River main channel (CIU - upstream and CID - downstream from Salto Cavalcante falls), Capivara Reservoir (CA) and Tibagi River (TI), as well as the site (ST) of a restocking event in the Laranjinha River. Source: modified from Google Earth, 2018 (https://www.google.com.br/maps).
FIGURE 3 in Different stocks of Prochilodus lineatus along the Cinzas River basin: contributions from a free-flowing tributary in the Capivara Reservoir region, Paranapanema River basin (upper Paraná River)
FIGURE 3 | Results from mtDNA (D-Loop) of Prochilodus lineatus in Cinzas River basin, Capivara Reservoir and Tibagi River. A. Mismatch distributions of mitochondrial haplotypes for all samples. B. Haplotype network, including GenBank data revealing previous records of some haplotypes in Paraná River main channel (PR) and in the Pardo (PA), Mogi-Guaçu (MG), Verde (VE), Peixe (PE), Aguapeí (AG), and Iguaçu (IG) rivers. Circle sizes are proportional to haplotype frequency.
High spatiotemporal resolution free surface detection using cost-effective video equipment and computer vision techniques in nearly stationary flow along a transparent wall in the laboratory
<p>The identification of the air-water interface in free surface flows traditionally involves intrusive techniques or costly equipment. Non-intrusive alternatives, such as computer vision, are emerging as highly effective substitutes or supplements for more invasive techniques in laboratory measurements, thanks to their straightforward implementation and cost efficiency. This research specifically delves in the conjunction of various naive techniques, exploring their collective precision in detecting the air-water interface along transparent walls in laboratory. A detection technique based on the double gradient of the image is applied and thoroughly examined. The study progresses through multiple refinement stages, culminating in a method that is both cost effective and easy to implement. This methodology allows for large-scale, high resolution measurements (200 mm × 1800 frames per video at a 0.25 mm, 50 Hz resolution), offering both spatial and temporal measurements by adeptly detecting the free surface along transparent walls.</p>
Flow velocity measurement data for the two desanding chambers of HPP Susasca before and after modification of tranquilizing racks in 2019 and 2021
<p>This dataset includes the flow velocity measurements in the sand trap of HPP Susasca before and after the modification of the tranquilizing racks, respectively. The study was conducted by the Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich.</p>
FVLLMONTI Application examples: Single reconfigurable VNWFET TCAD simulation and Reference logic cell DTCO flow
<p>This data set presents two application examples collecting and highlighting some of the key components to the TCAD, compact modelling and DTCO approaches carried out as part of the FVLLMONTI project in relation to Deliverable D3.2.</p> <p>First we present an example that demonstrates the functionality of the U-shaped reconfigurable transistor, switching the device behaviour between NMOS and PMOS using the Program Gate and giving the transistor characteristics of those devices.</p> <p>Secondly, this data set gives an introduction into the FVLLMONTI DTCO approach. The provided simulation project shows a reference DTCO flow from layout to library characteristics for previously delivered physical logic cell designs. The DTCO flow provides insight into logic cell parasitics, netlist for 3rd party SPICE analysis, and KPI determination and characterization.</p> <p>Both simulation projects can be seen as template for similar setups employed to overcome the challenges and answer the detailed questions faced in investigating a newly developed semiconductor technology such as the FVLLMONTI stack.</p>
Impact of Grid Resolution on Wave-mean Flow Interactions with High Resolution Mars Global Climate Model Simulations
<p>This dataset contains NetCDF files necessary to replicate results from the 2024 paper "<em>Impact of Grid Resolution on Wave-mean Flow Interactions with High Resolution Mars Global Climate Model Simulations</em>"</p> <p>The dataset contains NetCDF files with 1 year of zonally-averaged NASA Ames Mars Global Climate Model (MGCM) fields with 5-sol binning for each of the simulations presented in the paper: </p> <ul> <li>a "low-resolution" simulation with no parameterization for gravity waves</li> <li>a "high-resolution" simulation with no parameterization for gravity waves</li> <li>a "low-resolution" simulation with parameterizations for orographic and non-orographic gravity waves</li> </ul> <p>Also included are:</p> <ul> <li>a file describing the coordinates for the MGCM's vertical grids used in the study </li> <li> atmospheric fields not provided in the other NetCDF files and necessary to replicate figures 3 and supplemental figure FS2 from the paper.</li> <li>a README.txt detailing the content of each file in the dataset</li> </ul>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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