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36 results for “traveling waves”
P-S waves 3D velocity model of Los Humeros area from earthquake based travel-time tomography using CAT3D software (OGS)
<p>The dataset contains the 3D velocity model (VP (m/s), VS (m/s) and VP/VS) obtained from the tomographic inversion of seismological data in the area of Los Humeros (Mexico). The model was performed in the frame of the GEMex project (Mexico‐Europe Cooperation for research of enhanced geothermal systems and super-hot geothermal systems, WP5 ‘Detection of deep structures’, Jousset et al., D5.3, 2019).</p> <p>The inversion used 2661 P arrivals and 2272 S arrivals associated to 395 earthquakes recorded by 37 stations. The picking data was provided by Toledo et al., 2019.</p> <p>The inversion was performed by CAT3D software, a tomographic tool developed by OGS, which uses the SIRT method (Simultaneous Iterative Reconstruction Technique, Stewart, 1993) as inversion algorithm and the ray tracing procedure based on minimum time principle (Böhm et al., 1999). The velocities used as initial model for tomography were provided by the interpolated values obtained from the velocity analysis of four 2D seismic lines acquired inside the same investigated area by the tomographic inversion (See GEMex deliverable D5.3).</p> <p>The 3D velocity model is defined by a 3D grid of 61 nodes in X, 69 nodes in Y and 29 nodes in Z, equally spaced by 250 m in all directions. The total dimensions of the model is 15x17x7 km and the borders positions are (m) (WGS 84/UTM ZONE 14N):</p> <p>Xmin = 655000, Xmax = 670000</p> <p>Ymin = 2168000, Ymax = 2185000</p> <p>Zmin = -3000, Zmax = 4000</p>
Data for: From pattern to process? Dual travelling waves, with contrasting propagation speeds, best describe a self-organised spatio-temporal pattern in population growth of a cyclic rodent
<p>Centroid data used for the analysis in Roos et al. Eco Lett.</p> <p>Transects, up to 99 m in length (dependent on the field's length), were surveyed in linear stable landscape features (field, track or ditch margins) to estimate vole abundance from November 2011 until September 2017. Each transect was divided into 3 m sections (33 in total) and the presence or absence of one or more signs of vole activity (i.e., latrines by burrows, fresh vegetation clippings, and recent burrow excavations) in each section was noted. The proportion of sections with signs of vole presence per transect was then used as the abundance index. The number of surveys carried out at any time varied adaptively with the perceived risk of an outbreak (according to changes in estimated abundance in previous monitoring surveys).</p> <p>The response variable typically used in all models is proportional growth rate (r_{t,i}, where is the abundance index for site at time (Royama 1992; Berryman 2002). A benefit of using r_{t,i}, rather than ln(N_{t,i}), is that any multiplicative effects of site quality are cancelled out, provided they are constant over time. To calculate r_{t,i}, vole abundance indices are required at the same location in successive time periods (i.e., N_{t,i} and N_{t+1,i}). Given that exact transect locations were rarely reused in successive months, and all transect measurements took place throughout the year rather than discrete seasons, the data had to be aggregated to consistent locations and times to allow growth rate to be calculated. As such, transects were temporally aggregated into a respective yearly quarter (e.g., January to March 2014). Transects were spatially aggregated by sequentially selecting an unassigned transect as a reference point for the ith centroid and assigning all unassigned transects within a 5 km radius to the ith centroid, and repeating until all transects had been allocated (see Figure 2 for a summary of the number of transects assigned to each centroid, centroid locations, and time series of growth rate of each centroid). Once complete, the mean Julian day, X and Y UTM (Universal Transverse Mercator) and the mean index was calculated for all transects assigned to each centroid for each time period. Where a centroid had successive values of N_{t,i} and N_{t+1,i} available, the corresponding proportional growth rate was calculated.</p> <p>A constant of 3.03 was added to N_{t,i} to avoid zero entries (3.03 was the lowest non-zero value of <em>N</em> observed). The final dataset consisted of 3,751 observations.</p>
Supporting Datasets produced in Allen et al. (2018) Global Estimates of River Flow Wave Travel Times and Implications for Low-Latency Satellite Data"
<p><strong>Supporting datasets for Allen et al. (2018) - Global Estimates of River Flow Wave Travel Times and Implications for Low-Latency Satellite Data, <em>Geophysical Research Letters</em>, <a href="https://doi.org/10.1002/2018GL077914">https://doi.org/10.1002/2018GL077914</a></strong></p> <p>The code used to produce these data is available as a Github repository, permanently hosted on Zenodo: <a href="https://doi.org/10.5281/zenodo.1219784">https://doi.org/10.5281/zenodo.1219784</a></p> <p><strong>Abstract</strong></p> <p>Earth-orbiting satellites provide valuable observations of upstream river conditions worldwide. These observations can be used in real-time applications like early flood warning systems and reservoir operations, provided they are made available to users with sufficient lead time. Yet, the temporal requirements for access to satellite-based river data remain uncharacterized for time-sensitive applications. Here we present a global approximation of flow wave travel time to assess the utility of existing and future low-latency/near-real-time satellite products, with an emphasis on the forthcoming SWOT satellite. We apply a kinematic wave model to a global hydrography dataset and find that global flow waves traveling at their maximum speed take a median travel time of 6, 4 and 3 days to reach their basin terminus, the next downstream city and the next downstream dam respectively. Our findings suggest that a recently-proposed ≤2-day latency for a low-latency SWOT product is potentially useful for real-time river applications.</p> <p> </p> <p><strong>Description of repository datasets:</strong></p> <p>1. riverPolylines.zip contains ESRI shapefile polylines of river networks with outputs from main analysis. These continental-scale shapefiles contain the following attributes for each river segment:</p> <ul> <li>"ARCID" : unique identifier for each river segment line, defined as the river reach between river junctions/heads/mouths. The first 10 attributes are taken from Andreadis et al. (2013): https://doi.org/10.5281/zenodo.61758</li> <li>"UP_CELLS" : number of upstream cells (pixels)</li> <li>"AREA" : upstream drainage area (km<sup>2</sup>)</li> <li>"DISCHARGE" : discharge (m<sup>3</sup>/s)</li> <li>"WIDTH" : mean bankfull river width (m)</li> <li>"WIDTH5" : 5th percentile confidence interval bankfull river width (m)</li> <li>"WIDTH95" : 95th percentile confidence interval bankfull river width (m)</li> <li>"DEPTH" : mean bankfull river depth (m)</li> <li>"DEPTH5" : 5th percentile bankfull river depth (m)</li> <li>"DEPTH95" : 95th percentile confidence bankfull river depth (m)</li> <li>"LENGTH_KM" : segment length (km)</li> <li>"ORIG_FID" : original ID of segment</li> <li>"ELEV_M" : lowest elevation of segment (m). Derived from HydroSHEDS 15 sec hydrologically conditioned DEM: https://hydrosheds.cr.usgs.gov/datadownload.php?reqdata=15demg </li> <li>"POINT_X" : longitude of lowest point of segment (WGS84, decimal degrees)</li> <li>"POINT_Y" : latitude of lowest point of segment (WGS84, decimal degrees)</li> <li>"SLOPE" : average slope of segment (m/m)</li> <li>"CITY_JOINS" : an index associated with how likely a city/population center is located on the segment. Population center data from: http://web.ornl.gov/sci/landscan/ and http://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-populated-places/ </li> <li>"CITY_POP_M" : population of joined city (max N inhabitants) </li> <li>"DAM_JOINSC" : an index associated with how likely a dam is located on the segment. Dam data from Global Reservoir and Dam (GRanD) Database: http://www.gwsp.org/products/grand-database.html </li> <li>"DAM_AREA_S" : surface area of joined dam (m<sup>2</sup>)</li> <li>"DAM_CAP_MC" : volumetric capacity of joined dam (m<sup>3</sup>)</li> <li>"CELER_MPS" : modeled river flow wave celerity (m/s)</li> <li>"PROPTIME_D" : travel time of flow wave along segment (days)</li> <li>"hBASIN" : main basin UID for the hydroBASINS dataset: http://www.hydrosheds.org/page/hydrobasins</li> <li>"GLCC" : Global Land Cover Characterization at segment centroid: https://lta.cr.usgs.gov/glcc/globdoc2_0 </li> <li>"FLOODHAZAR" : flood hazard composite index from the DFO (via NASA Sedac): http://sedac.ciesin.columbia.edu/data/set/ndh-flood-hazard-frequency-distribution</li> <li>"SWOT_TRAC_" : SWOT track density (N overpasses per orbit cycle @ segment centroid). Created using SWOTtrack SWOTtracks_sciOrbit_sept15 polygon shapefile, uploaded here.</li> <li>"UPSTR_DIST" : upstream distance to the basin outlet (km) </li> <li>"UPSTR_TIME" : upstream flow wave travel time to the basin outlet (days)</li> <li>"CITY_UPSTR" : upstream flow wave travel time to the next downstream city (days)</li> <li>"DAM_UPSTR_" : upstream flow wave travel time to the next downstream dam (days)</li> <li>"MC_WIDTH" : mean of Monte Carlo simulated bankfull widths (m)</li> <li>"MC_DEPTH" : mean of Monte Carlo simulated bankfull depths (m)</li> <li>"MC_LENCOR" : mean of Monte Carlo simulated river length correction (km)</li> <li>"MC_LENGTH" : mean of Monte Carlo simulated river length (m)</li> <li>"MC_SLOPE" : mean of Monte Carlo simulated river slope (-)</li> <li>"MC_ZSLOPE" : mean of Monte Carlo simulated minimum slope threshold (m)</li> <li>"MC_N" : mean of Monte Carlo simulated Manning’s n (s/m^(1/3))</li> <li>"CONTINENT" : integer indicating the HydroSHEDS region of shapefile</li> </ul> <p>2. hydrosheds_connectivity.zip contains network connectivity CSVs for river polyline shapefiles. The tables do not contain headers:</p> <ul> <li>Col1: segment unique identifier (UID) corresponding to the ARCID column of the riverPolylines shapefiles</li> <li>Col2: Downstream UID</li> <li>Col3: Number of upstream UIDs</li> <li>Col4 – Col12: Upstream UIDs</li> </ul> <p>3. SWOTtracks_sciOrbit_sept15_density.zip contains a polygon shapefile derived from SWOTtracks_sciOrbit_sept15_completeOrbit containing the sampling frequency of SWOT (number of observations per complete orbit cycle). Polygon attributes correspond to each unique shape formed from overlapping swaths:</p> <ul> <li>FID : unique identifier of each polygon</li> <li>CENTROID_X : polygon centroid longitude (WGS84 - decimal degrees)</li> <li>CENTROID_Y : polygon centroid latitude (WGS84 - decimal degrees)</li> <li>COUNT_count: SWOT sampling frequency (N observations per complete orbit cycle)</li> </ul> <p>4. USGS_gauge_site_information.csv : table containing the list of USGS sites analyzed in the validation and obtained from http://nwis.waterdata.usgs.gov/nwis/dv Header descriptions contained within table. </p> <p>5. validation_gaugeBasedCelerity.zip contains polyline ESRI shapefiles covering North and Central America, where USGS gauges provided gauge-based celerity estimates. These files have FIDs and attributes corresponding to riverPolylines shapefiles described above and also contrain the folllowing fields:</p> <ul> <li>GAUGE_JOIN : an index associated with how likely a gauge is located on the segment. Gauge location information is contained in USGS_gauge_site_information.csv</li> <li>GAUGE_SITE: USGS gauge site number of joined gauge</li> <li>GAUGE_HUC8: which hydrological unit code the gauge is located in</li> <li>OBS_CEL_R: gauge-based correlation score (R). Upstream and downstream gauges were compared via lagged cross correlation analysis. The calculated celerity between the paired gauges were assigned to each segment between the two gauges. If there were multiple pairs of upstream and downstream gauges, the the mean celerity value was assigned, weighted by the quality of the correlation, R. Same weighted mean was applied in assigning R. </li> <li>OBS_CEL_MPS: gauge-based celerity estimate (m/s). </li> </ul> <p>6. tab1_latencies.csv contains data shown in Table 1 of the manuscript.</p> <p>7. figS3S4_monteCarloSim_global_runMeans.csv contains the mean of the Monte Carlo simulation inputs and outputs shown in Figure S3 and Figure S4. Column headers descriptions are given in riverPolylines (dataset #1 above). Some columns have rows with all the same value because these variables did not vary between ensemble runs.</p> <p>8. figS5_travelTimeEnsembleHistograms.zip contains data shown in Figure S5. Each csv corresponds to a figure component:</p> <ul> <li>tabdTT_b.csv : basin outlet travel times for all rivers</li> <li>tabdTT_b_swot.csv : basin outlet travel times for SWOT</li> <li>tabdTT_c.csv : next downstream city travel times for all rivers</li> <li>tabdTT_c_swot.csv : next downstream city travel times for SWOT</li> <li>tabdTT_d.csv : next downstream dam travel times for all rivers</li> <li>tabdTT_d_swot.csv : next downstream dam travel times for SWOT</li> </ul>
Delimiting the Neoproterozoic São Francisco Paleocontinental Block with P-wave travel-time tomography
<p>Tomographic data set for different depths (CSV-files with Longitude, Latitude anda Velocity Perturbation in percentage), Interpreted limit of the São Francisco Paleocontinent and the Abstract for the paper "Delimiting the Neoproterozoic São Francisco paleocontinental block with P-wave travel-time tomography" accepted by Geophysical Journal International.</p>
Dataset for: In-operando microwave scattering-parameter calibrated measurement of a Josephson travelling wave parametric amplifier
<p>Dataset for manuscript "In-operando microwave scattering-parameter calibrated measurement of a Josephson travelling wave parametric amplifier", <a href="https://arxiv.org/abs/2406.03063">arXiv:2406.03063</a></p> <p>Containing the uncalibrated raw measurement data and the calibrated dataset after applying the 8-term error model.</p>
W-Band Traveling Wave Tube Amplifier Based on Planar Slow Wave Structure
<p>Underlying data corresponding to the Journal paper: G. Ulisse and V. Krozer, "W-Band Traveling Wave Tube Amplifier Based on Planar Slow Wave Structure", IEEE Electron Device Letters, vol. 38, no. 1, January 2017.</p>
Kerr reversal in Josephson meta-material and traveling wave parametric amplification datasets
<p>This repository contains raw data for results presented in article "Kerr reversal in Josephson meta-material and traveling wave parametric amplification" (Preprint : arXiv:2101.05815). All data is stored in numpy(numpy.org) array format.</p> <p><strong>Please site any usage to original publication.</strong></p> <p><br> # Gain data</p> <p> The data used for generating Fig. 3(a) of main text:<br> <br> - Gain_6_freq contains frequency axis, gain_6 contains corresponding gain data when the device is pumped at 6 GHz<br> <br> - Gain_8_freq contains frequency axis, gain_8 contains corresponding gain data when the device is pumped at 8 GHz<br> <br> - Gain_10_freq contains frequency axis, gain_10 contains corresponding gain data when the device is pumped at 10 GHz</p> <p><br> # Saturation data</p> <p> The data used for generating Fig. 3(d) of main text:</p> <p> - saturation_6_pow contains input signal power at 6.05 GHz and saturation_6_gain contains gain as a function of the same when device is pumped at 8 GHz.</p> <p> - saturation_9.5_pow contains input signal power at 9.5 GHz and saturation_9.5_gain contains gain as a function of the same when device is pumped at 8 GHz.</p> <p><br> # Noise data</p> <p> The raw data used for fitting noise performance of the TWPA as depicted in Fig. 4 of main text:</p> <p> - noise_without_TWPA_temperature : contains noise temperature of the amplification chain without TWPA, in<br> Kelvin.<br> - noise_without_TWPA_sys_gain_dB : contains gain of the amplification chain without TWPA, in dB.</p> <p> - noise_freq : contains frequency axis for the measured PSD.</p> <p> - noise_thermal_source_temperature : contains temperature data of the thermal noise source.</p> <p> - noise_with_TWPA_PSD_vs_thermal_source_temperature : contains PSD measured with 200 MHz RBW as a function<br> of frequency and temperature of thermal noise source.</p> <p><br> # Transmission data</p> <p> Normalized transmission through the device.</p> <p> - transmission_flux : contains quantized flux axis for the measured transmission.</p> <p> - transmission_freq : contains frequency axis for the measured transmission.</p> <p> - transmission : contains transmission as a function of frequency and quantized flux, in dB.</p> <p><br> # Dispersion data</p> <p> Dispersion through the device.</p> <p> - dispersion_freq : contains frequency axis for the measured dispersion.</p> <p> - dispersion_phase_PCB : contains phase accumulation when RF switch is in PCB position as a function of<br> frequency, in radians.<br> - dispersion_flux_mA : contains flux axis for the measured dispersion, in mA.</p> <p> - dispersion_phase_device : contains phase accumulation when RF switch is in device position as a function<br> of frequency and flux, in radians.</p>
Traveling Waves: The Indonesian Tsunami Warning System - STS Webinar Series #1
<p>Previously published on YouTube: https://www.youtube.com/watch?v=CWK1_Nxi__Y</p> <p>(Indonesian)</p> <p>Seri webinar ini diadakan oleh Pusat Riset Kewilayahan (PRW) BRIN untuk mendiskusikan tema-tema terkait Science, Technology, and Society Studies, salah satu program kajian yang dikembangkan di PRW-BRIN. Webinar ini membahas kelindan antara diskursus, aktor, mesin deteksi, serta praktik sains dan teknologi untuk menghadapi risiko dan ketidakpastian sistem pengetahuan peringatan dini tsunami di Indonesia. Dengan menggunakan pendekatan etnografis, diskusi ini akan berusaha menjawab pertanyaan terkait teknologi sistem peringatan dini tsunami Indonesia sebagai infrastruktur sosial dan jaringan konektivitas pakar Jerman - Indonesia dalam pengembangan sistem teknologi tersebut di Indonesia.</p> <p>(English)</p> <p>This webinar series was held by the BRIN Regional Research Center (PRW) to discuss themes related to Science, Technology, and Society Studies, one of the study programs developed at PRW-BRIN. This webinar discusses the interplay between discourse, actors, detection machines, as well as science and technology practices to deal with the risks and uncertainties of the tsunami early warning knowledge system in Indonesia. Using an ethnographic approach, this discussion will attempt to answer questions related to the technology of the Indonesian tsunami early warning system as a social infrastructure and connectivity network of German-Indonesian experts in developing the technology system in Indonesia.</p>
Data from: Non-invasive biophysical measurement of travelling waves in the insect inner ear
Frequency analysis in the mammalian cochlea depends on the propagation of frequency information in the form of a travelling wave (TW) across tonotopically arranged auditory sensilla. TWs have been directly observed in the basilar papilla of birds and the ears of bush-crickets (Insecta: Orthoptera) and have also been indirectly inferred in the hearing organs of some reptiles and frogs. Existing experimental approaches to measure TW function in tetrapods and bush-crickets are inherently invasive, compromising the fine-scale mechanics of each system. Located in the forelegs, the bush-cricket ear exhibits outer, middle and inner components; the inner ear containing tonotopically arranged auditory sensilla within a fluid-filled cavity, and externally protected by the leg cuticle. Here, we report bush-crickets with transparent ear cuticles as potential model species for direct, non-invasive measuring of TWs and tonotopy. Using laser Doppler vibrometry and spectroscopy, we show that increased transmittance of light through the ear cuticle allows for effective non-invasive measurements of TWs and frequency mapping. More transparent cuticles allow several properties of TWs to be precisely recovered and measured in vivo from intact specimens. Our approach provides an innovative, non-invasive alternative to measure the natural motion of the sensilla-bearing surface embedded in the intact inner ear fluid.
Gain and output power of Traveling Wave Tube at W-band
<p>Results of the W-band TWT gain and output power simulated by using MAGIC 3D Particle in Cell Simulators. These results were presented in the paper title "W-band TWTs for New Generation High Capacity Wireless Networks" presented at the 17th International Vacuum Electronics Conference.</p>
Raw data : Observation of two-mode squeezing in a traveling wave parametric amplifier
<p>The raw data used to generate figures presented in the articles is available as qcodes datasets.</p> <p>Fig 2<br> The 100 million quadrature points used to construct statistics in figure 2 are stored at 10 datasets with ids 1 to 10, each containing 10 million points. The data was split in multiple databases to ease storage and processing.</p> <p>Fig 3<br> The pump phase sweep in figure 2 was recorded at 25 points from 0 to pi. Each sweep step is stored as a dataset in the database starting from run id 11 to 35, corresponding to 0 to pi in order.</p> <p>Fig 4<br> The quadratures recorded at delta values 20, 50, 100, 150 and 200 are stored with run id 36 to 40, respectively.</p> <p>Fig SNTJ gain calibration<br> The noise spectrum as a function of voltage applied to SNTJ is stored for frequencies corresponding to the delta values 20, 50, 100, 150 and 200, from run id 41 to 45, respectively.</p>
Photonic integrated circuit based continuous-travelling-wave parametric amplifier
<p>Dataset for the manuscript "Photonic integrated circuit based continuous-travelling-wave parametric amplifier".</p> <p>Contains all raw data and code used to produce the Figures and Extended Data Figures in the manuscript. </p> <p> </p> <p> </p> <p> </p>
Dispersion of the folded waveguide and output power of the Travelling Wave Tube amplifier in W band.
<p>Datasets of Dispersion of the folded waveguide and beam line (Fig1) and output power (Fig 2) of the paper "Fabrication of W-band TWT for 5G small cells backhaul" for IVEC 2017.</p> <p>Both MAGIC3D and CST- Particle StudioS were used for particle in cell simulations of the whole amplifier. Both the the simulators confirmed more that than 40 W on the full band 923 – 95 GHz as shown in Fig.2 The simulations included the couplers and the RF windows. Specific simulations for the design of the electron optics, the windows and the collector were performed.</p> <p> </p> <p> </p>
Unidirectional spin wave emission by travelling pair of magnetic field profiles
<p>This is the full data for this paper: https://arxiv.org/pdf/2307.12653.pdf</p> <p>G. P. anf J. W. K. would like to acknowledge the erasmus mundus MaMaSELF programm and the support from the National Science Center – Poland grant No. 2021/43/I/ST3/00550</p>
Data from: Non-invasive biophysical measurement of travelling waves in the insect inner ear
Open the record for dataset details and reuse information.
On the shape-dependent propulsion of nano- and microparticles by traveling ultrasound waves
<p>Supplementary data for the following manuscript: Johannes Voß, Raphael Wittkowski, "On the shape-dependent propulsion of nano- and microparticles by traveling ultrasound waves ", <em><strong>Nanoscale Adv.</strong></em>, 2020,<strong>2</strong>, 3890-3899, doi=10.1039/D0NA00099J</p>
Propulsion of nano- and microcones by a traveling ultrasound wave: dependence on orientation and aspect ratio of the particles
<p>Supplementary data for the following manuscript: Johannes Voß, Raphael Wittkowski, "Propulsion of nano- and microcones by a traveling ultrasound wave: dependence on orientation and aspect ratio of the particles"</p>
Propulsion of bullet- and cup-shaped nano- and microparticles by traveling ultrasound waves
<p>Supplementary data for the following manuscript: Johannes Voß, Raphael Wittkowski, "Propulsion of bullet- and cup-shaped nano- and microparticles by traveling ultrasound waves".</p>
Raw experimental data: Investigating pump harmonics generation in a SNAIL-based Traveling Wave Parametric Amplifier
<p>Raw experimental data that support the findings of the article "Investigating pump harmonics generation in a SNAIL-based Traveling Wave Parametric Amplifier" (<a href="https://arxiv.org/abs/2405.20096">https://arxiv.org/abs/2405.20096</a>).</p> <p>A jupyter notebook is provided to reproduce the experimental figure reported in the article.</p>
Breathing travelling wave solutions in a three-species competition-diffusion system
<p>We consider the situation where an exotic species <em>w</em> invades an ecosystem inhabited by two native species <em>u</em> and <em>v</em>. All species are competing for the same limited resource. Supposing that <em>u</em> and <em>v</em> are not able to coexist in the absence of the invader, we want to determine whether a successful invasion by <em>w</em> may allow all species to coexist (competitor-mediated coexistence). Mathematically, this problem can be modelled by the following three-species competition-diffusion system<br> <span class="math-tex">\( \left\{ \begin{alignedat}{6} u_t &= d_1 \, \Delta u &&+ (r_1 &&- u &&- b_{12} \, v &&- b_{13} \, w &&)\,u, \\ v_t &= d_2 \, \Delta v &&+ (r_2 &&- v &&- b_{21} \, u &&- b_{23} \, w &&)\,v, \\ w_t &= d_3 \, \Delta w &&+ (r_3 &&- w &&- b_{31} \, u &&- b_{32} \, v &&)\,w, \end{alignedat} \right.\)</span><br> where all parameters are positive constants.</p> <p>We are interested in the case in which the invading species is weaker than the native ones, i.e., it is not able to survive in the diffusion-free system obtained by setting <em>d</em><sub>1</sub> = <em>d</em><sub>2</sub> = <em>d</em><sub>3</sub> = 0.<br> We fix all parameters as<br> <span class="math-tex">\( \begin{aligned} & d_1 = d_2 = d_3 = 1, \\ & r_1 = r_2 = 28, \\ & \begin{aligned} b_{12} &= 22/21, & b_{13} &= 4, \\ b_{21} &= 1.87, & b_{23} &= 3/4, \\ b_{31} &= 26/21, & b_{32} &= 22/21, \\ \end{aligned} \end{aligned}\)</span><br> and leave <em>r</em><sub>3</sub>, which measures the strength of the exotic species, as a free parameter. Depending on the value of <em>r</em><sub>3</sub>, the invasion can be either successful or not and competitor-mediated coexistence may or may not occur.</p> <p>It turns out that if <em>r</em><sub>3</sub> lies in a certain range of values, the three-species competition-diffusion system admits a breathing travelling wave solution, i.e., a travelling pulse whose width is oscillating. Such a breathing wave is originated from a standard travelling pulse which is destabilized through a Hopf bifurcation. The period <em>T</em> of the breathing wave depends on the free parameter <em>r</em><sub>3</sub> and goes to infinity at one end of the solution branch.</p> <p>The movie "Breathing Travelling Wave Orbits" shows the evolution of the spatial profile of the breathing wave. After one period, the value of the parameter <em>r</em><sub>3</sub> is changed and the next solution on the breathing wave branch is displayed. Please note that the solution is plotted in a reference frame moving at the velocity of the breathing wave.</p> <p>The movie "Breathing Travelling Wave Features" shows several features of the breathing wave as <em>r</em><sub>3</sub> is changed. The first plot in the third row shows the current value of <em>r</em><sub>3</sub> and the position on the solution branch. The first row shows the space-time profiles of the solution. The first plot of the second row shows the evolution of the pulse width during one period. The remaining plots on the second row show the instantaneous velocities of the back and leading fronts of the oscillating pulse, while the plots immediately below show the density of the invading species <em>w</em> in correspondence of those two fronts.</p>
ScienceDex guides
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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.