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274 results for “collisions”
A Note on Aerosol Processing by Droplet Collision-Coalescence
<p>Simulation results for the above-mentioned publication. The files *_out.nc contain spectral data, *_out_time.nc time series. The prefixes small, medium, and large refer to the initial aerosol size distribution.</p>
JEWEL+PYTHIA simulated pp and PbPb collisions at 5020 GeV - Jet substructure variables
<p>Samples of simulated jets generated using JEWEL+PYTHIA for both Unquenched (pp) and Quenched (PbPb) cases, with the former being generated using the vacuum executable, jewel-vac, and the later using the medium executable, jewel-simple. For each case 320 000 events were produced with <span class="math-tex">\(\sqrt{s}=5020\)</span> GeV, <span class="math-tex">\(p_T \in [40, 250]\)</span> GeV, <span class="math-tex">\(|y|<2.5\)</span> . For the Quenched case, the medium settings were set to <span class="math-tex">\(\tau_i = 0.4\)</span> fm/c, <span class="math-tex">\(T_i = 440\)</span> MeV, <span class="math-tex">\(T_c = 170\)</span> MeV, and centrality <span class="math-tex">\(0-10\%\)</span>.</p> <p>Three Quenched samples are produced from the same JEWEL output eventfiles.</p> <ul> <li>One without thermal recoils</li> <li>One with thermal recoils but no background subtraction</li> <li>One with thermal recoils and background subtraction via the event-level subtraction prescription found in <a href="https://link.springer.com/article/10.1140/epjc/s10052-022-10954-1">Improved background subtraction and a fresh look at jet sub-structure in JEWEL</a></li> </ul> <p>More details about the simulation of the events and the jet variables please refer to the <a href="https://gitlab.com/lip_ml/jet-substructure-observables-ml-analysis">code repository</a> and <a href="https://arxiv.org/abs/2304.07196">Jet substructure observables for jet quenching in Quark Gluon Plasma: a Machine Learning driven analysis</a>, where they were first used.</p>
Dataset for "Terrane collision-induced subduction initiation: Mode selection and implications for western Pacific subduction system"
<p>Numerical results for "<strong>Terrane collision-induced subduction initiation: Mode selection and implications for western Pacific subduction system</strong>".</p>
Rate coefficients for inelastic Y+/Y-H collisions
<p>Rate coefficients in cm3s−1 for inelastic Y+H (include Y++H−) and Y++H (include Y2++H−) collisions at temperatures from T=1000K to T=10000K.<br> </p> <p>For details see ReadMe.txt.</p>
REDVID Collision Event Data – Linear Tracks and Hits
<p>An example, representative data set is generated using the REDuced VIrtual Detector (REDVID) simulation framework and contains complexity-reduced subatomic particle collision event data. Particle trajectory information and hit coordinates from interactions with reduced-order virtual detector models is included. The data is generated in 3D domain and follows the cylindrical coordinate system for hit point coordinates in space and trajectory function parameters.</p> <p>The included five tarballs each belong to a different data generation recipe. While all recipes include 10000 collision events, the number of tracks included in events varies from 1 track per event to 10000 tracks per event. This is noticeable from the tarball names.</p> <p>The data set is intended to be used as synthesised input for research involving ML-assisted pipeline design exploration, as well as ML model design exploration, e.g., Neural Architecture Search (NAS). To understand the data and its generation in detail, refer to the provided README file, as well as the related publication.</p>
Experimental data for "Fragmentation of ice particles: laboratory experiments on graupel-graupel and graupel-snowflake collisions"
<p>Graupel-graupel and graupel-snowflake collisions<br> experiments were carried out in still air conditions in the cold room of the Wind tunnel laboratory of the<br> Johannes Gutenberg University, Mainz. Graupel particles were first exposed to supersaturated air conditions, under which dendritic ice crystals have grown on their surface.<br> All fragments resulting from graupel-graupel collisions were<br> collected and investigated under a digital microscope. Fragments resulting from graupel-snowflake collisions were observed and recorded instantly after collision using a<br> holographic instrument. Number, size, area, and aspect ratio distributions of fragments were derived. </p> <p>These files contain data collected during the graupel-graupel and graupel-snowflake collisions. These data served for generating the figures in the publication.</p> <p>The files Graupel_graupel_number_CKE_mass_size.csv and Graupel_snowflake_CKE_mass_size give information on the experimental properties of the graupel-graupel and graupel-snowflake collisions, respectively.<br> For these two files, the first column gives the name of the experiment (e.g. snow1). Each line is corresponding to one collision experiment.</p> <p>The concrete data for each collision experiment is provided in the data file having the corresponding name, e.g., snow_data1.csv contains the data of collision experiment snow1, etc.</p> <p>Readme_graupel_data.txt and Readme_snow_data.txt contain collision kinetic energies of graupel-graupel and graupel-snowflake collisions, respectively.</p> <p>Graupel_crystal_surface.csv gives the estimated number of crystal for defined bins (Fig. 12 dashed line) on the surface of the 2 mm graupel before the collision.</p> <p> </p>
Supporting data and code for: Electron-electron collision effects on ISR temperature measurements
<p>CSV files are processed simulation output from EPPIC. Read these files with eppic_csvread.m, which also reads in the Brownian collision spectra, neb_th631.mat.</p> <p>singleparticle5.m is the code used for single-particle simulations of ISR spectra</p> <p>eppic_input_deck.i is the input file used for collisional ISR runs with EPPIC, see manuscript for Github link of EPPIC.</p> <p>electrostatic_meteor_ablation_sim-master.zip contains an archive of the EPPIC code when it was run, see manuscript for a Github link for the current version of EPPIC</p>
S61 | UJICCSLIB | Collision Cross Section (CCS) Library from UJI
<p>This is the collection associated with list S61 UJICCSLIB on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>A list of 970 collision cross section values from 556 compounds (both positive and negative ionization modes, and N2 as a drift gas using TWIMS-QTOF instrument) provided by Alberto Celma et al. (submitted)</p> <p>Update 10 April 2020: added charge information in an extra column to the xlsx and csv files, for PubChem integration. InChIKey and DTXSID files did not change. 30 Jul 2020: fixed adduct/charge mismatches in CSV.</p>
Data from: Effect of tower base painting on willow ptarmigan collision rates with wind turbines
<p>1. Birds colliding with turbine rotor blades is a well-known negative consequence of wind-power plants. However, there has been far less attention to the risk of birds colliding with the turbine towers, and how to mitigate this risk. 2. Based on data from the Smøla wind-power plant in Central Norway, it seems highly likely that willow ptarmigan (the only gallinaceous species found on the island) is prone to collide with turbine towers. By employing a BACI-approach, we tested if painting the lower parts of turbine towers black would reduce the collision risk. 3. Overall, there was a 48% reduction in the number of recorded ptarmigan carcasses per search at painted turbines relative to neighbouring control (unpainted) ones, with significant variation both within and between years. 4. Using contrast painting to the turbine towers resulted in significantly reduced number of ptarmigan carcasses found, emphasizing the effectiveness of such a relatively simple mitigation measure.</p>
Collision between biological process and statistical analysis revealed by mean-centering
<p>Animal ecologists often collect hierarchically-structured data and analyze these with linear mixed-effects models. Specific complications arise when the effect sizes of covariates vary on multiple levels (e.g., within vs among subjects). Mean-centering of covariates within subjects offers a useful approach in such situations, but is not without problems.</p> <p>A statistical model represents a hypothesis about the underlying biological process. Mean-centering within clusters assumes that the lower level responses (e.g. within subjects) depend on the deviation from the subject mean (relative) rather than on absolute values of the covariate. This may or may not be biologically realistic. We show that mismatch between the nature of the generating (i.e., biological) process and the form of the statistical analysis produce major conceptual and operational challenges for empiricists.</p> <p>We explored the consequences of mismatches by simulating data with three response-generating processes differing in the source of correlation between a covariate and the response. These data were then analyzed by three different analysis equations. We asked how robustly different analysis equations estimate key parameters of interest and under which circumstances biases arise. </p> <p>Mismatches between generating and analytical equations created several intractable problems for estimating key parameters. The most widely misestimated parameter was the among-subject variance in response. We found that no single analysis equation was robust in estimating all parameters generated by all equations. Importantly, even when response-generating and analysis equations matched mathematically, bias in some parameters arose when sampling across the range of the covariate was limited.</p> <p>Our results have general implications for how we collect and analyze data. They also remind us more generally that conclusions from statistical analysis of data are conditional on a hypothesis, sometimes implicit, for the process(es) that generated the attributes we measure. We discuss strategies for real data analysis in face of uncertainty about the underlying biological process.</p>
Contrasting Collision-induced Far-field Orogenesis Controlled by Thermo–rheological Properties of the Composite Terrane - Dataset
<p>This is the dataset for the paper "Contrasting Collision-induced Far-field Orogenesis Controlled by Thermo–rheological Properties of the Composite Terrane" that has been submitted to "Geophysical Research Letters".</p>
Integral and Differential Cross sections for 16O(3P)-N2, 17O(3P)-N2 and 18O(3P)-N2 collisions
<p>Raw data and codes used in Sanchit Kumar et. al. "Quantum scattering cross-sections for O(3P) + N2 collisions for planetary aeronomy" Monthly Notices of the Royal Astronomical Society 526, 5675–5681 (2023). https://doi.org/10.1093/mnras/stad3149</p><p>Please cite the original work as: </p><p>Kumar, Sanchit, et al. "Quantum scattering cross-sections for O (3 P)+ N2 collisions for planetary aeronomy." Monthly Notices of the Royal Astronomical Society 526.4 (2023): 5675-5681. <a href="https://doi.org/10.1093/mnras/stad3149">https://doi.org/10.1093/mnras/stad3149</a></p>
The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities
<p>Velocity field for the India-Eurasia collision zone from Sentinel-1 InSAR and GNSS data</p> <p>Citations:</p> <p>[1] Jin Fang, Gregory A Houseman, Tim J Wright, Lynn A Evans, Tim J Craig, John R Elliott and Andy Hooper (2023). The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10053499</p> <p>[2] Jin Fang, Gregory A Houseman, Tim J Wright, Lynn A Evans, Tim J Craig, John R Elliott and Andy Hooper (2024). The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities, Journal of Geophysical Research: Solid Earth, https://doi.org/10.1029/2023JB028571</p> <p>More details about the methodology to generate the velocity field can be found in Wright et al. (2023):</p> <p>[3] Tim J Wright, Greg Houseman, Jin Fang, Yasser Maghsoudi, Andy Hooper, John Elliott, Lynn Evans, Milan Lazecky, Qi Ou, Barry Parsons, Chris Rollins, Lin Shen, Hua Wang (2023). High-resolution geodetic strain rate field reveals dynamics of the India-Eurasia collision, submitted to Science, preprint available at https://doi.org/10.31223/X5G95R.</p>
Coordinates of jungle cat vehicle collision locations and background points
Open the record for dataset details and reuse information.
Forest gaps around wind turbines attract bat species with high collision risk
<p><span>Globally, renewable energy is highly demanded, leading to a recent increase in the number of wind turbines at forested sites. For the deployment of turbines, forest areas must be cleared, which results in significant habitat changes. To assess the ecological consequences of these changes for forest-associated bats, we quantified the activity of three bat foraging guilds at turbine clearings, adjacent forest edges, and nearby forest canopies. Specifically, open-space and edge-space foraging bats were more active at turbine clearings and forest edges than at forest canopies. Narrow-space foragers were also more often recorded at turbine clearings than at forest canopies. An increased activity of open- and edge-space foragers at turbine clearings may increase the risk for casualties. </span><span>Therefore, if turbines need to be placed in forests, strict curtailment schemes should be practiced to mitigate collision risk for these bats. This may impair the efficacy of wind energy production at forested sites.</span></p>
Dead mammal walking: a month-long march by a bison (Bison bison) after an ungulate-vehicle collision
<p>Globally, ungulate-vehicle collisions (UVC) are a major human safety concern and may also represent a significant source of mortality for some ungulate populations. However, records of UVC based on counts of roadside carcasses or reports by drivers involved in these incidents are assuredly underestimated because not all ungulates struck die immediately or at the roadside or are reported by drivers or authorities. Here, we provide an observation and analysis of the movements of a GPS-collared bison (<em>Bison bison</em>) that was involved in a UVC on the Alaska Highway and died in thick boreal forest 29 days later. During that time she moved 49.7 km from where she was hit. Her daily movement rate (km/hr) and daily net displacement (km/day) were significantly greater in the 29-day period before she was struck compared to 29 days afterward. This vivid example illustrates that individuals injured in a UVC can die several weeks later and at a considerable distance from where they were initially struck. Moreover, when they eventually die it may be where their carcass would not be found or associated with a UVC. Bison are the largest land mammal in North America and perhaps more robust to some lower-impact UVC than smaller-bodied species. Even so, if not for the GPS collar on this bison, we would have never known the fate of this individual, and the carcass likely never found. Taken together, the movements and final resting place of this bison illuminate how estimates of mortality as a result of UVC can be underestimated when the animal does not die immediately and in a location where it can be found. Given our data, we further urge managers to consider roadside counts of animals killed in UVC as a minimum estimate when considering options for mitigation.</p>
Data for "The influence of non-thermal collisions in Europa's atmosphere
<p>See "README.txt" for an explanation of how to read the data sets listed above in order to reproduce Figures 1-4 and the figure in the supplemental material.</p>
Vector Field Histogram (VFH) video results for vineyard navigation and collision avoidance
<p><strong>Video Results for PhD Thesis</strong>: This video demonstrates the implementation of Vector Field Histogram (VFH) navigation for collision avoidance within crop rows. The simulation is conducted in Gazebo, utilizing a Husky robot to showcase efficient path planning and obstacle avoidance in an agricultural setting.</p>
Hysteresis Performance and Design Optimization of Rubber Concrete Anti-collision Layer of Concrete Bridge Piers
<p>Static and hysteresis experiments were implemented firstly to obtain the compressive strength, elastic modulus and energy dissipation factor of rubber concrete. This study explores the static and dynamic properties of rubber concrete.</p>
Data release: Understanding binary neutron star collisions with hypermodels
<pre># Data release for "Understanding binary neutron star collisions with hypermodels" ## Summary For each event, we include sub-directories of the studies performed. In each directory, we provide the `bilby_pipe` configuration (`.ini`) files, `bilby` result file (`.json`). We also provide figures, samples (in the form of `.csv` files), and summary statistics where they are relevant. ## Hypermodel script All results obtained using the *hypermodel* technique use the script `multiwaveform.py`. We include this script at the top-level of this directory. To reproduce results, in each configuration file, replace the `analysis-executable` with the path to this script. ## Software versions All results obtained using * bilby_pipe=1.0.3: (CLEAN) 1220709 2021-05-14 06:28:48 -0700 * bilby=1.1.2: (CLEAN) e5481028 2021-07-05 16:45:01 +0100</pre>
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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.
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