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26 results for “numerical response”
Blind Prediction Competition - Sera.ta - Seismic Response of Masonry Cross Vaults: Shaking table tests and numerical validations
<p>Masonry vaults play a much relevant role in the seismic response of heritage masonry buildings, ranging from housing to the greatest cathedrals. Acting as both a ceiling and a structural horizontal diaphragm with significant mass, their mechanical behaviour affects the overall seismic response of buildings, in terms of strength, stiffness, and ductility. Moreover, local damage and collapse of vaults may produce significant losses in terms of cultural assets and casualties. In spite of the importance of this topic, the evaluation of the complex three-dimensional behaviour of vaults is still an important challenge for researchers. The main objectives of the present research project are:<br> 1) to better understand the seismic behaviour of masonry cross vaults by means of shaking table tests on both full-scale and small-scale models;<br> 2) to assess the capability of different modelling/analysis approaches to predict the seismic response of these masonry structures.</p> <p>In particular, three sets of shaking table tests are planned:<br> a. Tests on a 1:1 scale model of a brick unreinforced masonry cross vault: to investigate the behaviour of brick masonry cross vaults under different seismic inputs, in terms of damage, displacement capacity and peak acceleration.<br> b. Tests on a 1:1 scale model of a brick reinforced masonry cross vault: to evaluate the effectiveness of reinforcing techniques to repair the vaults tested in a).</p> <p>In addition to the experimental tests, a blind prediction competition is performed to assess the efficacy of different modelling strategies and analysis techniques. The final aims are to improve the safety assessment procedures proposed for historic masonry buildings in Eurocode 8.3 and to provide better seismic assessment techniques and strengthening measures.</p>
Site-Specific MCER Response Spectra for Los Angeles Region based on 3-D Numerical Simulations and the NGA West2 Equations
<p><strong>ABSTRACT</strong></p> <p>The Utilization of Ground Motion Simulation (UGMS) committee of the Southern California Earthquake Center (SCEC) developed site-specific, risk-targeted Maximum Considered Earthquake (MCER) response spectra for the Los Angeles region. The long period (T ≥ 2-sec) MCER response spectra were computed as the weighted average of MCER spectral accelerations derived from (1) 3-D numerical ground-motion simulations using the CyberShake computational platform, and (2) empirical ground-motion prediction equations (GMPEs) from the Pacific Earthquake Engineering Research (PEER) Center NGAWest2 project. The short period (T < 2- sec) MCER response spectra were computed exclusively from the NGAWest2 GMPEs. A web-based lookup tool was also developed so users can obtain the MCER response spectrum for a specified latitude and longitude and for a specified site class or 30-m average shear-wave velocity, VS30. The tool provides acceleration ordinates of the MCER response spectrum at 21 natural periods in the 0 to 10-sec band.</p> <p>This dataset includes a Java application to run queries. It serves as the backend data source for the web-based tool that can be found at: <a href="https://data2.scec.org/ugms-mcerGM-tool_v18.4/">https://data2.scec.org/ugms-mcerGM-tool_v18.4/</a>.</p> <p>For more information, please see <a href="https://www.scec.org/research/ugms">https://www.scec.org/research/ugms</a>.</p> <p><strong>DISCLAIMER</strong></p> <p>The UGMS MCER Tool is provided "as is" and without warranties of any kind. While SCEC and the UGMS Committee have made every effort to provide data from reliable sources or methodologies, SCEC and the UGMS Committee do not make any representations or warranties as to the accuracy, completeness, reliability, currency, or quality of any data provided herein. SCEC and the UGMS Committee do not intend the results provided by this tool to replace the sound judgment of a competent professional, who has knowledge and experience in the appropriate field(s) of practice. By using this tool, you accept to release SCEC and the UGMS Committee of any and all liability.</p> <p>Please note: The site-specific, design response spectral acceleration, Sa, returned by this tool for user-specified inputs, must be compared to the minimum Sa requirement described in Section 21.3 of ASCE 7-16 (second and third paragraphs). This minimum Sa is computed as 80% of the design response spectrum derived from the SDS, SD1, and TL values obtained from the ASCE tool at https://asce7hazardtool.online/. The larger of the site-specific Sa and the 80% minimum Sa at each period, T, is the final design response spectral acceleration. This final Sa x 1.5 is the final MCER response spectral acceleration.</p>
Numerical data for "Signatures of Kondo-Majorana interplay in ac response"
<p>Raw numerical data used to produce figures 2-8 in the article <em>Signatures of Kondo-Majorana interplay in ac response</em>, published as Phys. Rev. B <strong>109</strong>, 075432 (2024), DOI: 10.1103/PhysRevB.109.075432, and other data obtained within the same project.</p>
Dataset of the paper "Numerical Study of the Optical Response of ITO-In2O3 Core-Shell Nanocrystals for Multispectral Electromagnetic Shielding"
<p>This dataset provides the raw data of the paper "Numerical Study of the Optical Response of ITO-In2O3 Core-Shell Nanocrystals for Multispectral Electromagnetic Shielding"</p>
Probing temperature-responsivity of microgels and its interplay with a solid surface by superresolution microscopy and numerical simulations
<p>This dataset supports the publication ' Probing temperature-responsivity of microgels and its interplay with a solid surface by super resolution microscopy and numerical simulations' published on ACS Nano. DOI: https://doi.org/10.1021/acsnano.2c07569</p>
Postdiction Competition - Sera.ta - Seismic Response of Masonry Cross Vaults: Shaking table tests and numerical validations
<p>Once the blind prediction has been completed (DOI 10.5281/zenodo.7624666), new data are given to the research groups to perform the post diction simulations. The main objectives are:</p> <ul> <li>to calibrate the numerical model based on the dynamic properties;</li> <li>to emprove the capability of different modelling/analysis approaches and better justify the numerical assumptions.</li> </ul> <p>Below you can find the data set for the post diction analysis namely:</p> <ul> <li>Unstrengthened specimen results: </li> <li>Strengthened specimen results: </li> <li>Results of mix mortars used for the TRM technique:</li> </ul> <p> </p>
Numerical response of predator to prey: Dynamic interactions and population cycles in Eurasian lynx and roe deer
<p>The dynamic interactions between predators and their prey have two fundamental processes; numerical and functional responses. Numerical response is defined as predator growth rate as a function of prey density or both prey and predator densities [dP/dt = f(N, P)]. Functional response is defined as the kill rate by an individual predator being a function of prey density or prey and predator densities combined. Although there are relatively many studies on the functional response in mammalian predators, numerical response remains poorly documented. We studied numerical response of Eurasian lynx (<em>Lynx lynx</em>) to various densities of its primary prey species, roe deer (<em>Capreolus</em> <em>capreolus</em>), and to itself (lynx). We exploited an unusual natural situation, spanning three decades where lynx, after a period of absence in central and southern Sweden, during which roe deer populations had grown to high densities, subsequently recolonized region after region, from north to south. We divided the study area into seven regions, with increasing productivity from north to south. We found strong effects of both roe deer density and lynx density on lynx numerical response. Thus, both resources and intraspecific competition for these resources are important to understand the lynx population dynamic. We built a series of deterministic lynx–roe deer models and applied them to the seven regions. We found a very good fit between these Lotka-Volterra-type models and the data. The deterministic models produced almost cyclic dynamics or dampened cycles in five of the seven regions. Thus, we documented population cycles in this large-predator-large-herbivore system, which is rarely done. The amplitudes in the dampened cycles decreased towards the south. Thus, the dynamics between lynx and roe deer became more stable with increasing carrying capacity for roe deer, which is related to higher productivity in the environment. This increased stability could be explained by variation in predation risk, where human presence can act as prey refugia, and by a more diverse prey guild that will weaken the direct interaction between lynx and roe deer.</p>
Supplement to the "Response to the Referee" of the article "Simulation of marine stratocumulus using the super-droplet method: Numerical convergence and comparison to a double-moment bulk scheme"
<p>This is the supplement to the "Response to the Referee" of the article "Simulation of marine stratocumulus using the super-droplet method: Numerical convergence and comparison to a double-moment bulk scheme".</p> <p><a href="https://zenodo.org/api/files/87a1c801-d2f8-4c0c-b408-db56481cf97d/Movie%201_w_theta_v_t.mp4">Movie 1_w_theta_v_t.mp4</a>: Time evolution of vertical profiles of buoyancy production and its decomposition.</p> <p><a href="https://zenodo.org/api/files/6eabdd4d-61fd-4f7a-8656-0c9d1fb7ce6f/Movie%202_scatter_w_theta_v_t.mp4">Movie 2_scatter_w_theta_v_t.mp4</a>: Time evolution of scatter plots of w vs theta_v.</p> <p><a href="https://zenodo.org/api/files/6eabdd4d-61fd-4f7a-8656-0c9d1fb7ce6f/Movie%203_boy_incloud_real_z_t.mp4">Movie 3_boy_incloud_real_z_t.mp4</a>: Time evolution of buoyancy production and cloud fraction, and time series of cloud cover.</p> <p><a href="https://zenodo.org/api/files/2f5a17d9-6cb1-4769-88cd-65fd8efd0225/qr_cross.mp4">qr_cross.mp4</a>: Time evolution of cross section of rain water mixing ratio (q<sub>r</sub>) of SDM and SN14 simulation.</p>
Numerical response of predator to prey: Dynamic interactions and population cycles in Eurasian lynx and roe deer
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Evaluation of large language model chatbot responses to psychotic prompts: numerical ratings of prompt-response pairs
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Numerical response of predators to large variations of grassland vole abundance and long-term community change
<p>Voles can reach high densities with multi-annual population fluctuations of large amplitude, and they are at the base of predator communities in Northern Eurasia and Northern America. This status places them at the heart of management conflicts wherein crop protection and health concerns are often raised against conservation issues.<strong> Here, a 20-year survey describes the effects of large variations in grassland vole populations on the densities and the daily theoretical food intakes (TFI) of vole predators based on roadside counts.</strong> Our results show how the predator community responded to prey variations of large amplitude and how it reorganized with the increase in a dominant predator, here the red fox, which likely negatively impacted hare, European wildcat and domestic cat populations. This population increase did not lead to an increase in the average number of predators present in the study area, suggesting compensations among resident species due to intra-guild predation or competition. Large variations in vole predator number could be clearly attributed to the temporary increase in the populations of mobile birds of prey in response to grassland vole outbreaks. Our study provides empirical support for more timely and better focused actions in wildlife management and vole population control, and it supports an evidence-based and constructive dialogue about management targets and options between all stakeholders of such socio-ecosystems.</p> <p>The data set includes:</p> <p><b>S1 kml file</b>. Location of the study area (can be dropped in a Google Earth window or read from a GIS)</p> <p><b>S2 Excel file.</b> Road-side counts (sheet 1) and list of species observed (sheet 2)</p> <p><b>S3 Excel file.</b> Small mammal data</p> <p><b>S4 Excel file.</b> Data for computing theoretical daily food intakes</p> <p> </p>
Figure 2 in Functional and numerical responses of the predatory mite Amblyseius aerialis (Acari: Phytoseiidae) toAceria guerreronis (Acari: Eriophyidae)
Figure 2 Mean number of eggs laid byAmblyeius aerialis females provided with different densities
Figure 3 in Functional and numerical responses of the predatory mite Amblyseius aerialis (Acari: Phytoseiidae) toAceria guerreronis (Acari: Eriophyidae)
Figure 3 Average oviposition rates (eggs/female ± SE) byAmbyseius aerialis females given different
A Numerical Study of Tropical Cyclone and Ocean Responses to Air-sea Momentum Flux at High Winds
<p>The simulation data output from FIO-AOW for tropical cyclone study</p>
Identifying conceptual neural responses to symbolic numerals
<p>The goal of measuring conceptual processing in numerical cognition is distanced by the possibility that neural responses to symbolic numerals are influenced by physical stimulus confounds. Here, we targeted conceptual responses to parity (even <em>vs. </em>odd), using electroencephalographic (EEG) frequency-tagging with a symmetry/asymmetry design. Arabic numerals (2–9) were presented at 7.5 Hz in 50-s sequences; odd and even numbers were alternated to target differential, "asymmetry" responses to parity at 3.75 Hz (7.5 Hz/2). Parity responses were probed with four different stimulus sets, increasing in intra-numeral stimulus variability, and with two control conditions comprised of non-conceptual numeral alternations. Significant asymmetry responses were found over the occipitotemporal cortex to all conditions, even for the arbitrary controls. The large physical-differences control condition elicited the largest response in the stimulus set with the lowest level of variability (1 font). Only in the stimulus set with the highest level of variability (20 drawn, colored exemplars/numeral) did the response to parity surpass both control conditions. These findings show that physical differences across small sets of Arabic numerals can strongly influence, and even account for, automatic brain responses. However, carefully designed control conditions and highly variable stimulus sets may be used towards identifying truly conceptual neural responses.</p>
Topography Response to Horizontal Slab Tearing and Oblique Continental Collision: 3D Thermomechanical Numerical Modelling
<p>This repository submission provides the 3D thermo-mechanical numerical modelling code I3ELVIS developed by the workgroup of Prof. Taras Gerya (ETH Zürich). The code is based on finite-difference and marker-in-cell methods (Gerya & Yuen, 2003, 2007; Gerya, 2019). The code solves the momentum, continuity, and energy equations on the fixed Eulerian grid and transports the physical properties by Lagrangian markers using the velocity field. The code also accounts for the major phase transitions in the Earth’s mantle and internal heat sources arising from adiabatic, radiogenic, and frictional heating. Partial melting and melt extraction processes are neglected for the sake of simplicity. A more detailed description of the code, including the governing equations and the adopted rheological model can be found in various published literature (Andrić-Tomašević et al., 2023; Boonma et al., 2023; Maiti et al., 2024). Interested users are recommended to contact Prof. Taras Gerya (taras.gerya@erdw.ethz.ch). </p> <p>The repository also provides input and output files to run and reproduce model results and manuscript figures of Maiti et al., 2024 (JGR Solid Earth). Paraview States for processing .vtr result files and visualizing the model output data. Matlab script to visualise the topography from .grd files. </p>
Identifying conceptual neural responses to symbolic numerals
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Numerical response of predators to large variations of grassland vole abundance and long-term community change
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Generation-dependent functional and numerical responses of Neoseiulus californicus (Phytoseiidae) long-term reared on thorn apple pollen
<p>Raw data of Functional and numerical response of N. califrnicus on T. urticae in different generaaations</p>
Plan-form evolution of drainage basins in response to tectonic changes: Insights from experimental and numerical landscapes
<p>The following data contains raw DEMs from the experiment in the simulations referred to in the mentioned manuscript, as well as MATLAB scripts (and legend) used for the analysis of the data.</p> <p>Experiment DEMs are named after the time they were acquired within the experiment, i.e., 4.tif is the DEM of the landscape after a duration of 4 hours of experiment.</p> <p>In MATLAB scripts, the following legend is used:</p> <p>E = Experiment</p> <p>SL = Low concavity simulation</p> <p>SH = High concavity simulation</p> <p>HL = Hack's law analysis</p> <p>A = Asymmetry analysis</p> <p>C = Chi analysis</p> <p>CP = Chi prime analysis</p> <p>ADCP = Across-divide chi prime analysis</p> <p>DAI = Divide asymmetry index analysis</p> <p>CreateStructuredGrid = Function that generates GRIDobj from ASCII files</p> <p>Num_DEM_Load = Script that generates GRIDobj from ASCII files</p>
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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.