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6,498 results for “Physicality”
Two-fluid Physical Modeling of Superconducting Resonators in the ARTEMIS Framework
<p>Input files, data files and scripts to replicate results in "Two-fluid Physical Modeling of Superconducting Resonators in the ARTEMIS Framework" (Jambunathan et el.)</p> <p> </p> <p>Please direct any questions to the corresponding author, Revathi Jambunathan (rjambunathan [at] lbl.gov)</p> <p>Artemis simulations were run using the development branch of artemis <a href="https://github.com/ECP-WarpX/artemis">https://github.com/ECP-WarpX/artemis </a></p> <p>Some simulations in this paper were performed with different commit hashes of artemis and amrex, however, the input files should still work with the most recent development branch of artemis.</p> <p>In the attached tar file, the input files, data, and scripts used to analyse simulation results shown in Figures 1, 2, 3, 4, and 6 of the paper are provided. To run the simulations on perlmutter GPUs, the code is compiled with USE_GPU=TRUE and USE_LLG=FALSE</p> <p> </p> <p> </p> <p> </p>
Supporting data for: Physical controls and ecological implications of the timing of the spring phytoplankton bloom on the Newfoundland and Labrador shelf
<p>The Newfoundland and Labrador (NL) shelf and the Grand Banks of Newfoundland have been known as iconic fishing areas for centuries. In such areas with seasonal sea ice coverage, the timing of the spring bloom has been linked to sea ice melting, which stratifies the water column and promotes favorable conditions for phytoplankton to grow and accumulate. With sea ice gradually disappearing, we revisited the physical drivers controlling the initiation of the spring bloom in the region. We found that the timing of the phytoplankton bloom on the Grand Banks corresponds to the timing of ocean re-stratification following winter mixing. We also found that large-scale climate indicators are good proxies for the timing of the bloom and the abundance of<em> Calanus finmarchicus</em>, a key zooplankton species for the ecosystem. By revealing links between physical and biological processes, this work paves the way for an improved ecosystem approach to fisheries management.</p>
Reproducibility Package for Predictive Limitations of Physics-Informed Neural Networks in Vortex Shedding
<p>This archive contains the repro-pack for the paper at <a href="http://github.com/barbagroup/jcs_paper_pinn">https://github.com/barbagroup/jcs_paper_pinn</a>.</p>
Reproducibility Package (Raw Data) for Predictive Limitations of Physics-Informed Neural Networks in Vortex Shedding
<p>Raw data results from the simulations using PINN methods and PetIBM for the paper at: https://github.com/barbagroup/jcs_paper_pinn</p>
International E- Conference on "Recent Trends in Chemical Science, Physical Science, Life Science and Computer Technology (ICRTCPLCT–2022)
<p><strong>International E- Conference</strong> on “Recent Trends in Chemical Science, Physical Science, Life Science and Computer Technology (ICRTCPLCT–2022)” By Anjuman Islam Janjira Degree College of Science, Murud on <strong>29<sup>th</sup> March 2022.</strong></p>
Disentangling the relative contributions of factors determining seed physical defence: a global-scale data synthesis
<p>Physical defence investment in seeds varies greatly among plant species and is affected by many potential factors. However, the relative importance of influential factors has not yet been systematically evaluated. Here, we compiled a global database of the seed coat ratio (SCR), a proxy of seed physical defence, for 1,357 species. Using partial R<sup>2</sup> calculation that can estimate the reduction in explained variance of a given factor after removing it from a full model containing all the other factors, we disentangled the relative contributions of seven factors commonly used to explain variation in the SCR. Seed desiccation response and phylogeny explained a large amount of SCR variation among species, with a partial R<sup>2</sup><sub>lik</sub> value of 13.31% and 10.62%, respectively. Seed dormancy, plant growth form, seed mass, climate region, and fruit type showed little power to explain SCR variation, although they are often cited in the literature as potential factors. Our results highlight the important roles of seed desiccation response and phylogeny in shaping interspecific variation in the SCR. These findings provide a detailed interpretation of both evolutionary and ecological patterns underlying the physical defence mechanisms and allocation strategies of plant seeds.</p>
Dataset for the SIAM MPI23 project "Model inversion for complex physical systems using low-dimensional surrogates"
<p>This dataset contains 20,000 synthetic simulations of a simplified two-dimensional confined aquifer model of the Hanford Site. The inputs are the Kosambi-Karhunen-Loève expansion (KKLE) coefficients of the input log-transmissivity field. The outputs are the corresponding stationary pressure responses observed at 323 observation wells. Also included are the arrays necessary to reconstruct the log-transmissivity inputs from the KKLE coefficients.</p>
Replication package of "How Do Deep Learning Faults Affect AI-Enabled Cyber-Physical Systems in Operation? A Preliminary Study Based on DeepCrime Mutation Operators"
<p>Cyber-Physical Systems (CPSs) combine digital cyber technologies with physical processes. As in any other software system, in the case of CPSs, the use of Artificial Intelligence (AI) techniques in general, and Deep Neural Networks (DNNs) in particular, is contantly increasing. While recent studies have considerably advanced the field of testing AI-enabled systems, it has not yet been investigated how different Deep Learning (DL) bugs affect AI-enabled CPSs in operation. This work-in-progress paper presents a preliminary evaluation on how such bugs can affect CPSs in operation by using a mobile robot as a case study system. For that, we generated DL mutants by using operators proposed by Humbatova et al., which are operators based on real-world DL faults. Our preliminary investigation suggests that such bugs are more difficult to detect when they are deployed in operation rather than when testing their DNN in an off-line setup, which contrast with related studies.</p> <p> </p> <p>This repository provides the replication data employed in our study.</p>
DSC for pemetrexed, PEG, GABA, physical mixture of pemetrexed with PEG, physical mixture of pemetrexed with GABA, pemetrexed-PEG conjugate, and pemetrexed-GABA conjugate
<p>This data represent the figures of DSC for pemetrexed, PEG, GABA, physical mixture of pemetrexed with PEG, physical mixture of pemetrexed with GABA, pemetrexed-PEG conjugate, and pemetrexed-GABA conjugate</p>
Dataset for: Novel Physics Informed-Neural Networks for Estimation of Hydraulic Conductivity of Green Infrastructure as a Performance Metric by Solving Richards-Richardson PDE
<p><strong>Based on the Github respostitory: <a href="https://github.com/Khadrawi/Physics-Informed-Neural-Networks-for-Estimation-of-Hydraulic-Conductivity/tree/main">https://github.com/Khadrawi/Physics-Informed-Neural-Networks-for-Estimation-of-Hydraulic-Conductivity/tree/main</a></strong></p> <p>This repository contains the data used for the paper "Novel Physics Informed-Neural Networks for Estimation of Hydraulic Conductivity of Green Infrastructure as a Performance Metric by Solving Richards-Richardson PDE"<br> You'll find the csv files for the three simulated (Hydrus 1D) scenarios explained in the paper. These files were processed from the 'Nod_Inf.out' files to csv format.</p> <p><strong>Acknowledgments</strong><br> The publicly available data used for this study (scenario 1 & 2) as well as the code for the second PINN architecture (based on Dr. Maziar Raissi PINN code) and the code used to transform “Nod_inf.out” files from Hydrus 1D to csv files created by Dr. Toshiyuki Bandai and Dr. Teamrat A. Ghezzehei were helpfulfor this study.</p>
Data used in the physical-biogeochemical model of Danjiangkou Reservoir
<p>The date set includes meteorological, hydrological, water quality, and organic carbon loading data obtained in 2009 for the Danjiangkou Reservoir in China. The data were used to set the boundary conditions of the physical-biogeochemical model, which was adopted to simulate reservoir methane dynamics in the Danjiangkou Reservoir.</p>
Quantitative interpretation of the physical processes associated with the interannual variation of South Asian summer monsoon
<p>This is the CFRAM-A data of "Quantitative interpretation of the physical processes associated with the interannual variation of South Asian summer monsoon". The data includes 10 years: 1997, 1999, 2000, 2001, 2008, 2009, 2013, 2015, 2018, and 2019. Each year includes two "nc" data, one is the partial temperature, the other is the total temperature.</p>
BubbleML: A Multi-Physics Dataset and Benchmarks for Machine Learning
<p>In the field of phase change phenomena, the lack of accessible and diverse datasets suitable for machine learning (ML) training poses a significant challenge. Existing experimental datasets are often restricted, with limited availability and sparse ground truth data, impeding our understanding of this complex multi-physics phenomena. To bridge this gap, we present the <a href="https://github.com/HPCForge/BubbleML">BubbleML</a> Dataset which leverages physics-driven simulations to provide accurate ground truth information for various boiling scenarios, encompassing nucleate pool boiling, flow boiling, and sub-cooled boiling. This extensive dataset covers a wide range of parameters, including varying gravity conditions, flow rates, sub-cooling levels, and wall superheat, comprising 79 simulations. BubbleML is validated against experimental observations and trends, establishing it as an invaluable resource for ML research. Furthermore, we showcase its potential to facilitate exploration of diverse downstream tasks by introducing two benchmarks: (a) optical flow analysis to capture bubble dynamics, and (b) operator networks for learning temperature dynamics. The BubbleML dataset and its benchmarks serve as a catalyst for advancements in ML-driven research on multi-physics phase change phenomena, enabling the development and comparison of state-of-the-art techniques and models.</p>
Data for A Physical Model for the Observed Inverse Energy Cascade in Typhoon Boundary Layers
<p>This repository contains dataset for the paper entitled "A Physical Model for the Observed Inverse Energy Cascade in Typhoon Boundary Layers". The magnitude of inverse energy cascade flux is revised in version 2.0 according to Xia et al. (2009) (https://doi.org/10.1063/1.3275861). </p>
Dataset for "Data-driven empirical conductance relations during auroral precipitation using incoherent scatter radar and all sky imagers" JGR-Space Physics
<p><strong>Dataset for "Data-driven empirical conductance relations during auroral precipitation using incoherent scatter radar and all sky imagers" JGR-Space Physics.</strong></p> <p>Processed ACF level data can be provided by contacting me.</p> <p><strong>DOI of the publication:</strong></p> <p><strong>README file:</strong></p> <p>PFISRInversions_ASI_v1.1_Fang_01042023_v08162023.h5</p> <p>HallConductance: Altitude integrated Hall Conductance from 85-150 km altitude, [ntime], mho<br> PedersenConductance: Altitude integrated Pedersen Conductance from 85-150 km, [ntime], mho<br> EnergyFlux: Energy flux after integrating the differential number flux, [ntime], W/m^2<br> AverageEnergy: Average Energy after integrating the differential number flux, [ntime], eV<br> Measured_ElectronDensity: measured electron density from PFISR, [ntime, naltitude], #/m^3<br> Modeled_ElectronDensity: modeled electron density produced by the MEM version, [ntime,naltitude], #/m^3<br> UnixTime: time in seconds since 1970-01-01 00:00:00 UT, [ntime], seconds<br> NumberFlux: differential number flux, [ntime, nenergy], #/m^2 s^-1 eV^-1<br> EnergyGrid: energy grid spanning 1 keV - 100 keV in 25 steps, [nenergy], eV<br> ASIStatus: auroral identification number, [ntime], no units<br> 1: Discrete aurora<br> 2: Diffuse aurora<br> 3: Pulsating aurora<br> 8: Unidentified aurora<br> -1: data that was flagged as unsuitable:<br> either TEC was too low (no auroral E-region possibly associated with red aurora),<br> the modeled electron density was not consistent at all with the observed electron density (bad fit)</p> <p>ASIYEAR-Final_date.xlsx<br> This is an excel file that contains the original auroral image identification near the zenith direction<br> We used all sky imager videos located: http://optics.gi.alaska.edu/realtime/data/MPEG/PKR_DASC_256/<br> These files were converted into python pickle files and used internally for the rest of the investigation.</p> <p>The columns corresponds to days, and the rows correspond to time in UT as decimal hours:<br> The ASI Key is the following:<br> # 1 Discrete<br> # 2 Diffuse<br> # 3 Pulsating<br> # 4 Cloudy/Clear<br> # 5 Moon<br> # 6 Possible faint Aurora with moon out<br> # 7 Substorm Breakup<br> # 8 Cloudy with aurora (can't make out type)</p>
Glacier catalogue for IGM physics-informed deep-learning emulator pretraining
<p>This dataset was created with the iceflow glacier model CfsFlow to generate glacier extent and retreat in the Alps and New zealand with the goal to generate realistic and diverse glacier states for pretraining the physics-informed deep-learning emulator of IGM (https://github.com/jouvetg/igm).</p> <p>The data consists of distributed surface topography (usurf) and ice thickness (thk) of 8 snapshots of 37 glaciers in different stages (advance and retreat). The data is organized glacier-wise: each folder corresponds to one glacier, which contains a unique NetCDF file with 2D distributed raster data of surface elevation and ice thickness.</p>
Effectiveness of self-management of medication and self-monitoring of blood pressure, diet, and physical exercise on blood pressure in patients with poorly controlled hypertension (MEDICHY study): randomized and controlled trial
<p>Dataset study medichy ISRCTN144433778</p>
Data-set for "Reliability and operation cost of underdamped memories during cyclic erasures" (Research Article, No. apxr.202300074) to Advanced Physics Research.
<p><strong>Dataset for the article: "Reliability and operation cost of underdamped memories during cyclic erasures" (Research Article, No. apxr.202300074) to Advanced Physics Research. </strong></p> <ul> <li><strong>FigX.fig: </strong>Matlab format figure source used to draw all of the plots of the article, embedding the all data.</li> <li><strong>evolution_K.m:</strong> Matlab code file that implement the Repeated Erasure model and compute the success rate of the erasure from the temperature evolution computed from the model.</li> <li><strong>Model_Landauer_enchaine.m:</strong> Matlab code file. Function which uses the SE Model to calculate the evolution of all the energy quantities (average) during step 1 and step 2 starting at an initial temperature CI, and also renders the final temperature conditions to be used iteratively in the RE Model.</li> <li><strong>Toy_Model.m: </strong>Theoretical toy model for explaning the Landauer repeted erasures temperature evolution.</li> <li><strong>Analysis_repeated_erasure.m:</strong> Matlab code file to treat raw data .mat files of 45 repeated erasures to compute the energy, work and heat evolution. The average is done on the full raw data samples corresponding to thousands of 45 repeated erasures.</li> <li><strong>XmsRawData.zip:</strong> Raw data of 45 repeated erasures of duration X ms each. Those Matlab data files are meant to be analysed with the Analysis_repeated_erasure code. Among the usefull variables contained in those files we identifiy :</li> </ul> <ol> <li>V: applied protocol, position of center of the wells in nm</li> <li>x: position signal in nm</li> <li>sigma_m: calibration of the standard deviation used by the feedback</li> <li>x0: threshold of the virtual potential</li> <li>tau1: duration of step1</li> <li>tau2: duration of step 2</li> <li>tau4: half resting time between step1 and step2</li> <li>X1: initial half distance between the wells</li> <li>dt: acquisition sampling time</li> </ol> <p><br> </p> <p> </p>
Artifact Demonstration Video for Negotiating Autonomy in Cyber-Physical Systems at Run Time
<p>This artifact demonstration video shows a novel approach to dynamically negotiate autonomy in Cyber-Physical Systems (CPS). As CPS become increasingly integrated into our daily lives, sharing autonomy between human operators and autonomous agents within these systems becomes a critical issue. Business case developers also face the design and integration of CPS. Our research presents a novel architecture and methodology that seamlessly enables this sharing of autonomy. We aim to encourage business case developers to consider autonomic CPS. The video provides an insightful overview of our approach and highlights its practicality. It illustrates how our system enables business case developers to back CPS with a mechanism to negotiate and share autonomy at runtime. Our artifact demonstration video is not only a good opportunity to visually grasp the concept of negotiating autonomy in CPS, but also to experience the practical implementation of this approach. It serves as a valuable resource for researchers, engineers, and anyone interested in understanding the future of sharing autonomy in the context of cyber-physical systems.</p>
Physical oceanography during RV DONG FANG HONG 2 cruise NORC2017
<p>During the RV DONG FANG HONG 2 cruise to the northern South China Sea in August 2017, a SeaBird SBE 911Plus CTD was deployed at nine different stations, and various physical parameters were measured. The measured parameters and the respective sensor details were: Pressure (Paroscientific, Digiquartz), Temperature (2 x SBE 3, ITS-90 °C), Conductivity (2x SBE 4), and Turbidity (Seapoint). The NTUs were correlated by linear regression with the mass of SPM obtained from filtered water samples (n=25) and the following relationship: SPM=1.03×NTU.</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.