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37 results for “parameter interaction”
Interaction of waves with idealized high-relief bottom roughness, model parameters and code
Interactions between waves and large bottom roughness were investigated using Large Eddy Simulations of oscillatory flow over infinite hemisphere arrays. Simulations were made using the open source computational fluid dynamics code OpenFOAM. Wave amplitude, period, and hemisphere spacing were varied to investigate the dependence of kinematics and dynamics on dimensionless parameters, including the Keulegan-Carpenter number (KC), the ratio of wave orbital excursion to roughness element size. This archive includes the input files and user written routines to reproduce the simulations in the paper. Yu, X., J. H. Rosman, and J. L. Hench, 2018. Interaction of waves with idealized high-relief bottom roughness, Journal of Geophysical Research (Oceans), doi:10.1002/2017JC013515.
Advanced PySPAM: An Infrastructure to Constrain Underlying Interacting Galaxy Parameters Synthetic Results
<p>This database contains the results for the Chapter 3 of DOR's thesis. For a full description of these results and the way they were built, please see <em>Link to be added on publication</em>.</p> <p>The aim of this Chapter was to use MCMC methods with a fast, efficient simulation algorithm (APySPAM) to constrain the underyling parameters of observed interacting galaxy systems. This algorithm used a Chi-Squared distance minimisation between morphology distributions of observed and simulated images to constrain 13 underlying parameters of galaxy interaction. We applied our algorithm to to 50 of the 62 systems described in <a href="https://ui.adsabs.harvard.edu/abs/2016MNRAS.459..720H/abstract">Holincheck et al. (2016).</a></p> <p>We opted to use the Holincheck et al. sample as the underlying parameters of these systems had already been constrained using a Citizen Science project named <a href="https://mergers.galaxyzoo.org/">Galaxy Zoo: Mergers</a>. This gave us a ground truth to which compare our constraints to. We created synthetic observations of each image, and then ran our MCMC over them, achieving constraint across the sample and parameter space. However, when applied to observational data (we opted to use SDSS images of these systems) we are unable to constrain the full parameter space. This is particularily true of the orientations of the interacting system and their relative sizes.</p> <p>Exploring using velocity information in our constraints find that we improve almost all our constrains considerably. Therefore, adding in spectroscopic information to this method could drastically improve it. The main limitation of this approach, however, is computation time with each system taking approximately 20 hours on a well parallelised HPC to converge. Alternatives to improve performance lie in simulation based inference (SBI, an introduction can be found <a href="https://arxiv.org/pdf/2009.08459">here</a>) or including the use of GPUs (such as done by NVIDEA in fluid dynamics <a href="https://developer.nvidia.com/blog/ai-powered-simulation-tools-for-surrogate-modeling-engineering-workflows-with-siml-ai-and-nvidia-modulus/">here</a>)</p> <p>The results are portrayed as corner plots, with contour plots showing the distribution of likelihoods found in each MCMC run and the histograms on the side showing the marginalised posterior distributions.</p>
Developing and Benchmarking Sulfate and Sulfamate Force Field Parameters via Ab Initio Molecular Dynamics Simulations to Accurately Model Glycosaminoglycan Electrostatic Interactions
<p>To cite and for more details: Riopedre-Fernandez et al. <em>J. Chem. Inf. Model.</em> <strong>2024</strong>, 64 (18), 7122–7134. DOI: <a href="https://doi.org/10.1021/acs.jcim.4c00981">https://doi.org/10.1021/acs.jcim.4c00981</a></p> <p>The dataset includes molecular dynamics simulations of sulfated saccharides and their sulfated analogs in the presence of calcium cations in aqueous solution. Several force field parameter sets were compared (CHARMM36, GLYCAM06, AMOEBA, Drude) and new have been developed (prosECCo75 and GLYCAM-ECC75).</p> <p>The uploaded files contain the following simulation input files or/and simulation trajectories:</p> <p>1) Sulfated_Molecules_Umbrella_Sampling_AIMD: Umbrella sampling ab initio molecular dynamics simulations of calcium-methylsufate and calcium N-methylsulfamate ion pairs in water.</p> <p>2) Sulfated_Molecules_Umbrella_Sampling_FFMD: Umbrella sampling force field molecular dynamics simulations of calcium-methylsufate and calcium N-methylsulfamate ion pairs in water.</p> <p>3) Sulfated_Molecules_AWH_FFMD: Accelerated weight histogram force field molecular dynamics simulations of calcium interacting with both methylsufate and N-methylsulfamate in water.</p> <p>4) Disaccharides_FFMD: Unbiased force field molecular dynamics simulations of calcium-sulfated disaccharide aqueous solutions.</p> <p>UPD. Version 2.0 has updated one of the disaccharide-containing simulations (GLYCAM06, N-sulfation) due to incorrect calcium LJ parameters in the original upload.</p>
Understanding the Influence of Parameter Value Uncertainty on Climate Model Output: Developing an Interactive Web Dashboard
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Fig. 2. A B in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 2. A B-spline solid is a closed object whose shape can be adjusted by moving control points (dark points) that deforms the local portion of the object near the control point. The initial cylindrical shape in A is adjusted (B and C) by pulling out the points at the ends and drawing the points in the middle closer to the axis.
Fig. 1 in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 1. Body segments can be created using mass objects of different density and shape. Mass objects can be collected into mass sets to calculate their combined inertial properties; the most inclusive Tyrannosaurus mass set (whole body) is outlined here, as well as the trunk segment and its embedded mass objects.
Fig. 1 in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 1. Body segments can be created using mass objects of different density and shape. Mass objects can be collected into mass sets to calculate their combined inertial properties; the most inclusive Tyrannosaurus mass set (whole body) is outlined here, as well as the trunk segment and its embedded mass objects.
Data from: Genetic parameters in subtropical pine F1 hybrids: heritabilities, between-trait correlations and genotype-by-environment interactions
Growth and stem straightness traits of 29 Pinus caribaea var. hondurensis × Pinus tecunumanii (PCH × PTEC) and 26 P. caribaea var. hondurensis × Pinus oocarpa (PCH × POOC) hybrid pair-crosses plus a total of 16 intraspecific families were assessed at ages 5, 8 and 15 years from planting at two sites. The PCH × PTEC hybrid was the most productive, yielding 37 % more than a Pinus elliottii local control and was 21 % superior to either parental species in DBH growth. PCH × POOC hybrid was, on average, 16 % superior to either parental species for DBH. Narrow-sense heritability estimates were low to moderate for growth traits (average of 0.27) and stem straightness (0.16). The estimated additive genetic correlations between growth traits and ages within traits were high (>0.8) and positive, providing confidence in early selection based on diameter at breast height. The high proportion of estimated additive genetic variance compared to dominance variance in the F1 pine hybrids suggests that breeding strategies that maximize the use of additive genetic variance may be effective. The ranking of the 11 PCH parents based on general hybridizing ability predictions (estimated breeding values as hybrids) was somewhat inconsistent between PTEC and POOC hybrid crosses for all traits (r 9 d.f. = 0.38–0.45; p ∼0.15–0.25). There was no evidence of practically important G × E interaction for the hybrids except for PCH × PTEC height growth. This study suggests that a single, multi-hybrid breeding population seems appropriate in Zimbabwe if the trial sites are representative of the planting target zone.
United Atom Parameters for United Atom Multiscale Modelling Of Bio-Nano Interactions Of PEG Coated Nanoparticles
<p>Short-range surface adsorption potentials of carbohydrates, lipid fragments, and amino acid side chains in tabulated form.</p><p>Recovered from radial distribution functions</p><p>Force Fields: adapted CHARMM36.</p><p>Material: PEG</p><p>Status: updated on November 6, 2023</p>
United Atom Parameters for United Atom Multiscale Modelling Of Bio-Nano Interactions Of Zero-Valent Silver Nanoparticles
<p>Short-range surface adsorption potentials of carbohydrates, lipid fragments, and amino acid side chains in tabulated form.</p> <p>Calculated via AWT-MetaD (Gromacs/Plumed).</p> <p>Force Fields: INTERFACE/CHARMM36.</p> <p>Material: zero-valent silver</p>
United Atom Parameters for United Atom Multiscale Modelling Of Bio-Nano Interactions Of Zero-Valent Copper Nanoparticles
<p>Short-range surface adsorption potentials of carbohydrates, lipid fragments, and amino acid side chains in tabulated form.</p> <p>Calculated via AWT-MetaD (Gromacs/Plumed).</p> <p>Force Fields: INTERFACE/CHARMM36.</p> <p>Material: zero-valent copper</p>
Waveform data for the manuscript "Varying Shear Wave Splitting Parameters Suggest Interaction between Lithosphere and Asthenosphere in Arxan-Chaihe Volcanic Field, NE China"
<p>The folder contains the seismic waveform data (in SAC format) used for shear wave splitting measurements in this study, which has been filtered with corner frequencies of 0.02–1.00 Hz. </p>
United Atom Parameters for United Atom Multiscale Modelling Of Bio-Nano Interactions Of Citrate Trianion Stabilized Nanoparticles
<p>Short-range surface adsorption potentials of carbohydrates, lipid fragments, and amino acid side chains onto Cit(3-) stabilized surfaces (in tabulated form).</p> <p>Recovered from radial distribution functions</p> <p>Force Fields: adapted CHARMM36.</p> <p>Material: CIT(3-)</p>
United Atom Parameters for United Atom Multiscale Modelling Of Bio-Nano Interactions Of Zero-Valent Gold Nanoparticles
<p>Short-range surface adsorption potentials of carbohydrates, lipid fragments, and amino acid side chains in tabulated form.</p> <p>Calculated via AWT-MetaD (Gromacs/Plumed).</p> <p>Force Fields: INTERFACE/CHARMM36.</p> <p>Material: zero-valent gold</p>
OPEPv7 Force Field: Parameters of Non-Bonded Interaction Potentials
<p>Parameters defining interaction potentials between amino-acid beads in the OPEPv7 force field.</p> <p>CA stands for the C<span class="math-tex">\(\alpha\)</span> bead of any residue except for glycine, which has a dedicated bead type, labeled as CAG. The side-chain beads are each named after the corresponding residue.</p> <p>“12-6” represents the Lennard-Jones potential, in which case the <span class="math-tex">\(C_{12}\)</span> and <span class="math-tex">\(C_{6}\)</span> parameters are given in the table. “OPEP” denotes the OPEP side-chain potential with an attractive term while “OPEM” stands for the purely repulsive variant (see <a href="https://doi.org/10.1039/C4CS00048J">Chem. Soc. Rev., 2014,43, 4871-4893</a>). In both cases, the <span class="math-tex">\(r^{0}_{ij}\)</span> and <span class="math-tex">\(\varepsilon_{ij}\)</span> parameters are given.</p> <p>The parameters of the default OPEPv7 force field are listed in the <strong>OPEPv7.dat file</strong>. The <strong>OPEPv7_LJ_fit.dat</strong> file provides a simplified version of the force field where the “OPEP” potentials are replaced by Lennard-Jones fits.</p>
Parameter files for 3-D plume-slab interaction models
<p>Parameter files used in ASPECT for the four models presented in "Plume-driven subduction termination in 3-D mantle convection models"</p>
Gene-diet Interactions on Body Weight Regulation and Lifestyle Parameters.
ClinicalTrials.gov study NCT04699448. IPD Sharing: NO. Countries: 1. Publications: 2.
Study to Evaluate the Potential Pharmacokinetic Interaction and Pharmacodynamic Effects on Renal Parameters of Bumetanide (1mg) and Dapagliflozin (10 mg) When Co-administered in Healthy Subjects
ClinicalTrials.gov study NCT00930865. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Gut to Brain Interaction in Autism. Role of Probiotics on Clinical, Biochemical and Neurophysiological Parameters
ClinicalTrials.gov study NCT02708901. IPD Sharing: NO. Countries: 1. Publications: 5.
Data from: Genetic parameters in subtropical pine F1 hybrids: heritabilities, between-trait correlations and genotype-by-environment interactions
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