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11 results for “Monte-Carlo”
Combining Analytical Modeling, Realistic Simulation and Real Experimentation for the Optimization of Monte-Carlo Applications on the European Grid Infrastructure
<p>Data and scripts used to generate figures presented in the paper "Combining Analytical Modeling, Realistic Simulation and Real Experimentation for the Optimization of Monte-Carlo Applications on the European Grid Infrastructure" submitted to the Future Generation Computer Systems Journal.</p>
The Brain and Propranolol Pharmacokinetics in the Elderly-Figure 1.(a)Results of the Monte-Carlo simulations to describe pharmacokinetics of young patients with validation from the Taegtmeyer 2014 publication(Taegtmeyer et al., 2014)
<p>Propranolol has been found to be therapeutically effective, to obtain a clinical response by<br> beta-adrenoceptor blockade, at plasma levels of greater than 20 ng/mL(Coltart et al., 1971;<br> Frishman, 1988; Johnsson and Regàrdh, 1976). Thus, to display the data, we used highlighted<br> plasma concentration where the pharmacokinetic curve falls below 20ng/mL threshold for<br> therapeutic efficacy in the patient’s plasma.</p>
Figure 2.(a)Results of the Monte-Carlo simulations describing a population of elderly patients after a single oral dose of propranolol.-The Brain and Propranolol Pharmacokinetics in the Elderly
<p>In effort to identify the recommended Propranolol dosage for elderly patients, we identified<br> the patient package inserts from the Food and Drug Administration (FDA) Inderal label, who<br> manufacture propranolol. Based from FDA Wyeth Propranolol label, for dosing in the geriatric<br> population,the label states that there were not sufficient numbers of clinical study participants who<br> were 65-years and older to properly determine the difference in response young and elderly<br> patients.</p>
Figure 4. Simulation (Monte-Carlo, n=200) results elderly patients taking a 10mg oral dose resulting in similar Cmax, maximum plasma concentration, to the young patients taking a 40mg oral dose. The dotted lines illustrate the 10th and 90th percentiles of plasma levels of the elderly population with a 10mg oral administration of propranolol.-The Brain and Propranolol Pharmacokinetics in the Elderly
<p>Thus, the package insert (see 1) recommends clinicians start at the lower end of the dosing<br> range, without further details.<br> Similarly, Pfizer manufactures Inderal® LA (Propranolol HCI), which is the long-acting<br> form of propranolol and their package insert (see 2) states, “There is no information available for<br> elderly patients.” Though the kinetics for the long-acting formdiffers from the standard form,<br> manufactured by Wyeth, we would suspect a 10mg dose for the elderly would achieve a similar<br> maximum plasma concentration (Cmax) to that of the younger patient cohort.This 10mg, which is<br> 25% of the original 40mg, dosing schedule is based on our simulations at 10mg in the geriatric<br> population.</p>
Derived data supporting the analysis of surface albedo changes from Mars 2020 observations: Probabilistic distribution of the Amplitude Spectral Densities of Supercam microphone recordings and Monte-Carlo dust devil simulations.
<p>These files contain derived data used in the analysis submitted for publication in Journal of Geophysical Research: Planets, entitled "Dust Lifting Through Surface Albedo Changes at Jezero Crater, Mars" by Vicente-Retortillo et al. The article was initially submitted on November 14, 2022, and the revised version on March 1, 2023.</p> <p>Files include the derived data and information needed to generate Figures 4 (Microphone_Data.mat and Plot_ASD_from_Microphone_Data) and 6 (remaining files) of the article.</p>
Data for: Thermodynamics of a dilute Bose gas: a path-integral Monte-Carlo study
<p>Path-integral Monte-Carlo results of the thermodynamics of a homogeneous dilute Bose gas. Data used to plot the figures for internal energy, pressure, isothermal compressibility and contact parameter.</p>
The average transmitted wave in random particulate materials: Monte-Carlo data
<p><em>A Julia repo designed to replicate and validate wave scattering from a plate filled with randomly distributed particles. The code allows you to explore the effects of different parameters such as scatterer type, volume fraction, and frequency on wave scattering behavior.</em></p> <ol> <li> <p>Make sure you have Julia v1.6.7 or later, installed on your system.</p> </li> <li> <p>Open your preferred Julia environment (REPL or Jupyter Notebook).</p> </li> <li> <p>Navigate to the downloaded code directory within Julia.</p> </li> <li> <p>Load the necessary packages included in loaddata.jl.</p> </li> <li> <p>To load the data, modify the code's parameters using the provided flags:</p> </li> </ol> <p> </p> <ul> <li><code><code>is_sound_soft</code><span>: Set to </span><code>true</code><span> for sound-soft scatterers, and </span><code>false</code><span> for hard scatterers.</span></code></li> <li><code><code>is_two_wavenumbers</code><span>: Set to </span><code>true</code><span> for two wavenumbers, and </span><code>false</code><span> for three wavenumbers.</span></code></li> <li><code><code>is_low_freq</code>: Set to <code>true</code> for low frequency ka = 0.04, and <code>false</code> for frequenc ka = 0.36 or ka = 0.62.</code></li> <li><code><code>is_low_volfrac</code>: Set to <code>true</code> for 5% volume fraction, and <code>false</code> for 25% volume fraction.</code></li> </ul> <p> </p> <p>Run the code. The simulation will load the appropriate dataset based on your selected parameters and display the results.</p>
Monte-Carlo simulations of aerosol-based exposure to SARS-CoV-2 in a shop/bar
<p>Data from new Monte-Carlo simulations of aerosol-based exposure to SARS-CoV-2 in shop and bar environments. For background and previous results see V. Vuorinen et al. Safety Science 130 (2020): 104866.</p> <p>The files are collected to a tar file that includes all data files used for analysis (binary files with NumPy data type .npy) and a readme.</p>
Monte-Carlo Simulation Models of Animal Movement
This dataset consists of a series of computer programs in the PASCAL language that implement a simple model of animal movement. Results of model use were published in: Porter, J.H. and J.L. Dooley, Jr. 1993. Animal dispersal patterns: a reassessment of simple mathematical models. Ecology 74:2436-2443. MOVESIM.PAS, MOVESV1.PAS and MOVESV21.PAS are the model. Input is a set of coordinates for sampling points. The model randomly selects a starting point, then selects a random distance and direction. The distance to the nearest sampling point is then calculated and the process is repeated. There are a number of associated programs. MOVESTAT.SAS - SAS program to list frequency histogram for MOVESIM output. RANDGEN.PAS - Generate a set of random sampling stations within a given area. FITIT.PAS - Estimate fit of different movement models to output. ADJUST1.PAS - Estimate the number of individuals that would have been caught at a given distance if sample points had been evenly distributed.
Data For Hybrid Monte-Carlo Molecular Dynamics Methods For Estimation of Solidus and Liquidus Compositions, a Case Study in Cu-Ni and Au-Si
Open the record for dataset details and reuse information.
Monte-Carlo simulation of dike stability based on a coupled hydro-stability model
<p>With a large network of dikes that in the future will protect up to 15% of the worlds population from flooding, more extreme river discharges that result from climate change will dramatically increase the flood risk of these protected societies. Precise calculations of dike stability under adverse loading conditions will become increasingly important, though the hydrological impacts on dike stability, particularly the effects of groundwater flow, are often oversimplified in stability calculations. In order to better take account of groundwater flow processes, we use a coupled hydro-stability model to indicate relations between the geometry, subsurface materials, groundwater hydrology and stability of a dike. To calculate the stability, the most adverse drained loading conditions are applied to soil slip and basal sliding mechanisms. A database created by an extensive Monte Carlo analysis provides evidence for relations between geometry, material characteristics, hydrology and stability for three different failure processes. The database contains parameter combinations and the corresponding safety factor F. The database can be used to estimate failure probabilities for dike stretches that have not been assessed in detail, while including the uncertainties caused by a lack of in-situ data. </p>
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