A spatial kernel density method to estimate the diet composition of fish

We present a novel spatially explicit kernel density approach to estimate the proportional contribution of a prey to a predator’s diet by mass. First, we compared the spatial estimator to a traditional cluster-based approach using a Monte Carlo simulation study. Next, we compared the diet composition of three predators from Pamlico Sound, North Carolina, to evaluate how ignoring spatial correlation affects diet estimates. The spatial estimator had lower mean squared error values compared with the traditional cluster-based estimator for all Monte Carlo simulations. Incorporating spatial correlation when estimating the predator’s diet resulted in a consistent increase in precision across multiple levels of spatial correlation. Bias was often similar between the two estimators; however, when it differed it mostly favored the spatial estimator. The two estimators produced different estimates of proportional contribution of prey to the diets of the three field-collected predator species, especially when spatial correlation was strong and prey were consumed in patchy areas. Our simulation and empirical data provide strong evidence that data on food habits should be modeled using spatial approaches and not treated as spatially independent.

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A Monte Carlo simulation study of associated liquid crystals

Molecular Physics ◽

10.1080/002689799162966 ◽

1999 ◽

Vol 97 (11) ◽

pp. 1173-1184 ◽

Cited By ~ 1

Author(s):

R. Berardi, M. Fehervari, C. Zannoni

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Liquid Crystals ◽

Simulation Study ◽

Monte Carlo Simulation Study

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Examining the Performance Characteristics of Growth Mixture Modeling: A Monte Carlo Simulation Study

PsycEXTRA Dataset ◽

10.1037/e619812009-001 ◽

2009 ◽

Author(s):

James Peugh ◽

Xitao Fan

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Simulation Study ◽

Growth Mixture Modeling ◽

Mixture Modeling ◽

Performance Characteristics ◽

Monte Carlo Simulation Study ◽

Growth Mixture

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Porous Aluminophosphates as Adsorbents for the Separation of CO2/CH4 and CH4/N2 Mixtures – a Monte Carlo Simulation Study

10.26434/chemrxiv.6133346.v2 ◽

2018 ◽

Author(s):

Michael Fischer

Keyword(s):

Monte Carlo ◽

Landfill Gas ◽

Experimental Studies ◽

Chem Phys ◽

Monte Carlo Simulation Study ◽

Mixture Adsorption ◽

Vacuum Swing Adsorption ◽

Separation Of Co2 ◽

Pressure Swing ◽

Gcmc Simulations

<div>Aluminophosphates with zeolite-like topologies (AlPOs) have received considerable attention as potential adsorbents for use in the separation of methane-containing gas mixtures. Such separations, especially the removal of carbon dioxide and nitrogen from methane, are of great technological relevance in the context of the “upgrade” of natural gas, landfill gas, and biogas. While more than 50 zeolite frameworks have been synthesised in aluminophosphate composition or as heteroatom substituted AlPO derivatives, only a few of them have been characterised experimentally with regard to their adsorption and separation behaviour. In order to predict the potential of a variety of AlPO frameworks for applications in CO2/CH4 and CH4/N2 separations, atomistic grand-canonical Monte Carlo (GCMC) simulations were performed for 53 different structures. Building on previous work, which studied CO2/N2 mixture adsorption in AlPOs (M. Fischer, Phys. Chem. Chem. Phys., 2017, 19, 22801–22812), force field parameters for methane adsorption in AlPOs were validated through a comparison to available experimental adsorption data. Afterwards, CO2/CH4 and CH4/N2 mixture isotherms were computed for all 53 frameworks for room temperature and total pressures up to 1000 kPa (10 bar), allowing the prediction of selectivities and working capacities for conditions that are relevant for pressure swing adsorption (PSA) and vacuum swing adsorption (VSA). For CO2/CH4 mixtures, the GIS, SIV, and ATT frameworks were found to have the highest selectivities and CO2 working capacities under VSA conditions, whereas several frameworks, among them AFY, KFI, AEI, and LTA, show higher working capacities under PSA conditions. For CH4/N2 mixtures, all frameworks are moderately selective for methane over nitrogen, with ATV exhibiting a significantly higher selectivity than all other frameworks. While some of the most promising topologies are either not available in pure-AlPO4 composition or collapse upon calcination, others can be synthesised and activated, rendering them interesting candidates for future experimental studies. In addition to predictions of mixture adsorption isotherms, further simulations were performed for four selected systems in order to investigate the microscopic origins of the macroscopic adsorption behaviour, e.g. with regard to the very high CH4/N2 selectivity of ATV and the loading-dependent evolution of the heat of CO2 adsorption and CO2/CH4 selectivity of AEI and GME.</div>

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Using Approximation Non-Bayesian Computation with Fuzzy Data to Estimation Inverse Weibull Parameters and Reliability Function

Ibn AL- Haitham Journal For Pure and Applied Science ◽

10.30526/2017.ihsciconf.1811 ◽

2018 ◽

pp. 397

Author(s):

Nadia Hashim Al-Noor ◽

Shurooq A.K. Al-Sultany

Keyword(s):

Maximum Likelihood ◽

Expectation Maximization ◽

Mean Squared Error ◽

Maximum Likelihood Estimators ◽

Reliability Function ◽

Fuzzy Data ◽

Monte Carlo Simulation Study ◽

Weibull Parameters ◽

Squared Error ◽

Newton Raphson

In real situations all observations and measurements are not exact numbers but more or less non-exact, also called fuzzy. So, in this paper, we use approximate non-Bayesian computational methods to estimate inverse Weibull parameters and reliability function with fuzzy data. The maximum likelihood and moment estimations are obtained as non-Bayesian estimation. The maximum likelihood estimators have been derived numerically based on two iterative techniques namely “Newton-Raphson” and the “Expectation-Maximization” techniques. In addition, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the parameters and reliability function in terms of their mean squared error values and integrated mean squared error values respectively.

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