Function Of Ensemble Size Research Articles

An ensemble Kalman filtering algorithm for state estimation of jump Markov systems

This paper presents a target model whose dynamics is modelled as a jump Markov process. The problem of state estimation of jump Markov system using ensemble Kalman filter (EnKF) is dealt here. The EnKF is designed for higher order nonlinear state estimation. In target tracking using jump process, accuracy of the state estimate is defined as a function of ensemble size. The jump Markov system minimises state error at the state estimation and assumes that all probability distributions involved are to be Gaussian. This paper proposes state estimation of jump Markov systems using EnKF, at each step the ensemble of state estimates are calculated. From the result, calculate the error statistics from ensemble forecasts which are dynamically propagated through nonlinear system.

Cavity Cooling of an Ensemble Spin System

We describe how sideband cooling techniques may be applied to large spin ensembles in magnetic resonance. Using the Tavis-Cummings model in the presence of a Rabi drive, we solve a Markovian master equation describing the joint spin-cavity dynamics to derive cooling rates as a function of ensemble size. Our calculations indicate that the coupled angular momentum subspaces of a spin ensemble containing roughly 10(11) electron spins may be polarized in a time many orders of magnitude shorter than the typical thermal relaxation time. The described techniques should permit efficient removal of entropy for spin-based quantum information processors and fast polarization of spin samples. The proposed application of a standard technique in quantum optics to magnetic resonance also serves to reinforce the connection between the two fields, which has recently begun to be explored in further detail due to the development of hybrid designs for manufacturing noise-resilient quantum devices.

Stochastic Force Generation by Small Ensembles of Myosin II Motors

Forces in the actin cytoskeleton are generated by small groups of nonprocessive myosin II motors for which stochastic effects are highly relevant. Using a cross-bridge model with the assumptions of fast power-stroke kinetics and equal load sharing between equivalent states, we derive a one-step master equation for the activity of a finite-sized ensemble of mechanically coupled myosin II motors. For constant external load, this approach yields analytical results for duty ratio and force-velocity relation as a function of ensemble size. We find that stochastic effects cannot be neglected for ensemble sizes below 15. The one-step master equation can be used also for efficient computer simulations with linear elastic external load and reveals the sequence of buildup of force and ensemble rupture that is characteristic for reconstituted actomyosin contractility.

Potential and Limitations of Ensemble Docking

A major problem in structure-based virtual screening applications is the appropriate selection of a single or even multiple protein structures to be used in the virtual screening process. A priori it is unknown which protein structure(s) will perform best in a virtual screening experiment. We investigated the performance of ensemble docking, as a function of ensemble size, for eight targets of pharmaceutical interest. Starting from single protein structure docking results, for each ensemble size up to 500,000 combinations of protein structures were generated, and, for each ensemble, pose prediction and virtual screening results were derived. Comparison of single to multiple protein structure results suggests improvements when looking at the performance of the worst and the average over all single protein structures to the performance of the worst and average over all protein ensembles of size two or greater, respectively. We identified several key factors affecting ensemble docking performance, including the sampling accuracy of the docking algorithm, the choice of the scoring function, and the similarity of database ligands to the cocrystallized ligands of ligand-bound protein structures in an ensemble. Due to these factors, the prospective selection of optimum ensembles is a challenging task, shown by a reassessment of published ensemble selection protocols.

Probabilistic Precipitation Forecast Skill as a Function of Ensemble Size and Spatial Scale in a Convection-Allowing Ensemble

Probabilistic quantitative precipitation forecasts (PQPFs) from the storm-scale ensemble forecast system run by the Center for Analysis and Prediction of Storms during the spring of 2009 are evaluated using area under the relative operating characteristic curve (ROC area). ROC area, which measures discriminating ability, is examined for ensemble size n from 1 to 17 members and for spatial scales ranging from 4 to 200 km. Expectedly, incremental gains in skill decrease with increasing n. Significance tests comparing ROC areas for each n to those of the full 17-member ensemble revealed that more members are required to reach statistically indistinguishable PQPF skill relative to the full ensemble as forecast lead time increases and spatial scale decreases. These results appear to reflect the broadening of the forecast probability distribution function (PDF) of future atmospheric states associated with decreasing spatial scale and increasing forecast lead time. They also illustrate that efficient allocation of computing resources for convection-allowing ensembles requires careful consideration of spatial scale and forecast length desired.

Structural Organization of Gold Nanoparticles onto the ITO Surface and Its Optical Properties as a Function of Ensemble Size

Self-assembly of citrate-stabilized gold nanoparticles (AuNPs) onto an optically transparent indium tin oxide (ITO) surface followed by neutralization of these particles using dodecanethiol as a surfactant have been demonstrated. X-ray photoelectron spectroscopic (XPS) studies revealed the partial removal of citrate ions from the immobilized AuNPs, which advances the dilution of electrostatic attraction between AuNPs and the APS (amino-terminated monolayer)-functionalized ITO surface. The resultant AuNPs restore their mobility to some extent and form small ensembles. Some of the immobilized AuNPs were completely removed from the surface due to neutralization, as confirmed by XPS studies. Interparticle distance and size of ensembles were manipulated by consecutive cycles of immobilization and neutralization of AuNPs. Controlled nanostructural fabrication progression, which leads to two-dimensional lateral growth of AuNPs, provides a method for systematically shifting the surface plasmon resonance band based on the increase in plasmon coupling among the closely placed AuNPs of an ensemble. The magnitude of shift increases with the size of ensemble. This manipulated chemical strategy offers a convenient and simple method to tune the optical properties of materials on a nanoscale.