Two-dimensional Monte Carlo simulations of H i line formation in massive young stellar object disc winds

Massive young stellar objects (YSOs) are powerful infrared Hi line emitters. It has been suggested that these lines form in an outflow from a disc surrounding the YSO. Here, new two-dimensional Monte Carlo radiative transfer calculations are described which test this hypothesis. Infrared spectra are synthesized for a YSO disc wind model based on earlier hydrodynamical calculations. The model spectra are in qualitative agreement with the observed spectra from massive YSOs, and therefore provide support for a disc wind explanation for the Hi lines. However, there are some significant differences: the models tend to overpredict the Bra/Br? ratio of equivalent widths and produce line profiles which are slightly too broad and, in contrast to typical observations, are double-peaked. The interpretation of these differences within the context of the disc wind picture and suggestions for their resolution via modifications to the assumed disc and outflow structure are discussed. © 2005 RAS.

Empirical performance of RSSI-based Monte Carlo localisation for active RFID patient tracking systems

The range of potential applications for indoor and campus based personnel localisation has led researchers to create a wide spectrum of different algorithmic approaches and systems. However, the majority of the proposed systems overlook the unique radio environment presented by the human body leading to systematic errors and inaccuracies when deployed in this context. In this paper RSSI-based Monte Carlo Localisation was implemented using commercial 868 MHz off the shelf hardware and empirical data was gathered across a relatively large number of scenarios within a single indoor office environment. This data showed that the body shadowing effect caused by the human body introduced path skew into location estimates. It was also shown that, by using two body-worn nodes in concert, the effect of body shadowing can be mitigated by averaging the estimated position of the two nodes worn on either side of the body. © Springer Science+Business Media, LLC 2012.

Monte Carlo simulation of complex cohesive fracture in random heterogeneous quasi-brittle materials

A numerical method is developed to simulate complex two-dimensional crack propagation in quasi-brittle materials considering random heterogeneous fracture properties. Potential cracks are represented by pre-inserted cohesive elements with tension and shear softening constitutive laws modelled by spatially varying Weibull random fields. Monte Carlo simulations of a concrete specimen under uni-axial tension were carried out with extensive investigation of the effects of important numerical algorithms and material properties on numerical efficiency and stability, crack propagation processes and load-carrying capacities. It was found that the homogeneous model led to incorrect crack patterns and load–displacement curves with strong mesh-dependence, whereas the heterogeneous model predicted realistic, complicated fracture processes and load-carrying capacity of little mesh-dependence. Increasing the variance of the tensile strength random fields with increased heterogeneity led to reduction in the mean peak load and increase in the standard deviation. The developed method provides a simple but effective tool for assessment of structural reliability and calculation of characteristic material strength for structural design.

Body shadowing mitigation using differentiated LOS/NLOS channel models for RSSI-based Monte Carlo personnel localization

Research into localization has produced a wealth of algorithms and techniques to estimate the location of wireless network nodes, however the majority of these schemes do not explicitly account for non-line of sight conditions. Disregarding this common situation reduces their accuracy and their potential for exploitation in real world applications. This is a particular problem for personnel tracking where the user's body itself will inherently cause time-varying blocking according to their movements. Using empirical data, this paper demonstrates that, by accounting for non-line of sight conditions and using received signal strength based Monte Carlo localization, meter scale accuracy can be achieved for a wrist-worn personnel tracking tag in a 120 m indoor office environment. © 2012 IEEE.

Monte Carlo studies of quantum and classical annealing on a double well

We present results for a variety of Monte Carlo annealing approaches, both classical and quantum, benchmarked against one another for the textbook optimization exercise of a simple one-dimensional double well. In classical (thermal) annealing, the dependence upon the move chosen in a Metropolis scheme is studied and correlated with the spectrum of the associated Markov transition matrix. In quantum annealing, the path integral Monte Carlo approach is found to yield nontrivial sampling difficulties associated with the tunneling between the two wells. The choice of fictitious quantum kinetic energy is also addressed. We find that a "relativistic" kinetic energy form, leading to a higher probability of long real-space jumps, can be considerably more effective than the standard nonrelativistic one.

Monte Carlo calculations of quantum yield in inhomogeneous PtSi/p-Si Schottky barriers

Monte Carlo calculations of quantum yield in PtSi/p-Si infrared detectors are carried out taking into account the presence of a spatially distributed barrier potential. In the 1-4 mu m wavelength range it is found that the spatial inhomogeneity of the barrier has no significant effect on the overall device photoresponse. However, above lambda = 4.0 mu m and particularly as the cut-off wavelength (lambda approximate to 5.5 mu m) is approached, these calculations reveal a difference between the homogeneous and inhomogeneous barrier photoresponse which becomes increasingly significant and exceeds 50% at lambda = 5.3 mu m. It is, in fact, the inhomogeneous barrier which displays an increased photoyield, a feature that is confirmed by approximate analytical calculations assuming a symmetric Gaussian spatial distribution of the barrier. Furthermore, the importance of the silicide layer thickness in optimizing device efficiency is underlined as a trade-off between maximizing light absorption in the silicide layer and optimizing the internal yield. The results presented here address important features which determine the photoyield of PtSi/Si Schottky diodes at energies below the Si absorption edge and just above the Schottky barrier height in particular.

Root Cause Analysis by a Combined Sparse Classification and Monte Carlo Approach

Classification methods with embedded feature selection capability are very appealing for the analysis of complex processes since they allow the analysis of root causes even when the number of input variables is high. In this work, we investigate the performance of three techniques for classification within a Monte Carlo strategy with the aim of root cause analysis. We consider the naive bayes classifier and the logistic regression model with two different implementations for controlling model complexity, namely, a LASSO-like implementation with a L1 norm regularization and a fully Bayesian implementation of the logistic model, the so called relevance vector machine. Several challenges can arise when estimating such models mainly linked to the characteristics of the data: a large number of input variables, high correlation among subsets of variables, the situation where the number of variables is higher than the number of available data points and the case of unbalanced datasets. Using an ecological and a semiconductor manufacturing dataset, we show advantages and drawbacks of each method, highlighting the superior performance in term of classification accuracy for the relevance vector machine with respect to the other classifiers. Moreover, we show how the combination of the proposed techniques and the Monte Carlo approach can be used to get more robust insights into the problem under analysis when faced with challenging modelling conditions.

Self-consistent modelling of line-driven hot-star winds with Monte Carlo radiation hydrodynamics

Radiative pressure exerted by line interactions is a prominent driver of outflows in astrophysical systems, being at work in the outflows emerging from hot stars or from the accretion discs of cataclysmic variables, massive young stars and active galactic nuclei. In this work, a new radiation hydrodynamical approach to model line-driven hot-star winds is presented. By coupling a Monte Carlo radiative transfer scheme with a finite volume fluid dynamical method, line-driven mass outflows may be modelled self-consistently, benefiting from the advantages of Monte Carlo techniques in treating multiline effects, such as multiple scatterings, and in dealing with arbitrary multidimensional configurations. In this work, we introduce our approach in detail by highlighting the key numerical techniques and verifying their operation in a number of simplified applications, specifically in a series of self-consistent, one-dimensional, Sobolev-type, hot-star wind calculations. The utility and accuracy of our approach are demonstrated by comparing the obtained results with the predictions of various formulations of the so-called CAK theory and by confronting the calculations with modern sophisticated techniques of predicting the wind structure. Using these calculations, we also point out some useful diagnostic capabilities our approach provides. Finally, we discuss some of the current limitations of our method, some possible extensions and potential future applications.

Simulation study of Pd submonolayer films on Au(hkl) and Pt(hkl) and their relationship to underpotential deposition

The structure and stability of palladium adlayers on Au(hkl) and Pt(hkl) were studied at different coverage degrees by means of Monte Carlo simulations using the interatomic potentials of the embedded atom model. In all cases the Pd films were found to grow epitaxially and pseudomorphically with the crystallographic orientation of the substrate. The differences and similarities of the adlayer with the substrate were analyzed.

Evaluation of genetic substructure in the Irish Study of High-Density Schizophrenia Families

The presence of genetic substructure has the potential to diminish the chances of detecting a linkage signal. Using a Markov chain Monte Carlo procedure developed by Pritchard and colleagues and implemented in the program STRUCTURE, we evaluated the evidence for genetic substructure using genotypes from 37 microsatellite markers in affected individuals selected at random from 263 multiplex families in the Irish Study of High-Density Schizophrenia Families. We found no evidence for the presence of genetic substructure in this sample.

Multiple Biomarkers for the prediction of ischemic stroke: the PRIME study

Objective: To simultaneously evaluate 14 biomarkers from distinct biological pathways for risk prediction of ischemic stroke, including biomarkers of hemostasis, inflammation, and endothelial activation as well as chemokines and adipocytokines.
Methods and Results: The Prospective Epidemiological Study on Myocardial Infarction (PRIME) is a cohort of 9771 healthy men 50 to 59 years of age who were followed up over 10 years. In a nested case–control study, 95 ischemic stroke cases were matched with 190 controls. After multivariable adjustment for traditional risk factors, fibrinogen (odds ratio [OR], 1.53; 95% confidence interval [CI], 1.03–2.28), E-selectin (OR, 1.76; 95% CI, 1.06–2.93), interferon-γ-inducible-protein-10 (OR, 1.72; 95% CI, 1.06–2.78), resistin (OR, 2.86; 95% CI, 1.30–6.27), and total adiponectin (OR, 1.82; 95% CI, 1.04–3.19) were significantly associated with ischemic stroke. Adding E-selectin and resistin to a traditional risk factor model significantly increased the area under the receiver-operating characteristic curve from 0.679 (95% CI, 0.612–0.745) to 0.785 and 0.788, respectively, and yielded a categorical net reclassification improvement of 29.9% (P=0.001) and 28.4% (P=0.002), respectively. Their simultaneous inclusion in the traditional risk factor model increased the area under the receiver-operating characteristic curve to 0.824 (95% CI, 0.770–0.877) and resulted in an net reclassification improvement of 41.4% (P<0.001). Results were confirmed when using continuous net reclassification improvement.
Conclusion: Among multiple biomarkers from distinct biological pathways, E-selectin and resistin provided incremental and additive value to traditional risk factors in predicting ischemic stroke.