SATURATED AND NEAR-DIFFRACTION-LIMITED OPERATION OF AN XUV LASER AT 23.6 NM

Amplification of spontaneous emission (ASE) at 23.6 nm has been studied in a Ge plasma heated by a 1 TW infrared laser pulse. The exponent of the axial gain reached 21 in a geometry with Fresnel number less-than-or-equal-to 1. Two plasma columns of combined length up to 36 mm were used with an extreme ultraviolet mirror giving double-pass amplification. Saturation of the ASE output was observed. The beam divergence was about 8 x diffraction limited with a brightness estimated at 10(14) W cm-2 sr-1. The feedback from the mirror was significantly reduced probably by radiation damage from the plasma.

A Bayesian network hybrid model for representing accident and emergency waiting times

The paper introduces a new modeling approach that represents the waiting times in an accident and emergency (A&E) department in a UK based national health service (NHS) hospital. The technique uses Bayesian networks to capture the heterogeneity of arriving patients by representing how patient covariates interact to influence their waiting times in the department. Such waiting times have been reviewed by the NHS as a means of investigating the efficiency of A&E departments (emergency rooms) and how they operate. As a result activity targets are now established based on the patient total waiting times with much emphasis on trolley waits.

Multicamera sport player tracking with Bayesian estimation of measurements

We propose a complete application capable of tracking multiple objects in an environment monitored by multiple cameras. The system has been specially developed to be applied to sport games, and it has been evaluated in a real association-football stadium. Each target is tracked using a local importance-sampling particle filter in each camera, but the final estimation is made by combining information from the other cameras using a modified unscented Kalman filter algorithm. Multicamera integration enables us to compensate for bad measurements or occlusions in some cameras thanks to the other views it offers. The final algorithm results in a more accurate system with a lower failure rate. (C) 2009 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3114605]

A Bayesian analysis of the chromosome architecture of human disorders by integrating reductionist data

In this paper, we present a Bayesian approach to estimate a chromosome and a disorder network from the Online Mendelian Inheritance in Man (OMIM) database. In contrast to other approaches, we obtain statistic rather than deterministic networks enabling a parametric control in the uncertainty of the underlying disorder-disease gene associations contained in the OMIM, on which the networks are based. From a structural investigation of the chromosome network, we identify three chromosome subgroups that reflect architectural differences in chromosome-disorder associations that are predictively exploitable for a functional analysis of diseases.

Improving multi-site benefit functions via Bayesian model averaging: A new approach to benefit transfer

A benefit function transfer obtains estimates of willingness-to-pay (WTP) for the evaluation of a given policy at a site by combining existing information from different study sites. This has the advantage that more efficient estimates are obtained, but it relies on the assumption that the heterogeneity between sites is appropriately captured in the benefit transfer model. A more expensive alternative to estimate WTP is to analyze only data from the policy site in question while ignoring information from other sites. We make use of the fact that these two choices can be viewed as a model selection problem and extend the set of models to allow for the hypothesis that the benefit function is only applicable to a subset of sites. We show how Bayesian model averaging (BMA) techniques can be used to optimally combine information from all models. The Bayesian algorithm searches for the set of sites that can form the basis for estimating a benefit function and reveals whether such information can be transferred to new sites for which only a small data set is available. We illustrate the method with a sample of 42 forests from U.K. and Ireland. We find that BMA benefit function transfer produces reliable estimates and can increase about 8 times the information content of a small sample when the forest is 'poolable'. © 2008 Elsevier Inc. All rights reserved.

Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data

The relationships among organisms and their surroundings can be of immense complexity. To describe and understand an ecosystem as a tangled bank, multiple ways of interaction and their effects have to be considered, such as predation, competition, mutualism and facilitation. Understanding the resulting interaction networks is a challenge in changing environments, e.g. to predict knock-on effects of invasive species and to understand how climate change impacts biodiversity. The elucidation of complex ecological systems with their interactions will benefit enormously from the development of new machine learning tools that aim to infer the structure of interaction networks from field data. In the present study, we propose a novel Bayesian regression and multiple changepoint model (BRAM) for reconstructing species interaction networks from observed species distributions. The model has been devised to allow robust inference in the presence of spatial autocorrelation and distributional heterogeneity. We have evaluated the model on simulated data that combines a trophic niche model with a stochastic population model on a 2-dimensional lattice, and we have compared the performance of our model with L1-penalized sparse regression (LASSO) and non-linear Bayesian networks with the BDe scoring scheme. In addition, we have applied our method to plant ground coverage data from the western shore of the Outer Hebrides with the objective to infer the ecological interactions. (C) 2012 Elsevier B.V. All rights reserved.

Analysis of Two-Speed Wind Farm Operation From Grid-Side Measurements

This paper analyzes data captured by a phasor measurement unit at a wind farm, employing two-speed induction generators, and investigates aspects of the control system's interaction with the power system. Composite superimposed transient events are proposed as a method to improve the quality of the analysis and reduce errors caused by unknowns, such as wind speed variation. A Mathworks SimPowerSystems model validates the inertia contribution of the wind farm, which is an important parameter in power systems with high wind penetration. Transients caused by turbine speed transitions are identified and explained. The analysis also highlights areas where wind farm control should be improved if useful inertia contribution is to be provided.