Investigations of ultrafast charge dynamics in laser-irradiated targets by a self probing technique employing laser driven protons

The divergent and broadband proton beams produced by the target normal sheath acceleration mechanism provide the unique opportunity to probe, in a point-projection imaging scheme, the dynamics of the transient electric and magnetic fields produced during laser-plasma interactions. Commonly such experimental setup entails two intense laser beams, where the interaction produced by one beam is probed with the protons produced by the second. We present here experimental studies of the ultra-fast charge dynamics along a wire connected to laser irradiated target carried out by employing a ‘self’ proton probing arrangement – i.e. by connecting the wire to the target generating the probe protons. The experimental data shows that an electromagnetic pulse carrying a significant amount of charge is launched along the wire, which travels as a unified pulse of 10s of ps duration with a velocity close to speed of light. The experimental capabilities and the analysis procedure of this specific type of proton probing technique are discussed.

New methods for meta-analysis under a fixed effects framework, with frequentist and Bayesian estimation.

Thesis (Ph.D.)--University of Washington, 2015

A review of dynamic Bayesian network techniques with applications in healthcare risk modelling

Coping with an ageing population is a major concern for healthcare organisations around the world. The average cost of hospital care is higher than social care for older and terminally ill patients. Moreover, the average cost of social care increases with the age of the patient. Therefore, it is important to make efficient and fair capacity planning which also incorporates patient centred outcomes. Predictive models can provide predictions which their accuracy can be understood and quantified. Predictive modelling can help patients and carers to get the appropriate support services, and allow clinical decision-makers to improve care quality and reduce the cost of inappropriate hospital and Accident and Emergency admissions. The aim of this study is to provide a review of modelling techniques and frameworks for predictive risk modelling of patients in hospital, based on routinely collected data such as the Hospital Episode Statistics database. A number of sub-problems can be considered such as Length-of-Stay and End-of-Life predictive modelling. The methodologies in the literature are mainly focused on addressing the problems using regression methods and Markov models, and the majority lack generalisability. In some cases, the robustness, accuracy and re-usability of predictive risk models have been shown to be improved using Machine Learning methods. Dynamic Bayesian Network techniques can represent complex correlations models and include small probabilities into the solution. The main focus of this study is to provide a review of major time-varying Dynamic Bayesian Network techniques with applications in healthcare predictive risk modelling.

Separating sequential equilibrium in a Bayesian international duopoly

We consider two Cournot firms, one located in the home country and the other in the foreign country, producing substitute goods for consumption in a third country. We suppose that neither the home government nor the foreign firm know the costs of the home firm, while the foreign firm cost is common knowledge. We determine the separating sequential equilibrium outputs.

Hydrogeophysical data integration at larger scales: Application of Bayesian sequential simulation for the characterization of heterogeneous alluvial aquifers

Knowledge of the spatial distribution of hydraulic conductivity (K) within an aquifer is critical for reliable predictions of solute transport and the development of effective groundwater management and/or remediation strategies. While core analyses and hydraulic logging can provide highly detailed information, such information is inherently localized around boreholes that tend to be sparsely distributed throughout the aquifer volume. Conversely, larger-scale hydraulic experiments like pumping and tracer tests provide relatively low-resolution estimates of K in the investigated subsurface region. As a result, traditional hydrogeological measurement techniques contain a gap in terms of spatial resolution and coverage, and they are often alone inadequate for characterizing heterogeneous aquifers. Geophysical methods have the potential to bridge this gap. The recent increased interest in the application of geophysical methods to hydrogeological problems is clearly evidenced by the formation and rapid growth of the domain of hydrogeophysics over the past decade (e.g., Rubin and Hubbard, 2005).

Toxic substances in blood: an analysis of current recommendations under a Bayesian (decision) approach

This article extends existing discussion in literature on probabilistic inference and decision making with respect to continuous hypotheses that are prevalent in forensic toxicology. As a main aim, this research investigates the properties of a widely followed approach for quantifying the level of toxic substances in blood samples, and to compare this procedure with a Bayesian probabilistic approach. As an example, attention is confined to the presence of toxic substances, such as THC, in blood from car drivers. In this context, the interpretation of results from laboratory analyses needs to take into account legal requirements for establishing the 'presence' of target substances in blood. In a first part, the performance of the proposed Bayesian model for the estimation of an unknown parameter (here, the amount of a toxic substance) is illustrated and compared with the currently used method. The model is then used in a second part to approach-in a rational way-the decision component of the problem, that is judicial questions of the kind 'Is the quantity of THC measured in the blood over the legal threshold of 1.5 μg/l?'. This is pointed out through a practical example.

Probing cell surfaces of host and nonhost species of a mycoparasite, using FITC-labeled lectins

Cell surfaces of susceptible host species (Mortierella pusllla and Cboanepilora cucurbitarum ), resistant host (Pilascolomyces articulosus ), nonhost (Mortierella candelabrum ) and the mycoparasite (Piptocepilalis virginiana) were examined for sugar distribution patterns using light and fluorescent microscopy techniques. The susceptible host, resistant host and the mycoparasite species exhibited a similar sugar distribution profile; they all showed N-acetyl glucosamine and D-glucose on their cell wall surfaces. The nonhost cell wall surface showed a positive binding reaction to FITClectins specific for N-acetyl glucosamine and also for OI.-fucose, N-acetyl galactosamine and galactose. Treatment of these fungi with mild concentrations of proteinases (both commercial as well as the mycoparasiteproteinase) resulted in the revelation of additional sugars on the fungal cell walls. The susceptible host treated with proteinase expressed higher levels of N-acetyl glucosamine and D-glucose. The susceptible host also showed the presence of OI.-fucose, N-acetyl galactosamine and galactose. The proteinasetreated susceptible host cell walls also showed an increase in the levels of attachment with the mycoparasite. Treatment of the resistant host with proteinases revealed OI.-fucose in addition to N-acetyl glucosamine and D-glucose. Treatment of the nonhost cell wall with proteinase resulted in the exposure of low levels of D-glucose, in addition to sugars found on the untreated nonhost cell wall surface. The mycoparasite treated with proteinase revealed OI.-fucose, N-acetyl galactosamine and galactose on its cell surface in addition to the sugars N-acetyl glucosamine and D-glucose. Protoplasts were isolated from hosts and nonhost fungi and their surfaces were examined for sugar distribution patterns. The susceptible host and nonhost protoplast membranes showed all the sugars (N-acetyl glucosamine, D-glucose, (It.-fucose, N-acetyl galactosamine and galactose) tested for. The resistant host protoplast membrane however, had only N-acetyl glucosamine and D-glucose exposed. This sugar distribution profile resembles that exhibited by the untreated resistant host cell wall, as well as that shown by the untreated mycoparasite cell surface. Only susceptible host protoplasts were successful in attaching to the mycoparasite surface. Resistant host protoplasts did not show any interaction with the i mycoparasite cell surface. Both susceptible as well as resistant host protoplasts were incapable of attaching to agarose beads surface-coated with specific carbohydrates. The mycoparasite however, did attach to agarose beads surface-coated with either N-acetyl glucosamine, D-glucose/Dmannose or o:,- methyl-D-mannose. The relevance of the cell wall and the protoplast membrane in the light of the present results, in reacting appropriately to bring about either a susceptible, a resistant or a nonhost response has been discussed.

Using a Bayesian model for bankruptcy prediction : a comparative approach

The purpose of this study is to examine the impact of the choice of cut-off points, sampling procedures, and the business cycle on the accuracy of bankruptcy prediction models. Misclassification can result in erroneous predictions leading to prohibitive costs to firms, investors and the economy. To test the impact of the choice of cut-off points and sampling procedures, three bankruptcy prediction models are assessed- Bayesian, Hazard and Mixed Logit. A salient feature of the study is that the analysis includes both parametric and nonparametric bankruptcy prediction models. A sample of firms from Lynn M. LoPucki Bankruptcy Research Database in the U. S. was used to evaluate the relative performance of the three models. The choice of a cut-off point and sampling procedures were found to affect the rankings of the various models. In general, the results indicate that the empirical cut-off point estimated from the training sample resulted in the lowest misclassification costs for all three models. Although the Hazard and Mixed Logit models resulted in lower costs of misclassification in the randomly selected samples, the Mixed Logit model did not perform as well across varying business-cycles. In general, the Hazard model has the highest predictive power. However, the higher predictive power of the Bayesian model, when the ratio of the cost of Type I errors to the cost of Type II errors is high, is relatively consistent across all sampling methods. Such an advantage of the Bayesian model may make it more attractive in the current economic environment. This study extends recent research comparing the performance of bankruptcy prediction models by identifying under what conditions a model performs better. It also allays a range of user groups, including auditors, shareholders, employees, suppliers, rating agencies, and creditors' concerns with respect to assessing failure risk.

Bayesian Analysis for a Theory of Random Consumer Demand: The Case of Indivisible Goods

McCausland (2004a) describes a new theory of random consumer demand. Theoretically consistent random demand can be represented by a \"regular\" \"L-utility\" function on the consumption set X. The present paper is about Bayesian inference for regular L-utility functions. We express prior and posterior uncertainty in terms of distributions over the indefinite-dimensional parameter set of a flexible functional form. We propose a class of proper priors on the parameter set. The priors are flexible, in the sense that they put positive probability in the neighborhood of any L-utility function that is regular on a large subset bar(X) of X; and regular, in the sense that they assign zero probability to the set of L-utility functions that are irregular on bar(X). We propose methods of Bayesian inference for an environment with indivisible goods, leaving the more difficult case of indefinitely divisible goods for another paper. We analyse individual choice data from a consumer experiment described in Harbaugh et al. (2001).

Bayesian inference and model comparison for random choice structures

We complete the development of a testing ground for axioms of discrete stochastic choice. Our contribution here is to develop new posterior simulation methods for Bayesian inference, suitable for a class of prior distributions introduced by McCausland and Marley (2013). These prior distributions are joint distributions over various choice distributions over choice sets of di fferent sizes. Since choice distributions over di fferent choice sets can be mutually dependent, previous methods relying on conjugate prior distributions do not apply. We demonstrate by analyzing data from a previously reported experiment and report evidence for and against various axioms.