Progress in proton radiography for diagnosis of ICF-relevant plasmas

Proton radiography using laser-driven sources has been developed as a diagnostic since the beginning of the decade, and applied successfully to a range of experimental situations. Multi-MeV protons driven from thin foils via the Target Normal Sheath Acceleration mechanism, offer, under optimal conditions, the possibility of probing laser-plasma interactions, and detecting electric and magnetic fields as well as plasma density gradients with similar to ps temporal resolution and similar to 5-10 mu m spatial resolution. In view of these advantages, the use of proton radiography as a diagnostic in experiments of relevance to Inertial Confinement Fusion is currently considered in the main fusion laboratories. This paper will discuss recent advances in the application of laser-driven radiography to experiments of relevance to Inertial Confinement Fusion. In particular we will discuss radiography of hohlraum and gasbag targets following the interaction of intense ns pulses. These experiments were carried out at the HELEN laser facility at AWE (UK), and proved the suitability of this diagnostic for studying, with unprecedented detail, laser-plasma interaction mechanisms of high relevance to Inertial Confinement Fusion. Non-linear solitary structures of relevance to space physics, namely phase space electron holes, have also been highlighted by the measurements. These measurements are discussed and compared to existing models.

Discrete Conditional Phase-type models utilising classification trees: Application to modelling health service capacities

Discrete Conditional Phase-type (DC-Ph) models consist of a process component (survival distribution) preceded by a set of related conditional discrete variables. This paper introduces a DC-Ph model where the conditional component is a classification tree. The approach is utilised for modelling health service capacities by better predicting service times, as captured by Coxian Phase-type distributions, interfaced with results from a classification tree algorithm. To illustrate the approach, a case-study within the healthcare delivery domain is given, namely that of maternity services. The classification analysis is shown to give good predictors for complications during childbirth. Based on the classification tree predictions, the duration of childbirth on the labour ward is then modelled as either a two or three-phase Coxian distribution. The resulting DC-Ph model is used to calculate the number of patients and associated bed occupancies, patient turnover, and to model the consequences of changes to risk status.