Lisa McCrink

INTENSITY ENHANCEMENT OF O VI ULTRAVIOLET EMISSION LINES IN SOLAR SPECTRA DUE TO OPACITY

Opacity is a property of many plasmas. It is normally expected that if an emission line in a plasma becomes optically thick, then its intensity ratio to that of another transition that remains optically thin should decrease. However, radiative transfer calculations undertaken both by ourselves and others predict that under certain conditions the intensity ratio of an optically thick to an optically thin line can show an increase over the optically thin value, indicating an enhancement in the former. These conditions include the geometry of the emitting plasma and its orientation to the observer. A similar effect can take place between lines of differing optical depths. While previous observational studies have focused on stellar point sources, here we investigate the spatially resolved solar atmosphere using measurements of the I(1032 A)/I(1038 A) intensity ratio of O VI in several regions obtained with the Solar Ultraviolet Measurements of Emitted Radiation instrument on board the Solar and Heliospheric Observatory satellite. We find several I(1032 A)/I(1038 A) ratios observed on the disk to be significantly larger than the optically thin value of 2.0, providing the first detection (to our knowledge) of intensity enhancement in the ratio arising from opacity effects in the solar atmosphere. The agreement between observation and theory is excellent and confirms that the O VI emission originates from a slab-like geometry in the solar atmosphere, rather than from cylindrical structures.

Discrete Conditional Phase-type model (DC_Ph) for patient waiting time with a logistic regression component to predict patient admission to hospital

Discrete Conditional Phase-type (DC-Ph) models are a family of models which represent skewed survival data conditioned on specific inter-related discrete variables. The survival data is modeled using a Coxian phase-type distribution which is associated with the inter-related variables using a range of possible data mining approaches such as Bayesian networks (BNs), the Naïve Bayes Classification method and classification regression trees. This paper utilizes the Discrete Conditional Phase-type model (DC-Ph) to explore the modeling of patient waiting times in an Accident and Emergency Department of a UK hospital. The resulting DC-Ph model takes on the form of the Coxian phase-type distribution conditioned on the outcome of a logistic regression model.