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.

Modulational instability and localized excitations involving two nonlinearly coupled upper-hybrid waves in plasmas

The nonlinear coupling between two perpendicularly propagating ( with respect to the external magnetic field direction) upper-hybrid ( UH) waves in a uniform magnetoplasma is considered, taking into account quasi-stationary density perturbations which are driven by the UH wave ponderomotive force. This interaction is governed by a pair of coupled nonlinear Schrodinger equations ( CNLSEs) for the UH wave envelopes. The CNLSEs are used to investigate the occurrence of modulational instability. Waves in the vicinity of the UH resonance are considered, so that the group dispersion terms for both waves are approximately equal, but the UH wave group velocities may be different. It is found that a pair of unstable UH waves ( obeying anomalous group dispersion) yields an increased instability growth rate, while a pair of stable UH waves ( individually obeying normal group dispersion) remains stable for equal group velocities, although it is destabilized by a finite group velocity mismatch. Stationary nonlinear solutions of the CNLSEs are presented.

Wave mixing of hybrid Bogoliubov modes in a Bose-Einstein condensate

Mode-mixing of coherent excitations of a trapped Bose-Einstein condensate is modeled using the Bogoliubov approximation. Calculations are presented for second-harmonic generation between the two lowest-lying even-parity m=0 modes in an oblate spheroidal trap. Hybridization of the modes of the breather (l=0) and surface (l=4) states leads to the formation of a Bogoliubov dark state near phase-matching resonance so that a single mode is coherently populated. Efficient harmonic generation requires a strong coupling rate, sharply-defined and well-separated frequency spectrum, and good phase matching. We find that in all three respects the quantal results are significantly different from hydrodynamic predictions. Typically the second-harmonic conversion rate is half that given by an equivalent hydrodynamic estimate.

A hybrid metaheuristic approach to the university course timetabling problem

This paper describes the development of a novel metaheuristic that combines an electromagnetic-like mechanism (EM) and the great deluge algorithm (GD) for the University course timetabling problem. This well-known timetabling problem assigns lectures to specific numbers of timeslots and rooms maximizing the overall quality of the timetable while taking various constraints into account. EM is a population-based stochastic global optimization algorithm that is based on the theory of physics, simulating attraction and repulsion of sample points in moving toward optimality. GD is a local search procedure that allows worse solutions to be accepted based on some given upper boundary or ‘level’. In this paper, the dynamic force calculated from the attraction-repulsion mechanism is used as a decreasing rate to update the ‘level’ within the search process. The proposed method has been applied to a range of benchmark university course timetabling test problems from the literature. Moreover, the viability of the method has been tested by comparing its results with other reported results from the literature, demonstrating that the method is able to produce improved solutions to those currently published. We believe this is due to the combination of both approaches and the ability of the resultant algorithm to converge all solutions at every search process.