Background: Hospital in-patients need sleep so that restorative process and healing can take place. However, over one third of in-patients experience sleep disturbance, often caused by noise. This can compromise patients’ perceptions of care quality and cause physical and psychological ill health. Aims: To assess 1) in-patients sleep quality, quantity, reported sources of sleep disturbance and their suggestions for improvement 2) objectively measure decibel levels recorded at night. Methods: This descriptive study conducted in a Medical Assessment Unit used multi-methods; a semi-structured ‘sleep experience’ questionnaire administered to a purposive sample of in-patients; recording of night-time noise levels, on 52 consecutive nights, using two calibrated Casella sound level meters. Results: Patient ratings of ‘in-hospital’ sleep quantity (3.25; 2.72 SD) and quality (2.91; 2.56 SD) was poorer compared to ‘home’ sleep quantity (5.07; 2.81 SD) and quality (5.52; 2.79 SD). The difference in sleep quality (p<0.001) and quantity (p<0.001) ratings whilst in hospital, compared to at home, was statistically significant. Care processes, noise from other patients and the built environment were common sources of sleep disturbance. Participants’ suggestions for improvement were similar to interventions identified in current research. The constant noise level ranged from 38-57 decibels (equivalent to an office environment), whilst peak levels reached a maximum of 116 decibels, (equivalent to banging a car door one metre away). Conclusion: The self-rated patient sleep experience was significantly poorer in hospital, compared to home. Noise at night contributed to sleep disturbance. Decibel levels were equivalent to those reported in other international studies. Data informed the development of a ‘Sleep Smart’ toolkit designed to improve the in-patient sleep experience.
The Particle-in-Cell modelling of gridded ion engine plume neutralisation has been simplified when compared to traditional methods. This results in significantly less computational resources being required. The NSTAR engine was modelled as a reference, where simulated specific impulse values were found to be 5% higher than the real engine. This method will be most suited to rapid prototyping and optimisation studies, where speed of simulations is an important factor.
This study presents an extension of a previous study (On an Exact Step Length in Gradient-Based Aerodynamic Shape Optimization) to viscous transonic flows. In this work, we showed that the same procedure to derive an explicit expression for an exact step length βexact in a gradient-based optimization method for inviscid transonic flows can be employed for viscous transonic flows. The extended numerical method was evaluated for the viscous flows over the transonic RAE 2822 airfoil at two common flow conditions in the transonic regime. To do so, the RAE 2822 airfoil was reconstructed by a Bezier curve of degree 16. The numerical solution of the transonic turbulent flow over the airfoil was performed using the solver ANSYS Fluent (using the Spalart–Allmaras turbulence model). Using the proposed step length, a gradient-based optimization method was employed to minimize the drag-to-lift ratio of the airfoil. The gradient of the objective function with respect to design variables was calculated by the finite-difference method. Efficiency and accuracy of the proposed method were investigated through two test cases.
The BLOODHOUND SSC project was publicly announced in October 2008, with a primary engineering objective of designing, constructing and running a vehicle capable of achieving a speed of 1000 mph on land.The aerodynamic design of this vehicle is to be accomplished using computational simulation only and this paper describes the development and application of the approach adopted.The computational model employs a cell vertex finite volume algorithm for the solution of compressible viscous flow problems on unstructured hybrid meshes.A one equation turbulence model is adopted and the solution of the steady flow equations is obtained by explicit relaxation.For the combination of high Mach number, complex geometry and complex boundary conditions, involving rotating surfaces and a rolling ground, a consistent HLLC numerical flux function is adopted to ensure a stable procedure.To illustrate the impact of the approach upon the final configuration, a number of simulations undertaken to aid the aerodynamic design are described.
This chapter investigates the soybean-oil "crush" spread, that is the profit margin gained by processing soybeans into soyoil. Soybeans form a large proportion (over 1/5th) of the agricultural output of US farmers and the profit margins gained will therefore have a wide impact on the US economy in general. The chapter uses a number of techniques to forecast and trade the soybean crush spread. A traditional regression analysis is used as a benchmark against more sophisticated models such as a MultiLayer Perceptron (MLP), Recurrent Neural Networks and Higher Order Neural Networks. These are then used to trade the spread, the implementation of a number of filtering techniques as used in the literature are utilised to further refine the trading statistics of the models. The results show that the best model before transactions costs both in- and out-of-sample is the Recurrent Network generating a superior risk adjusted return to all other models investigated. However in the case of most of the models investigated the cost of trading the spread all but eliminates any profit potential. Request access from your librarian to read this chapter's full text.
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