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.

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.

Development and Validation of a Forklift Truck Powertrain Simulation

Fuel economy has become an important consideration in forklift truck design, particularly in Europe. A simulation of the fuel consumption and performance of a forklift truck has been developed, validated and subsequently used to determine the energy consumed by individual powertrain components during drive cycles.
The truck used in this study has a rated lifting capacity of 2500kg, and is powered by a 2.6 litre naturally aspirated diesel engine with a fuel pump containing a mechanical variable-speed governor. The drivetrain consisted of a torque convertor, hydraulic clutch and single speed transmission.
AVL Cruise was used to simulate the vehicle powertrain, with coupled Mathworks Simulink models used to simulate the hydraulic and control systems and governor. The vehicle has been simulated on several performance and fuel consumption drive cycles with the main focus being the VDI 2198 fuel consumption drive cycle.
To validate the model, a truck was instrumented and measurements taken to compare the performance and instantaneous fuel consumption to simulated values. The fuel injector pump was modified and calibrated to enable instantaneous fuel flow to be measured.
The model has been validated to within acceptable limits and has been used to investigate the effect four different torque converters have on the fuel consumption and performance of the forklift truck. The study demonstrates how the model can be used to compare the fuel consumption and performance trade-offs when selecting drivetrain components.

The use of a dynamic truck-trailer drive-by system to monitor bridge damping

Bridge structures are continuously subject to degradation due to the environment, ageing and excess loading. Periodic monitoring of bridges is therefore a key part of any maintenance strategy as it can give early warning if a bridge becomes unsafe. This article investigates an alternative method for the monitoring of bridge dynamic behaviour: a truck-trailer vehicle system, with accelerometers fitted to the axles of the trailer. The method aims to detect changes in the damping of a bridge, which may indicate the existence of damage. A simplified vehicle-bridge interaction model is used in theoretical simulations to assess the effectiveness of the method in detecting those changes. The influence of road profile roughness on the vehicle vibration is overcome by recording accelerations from both axles of a trailer and then analysing the spectra of the difference in the accelerations between the two axles. The effectiveness of the approach in detecting damage simulated as a loss in stiffness is also investigated. In addition, the sensitivity of the approach to the vehicle speed, road roughness class, bridge span length, changes in the equal axle properties and noise is investigated.