Influence of prolonged hospitalization on overall bed occupancy: a five-year single-centre study

Background: Effective bed use is crucial to an efficient NHS. Current targets suggest a decrease in mean occupancy as the most appropriate method of improving overall efficiency. The elderly and those suffering from complex medical problems are thought to account for a high proportion of overall bed occupancy.

Aim: To assess the effect of prolonged hospital stay (>100 days) on overall bed occupancy in a modern teaching hospital.

Design: Retrospective analysis.

Methods: Analysis of all admission episodes (n = 117 178) over a five-year period in a large teaching hospital in a single UK region, serving a population of approximately 200 000. A logistic regression multi-factorial model was used to assess the effect of demographic and diagnostic variables on duration of stay.

Results: A prolonged stay (>100 days) was seen in 648 admission episodes (0.6%). These accounted for 11% of the overall bed occupancy over the 5-year period. Excluding all prolonged admission episodes from our analysis made no difference to the overall median length of stay.

Discussion: Prolonged hospitalizations have a significant impact on bed occupancy. Targeting these very long (>100 days) hospital stays may better improve overall efficiency, compared to targeting mean or median length of stay.

A wavelet-based damage detection algorithm based on bridge acceleration response to a vehicle

Previous research based on theoretical simulations has shown the potential of the wavelet transform to detect damage in a beam by analysing the time-deflection response due to a constant moving load. However, its application to identify damage from the response of a bridge to a vehicle raises a number of questions. Firstly, it may be difficult to record the difference in the deflection signal between a healthy and a slightly damaged structure to the required level of accuracy and high scanning frequencies in the field. Secondly, the bridge is going to have a road profile and it will be loaded by a sprung vehicle and time-varying forces rather than a constant load. Therefore, an algorithm based on a plot of wavelet coefficients versus time to detect damage (a singularity in the plot) appears to be very sensitive to noise. This paper addresses these questions by: (a) using the acceleration signal, instead of the deflection signal, (b) employing a vehicle-bridge finite element interaction model, and (c) developing a novel wavelet-based approach using wavelet energy content at each bridge section which proves to be more sensitive to damage than a wavelet coefficient line plot at a given scale as employed by others.

Identification of damping in a bridge using a moving instrumented vehicle

In recent years, there has been a significant increase in the number of bridges which are being instrumented and monitored on an ongoing basis. This is in part due to the introduction of bridge management systems designed to provide a high level of protection to the public and early warning if the bridge becomes unsafe. This paper investigates a novel alternative; a low-cost method consisting of the use of a vehicle fitted with accelerometers on its axles to monitor the dynamic behaviour of bridges. A simplified half-car vehicle-bridge interaction model is used in theoretical simulations to test the effectiveness of the approach in identifying the damping ratio of the bridge. The method is tested for a range of bridge spans and vehicle velocities using theoretical simulations and the influences of road roughness, initial vibratory condition of the vehicle, signal noise, modelling errors and frequency matching on the accuracy of the results are investigated.

Dynamic Axle Force and Road Profile Identification Using a Moving Vehicle

In the interaction between vehicles, pavements and bridges, it is essential to aim towards a reduction of vehicle axle forces to promote longer pavement life spans and to prevent bridges loads becoming too high. Moreover, as the road surface roughness affects the vehicle dynamic forces, an efficient monitoring of pavement condition is also necessary to achieve this aim. This paper uses a novel algorithm to identify the dynamic interaction forces and pavement roughness from vehicle accelerations in both theoretical simulations and a laboratory experiment; moving force identification theory is applied to a vehicle model for this purpose. Theoretical simulations are employed to evaluate the ability of the algorithm to predict forces over a range of bridge spans and to evaluate the influence of road roughness level on the accuracy of the results. Finally, in addressing the challenge for the real-world problem, the effects of vehicle configuration and speed on the predicted road roughness are also investigated in a laboratory experiment.

Vehicle-barrier tracking of a scaled crash test for roadside barrier design

In this paper the tracking system used to perform a scaled vehicle-barrier crash test is reported. The scaled crash test was performed as part of a wider project aimed at designing a new safety barrier making use of natural building materials. The scaled crash test was designed and performed as a proof of concept of the new mass-based safety barriers and the study was composed of two parts: the scaling technique and of a series of performed scaled crash tests. The scaling method was used for 1) setting the scaled test impact velocity so that energy dissipation and momentum transferring, from the car to the barrier, can be reproduced and 2) predicting the acceleration, velocity and displacement values occurring in the full-scale impact from the results obtained in a scaled test. To achieve this goal the vehicle and barrier displacements were to be recorded together with the vehicle accelerations and angular velocities. These quantities were measured during the tests using acceleration sensors and a tracking system. The tracking system was composed of a high speed camera and a set of targets to measure the vehicle linear and angular velocities. A code was developed to extract the target velocities from the videos and the velocities obtained were then compared with those obtained integrating the accelerations provided by the sensors to check the reliability of the method.

A self-learning teaching-learning based optimization for dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints, such as the valve point effect, power balance and ramp rate limits. The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model. Self-learning teaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.

Risks of high-powered motorcycles among younger adults

OBJECTIVES:
We assessed whether policies designed to safeguard young motorcyclists would be effective given shifts in ownership toward high-powered motorcycles.
METHODS:
We investigated population-wide motor vehicle driver and motorcyclist casualties (excluding passengers) recorded in Britain between 2002 and 2009. To adjust for exposure and measure individual risk, we used the estimated number of trips of motorcyclists and drivers, which had been collected as part of a national travel survey.
RESULTS:
Motorcyclists were 76 times more likely to be killed than were drivers for every trip. Older motorcyclist age-strongly linked to experience, skill set, and riding behavior-did not abate the risks of high-powered motorcycles. Older motorcyclists made more trips on high-powered motorcycles.
CONCLUSIONS:
Tighter engine size restrictions would help reduce the use of high-powered motorcycles. Policymakers should introduce health warnings on the risks of high-powered motorcycles and the benefits of safety equipment.

Monitoring Bridge Dynamic Behaviour using an Instrumented Two Axle Vehicle

Highway structures such as bridges are subject to continuous degradation primarily due to ageing, loading and environmental factors. A rational transport policy must monitor and provide adequate maintenance to this infrastructure to guarantee the required levels of transport service and safety. Increasingly in recent years, bridges are being instrumented and monitored on an ongoing basis due to the implementation of Bridge Management Systems. This is very effective and provides a high level of protection to the public and early warning if the bridge becomes unsafe. However, the process can be expensive and time consuming, requiring the installation of sensors and data acquisition electronics on the bridge. This paper investigates the use of an instrumented 2-axle vehicle fitted with accelerometers to monitor the dynamic behaviour of a bridge network in a simple and cost-effective manner. A simplified half car-beam interaction model is used to simulate the passage of a vehicle over a bridge. This investigation involves the frequency domain analysis of the axle accelerations as the vehicle crosses the bridge. The spectrum of the acceleration record contains noise, vehicle, bridge and road frequency components. Therefore, the bridge dynamic behaviour is monitored in simulations for both smooth and rough road surfaces. The vehicle mass and axle spacing are varied in simulations along with bridge structural damping in order to analyse the sensitivity of the vehicle accelerations to a change in bridge properties. These vehicle accelerations can be obtained for different periods of time and serve as a useful tool to monitor the variation of bridge frequency and damping with time.

Extracting statistically dominant modes of a steel truss bridge from vehicle-induced vibrations

This study is intended to investigate the validity of the stability diagram (SD) aided multivariate autoregressive (MAR) analysis for identifying modal parameters of a real truss bridge. The MAR models are adopted to fit the time series of the dynamic accelerations recorded from a number of observation points on the bridge; then the modal parameters are extracted from the MAR model coefficient matrix. The SD is adopted to determine statistically dominant modes. In plotting the SD, a number of stability criteria are further adopted for filtering out those modes with unstable modal parameters. By the present method, the first five modal frequencies and mode shapes are identified with very high precision, while the damping ratios are identified with high precision for the 1st mode but with poorer precision for higher modes. Moreover, the ability of the SD in selecting structural modes without getting involved in any model-order optimization problem is highlighted through a comparison study.

Effect of vehicle characteristics on safety, fuel use and emissions

Nos últimos anos, o número de vítimas de acidentes de tráfego por milhões de habitantes em Portugal tem sido mais elevado do que a média da União Europeia. Ao nível nacional torna-se premente uma melhor compreensão dos dados de acidentes e sobre o efeito do veículo na gravidade do mesmo. O objetivo principal desta investigação consistiu no desenvolvimento de modelos de previsão da gravidade do acidente, para o caso de um único veículo envolvido e para caso de uma colisão, envolvendo dois veículos. Além disso, esta investigação compreendeu o desenvolvimento de uma análise integrada para avaliar o desempenho do veículo em termos de segurança, eficiência energética e emissões de poluentes. Os dados de acidentes foram recolhidos junto da Guarda Nacional Republicana Portuguesa, na área metropolitana do Porto para o período de 2006-2010. Um total de 1,374 acidentes foram recolhidos, 500 acidentes envolvendo um único veículo e 874 colisões. Para a análise da segurança, foram utilizados modelos de regressão logística. Para os acidentes envolvendo um único veículo, o efeito das características do veículo no risco de feridos graves e/ou mortos (variável resposta definida como binária) foi explorado. Para as colisões envolvendo dois veículos foram criadas duas variáveis binárias adicionais: uma para prever a probabilidade de feridos graves e/ou mortos num dos veículos (designado como veículo V1) e outra para prever a probabilidade de feridos graves e/ou mortos no outro veículo envolvido (designado como veículo V2). Para ultrapassar o desafio e limitações relativas ao tamanho da amostra e desigualdade entre os casos analisados (apenas 5.1% de acidentes graves), foi desenvolvida uma metodologia com base numa estratégia de reamostragem e foram utilizadas 10 amostras geradas de forma aleatória e estratificada para a validação dos modelos. Durante a fase de modelação, foi analisado o efeito das características do veículo, como o peso, a cilindrada, a distância entre eixos e a idade do veículo. Para a análise do consumo de combustível e das emissões, foi aplicada a metodologia CORINAIR. Posteriormente, os dados das emissões foram modelados de forma a serem ajustados a regressões lineares. Finalmente, foi desenvolvido um indicador de análise integrada (denominado “SEG”) que proporciona um método de classificação para avaliar o desempenho do veículo ao nível da segurança rodoviária, consumos e emissões de poluentes.Face aos resultados obtidos, para os acidentes envolvendo um único veículo, o modelo de previsão do risco de gravidade identificou a idade e a cilindrada do veículo como estatisticamente significativas para a previsão de ocorrência de feridos graves e/ou mortos, ao nível de significância de 5%. A exatidão do modelo foi de 58.0% (desvio padrão (D.P.) 3.1). Para as colisões envolvendo dois veículos, ao prever a probabilidade de feridos graves e/ou mortos no veículo V1, a cilindrada do veículo oposto (veículo V2) aumentou o risco para os ocupantes do veículo V1, ao nível de significância de 10%. O modelo para prever o risco de gravidade no veículo V1 revelou um bom desempenho, com uma exatidão de 61.2% (D.P. 2.4). Ao prever a probabilidade de feridos graves e/ou mortos no veículo V2, a cilindrada do veículo V1 aumentou o risco para os ocupantes do veículo V2, ao nível de significância de 5%. O modelo para prever o risco de gravidade no veículo V2 também revelou um desempenho satisfatório, com uma exatidão de 40.5% (D.P. 2.1). Os resultados do indicador integrado SEG revelaram que os veículos mais recentes apresentam uma melhor classificação para os três domínios: segurança, consumo e emissões. Esta investigação demonstra que não existe conflito entre a componente da segurança, a eficiência energética e emissões relativamente ao desempenho dos veículos.

Internalising the external costs of light and heavy goods vehicle transport in London

Report produced as part of the Green Logistics project (EPSRC and Department for Transport funded). This report provides estimates of the total external costs of LGV and HGV operations in London. In 2006, total LGV and HGV activity imposed external costs of approximately £1.75-£1.8 billion using low, medium and high emission cost values. About 27 per cent of these costs were internalised by duties and taxes paid by LGV operators, compared with 26% in the case of HGVs. If congestion costs are excluded, taxes and duties paid by LGV operators are estimated to be 155% of LGVs' allocated infrastructural and environmental costs, compared with 85% in the case of HGVs. When using the medium emission cost values, LGVs accounted for 56% of these external costs in London and HGVs for 44%.

The electric vehicle integration into the power system: an application to the portuguese case

Electric vehicles (EV) offer a great potential to address the integration of renewable energy sources (RES) in the power grid, and thus reduce the dependence on oil as well as the greenhouse gases (GHG) emissions. The high share of wind energy in the Portuguese energy mix expected for 2020 can led to eventual curtailment, especially during the winter when high levels of hydro generation occur. In this paper a methodology based on a unit commitment and economic dispatch is implemented, and a hydro-thermal dispatch is performed in order to evaluate the impact of the EVs integration into the grid. Results show that the considered 10 % penetration of EVs in the Portuguese fleet would increase load in 3 % and would not integrate a significant amount of wind energy because curtailment is already reduced in the absence of EVs. According to the results, the EV is charged mostly with thermal generation and the associated emissions are much higher than if they were calculated based on the generation mix.

Application-specific modified particle swarm optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding he management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Is the electric vehicle a solution for the wind power integration in the portuguese power system?

The integration of Plug-in electric vehicles in the transportation sector has a great potential to reduce oil dependency, the GHG emissions and to contribute for the integration of renewable sources into the electricity generation mix. Portugal has a high share of wind energy, and curtailment may occur, especially during the off-peak hours with high levels of hydro generation. In this context, the electric vehicles, seen as a distributed storage system, can help to reduce the potential wind curtailments and, therefore, increase the integration of wind power into the power system. In order to assess the energy and environmental benefits of this integration, a methodology based on a unit commitment and economic dispatch is adapted and implemented. From this methodology, the thermal generation costs, the CO2 emissions and the potential wind generation curtailment are computed. Simulation results show that a 10% penetration of electric vehicles in the Portuguese fleet would increase electrical load by 3% and reduce wind curtailment by only 26%. This results from the fact that the additional generation required to supply the electric vehicles is mostly thermal. The computed CO2 emissions of the EV are 92 g CO2/kWh which become closer to those of some new ICE engines.

Evaluation of the Electric Vehicle Impact in the Power Demand Curve in a Smart Grid Environment

Smart grids with an intensive penetration of distributed energy resources will play an important role in future power system scenarios. The intermittent nature of renewable energy sources brings new challenges, requiring an efficient management of those sources. Additional storage resources can be beneficially used to address this problem; the massive use of electric vehicles, particularly of vehicle-to-grid (usually referred as gridable vehicles or V2G), becomes a very relevant issue. This paper addresses the impact of Electric Vehicles (EVs) in system operation costs and in power demand curve for a distribution network with large penetration of Distributed Generation (DG) units. An efficient management methodology for EVs charging and discharging is proposed, considering a multi-objective optimization problem. The main goals of the proposed methodology are: to minimize the system operation costs and to minimize the difference between the minimum and maximum system demand (leveling the power demand curve). The proposed methodology perform the day-ahead scheduling of distributed energy resources in a distribution network with high penetration of DG and a large number of electric vehicles. It is used a 32-bus distribution network in the case study section considering different scenarios of EVs penetration to analyze their impact in the network and in the other energy resources management.