A Bayesian spatio-temporal model of panel design data: airborne particle number concentration in Brisbane, Australia

This paper outlines a methodology for semi-parametric spatio-temporal modelling of data which is dense in time but sparse in space, obtained from a split panel design, the most feasible approach to covering space and time with limited equipment. The data are hourly averaged particle number concentration (PNC) and were collected, as part of the Ultrafine Particles from Transport Emissions and Child Health (UPTECH) project. Two weeks of continuous measurements were taken at each of a number of government primary schools in the Brisbane Metropolitan Area. The monitoring equipment was taken to each school sequentially. The school data are augmented by data from long term monitoring stations at three locations in Brisbane, Australia. Fitting the model helps describe the spatial and temporal variability at a subset of the UPTECH schools and the long-term monitoring sites. The temporal variation is modelled hierarchically with penalised random walk terms, one common to all sites and a term accounting for the remaining temporal trend at each site. Parameter estimates and their uncertainty are computed in a computationally efficient approximate Bayesian inference environment, R-INLA. The temporal part of the model explains daily and weekly cycles in PNC at the schools, which can be used to estimate the exposure of school children to ultrafine particles (UFPs) emitted by vehicles. At each school and long-term monitoring site, peaks in PNC can be attributed to the morning and afternoon rush hour traffic and new particle formation events. The spatial component of the model describes the school to school variation in mean PNC at each school and within each school ground. It is shown how the spatial model can be expanded to identify spatial patterns at the city scale with the inclusion of more spatial locations.Comment: Draft of this paper presented at ISBA 2012 as poster, part of UPTECH projec

Applying hierarchical task analysis to medication administration errors

Medication use in hospitals is a complex process and is dependent on the successful interaction of health professionals functioning within different disciplines. Errors can occur at any one of the five main stages of prescribing, documenting, dispensing or preparation, administering and monitoring. The responsibility for the error is often placed on the nurse, as she or he is the last person in the drug administration chain whilst more pressing underlying causal factors remain unresolved. This paper demonstrates how hierarchical task analysis can be used to model drug administration and then uses the systematic human error reduction and prediction approach to predict which errors are likely to occur. The paper also puts forward design solutions to mitigate these errors

A survey of life support system automation and control

The level of automation and control necessary to support advanced life support systems for use in the manned space program is steadily increasing. As the length and complexity of manned missions increase, life support systems must be able to meet new space challenges. Longer, more complex missions create new demands for increased automation, improved sensors, and improved control systems. It is imperative that research in these key areas keep pace with current and future developments in regenerative life support technology. This paper provides an overview of past and present research in the areas of sensor development, automation, and control of life support systems for the manned space program, and it discusses the impact continued research in several key areas will have on the feasibility, operation, and design of future life support systems

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial