A realistic evaluation of track and trigger systems and acute care training for early recognition and treatment of deteriorating ward-based patients: research protocol.

Aim The aim of the study is to evaluate factors that enable or constrain the implementation and service delivery of early warnings systems or acute care training in practice. Background To date there is limited evidence to support the effectiveness of acute care initiatives (early warning systems, acute care training, outreach) in reducing the number of adverse events (cardiac arrest, death, unanticipated Intensive Care admission) through increased recognition and management of deteriorating ward based patients in hospital [1-3]. The reasons posited are that previous research primarily focused on measuring patient outcomes following the implementation of an intervention or programme without considering the social factors (the organisation, the people, external influences) which may have affected the process of implementation and hence measured end-points. Further research which considers the social processes is required in order to understand why a programme works, or does not work, in particular circumstances [4]. Method The design is a multiple case study approach of four general wards in two acute hospitals where Early Warning Systems (EWS) and Acute Life-threatening Events Recognition and Treatment (ALERT) course have been implemented. Various methods are being used to collect data about individual capacities, interpersonal relationships and institutional balance and infrastructures in order to understand the intended and unintended process outcomes of implementing EWS and ALERT in practice. This information will be gathered from individual and focus group interviews with key participants (ALERT facilitators, nursing and medical ALERT instructors, ward managers, doctors, ward nurses and health care assistants from each hospital); non-participant observation of ward organisation and structure; audit of patients' EWS charts and audit of the medical notes of patients who deteriorated during the study period to ascertain whether ALERT principles were followed. Discussion & progress to date This study commenced in January 2007. Ethical approval has been granted and data collection is ongoing with interviews being conducted with key stakeholders. The findings from this study will provide evidence for policy-makers to make informed decisions regarding the direction for strategic and service planning of acute care services to improve the level of care provided to acutely ill patients in hospital. References 1. Esmonde L, McDonnell A, Ball C, Waskett C, Morgan R, Rashidain A et al. Investigating the effectiveness of Critical Care Outreach Services: A systematic review. Intensive Care Medicine 2006; 32: 1713-1721 2. McGaughey J, Alderdice F, Fowler R, Kapila A, Mayhew A, Moutray M. Outreach and Early Warning Systems for the prevention of Intensive Care admission and death of critically ill patients on general hospital wards. Cochrane Database of Systematic Reviews 2007, Issue 3. www.thecochranelibrary.com 3. Winters BD, Pham JC, Hunt EA, Guallar E, Berenholtz S, Pronovost PJ (2007) Rapid Response Systems: A systematic review. Critical Care Medicine 2007; 35 (5): 1238-43 4. Pawson R and Tilley N. Realistic Evaluation. London; Sage: 1997

The monetary benefit of early flood warnings in Europe

Effective disaster risk management relies on science-based solutions to close the gap between prevention and preparedness measures. The consultation on the United Nations post-2015 framework for disaster risk reduction highlights the need for cross-border early warning systems to strengthen the preparedness phases of disaster risk management, in order to save lives and property and reduce the overall impact of severe events. Continental and global scale flood forecasting systems provide vital early flood warning information to national and international civil protection authorities, who can use this information to make decisions on how to prepare for upcoming floods. Here the potential monetary benefits of early flood warnings are estimated based on the forecasts of the continental-scale European Flood Awareness System (EFAS) using existing flood damage cost information and calculations of potential avoided flood damages. The benefits are of the order of 400 Euro for every 1 Euro invested. A sensitivity analysis is performed in order to test the uncertainty in the method and develop an envelope of potential monetary benefits of EFAS warnings. The results provide clear evidence that there is likely a substantial monetary benefit in this cross-border continental-scale flood early warning system. This supports the wider drive to implement early warning systems at the continental or global scale to improve our resilience to natural hazards.

The warning-sign hierarchy between quantitative subcortical motor mapping and continuous motor evoked potential monitoring during resection of supratentorial brain tumors

Mapping and monitoring are believed to provide an early warning sign to determine when to stop tumor removal to avoid mechanical damage to the corticospinal tract (CST). The objective of this study was to systematically compare subcortical monopolar stimulation thresholds (1-20 mA) with direct cortical stimulation (DCS)-motor evoked potential (MEP) monitoring signal abnormalities and to correlate both with new postoperative motor deficits. The authors sought to define a mapping threshold and DCS-MEP monitoring signal changes indicating a minimal safe distance from the CST.

Milk fatty acids as possible biomarkers to early diagnose elevated concentrations of blood plasma nonesterified fatty acids in dairy cows

Most cows encounter a state of negative energy balance during the periparturient period, which may lead to metabolic disorders and impaired fertility. The aim of this study was to assess the potential of milk fatty acids as diagnostic tools of detrimental levels of blood plasma nonesterified fatty acids (NEFA), defined as NEFA concentrations beyond 0.6 mmol/L, in a data set of 92 early lactating cows fed a glucogenic or lipogenic diet and subjected to 0-, 30-, or 60-d dry period before parturition. Milk was collected in wk 2, 3, 4, and 8 (n = 368) and blood was sampled weekly from wk 2 to 8 after parturition. Milk was analyzed for milk fatty acids and blood plasma for NEFA. Data were classified as "at risk of detrimental blood plasma NEFA" (NEFA ≥ 0.6 mmol/L) and "not at risk of detrimental blood plasma NEFA" (NEFA <0.6 mmol/L). Concentrations of 45 milk fatty acids and milk fat C18:1 cis-9-to-C15:0 ratio were subjected to a discriminant analysis. Milk fat C18:1 cis-9 revealed the most discriminating variable to identify detrimental blood plasma NEFA. A false positive rate of 10% allowed us to diagnose 46% of the detrimental blood plasma NEFA cases based on a milk fat C18:1 cis-9 concentration of at least 230 g/kg of milk fatty acids. Additionally, it was assessed whether the milk fat C18:1 cis-9 concentrations of wk 2 could be used as an early warning for detrimental blood plasma NEFA risk during the first 8 wk in lactation. Cows with at least 240 g/kg of C18:1 cis-9 in milk fat had about 50% chance to encounter blood plasma NEFA values of 0.6 mmol/L or more during the first 8 wk of lactation, with a false positive rate of 11.4%. Profit simulations were based on costs for cows suffering from detrimental blood plasma NEFA, and costs for preventive treatment based on daily dosing of propylene glycol for 3 wk. Given the relatively low incidence rate (8% of all observations), continuous monitoring of milk fatty acids during the first 8 wk of lactation to diagnose detrimental blood plasma NEFA does not seem cost effective. On the contrary, milk fat C18:1 cis-9 of the second lactation week could be an early warning of cows at risk of detrimental blood NEFA. In this case, selective treatment may be cost effective.

Early warnings of heart rate deterioration

Hospitals can experience difficulty in detecting and responding to early signs of patient deterioration leading to late intensive care referrals, excess mortality and morbidity, and increased hospital costs. Our study aims to explore potential indicators of physiological deterioration by the analysis of vital-signs. The dataset used comprises heart rate (HR) measurements from MIMIC II waveform database, taken from six patients admitted to the Intensive Care Unit (ICU) and diagnosed with severe sepsis. Different indicators were considered: 1) generic early warning indicators used in ecosystems analysis (autocorrelation at-1-lag (ACF1), standard deviation (SD), skewness, kurtosis and heteroskedasticity) and 2) entropy analysis (kernel entropy and multi scale entropy). Our preliminary findings suggest that when a critical transition is approaching, the equilibrium state changes what is visible in the ACF1 and SD values, but also by the analysis of the entropy. Entropy allows to characterize the complexity of the time series during the hospital stay and can be used as an indicator of regime shifts in a patient’s condition. One of the main problems is its dependency of the scale used. Our results demonstrate that different entropy scales should be used depending of the level of entropy verified.

Patient physiological status at the emergency department-ward interface and emergency calls for clinical deterioration during early hospital admission

AIMS: To examine the relationship between physiological status at the emergency department-ward interface and emergency calls (medical emergency team or cardiac arrest team activation) during the first 72 hours of hospital admission. BACKGROUND: Ward adverse events are related to abnormal physiology in emergency department however the relationship between physiology at the emergency department-ward interface and ward adverse events is unknown. DESIGN: Descriptive and exploratory design. METHODS: The study involved 1980 patients at three hospitals in Melbourne Australia: i) 660 randomly selected adults admitted via the emergency department to medical or surgical wards during 2012 and who had an emergency call; and ii) 1320 adults without emergency calls matched for gender, triage category, usual residence, admitting unit and age. RESULTS/FINDINGS: The median age was 78 years and 48·8% were males. The median time to the first emergency call was 18·8 hours and ≥1 abnormal parameters were documented in 34·9% of patients during the last hour of ED care and 47·1% of patients during first hour of ward care. Emergency calls were significantly more common in patients with heart rate and conscious state abnormalities during the last hour of emergency care and abnormal oxygen saturation, heart rate or respiratory rate during the first hour of ward care. Medical emergency team afferent limb failure occurred in 55·3% patients with medical emergency team activation criteria during first hour of ward care. CONCLUSION: The use of physiological status at the emergency department-ward interface to guide care planning and reasons for and outcomes of medical emergency team afferent limb failure are important areas for future research.

Monitoring the Performance of Complex Projects from Multiple Perspectives over Multiple Time Frames

When complex projects go wrong they can go horribly wrong with severe financial consequences. We are undertaking research to develop leading performance indicators for complex projects, metrics to provide early warning of potential difficulties. The assessment of success of complex projects can be made by a range of stakeholders over different time scales, against different levels of project results: the project’s outputs at the end of the project; the project’s outcomes in the months following project completion; and the project’s impact in the years following completion. We aim to identify leading performance indicators, which may include both success criteria and success factors, and which can be measured by the project team during project delivery to forecast success as assessed by key stakeholders in the days, months and years following the project. The hope is the leading performance indicators will act as alarm bells to show if a project is diverting from plan so early corrective action can be taken. It may be that different combinations of the leading performance indicators will be appropriate depending on the nature of project complexity. In this paper we develop a new model of project success, whereby success is assessed by different stakeholders over different time frames against different levels of project results. We then relate this to measurements that can be taken during project delivery. A methodology is described to evaluate the early parts of this model. Its implications and limitations are described. This paper describes work in progress.

Socio-environmental factors and suicide in Queensland, Australia

Suicide has drawn much attention from both the scientific community and the public. Examining the impact of socio-environmental factors on suicide is essential in developing suicide prevention strategies and interventions, because it will provide health authorities with important information for their decision-making. However, previous studies did not examine the impact of socio-environmental factors on suicide using a spatial analysis approach. The purpose of this study was to identify the patterns of suicide and to examine how socio-environmental factors impact on suicide over time and space at the Local Governmental Area (LGA) level in Queensland. The suicide data between 1999 and 2003 were collected from the Australian Bureau of Statistics (ABS). Socio-environmental variables at the LGA level included climate (rainfall, maximum and minimum temperature), Socioeconomic Indexes for Areas (SEIFA) and demographic variables (proportion of Indigenous population, unemployment rate, proportion of population with low income and low education level). Climate data were obtained from Australian Bureau of Meteorology. SEIFA and demographic variables were acquired from ABS. A series of statistical and geographical information system (GIS) approaches were applied in the analysis. This study included two stages. The first stage used average annual data to view the spatial pattern of suicide and to examine the association between socio-environmental factors and suicide over space. The second stage examined the spatiotemporal pattern of suicide and assessed the socio-environmental determinants of suicide, using more detailed seasonal data. In this research, 2,445 suicide cases were included, with 1,957 males (80.0%) and 488 females (20.0%). In the first stage, we examined the spatial pattern and the determinants of suicide using 5-year aggregated data. Spearman correlations were used to assess associations between variables. Then a Poisson regression model was applied in the multivariable analysis, as the occurrence of suicide is a small probability event and this model fitted the data quite well. Suicide mortality varied across LGAs and was associated with a range of socio-environmental factors. The multivariable analysis showed that maximum temperature was significantly and positively associated with male suicide (relative risk [RR] = 1.03, 95% CI: 1.00 to 1.07). Higher proportion of Indigenous population was accompanied with more suicide in male population (male: RR = 1.02, 95% CI: 1.01 to 1.03). There was a positive association between unemployment rate and suicide in both genders (male: RR = 1.04, 95% CI: 1.02 to 1.06; female: RR = 1.07, 95% CI: 1.00 to 1.16). No significant association was observed for rainfall, minimum temperature, SEIFA, proportion of population with low individual income and low educational attainment. In the second stage of this study, we undertook a preliminary spatiotemporal analysis of suicide using seasonal data. Firstly, we assessed the interrelations between variables. Secondly, a generalised estimating equations (GEE) model was used to examine the socio-environmental impact on suicide over time and space, as this model is well suited to analyze repeated longitudinal data (e.g., seasonal suicide mortality in a certain LGA) and it fitted the data better than other models (e.g., Poisson model). The suicide pattern varied with season and LGA. The north of Queensland had the highest suicide mortality rate in all the seasons, while there was no suicide case occurred in the southwest. Northwest had consistently higher suicide mortality in spring, autumn and winter. In other areas, suicide mortality varied between seasons. This analysis showed that maximum temperature was positively associated with suicide among male population (RR = 1.24, 95% CI: 1.04 to 1.47) and total population (RR = 1.15, 95% CI: 1.00 to 1.32). Higher proportion of Indigenous population was accompanied with more suicide among total population (RR = 1.16, 95% CI: 1.13 to 1.19) and by gender (male: RR = 1.07, 95% CI: 1.01 to 1.13; female: RR = 1.23, 95% CI: 1.03 to 1.48). Unemployment rate was positively associated with total (RR = 1.40, 95% CI: 1.24 to 1.59) and female (RR=1.09, 95% CI: 1.01 to 1.18) suicide. There was also a positive association between proportion of population with low individual income and suicide in total (RR = 1.28, 95% CI: 1.10 to 1.48) and male (RR = 1.45, 95% CI: 1.23 to 1.72) population. Rainfall was only positively associated with suicide in total population (RR = 1.11, 95% CI: 1.04 to 1.19). There was no significant association for rainfall, minimum temperature, SEIFA, proportion of population with low educational attainment. The second stage is the extension of the first stage. Different spatial scales of dataset were used between the two stages (i.e., mean yearly data in the first stage, and seasonal data in the second stage), but the results are generally consistent with each other. Compared with other studies, this research explored the variety of the impact of a wide range of socio-environmental factors on suicide in different geographical units. Maximum temperature, proportion of Indigenous population, unemployment rate and proportion of population with low individual income were among the major determinants of suicide in Queensland. However, the influence from other factors (e.g. socio-culture background, alcohol and drug use) influencing suicide cannot be ignored. An in-depth understanding of these factors is vital in planning and implementing suicide prevention strategies. Five recommendations for future research are derived from this study: (1) It is vital to acquire detailed personal information on each suicide case and relevant information among the population in assessing the key socio-environmental determinants of suicide; (2) Bayesian model could be applied to compare mortality rates and their socio-environmental determinants across LGAs in future research; (3) In the LGAs with warm weather, high proportion of Indigenous population and/or unemployment rate, concerted efforts need to be made to control and prevent suicide and other mental health problems; (4) The current surveillance, forecasting and early warning system needs to be strengthened, to trace the climate and socioeconomic change over time and space and its impact on population health; (5) It is necessary to evaluate and improve the facilities of mental health care, psychological consultation, suicide prevention and control programs; especially in the areas with low socio-economic status, high unemployment rate, extreme weather events and natural disasters.

A hidden Markov model and relative entropy rate approach to vision-based dim target detection for UAV sense-and-avoid

Uninhabited aerial vehicles (UAVs) are a cutting-edge technology that is at the forefront of aviation/aerospace research and development worldwide. Many consider their current military and defence applications as just a token of their enormous potential. Unlocking and fully exploiting this potential will see UAVs in a multitude of civilian applications and routinely operating alongside piloted aircraft. The key to realising the full potential of UAVs lies in addressing a host of regulatory, public relation, and technological challenges never encountered be- fore. Aircraft collision avoidance is considered to be one of the most important issues to be addressed, given its safety critical nature. The collision avoidance problem can be roughly organised into three areas: 1) Sense; 2) Detect; and 3) Avoid. Sensing is concerned with obtaining accurate and reliable information about other aircraft in the air; detection involves identifying potential collision threats based on available information; avoidance deals with the formulation and execution of appropriate manoeuvres to maintain safe separation. This thesis tackles the detection aspect of collision avoidance, via the development of a target detection algorithm that is capable of real-time operation onboard a UAV platform. One of the key challenges of the detection problem is the need to provide early warning. This translates to detecting potential threats whilst they are still far away, when their presence is likely to be obscured and hidden by noise. Another important consideration is the choice of sensors to capture target information, which has implications for the design and practical implementation of the detection algorithm. The main contributions of the thesis are: 1) the proposal of a dim target detection algorithm combining image morphology and hidden Markov model (HMM) filtering approaches; 2) the novel use of relative entropy rate (RER) concepts for HMM filter design; 3) the characterisation of algorithm detection performance based on simulated data as well as real in-flight target image data; and 4) the demonstration of the proposed algorithm's capacity for real-time target detection. We also consider the extension of HMM filtering techniques and the application of RER concepts for target heading angle estimation. In this thesis we propose a computer-vision based detection solution, due to the commercial-off-the-shelf (COTS) availability of camera hardware and the hardware's relatively low cost, power, and size requirements. The proposed target detection algorithm adopts a two-stage processing paradigm that begins with an image enhancement pre-processing stage followed by a track-before-detect (TBD) temporal processing stage that has been shown to be effective in dim target detection. We compare the performance of two candidate morphological filters for the image pre-processing stage, and propose a multiple hidden Markov model (MHMM) filter for the TBD temporal processing stage. The role of the morphological pre-processing stage is to exploit the spatial features of potential collision threats, while the MHMM filter serves to exploit the temporal characteristics or dynamics. The problem of optimising our proposed MHMM filter has been examined in detail. Our investigation has produced a novel design process for the MHMM filter that exploits information theory and entropy related concepts. The filter design process is posed as a mini-max optimisation problem based on a joint RER cost criterion. We provide proof that this joint RER cost criterion provides a bound on the conditional mean estimate (CME) performance of our MHMM filter, and this in turn establishes a strong theoretical basis connecting our filter design process to filter performance. Through this connection we can intelligently compare and optimise candidate filter models at the design stage, rather than having to resort to time consuming Monte Carlo simulations to gauge the relative performance of candidate designs. Moreover, the underlying entropy concepts are not constrained to any particular model type. This suggests that the RER concepts established here may be generalised to provide a useful design criterion for multiple model filtering approaches outside the class of HMM filters. In this thesis we also evaluate the performance of our proposed target detection algorithm under realistic operation conditions, and give consideration to the practical deployment of the detection algorithm onboard a UAV platform. Two fixed-wing UAVs were engaged to recreate various collision-course scenarios to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. Based on this collected data, our proposed detection approach was able to detect targets out to distances ranging from about 400m to 900m. These distances, (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning ahead of impact that approaches the 12.5 second response time recommended for human pilots. Furthermore, readily available graphic processing unit (GPU) based hardware is exploited for its parallel computing capabilities to demonstrate the practical feasibility of the proposed target detection algorithm. A prototype hardware-in- the-loop system has been found to be capable of achieving data processing rates sufficient for real-time operation. There is also scope for further improvement in performance through code optimisations. Overall, our proposed image-based target detection algorithm offers UAVs a cost-effective real-time target detection capability that is a step forward in ad- dressing the collision avoidance issue that is currently one of the most significant obstacles preventing widespread civilian applications of uninhabited aircraft. We also highlight that the algorithm development process has led to the discovery of a powerful multiple HMM filtering approach and a novel RER-based multiple filter design process. The utility of our multiple HMM filtering approach and RER concepts, however, extend beyond the target detection problem. This is demonstrated by our application of HMM filters and RER concepts to a heading angle estimation problem.

Vulnerability of eco-environmental health to climate change : the views of government stakeholders and other specialists in Queensland, Australia

Background: There is overwhelming scientific evidence that human activities have changed and will continue to change the climate of the Earth. Eco-environmental health, which refers to the interdependencies between ecological systems and population health and well-being, is likely to be significantly influenced by climate change. The aim of this study was to examine perceptions from government stakeholders and other relevant specialists about the threat of climate change, their capacity to deal with it, and how to develop and implement a framework for assessing vulnerability of eco-environmental health to climate change.---------- Methods: Two focus groups were conducted in Brisbane, Australia with representatives from relevant government agencies, non-governmental organisations, and the industry sector (n = 15) involved in the discussions. The participants were specialists on climate change and public health from governmental agencies, industry, and nongovernmental organisations in South-East Queensland.---------- Results: The specialists perceived climate change to be a threat to eco-environmental health and had substantial knowledge about possible implications and impacts. A range of different methods for assessing vulnerability were suggested by the participants and the complexity of assessment when dealing with multiple hazards was acknowledged. Identified factors influencing vulnerability were perceived to be of a social, physical and/or economic nature. They included population growth, the ageing population with associated declines in general health and changes in the vulnerability of particular geographical areas due to for example, increased coastal development, and financial stress. Education, inter-sectoral collaboration, emergency management (e.g. development of early warning systems), and social networks were all emphasised as a basis for adapting to climate change. To develop a framework, different approaches were discussed for assessing eco-environmental health vulnerability, including literature reviews to examine the components of vulnerability such as natural hazard risk and exposure and to investigate already existing frameworks for assessing vulnerability.---------- Conclusion: The study has addressed some important questions in regard to government stakeholders and other specialists’ views on the threat of climate change and its potential impacts on eco-environmental health. These findings may have implications in climate change and public health decision-making.

Sustainable fashion textiles design : value adding through technological enhancement

A key concern in the field of contemporary fashion/textiles design is the emergence of ‘fast fashion’: best explained as "buy it Friday, wear it Saturday and throw it away on Sunday" (O'Loughlin, 2007). In this contemporary retail atmosphere of “pile it high: sell it cheap” and “quick to market”, even designer goods have achieved a throwaway status. This modern culture of consumerism is the antithesis of sustainability and is proving a dilemma surrounding sustainable practice for designers and producers in the disciplines (de Blas, 2010). Design researchers including those in textiles/fashion have begun to explore what is a key question in the 21st century in order to create a vision and reason for their disciplines: Can products be designed to have added value to the consumer and hence contribute to a more sustainable industry? Fashion Textiles Design has much to answer for in contributing to the problems of unsustainable practices on a global scale in design, production and waste. However, designers within this field also have great potential to contribute to practical ‘real world’ solutions. ----- ----- This paper provides an overview of some of the design and technological developments from the fashion/textiles industry, endorsing a model where designers and technicians use their transferrable skills for wellbeing rather than desire. Smart materials in the form of responsive and adaptive fibres and fabrics combined with electro active devices, and ICT are increasingly shaping many aspects of society particularly in the leisure industry and interactive consumer products are ever more visible in healthcare. Combinations of biocompatible delivery devices with bio sensing elements can create analyse, sense and actuate early warning and monitoring systems which can be linked to data logging and patient records via intelligent networks. Patient sympathetic, ‘smart’ fashion/textiles applications based on interdisciplinary expertise utilising textiles design and technology is emerging. An analysis of a series of case studies demonstrates the potential of fashion textiles design practitioners to exploit the concept of value adding through technological garment and textiles applications and enhancement for health and wellbeing and in doing so contribute to a more sustainable future fashion/textiles design industry.

Improved salinity management for the Nebine / Mungallala / Wallam catchment

The low stream salinity naturally in the Nebine-Mungallala Catchment, extent of vegetation retention, relatively low rainfall and high evaporation indicates that there is a relatively low risk of rising shallow groundwater tables in the catchment. Scalding caused by wind and water erosion exposing highly saline sub-soils is a more important regional issue, such as in the Homeboin area. Local salinisation associated with evaporation of bore water from free flowing bore drains and bores is also an important land degradation issue particularly in the lower Nebine, Wallam and Mungallala Creeks. The replacement of free flowing artesian bores and bore drains with capped bores and piped water systems under the Great Artesian Basin bore rehabilitation program is addressing local salinisation and scalding in the vicinity of bore drains and preventing the discharge of saline bore water to streams. Three principles for the prevention and control of salinity in the Nebine Mungallala catchment have been identified in this review: • Avoid salinity through avoiding scalds – i.e. not exposing the near-surface salt in landscape through land degradation; • Riparian zone management: Scalding often occurs within 200m or so of watering lines. Natural drainage lines are most likely to be overstocked, and thus have potential for scalding. Scalding begins when vegetation is removed, and without that binding cover, wind and water erosion exposes the subsoil; and • Monitoring of exposed or grazed soil areas. Based on the findings of the study, we make the following recommendations: 1. Undertake a geotechnical study of existing maps and other data to help identify and target areas most at risk of rising water tables causing salinity. Selected monitoring should then be established using piezometers as an early warning system. 2. SW NRM should financially support scald reclamation activity through its various funding programs. However, for this to have any validity in the overall management of salinity risk, it is critical that such funding require the landholder to undertake a salinity hazard/risk assessment of his/her holding. 3. A staged approach to funding may be appropriate. In the first instance, it would be reasonable to commence funding some pilot scald reclamation work with a view to further developing and piloting the farm hazard/risk assessment tools, and exploring how subsequent grazing management strategies could be incorporated within other extension and management activities. Once the details of the necessary farm level activities have been more clearly defined, and following the outcomes of the geotechnical review recommended above, a more comprehensive funding package could be rolled out to priority areas. 4. We recommend that best-practice grazing management training currently on offer should be enhanced with information about salinity risk in scald-prone areas, and ways of minimising the likelihood of scald formation. 5. We recommend that course material be developed for local students in Years 6 and 7, and that arrangements be made with local schools to present this information. Given the constraints of existing syllabi, we envisage that negotiations may have to be undertaken with the Department of Education in order for this material to be permitted to be used. We have contact with key people who could help in this if required. 6. We recommend that SW NRM continue to support existing extension activities such as Grazing Land Management and the Monitoring Made Easy tools. These aids should be able to be easily expanding to incorporate techniques for monitoring, addressing and preventing salinity and scalding. At the time of writing staff of SW NRM were actively involved in this process. It is important that these activities are adequately resourced to facilitate the uptake by landholders of the perception that salinity is an issue that needs to be addressed as part of everyday management. 7. We recommend that SW NRM consider investing in the development and deployment of a scenario-modelling learning support tool as part of the awareness raising and education activities. Secondary salinity is a dynamic process that results from ongoing human activity which mobilises and/or exposes salt occurring naturally in the landscape. Time scales can be short to very long, and the benefits of management actions can similarly have immediate or very long time frames. One way to help explain the dynamics of these processes is through scenario modelling.

Machine prognostics based on health state probability estimation

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation and can also improve productivity and enhance system’s safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. Although a variety of prognostic methodologies have been reported recently, their application in industry is still relatively new and mostly focused on the prediction of specific component degradations. Furthermore, they required significant and sufficient number of fault indicators to accurately prognose the component faults. Hence, sufficient usage of health indicators in prognostics for the effective interpretation of machine degradation process is still required. Major challenges for accurate longterm prediction of remaining useful life (RUL) still remain to be addressed. Therefore, continuous development and improvement of a machine health management system and accurate long-term prediction of machine remnant life is required in real industry application. This thesis presents an integrated diagnostics and prognostics framework based on health state probability estimation for accurate and long-term prediction of machine remnant life. In the proposed model, prior empirical (historical) knowledge is embedded in the integrated diagnostics and prognostics system for classification of impending faults in machine system and accurate probability estimation of discrete degradation stages (health states). The methodology assumes that machine degradation consists of a series of degraded states (health states) which effectively represent the dynamic and stochastic process of machine failure. The estimation of discrete health state probability for the prediction of machine remnant life is performed using the ability of classification algorithms. To employ the appropriate classifier for health state probability estimation in the proposed model, comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault data of three different faults in a high pressure liquefied natural gas (HP-LNG) pump. As a result of this comparison study, SVMs were employed in heath state probability estimation for the prediction of machine failure in this research. The proposed prognostic methodology has been successfully tested and validated using a number of case studies from simulation tests to real industry applications. The results from two actual failure case studies using simulations and experiments indicate that accurate estimation of health states is achievable and the proposed method provides accurate long-term prediction of machine remnant life. In addition, the results of experimental tests show that the proposed model has the capability of providing early warning of abnormal machine operating conditions by identifying the transitional states of machine fault conditions. Finally, the proposed prognostic model is validated through two industrial case studies. The optimal number of health states which can minimise the model training error without significant decrease of prediction accuracy was also examined through several health states of bearing failure. The results were very encouraging and show that the proposed prognostic model based on health state probability estimation has the potential to be used as a generic and scalable asset health estimation tool in industrial machinery.

Exposure to hot and cold temperatures and ambulance attendances in Brisbane, Australia: A time series study

ABSTRACT Objectives: To investigate the effect of hot and cold temperatures on ambulance attendances. Design: An ecological time series study. Setting and participants: The study was conducted in Brisbane, Australia. We collected information on 783 935 daily ambulance attendances, along with data of associated meteorological variables and air pollutants, for the period of 2000–2007. Outcome measures: The total number of ambulance attendances was examined, along with those related to cardiovascular, respiratory and other non-traumatic conditions. Generalised additive models were used to assess the relationship between daily mean temperature and the number of ambulance attendances. Results: There were statistically significant relationships between mean temperature and ambulance attendances for all categories. Acute heat effects were found with a 1.17% (95% CI: 0.86%, 1.48%) increase in total attendances for 1 °C increase above threshold (0–1 days lag). Cold effects were delayed and longer lasting with a 1.30% (0.87%, 1.73%) increase in total attendances for a 1 °C decrease below the threshold (2–15 days lag). Harvesting was observed following initial acute periods of heat effects, but not for cold effects. Conclusions: This study shows that both hot and cold temperatures led to increases in ambulance attendances for different medical conditions. Our findings support the notion that ambulance attendance records are a valid and timely source of data for use in the development of local weather/health early warning systems.

Exposure to hot and cold temperatures and ambulance attendances in Brisbane, Australia

Objectives: To investigate the effect of hot and cold temperatures on ambulance attendances. Design: An ecological time series study. Setting and participants: The study was conducted in Brisbane, Australia. We collected information on 783 935 daily ambulance attendances, along with data of associated meteorological variables and air pollutants, for the period of 2000–2007. Outcome measures: The total number of ambulance attendances was examined, along with those related to cardiovascular, respiratory and other non-traumatic conditions. Generalised additive models were used to assess the relationship between daily mean temperature and the number of ambulance attendances. Results: There were statistically significant relationships between mean temperature and ambulance attendances for all categories. Acute heat effects were found with a 1.17% (95% CI: 0.86%, 1.48%) increase in total attendances for 1 °C increase above threshold (0–1 days lag). Cold effects were delayed and longer lasting with a 1.30% (0.87%, 1.73%) increase in total attendances for a 1 °C decrease below the threshold (2–15 days lag). Harvesting was observed following initial acute periods of heat effects, but not for cold effects. Conclusions: This study shows that both hot and cold temperatures led to increases in ambulance attendances for different medical conditions. Our findings support the notion that ambulance attendance records are a valid and timely source of data for use in the development of local weather/health early warning systems.