Structural reliability prediction of a steel bridge element using Dynamic Object Oriented Bayesian Network (DOOBN)

Different from conventional methods for structural reliability evaluation, such as, first/second-order reliability methods (FORM/SORM) or Monte Carlo simulation based on corresponding limit state functions, a novel approach based on dynamic objective oriented Bayesian network (DOOBN) for prediction of structural reliability of a steel bridge element has been proposed in this paper. The DOOBN approach can effectively model the deterioration processes of a steel bridge element and predict their structural reliability over time. This approach is also able to achieve Bayesian updating with observed information from measurements, monitoring and visual inspection. Moreover, the computational capacity embedded in the approach can be used to facilitate integrated management and maintenance optimization in a bridge system. A steel bridge girder is used to validate the proposed approach. The predicted results are compared with those evaluated by FORM method.

Moving tele-monitoring and tele-treatment from promise to practice : a business model approach for a chronic lower back pain application

The availability of new information and communication technologies creates opportunities for new, mobile tele-health services. While many promising tele-health projects deliver working R&D prototypes, they often do not result in actual deployment. We aim to identify critical issues than can increase our understanding and enhance the viability of the mobile tele-health services beyond the R&D phase by developing a business model. The present study describes the systematic development and evaluation of a service-oriented business model for tele-monitoring and -treatment of chronic lower back pain patients based on a mobile technology prototype. We address challenges of multi-sector collaboration and disruptive innovation.

Monitoring mesenchymal stem cell cultures using image processing and pattern recognition techniques

Stem cells have attracted tremendous interest in recent times due to their promise in providing innovative new treatments for a great range of currently debilitating diseases. This is due to their potential ability to regenerate and repair damaged tissue, and hence restore lost body function, in a manner beyond the body's usual healing process. Bone marrow-derived mesenchymal stem cells or bone marrow stromal cells are one type of adult stem cells that are of particular interest. Since they are derived from a living human adult donor, they do not have the ethical issues associated with the use of human embryonic stem cells. They are also able to be taken from a patient or other donors with relative ease and then grown readily in the laboratory for clinical application. Despite the attractive properties of bone marrow stromal cells, there is presently no quick and easy way to determine the quality of a sample of such cells. Presently, a sample must be grown for weeks and subject to various time-consuming assays, under the direction of an expert cell biologist, to determine whether it will be useful. Hence there is a great need for innovative new ways to assess the quality of cell cultures for research and potential clinical application. The research presented in this thesis investigates the use of computerised image processing and pattern recognition techniques to provide a quicker and simpler method for the quality assessment of bone marrow stromal cell cultures. In particular, aim of this work is to find out whether it is possible, through the use of image processing and pattern recognition techniques, to predict the growth potential of a culture of human bone marrow stromal cells at early stages, before it is readily apparent to a human observer. With the above aim in mind, a computerised system was developed to classify the quality of bone marrow stromal cell cultures based on phase contrast microscopy images. Our system was trained and tested on mixed images of both healthy and unhealthy bone marrow stromal cell samples taken from three different patients. This system, when presented with 44 previously unseen bone marrow stromal cell culture images, outperformed human experts in the ability to correctly classify healthy and unhealthy cultures. The system correctly classified the health status of an image 88% of the time compared to an average of 72% of the time for human experts. Extensive training and testing of the system on a set of 139 normal sized images and 567 smaller image tiles showed an average performance of 86% and 85% correct classifications, respectively. The contributions of this thesis include demonstrating the applicability and potential of computerised image processing and pattern recognition techniques to the task of quality assessment of bone marrow stromal cell cultures. As part of this system, an image normalisation method has been suggested and a new segmentation algorithm has been developed for locating cell regions of irregularly shaped cells in phase contrast images. Importantly, we have validated the efficacy of both the normalisation and segmentation method, by demonstrating that both methods quantitatively improve the classification performance of subsequent pattern recognition algorithms, in discriminating between cell cultures of differing health status. We have shown that the quality of a cell culture of bone marrow stromal cells may be assessed without the need to either segment individual cells or to use time-lapse imaging. Finally, we have proposed a set of features, that when extracted from the cell regions of segmented input images, can be used to train current state of the art pattern recognition systems to predict the quality of bone marrow stromal cell cultures earlier and more consistently than human experts.

Caseload management, work-related stress and case manager self-efficacy among Victorian mental health case managers

Objective: In Australia and comparable countries, case management has become the dominant process by which public mental health services provide outpatient clinical services to people with severe mental illness. There is recognition that caseload size impacts on service provision and that management of caseloads is an important dimension of overall service management. There has been little empirical investigation, however, of caseload and its management. The present study was undertaken in the context of an industrial agreement in Victoria, Australia that required services to introduce standardized approaches to caseload management. The aims of the present study were therefore to (i) investigate caseload size and approaches to caseload management in Victoria's mental health services; and (ii) determine whether caseload size and/or approach to caseload management is associated with work-related stress or case manager self-efficacy among community mental health professionals employed in Victoria's mental health services. Method: A total of 188 case managers responded to an online cross-sectional survey with both purpose-developed items investigating methods of case allocation and caseload monitoring, and standard measures of work-related stress and case manager personal efficacy. Results: The mean caseload size was 20 per full-time case manager. Both work-related stress scores and case manager personal efficacy scores were broadly comparable with those reported in previous studies. Higher caseloads were associated with higher levels of work-related stress and lower levels of case manager personal efficacy. Active monitoring of caseload was associated with lower scores for work-related stress and higher scores for case manager personal efficacy, regardless of size of caseload. Although caseloads were most frequently monitored by the case manager, there was evidence that monitoring by a supervisor was more beneficial than self-monitoring. Conclusion: Routine monitoring of caseload, especially by a workplace supervisor, may be effective in reducing work-related stress and enhancing case manager personal efficacy. Keywords: case management, caseload, stress

Is the iPhone an accurate and useful tool for the monitoring of spinal deformity?

The progression of spinal deformity is traditionally monitored by spinal surgeons using the Cobb method on hardcopy radiographs with a protractor and pencil. The rotation of the spine and ribcage (rib hump) in scoliosis is measured with a simple hand-held inclinometer (Scoliometer). The iPhone and other smart phones have the capability to accurately sense inclination, and can therefore be used to measure Cobb angles and rib hump angulation. The purpose of this study was to quantify the performance of the iPhone compared to a standard protractor for measuring Cobb angles and the Scoliometer for measuring rib humps. The study concluded that the iPhone is a clinically equivalent measuring tool to the traditional protractor and Scoliometer

Journeys to health services in Great Britain : an analysis of changing travel patterns 1985-2006

This paper examines changing patterns in the utilisation and geographic access to health services in Great Britain using National Travel Survey data (1985-2006). The utilisation rate was derived using the proportion of journeys made to access health services. Geographic access was analysed by separating the concept into its accessibility and mobility dimensions. Regression analyses were conducted to investigate the differences between different socio-spatial groups in these indicators over the period 1985-2006. This study found that journey distances to health facilities were significantly shorter and also gradually reduced over the period in question for Londoners, females, those without a car or on low incomes, and older people. However, most of their rates of utilisation of health services were found to be significantly lower because their journey times were significantly longer and also gradually increased over the periods. These findings indicate that the rate of utilisation of health services largely depends on mobility level although previous research studies have traditionally overlooked the mobility dimension.

Room ventilation and the risk of airborne infection transmission in three health care settings within a large teaching hospital

Background: Room ventilation is a key determinant of airborne disease transmission. Despite this, ventilation guidelines in hospitals are not founded on robust scientific evidence related to prevention of airborne transmission. Methods: We sought to assess the effect of ventilation rates on influenza, tuberculosis (TB) and rhinovirus infection risk within three distinct rooms in a major urban hospital; a Lung Function Laboratory, Emergency Department (ED) Negative-pressure Isolation Room and an Outpatient Consultation Room were investigated. Air exchange rate measurements were performed in each room using CO2 as a tracer. Gammaitoni and Nucci’s model was employed to estimate infection risk. Results: Current outdoor air exchange rates in the Lung Function Laboratory and ED Isolation Room limited infection risks to between 0.1 and 3.6%. Influenza risk for individuals entering an Outpatient Consultation Room after an infectious individual departed ranged from 3.6 to 20.7%, depending on the duration for which each person occupied the room. Conclusions: Given the absence of definitive ventilation guidelines for hospitals, air exchange measurements combined with modelling afford a useful means of assessing, on a case-by-case basis, the suitability of room ventilation at preventing airborne disease transmission.

Adherence, adaptation and acceptance of elderly chronic heart failure patients to receiving healthcare via telephone-monitoring

Background: Although the potential to reduce hospitalisation and mortality in chronic heart failure (CHF) is well reported, the feasibility of receiving healthcare by structured telephone support or telemonitoring is not. Aims: To determine; adherence, adaptation and acceptability to a national nurse-coordinated telephone-monitoring CHF management strategy. The Chronic Heart Failure Assistance by Telephone Study (CHAT). Methods: Triangulation of descriptive statistics, feedback surveys and qualitative analysis of clinical notes. Cohort comprised of standard care plus intervention (SC + I) participants who completed the first year of the study. Results: 30 GPs (70% rural) randomised to SC + I recruited 79 eligible participants, of whom 60 (76%) completed the full 12 month follow-up period. During this time 3619 calls were made into the CHAT system (mean 45.81 SD ± 79.26, range 0-369), Overall there was an adherence to the study protocol of 65.8% (95% CI 0.54-0.75; p = 0.001) however, of the 60 participants who completed the 12 month follow-up period the adherence was significantly higher at 92.3% (95% CI 0.82-0.97, p ≤ 0.001). Only 3% of this elderly group (mean age 74.7 ±9.3 years) were unable to learn or competently use the technology. Participants rated CHAT with a total acceptability rate of 76.45%. Conclusion: This study shows that elderly CHF patients can adapt quickly, find telephone-monitoring an acceptable part of their healthcare routine, and are able to maintain good adherence for a least 12 months. © 2007.

Condition monitoring and diagnosis of injector faults in a diesel engine using in-cylinder pressure and acoustic emission techniques

Failing injectors are one of the most common faults in diesel engines. The severity of these faults could have serious effects on diesel engine operations such as engine misfire, knocking, insufficient power output or even cause a complete engine breakdown. It is thus essential to prevent such faults from occurring by monitoring the condition of these injectors. In this paper, the authors present the results of an experimental investigation on identifying the signal characteristics of a simulated incipient injector fault in a diesel engine using both in-cylinder pressure and acoustic emission (AE) techniques. A time waveform event driven synchronous averaging technique was used to minimize or eliminate the effect of engine speed variation and amplitude fluctuation. It was found that AE is an effective method to detect the simulated injector fault in both time (crank angle) and frequency (order) domains. It was also shown that the time domain in-cylinder pressure signal is a poor indicator for condition monitoring and diagnosis of the simulated injector fault due to the small effect of the simulated fault on the engine combustion process. Nevertheless, good correlations between the simulated injector fault and the lower order components of the enveloped in-cylinder pressure spectrum were found at various engine loading conditions.

Preliminary study on bridge health prediction using Dynamic Objective Oriented Bayesian Network (DOOBN)

The availability of bridges is crucial to people’s daily life and national economy. Bridge health prediction plays an important role in bridge management because maintenance optimization is implemented based on prediction results of bridge deterioration. Conventional bridge deterioration models can be categorised into two groups, namely condition states models and structural reliability models. Optimal maintenance strategy should be carried out based on both condition states and structural reliability of a bridge. However, none of existing deterioration models considers both condition states and structural reliability. This study thus proposes a Dynamic Objective Oriented Bayesian Network (DOOBN) based method to overcome the limitations of the existing methods. This methodology has the ability to act upon as a flexible unifying tool, which can integrate a variety of approaches and information for better bridge deterioration prediction. Two demonstrative case studies are conducted to preliminarily justify the feasibility of the methodology

A review of commuter exposure to ultrafine particles and its health effects

Ultrafine particles (UFPs, <100 nm) are produced in large quantities by vehicular combustion and are implicated in causing several adverse human health effects. Recent work has suggested that a large proportion of daily UFP exposure may occur during commuting. However, the determinants, variability and transport mode-dependence of such exposure are not well-understood. The aim of this review was to address these knowledge gaps by distilling the results of ‘in-transit’ UFP exposure studies performed to-date, including studies of health effects. We identified 47 exposure studies performed across 6 transport modes: automobile, bicycle, bus, ferry, rail and walking. These encompassed approximately 3000 individual trips where UFP concentrations were measured. After weighting mean UFP concentrations by the number of trips in which they were collected, we found overall mean UFP concentrations of 3.4, 4.2, 4.5, 4.7, 4.9 and 5.7 × 10^4 particles cm^-3 for the bicycle, bus, automobile, rail, walking and ferry modes, respectively. The mean concentration inside automobiles travelling through tunnels was 3.0 × 10^5 particles cm^-3. While the mean concentrations were indicative of general trends, we found that the determinants of exposure (meteorology, traffic parameters, route, fuel type, exhaust treatment technologies, cabin ventilation, filtration, deposition, UFP penetration) exhibited marked variability and mode-dependence, such that it is not necessarily appropriate to rank modes in order of exposure without detailed consideration of these factors. Ten in-transit health effects studies have been conducted and their results indicate that UFP exposure during commuting can elicit acute effects in both healthy and health-compromised individuals. We suggest that future work should focus on further defining the contribution of in-transit UFP exposure to total UFP exposure, exploring its specific health effects and investigating exposures in the developing world. Keywords: air pollution; transport modes; acute health effects; travel; public transport

Using mobile social media and GIS in health and place research

This paper presents new research methods that combine the use of location-based, social media on mobile phones with geographic information systems (GIS) to explore connections between people, place and health. It discusses the feasibility, limitations, and benefits of using these methods, which enable real-time, location-based, quantitative data to be collected on the recreation, consumption, and physical activity patterns of urban residents in Brisbane, Queensland. The study employs mechanisms already inherent in popular mobile social media applications (Facebook, Twitter and Foursquare) to collect this data. The research methods presented in this paper are innovative and potentially applicable to an increasing number of academic research areas, as well as to a growing range of service providers that benefit from monitoring consumer behaviour, and responding to emerging changes in these patterns and trends. The ability to both collect and map objective, real-time data about the consumption, leisure, recreation, and physical activity patterns amongst urban communities has direct implications for a range of research disciplines including media studies, advertising, health promotion, social marketing, public health inequalities, and urban design.

Gaseous and particle emissions from an ethanol fumigated compression ignition engine

A 4-cylinder Ford 2701C test engine was used in this study to explore the impact of ethanol fumigation on gaseous and particle emission concentrations. The fumigation technique delivered vaporised ethanol into the intake manifold of the engine, using an injector, a pump and pressure regulator, a heat exchanger for vaporising ethanol and a separate fuel tank and lines. Gaseous (Nitric oxide (NO), Carbon monoxide (CO) and hydrocarbons (HC)) and particulate emissions (particle mass (PM2.5) and particle number) testing was conducted at intermediate speed (1700 rpm) using 4 load settings with ethanol substitution percentages ranging from 10-40 % (by energy). With ethanol fumigation, NO and PM2.5 emissions were reduced, whereas CO and HC emissions increased considerably and particle number emissions increased at most test settings. It was found that ethanol fumigation reduced the excess air factor for the engine and this led to increased emissions of CO and HC, but decreased emissions of NO. PM2.5 emissions were reduced with ethanol fumigation, as ethanol has a very low “sooting” tendency. This is due to the higher hydrogen-to-carbon ratio of this fuel, and also because ethanol does not contain aromatics, both of which are known soot precursors. The use of a diesel oxidation catalyst (as an after-treatment device) is recommended to achieve a reduction in the four pollutants that are currently regulated for compression ignition engines. The increase in particle number emissions with ethanol fumigation was due to the formation of volatile (organic) particles; consequently, using a diesel oxidation catalyst will also assist in reducing particle number emissions.