Reliability prediction using the non-parametric explicit hazard model : a case study

Survival probability prediction using covariate-based hazard approach is a known statistical methodology in engineering asset health management. We have previously reported the semi-parametric Explicit Hazard Model (EHM) which incorporates three types of information: population characteristics; condition indicators; and operating environment indicators for hazard prediction. This model assumes the baseline hazard has the form of the Weibull distribution. To avoid this assumption, this paper presents the non-parametric EHM which is a distribution-free covariate-based hazard model. In this paper, an application of the non-parametric EHM is demonstrated via a case study. In this case study, survival probabilities of a set of resistance elements using the non-parametric EHM are compared with the Weibull proportional hazard model and traditional Weibull model. The results show that the non-parametric EHM can effectively predict asset life using the condition indicator, operating environment indicator, and failure history.

Productivity performance of Singapore’s retail sector : a two-stage non-parametric approach

This paper seeks to identify and quantify sources of the lagging productivity in Singapore’s retail sector as reported in the Economic Strategies Committee 2010 report. A two-stage analysis is adopted. In the first stage, the Malmquist productivity index is employed which provides measures of productivity change, technological change and efficiency change. In the second stage, technical efficiency estimates are regressed against explanatory variables based on a truncated regression model. Sources of technical efficiency were attributed to quality of workers while product assortment and competition negatively impacted on efficiency.

Non-parametric techniques for fast and robust stereo matching

The mining environment presents a challenging prospect for stereo vision. Our objective is to produce a stereo vision sensor suited to close-range scenes consisting mostly of rocks. This sensor should produce a dense depth map within real-time constraints. Speed and robustness are of foremost importance for this application. This paper compares a number of stereo matching algorithms in terms of robustness and suitability to fast implementation. These include traditional area-based algorithms, and algorithms based on non-parametric transforms, notably the rank and census transforms. Our experimental results show that the rank and census transforms are robust with respect to radiometric distortion and introduce less computational complexity than conventional area-based matching techniques.

Suitability of non-parametric stereo matching algorithms for mining automation

The mining environment, being complex, irregular and time varying, presents a challenging prospect for stereo vision. For this application, speed, reliability, and the ability to produce a dense depth map are of foremost importance. This paper assesses the suitability of a number of matching techniques for use in a stereo vision sensor for close range scenes consisting primarily of rocks. These include traditional area-based matching metrics, and non-parametric transforms, in particular, the rank and census transforms. Experimental results show that the rank and census transforms exhibit a number of clear advantages over area-based matching metrics, including their low computational complexity, and robustness to certain types of distortion.

Frame-rate stereopsis using non-parametric transforms and programmable logic

A frame-rate stereo vision system, based on non-parametric matching metrics, is described. Traditional metrics, such as normalized cross-correlation, are expensive in terms of logic. Non-parametric measures require only simple, parallelizable, functions such as comparators, counters and exclusive-or, and are thus very well suited to implementation in reprogrammable logic.

A near-to-far non-parametric learning approach for estimating traversability in deformable terrain

It is well recognized that many scientifically interesting sites on Mars are located in rough terrains. Therefore, to enable safe autonomous operation of a planetary rover during exploration, the ability to accurately estimate terrain traversability is critical. In particular, this estimate needs to account for terrain deformation, which significantly affects the vehicle attitude and configuration. This paper presents an approach to estimate vehicle configuration, as a measure of traversability, in deformable terrain by learning the correlation between exteroceptive and proprioceptive information in experiments. We first perform traversability estimation with rigid terrain assumptions, then correlate the output with experienced vehicle configuration and terrain deformation using a multi-task Gaussian Process (GP) framework. Experimental validation of the proposed approach was performed on a prototype planetary rover and the vehicle attitude and configuration estimate was compared with state-of-the-art techniques. We demonstrate the ability of the approach to accurately estimate traversability with uncertainty in deformable terrain.

World emissions and economic growth: Application of non-parametric methods

The Environmental Kuznets Curve (EKC) hypothesises an inverse U-shaped relationship between a measure of environmental pollution and per capita income levels. In this study, we apply non-parametric estimation of local polynomial regression (local quadratic fitting) to allow more flexibility in local estimation. This study uses a larger and globally representative sample of many local and global pollutants and natural resources including Biological Oxygen Demand (BOD) emission, CO2 emission, CO2 damage, energy use, energy depletion, mineral depletion, improved water source, PM10, particulate emission damage, forest area and net forest depletion. Copyright © 2009 Inderscience Enterprises Ltd.

Non-parametric consistency test for multiple-sensing-modality data fusion

Fusing data from multiple sensing modalities, e.g. laser and radar, is a promising approach to achieve resilient perception in challenging environmental conditions. However, this may lead to \emph{catastrophic fusion} in the presence of inconsistent data, i.e. when the sensors do not detect the same target due to distinct attenuation properties. It is often difficult to discriminate consistent from inconsistent data across sensing modalities using local spatial information alone. In this paper we present a novel consistency test based on the log marginal likelihood of a Gaussian process model that evaluates data from range sensors in a relative manner. A new data point is deemed to be consistent if the model statistically improves as a result of its fusion. This approach avoids the need for absolute spatial distance threshold parameters as required by previous work. We report results from object reconstruction with both synthetic and experimental data that demonstrate an improvement in reconstruction quality, particularly in cases where data points are inconsistent yet spatially proximal.

A constraint to improve the reliability of stereo matching using the rank transform

The rank transform is a non-parametric technique which has been recently proposed for the stereo matching problem. The motivation behind its application to the matching problem is its invariance to certain types of image distortion and noise, as well as its amenability to real-time implementation. This paper derives an analytic expression for the process of matching using the rank transform, and then goes on to derive one constraint which must be satisfied for a correct match. This has been dubbed the rank order constraint or simply the rank constraint. Experimental work has shown that this constraint is capable of resolving ambiguous matches, thereby improving matching reliability. This constraint was incorporated into a new algorithm for matching using the rank transform. This modified algorithm resulted in an increased proportion of correct matches, for all test imagery used.

The individual and interactive relationship between long chain omega-3 polyunsaturated fatty acids and physical activity as predictors of cognition in cognitively impaired and non-impaired older adults

Alterations in cognitive function are characteristic of the aging process in humans and other animals. However, the nature of these age related changes in cognition is complex and is likely to be influenced by interactions between genetic predispositions and environmental factors resulting in dynamic fluctuations within and between individuals. These inter and intra-individual fluctuations are evident in both so-called normal cognitive aging and at the onset of cognitive pathology. Mild Cognitive Impairment (MCI), thought to be a prodromal phase of dementia, represents perhaps the final opportunity to mitigate cognitive declines that may lead to terminal conditions such as dementia. The prognosis for people with MCI is mixed with the evidence suggesting that many will remain stable within 10-years of diagnosis, many will improve, and many will transition to dementia. If the characteristics of people who do not progress to dementia from MCI can be identified and replicated in others it may be possible to reduce or delay dementia onset, thus reducing a growing personal and public health burden. Furthermore, if MCI onset can be prevented or delayed, the burden of cognitive decline in aging populations worldwide may be reduced. A cognitive domain that is sensitive to the effects of advancing age, and declines in which have been shown to presage the onset of dementia in MCI patients, is executive function. Moreover, environmental factors such as diet and physical activity have been shown to affect performance on tests of executive function. For example, improvements in executive function have been demonstrated as a result of increased aerobic and anaerobic physical activity and, although the evidence is not as strong, findings from dietary interventions suggest certain nutrients may preserve or improve executive functions in old age. These encouraging findings have been demonstrated in older adults with MCI and their non-impaired peers. However, there are some gaps in the literature that need to be addressed. For example, little is known about the effect on cognition of an interaction between diet and physical activity. Both are important contributors to health and wellbeing, and a growing body of evidence attests to their importance in mental and cognitive health in aging individuals. Yet physical activity and diet are rarely considered together in the context of cognitive function. There is also little known about potential underlying biological mechanisms that might explain the physical activity/diet/cognition relationship. The first aim of this program of research was to examine the individual and interactive role of physical activity and diet, specifically long chain polyunsaturated fatty acid consumption(LCn3) as predictors of MCI status. The second aim is to examine executive function in MCI in the context of the individual and interactive effects of physical activity and LCn3.. A third aim was to explore the role of immune and endocrine system biomarkers as possible mediators in the relationship between LCn3, physical activity and cognition. Study 1a was a cross-sectional analysis of MCI status as a function of erythrocyte proportions of an interaction between physical activity and LCn3. The marine based LCn3s eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) have both received support in the literature as having cognitive benefits, although comparisons of the relative benefits of EPA or DHA, particularly in relation to the aetiology of MCI, are rare. Furthermore, a limited amount of research has examined the cognitive benefits of physical activity in terms of MCI onset. No studies have examined the potential interactive benefits of physical activity and either EPA or DHA. Eighty-four male and female adults aged 65 to 87 years, 50 with MCI and 34 without, participated in Study 1a. A logistic binary regression was conducted with MCI status as a dependent variable, and the individual and interactive relationships between physical activity and either EPA or DHA as predictors. Physical activity was measured using a questionnaire and specific physical activity categories were weighted according to the metabolic equivalents (METs) of each activity to create a physical activity intensity index (PAI). A significant relationship was identified between MCI outcome and the interaction between the PAI and EPA; participants with a higher PAI and higher erythrocyte proportions of EPA were more likely to be classified as non-MCI than their less active peers with less EPA. Study 1b was a randomised control trial using the participants from Study 1a who were identified with MCI. Given the importance of executive function as a determinant of progression to more severe forms of cognitive impairment and dementia, Study 1b aimed to examine the individual and interactive effect of physical activity and supplementation with either EPA or DHA on executive function in a sample of older adults with MCI. Fifty male and female participants were randomly allocated to supplementation groups to receive 6-months of supplementation with EPA, or DHA, or linoleic acid (LA), a long chain polyunsaturated omega-6 fatty acid not known for its cognitive enhancing properties. Physical activity was measured using the PAI from Study 1a at baseline and follow-up. Executive function was measured using five tests thought to measure different executive function domains. Erythrocyte proportions of EPA and DHA were higher at follow-up; however, PAI was not significantly different. There was also a significant improvement in three of the five executive function tests at follow-up. However, regression analyses revealed that none of the variance in executive function at follow-up was predicted by EPA, DHA, PAI, the EPA by PAI interaction, or the DHA by PAI interaction. The absence of an effect may be due to a small sample resulting in limited power to find an effect, the lack of change in physical activity over time in terms of volume and/or intensity, or a combination of both reduced power and no change in physical activity. Study 2a was a cross-sectional study using cognitively unimpaired older adults to examine the individual and interactive effects of LCn3 and PAI on executive function. Several possible explanations for the absence of an effect were identified. From this consideration of alternative explanations it was hypothesised that post-onset interventions with LCn3 either alone or in interation with self-reported physical activity may not be beneficial in MCI. Thus executive function responses to the individual and interactive effects of physical activity and LCn3 were examined in a sample of older male and female adults without cognitive impairment (n = 50). A further aim of study 2a was to operationalise executive function using principal components analysis (PCA) of several executive function tests. This approach was used firstly as a data reduction technique to overcome the task impurity problem, and secondly to examine the executive function structure of the sample for evidence of de-differentiation. Two executive function components were identified as a result of the PCA (EF 1 and EF 2). However, EPA, DHA, the PAI, or the EPA by PAI or DHA by PAI interactions did not account for any variance in the executive function components in subsequent hierarchical multiple regressions. Study 2b was an exploratory correlational study designed to explore the possibility that immune and endocrine system biomarkers may act as mediators of the relationship between LCn3, PAI, the interaction between LCn3 and PAI, and executive functions. Insulin-like growth factor-1 (IGF-1), an endocrine system growth hormone, and interleukin-6 (IL-6) an immune system cytokine involved in the acute inflammatory response, have both been shown to affect cognition including executive functions. Moreover, IGF-1 and IL-6 have been shown to be antithetical in so far as chronically increased IL-6 has been associated with reduced IGF-1 levels, a relationship that has been linked to age related morbidity. Further, physical activity and LCn3 have been shown to modulate levels of both IGF-1 and IL-6. Thus, it is possible that the cognitive enhancing effects of LCn3, physical activity or their interaction are mediated by changes in the balance between IL-6 and IGF-1. Partial and non-parametric correlations were conducted in a subsample of participants from Study 2a (n = 13) to explore these relationships. Correlations of interest did not reach significance; however, the coefficients were quite large for several relationships suggesting studies with larger samples may be warranted. In summary, the current program of research found some evidence supporting an interaction between EPA, not DHA, and higher energy expenditure via physical activity in differentiating between older adults with and without MCI. However, a RCT examining executive function in older adults with MCI found no support for increasing EPA or DHA while maintaining current levels of energy expenditure. Furthermore, a cross-sectional study examining executive function in older adults without MCI found no support for better executive function performance as a function of increased EPA or DHA consumption, greater energy expenditure via physical activity or an interaction between physical activity and either EPA or DHA. Finally, an examination of endocrine and immune system biomarkers revealed promising relationships in terms of executive function in non-MCI older adults particularly with respect to LCn3 and physical activity. Taken together, these findings demonstrate a potential benefit of increasing physical activity and LCn3 consumption, particularly EPA, in mitigating the risk of developing MCI. In contrast, no support was found for a benefit to executive function as a result of increased physical activity, LCn3 consumption or an interaction between physical activity and LCn3, in participants with and without MCI. These results are discussed with reference to previous findings in the literature including possible limitations and opportunities for future research.

A parametric analysis technique for design of fuel cell and hybrid-electric vehicles

This paper presents a new simplified parametric analysis technique for the design of fuel cell and hybrid-electric vehicles. The technique utilizes a comprehensive set of ∼30 parameters to fully characterize the vehicle platform, powertrain components, vehicle performance requirements and driving conditions. It is best applied to the sizing of powertrain components and prediction of energy consumption in a vehicle. This new parametric technique makes a good complement to existing vehicle simulation software packages and therefore represents a potentially valuable tool for the hybrid vehicle designer.

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

Forecasting time-varying value-at-risk

In this thesis we are interested in financial risk and the instrument we want to use is Value-at-Risk (VaR). VaR is the maximum loss over a given period of time at a given confidence level. Many definitions of VaR exist and some will be introduced throughout this thesis. There two main ways to measure risk and VaR: through volatility and through percentiles. Large volatility in financial returns implies greater probability of large losses, but also larger probability of large profits. Percentiles describe tail behaviour. The estimation of VaR is a complex task. It is important to know the main characteristics of financial data to choose the best model. The existing literature is very wide, maybe controversial, but helpful in drawing a picture of the problem. It is commonly recognised that financial data are characterised by heavy tails, time-varying volatility, asymmetric response to bad and good news, and skewness. Ignoring any of these features can lead to underestimating VaR with a possible ultimate consequence being the default of the protagonist (firm, bank or investor). In recent years, skewness has attracted special attention. An open problem is the detection and modelling of time-varying skewness. Is skewness constant or there is some significant variability which in turn can affect the estimation of VaR? This thesis aims to answer this question and to open the way to a new approach to model simultaneously time-varying volatility (conditional variance) and skewness. The new tools are modifications of the Generalised Lambda Distributions (GLDs). They are four-parameter distributions, which allow the first four moments to be modelled nearly independently: in particular we are interested in what we will call para-moments, i.e., mean, variance, skewness and kurtosis. The GLDs will be used in two different ways. Firstly, semi-parametrically, we consider a moving window to estimate the parameters and calculate the percentiles of the GLDs. Secondly, parametrically, we attempt to extend the GLDs to include time-varying dependence in the parameters. We used the local linear regression to estimate semi-parametrically conditional mean and conditional variance. The method is not efficient enough to capture all the dependence structure in the three indices —ASX 200, S&P 500 and FT 30—, however it provides an idea of the DGP underlying the process and helps choosing a good technique to model the data. We find that GLDs suggest that moments up to the fourth order do not always exist, there existence appears to vary over time. This is a very important finding, considering that past papers (see for example Bali et al., 2008; Hashmi and Tay, 2007; Lanne and Pentti, 2007) modelled time-varying skewness, implicitly assuming the existence of the third moment. However, the GLDs suggest that mean, variance, skewness and in general the conditional distribution vary over time, as already suggested by the existing literature. The GLDs give good results in estimating VaR on three real indices, ASX 200, S&P 500 and FT 30, with results very similar to the results provided by historical simulation.

Monitoring and analysis of internet traffic targeting unused address spaces

Today’s evolving networks are experiencing a large number of different attacks ranging from system break-ins, infection from automatic attack tools such as worms, viruses, trojan horses and denial of service (DoS). One important aspect of such attacks is that they are often indiscriminate and target Internet addresses without regard to whether they are bona fide allocated or not. Due to the absence of any advertised host services the traffic observed on unused IP addresses is by definition unsolicited and likely to be either opportunistic or malicious. The analysis of large repositories of such traffic can be used to extract useful information about both ongoing and new attack patterns and unearth unusual attack behaviors. However, such an analysis is difficult due to the size and nature of the collected traffic on unused address spaces. In this dissertation, we present a network traffic analysis technique which uses traffic collected from unused address spaces and relies on the statistical properties of the collected traffic, in order to accurately and quickly detect new and ongoing network anomalies. Detection of network anomalies is based on the concept that an anomalous activity usually transforms the network parameters in such a way that their statistical properties no longer remain constant, resulting in abrupt changes. In this dissertation, we use sequential analysis techniques to identify changes in the behavior of network traffic targeting unused address spaces to unveil both ongoing and new attack patterns. Specifically, we have developed a dynamic sliding window based non-parametric cumulative sum change detection techniques for identification of changes in network traffic. Furthermore we have introduced dynamic thresholds to detect changes in network traffic behavior and also detect when a particular change has ended. Experimental results are presented that demonstrate the operational effectiveness and efficiency of the proposed approach, using both synthetically generated datasets and real network traces collected from a dedicated block of unused IP addresses.

Application of an appearance-based intervention to improve sun protection outcomes of outdoor workers in Queensland, Australia

Outdoor workers are exposed to high levels of ultraviolet radiation (UVR) and may thus be at greater risk to experience UVR-related health effects such as skin cancer, sun burn, and cataracts. A number of intervention trials (n=14) have aimed to improve outdoor workers’ work-related sun protection cognitions and behaviours. Only one study however has reported the use of UV-photography as part of a multi-component intervention. This study was performed in the USA and showed long-term (12 months) improvements in work-related sun protection behaviours. Intervention effects of the other studies have varied greatly, depending on the population studied, intervention applied, and measurement of effect. Previous studies have not assessed whether: - Interventions are similarly effective for workers in stringent and less stringent policy organisations; - Policy effect is translated into workers’ leisure time protection; - Implemented interventions are effective in the long-term; - The facial UV-photograph technique is effective in Australian male outdoor workers without a large additional intervention package, and; - Such interventions will also affect workers’ leisure time sun-related cognitions and behaviours. Therefore, the present Protection of Outdoor Workers from Environmental Radiation [POWER]-study aimed to fill these gaps and had the objectives of: a) assessing outdoor workers’ sun-related cognitions and behaviours at work and during leisure time in stringent and less stringent sun protection policy environments; b) assessing the effect of an appearance-based intervention on workers’ risk perceptions, intentions and behaviours over time; c) assessing whether the intervention was equally effective within the two policy settings; and d) assessing the immediate post-intervention effect. Effectiveness was described in terms of changes in sun-related risk perceptions and intentions (as these factors were shown to be main precursors of behaviour change in many health promotion theories) and behaviour. The study purposefully selected and recruited two organisations with a large outdoor worker contingent in Queensland, Australia within a 40 kilometre radius of Brisbane. The two organisations differed in the stringency of implementation and reinforcement of their organisational sun protection policy. Data were collected from 154 male predominantly Australian born outdoor workers with an average age of 37 years and predominantly medium to fair skin (83%). Sun-related cognitions and behaviours of workers were assessed using self-report questionnaires at baseline and six to twelve months later. Variation in follow-up time was due to a time difference in the recruitment of the two organisations. Participants within each organisation were assigned to an intervention or control group. The intervention group participants received a one-off personalised Skin Cancer Risk Assessment Tool [SCRAT]-letter and a facial UV-photograph with detailed verbal information. This was followed by an immediate post-intervention questionnaire within three months of the start of the study. The control group only received the baseline and follow-up questionnaire. Data were analysed using a variety of techniques including: descriptive analyses, parametric and non-parametric tests, and generalised estimating equations. A 15% proportional difference observed was deemed of clinical significance, with the addition of reported statistical significance (p<0.05) where applicable. Objective 1: Assess and compare the current sun-related risk perceptions, intentions, behaviours, and policy awareness of outdoor workers in stringent and less stringent sun protection policy settings. Workers within the two organisations (stringent n=89 and less stringent n=65) were similar in their knowledge about skin cancer, self efficacy, attitudes, and social norms regarding sun protection at work and during leisure time. Participants were predominantly in favour of sun protection. Results highlighted that compared to workers in a less stringent policy organisation working for an organisation with stringent sun protection policies and practices resulted in more desirable sun protection intentions (less willing to tan p=0.03) ; actual behaviours at work (sufficient use of upper and lower body protection, headgear, and sunglasses (p<0.001 for all comparisons), and greater policy awareness (awareness of repercussions if Personal Protective Equipment (PPE) was not used, p<0.001)). However the effect of the work-related sun protection policy was found not to extend to leisure time sun protection. Objective 2: Compare changes in sun-related risk perceptions, intentions, and behaviours between the intervention and control group. The effect of the intervention was minimal and mainly resulted in a clinically significant reduction in work-related self-perceived risk of developing skin cancer in the intervention compared to the control group (16% and 32% for intervention and control group, respectively estimated their risk higher compared to other outdoor workers: , p=0.11). No other clinical significant effects were observed at 12 months follow-up. Objective 3: Assess whether the intervention was equally effective in the stringent sun protection policy organisation and the less stringent sun protection policy organisation. The appearance-based intervention resulted in a clinically significant improvement in the stringent policy intervention group participants’ intention to protect from the sun at work (workplace*time interaction, p=0.01). In addition to a reduction in their willingness to tan both at work (will tan at baseline: 17% and 61%, p=0.06, at follow-up: 54% and 33%, p=0.07, stringent and less stringent policy intervention group respectively. The workplace*time interaction was significant p<0.001) and during leisure time (will tan at baseline: 42% and 78%, p=0.01, at follow-up: 50% and 63%, p=0.43, stringent and less stringent policy intervention group respectively. The workplace*time interaction was significant p=0.01) over the course of the study compared to the less stringent policy intervention group. However, no changes in actual sun protection behaviours were found. Objective 4: Examine the effect of the intervention on level of alarm and concern regarding the health of the skin as well as sun protection behaviours in both organisations. The immediate post-intervention results showed that the stringent policy organisation participants indicated to be less alarmed (p=0.04) and concerned (p<0.01) about the health of their skin and less likely to show the facial UV-photograph to others (family p=0.03) compared to the less stringent policy participants. A clinically significantly larger proportion of participants from the stringent policy organisation reported they worried more about skin cancer (65%) and skin freckling (43%) compared to those in the less stringent policy organisation (46%,and 23% respectively , after seeing the UV-photograph). In summary the results of this study suggest that the having a stringent work-related sun protection policy was significantly related to for work-time sun protection practices, but did not extend to leisure time sun protection. This could reflect the insufficient level of sun protection found in the general Australian population during leisure time. Alternatively, reactance caused by being restricted in personal decisions through work-time policy could have contributed to lower leisure time sun protection. Finally, other factors could have also contributed to the less than optimal leisure time sun protection behaviours reported, such as unmeasured personal or cultural barriers. All these factors combined may have lead to reduced willingness to take proper preventive action during leisure time exposure. The intervention did not result in any measurable difference between the intervention and control groups in sun protection behaviours in this population, potentially due to the long lag time between the implementation of the intervention and assessment at 12-months follow-up. In addition, high levels of sun protection behaviours were found at baseline (ceiling effect) which left little room for improvement. Further, this study did not assess sunscreen use, which was the predominant behaviour assessed in previous effective appearance-based interventions trials. Additionally, previous trials were mainly conducted in female populations, whilst the POWER-study’s population was all male. The observed immediate post-intervention result could be due to more emphasis being placed on sun protection and risks related to sun exposure in the stringent policy organisation. Therefore participants from the stringent policy organisation could have been more aware of harmful effects of UVR and hence, by knowing that they usually protect adequately, not be as alarmed or concerned as the participants from the less stringent policy organisation. In conclusion, a facial UV-photograph and SCRAT-letter information alone may not achieve large changes in sun-related cognitions and behaviour, especially of assessed 6-12 months after the intervention was implemented and in workers who are already quite well protected. Differences found between workers in the present study appear to be more attributable to organisational policy. However, against a background of organisational policy, this intervention may be a useful addition to sun-related workplace health and safety programs. The study findings have been interpreted while respecting a number of limitations. These have included non-random allocation of participants due to pre-organised allocation of participants to study group in one organisation and difficulty in separating participants from either study group. Due to the transient nature of the outdoor worker population, only 105 of 154 workers available at baseline could be reached for follow-up. (attrition rate=32%). In addition the discrepancy in the time to follow-up assessment between the two organisations was a limitation of the current study. Given the caveats of this research, the following recommendations were made for future research: - Consensus should be reached to define "outdoor worker" in terms of time spent outside at work as well as in the way sun protection behaviours are measured and reported. - Future studies should implement and assess the value of the facial UV-photographs in a wide range of outdoor worker organisations and countries. - More timely and frequent follow-up assessments should be implemented in intervention studies to determine the intervention effect and to identify the best timing of booster sessions to optimise results. - Future research should continue to aim to target outdoor workers’ leisure time cognitions and behaviours and improve these if possible. Overall, policy appears to be an important factor in workers’ compliance with work-time use of sun protection. Given the evidence generated by this research, organisations employing outdoor workers should consider stringent implementation and reinforcement of a sun protection policy. Finally, more research is needed to improve ways to generate desirable behaviour in this population during leisure time.