Statistical modelling of wind effects on signal propagation for wireless sensor networks

A wireless sensor network system must have the ability to tolerate harsh environmental conditions and reduce communication failures. In a typical outdoor situation, the presence of wind can introduce movement in the foliage. This motion of vegetation structures causes large and rapid signal fading in the communication link and must be accounted for when deploying a wireless sensor network system in such conditions. This thesis examines the fading characteristics experienced by wireless sensor nodes due to the effect of varying wind speed in a foliage obstructed transmission path. It presents extensive measurement campaigns at two locations with the approach of a typical wireless sensor networks configuration. The significance of this research lies in the varied approaches of its different experiments, involving a variety of vegetation types, scenarios and the use of different polarisations (vertical and horizontal). Non–line of sight (NLoS) scenario conditions investigate the wind effect based on different vegetation densities including that of the Acacia tree, Dogbane tree and tall grass. Whereas the line of sight (LoS) scenario investigates the effect of wind when the grass is swaying and affecting the ground-reflected component of the signal. Vegetation type and scenarios are envisaged to simulate real life working conditions of wireless sensor network systems in outdoor foliated environments. The results from the measurements are presented in statistical models involving first and second order statistics. We found that in most of the cases, the fading amplitude could be approximated by both Lognormal and Nakagami distribution, whose m parameter was found to depend on received power fluctuations. Lognormal distribution is known as the result of slow fading characteristics due to shadowing. This study concludes that fading caused by variations in received power due to wind in wireless sensor networks systems are found to be insignificant. There is no notable difference in Nakagami m values for low, calm, and windy wind speed categories. It is also shown in the second order analysis, the duration of the deep fades are very short, 0.1 second for 10 dB attenuation below RMS level for vertical polarization and 0.01 second for 10 dB attenuation below RMS level for horizontal polarization. Another key finding is that the received signal strength for horizontal polarisation demonstrates more than 3 dB better performances than the vertical polarisation for LoS and near LoS (thin vegetation) conditions and up to 10 dB better for denser vegetation conditions.

Statistical analysis of conflict involvements in port water navigation

With increasing rate of shipping traffic, the risk of collisions in busy and congested port waters is likely to rise. However, due to low collision frequencies in port waters, it is difficult to analyze such risk in a sound statistical manner. A convenient approach of investigating navigational collision risk is the application of the traffic conflict techniques, which have potential to overcome the difficulty of obtaining statistical soundness. This study aims at examining port water conflicts in order to understand the characteristics of collision risk with regard to vessels involved, conflict locations, traffic and kinematic conditions. A hierarchical binomial logit model, which considers the potential correlations between observation-units, i.e., vessels, involved in the same conflicts, is employed to evaluate the association of explanatory variables with conflict severity levels. Results show higher likelihood of serious conflicts for vessels of small gross tonnage or small overall length. The probability of serious conflict also increases at locations where vessels have more varied headings, such as traffic intersections and anchorages; becoming more critical at night time. Findings from this research should assist both navigators operating in port waters as well as port authorities overseeing navigational management.

A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.

A review of current statistical methodologies for in storage sampling and surveillance in the grains industry

Effective, statistically robust sampling and surveillance strategies form an integral component of large agricultural industries such as the grains industry. Intensive in-storage sampling is essential for pest detection, Integrated Pest Management (IPM), to determine grain quality and to satisfy importing nation’s biosecurity concerns, while surveillance over broad geographic regions ensures that biosecurity risks can be excluded, monitored, eradicated or contained within an area. In the grains industry, a number of qualitative and quantitative methodologies for surveillance and in-storage sampling have been considered. Primarily, research has focussed on developing statistical methodologies for in storage sampling strategies concentrating on detection of pest insects within a grain bulk, however, the need for effective and statistically defensible surveillance strategies has also been recognised. Interestingly, although surveillance and in storage sampling have typically been considered independently, many techniques and concepts are common between the two fields of research. This review aims to consider the development of statistically based in storage sampling and surveillance strategies and to identify methods that may be useful for both surveillance and in storage sampling. We discuss the utility of new quantitative and qualitative approaches, such as Bayesian statistics, fault trees and more traditional probabilistic methods and show how these methods may be used in both surveillance and in storage sampling systems.

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.

Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals

Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.

The Impact of Indigenous status on adult sentencing : a review of the statistical research literature.

The gross overrepresentation of Indigenous peoples in prison populations suggests that sentencing may be a discriminatory process. Using findings from recent (1991–2011) multivariate statistical sentencing analyses from the United States, Canada, and Australia, we review the 3 key hypotheses advanced as plausible explanations for baseline sentencing discrepancies between Indigenous and non-Indigenous adult criminal defendants: (a) differential involvement, (b) negative discrimination, and (c) positive discrimination. Overall, the prior research shows strong support for the differential involvement thesis and some support for the discrimination theses (positive and negative). We argue that where discrimination is found, it may be explained by the lack of a more complete set of control variables in researchers’ multivariate models and/or differing political and social contexts.

Statistical and scientometric analysis of international research in geographical and environmental education

Certain statistic and scientometric features of articles published in the journal “International Research in Geographical and Environmental Education” are examined in this paper, for the period 1992-2009, by applying nonparametric statistics and Shannon’s entropy (diversity) formula. The main findings of this analysis are: a) after 2004 the research priorities of researchers in geographical and environmental education seem to have changed, b) “teacher education” has been the most recurrent theme throughout these 18 years, followed by “values & attitudes” and “inquiry & problem solving” c) the themes “GIS” and “Sustainability” were the most “stable” throughout the 18 years, meaning that they maintained their ranks as publication priorities more than other themes, d) citations of IRGEE increase annually, e) the average thematic diversity of articles published during the period 1992-2009 is 82.7% of the maximum thematic diversity (very high), meaning that the Journal has the capacity to attract a wide readership for the 10 themes it has successfully covered throughout the 18 years of its publication.

Linking three-dimensional geological modelling and multivariate statistical analysis to define groundwater chemistry baseline and identify inter-aquifer connectivity within the Clarence-Moreton Basin, Southeast Queensland, Australia

The Clarence-Moreton Basin (CMB) covers approximately 26000 km2 and is the only sub-basin of the Great Artesian Basin (GAB) in which there is flow to both the south-west and the east, although flow to the south-west is predominant. In many parts of the basin, including catchments of the Bremer, Logan and upper Condamine Rivers in southeast Queensland, the Walloon Coal Measures are under exploration for Coal Seam Gas (CSG). In order to assess spatial variations in groundwater flow and hydrochemistry at a basin-wide scale, a 3D hydrogeological model of the Queensland section of the CMB has been developed using GoCAD modelling software. Prior to any large-scale CSG extraction, it is essential to understand the existing hydrochemical character of the different aquifers and to establish any potential linkage. To effectively use the large amount of water chemistry data existing for assessment of hydrochemical evolution within the different lithostratigraphic units, multivariate statistical techniques were employed.

Review of statistical methods for disease mapping

Cancer poses an undeniable burden to the health and wellbeing of the Australian community. In a recent report commissioned by the Australian Institute for Health and Welfare(AIHW, 2010), one in every two Australians on average will be diagnosed with cancer by the age of 85, making cancer the second leading cause of death in 2007, preceded only by cardiovascular disease. Despite modest decreases in standardised combined cancer mortality over the past few decades, in part due to increased funding and access to screening programs, cancer remains a significant economic burden. In 2010, all cancers accounted for an estimated 19% of the country's total burden of disease, equating to approximately $3:8 billion in direct health system costs (Cancer Council Australia, 2011). Furthermore, there remains established socio-economic and other demographic inequalities in cancer incidence and survival, for example, by indigenous status and rurality. Therefore, in the interests of the nation's health and economic management, there is an immediate need to devise data-driven strategies to not only understand the socio-economic drivers of cancer but also facilitate the implementation of cost-effective resource allocation for cancer management...

On the statistical determination of optimal camera configurations in large scale surveillance networks

The selection of optimal camera configurations (camera locations, orientations etc.) for multi-camera networks remains an unsolved problem. Previous approaches largely focus on proposing various objective functions to achieve different tasks. Most of them, however, do not generalize well to large scale networks. To tackle this, we introduce a statistical formulation of the optimal selection of camera configurations as well as propose a Trans-Dimensional Simulated Annealing (TDSA) algorithm to effectively solve the problem. We compare our approach with a state-of-the-art method based on Binary Integer Programming (BIP) and show that our approach offers similar performance on small scale problems. However, we also demonstrate the capability of our approach in dealing with large scale problems and show that our approach produces better results than 2 alternative heuristics designed to deal with the scalability issue of BIP.

A web application to perform linkage disequilibrium and linkage analyses on a computational grid

Motivation: Unravelling the genetic architecture of complex traits requires large amounts of data, sophisticated models and large computational resources. The lack of user-friendly software incorporating all these requisites is delaying progress in the analysis of complex traits. Methods: Linkage disequilibrium and linkage analysis (LDLA) is a high-resolution gene mapping approach based on sophisticated mixed linear models, applicable to any population structure. LDLA can use population history information in addition to pedigree and molecular markers to decompose traits into genetic components. Analyses are distributed in parallel over a large public grid of computers in the UK. Results: We have proven the performance of LDLA with analyses of simulated data. There are real gains in statistical power to detect quantitative trait loci when using historical information compared with traditional linkage analysis. Moreover, the use of a grid of computers significantly increases computational speed, hence allowing analyses that would have been prohibitive on a single computer. © The Author 2009. Published by Oxford University Press. All rights reserved.

Comparative study of analytical rental model and statistical models for predicting house rental levels

The need for a house rental model in Townsville, Australia is addressed. Models developed for predicting house rental levels are described. An analytical model is built upon a priori selected variables and parameters of rental levels. Regression models are generated to provide a comparison to the analytical model. Issues in model development and performance evaluation are discussed. A comparison of the models indicates that the analytical model performs better than the regression models.