News media representations of homelessness : do economic news production pressures prevent journalists from adequately reporting complex social issues

This study used both content and frame analyses to test news-media representations of homelessness in The Courier-Mail newspaper for evidence of restricted journalism practice. Specifically, it sought signs of either direct manipulation of issue representation based on ideological grounds, and also evidence of news organisations prioritising low-cost news production over Public Sphere journalistic news values. The study found that news stories from the earlier parts of the longitudinal study showed stereotypical misrepresentations of homelessness for public deliberation which might be attributed to either, or both of the nominated restricting factors. However news stories from the latter part of the study saw a distinct change in the way the issue was represented, indicating a journalistic capacity to thoughtfully and sensitively represent a complex social issue to the public. Further study is recommended to ascertain how and why this change occurred, so that journalistic practice might be further improved.

Achieving safe road use in a rapidly motorising country : The influence of longstanding beliefs on risky driver behaviour in Pakistan

Road crashes are one of the most significant public health problems in Pakistan; however the factors that contribute to road crashes in Pakistan are not well-researched. Traditional beliefs and values can act as a barrier to health-promoting and injury prevention behaviours, in general and especially in relation to road safety, and may also contribute to risk-taking behaviours. Such beliefs can present significant challenges for health advocates who aim to change behaviour in order to avert road crashes and diminish their consequences. Qualitative research was undertaken in Islamabad, Rawalpindi and Lahore with a range of drivers, religious orators, police and policy makers to explore cultural and religious beliefs and their association with risky road use, and to understand how they might affect development of road safety interventions. The findings highlight a range of issues, including the identification of aspects of beliefs that have complex social implications when designing safety intervention strategies. The pervasive nature of religious and superstitious beliefs in Pakistan can affect road user behaviour by reinforcing the presumption that the individual has no part to play in safety, thereby supporting continued risk taking behaviour. It is anticipated that the findings could be used to inform the design of interventions aimed at influencing broad-spectrum health attitudes and practices among the communities where such beliefs are prevalent.

Internet interventions for treatment of alcohol related problems

This is the protocol for a review and there is no abstract. The objectives are as follows: The primary objective of this review will be to assess the effect of internet and mobile phone alcohol-related interventions on reducing participants' alcohol consumption. Secondary objectives will include assessing the impact of the interventions on symptoms of alcohol-related disorders and degree of alcohol dependence, the effects on functioning, and patient acceptability. The review will also assess whether there are different outcomes for different diagnostic groups, and which intervention approaches or components (including therapeutic support) may be contributing to any positive effects.

The public health challenges of mental health problems and associated difficulties

Mental health is a major global health issue. Neuropsychiatric conditions are the most significant cause of disability worldwide, and account for 14% of the global burden of disease. Depression in particular places a huge burden on society, with the Global Burden of Disease 2000 study listing it as the fourth leading cause of disease burden worldwide and the largest non-fatal disease burden. In Australia, mental disorders are startlingly common and related to significant disability. The 2007 National Survey of Mental Health and Wellbeing revealed that the lifetime prevalence of any mental disorder was 45%, and within the last 12 months 20% of Australians met criteria for a mental disorder. Many of the articles in this issue explore mental health issues in young people. Indeed, mental health issues account for a large proportion of the disease burden in young people. Across the globe, mental health disorders caused the greatest number of years lost to disability(YLDs) amongst young people aged 10 to 24 years (45% of total YLDs). Depression caused the highest number of disability-adjusted life-years (DALYs) across this age group, accounting for 8. 2% of DALYs alone.6 It is clear that mental health is a critical area of focus for researchers, practitioners, and policy makers.

Bayesian algorithms with applications

Advances in algorithms for approximate sampling from a multivariable target function have led to solutions to challenging statistical inference problems that would otherwise not be considered by the applied scientist. Such sampling algorithms are particularly relevant to Bayesian statistics, since the target function is the posterior distribution of the unobservables given the observables. In this thesis we develop, adapt and apply Bayesian algorithms, whilst addressing substantive applied problems in biology and medicine as well as other applications. For an increasing number of high-impact research problems, the primary models of interest are often sufficiently complex that the likelihood function is computationally intractable. Rather than discard these models in favour of inferior alternatives, a class of Bayesian "likelihoodfree" techniques (often termed approximate Bayesian computation (ABC)) has emerged in the last few years, which avoids direct likelihood computation through repeated sampling of data from the model and comparing observed and simulated summary statistics. In Part I of this thesis we utilise sequential Monte Carlo (SMC) methodology to develop new algorithms for ABC that are more efficient in terms of the number of model simulations required and are almost black-box since very little algorithmic tuning is required. In addition, we address the issue of deriving appropriate summary statistics to use within ABC via a goodness-of-fit statistic and indirect inference. Another important problem in statistics is the design of experiments. That is, how one should select the values of the controllable variables in order to achieve some design goal. The presences of parameter and/or model uncertainty are computational obstacles when designing experiments but can lead to inefficient designs if not accounted for correctly. The Bayesian framework accommodates such uncertainties in a coherent way. If the amount of uncertainty is substantial, it can be of interest to perform adaptive designs in order to accrue information to make better decisions about future design points. This is of particular interest if the data can be collected sequentially. In a sense, the current posterior distribution becomes the new prior distribution for the next design decision. Part II of this thesis creates new algorithms for Bayesian sequential design to accommodate parameter and model uncertainty using SMC. The algorithms are substantially faster than previous approaches allowing the simulation properties of various design utilities to be investigated in a more timely manner. Furthermore the approach offers convenient estimation of Bayesian utilities and other quantities that are particularly relevant in the presence of model uncertainty. Finally, Part III of this thesis tackles a substantive medical problem. A neurological disorder known as motor neuron disease (MND) progressively causes motor neurons to no longer have the ability to innervate the muscle fibres, causing the muscles to eventually waste away. When this occurs the motor unit effectively ‘dies’. There is no cure for MND, and fatality often results from a lack of muscle strength to breathe. The prognosis for many forms of MND (particularly amyotrophic lateral sclerosis (ALS)) is particularly poor, with patients usually only surviving a small number of years after the initial onset of disease. Measuring the progress of diseases of the motor units, such as ALS, is a challenge for clinical neurologists. Motor unit number estimation (MUNE) is an attempt to directly assess underlying motor unit loss rather than indirect techniques such as muscle strength assessment, which generally is unable to detect progressions due to the body’s natural attempts at compensation. Part III of this thesis builds upon a previous Bayesian technique, which develops a sophisticated statistical model that takes into account physiological information about motor unit activation and various sources of uncertainties. More specifically, we develop a more reliable MUNE method by applying marginalisation over latent variables in order to improve the performance of a previously developed reversible jump Markov chain Monte Carlo sampler. We make other subtle changes to the model and algorithm to improve the robustness of the approach.

Biomechanical performance of polycaprolactone (PCL)-based scaffold with rhBMP-2 in a sheep thoracic spine fusion model

Adolescent idiopathic scoliosis is a complex three dimensional deformity affecting 2-3% of the general population. Resulting spine deformities include progressive coronal curvature, hypokyphosis, or frank lordosis in the thoracic spine and vertebral rotation in the axial plane with posterior elements turned into the curve concavity. The potential for curve progression is heightened during the adolescent growth spurt. Success of scoliosis deformity correction depends on solid bony fusion between adjacent vertebrae after the intervertebral discs have been surgically cleared and the disc spaces filled with graft material. Problems with bone graft harvest site morbidity as well as limited bone availability have led to the search for bone graft substitutes. Recently, a bioactive and resorbable scaffold fabricated from medical grade polycaprolactone (PCL) has been developed for bone regeneration at load bearing sites. Combined with recombinant human bone morphogenic protein–2 (rhBMP-2), this has been shown to be successful in acting as a bone graft substitute in acting as a bone graft substitute in a porcine lumbar interbody fusion model when compared to autologous bone graft. This in vivo sheep study intends to evaluate the suitability of a custom designed medical grade PCL scaffold in combination with rhBMP-2 as a bone graft substitute in the setting of mini–thoracotomy surgery as a platform for ongoing research to benefit patients with adolescent idiopathic scoliosis.

Modeling and detection of complex attacks

A complex attack is a sequence of temporally and spatially separated legal and illegal actions each of which can be detected by various IDS but as a whole they constitute a powerful attack. IDS fall short of detecting and modeling complex attacks therefore new methods are required. This paper presents a formal methodology for modeling and detection of complex attacks in three phases: (1) we extend basic attack tree (AT) approach to capture temporal dependencies between components and expiration of an attack, (2) using enhanced AT we build a tree automaton which accepts a sequence of actions from input message streams from various sources if there is a traversal of an AT from leaves to root, and (3) we show how to construct an enhanced parallel automaton that has each tree automaton as a subroutine. We use simulation to test our methods, and provide a case study of representing attacks in WLANs.

Finite element shoulder models

Shoulder joint is a complex integration of soft and hard tissues. It plays an important role in performing daily activities and can be considered as a perfect compromise between mobility and stability. However, shoulder is vulnerable to complications such as dislocations and osteoarthritis. Finite element (FE) models have been developed to understand shoulder injury mechanisms, implications of disease on shoulder complex and in assessing the quality of shoulder implants. Further, although few, Finite element shoulder models have also been utilized to answer important clinical questions such as the difference between a normal and osteoarthritic shoulder joint. However, due to the absence of experimental validation, it is questionable whether the constitutive models applied in these FE models are adequate to represent mechanical behaviors of shoulder elements (Cartilages, Ligaments, Muscles etc), therefore the confidence of using current models in answering clinically relevant question. The main objective of this review is to critically evaluate the existing FE shoulder models that have been used to investigate clinical problems. Due concern is given to check the adequacy of representative constitutive models of shoulder elements in drawing clinically relevant conclusion. Suggestions have been given to improve the existing shoulder models by inclusion of adequate constitutive models for shoulder elements to confidently answer clinically relevant questions.

A hybrid smoothed finite element method (H-SFEM) to solid mechanics problems

In this paper, a hybrid smoothed finite element method (H-SFEM) is developed for solid mechanics problems by combining techniques of finite element method (FEM) and Node-based smoothed finite element method (NS-FEM) using a triangular mesh. A parameter is equipped into H-SFEM, and the strain field is further assumed to be the weighted average between compatible stains from FEM and smoothed strains from NS-FEM. We prove theoretically that the strain energy obtained from the H-SFEM solution lies in between those from the compatible FEM solution and the NS-FEM solution, which guarantees the convergence of H-SFEM. Intensive numerical studies are conducted to verify these theoretical results and show that (1) the upper and lower bound solutions can always be obtained by adjusting ; (2) there exists a preferable at which the H-SFEM can produce the ultrasonic accurate solution.

A review of the Ustilago-Sporisorium-Macalpinomyces complex

The fungal genera Ustilago, Sporisorium and Macalpinomyces represent an unresolved complex. Taxa within the complex often possess characters that occur in more than one genus, creating uncertainty for species placement. Previous studies have indicated that the genera cannot be separated by morphology alone. Here we chronologically review the history of the Ustilago-Sporisorium-Macalpinomyces complex, argue for its resolution and suggest methods to accomplish a stable taxonomy. A combined molecular and morphological approach is required to identify synapomorphic characters that underpin a new classification. Ustilago, Sporisorium and Macalpinomyces require explicit re-description and new genera, based on monophyletic groups, are needed to accommodate taxa that no longer fit the emended descriptions. A resolved classification will end the taxonomic confusion that surrounds generic placement of these smut fungi.

Preparing citizens for a complex world : the grand challenge of teaching socio-scientific issues in science education

The dawn of the twenty-first century encouraged a number of scientific and technological organisations to identify what they saw as ‘Grand Challenges and Opportunities’. Issues of environment and health featured very prominently in these quite short lists, as can be seen from a sample of these challenges in Table 1. Indeed, the first two lists of challenges in Table 1 were identified as for the environment and for health, respectively.