Hypercapitalism : an investigation into the relationship between language, new media, and social perceptions of value

Overall, this thesis purports to make two significant contributions to knowledge. The first is a foundational critique of political economy in the context of an emergent global knowledge economy. The second is a method for analysing evaluations in language. The relationships that give coherence to those two contributions are as follows. The widely-heralded emergence of a knowledge economy indicates that more intimate aspects of human activity have become exposed to commodification on a massive scale, specifically, activities associated with thought and language. Correspondingly, more abstract forms of value have developed as the products of thought and language have become dominant commodity forms. Historical investigation shows that value has moved from an objective category in political economy, pertaining to such substances as precious metals and land, to become situated today predominantly in “expert” expressions of language, or more precisely, their institutional contexts of production. These are now propagated and circulated on a global scale. Legal, political, and technological developments are key in the development of new, more abstract forms of labour and value, although the relationships connecting these are neither simple nor direct. They are, however, inseparably related in the trajectories that this thesis describes. Consequently they are dealt with inseparably throughout.

Globalization, frontier masculinities and violence : booze, blokes and brawls

Over the last two decades, two new trajectories have taken hold in criminology - the study of masculinity and crime, after a century of neglect, and the geography of crime. This article brings both those fields together to analyse the impact of globalisation in the resources sector on frontier cultures of violence. This paper approaches this issue through a case study of frontier masculinities and violence in communities at the forefront of generating resource extraction for global economies. This paper argues that the high rates of violence among men living in work camps in these socio-spatial contexts cannot simply be understood as individualised expressions of psycho-pathological deficit or social disorganisation. Explanations for these patterns of violence must also consider a number of key subterranean convergences between globalising processes and the social dynamics of male-on-male violence in such settings.

Kinematics of intracellular chlamydiae provide evidence for contact-dependent development

A crucial process of chlamydial development involves differentiation of the replicative reticulate body (RB) into the infectious elementary body (EB). We present experimental evidence to provide support for a contact-dependent hypothesis for explaining the trigger involved in differentiation. We recorded live-imaging of Chlamydia trachomatis-infected McCoy cells at key times during development and tracked the temporospatial trajectories of individual chlamydial particles. We found that movement of the particles is related to development. Early to mid-developmental stages involved slight wobbling of RBs. The average speed of particles increased sharply at 24 h postinfection (after the estimated onset of RB to EB differentiation). We also investigated a penicillin-supplemented culture containing EBs, RBs, and aberrantly enlarged, stressed chlamydiae. Near-immobile enlarged particles are consistent with their continued tethering to the chlamydial inclusion membrane (CIM). We found a significantly negative, nonlinear association between speed and size/type of particles, providing further support for the hypothesis that particles become untethered near the onset of RB to EB differentiation. This study establishes the relationship between the motion properties of the chlamydiae and developmental stages, whereby wobbling RBs gradually lose contact with the CIM, and RB detachment from the CIM is coincidental with the onset of late differentiation.

Innovation

Innovation can be defined broadly to include the development and uptake of new technology, the introduction of new products, the utilisation of new market opportunities and the implementation of new business processes including new forms of work organisation or management structures and approaches. Innovation, or the commercial application of new knowledge, is of increasing importance to economic competitiveness given the growth in production and trade in high technology industries and knowledge intensive service sectors such as business services (Edquist, Hommen and McKelvey 2001). An important field of innovation in modern economies is associated with the rapid development and application of information and communications technologies (ICTs). ICTs constitute an increasing share of value added, growth and employment and also impact on employment and productivity in other industry sectors. The structural transformation of modern economies associated with ICTs has led to an increase in the importance of information and knowledge resources (rather than physical capital) as inputs or factors of production. Technology and product innovations are often given central attention in innovation research, however, organisational and managerial changes have been recognised as critical. Over the last two decades, understandings of the nature and process of innovation have advanced significantly. In the 1950s and 1960s, there was a view that innovation resulted from basic research, or in essence that scientific research acted as a 'push' for innovation. As such there was a great deal of emphasis on formal research and development, undertaken either by governments or research and development units within business organisations. Radical innovations involving new products and new technological trajectories were thought to derive from basic research (Freeman 1995).

Building resilience to prevent mental health problems in young people : The resourceful adolescent programme (RAP)

This chapter describes an evidence-based programme called the Resourceful Adolescent Program (RAP), which has been successful in building resilience in young people to prevent depressive symptoms developing.The programme adopts a strengths-focused approach. It aims to build a range of coping resources that foster teenagers’ abilities to maintain a positive sense of self and regulate emotions in the face of the vicissitudes of everyday struggles and difficult life events.This groupbased programme can be implemented routinely in schools or by counselling professionals as an early intervention or prevention programme. While there is no universal definition, ‘resilience’ generally means the process of avoiding the negative trajectories associated with exposure to risk factors (Fergus and Zimmerman, 2005). Current models of resilience are also very clear that there ‘are many pathways to resilience’ (Bonanno, 2004) and there is no

Perceptions of aging across 26 cultures and their culture-level associates

College students (N = 3,435) in 26 cultures reported their perceptions of age-related changes in physical, cognitive, and socioemotional areas of functioning and rated societal views of aging within their culture. There was widespread cross-cultural consensus regarding the expected direction of aging trajectories with (1) perceived declines in societal views of aging, physical attractiveness, the ability to perform everyday tasks, and new learning, (2) perceived increases in wisdom, knowledge, and received respect, and (3) perceived stability in family authority and life satisfaction. Cross-cultural variations in aging perceptions were associated with culture-level indicators of population aging, education levels, values, and national character stereotypes. These associations were stronger for societal views on aging and perceptions of socioemotional changes than for perceptions of physical and cognitive changes. A consideration of culture-level variables also suggested that previously reported differences in aging perceptions between Asian and Western countries may be related to differences in population structure.

Theoretical investigation of mechanisms of formation and interaction of nanoparticles

In this thesis an investigation into theoretical models for formation and interaction of nanoparticles is presented. The work presented includes a literature review of current models followed by a series of five chapters of original research. This thesis has been submitted in partial fulfilment of the requirements for the degree of doctor of philosophy by publication and therefore each of the five chapters consist of a peer-reviewed journal article. The thesis is then concluded with a discussion of what has been achieved during the PhD candidature, the potential applications for this research and ways in which the research could be extended in the future. In this thesis we explore stochastic models pertaining to the interaction and evolution mechanisms of nanoparticles. In particular, we explore in depth the stochastic evaporation of molecules due to thermal activation and its ultimate effect on nanoparticles sizes and concentrations. Secondly, we analyse the thermal vibrations of nanoparticles suspended in a fluid and subject to standing oscillating drag forces (as would occur in a standing sound wave) and finally on lattice surfaces in the presence of high heat gradients. We have described in this thesis a number of new models for the description of multicompartment networks joined by a multiple, stochastically evaporating, links. The primary motivation for this work is in the description of thermal fragmentation in which multiple molecules holding parts of a carbonaceous nanoparticle may evaporate. Ultimately, these models predict the rate at which the network or aggregate fragments into smaller networks/aggregates and with what aggregate size distribution. The models are highly analytic and describe the fragmentation of a link holding multiple bonds using Markov processes that best describe different physical situations and these processes have been analysed using a number of mathematical methods. The fragmentation of the network/aggregate is then predicted using combinatorial arguments. Whilst there is some scepticism in the scientific community pertaining to the proposed mechanism of thermal fragmentation,we have presented compelling evidence in this thesis supporting the currently proposed mechanism and shown that our models can accurately match experimental results. This was achieved using a realistic simulation of the fragmentation of the fractal carbonaceous aggregate structure using our models. Furthermore, in this thesis a method of manipulation using acoustic standing waves is investigated. In our investigation we analysed the effect of frequency and particle size on the ability for the particle to be manipulated by means of a standing acoustic wave. In our results, we report the existence of a critical frequency for a particular particle size. This frequency is inversely proportional to the Stokes time of the particle in the fluid. We also find that for large frequencies the subtle Brownian motion of even larger particles plays a significant role in the efficacy of the manipulation. This is due to the decreasing size of the boundary layer between acoustic nodes. Our model utilises a multiple time scale approach to calculating the long term effects of the standing acoustic field on the particles that are interacting with the sound. These effects are then combined with the effects of Brownian motion in order to obtain a complete mathematical description of the particle dynamics in such acoustic fields. Finally, in this thesis, we develop a numerical routine for the description of "thermal tweezers". Currently, the technique of thermal tweezers is predominantly theoretical however there has been a handful of successful experiments which demonstrate the effect it practise. Thermal tweezers is the name given to the way in which particles can be easily manipulated on a lattice surface by careful selection of a heat distribution over the surface. Typically, the theoretical simulations of the effect can be rather time consuming with supercomputer facilities processing data over days or even weeks. Our alternative numerical method for the simulation of particle distributions pertaining to the thermal tweezers effect use the Fokker-Planck equation to derive a quick numerical method for the calculation of the effective diffusion constant as a result of the lattice and the temperature. We then use this diffusion constant and solve the diffusion equation numerically using the finite volume method. This saves the algorithm from calculating many individual particle trajectories since it is describes the flow of the probability distribution of particles in a continuous manner. The alternative method that is outlined in this thesis can produce a larger quantity of accurate results on a household PC in a matter of hours which is much better than was previously achieveable.

The silk road project : dance and embodiment in the age of motion capture

The Silk Road Project was a practice-based research project investigating the potential of motion capture technology to inform perceptions of embodiment in dance performance. The project created a multi-disciplinary collaborative performance event using dance performance and real-time motion capture at Deakin University’s Deakin Motion Lab. Several new technological advances in producing real-time motion capture performance were produced, along with a performance event that examined the aesthetic interplay between a dancer’s movement and the precise mappings of its trajectories created by motion capture and real-time motion graphic visualisations.

Vision-based detection and tracking of aerial targets for UAV collision avoidance

Machine vision represents a particularly attractive solution for sensing and detecting potential collision-course targets due to the relatively low cost, size, weight, and power requirements of the sensors involved (as opposed to radar). This paper describes the development and evaluation of a vision-based collision detection algorithm suitable for fixed-wing aerial robotics. The system was evaluated using highly realistic vision data of the moments leading up to a collision. Based on the collected data, our detection approaches were able to detect targets at distances ranging from 400m to about 900m. These distances (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning of between 8-10 seconds ahead of impact, which approaches the 12.5 second response time recommended for human pilots. We make use of the enormous potential of graphic processing units to achieve processing rates of 30Hz (for images of size 1024-by- 768). Currently, integration in the final platform is under way.

Airborne vision-based collision-detection system

Machine vision represents a particularly attractive solution for sensing and detecting potential collision-course targets due to the relatively low cost, size, weight, and power requirements of vision sensors (as opposed to radar and TCAS). This paper describes the development and evaluation of a real-time vision-based collision detection system suitable for fixed-wing aerial robotics. Using two fixed-wing UAVs to recreate various collision-course scenarios, we were able to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. This type of image data is extremely scarce and was invaluable in evaluating the detection performance of two candidate target detection approaches. Based on the collected data, our detection approaches were able to detect targets at distances ranging from 400m to about 900m. These distances (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning of between 8-10 seconds ahead of impact, which approaches the 12.5 second response time recommended for human pilots. We overcame the challenge of achieving real-time computational speeds by exploiting the parallel processing architectures of graphics processing units found on commercially-off-the-shelf graphics devices. Our chosen GPU device suitable for integration onto UAV platforms can be expected to handle real-time processing of 1024 by 768 pixel image frames at a rate of approximately 30Hz. Flight trials using manned Cessna aircraft where all processing is performed onboard will be conducted in the near future, followed by further experiments with fully autonomous UAV platforms.

UAS mission path planning system (MPPS) using hybrid-game coupled to multi-objective optimiser

This paper presents the application of advanced optimization techniques to unmanned aerial system mission path planning system (MPPS) using multi-objective evolutionary algorithms (MOEAs). Two types of multi-objective optimizers are compared; the MOEA nondominated sorting genetic algorithm II and a hybrid-game strategy are implemented to produce a set of optimal collision-free trajectories in a three-dimensional environment. The resulting trajectories on a three-dimensional terrain are collision-free and are represented by using Bézier spline curves from start position to target and then target to start position or different positions with altitude constraints. The efficiency of the two optimization methods is compared in terms of computational cost and design quality. Numerical results show the benefits of adding a hybrid-game strategy to a MOEA and for a MPPS.

Optimal mission path planning (MPP) for an air sampling unmanned aerial system

This paper presents advanced optimization techniques for Mission Path Planning (MPP) of a UAS fitted with a spore trap to detect and monitor spores and plant pathogens. The UAV MPP aims to optimise the mission path planning search and monitoring of spores and plant pathogens that may allow the agricultural sector to be more competitive and more reliable. The UAV will be fitted with an air sampling or spore trap to detect and monitor spores and plant pathogens in remote areas not accessible to current stationary monitor methods. The optimal paths are computed using a Multi-Objective Evolutionary Algorithms (MOEAs). Two types of multi-objective optimisers are compared; the MOEA Non-dominated Sorting Genetic Algorithms II (NSGA-II) and Hybrid Game are implemented to produce a set of optimal collision-free trajectories in three-dimensional environment. The trajectories on a three-dimension terrain, which are generated off-line, are collision-free and are represented by using Bézier spline curves from start position to target and then target to start position or different position with altitude constraints. The efficiency of the two optimization methods is compared in terms of computational cost and design quality. Numerical results show the benefits of coupling a Hybrid-Game strategy to a MOEA for MPP tasks. The reduction of numerical cost is an important point as the faster the algorithm converges the better the algorithms is for an off-line design and for future on-line decisions of the UAV.

UAS Mission Path Planning System, (MPPS) using hybrid-game coupled to multi-objective optimizer

This paper presents the application of advanced optimization techniques to unmanned aerial system mission path planning system (MPPS) using multi-objective evolutionary algorithms (MOEAs). Two types of multi-objective optimizers are compared; the MOEA nondominated sorting genetic algorithm II and a hybrid-game strategy are implemented to produce a set of optimal collision-free trajectories in a three-dimensional environment. The resulting trajectories on a three-dimensional terrain are collision-free and are represented by using Bézier spline curves from start position to target and then target to start position or different positions with altitude constraints. The efficiency of the two optimization methods is compared in terms of computational cost and design quality. Numerical results show the benefits of adding a hybrid-game strategy to a MOEA and for a MPPS.

Bayesian models for longitudinal data

Longitudinal data, where data are repeatedly observed or measured on a temporal basis of time or age provides the foundation of the analysis of processes which evolve over time, and these can be referred to as growth or trajectory models. One of the traditional ways of looking at growth models is to employ either linear or polynomial functional forms to model trajectory shape, and account for variation around an overall mean trend with the inclusion of random eects or individual variation on the functional shape parameters. The identification of distinct subgroups or sub-classes (latent classes) within these trajectory models which are not based on some pre-existing individual classification provides an important methodology with substantive implications. The identification of subgroups or classes has a wide application in the medical arena where responder/non-responder identification based on distinctly diering trajectories delivers further information for clinical processes. This thesis develops Bayesian statistical models and techniques for the identification of subgroups in the analysis of longitudinal data where the number of time intervals is limited. These models are then applied to a single case study which investigates the neuropsychological cognition for early stage breast cancer patients undergoing adjuvant chemotherapy treatment from the Cognition in Breast Cancer Study undertaken by the Wesley Research Institute of Brisbane, Queensland. Alternative formulations to the linear or polynomial approach are taken which use piecewise linear models with a single turning point, change-point or knot at a known time point and latent basis models for the non-linear trajectories found for the verbal memory domain of cognitive function before and after chemotherapy treatment. Hierarchical Bayesian random eects models are used as a starting point for the latent class modelling process and are extended with the incorporation of covariates in the trajectory profiles and as predictors of class membership. The Bayesian latent basis models enable the degree of recovery post-chemotherapy to be estimated for short and long-term followup occasions, and the distinct class trajectories assist in the identification of breast cancer patients who maybe at risk of long-term verbal memory impairment.

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

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