Punitiveness and the criminalisation of the other : State wards, unlawful non-citizens and Indigenous youth

This paper explores the genealogies of bio-power that cut across punitive state interventions aimed at regulating or normalising several distinctive ‘problem’ or ‘suspect’ deviant populations, such as state wards, non-lawful citizens and Indigenous youth. It begins by making some general comments about the theoretical approach to bio-power taken in this paper. It then outlines the distinctive features of bio-power in Australia and how these intersected with the emergence of penal welfarism to govern the unruly, unchaste, unlawful, and the primitive. The paper draws on three examples to illustrate the argument – the gargantuan criminalisation rates of Aboriginal youth, the history of incarcerating state wards in state institutions, and the mandatory detention of unlawful non-citizens and their children. The construction of Indigenous people as a dangerous presence, alongside the construction of the unruly neglected children of the colony — the larrikin descendants of convicts as necessitating special regimes of internal controls and institutions, found a counterpart in the racial and other exclusionary criteria operating through immigration controls for much of the twentieth century. In each case the problem child or population was expelled from the social body through forms of bio-power, rationalised as strengthening, protecting or purifying the Australian population.

Monitoring energy performance of air conditioners in classrooms

Air quality and temperatures in classrooms are important factors influencing the student learning process. To improve the thermal comfort of classrooms for Queensland State Schools, Queensland Government initiated the "Cooler Schools Program". One of the key objectives under this program was to develop low energy cooling systems as an alternative to high energy demand conventioanl split system of air conditioning (AC) systems. In order to compare and evaluate the energy performance of different types of air conditioners installed in classrooms, monitoring systems were installed in a state primary school located in the greater outer urban area of Brisbane, Australia. It was found that the installation of monitoring systems could have a significant impact on the accuracy of the data being collected. By comparing the estimated energy efficiency ratio (EER)for four qualified air conditioners included in this study, it was also found that AC6, a hybrid air conditioner newly developed by the Queensland Department of Public Works (DPW), had the best energy performance, although the current data were not able to show the full advantages of the system.

Will insulation always bring benefits in energy saving and thermal comfort?

Building insulation is often used to reduce the conduction heat transfer through building envelope. With a higher level of insulation (or a greater R-value), the less the conduction heat would transfer through building envelope. In this paper, using building computer simulation techniques, the effects of building insulation levels on the thermal and energy performance of a sample air-conditioned office building in Australia are studied. It is found that depending on the types of buildings and the climates of buildings located, increasing the level of building insulation will not always bring benefits in energy saving and thermal comfort, particularly for internal-load dominated office buildings located in temperate/tropical climates. The possible implication of building insulation in face of global warming has also been examined. Compared with the influence of insulation on building thermal performance, the influence on building energy use is relatively small.

Inclusion of PMU current phasor measurements in a power system state estimator

Three different methods of inclusion of current measurements by phasor measurement units (PMUs) in a power sysetm state estimator is investigated. A comprehensive formulation of the hybrid state estimator incorporating conventional, as well as PMU measurements, is presented for each of the three methods. The behaviour of the elements because of the current measurements in the measurement Jacobian matrix is examined for any possible ill-conditioning of the state estimator gain matrix. The performance of the state estimators are compared in terms of the convergence properties and the varian in the estimated states. The IEEE 14-bus and IEEE 300-bus systems are used as test beds for the study.

A national roll out of an insufficiently evaluated practice : how evidence based are our end-of-life care policies?

There has been increasing international efforts to ensure that health care policies are evidence based. One area where there is a lack of ‘effectiveness’ evidence is in the use of end-of-life care pathways (EOLCP) (1). Despite the lack of evidence supporting the efficacy of the EOCLP, their use has been endorsed in the recent national palliative care strategy document in the UK (2). In addition, a publication endorsed by the Australian Government (titled: Supporting Australians to live well at the End of Life- National Palliative Care Strategy 2010) (3), recommended a national roll out of EOLCP across all sectors (primary, acute and aged care) in Australia. According to this document, it is a measure of “appropriateness” and “effectiveness” for promoting quality end-of-life care.

Nation-state or country-state : how do we discuss belonging in an age of fluidity?

This is the opening article of a two-part exchange between Jean-Paul Gagnon and Michael Gardiner on the nation-state.

Voluntary employment agreements in the pioneer state

Research Paper examining the introduction of VEA’s on the industrial and political framework of Queensland

A solid state Marx generator with a novel configuration

The new configuration proposed in this paper for Marx Generator (MG) aims to generate high voltage for pulsed power applications through reduced number of semiconductor components with a more efficient load supplying process. The main idea is to charge two groups of capacitors in parallel through an inductor and take advantage of resonant phenomenon in charging each capacitor up to a double input voltage level. In each resonant half a cycle, one of those capacitor groups are charged, and eventually the charged capacitors will be connected in series and the summation of the capacitor voltages can be appeared at the output of the topology. This topology can be considered as a modified Marx generator which works based on the resonant concept. Simulated models of this converter have been investigated in Matlab/SIMULINK platform and a prototype set up has been implemented in laboratory. The acquired results of either fully satisfy the anticipations in proper operation of the converter.

A solid state Marx generator with a novel configuration

The new configuration proposed in this paper for Marx Generator (MG.) aims to generate high voltage for pulsed power applications through reduced number of semiconductor components with a more efficient load supplying process. The main idea is to charge two groups of capacitors in parallel through an inductor and take the advantage of resonant phenomenon in charging each capacitor up to a double input voltage level. In each resonant half a cycle, one of those capacitor groups are charged, and eventually the charged capacitors will be connected in series and the summation of the capacitor voltages can be appeared at the output of the topology. This topology can be considered as a modified Marx generator which works based on the resonant concept. Simulated models of this converter have been investigated in Matlab/SIMULINK platform and the acquired results fully satisfy the anticipations in proper operation of the converter.

The legal role of medical professionals in decisions to withhold or withdraw life-sustaining treatment : Part 1 (New South Wales)

This is the first article in a series of three that examines the legal role of medical professionals in decisions to withhold or withdraw life-sustaining treatment from adults who lack capacity. This article considers the position in New South Wales. A review of the law in this State reveals that medical professionals play significant legal roles in these decisions. However, the law is problematic in a number of respects and this is likely to impede medical professionals’ legal knowledge in this area. The article examines the level of training medical professionals receive on issues such as advance directives and substitute decision-making, and the available empirical evidence as to the state of medical professionals’ knowledge of the law at the end of life. It concludes that there are gaps in legal knowledge and that law reform is needed in New South Wales.

The legal role of medical professionals in decisions to withhold or withdraw life-sustaining treatment : Part 2 (Queensland)

This is the second article in a series of three that examines the legal role of medical professionals in decisions to withhold or withdraw life-sustaining treatment from adults who lack capacity. This article considers the position in Queensland, including the parens patriae jurisdiction of the Supreme Court. A review of the law in this State reveals that medical professionals play significant legal roles in these decisions. However, the law is problematic in a number of respects and this is likely to impede medical professionals’ legal knowledge in this area. The article examines the level of training medical professionals receive on issues such as advance health directives and substitute decision-making, and the available empirical evidence as to the state of medical professionals’ knowledge of the law at the end of life. It concludes that there are gaps in legal knowledge and that law reform is needed in Queensland.

Asset life prediction and maintenance decision-making using a non-linear non-Gaussian state space model

Estimating and predicting degradation processes of engineering assets is crucial for reducing the cost and insuring the productivity of enterprises. Assisted by modern condition monitoring (CM) technologies, most asset degradation processes can be revealed by various degradation indicators extracted from CM data. Maintenance strategies developed using these degradation indicators (i.e. condition-based maintenance) are more cost-effective, because unnecessary maintenance activities are avoided when an asset is still in a decent health state. A practical difficulty in condition-based maintenance (CBM) is that degradation indicators extracted from CM data can only partially reveal asset health states in most situations. Underestimating this uncertainty in relationships between degradation indicators and health states can cause excessive false alarms or failures without pre-alarms. The state space model provides an efficient approach to describe a degradation process using these indicators that can only partially reveal health states. However, existing state space models that describe asset degradation processes largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires that failures and inspections only happen at fixed intervals. The discrete state assumption entails discretising continuous degradation indicators, which requires expert knowledge and often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This research proposes a Gamma-based state space model that does not have discrete time, discrete state, linear and Gaussian assumptions to model partially observable degradation processes. Monte Carlo-based algorithms are developed to estimate model parameters and asset remaining useful lives. In addition, this research also develops a continuous state partially observable semi-Markov decision process (POSMDP) to model a degradation process that follows the Gamma-based state space model and is under various maintenance strategies. Optimal maintenance strategies are obtained by solving the POSMDP. Simulation studies through the MATLAB are performed; case studies using the data from an accelerated life test of a gearbox and a liquefied natural gas industry are also conducted. The results show that the proposed Monte Carlo-based EM algorithm can estimate model parameters accurately. The results also show that the proposed Gamma-based state space model have better fitness result than linear and Gaussian state space models when used to process monotonically increasing degradation data in the accelerated life test of a gear box. Furthermore, both simulation studies and case studies show that the prediction algorithm based on the Gamma-based state space model can identify the mean value and confidence interval of asset remaining useful lives accurately. In addition, the simulation study shows that the proposed maintenance strategy optimisation method based on the POSMDP is more flexible than that assumes a predetermined strategy structure and uses the renewal theory. Moreover, the simulation study also shows that the proposed maintenance optimisation method can obtain more cost-effective strategies than a recently published maintenance strategy optimisation method by optimising the next maintenance activity and the waiting time till the next maintenance activity simultaneously.

Machine prognostics based on health state probability estimation

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation and can also improve productivity and enhance system’s safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. Although a variety of prognostic methodologies have been reported recently, their application in industry is still relatively new and mostly focused on the prediction of specific component degradations. Furthermore, they required significant and sufficient number of fault indicators to accurately prognose the component faults. Hence, sufficient usage of health indicators in prognostics for the effective interpretation of machine degradation process is still required. Major challenges for accurate longterm prediction of remaining useful life (RUL) still remain to be addressed. Therefore, continuous development and improvement of a machine health management system and accurate long-term prediction of machine remnant life is required in real industry application. This thesis presents an integrated diagnostics and prognostics framework based on health state probability estimation for accurate and long-term prediction of machine remnant life. In the proposed model, prior empirical (historical) knowledge is embedded in the integrated diagnostics and prognostics system for classification of impending faults in machine system and accurate probability estimation of discrete degradation stages (health states). The methodology assumes that machine degradation consists of a series of degraded states (health states) which effectively represent the dynamic and stochastic process of machine failure. The estimation of discrete health state probability for the prediction of machine remnant life is performed using the ability of classification algorithms. To employ the appropriate classifier for health state probability estimation in the proposed model, comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault data of three different faults in a high pressure liquefied natural gas (HP-LNG) pump. As a result of this comparison study, SVMs were employed in heath state probability estimation for the prediction of machine failure in this research. The proposed prognostic methodology has been successfully tested and validated using a number of case studies from simulation tests to real industry applications. The results from two actual failure case studies using simulations and experiments indicate that accurate estimation of health states is achievable and the proposed method provides accurate long-term prediction of machine remnant life. In addition, the results of experimental tests show that the proposed model has the capability of providing early warning of abnormal machine operating conditions by identifying the transitional states of machine fault conditions. Finally, the proposed prognostic model is validated through two industrial case studies. The optimal number of health states which can minimise the model training error without significant decrease of prediction accuracy was also examined through several health states of bearing failure. The results were very encouraging and show that the proposed prognostic model based on health state probability estimation has the potential to be used as a generic and scalable asset health estimation tool in industrial machinery.