92 resultados para breakdown point


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Characteristics of electrical breakdown of a planar magnetron enhanced with an electromagnet and a hollow-cathode structure, are studied experimentally and numerically. At lower pressures the breakdown voltage shows a dependence on the applied magnetic field, and the voltage necessary to achieve the self-sustained discharge regime can be significantly reduced. At higher pressures, the dependence is less sensitive to the magnetic field magnitude and shows a tendency of increased breakdown voltage at the stronger magnetic fields. A model of the magnetron discharge breakdown is developed with the background gas pressure and the magnetic field used as parameters. The model describes the motion of electrons, which gain energy by passing the electric field across the magnetic field and undergo collisions with neutrals, thus generating new bulk electrons. The electrons are in turn accelerated in the electric field and effectively ionize a sufficient amount of neutrals to enable the discharge self-sustainment regime. The model is based on the assumption about the combined classical and near-wall mechanisms of electron conductivity across the magnetic field, and is consistent with the experimental results. The obtained results represent a significant advance toward energy-efficient multipurpose magnetron discharges.

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The formation of vapor layers around an electrode immersed in a conducting liquid prior to generation of a plasma discharge is studied using numerical simulations. This study quantifies and explains the effects of the electrode geometry and applied voltage pulses, as well as the electrical and thermal properties of the liquids on the temporal dynamics of the pre-breakdown conditions in the vapor layer. This model agrees well with experimental data, in particular, the time needed to reach the electrical breakdown threshold. Because the time needed for discharge ignition can be accurately predicted from the model, the parameters such as the pulse shape, voltage, and electrode configuration can be optimized under different liquid conditions, which facilitates a faster and more energy-efficient plasma generation.

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This study reports on the utilisation of the Manchester Driver Behaviour Questionnaire (DBQ) to examine the self-reported driving behaviours of a large sample of Australian fleet drivers (N = 3414). Surveys were completed by employees before they commenced a one day safety workshop intervention. Factor analysis techniques identified a three factor solution similar to previous research, which was comprised of: (a) errors, (b) highway-code violations and (c) aggressive driving violations. Two items traditionally related with highway-code violations were found to be associated with aggressive driving behaviours among the current sample. Multivariate analyses revealed that exposure to the road, errors and self-reported offences predicted crashes at work in the last 12 months, while gender, highway violations and crashes predicted offences incurred while at work. Importantly, those who received more fines at work were at an increased risk of crashing the work vehicle. However, overall, the DBQ demonstrated limited efficacy at predicting these two outcomes. This paper outlines the major findings of the study in regards to identifying and predicting aberrant driving behaviours and also highlights implications regarding the future utilisation of the DBQ within fleet settings.

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We investigate whether framing effects of voluntary contributions are significant in a provision point mechanism. Our results show that framing significantly affects individuals of the same type: cooperative individuals appear to be more cooperative in the public bads game than in the public goods game, whereas individualistic subjects appear to be less cooperative in the public bads game than in the public goods game. At the aggregate level of pooling all individuals, the data suggests that framing effects are negligible, which is in contrast with the established result.

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Spatial data are now prevalent in a wide range of fields including environmental and health science. This has led to the development of a range of approaches for analysing patterns in these data. In this paper, we compare several Bayesian hierarchical models for analysing point-based data based on the discretization of the study region, resulting in grid-based spatial data. The approaches considered include two parametric models and a semiparametric model. We highlight the methodology and computation for each approach. Two simulation studies are undertaken to compare the performance of these models for various structures of simulated point-based data which resemble environmental data. A case study of a real dataset is also conducted to demonstrate a practical application of the modelling approaches. Goodness-of-fit statistics are computed to compare estimates of the intensity functions. The deviance information criterion is also considered as an alternative model evaluation criterion. The results suggest that the adaptive Gaussian Markov random field model performs well for highly sparse point-based data where there are large variations or clustering across the space; whereas the discretized log Gaussian Cox process produces good fit in dense and clustered point-based data. One should generally consider the nature and structure of the point-based data in order to choose the appropriate method in modelling a discretized spatial point-based data.

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This paper is about localising across extreme lighting and weather conditions. We depart from the traditional point-feature-based approach as matching under dramatic appearance changes is a brittle and hard thing. Point feature detectors are fixed and rigid procedures which pass over an image examining small, low-level structure such as corners or blobs. They apply the same criteria applied all images of all places. This paper takes a contrary view and asks what is possible if instead we learn a bespoke detector for every place. Our localisation task then turns into curating a large bank of spatially indexed detectors and we show that this yields vastly superior performance in terms of robustness in exchange for a reduced but tolerable metric precision. We present an unsupervised system that produces broad-region detectors for distinctive visual elements, called scene signatures, which can be associated across almost all appearance changes. We show, using 21km of data collected over a period of 3 months, that our system is capable of producing metric localisation estimates from night-to-day or summer-to-winter conditions.

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The Driver Behaviour Questionnaire (DBQ) continues to be the most widely utilised self-report scale globally to assess crash risk and aberrant driving behaviours among motorists. However, the scale also attracts criticism regarding its perceived limited ability to accurately identify those most at risk of crash involvement. This study reports on the utilisation of the DBQ to examine the self-reported driving behaviours (and crash outcomes) of drivers in three separate Australian fleet samples (N = 443, N = 3414, & N = 4792), and whether combining the samples increases the tool’s predictive ability. Either on-line or paper versions of the questionnaire were completed by fleet employees in three organisations. Factor analytic techniques identified either three or four factor solutions (in each of the separate studies) and the combined sample produced expected factors of: (a) errors, (b) highway-code violations and (c) aggressive driving violations. Highway code violations (and mean scores) were comparable across the studies. However, across the three samples, multivariate analyses revealed that exposure to the road was the best predictor of crash involvement at work, rather than DBQ constructs. Furthermore, combining the scores to produce a sample of 8649 drivers did not improve the predictive ability of the tool for identifying crashes (e.g., 0.4% correctly identified) or for demerit point loss (0.3%). The paper outlines the major findings of this comparative sample study in regards to utilising self-report measurement tools to identify “at risk” drivers as well as the application of such data to future research endeavours.

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PURPOSE Every health care sector including hospice/palliative care needs to systematically improve services using patient-defined outcomes. Data from the national Australian Palliative Care Outcomes Collaboration aims to define whether hospice/palliative care patients' outcomes and the consistency of these outcomes have improved in the last 3 years. METHODS Data were analysed by clinical phase (stable, unstable, deteriorating, terminal). Patient-level data included the Symptom Assessment Scale and the Palliative Care Problem Severity Score. Nationally collected point-of-care data were anchored for the period July-December 2008 and subsequently compared to this baseline in six 6-month reporting cycles for all services that submitted data in every time period (n = 30) using individual longitudinal multi-level random coefficient models. RESULTS Data were analysed for 19,747 patients (46 % female; 85 % cancer; 27,928 episodes of care; 65,463 phases). There were significant improvements across all domains (symptom control, family care, psychological and spiritual care) except pain. Simultaneously, the interquartile ranges decreased, jointly indicating that better and more consistent patient outcomes were being achieved. CONCLUSION These are the first national hospice/palliative care symptom control performance data to demonstrate improvements in clinical outcomes at a service level as a result of routine data collection and systematic feedback.

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Partial shading and rapidly changing irradiance conditions significantly impact on the performance of photovoltaic (PV) systems. These impacts are particularly severe in tropical regions where the climatic conditions result in very large and rapid changes in irradiance. In this paper, a hybrid maximum power point (MPP) tracking (MPPT) technique for PV systems operating under partially shaded conditions witapid irradiance change is proposed. It combines a conventional MPPT and an artificial neural network (ANN)-based MPPT. A low cost method is proposed to predict the global MPP region when expensive irradiance sensors are not available or are not justifiable for cost reasons. It samples the operating point on the stairs of I–V curve and uses a combination of the measured current value at each stair to predict the global MPP region. The conventional MPPT is then used to search within the classified region to get the global MPP. The effectiveness of the proposed MPPT is demonstrated using both simulations and an experimental setup. Experimental comparisons with four existing MPPTs are performed. The results show that the proposed MPPT produces more energy than the other techniques and can effectively track the global MPP with a fast tracking speed under various shading patterns.

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Intramedullary nailing is the standard fixation method for displaced diaphyseal fractures of tibia. Selection of the correct nail insertion point is important for axial alignment of bone fragments and to avoid iatrogenic fractures. However, the standard entry point (SEP) may not always optimise the bone-nail fit due to geometric variations of bones. This study aimed to investigate the optimal entry for a given bone-nail pair using the fit quantification software tool previously developed by the authors. The misfit was quantified for 20 bones with two nail designs (ETN and ETN-Proximal Bend) related to the SEP and 5 entry points which were 5 mm and 10 mm away from the SEP. The SEP was the optimal entry point for 50% of the bones used. For the remaining bones, the optimal entry point was located 5 mm away from the SEP, which improved the overall fit by 40% on average. However, entry points 10 mm away from the SEP doubled the misfit. The optimised bone-nail fit can be achieved through the SEP and within the range of a 5 mm radius, except posteriorly. The study results suggest that the optimal entry point should be selected by considering the fit during insertion and not only at the final position.

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Bushfire responsive design and management strategy at the bioregion scale. 248 Page document containing text, original designs, photographs, masterplans and critique - created as an alternative community-based strategy for risk mitigation and management reponse to bushfire in the Point Henry and Bremer Bay region of Western Australia. Document drafted as an alternative to a local government commissioned plan which had many shortcomings. It was presented as a 'powerpoint' presentaion at a public meeting in Bremer Bay on 7th April 2014 and disseminated to local community members and councillors to encourage public debate and feedback to the Shire of Jerramungup, WA.