A proposed qualitative framework for heterogeneous burning of metallic materials : the 'melting rate triangle'

This paper presents a proposed qualitative framework to discuss the heterogeneous burning of metallic materials, through parameters and factors that influence the melting rate of the solid metallic fuel (either in a standard test or in service). During burning, the melting rate is related to the burning rate and is therefore an important parameter for describing and understanding the burning process, especially since the melting rate is commonly recorded during standard flammability testing for metallic materials and is incorporated into many relative flammability ranking schemes. However, whilst the factors that influence melting rate (such as oxygen pressure or specimen diameter) have been well characterized, there is a need for an improved understanding of how these parameters interact as part of the overall melting and burning of the system. Proposed here is the ‘Melting Rate Triangle’, which aims to provide this focus through a conceptual framework for understanding how the melting rate (of solid fuel) is determined and regulated during heterogeneous burning. In the paper, the proposed conceptual model is shown to be both (a) consistent with known trends and previously observed results, and (b)capable of being expanded to incorporate new data. Also shown are examples of how the Melting Rate Triangle can improve the interpretation of flammability test results. Slusser and Miller previously published an ‘Extended Fire Triangle’ as a useful conceptual model of ignition and the factors affecting ignition, providing industry with a framework for discussion. In this paper it is shown that a ‘Melting Rate Triangle’ provides a similar qualitative framework for burning, leading to an improved understanding of the factors affecting fire propagation and extinguishment.

Trauma type and posttrauma outcomes : differences between survivors of motor vehicle accidents, sexual assault, and bereavement

Research examining post-trauma pathology indicates negative outcomes can differ as a function of the type of trauma experienced. Such research has yet to be published when looking at positive post-trauma changes. Ninety-Four survivors of trauma, forming three groups, completed the Posttraumatic Growth Inventory (PTGI) and Impact of Events Scale-Revised (IES-R). Groups comprised survivors of i) sexual abuse ii) motor vehicle accidents iii) bereavement. Results indicted differences in growth between the groups with the bereaved reporting higher levels of growth than other survivors and sexual abuse survivors demonstrated higher levels of PTSD symptoms than the other groups. However, this did not preclude sexual abuse survivors from also reporting moderate levels of growth. Results are discussed with relation to fostering growth through clinical practice.

‘Keep telling until someone listens’ : understanding prevention concepts in children’s picture books dealing with child sexual abuse

Children’s picture books dealing with the topic of child sexual abuse appeared in the 1980s with the aim of addressing the need for age-appropriate texts to teach sexual abuse prevention concepts and to provide support for young children who may be at risk of or have already experienced sexual abuse. Despite the apparent potential of children’s picture books to convey child sexual abuse prevention concepts, very few studies have addressed the topic of child sexual abuse in children’s literature. This article critically examines a selection of 15 picture books (published in the US, Canada and Australia) for children aged 3–8 years dealing with this theme. It makes use of an established set of evaluative criteria to conduct an audit of the books’ content and applies techniques of literary discourse analysis to explain how these picture books satisfy criteria for child sexual abuse prevention. The analysis is used as a way to understand the discourses available to readers, both adults and children, on the topic of child sexual abuse. Key themes in the books include children’s empowerment and agency, and the need for persistence and hope.

Preventing sexual violence?

This article critically assesses the main social policy responses to preventing rape following much feminist struggle to make sexual violence a public matter of legitimate concern. It considers the preventative potential of legal measures, anti-violence campaigns waged by feminist and men's groups in the US and Australia, public education campaigns in Schools and Universities, and public awareness campaigns sponsored by the state.We argue that sexual violence is not amenable to quick fix strategies that place responsibility for prevention entirely on individual men or women. While we recognise that responsibilising victims and individualising offenders is consistent with wider global shifts in social policy calling upon individuals to manage their own risk, we argue that the increasing reliance on such neo-liberal social policy is especially problematic in preventing rape. The paper suggests ways to resist this which place greater emphasis on the promotion of sexual ethics; the eroticisation of consent; the reinvention of the norms of romance to include both these, and the complete separation of the psycho-social-symbolic connections between sex and violence, and ultimately the re-evaluation of the cultural expectations of masculinity and femininity.

Teachers reporting child sexual abuse : towards evidence-based reform of law, policy and practice

This report presents the results of the largest study ever conducted into the law, policy and practice of primary school teachers’ reporting of child sexual abuse in New South Wales, Queensland and Western Australia. The study included the largest Australian survey of teachers about reporting sexual abuse, in both government and non-government schools (n=470). Our research has produced evidence-based findings to enhance law, policy and practice about teachers’ reporting of child sexual abuse. The major benefits of our findings and recommendations are to: • Show how the legislation in each State can be improved; • Show how the policies in government and non-government school sectors can be improved; and • Show how teacher training can be improved. These improvements can enhance the already valuable contribution that teachers are making to identify cases of child sexual abuse. Based on the findings of our research, this report proposes solutions to issues in seven key areas of law, policy and practice. These solutions are relevant for State Parliaments, government and non-government educational authorities, and child protection departments. The solutions in each State are practicable, low-cost, and align with current government policy approaches. Implementing these solutions will: • protect more children from sexual abuse; • save cost to governments and society; • develop a professional teacher workforce better equipped for their child protection role; and • protect government and school authorities from legal liability.

Teachers’ attitudes toward reporting child sexual abuse : problems with existing research leading to new scale development

This paper details a systematic literature review identifying problems in extant research relating to teachers’ attitudes towards reporting child sexual abuse, and offers a model for new attitude scale development and testing. Scale development comprised a five-phase process grounded in contemporary attitude theories including: a) developing the initial item pool; b) conducting a panel review; c) refining the scale via an expert focus group; d) building content validity through cognitive interviews; e) assessing internal consistency via field testing. The resulting 21-item scale displayed construct validity in preliminary testing. The scale may prove useful as a research tool, given the theoretical supposition that attitudes may be changed with time, context, experience, and education. Further investigation with a larger sample is warranted.

Outrage management in cases of sexual harassment as revealed in judicial decisions

Sexual harassment can be conceptualized as a series of interactions between harassers and targets that either inhibit or increase outrage by third parties. The outrage management model predicts the kinds of actions likely to be used by perpetrators to minimize outrage, predicts the consequences of failing to use these tactics—namely backfire, and recommends countertactics to increase outrage. Using this framework, our archival study examined outrage-management tactics reported as evidence in 23 judicial decisions of sexual harassment cases in Australia. The decisions contained precise, detailed information about the circumstances leading to the claim; the events which transpired in the courtroom, including direct quotations; and the judges' interpretations and findings. We found evidence that harassers minimize outrage by covering up the actions, devaluing the target, reinterpreting the events, using official channels to give an appearance of justice, and intimidating or bribing people involved. Targets can respond using countertactics of exposure, validation, reframing, mobilization of support, and resistance. Although there are limitations to using judicial decisions as a source of information, our study points to the value of studying tactics and the importance to harassers of minimizing outrage from their actions. The findings also highlight that, given the limitations of statutory and organizational protections in reducing the incidence and severity of sexual harassment in the community, individual responses may be effective as part of a multilevel response in reducing the incidence and impact of workplace sexual harassment as a gendered harm.

Using accelerometer, high sample rate GPS and magnetometer data to develop a cattle movement and behaviour model

The study described in this paper developed a model of animal movement, which explicitly recognised each individual as the central unit of measure. The model was developed by learning from a real dataset that measured and calculated, for individual cows in a herd, their linear and angular positions and directional and angular speeds. Two learning algorithms were implemented: a Hidden Markov model (HMM) and a long-term prediction algorithm. It is shown that a HMM can be used to describe the animal's movement and state transition behaviour within several “stay” areas where cows remained for long periods. Model parameters were estimated for hidden behaviour states such as relocating, foraging and bedding. For cows’ movement between the “stay” areas a long-term prediction algorithm was implemented. By combining these two algorithms it was possible to develop a successful model, which achieved similar results to the animal behaviour data collected. This modelling methodology could easily be applied to interactions of other animal species.

Poster abstract : Energy-efficient rate adaptive MAC Protocol (RA-MAC) for long-lived sensor networks

This paper introduces an energy-efficient Rate Adaptive MAC (RA-MAC) protocol for long-lived Wireless Sensor Networks (WSN). Previous research shows that the dynamic and lossy nature of wireless communication is one of the major challenges to reliable data delivery in a WSN. RA-MAC achieves high link reliability in such situations by dynamically trading off radio bit rate for signal processing gain. This extra gain reduces the packet loss rate which results in lower energy expenditure by reducing the number of retransmissions. RA-MAC selects the optimal data rate based on channel conditions with the aim of minimizing energy consumption. We have implemented RA-MAC in TinyOS on an off-the-shelf sensor platform (TinyNode), and evaluated its performance by comparing RA-MAC with state-ofthe- art WSN MAC protocol (SCP-MAC) by experiments.

Use of IP addresses for high rate flooding attack detection

High-rate flooding attacks (aka Distributed Denial of Service or DDoS attacks) continue to constitute a pernicious threat within the Internet domain. In this work we demonstrate how using packet source IP addresses coupled with a change-point analysis of the rate of arrival of new IP addresses may be sufficient to detect the onset of a high-rate flooding attack. Importantly, minimizing the number of features to be examined, directly addresses the issue of scalability of the detection process to higher network speeds. Using a proof of concept implementation we have shown how pre-onset IP addresses can be efficiently represented using a bit vector and used to modify a “white list” filter in a firewall as part of the mitigation strategy.

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.