Computer simulation of the fatigue behaviour of roof cladding during the passage of a tropical cyclone

This paper describes the development of an analytical model used to simulate the fatigue behaviour of roof cladding during the passage of a tropical cyclone. The model incorporated into a computer program uses wind pressure data from wind tunnel tests in combination with time history information on wind speed and direction during a tropical cyclone, and experimental fatigue characteristics data of roof claddings. The wind pressure data is analysed using a rainflow form of analysis, and a fatigue damage index calculated using a modified form of Miner's rule. Some of the results obtained to date and their significance in relation to the review of current fatigue tests are presented. The model appears to be reasonable for comparative estimation of fatigue life, but an improvement of Miner's rule is required for the prediction of actual fatigue life.

Towards an appropriate fatigue loading sequence for roof claddings in cyclone prone areas

Currently two different fatigue tests are being used to investigate the fatigue susceptibility of roof claddings in the cyclone prone areas of Australia. In order to resolve this issue a detailed investigation was conducted to study the nature of cyclonic wind forces using wind tunnel testing and computer modelling and the fatigue behaviour of metal roof claddings using structural testing. This led to the development of an accurate, but complicated loading matrix for a design cyclone. Based on this matrix, a simplified low-high-low loading sequence has been developed for the testing of roofing systems in cyclone prone areas. This paper first reviews the currently used fatigue loading sequences, then presents details of the cyclonic wind loading matrix and finally the development of the new simplified loading sequence. This simplified sequence should become the only suitable test for most of the cyclone prone areas of Australia covered by Region C which suffers from Category 4 cyclones. For Region D which suffers from Category 5 cyclones, the same loading sequence with 20% increased cycles has been recommended. An experimental programme to validate the new simplified loading sequence has been proposed.

What roles do team climate, roster control, and work life conflict play in shiftworkers’ fatigue longitudinally?

The study aimed to examine shiftworkers fatigue and the longitudinal relationships that impact on fatigue such as team climate, work life conflict, control of shifts and shift type in shift working nurses. We used a quantitative survey methodology and analysed data with a moderated hierarchical multiple regression. After matching across two time periods 18 months apart, the sample consisted of 166 nurses from one Australian hospital. Of these nurses, 61 worked two rotating day shifts (morning & afternoon/evening) and 105 were rotating shiftworkers who worked three shifts (morning afternoon/evening and nights). The findings suggest that control over shift scheduling can have significant effects on fatigue for both two-shift and three-shift workers. A significant negative relationship between positive team climate and fatigue was moderated by shift type. At both Time 1 and Time 2, work life conflict was the strongest predictor of concurrent fatigue, but over time it was not.

Intrusion detection in distributed systems, an approach based on taint marking

This paper presents a new framework for distributed intrusion detection based on taint marking. Our system tracks information flows between applications of multiple hosts gathered in groups (i.e., sets of hosts sharing the same distributed information flow policy) by attaching taint labels to system objects such as files, sockets, Inter Process Communication (IPC) abstractions, and memory mappings. Labels are carried over the network by tainting network packets. A distributed information flow policy is defined for each group at the host level by labeling information and defining how users and applications can legally access, alter or transfer information towards other trusted or untrusted hosts. As opposed to existing approaches, where information is most often represented by two security levels (low/high, public/private, etc.), our model identifies each piece of information within a distributed system, and defines their legal interaction in a fine-grained manner. Hosts store and exchange security labels in a peer to peer fashion, and there is no central monitor. Our IDS is implemented in the Linux kernel as a Linux Security Module (LSM) and runs standard software on commodity hardware with no required modification. The only trusted code is our modified operating system kernel. We finally present a scenario of intrusion in a web service running on multiple hosts, and show how our distributed IDS is able to report security violations at each host level.

Real-time video event detection in crowded scenes using MPEG derived features : a multiple instance learning approach

This paper presents an investigation into event detection in crowded scenes, where the event of interest co-occurs with other activities and only binary labels at the clip level are available. The proposed approach incorporates a fast feature descriptor from the MPEG domain, and a novel multiple instance learning (MIL) algorithm using sparse approximation and random sensing. MPEG motion vectors are used to build particle trajectories that represent the motion of objects in uniform video clips, and the MPEG DCT coefficients are used to compute a foreground map to remove background particles. Trajectories are transformed into the Fourier domain, and the Fourier representations are quantized into visual words using the K-Means algorithm. The proposed MIL algorithm models the scene as a linear combination of independent events, where each event is a distribution of visual words. Experimental results show that the proposed approaches achieve promising results for event detection compared to the state-of-the-art.

Advances in skin cancer early detection and diagnosis

OBJECTIVES: To provide an overview of 1) traditional methods of skin cancer early detection, 2) current technologies for skin cancer detection, and 3) evolving practice models of early detection. DATA SOURCES: Peer-reviewed databased articles and reviews, scholarly texts, and Web-based resources. CONCLUSION: Early detection of skin cancer through established methods or newer technologies is critical for reducing both skin cancer mortality and the overall skin cancer burden. IMPLICATIONS FOR NURSING PRACTICE: A basic knowledge of recommended skin examination guidelines and risk factors for skin cancer, traditional methods to further examine lesions that are suspicious for skin cancer and evolving detection technologies can guide patient education and skin inspection decisions.

Skin cancer detection by one click - are we any closer?

To the editor...

The detection of incipient faults in small multi-cylinder diesel engines using multiple acoustic emission sensors

This thesis investigates condition monitoring (CM) of diesel engines using acoustic emission (AE) techniques. The AE signals recorded from a small size diesel engine are mixtures of multiple sources from multiple cylinders. Thus, it is difficult to interpret the information conveyed in the signals for CM purposes. This thesis develops a series of practical signal processing techniques to overcome this problem. Various experimental studies conducted to assess the CM capabilities of AE analysis for diesel engines. A series of modified signal processing techniques were proposed. These techniques showed promising results of capability for CM of multiple cylinders diesel engine using multiple AE sensors.

Enabling aircraft emergency landings using active visual site detection

The ability to automate forced landings in an emergency such as engine failure is an essential ability to improve the safety of Unmanned Aerial Vehicles operating in General Aviation airspace. By using active vision to detect safe landing zones below the aircraft, the reliability and safety of such systems is vastly improved by gathering up-to-the-minute information about the ground environment. This paper presents the Site Detection System, a methodology utilising a downward facing camera to analyse the ground environment in both 2D and 3D, detect safe landing sites and characterise them according to size, shape, slope and nearby obstacles. A methodology is presented showing the fusion of landing site detection from 2D imagery with a coarse Digital Elevation Map and dense 3D reconstructions using INS-aided Structure-from-Motion to improve accuracy. Results are presented from an experimental flight showing the precision/recall of landing sites in comparison to a hand-classified ground truth, and improved performance with the integration of 3D analysis from visual Structure-from-Motion.

An evaluation of different features and learning models for anomalous event detection

The huge amount of CCTV footage available makes it very burdensome to process these videos manually through human operators. This has made automated processing of video footage through computer vision technologies necessary. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned ‘normal’ model. There is no precise and exact definition for an abnormal activity; it is dependent on the context of the scene. Hence there is a requirement for different feature sets to detect different kinds of abnormal activities. In this work we evaluate the performance of different state of the art features to detect the presence of the abnormal objects in the scene. These include optical flow vectors to detect motion related anomalies, textures of optical flow and image textures to detect the presence of abnormal objects. These extracted features in different combinations are modeled using different state of the art models such as Gaussian mixture model(GMM) and Semi- 2D Hidden Markov model(HMM) to analyse the performances. Further we apply perspective normalization to the extracted features to compensate for perspective distortion due to the distance between the camera and objects of consideration. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.

Complete genomic sequence of a Rubus yellow net virus isolate and detection of genome-wide pararetrovirus-derived small RNAs

Rubus yellow net virus (RYNV) was cloned and sequenced from a red raspberry (Rubus idaeus L.) plant exhibiting symptoms of mosaic and mottling in the leaves. Its genomic sequence indicates that it is a distinct member of the genus Badnavirus, with 7932. bp and seven ORFs, the first three corresponding in size and location to the ORFs found in the type member Commelina yellow mottle virus. Bioinformatic analysis of the genomic sequence detected several features including nucleic acid binding motifs, multiple zinc finger-like sequences and domains associated with cellular signaling. Subsequent sequencing of the small RNAs (sRNAs) from RYNV-infected R. idaeus leaf tissue was used to determine any RYNV sequences targeted by RNA silencing and identified abundant virus-derived small RNAs (vsRNAs). The majority of the vsRNAs were 22-nt in length. We observed a highly uneven genome-wide distribution of vsRNAs with strong clustering to small defined regions distributed over both strands of the RYNV genome. Together, our data show that sequences of the aphid-transmitted pararetrovirus RYNV are targeted in red raspberry by the interfering RNA pathway, a predominant antiviral defense mechanism in plants. © 2013.

HMM relative entropy rate concepts for vision-based aircraft manoeuvre detection

Machine vision is emerging as a viable sensing approach for mid-air collision avoidance (particularly for small to medium aircraft such as unmanned aerial vehicles). In this paper, using relative entropy rate concepts, we propose and investigate a new change detection approach that uses hidden Markov model filters to sequentially detect aircraft manoeuvres from morphologically processed image sequences. Experiments using simulated and airborne image sequences illustrate the performance of our proposed algorithm in comparison to other sequential change detection approaches applied to this application.

The human cost of change : tales from the campus about personal change fatigue, resistance, and resilience

For the last decade, one question has haunted me: what helps people to cope with large-scale organisational change in their workplace? This study explores the construct of personal change resilience, and its potential for identifying solutions to the problems of change fatigue and change resistance. The thesis has emerged from the fields of change management, leadership, training, mentoring, evaluation, management and trust within the context of higher education in Australia at the beginning of the twenty-first century. In this thesis I present a theoretical model of the factors to consider in increasing peoples’ personal change resilience as they navigate large-scale organisational change at work, thereby closing a gap in the literature on the construct of change resilience. The model presented is based on both the literature in the realms of business and education, and on the findings of the research. In this thesis, an autoethnographic case study of two Australian university projects is presented as one narrative, resulting in a methodological step forward in the use of multiple research participants’ stories in the development of a single narrative. The findings describe the experiences of workers in higher education and emphasise the importance of considerate management in the achievement of positive experiences of organisational change. This research makes a significant contribution to new knowledge in three ways. First, it closes a gap in the literature in the realm of change management around personal change resilience as a solution to the problem of change fatigue by presenting models of both change failure and personal change resilience. Second, it is methodologically innovative in the use of personae to tell the stories of multiple participants in one coherent tale presented as a work of ethnographic fiction seen through an autoethnographic lens. By doing so, it develops a methodology for giving a voice to those to whom change is done in the workplace. Third, it provides a perspective on organisational change management from the view of the actual workers affected by change, thereby adding to the literature that currently exists, which is based on the views of those with responsibility for leading or managing change rather than those it affects. This thesis is intended as a practical starting point for conversations by actual change managers in higher education, and it is written in such a way as to help them see how theory can be applied in real life, and how empowering and enabling the actual working staff members, and engaging with them in a considerate way before, during and even after the change process, can help to make them resilient enough to cope with the change, rather than leaving them burned out or disengaged and no longer a well-functioning member of the institution. This thesis shows how considerately managed large-scale organisational change can result in positive outcomes for both the organisation and the individuals who work in it.