72 resultados para On-line communities


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This paper examines the effects of an eco-driving message on driver distraction. Two in-vehicle distracter tasks were compared with an eco-driving task and a baseline task in an advanced driving simulator. N = 22 subjects were asked to perform an eco-driving, CD changing, and a navigation task while engaged in critical manoeuvres during which they were expected to respond to a peripheral detection task (PDT) with total duration of 3.5 h. The study involved two sessions over two consecutive days. The results show that drivers’ mental workloads are significantly higher during navigation and CD changing tasks in comparison to the two other scenarios. However, eco-driving mental workload is still marginally significant (p ∼ .05) across different manoeuvres. Similarly, event detection tasks show that drivers miss significantly more events in the navigation and CD changing scenarios in comparison to both the baseline and eco-driving scenario. Analysis of the practice effect shows that drivers’ baseline scenario and navigation scenario exhibit significantly less demand on the second day. Drivers also can detect significantly more events on the second day for all scenarios. The authors conclude that even reading a simple message while driving could potentially lead to missing an important event, especially when executing critical manoeuvres. However, there is some evidence of a practice effect which suggests that future research should focus on performance with habitual rather than novel tasks. It is recommended that sending text as an eco-driving message analogous to the study circumstances should not be delivered to drivers on-line when vehicle is in motion.

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This paper describes a new approach to establish the probabilistic cable rating based on cable thermal environment studies. Knowledge of cable parameters has been well established. However the environment in which the cables are buried is not so well understood. Research in Queensland University of Technology has been aimed at obtaining and analysing actual daily field values of thermal resistivity and diffusivity of the soil around power cables. On-line monitoring systems have been developed and installed with a data logger system and buried spheres that use an improved technique to measure thermal resistivity and diffusivity over a short period. Based on the long-term continuous field data for more than 4 years, a probabilistic approach is developed to establish the correlation between the measured field thermal resistivity values and rainfall data from weather bureau records. Hence, a probabilistic cable rating can be established based on monthly probabilistic distribution of thermal resistivity

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Large scale sugarcane bagasse storage in uncovered stockpiles has the potential to result in adverse impacts on the environment and surrounding communities through hazards associated with nuisance dust, groundwater seepage, spontaneous combustion and generation of contaminated leachates. Managing these hazards will assist in improved health and safety outcomes for factory staff and reduced potential environmental impacts on surrounding communities. Removal of the smaller fibres (pith) from bagasse prior to stockpiling reduced the dust number of bagasse by 50% and modelling suggests peak ground level PM10 dust emissions would reduce by 70%. Depithed bagasse has much lower water holding capacity (~43%) than whole bagasse. This experimental and modelling study investigated the physical properties of depithed and whole bagasse. Dust dispersion modelling was undertaken to determine the likely effects associated with storage of whole and depithed sugarcane bagasse.

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Aerosol mass spectrometers (AMS) are powerful tools in the analysis of the chemical composition of airborne particles, particularly organic aerosols which are gaining increasing attention. However, the advantages of AMS in providing on-line data can be outweighed by the difficulties involved in its use in field measurements at multiple sites. In contrast to the on-line measurement by AMS, a method which involves sample collection on filters followed by subsequent analysis by AMS could significantly broaden the scope of AMS application. We report the application of such an approach to field studies at multiple sites. An AMS was deployed at 5 urban schools to determine the sources of the organic aerosols at the schools directly. PM1 aerosols were also collected on filters at these and 20 other urban schools. The filters were extracted with water and the extract run through a nebulizer to generate the aerosols, which were analysed by an AMS. The mass spectra from the samples collected on filters at the 5 schools were found to have excellent correlations with those obtained directly by AMS, with r2 ranging from 0.89 to 0.98. Filter recoveries varied between the schools from 40 -115%, possibly indicating that this method provides qualitative rather than quantitative information. The stability of the organic aerosols on Teflon filters was demonstrated by analysing samples stored for up to two years. Application of the procedure to the remaining 20 schools showed that secondary organic aerosols were the main source of aerosols at the majority of the schools. Overall, this procedure provides accurate representation of the mass spectra of ambient organic aerosols and could facilitate rapid data acquisition at multiple sites where AMS could not be deployed for logistical reasons.

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Objective: To understand the journey of advanced prostate cancer patients for supporting development of an innovative patient journey browser. Background: Prostate cancer is one of the common cancers in Australia. Due to the chronic nature of the disease, it is important to have effective disease management strategy and care model. Multi-disciplinary care is a well-proven approach for chronic disease management. The Multi-disciplinary team (MDT) can function more effectively if all the required information is available for the clinical decision support. The development of innovative technology relies on an accurate understanding of the advanced prostate cancer patient’s journey over a prolonged period. This need arises from the fact that advanced prostate cancer patients may follow various treatment paths and change their care providers. As a result of this, it is difficult to understand the actual sources of patient’s clinical records and their treatment patterns. The aim of the research is to understand variable sources of clinical records, treatment patterns, alternative therapies, over the counter (OTC) medications of advanced prostate cancer patients. This study provides better and holistic understanding of advanced prostate cancer journey. Methods: The study was conducted through an on-line survey developed to seek and analyse the responses from the participants. The on-line questionnaire was carefully developed through consultations with the clinical researchers at the Australian Prostate Cancer Research Centre-Queensland, prostate cancer support group representatives and health informaticians at the Australian e-Health Research Centre. The non-identifying questionnaire was distributed to the patients through prostate cancer support groups in Queensland, Australia. The pilot study was carried out between August 2010 and December 2010. Results: The research made important observations about the advanced prostate cancer journey. It showed that General Practitioner (GP) was the common source of patient’s clinical records (41%) followed by Urologist (14%) and other clinicians (14%). The data analysis also showed that selenium was the common complementary supplement (55%) used by the patients and about 48% patients did not use any OTC drugs. The most common OTC used by the patients was Paracetamol (about 45%). Conclusion: The results have provided a foundation to the architecture of the proposed technology solution. The outcomes of this study are incorporated in design of the proposed patient journey browser system. A basic version of the system is currently being used at the advanced prostate cancer MDT meetings.

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Frequency domain spectroscopy (FDS) is being used to assess the insulation condition of oil–paper power transformers. However, it has to date only been implemented on de-energised transformers, which requires the transformers to be shut down for an extended period and may cause significant costs. To solve this issue, a newly improved monitoring method based on the FDS principle is proposed to implement the dielectric measurement on energised transformers. Moreover, a chirp waveform excitation and its novel processing method are introduced. Compared with the conventional FDS results, dielectric results from the energised insulation system have higher tanδ values because of the increased losses. To further understand the insulation ageing process, the effects of the geometric capacitance are removed from the measured imaginary admittance of the insulation system to enhance the ageing signature. The resulting imaginary admittance is then shown to correlate well with the central time constant in return voltage measurements results. The proposed methods address the issues on techniques used on energised transformers and provide a clue for on-line FDS diagnostic application.

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Purpose – The purpose of this paper is to describe an innovative compliance control architecture for hybrid multi‐legged robots. The approach was verified on the hybrid legged‐wheeled robot ASGUARD, which was inspired by quadruped animals. The adaptive compliance controller allows the system to cope with a variety of stairs, very rough terrain, and is also able to move with high velocity on flat ground without changing the control parameters. Design/methodology/approach – The paper shows how this adaptivity results in a versatile controller for hybrid legged‐wheeled robots. For the locomotion control we use an adaptive model of motion pattern generators. The control approach takes into account the proprioceptive information of the torques, which are applied on the legs. The controller itself is embedded on a FPGA‐based, custom designed motor control board. An additional proprioceptive inclination feedback is used to make the same controller more robust in terms of stair‐climbing capabilities. Findings – The robot is well suited for disaster mitigation as well as for urban search and rescue missions, where it is often necessary to place sensors or cameras into dangerous or inaccessible areas to get a better situation awareness for the rescue personnel, before they enter a possibly dangerous area. A rugged, waterproof and dust‐proof corpus and the ability to swim are additional features of the robot. Originality/value – Contrary to existing approaches, a pre‐defined walking pattern for stair‐climbing was not used, but an adaptive approach based only on internal sensor information. In contrast to many other walking pattern based robots, the direct proprioceptive feedback was used in order to modify the internal control loop, thus adapting the compliance of each leg on‐line.

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Within online learning communities, receiving timely and meaningful insights into the quality of learning activities is an important part of an effective educational experience. Commonly adopted methods – such as the Community of Inquiry framework – rely on manual coding of online discussion transcripts, which is a costly and time consuming process. There are several efforts underway to enable the automated classification of online discussion messages using supervised machine learning, which would enable the real-time analysis of interactions occurring within online learning communities. This paper investigates the importance of incorporating features that utilise the structure of on-line discussions for the classification of "cognitive presence" – the central dimension of the Community of Inquiry framework focusing on the quality of students' critical thinking within online learning communities. We implemented a Conditional Random Field classification solution, which incorporates structural features that may be useful in increasing classification performance over other implementations. Our approach leads to an improvement in classification accuracy of 5.8% over current existing techniques when tested on the same dataset, with a precision and recall of 0.630 and 0.504 respectively.