304 resultados para Eletric car
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Drink driving remains a major cause of serious and fatal car crashes in Australia and internationally. While this problem is more prevalent among male drivers, the rates of female intoxicated drivers have increased steadily over the past decades in many motorised countries. A combination of police enforcement, media awareness campaigns, and community initiatives has played a key role in reducing incidents of illegal drink driving by targeting public drink driving attitudes. However, important cultural differences in regards to the tolerance towards drink driving have been noted. While many countries, including Australia, have a legal Blood Alcohol Concentration (BAC) limit of .05 or higher, some countries have moved towards a zero –or low tolerance approach to drink driving; several European countries, including Sweden, Hungary, Slovakia, and Estonia currently enforce .00 or .02 BAC limits.
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The purpose of this investigation is to present an overview of roadside drug driving enforcement and detections in Queensland, Australia since the introduction of oral fluid screening. Drug driving is a problematic issue for road safety and investigations of the prevalence and impact of drug driving suggest that, in particular, the use of illicit drugs may increase a driver’s involvement in a road crash when compared to a driver who is drug free. In response to the potential increased crash involvement of drug impaired drivers, Australian police agencies have adopted the use of oral fluid analysis to detect the presence of illicit drugs in drivers. This paper describes the results of roadside drug testing for over 80,000 drivers in Queensland, Australia, from December 2007 to June 2012. It provides unique data on the prevalence of methamphetamine, cannabis and ecstasy in the screened population for the period. When prevalence rates are examined over time, drug driving detection rates have almost doubled from around 2.0% at the introduction of roadside testing operations to just under 4.0% in the latter years. The most common drug type detected was methamphetamine (40.8%) followed by cannabis (29.8%) and methamphetamine/cannabis combination (22.5%). By comparison, the rate of ecstasy detection was very low (1.7%). The data revealed a number of regional, age and gender patterns and variations of drug driving across the state. Younger drivers were more likely to test positive for cannabis whilst older drivers were more likely to test positive for methamphetamine. The overall characteristics of drivers who tested positive to the presence of at least one of the target illicit drugs are they are likely to be male, aged 30-39 years, be driving a car on Friday, Saturday or Sunday between 6:00PM and 6:00AM and to test positive for methamphetamine.
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Sharing some closely related themes and a common theoretical orientation based on the governmentality analytic, these are nevertheless two very different contributions to criminological knowledge and theory. The first, The Currency of Justice: Fines and Damages in Consumer Societies (COJ), is a sustained and highly original analysis of that most pervasive yet overlooked feature of modern legal orders; their reliance on monetary sanctions. Crime and Risk (CAR), on the other hand, is a short synoptic overview of the many dimensions and trajectories of risk in contemporary debate and practice, both the practices of crime and the governance of crime. It is one of the first in a new series by Sage, 'Compact Criminology', in which authors survey in little more than a hundred pages some current field of debate. With this small gem, Pat O'Malley has set the bar very high for those who follow. For all its brevity, CAR traverses a massive expanse of research, debates and issues, while also opening up new and challenging questions around the politics of risk and the relationship between criminal risk-taking and the governance of risk and crime. The two books draw together various threads of O'Malley's rich body of work on these issues, and once again demonstrate that he is one of the foremost international scholars of risk inside and outside criminology.
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Twitter and other social media have become increasingly important tools for maintaining the relationships between fans and their idols across a range of activities, from politics and the arts to celebrity and sports culture. Twitter, Inc. itself has initiated several strategic approaches, especially to entertainment and sporting organisations; late in 2012, for example, a Twitter, Inc. delegation toured Australia in order to develop formal relationships with a number of key sporting bodies covering popular sports such as Australian Rules Football, A-League football (soccer), and V8 touring car racing, as well as to strengthen its connections with key Australian broadcasters and news organisations (Jackson & Christensen, 2012). Similarly, there has been a concerted effort between Twitter Germany and the German Bundesliga clubs and football association to coordinate the presence of German football on Twitter ahead of the 2012–2013 season: the Twitter accounts of almost all first-division teams now bear the official Twitter verification mark, and a system of ‘official’ hashtags for tweeting about individual games (combining the abbreviations of the two teams, e.g. #H96FCB) has also been instituted (Twitter auf Deutsch, 2012).
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In this paper we present a method for autonomously tuning the threshold between learning and recognizing a place in the world, based on both how the rodent brain is thought to process and calibrate multisensory data and the pivoting movement behaviour that rodents perform in doing so. The approach makes no assumptions about the number and type of sensors, the robot platform, or the environment, relying only on the ability of a robot to perform two revolutions on the spot. In addition, it self-assesses the quality of the tuning process in order to identify situations in which tuning may have failed. We demonstrate the autonomous movement-driven threshold tuning on a Pioneer 3DX robot in eight locations spread over an office environment and a building car park, and then evaluate the mapping capability of the system on journeys through these environments. The system is able to pick a place recognition threshold that enables successful environment mapping in six of the eight locations while also autonomously flagging the tuning failure in the remaining two locations. We discuss how the method, in combination with parallel work on autonomous weighting of individual sensors, moves the parameter dependent RatSLAM system significantly closer to sensor, platform and environment agnostic operation.
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In outdoor environments shadows are common. These typically strong visual features cause considerable change in the appearance of a place, and therefore confound vision-based localisation approaches. In this paper we describe how to convert a colour image of the scene to a greyscale invariant image where pixel values are a function of underlying material property not lighting. We summarise the theory of shadow invariant images and discuss the modelling and calibration issues which are important for non-ideal off-the-shelf colour cameras. We evaluate the technique with a commonly used robotic camera and an autonomous car operating in an outdoor environment, and show that it can outperform the use of ordinary greyscale images for the task of visual localisation.
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Introduction Sleep restriction and missing 1 night’s continuous positive air pressure (CPAP) treatment are scenarios faced by obstructive sleep apnoea (OSA) patients, who must then assess their own fitness to drive. This study aims to assess the impact of this on driving performance. Method 11 CPAP treated participants (50–75 yrs), drove an interactive car simulator under monotonous motorway conditions for 2 hours on 3 afternoons, following;(i)normal night’s sleep (average 8.2 h) with CPAP (ii) sleep restriction (5 h), with CPAP (iii)normal length of sleep, without CPAP. Driving incidents were noted if the car came out of the designated driving lane. EEG was recorded continually and KSS reported every 200 seconds. Results Driving incidents: Incidents were more prevalent following CPAP withdrawal during hour 1, demonstrating a significant condition time interaction [F(6,60) = 3.40, p = 0.006]. KSS: At the start of driving participants felt sleepiest following CPAP withdrawal, by the end of the task KSS levels were similar following CPAP withdrawal and sleep restriction, demonstrating a significant condition, time interaction [F(3.94,39.41) = 3.39, p = 0.018]. EEG: There was a non significant trend for combined alpha and theta activity to be highest throughout the drive following CPAP withdrawal. Discussion CPAP withdrawal impairs driving simulator performance sooner than restricting sleep to 5 h with CPAP. Participants had insight into this increased sleepiness reflected by the higher KSS reported following CPAP withdrawal. In the practical terms of driving any one incident could be fatal. The earlier impairment reported here demonstrates the potential danger of missing CPAP treatment and highlights the benefit of CPAP treatment even when sleep time is short.
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Objectives The UK Department for Transport recommends taking a break from driving every 2 h. This study investigated: (i) if a 2 h drive time on a monotonous road is appropriate for OSA patients treated with CPAP, compared with healthy age matched controls, (ii) the impact of a night’s sleep restriction (with CPAP) and (iii) what happens if these patients miss one nights’ CPAP treatment. Methods About 19 healthy men aged 52–74 y (m = 66.2 y) and 19 OSA participants aged 50–75 y (m = 64.4 y) drove an interactive car simulator under monotonous motorway conditions for 2 h on two afternoons, in a counterbalanced design; (1) following a normal night’s sleep (8 h). (2) following a restricted night’s sleep (5 h), with normal CPAP use (3) following a night without CPAP treatment. (n = 11) Lane drifting incidents, indicative of falling asleep, were recorded for up to 2 h depending on competence to continue driving. Results Normal sleep: Controls drove for an average of 95.9 min (s.d. 37 min) and treated OSA drivers for 89.6 min (s.d. 29 min) without incident. 63.2% of controls and 42.1% of OSA drivers successfully completed the drive without an incident. Sleep restriction: 47.4% of controls and 26.3% OSA drivers finished without incident. Overall: controls drove for an average of 89.5 min (s.d. 39 min) and treated OSA drivers 65 min (s.d. 42 min) without incident. The effect of condition was significant [F(1.36) = 9.237, P < 0.05, eta2 = 0.204]. Stopping CPAP: 18.2% of drivers successfully completed the drive. Overall, participants drove for an average of 50.1 min (s.d. 38 min) without incident. The effect of condition was significant [F(2) = 8.8, P < 0.05, eta2 = 0.468]. Conclusion 52.6% of all drivers were able to complete a 2 hour drive under monotonous conditions after a full night’s sleep. Sleep restriction significantly affected both control and OSA drivers. We find evidence that treated OSA drivers are more impaired by sleep restriction than healthy control, as they were less able to sustain safely the 2 h drive without incidents. OSA drivers should be aware that non-compliance with CPAP can significantly impair driving performance. It may be appropriate to recommend older drivers take a break from driving every 90 min especially when undertaking a monotonous drive, as was the case here.
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Background In China, as in many developing countries, rapid increases in car ownership and new drivers have been coupled with a large trauma burden. The World Health Organization has identified key risk factors including speeding, drink-driving, helmet and restraint non-use, overloaded vehicles, and fatigued-driving in many rapidly motorising countries, including China. Levels of awareness of these risk factors among road users are not well understood. Although research identifies speeding as the major factor contributing to road crashes in China, there appears to be widespread acceptance of it among the broader community. Purpose To assess self-reported speeding and awareness of crash risk factors among Chinese drivers in Beijing. Methods Car drivers (n=299) were recruited from car washing locations and car parks to complete an anonymous questionnaire. Perceptions of the relative risk of drink-driving, fatigued-driving and speeding, and attitudes towards speeding and self-reported driving speeds were assessed. Results Overall, driving speeds of >10km/hr above posted limits on two road types (60 and 80 km/hour zones) were reported by more than one third of drivers. High-range speeding (i.e., >30 km/hour in a 60 km/hour zone and >40 km/hour in an 80 km/hour zone) was reported by approximately 5% of the sample. Attitudinal measures indicated that approximately three quarters of drivers reported attitudes that were not supportive of speeding. Drink-driving was identified as the most risky behaviour; 18% reported the perception that drink-driving had the same level of danger as speeding and 82% reported it as more dangerous. For fatigued-driving, 1% reported the perception that it was not as dangerous as speeding; 27.4% reported it as the same level and 71.6% perceived it as more dangerous. Conclusion Driving speeds well above posted speed limits were commonly reported by drivers. Speeding was rated as the least dangerous on-road behaviour, compared to drink-driving and fatigued-driving. One third of drivers reported regularly engaging in speeds at least 10km/hr above posted limits, despite speeding being the major reported contributor to crashes. Greater awareness of the risks associated with speeding is needed to help reduce the road trauma burden in China and promote greater speed limit compliance.
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Nitrous oxide (N2O) is one of the greenhouse gases that can contribute to global warming. Spatial variability of N2O can lead to large uncertainties in prediction. However, previous studies have often ignored the spatial dependency to quantify the N2O - environmental factors relationships. Few researches have examined the impacts of various spatial correlation structures (e.g. independence, distance-based and neighbourhood based) on spatial prediction of N2O emissions. This study aimed to assess the impact of three spatial correlation structures on spatial predictions and calibrate the spatial prediction using Bayesian model averaging (BMA) based on replicated, irregular point-referenced data. The data were measured in 17 chambers randomly placed across a 271 m(2) field between October 2007 and September 2008 in the southeast of Australia. We used a Bayesian geostatistical model and a Bayesian spatial conditional autoregressive (CAR) model to investigate and accommodate spatial dependency, and to estimate the effects of environmental variables on N2O emissions across the study site. We compared these with a Bayesian regression model with independent errors. The three approaches resulted in different derived maps of spatial prediction of N2O emissions. We found that incorporating spatial dependency in the model not only substantially improved predictions of N2O emission from soil, but also better quantified uncertainties of soil parameters in the study. The hybrid model structure obtained by BMA improved the accuracy of spatial prediction of N2O emissions across this study region.
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Public health research consistently demonstrates the salience of neighbourhood as a determinant of both health-related behaviours and outcomes across the human life course. This paper will report on the findings from a mixed-methods Brisbane-based study that explores how mothers with primary school children from both high and low socioeconomic suburbs use the local urban environment for the purpose of physical activity. Firstly, we demonstrate findings from an innovative methodology using the geographic information systems (GIS) embedded in social media platforms on mobile phones to track locations, resource-use, distances travelled, and modes of transport of the families in real-time; and secondly, we report on qualitative data that provides insight into reasons for differential use of the environment by both groups. Spatial/mapping and statistical data showed that while the mothers from both groups demonstrated similar daily routines, the mothers from the high SEP suburb engaged in increased levels of physical activity, travelled less frequently and less distance by car, and walked more for transport. The qualitative data revealed differences in the psychosocial processes and characteristics of the households and neighbourhoods of the respective groups, with mothers in the lower SEP suburb reporting more stress, higher conflict, and lower quality relationships with neighbours.
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BACKGROUND Pandemic influenza A (H1N1) has a significant public health impact. This study aimed to examine the effect of socio-ecological factors on the transmission of H1N1 in Brisbane, Australia. METHODOLOGY We obtained data from Queensland Health on numbers of laboratory-confirmed daily H1N1 in Brisbane by statistical local areas (SLA) in 2009. Data on weather and socio-economic index were obtained from the Australian Bureau of Meteorology and the Australian Bureau of Statistics, respectively. A Bayesian spatial conditional autoregressive (CAR) model was used to quantify the relationship between variation of H1N1 and independent factors and to determine its spatiotemporal patterns. RESULTS Our results show that average increase in weekly H1N1 cases were 45.04% (95% credible interval (CrI): 42.63-47.43%) and 23.20% (95% CrI: 16.10-32.67%), for a 1 °C decrease in average weekly maximum temperature at a lag of one week and a 10mm decrease in average weekly rainfall at a lag of one week, respectively. An interactive effect between temperature and rainfall on H1N1 incidence was found (changes: 0.71%; 95% CrI: 0.48-0.98%). The auto-regression term was significantly associated with H1N1 transmission (changes: 2.5%; 95% CrI: 1.39-3.72). No significant association between socio-economic indexes for areas (SEIFA) and H1N1 was observed at SLA level. CONCLUSIONS Our results demonstrate that average weekly temperature at lag of one week and rainfall at lag of one week were substantially associated with H1N1 incidence at a SLA level. The ecological factors seemed to have played an important role in H1N1 transmission cycles in Brisbane, Australia.
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Governments are challenged by the need to ensure that ageing populations stay active and engaged as they age. Therefore, it is critical to investigate the role of mobility in older people's engagement in out-of-home activities, and to identify the experiences they have within their communities. This research investigates the use of transportation by older people and its implications for their out-of-home activities within suburban environments. The qualitative, mixed-method approach employs data collection methods which include a daily travel diary (including a questionnaire), Global Positioning System (GPS) tracking and semi-structured interviews with older people living in suburban environments in Brisbane, Australia. Results show that older people are mobile throughout the city, and their car provides them with that opportunity to access desired destinations. This ability to drive allows older people to live independently and to assist others who do not drive, particularly where transport alternatives are not as accessible. The ability to transport goods and other people is a significant advantage of the private car over other transport options. People with no access to private transportation who live in low-density environments are disadvantaged when it comes to participation within the community. Further research is needed to better understand the relationship between transportation and participation within the community environment, to assist policy makers and city and transportation planners to develop strategies for age-friendly environments within the community.
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The car has arguably had more influence on our lifestyle and urban environment than any other consumer product; allowing unprecedented freedom for living, working and recreation where and when we choose. However, problems of pollution, congestion, road trauma, inefficient land use and social inequality are associated with car use. Despite 100 years of design and technology refinements, the aforementioned problems are significant and persistent: many argue that resolving these problems requires a fundamental redesign of the car. Redesigned vehicles have been proposed such as the MIT CityCar and others such as the Renault Twizy, commercialized. None however have successfully brought about significant change and the study of disruptive innovation offers an explanation for this. Disruptive innovation, by definition, disrupts a market. It also disrupts the product ecosystem. The existing product ecosystem has co-evolved to support the conventional car and is not optimized for the new design: which will require a redesigned ecosystem to support it. A literature review identifies a lack of methodology for identifying the components of product ecosystems and the changes required for disruptive innovation implementation. This paper proposes such a methodology based on Design Thinking, Actor Network Theory, Disruptive Innovation and the CityCar scenarios.
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The use of hierarchical Bayesian spatial models in the analysis of ecological data is increasingly prevalent. The implementation of these models has been heretofore limited to specifically written software that required extensive programming knowledge to create. The advent of WinBUGS provides access to Bayesian hierarchical models for those without the programming expertise to create their own models and allows for the more rapid implementation of new models and data analysis. This facility is demonstrated here using data collected by the Missouri Department of Conservation for the Missouri Turkey Hunting Survey of 1996. Three models are considered, the first uses the collected data to estimate the success rate for individual hunters at the county level and incorporates a conditional autoregressive (CAR) spatial effect. The second model builds upon the first by simultaneously estimating the success rate and harvest at the county level, while the third estimates the success rate and hunting pressure at the county level. These models are discussed in detail as well as their implementation in WinBUGS and the issues arising therein. Future areas of application for WinBUGS and the latest developments in WinBUGS are discussed as well.