960 resultados para road network
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This report identifies the outcomes of a program evaluation of the five year Workplace Health and Safety Strategy (2012-2017), specifically, the engagement component within the Queensland Ambulance Service. As part of the former Department of Community Safety, their objective was to work towards harmonising the occupational health and safety policies and process to improve the workplace culture. The report examines and assess the process paths and resource inputs into the strategy, provides feedback on progress to achieving identified goals as well as identify opportunities for improvements and barriers to progress. Consultations were held with key stakeholders within QAS and focus groups were facilitated with managers and health and safety representatives of each Local Area Service Network.
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Genetic correlation (rg) analysis determines how much of the correlation between two measures is due to common genetic influences. In an analysis of 4 Tesla diffusion tensor images (DTI) from 531 healthy young adult twins and their siblings, we generalized the concept of genetic correlation to determine common genetic influences on white matter integrity, measured by fractional anisotropy (FA), at all points of the brain, yielding an NxN genetic correlation matrix rg(x,y) between FA values at all pairs of voxels in the brain. With hierarchical clustering, we identified brain regions with relatively homogeneous genetic determinants, to boost the power to identify causal single nucleotide polymorphisms (SNP). We applied genome-wide association (GWA) to assess associations between 529,497 SNPs and FA in clusters defined by hubs of the clustered genetic correlation matrix. We identified a network of genes, with a scale-free topology, that influences white matter integrity over multiple brain regions.
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A major challenge in neuroscience is finding which genes affect brain integrity, connectivity, and intellectual function. Discovering influential genes holds vast promise for neuroscience, but typical genome-wide searches assess approximately one million genetic variants one-by-one, leading to intractable false positive rates, even with vast samples of subjects. Even more intractable is the question of which genes interact and how they work together to affect brain connectivity. Here, we report a novel approach that discovers which genes contribute to brain wiring and fiber integrity at all pairs of points in a brain scan. We studied genetic correlations between thousands of points in human brain images from 472 twins and their nontwin siblings (mean age: 23.7 2.1 SD years; 193 male/279 female).Wecombined clustering with genome-wide scanning to find brain systems withcommongenetic determination.Wethen filtered the image in a new way to boost power to find causal genes. Using network analysis, we found a network of genes that affect brain wiring in healthy young adults. Our new strategy makes it computationally more tractable to discover genes that affect brain integrity. The gene network showed small-world and scale-free topologies, suggesting efficiency in genetic interactions and resilience to network disruption. Genetic variants at hubs of the network influence intellectual performance by modulating associations between performance intelligence quotient and the integrity of major white matter tracts, such as the callosal genu and splenium, cingulum, optic radiations, and the superior longitudinal fasciculus.
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Modern non-invasive brain imaging technologies, such as diffusion weighted magnetic resonance imaging (DWI), enable the mapping of neural fiber tracts in the white matter, providing a basis to reconstruct a detailed map of brain structural connectivity networks. Brain connectivity networks differ from random networks in their topology, which can be measured using small worldness, modularity, and high-degree nodes (hubs). Still, little is known about how individual differences in structural brain network properties relate to age, sex, or genetic differences. Recently, some groups have reported brain network biomarkers that enable differentiation among individuals, pairs of individuals, and groups of individuals. In addition to studying new topological features, here we provide a unifying general method to investigate topological brain networks and connectivity differences between individuals, pairs of individuals, and groups of individuals at several levels of the data hierarchy, while appropriately controlling false discovery rate (FDR) errors. We apply our new method to a large dataset of high quality brain connectivity networks obtained from High Angular Resolution Diffusion Imaging (HARDI) tractography in 303 young adult twins, siblings, and unrelated people. Our proposed approach can accurately classify brain connectivity networks based on sex (93% accuracy) and kinship (88.5% accuracy). We find statistically significant differences associated with sex and kinship both in the brain connectivity networks and in derived topological metrics, such as the clustering coefficient and the communicability matrix.
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As connectivity analyses become more popular, claims are often made about how the brain's anatomical networks depend on age, sex, or disease. It is unclear how results depend on tractography methods used to compute fiber networks. We applied 11 tractography methods to high angular resolution diffusion images of the brain (4-Tesla 105-gradient HARDI) from 536 healthy young adults. We parcellated 70 cortical regions, yielding 70×70 connectivity matrices, encoding fiber density. We computed popular graph theory metrics, including network efficiency, and characteristic path lengths. Both metrics were robust to the number of spherical harmonics used to model diffusion (4th-8th order). Age effects were detected only for networks computed with the probabilistic Hough transform method, which excludes smaller fibers. Sex and total brain volume affected networks measured with deterministic, tensor-based fiber tracking but not with the Hough method. Each tractography method includes different fibers, which affects inferences made about the reconstructed networks.
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The reliance on police data for the counting of road crash injuries can be problematic, as it is well known that not all road crash injuries are reported to police which under-estimates the overall burden of road crash injuries. The aim of this study was to use multiple linked data sources to estimate the extent of under-reporting of road crash injuries to police in the Australian state of Queensland. Data from the Queensland Road Crash Database (QRCD), the Queensland Hospital Admitted Patients Data Collection (QHAPDC), Emergency Department Information System (EDIS), and the Queensland Injury Surveillance Unit (QISU) for the year 2009 were linked. The completeness of road crash cases reported to police was examined via discordance rates between the police data (QRCD) and the hospital data collections. In addition, the potential bias of this discordance (under-reporting) was assessed based on gender, age, road user group, and regional location. Results showed that the level of under-reporting varied depending on the data set with which the police data was compared. When all hospital data collections are examined together the estimated population of road crash injuries was approximately 28,000, with around two-thirds not linking to any record in the police data. The results also showed that the under-reporting was more likely for motorcyclists, cyclists, males, young people, and injuries occurring in Remote and Inner Regional areas. These results have important implications for road safety research and policy in terms of: prioritising funding and resources; targeting road safety interventions into areas of higher risk; and estimating the burden of road crash injuries.
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Successful project management depends upon forming and maintaining relationships between and among project team members and stakeholder groups. The nature of these relationships and the patterns that they form affect communication, collaboration and resource flows. Networks affect us directly, and we use them to influence people and processes. Social Network Analysis (SNA) can be an extremely valuable research tool to better understand how critical social networks develop and influence work processes, particularly as projects become larger and more complex. This chapter introduces foundational network concepts, helps you determine if SNA could help you answer your research questions, and explains how to design and implement a social network study. At the end of this chapter, the reader can: understand foundational concepts about social networks; decide if SNA is an appropriate research methodology to address particular questions or problems; design and implement a basic social network study.
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As China continues to motorise rapidly, solutions are needed to reduce the burden of road trauma that is spread inequitably across the community. Little is currently known about how new drivers are trained to deal with on-road challenges, and little is also known about the perceptions, behaviours and attitudes of road users in China. This paper reports on a pilot study conducted in a driver retraining facility in one Chinese city where people who have had their licence suspended for accrual of 12 demerit points in a one year period must attend compulsory retraining in order to regain their licence. A sample of 239 suspended drivers responded to an anonymous questionnaire that sought information about preferred driving speeds and perceptions of safe driving speeds across two speed zones. Responses indicated that speeds higher than the posted limits were commonly reported, and that there was incongruence between preferred and safe speeds, such that a greater proportion of drivers reported preferred speeds that were substantially faster than what were reported as safe speeds. Participants with more driving experience reported significantly fewer crashes than newly licensed drivers (less than 2 years licensed) but no differences were found in offences when compared across groups with different levels of driving experience. Perceptions of risky behaviours were assessed by asking participants to describe what they considered to be the most dangerous on-road behaviours. Speeding and drink driving were the most commonly reported by far, followed by issues such as fatigue, ignoring traffic rules, not obeying traffic rules, phone use while driving, and non-use of seatbelts, which attracted an extremely low response which seems consistent with previously reported low belt wearing rates, unfavourable attitudes towards seatbelt use, and low levels of enforcement. Finally, observations about culturally specific considerations are made from previous research conducted by the authors and others. Specifically, issues of saving face and the importance and pervasiveness of social networks and social influence are discussed with particular regard to how any future countermeasures need to be informed by a thorough understanding of Chinese customs and culture.
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Although road construction and use provides significant economic and social benefits, its environmental impact is of growing concern. Roads are one of the greatest greenhouse gas contributors, both directly through fossil energy consumed in mining, transporting, earthworks and paving work, and through the emissions from road use by vehicles. Further,according to the Australian Government, when combined with expected population growth and internal migration,expected changes in temperature and rainfall are expected to increase road maintenance costs. This discussion paper will outline opportunities within the Australian context for reducing environmental and carbon pressure from road building, and provide a framework for considering the potential pressures that will affect the resilience of roads to the impacts of climate change and oil vulnerability.
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The practice of road construction and maintenance is inherently lean and efficient; a result of the economic benefits that are gained by minimizing wasted resources. In this age of conservation and environmental management, the inbuilt sustainability of existing road construction practices is being developed and extended to produce variety of environmentally sustainable options. A new concept of a “sustainable road” has emerged through both academia and industry, and is defined to be a road that is: - constructed to reduce environmental impacts; - designed to optimise the alignment (vertical and horizontal including considerations of ecological constraints and operational use by vehicles); - resilient to future environmental and economic pressures (e.g. climate change and resource scarcity); - adaptable to changing uses including increased travel volumes, greater demand for public and active (cycling and walking) transport, and; - able to harvest the energy to power itself.
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Green infrastructure is considered as a strategic approach to address the ecological and social impacts of urban sprawl. The main elements of green infrastructure have been well established and include a series of multifunctional ecological systems, such as green urban space, green road infrastructure and the links between these systems. However, it should be noted that the elements of green road infrastructure have only been briefly mentioned in isolated life cycle stages, e.g. design, procurement, construction, maintenance and operation. The definition of green road infrastructure and the elements in green road infrastructure projects remain largely unknown. To explore the elements in green road infrastructure, a critical review was adopted. As the development of green road infrastructure projects is guided by rating systems, a comparison of three major green roads rating systems, including GreenroadsTM, EnvisionTM and Infrastructure Sustainability Rating Tool—IS, was conducted. The comparison reveals that green roads can be defined as road projects that have superior performance in economic, social and environmental sustainability. The sustainability features in green roads mainly include environmental sustainability, social sustainability, economic sustainability, quality, pavement technology and innovation. The results will contribute to an increased understanding of green roads and will be useful to improve the performance of road projects on these sustainability features.
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Road transport plays a significant role in various industries and mobility services around the globe and has a vital impact on our daily lives. However it also has serious impacts on both public health and the environment. In-vehicle feedback systems are a relatively new approach to encouraging driver behavior change for improving fuel efficiency and safety in automotive environments. While many studies claim that the adoption of eco-driving practices, such as eco-driving training programs and in-vehicle feedback to drivers, has the potential to improve fuel efficiency, limited research has integrated safety and eco-driving. Therefore, it is crucial to understand the human factors related theories and practices which will inform the design of an in-vehicle Human Machine Interface (HMI) that could provide real-time driver feedback and consequently improve both fuel efficiency and safety. This paper provides a comprehensive review of the current state of published literature on in-vehicle systems to identify and evaluate the impact of eco-driving and safety feedback systems. This paper also discusses how these factors may conflict with one another and have a negative effect on road safety, while also exploring possible eco-driving practices that could encourage more sustainable, environmentally-conscious and safe driving behavior. The review revealed a lack of comprehensive theoretical research integrating eco-driving and safe driving, and no current available HMI covering both aspects simultaneously. Furthermore, the review identified that some eco-driving in-vehicle systems may enhance fuel efficiency without compromising safety. The review has identified a range of concepts which can be developed to influence driver acceptance of safety and eco-driving systems within the area of HMI. This can promote new research aimed at enhancing our understanding of the relationship between eco-driving and safety from the human factors viewpoint. This provides a foundation for developing innovative, persuasive and acceptable in-vehicle HMI systems to improve fuel efficiency and road safety.
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Pedestrian safety is a critical issue in Ethiopia. Reports show that 50 to 60% of traffic fatality victims in the country are pedestrians. The primary aim of this research was to examine the possible causes of and contributing factors to crashes with pedestrians in Ethiopia, and improve pedestrian safety by recommending possible countermeasures. The secondary aim was to develop appropriate pedestrian crash models for two-way two-lane rural roads and roundabouts in the capital city of Ethiopia. This research uses quantitative methods throughout the process of the investigation. The research has applied various statistical methods. The results of this research support the idea that geometric and operational features have significant influence on pedestrian safety and crashes. Accordingly, policies and strategies are needed to safeguard pedestrians in Ethiopia.
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How can obstacles to innovation be overcome in road construction? Using a focus group methodology, and based on two prior rounds of empirical work, the analysis in this chapter generates a set of four key solutions to two main construction innovation obstacles: (1) restrictive tender assessment and (2) disagreement over who carries the risk of new product failure. The four key solutions uncovered were: 1) pre-project product certification; 2) past innovation performance assessment; 3) earlier involvement of product suppliers and road asset operators; and 4) performance-based specifications. Additional research is suggested in order to illicit deeper insights into possible solutions to construction innovation obstacles, and should emphasise furthering the theoretical interpretation of empirical phenomena.
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Deterrence-based initiatives form a cornerstone of many road safety countermeasures. This approach is informed by Classical Deterrence Theory, which proposes that individuals will be deterred from committing offences if they fear the perceived consequences of the act, especially the perceived certainty, severity and swiftness of sanctions. While deterrence-based countermeasures have proven effective in reducing a range of illegal driving behaviours known to cause crashes such as speeding and drink driving, the exact level of exposure, and how the process works, remains unknown. As a result the current study involved a systematic review of the literature to identify theoretical advancements within deterrence theory that has informed evidence-based practice. Studies that reported on perceptual deterrence between 1950 and June 2015 were searched in electronic databases including PsychINFO and ScienceDirect, both within road safety and non-road safety fields. This review indicated that scientific efforts to understand deterrence processes for road safety were most intense during the 1970s and 1980s. This era produced competing theories that postulated both legal and non-legal factors can influence offending behaviours. Since this time, little theoretical progression has been made in the road safety arena, apart from Stafford and Warr's (1993) reconceptualisation of deterrence that illuminated the important issue of punishment avoidance. In contrast, the broader field of criminology has continued to advance theoretical knowledge by investigating a range of individual difference-based factors proposed to influence deterrent processes, including: moral inhibition, social bonding, self-control, tendencies to discount the future, etc. However, this scientific knowledge has not been directed towards identifying how to best utilise deterrence mechanisms to improve road safety. This paper will highlight the implications of this lack of progression and provide direction for future research.