The unequal distribution of road trauma in China : understanding risk factors to improve public health

The World Health Organization identifies road trauma as a major public health issue in every country; most notably among low-to-middle income countries. More than 90% of all road fatalities occur in these countries, although they have only 48% of all registered vehicles [1]. Unprecedented focus has been placed on reducing the global road trauma burden through the United Nations Decade of Action for Road Safety (2011-2020). China is rapidly transitioning from a nation of bicycle riders and pedestrians to one where car ownership and use is increasing. This transition presents important public health, mobility, and safety challenges. Rapid motorisation has resulted in an increased road trauma burden, shouldered disproportionately among the population. Vulnerable road users (bicyclists, pedestrians, and motorcyclists) are of particular concern, representing 70% of all road-related fatalities [1]. Furthermore, those at greatest risk of sustaining a crash-related disability are: male, older, less educated, and earning a lower income [2] and residing in urban areas [3], with higher fatality rates in north-western poorer provinces [3]. Speeding is a key factor in road crashes in China [1, 4] and is one of two risk factors targeted in the Bloomberg Philanthropies-funded Global Road Safety Program operating in two Chinese cities over five year [5] to which the first author has provided expert advice. However, little evidence exists to help understand the factors underpinning speeding behaviour. Previous research conducted by the authors in Beijing and Hangzhou explored personal, social, and legal factors relating to speeding to assist in better understanding the motivations for non-compliance with speed limits. Qualitative and quantitative research findings indicated that speeding is relatively common, including self-reported travel speeds of greater than 30 km/hour above posted speed limits [6], and that the road safety laws and enforcement practices may, in some circumstances, contribute to this [7]. Normative factors were also evident; the role of friends, family members and driving instructors were influential. Additionally, using social networks to attempt to avoid detection and penalty was reported, thereby potentially reinforcing community perceptions that speeding is acceptable [8, 9]. The authors established strong collaborative links with the Chinese Academy of Sciences and Zhejiang Police College to conduct this research. The first author has worked in both institutions for extended time periods and recognises that research must include an understanding of culturally-relevant issues if road safety is to improve in China. Future collaborations to assist in enhancing our understanding of such issues are welcomed. References [1] World Health Organization. (2009). Global status report on road safety: Time for action; Geneva. [2] Chen, H., Du, W., & Li, N. (2013). The socioeconomic inequality in traffic-related disability among Chinese adults: the application of concentration index. Accident Analysis & Prevention, 55(101-106). [3] Wang, S. Y., Li, Y. H., Chi, G. B., Xiao, S. Y., Ozanne-Smith, J., Stevenson, M., & Phillips, M. (2008). Injury-related fatalities in China: an under-recognised public-health problem. The Lancet (British edition), 372(9651), 1765-1773. [4] He, J., King, M. J., Watson, B., Rakotonirainy, A., & Fleiter, J. J. (2013). Speed enforcement in China: National, provincial and city initiatives and their success. Accident Analysis & Prevention, 50, 282-288. [5] Bhalla, K., Li, Q., Duan, L., Wang, Y., Bishai, D., & Hyder, A. A. (2013). The prevalence of speeding and drink driving in two cities in China: a mid project evaluation of ongoing road safety interventions. Injury, 44, 49-56. doi:10.1016/S0020-1383(13)70213-4. [6] Fleiter, J. J., Watson, B., & Lennon, A. (2013). Awareness of risky behaviour among Chinese drivers. Peer-reviewed paper presented at 23rd Canadian Multidisciplinary Road Safety Conference, Montréal, Québec. [7] Fleiter, J. J., Watson, B., Lennon, A., King, M. J., & Shi, K. (2009). Speeding in Australia and China: A comparison of the influence of legal sanctions and enforcement practices on car drivers. Peer-reviewd paper presented at Australasian Road Safety Research Policing Education Conference, Sydney. [8] Fleiter, J. J., Watson, B., Lennon, A., King, M. J., & Shi, K. (2011). Social influences on drivers in China. Journal of the Australasian College of Road Safety, 22(2), 29-36. [9] Fleiter, J. J., Watson, B., Guan, M. Q., Ding, J. Y., & Xu, C. (2013). Characteristics of Chinese Drivers Attending a Mandatory Training Course Following Licence Suspension. Peer-reviewed paper presented at Road Safety on Four Continents, Beijing, China.

Impact of PV units in LV feeders on distribution system reliability

Re-supplying loads on outage through cross-connect from adjacent feeders in a distribution system may cause voltage drop and hence require load shedding. However, the surplus PV generated in some of the LV feeders can prevent load shedding, and improve reliability. In order to measure these effects, this paper proposes the application of Direct Load Flow method[1] in reliability evaluation of distribution systems with PV units. As part of this study, seasonal impacts on load consumption together with surplus PV output power injection to higher voltage networks are also considered. New indices are proposed to measure yearly expected energy export, from LV to MV and from MV to higher voltage network.

A quantitative metric to identify critical elements within seafood supply networks

A theoretical basis is required for comparing key features and critical elements in wild fisheries and aquaculture supply chains under a changing climate. Here we develop a new quantitative metric that is analogous to indices used to analyse food-webs and identify key species. The Supply Chain Index (SCI) identifies critical elements as those elements with large throughput rates, as well as greater connectivity. The sum of the scores for a supply chain provides a single metric that roughly captures both the resilience and connectedness of a supply chain. Standardised scores can facilitate cross-comparisons both under current conditions as well as under a changing climate. Identification of key elements along the supply chain may assist in informing adaptation strategies to reduce anticipated future risks posed by climate change. The SCI also provides information on the relative stability of different supply chains based on whether there is a fairly even spread in the individual scores of the top few key elements, compared with a more critical dependence on a few key individual supply chain elements. We use as a case study the Australian southern rock lobster Jasus edwardsii fishery, which is challenged by a number of climate change drivers such as impacts on recruitment and growth due to changes in large-scale and local oceanographic features. The SCI identifies airports, processors and Chinese consumers as the key elements in the lobster supply chain that merit attention to enhance stability and potentially enable growth. We also apply the index to an additional four real-world Australian commercial fishery and two aquaculture industry supply chains to highlight the utility of a systematic method for describing supply chains. Overall, our simple methodological approach to empirically-based supply chain research provides an objective method for comparing the resilience of supply chains and highlighting components that may be critical.

Eliciting and encoding expert knowledge on variable selection into classical or Bayesian species distribution models

The quality of species distribution models (SDMs) relies to a large degree on the quality of the input data, from bioclimatic indices to environmental and habitat descriptors (Austin, 2002). Recent reviews of SDM techniques, have sought to optimize predictive performance e.g. Elith et al., 2006. In general SDMs employ one of three approaches to variable selection. The simplest approach relies on the expert to select the variables, as in environmental niche models Nix, 1986 or a generalized linear model without variable selection (Miller and Franklin, 2002). A second approach explicitly incorporates variable selection into model fitting, which allows examination of particular combinations of variables. Examples include generalized linear or additive models with variable selection (Hastie et al. 2002); or classification trees with complexity or model based pruning (Breiman et al., 1984, Zeileis, 2008). A third approach uses model averaging, to summarize the overall contribution of a variable, without considering particular combinations. Examples include neural networks, boosted or bagged regression trees and Maximum Entropy as compared in Elith et al. 2006. Typically, users of SDMs will either consider a small number of variable sets, via the first approach, or else supply all of the candidate variables (often numbering more than a hundred) to the second or third approaches. Bayesian SDMs exist, with several methods for eliciting and encoding priors on model parameters (see review in Low Choy et al. 2010). However few methods have been published for informative variable selection; one example is Bayesian trees (O’Leary 2008). Here we report an elicitation protocol that helps makes explicit a priori expert judgements on the quality of candidate variables. This protocol can be flexibly applied to any of the three approaches to variable selection, described above, Bayesian or otherwise. We demonstrate how this information can be obtained then used to guide variable selection in classical or machine learning SDMs, or to define priors within Bayesian SDMs.

Predicting the temporal response of seagrass meadows to dredging using Dynamic Bayesian Networks

Predicting temporal responses of ecosystems to disturbances associated with industrial activities is critical for their management and conservation. However, prediction of ecosystem responses is challenging due to the complexity and potential non-linearities stemming from interactions between system components and multiple environmental drivers. Prediction is particularly difficult for marine ecosystems due to their often highly variable and complex natures and large uncertainties surrounding their dynamic responses. Consequently, current management of such systems often rely on expert judgement and/or complex quantitative models that consider only a subset of the relevant ecological processes. Hence there exists an urgent need for the development of whole-of-systems predictive models to support decision and policy makers in managing complex marine systems in the context of industry based disturbances. This paper presents Dynamic Bayesian Networks (DBNs) for predicting the temporal response of a marine ecosystem to anthropogenic disturbances. The DBN provides a visual representation of the problem domain in terms of factors (parts of the ecosystem) and their relationships. These relationships are quantified via Conditional Probability Tables (CPTs), which estimate the variability and uncertainty in the distribution of each factor. The combination of qualitative visual and quantitative elements in a DBN facilitates the integration of a wide array of data, published and expert knowledge and other models. Such multiple sources are often essential as one single source of information is rarely sufficient to cover the diverse range of factors relevant to a management task. Here, a DBN model is developed for tropical, annual Halophila and temperate, persistent Amphibolis seagrass meadows to inform dredging management and help meet environmental guidelines. Specifically, the impacts of capital (e.g. new port development) and maintenance (e.g. maintaining channel depths in established ports) dredging is evaluated with respect to the risk of permanent loss, defined as no recovery within 5 years (Environmental Protection Agency guidelines). The model is developed using expert knowledge, existing literature, statistical models of environmental light, and experimental data. The model is then demonstrated in a case study through the analysis of a variety of dredging, environmental and seagrass ecosystem recovery scenarios. In spatial zones significantly affected by dredging, such as the zone of moderate impact, shoot density has a very high probability of being driven to zero by capital dredging due to the duration of such dredging. Here, fast growing Halophila species can recover, however, the probability of recovery depends on the presence of seed banks. On the other hand, slow growing Amphibolis meadows have a high probability of suffering permanent loss. However, in the maintenance dredging scenario, due to the shorter duration of dredging, Amphibolis is better able to resist the impacts of dredging. For both types of seagrass meadows, the probability of loss was strongly dependent on the biological and ecological status of the meadow, as well as environmental conditions post-dredging. The ability to predict the ecosystem response under cumulative, non-linear interactions across a complex ecosystem highlights the utility of DBNs for decision support and environmental management.

Decentralised key management for delay tolerant networks

Public key authentication is the verification of the identity-public key binding, and is foundational to the security of any network. The contribution of this thesis has been to provide public key authentication for a decentralised and resource challenged network such as an autonomous Delay Tolerant Network (DTN). It has resulted in the development and evaluation of a combined co-localisation trust system and key distribution scheme evaluated on a realistic large geographic scale mobility model. The thesis also addresses the problem of unplanned key revocation and replacement without any central authority.