Boosting the effects of a curriculum based injury prevention program through a school connectedness intervention : final report to NRMA-ACT Road Safety Trust

Injury is the leading cause of death among young people (AIHW, 2008). A primary contributing factor to injury among adolescents is risk taking behaviour, including road related risks such as risky bicycle and motorcycle use and riding with dangerous or drink-drivers. Injury rates increase dramatically throughout adolescence, at the same time as adolescents are becoming more involved in risk taking behaviour. Also throughout this period, adolescents‟ connectedness to school is decreasing (Monahan, Oesterle & Hawkins, 2010; Whitlock, 2004). School connectedness refers to „the extent to which students feel personally accepted, respected, included, and supported by others in the school‟ (Goodenow, 1993, p. 80), and has been repeatedly shown to be a critical protective factor in adolescent development. For example, school connectedness has been shown to be associated with decreased risk taking behaviour, including violence and alcohol and other drug use (e.g., Resnick et al., 1997), as well as with decreased transport risk taking and vehicle related injuries (Chapman et al., accepted April 2011). This project involved the pilot evaluation of a school connectedness intervention (a professional development program for teachers) to reduce adolescent risk taking behaviour and injury. This intervention has been developed for use as a component of the Skills for Preventing Injury in Youth (SPIY) curriculum based injury prevention program for early adolescents. The objectives of this research were to: 1. Implement a trial School Connectedness intervention (professional development program for teachers) in ACT high schools, and evaluate using comparison high schools. 2. Determine whether the School Connectedness program impacts on adolescent risk taking behaviour and associated injuries (particularly transport risks and injuries). 3. Evaluate the process effectiveness of the School Connectedness program.

Fine-mapping the HOXB region detects common variants tagging a rare coding allele : evidence for synthetic association in prostate cancer

The HOXB13 gene has been implicated in prostate cancer (PrCa) susceptibility. We performed a high resolution fine-mapping analysis to comprehensively evaluate the association between common genetic variation across the HOXB genetic locus at 17q21 and PrCa risk. This involved genotyping 700 SNPs using a custom Illumina iSelect array (iCOGS) followed by imputation of 3195 SNPs in 20,440 PrCa cases and 21,469 controls in The PRACTICAL consortium. We identified a cluster of highly correlated common variants situated within or closely upstream of HOXB13 that were significantly associated with PrCa risk, described by rs117576373 (OR 1.30, P = 2.62×10(-14)). Additional genotyping, conditional regression and haplotype analyses indicated that the newly identified common variants tag a rare, partially correlated coding variant in the HOXB13 gene (G84E, rs138213197), which has been identified recently as a moderate penetrance PrCa susceptibility allele. The potential for GWAS associations detected through common SNPs to be driven by rare causal variants with higher relative risks has long been proposed; however, to our knowledge this is the first experimental evidence for this phenomenon of synthetic association contributing to cancer susceptibility.

A pseudo-marginal sequential Monte Carlo algorithm for random effects models in Bayesian sequential design

A computationally efficient sequential Monte Carlo algorithm is proposed for the sequential design of experiments for the collection of block data described by mixed effects models. The difficulty in applying a sequential Monte Carlo algorithm in such settings is the need to evaluate the observed data likelihood, which is typically intractable for all but linear Gaussian models. To overcome this difficulty, we propose to unbiasedly estimate the likelihood, and perform inference and make decisions based on an exact-approximate algorithm. Two estimates are proposed: using Quasi Monte Carlo methods and using the Laplace approximation with importance sampling. Both of these approaches can be computationally expensive, so we propose exploiting parallel computational architectures to ensure designs can be derived in a timely manner. We also extend our approach to allow for model uncertainty. This research is motivated by important pharmacological studies related to the treatment of critically ill patients.

A Memetic Algorithm for the location-based continuously operating reference stations placement problem in Network Real-Time Kinematic

Network Real-Time Kinematic (NRTK) is a technology that can provide centimeter-level accuracy positioning services in real time, and it is enabled by a network of Continuously Operating Reference Stations (CORS). The location-oriented CORS placement problem is an important problem in the design of a NRTK as it will directly affect not only the installation and operational cost of the NRTK, but also the quality of positioning services provided by the NRTK. This paper presents a Memetic Algorithm (MA) for the location-oriented CORS placement problem, which hybridizes the powerful explorative search capacity of a genetic algorithm and the efficient and effective exploitative search capacity of a local optimization. Experimental results have shown that the MA has better performance than existing approaches. In this paper we also conduct an empirical study about the scalability of the MA, effectiveness of the hybridization technique and selection of crossover operator in the MA.

BOSTER : an efficient algorithm for mining frequent unordered induced subtrees

Extracting frequent subtrees from the tree structured data has important applications in Web mining. In this paper, we introduce a novel canonical form for rooted labelled unordered trees called the balanced-optimal-search canonical form (BOCF) that can handle the isomorphism problem efficiently. Using BOCF, we define a tree structure guided scheme based enumeration approach that systematically enumerates only the valid subtrees. Finally, we present the balanced optimal search tree miner (BOSTER) algorithm based on BOCF and the proposed enumeration approach, for finding frequent induced subtrees from a database of labelled rooted unordered trees. Experiments on the real datasets compare the efficiency of BOSTER over the two state-of-the-art algorithms for mining induced unordered subtrees, HybridTreeMiner and UNI3. The results are encouraging.

BEST : an efficient algorithm for mining frequent unordered embedded subtrees

This paper presents an algorithm for mining unordered embedded subtrees using the balanced-optimal-search canonical form (BOCF). A tree structure guided scheme based enumeration approach is defined using BOCF for systematically enumerating the valid subtrees only. Based on this canonical form and enumeration technique, the balanced optimal search embedded subtree mining algorithm (BEST) is introduced for mining embedded subtrees from a database of labelled rooted unordered trees. The extensive experiments on both synthetic and real datasets demonstrate the efficiency of BEST over the two state-of-the-art algorithms for mining embedded unordered subtrees, SLEUTH and U3.

Regulating audit quality : restoring trust and legitimacy

The global financial crsis, corporate failures and scandals in amny countries raise significant questions audit quality. In the UK, the FRC took the unprecedented step of codifying audit quality in its ‘Audit Quality Framework’. We analyze the extent to which audit firms, professional bodies, and investors considered the FRC proposals sufficient for addressing concerns about audit quality. Using impression management and legitimacy as a framework to analyze stakeholder responses we go beyond audit quality drivers identified by the FRC. In contrast to the drivers identified by the FRC, our focus on transparency, expertise, professionalism and commercialization of the audit shows that FRC, audit firms and professional bodies have mainly focused on issues which possibly do not pose a threat to the commercial interest of audit firms. Overall, our analysis shows that regulatory and professional bodies engaged in image management and the promotion of audit quality in an attempt to remedy tarnished image and augment their legitimacy and standing. In attempting to restore trust and legitimacy regulatory bodies, such as the FRC, have to reconcile complex often contradictory stakeholder demands.

Genetic divergence and echolocation call frequency in cryptic species of Hipposideros larvatus s.l. (Chiroptera : Hipposideridae) from the Indo-Malayan region

The intermediate leaf-nosed bat (Hipposideros larvatus) is a medium-sized bat distributed throughout the Indo-Malay region. In north-east India, bats identified as H. larvatus captured at a single cave emitted echolocation calls with a bimodal distribution of peak frequencies, around either 85 kHz or 98 kHz. Individuals echolocating at 85 kHz had larger ears and longer forearms than those echolocating at 98 kHz, although no differences were detected in either wing morphology or diet, suggesting limited resource partitioning. A comparison of mitochondrial control region haplotypes of the two phonic types with individuals sampled from across the Indo-Malay range supports the hypothesis that, in India, two cryptic species are present. The Indian 98-kHz phonic bats formed a monophyletic clade with bats from all other regional populations sampled, to the exclusion of the Indian 85-kHz bats. In India, the two forms showed 12–13% sequence divergence and we propose that the name Hipposideros khasiana for bats of the 85-kHz phonic type. Bats of the 98-kHz phonic type formed a monophyletic group with bats from Myanmar, and corresponded to Hipposideros grandis, which is suggested to be a species distinct from Hipposideros larvatus. Differences in echolocation call frequency among populations did not reflect phylogenetic relationships, indicating that call frequency is a poor indicator of evolutionary history. Instead, divergence in call frequency probably occurs in allopatry, possibly augmented by character displacement on secondary contact to facilitate intraspecific communication.

Pollen image classification using the classifynder system: Algorithm comparison and a case study on New Zealand honey

We describe an investigation into how Massey University’s Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University’s pollen reference collection (2,890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set.We additionally work through a real world case study where we assess the ability of the system to determine the pollen make-up of samples of New Zealand honey. In addition to the Classifynder’s native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples.

Tubule detection in testis images using boundary weighting and circular shortest path

In studies of germ cell transplantation, measureing tubule diameters and counting cells from different populations using antibodies as markers are very important. Manual measurement of tubule sizes and cell counts is a tedious and sanity grinding work. In this paper, we propose a new boundary weighting based tubule detection method. We first enhance the linear features of the input image and detect the approximate centers of tubules. Next, a boundary weighting transform is applied to the polar transformed image of each tubule region and a circular shortest path is used for the boundary detection. Then, ellipse fitting is carried out for tubule selection and measurement. The algorithm has been tested on a dataset consisting of 20 images, each having about 20 tubules. Experiments show that the detection results of our algorithm are very close to the results obtained manually. © 2013 IEEE.

A communication model and partitioning algorithm for streaming applications for an embedded MPSoC

Energy efficient embedded computing enables new application scenarios in mobile devices like software-defined radio and video processing. The hierarchical multiprocessor considered in this work may contain dozens or hundreds of resource efficient VLIW CPUs. Programming this number of CPU cores is a complex task requiring compiler support. The stream programming paradigm provides beneficial properties that help to support automatic partitioning. This work describes a compiler for streaming applications targeting the self-build hierarchical CoreVA-MPSoC multiprocessor platform. The compiler is supported by a programming model that is tailored to fit the streaming programming paradigm. We present a novel simulated-annealing (SA) based partitioning algorithm, called Smart SA. The overall speedup of Smart SA is 12.84 for an MPSoC with 16 CPU cores compared to a single CPU implementation. Comparison with a state of the art partitioning algorithm shows an average performance improvement of 34.07%.

An efficient forward-backward algorithm to MSDEPP including batteries and voltage control devices

Electric distribution networks are now in the era of transition from passive to active distribution networks with the integration of energy storage devices. Optimal usage of batteries and voltage control devices along with other upgrades in network needs a distribution expansion planning (DEP) considering inter-temporal dependencies of stages. This paper presents an efficient approach for solving multi-stage distribution expansion planning problems (MSDEPP) based on a forward-backward approach considering energy storage devices such as batteries and voltage control devices such as voltage regulators and capacitors. The proposed algorithm is compared with three other techniques including full dynamic, forward fill-in, backward pull-out from the point of view of their precision and their computational efficiency. The simulation results for the IEEE 13 bus network show the proposed pseudo-dynamic forward-backward approach presents good efficiency in precision and time of optimization.