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The objective of this paper is to assess whether existing pavement temperature prediction algorithms can be used to reliably determine Performance Grade (PG) design temperatures for Australian asphalt pavements. The results show good agreement between internationally and locally developed pavement temperature algorithms for the prediction of high pavement design temperature. There is more variability between models in the prediction of low pavement design temperature. The paper also provides a set of PG grading results for a range of Australian asphalt binders. The findings indicate that for a given design situation harder bitumen, ormore highly modified binder would be specified in Australia than in jurisdictions using the PG system. [ABSTRACT FROM AUTHOR]

The Satin Bowerbird Optimisation (SBO) was inspired by the Satin Bowerbirds living in Australia's rainforests and other mesic habitats. Like other meta-heuristic algorithms, the main problem faced by the SBO is that it has been empirically demonstrated to become easily trapped into local optimal solutions, creating low precision and slow convergence speeds. To overcome these deficiencies, we propose herein the Adaptive Chaotic Satin Bowerbird Optimisation algorithm (AC-SBO). Within the AC-SBO algorithm, a chaotic map is introduced to modify the search process, with which to enhance global convergence speeds, and to obtain better performance. We introduced the chaos theory into the SBO optimisation process, in order to replace the main parameter's greatest step size ( α), which assists in controlling the balance between both exploration and exploitation. The search accuracy and performance of the AC-SBO algorithm were verified on ten classical benchmark functions. In addition, in the experimental CEC2014 results showed that for almost all functions, the AC-SBO technique proved superior to the other comparative algorithms optimisations. The Wilcoxon rank-sum statistical test was performed in order to judge the significance of the results, and further demonstrated the improved performance of the proposed AC-SBO algorithm. [ABSTRACT FROM AUTHOR]

The utility of inertial measurement units (IMUs) for sporting skill and performance analysis during training and competition is advantageous for enhancing the objectivity of athlete monitoring. This study aimed to classify Australian Rules football (AF) kick types in an applied environment using ankle-mounted IMUs. IMUs and video capture of a controlled protocol, including four kick types at varying distances, were recorded during a single testing session with female AF athletes (n = 20). Processed IMU data were modelled using support vector machine classifier, random forest, and k-nearest neighbour algorithms under a 2-Kick, 4-Kick, and kick distance (10, 20, 30 m) conditions. The random forest model showed the highest results for overall classification accuracy (83% 2-Kick and 80% 4-Kick), test F1-score (0.76 2-Kick and 0.81 4-Kick), and AUC score (0.58 2-Kick and 0.60 4-Kick). Kick distance classification showed a model test and class weighted F1-score of 0.63 and overall accuracy of 64%, respectively. This study highlights the potential for an applied semi-automated AF training kick detection and type classification system using IMUs. [ABSTRACT FROM AUTHOR]

Micro-sensors were used to quantify macro kinematics of classical cross-country skiing techniques and measure cycle rates and cycle lengths during on-snow training. Data were collected from seven national level participants skiing at two submaximal intensities while wearing a micro-sensor unit (MinimaxX™). Algorithms were developed identifying double poling (DP), diagonal striding (DS), kick-double poling (KDP), tucking (Tuck), and turning (Turn). Technique duration (T-time), cycle rates, and cycle counts were compared to video-derived data to assess system accuracy. There was good reliability between micro-sensor and video calculated cycle rates for DP, DS, and KDP, with small mean differences (Mdiff% = −0.2 ± 3.2, −1.5 ± 2.2 and −1.4 ± 6.2) and trivial to small effect sizes (ES = 0.20, 0.30 and 0.13). Very strong correlations were observed for DP, DS, and KDP for T-time (r = 0.87–0.99) and cycle count (r = 0.87–0.99), while mean values were under-reported by the micro-sensor. Incorrect Turn detection was a major factor in technique cycle misclassification. Data presented highlight the potential of automated ski technique classification in cross-country skiing research. With further refinement, this approach will allow many applied questions associated with pacing, fatigue, technique selection and power output during training and competition to be answered. [ABSTRACT FROM PUBLISHER]

Introduction and aims: Illicit methamphetamine (MA) use is an important public health concern. There is a dearth of knowledge about effective and cost-effective treatments for methamphetamine (MA) dependence in Australia. This article evaluates the cost-effectiveness of counselling as a treatment option for illicit MA use compared with no treatment option. Design and methods: Data are from 501 individuals recruited into Methamphetamine Treatment Evaluation Study (MATES). The population of MA users from MATES is extrapolated to a total number of 1000 MA users in the intervention group (counselling treatment) and control group (non-treatment group). A decision analytic model is developed that examines the costs and health outcomes [measures as quality adjusted life years (QALYs) gained] for the treatment and comparison group over a 3-year period. A societal perspective is adopted and model inputs are subject to sensitivity and uncertainty analysis to test the robustness of results to parameter variability. Results are discounted by using 3% discount rate and expressed in 2011 Australian dollars. Results: The incremental cost-effectiveness analysis suggests that counselling is a dominant health care intervention, i.e. saves money and is more effective than a do nothing intervention. The incremental difference in costs is −AU$18.36 million (95% CI −AU$22.80 million to −AU$14.31 million) and the incremental difference in QALY is 107 (95% CI −640 to 820) with a probability of 78.64% of counselling being a dominant and cost-effective treatment within the acceptable incremental cost-effectiveness ratio (ICER) of $63 832 per QALY in the Australian society. The results of the sensitivity analysis show that the ICER is most sensitive to change in five major inputs: baseline utility, utility at 3 months, dealing crime costs, property crime costs and fraud crime costs. Discussion and Conclusions: The economic evaluation of the cost-effectiveness of counselling for MA dependence, as a first cost-effectiveness study to assess psychosocial treatment options for MA dependence, shows that greater investment in this cost-effective strategy will produce significant cost-savings and improve health outcomes as well as improve a lot of externality issues associated with drug use. [ABSTRACT FROM AUTHOR]

Clustering of cumulative grainsize distribution curves was trialled with the publicly available software program CLARA as a means of finding sediment samples and geographical areas or parts of the geological record with highly similar particle-size characteristics. CLARA proved effective for this purpose. Tests were made with large datasets from the shallow-marine environments of Sydney Harbour (Australia), Oronsay (Inner Hebrides, Scotland), and Darss Sill (Baltic Sea). CLARA has four possible configurations depending on choices of distance metric and standardisation. One configuration identified outliers and small groups of samples most dissimilar from others, a very useful function. A second configuration clustered cumulative curves in a geometrical fashion similar to manual clustering. Compared to CLARA, an entropy algorithm was several orders of magnitude slower and did not identify outliers. When smaller numbers of clusters were requested, cumulative curves with strongly opposite curvature were grouped by the entropy algorithm, and by CLARA for non-standardised (but not standardised) variables. This potential problem is removed for CLARA by initially forming more clusters than suggested by statistical methods, in conjunction with outlier detection and removal. Entropy and CLARA clustering of frequency distributions provided the best resolution of size modes, and best separation of overlapping size modes, but clustering of cumulative curves provided better overall groupings. However, CLARA is not suitable for direct clustering of all conceivable frequency distributions, a problem not occurring with the corresponding cumulative curves. [ABSTRACT FROM AUTHOR]