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One factor that limits development of fundamental research on the influence of coke microstructure on its strength is the difficulty in quantifying the way that microstructure is both classified and distributed in three dimensions. To support such fundamental studies, this study evaluated a novel volumetric approach for classifying small (approx. 450 μm3) blocks of coke microstructure from 3D computed tomography scans. An automated process for classifying microstructure blocks was described. It is based on Landmark Multi-Dimensional Scaling and uses the Bhattacharyya metric and k-means clustering. The approach was evaluated using 27 coke samples across a range of coke with different properties and reliably identified 6 ordered class of coke microstructure based on the distribution of voxel intensities associated with structural density. The lower class (1–2) subblocks tend to be dominated by pores and thin walls. Typically, there is an increase in wall thickness and reduced pore sizes in the higher classes. Inert features are also likely to be seen in higher classes (5–6). In general, this approach provides an efficient automated means for identifying the 3D spatial distribution of microstructure in CT scans of coke.

The accurate prediction of coke quality is important for the selection and valuation of metallurgical coals. Whilst many prediction models exist, they tend to perform poorly for coals beyond which the model was developed. Further, these models general fail to directly account for physical interactions occurring between the blend components, through the assumption that the aggregate properties of the blend are suitably representative of the overall behavior of the blend. To study this assumption, a parameter termed the vitrinite distribution category was introduced to directly account for the distribution of one of these commonly aggregated parameters, the vitrinite reflectance. The introduction of this parameter in a regression model for coke quality prediction improved the model fit. The vitrinite distribution category was demonstrated to provide new information about coal blending decisions, and was found to be capable of providing insight into the behavior of different blending structures. Residual analysis was applied to explore the behavior of the coke quality prediction model, with the vitrinite distribution category found to explain more than just the presence or absence of coals within a blend. This work provides the foundation of future studies in examining coal blending decisions, with the proposed parameter having the potential to be applied as part of a coke quality prediction model to optimize coal blending decisions.

Childhood obesity is becoming increasingly prolific and problematic. Contributors to childhood obesity include decreased levels of physical activity and increased sedentary behavior. Screen-based entertainment may be an important factor in the development of childhood obesity as children and adolescents prefer to spend time using electronic devices than exercising. While it is difficult to encourage children to cease playing video games, it is possible to change these passive screens to active ones. Information and Communication Technologies (ICTs) have been utilized by academics and researchers to promote levels of physical activity among young people. This paper is part of our continuous research into the use of technology in the facilitation and motivation of children to be more physically active. This paper presents the "MySteps " framework that has been developed to manage youth screen time and exercise performance statistics. By developing technology-based solutions, we intend to increase children and adolescents awareness of their levels of physical activity and screen time. Consequently, self-monitoring and management of screen time and physical activity levels may lead to more active living beginning at an early age and continuing throughout life.

In–match player performance, measured by data from Geographical Positioning System (GPS) devices, was predicted with a correlation coefficient of greater than 0.7. Predictions were based on heart rate variability measures and used advanced regression techniques based on machine learning. These techniques included methods for the selection of variables to be included in the regression study. Results indicate that variable selection using a wrapper subset method with a genetic algorithm outperformed both principal components analysis and the default method of using all variables. The success of prediction of match performance suggests a potential for new tools to assist the team coach in player selection and management of player training. This work also provides the possibility for a training programme to be adjusted specifically to meet the challenges of the size of the playing field and the temperature likely to be encountered on the day of the match.

In this paper we set out to verify the existence of Miles and Snow strategy types in Australian small- and medium-size enterprises (SMEs) through objective classification. Australian SMEs, in particular, are interesting as they are reported to have some unique characteristics, with as many as 70% following a low growth or life-style pathway. While numerous empirical studies have been conducted to validate the existence and characteristics of the Miles and Snow strategy types in different domains for both larger and smaller enterprises, these studies typically rely on a subjective, ‘self-typing’ approach. In this study we employ a more objective approach by identifying measures from existing survey data that capture the strategic dimensions proposed by Miles and Snow. We use these objective measures in a K-means cluster analysis to identify four different strategic groups. Three of the groups correspond to the three ‘viable’ Miles and Snow strategy types of Defender, Prospector and Analyser; however, we also identify a ‘Static’ strategy type that constitutes 42% of SMEs in the sample.