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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. [ABSTRACT FROM AUTHOR]

In ubiquitous computing, there is a trade-off between adequate display of information and how the layering of data from multiple devices may result in increased imposition upon cognitive load. An emerging class of ubiquitous technologies referred to as ambient displays aim to reduce this imposition through trading off highly detailed visualisation and notification capabilities in the pursuit of systems which impose low levels of workload. What is yet to be explored in detail is how these systems may impose upon cognitive load in a quantified manner. In response, this study focuses on two research questions: RQ1—What methods may be appropriate in the measure of Ambient Display induced Cognitive Load and RQ2—How does one prototype display contribute to levels of Cognitive Load in a laboratory setting. These questions are investigated through a dual-task measure of cognitive load. A repeated measures within-groups laboratory experiment was conducted where participants were required to complete an n-back task in a display-present and display-absent condition. The n-back task was found to be an appropriate method of manipulating performance in both the primary and secondary tasks, although the presence of the evaluated ambient display was not found to induce additional workload. This is despite participants indicating in a subjective measure that they were able to perceive events visualised on the ambient display. [ABSTRACT FROM AUTHOR]

Ambient Displays, a sub-class of ubiquitous computing, aim to present non-critical information using peripheral visualisation with minimal distraction. The utility of Ambient Displays relies on providing useful, well-designed information in a way that does not increase the users cognitive load. Assessing the cognitive load of an Ambient Display is thus an important part of the development process. In this paper we review the key design dimensions of Ambient Displays and consider how they impact on cognitive load. We then examine various approaches for measuring cognitive load before describing a study that investigates a novel use of a dual-task measure to evaluate the cognitive load of a specific Ambient Display. A between-subjects design with 40 participants was used, with the Ambient Display active for half of these participants. All participants completed three different primary tasks (n-back, visual digit span, and auditory digit span) alongside the secondary, Detection Response Task. The results show that the n-back task is the most appropriate for manipulating primary task load when evaluating such displays and that the dual-task paradigm can be used to provide an objective measure of workload. Analysis of the participants primary and secondary task performance indicates that the evaluated Ambient Display imposed no additional cognitive load. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Many psychological experiments require participants to complete lots of trials in a monotonous task, which often induces boredom. An increasingly popular approach to alleviate such boredom is to incorporate gamelike features into standard experimental tasks. Games are assumed to be interesting and, hence, motivating, and better motivated participants might produce better data (with fewer lapses in attention and greater accuracy). Despite its apparent prevalence, the assumption that gamelike features improve data is almost completely untested. We test this assumption by presenting a choice task and a change detection task in both gamelike and standard forms. Response latency, accuracy, and overall task performance were unchanged by gamelike features in both experiments. We present a novel cognitive model for the choice task, based on particle filtering, to decorrelate the dependent variables and measure performance in a more psychologically meaningful manner. The model-based analyses are consistent with the hypothesis that gamelike features did not alter cognition. A postexperimental questionnaire indicated that the gamelike version provided a more positive and enjoyable experience for participants than the standard task, even though this subjective experience did not translate into data effects. Although our results hold only for the two experiments examined, the gamelike features we incorporated into both tasks were typical of-and at least as salient and interesting as those usually used by-experimental psychologists. Our results suggest that modifying an experiment to include gamelike features, while leaving the basic task unchanged, may not improve the quality of the data collected, but it may provide participants with a better experimental experience. [ABSTRACT FROM AUTHOR]