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International research has shown that live sports commentary exhibits racial bias. Specifically, non-White players are more likely to be praised in terms of their physicality, while White players are more likely to be praised in terms of their intellect and character. The current study, which utilised a quantitative content analysis design, examined whether the speech of AFL commentators exhibited racial bias. The study randomly selected 50 men's AFL game quarters from the 2019 AFL season and analysed 1368 applicable statements directed at 382 unique players. Based on prior research, a coding instrument was developed that incorporated three main categories (physical, cognitive, and character attributes), and six subcategories (physical ability, appearance, cognitive ability, intelligence, general character, and hard work). In contrast to the international literature, findings revealed that there were no significant between-race differences for each main attribute category. However, non-White players received a higher proportion of statements related to their physical ability, and a lower proportion of statements related to their appearance compared to White players. Non-White players also received a higher proportion of negative statements related to their cognitive ability compared to White players. There was no evidence found to suggest that players of any race were discussed in terms of their physical ability being innate, natural, or instinctual. Given the strong, but also dated, evidence showing racial bias within both American and European sports commentary, the current study provides only weak evidence for the existence of racial bias within contemporary AFL live commentary. [ABSTRACT FROM AUTHOR]

The United States (US) Army became the first agency in the federal government to apply the Global Reporting Initiative guidelines to its sustainability program in 2007. It fully reported on 28 performance indicators and partially reported on another 12 in its first annual report. The second annual report for 2008 fully reported on 25 performance indicators and partially reported on 22. For each year, the US Army achieved the Global Reporting Initiative Administrative Level B for its sustainability program. The US Army has established sustainability as department policy, developed courses to indoctrinate senior leaders to the concept, and made a commitment to practicing sustainability. This report describes the evolution of sustainability as a multidimensional management paradigm in the US Army, identifies the Army Triple Bottom Line, and explores next steps, including the use of sustainability as a force multiplier in contingency operations. [ABSTRACT FROM AUTHOR]

Worldwide demographic projections point to a progressively older population. This fact has fostered research on Ambient Assisted Living, which includes developments on smart homes and social robots. To endow such environments with truly autonomous behaviours, algorithms must extract semantically meaningful information from whichever sensor data is available. Human activity recognition is one of the most active fields of research within this context. Proposed approaches vary according to the input modality and the environments considered. Different from others, this paper addresses the problem of recognising heterogeneous activities of daily living centred in home environments considering simultaneously data from videos, wearable IMUs and ambient sensors. For this, two contributions are presented. The first is the creation of the Heriot-Watt University/University of Sao Paulo (HWU-USP) activities dataset, which was recorded at the Robotic Assisted Living Testbed at Heriot-Watt University. This dataset differs from other multimodal datasets due to the fact that it consists of daily living activities with either periodical patterns or long-term dependencies, which are captured in a very rich and heterogeneous sensing environment. In particular, this dataset combines data from a humanoid robot's RGBD (RGB + depth) camera, with inertial sensors from wearable devices, and ambient sensors from a smart home. The second contribution is the proposal of a Deep Learning (DL) framework, which provides multimodal activity recognition based on videos, inertial sensors and ambient sensors from the smart home, on their own or fused to each other. The classification DL framework has also validated on our dataset and on the University of Texas at Dallas Multimodal Human Activities Dataset (UTD-MHAD), a widely used benchmark for activity recognition based on videos and inertial sensors, providing a comparative analysis between the results on the two datasets considered. Results demonstrate that the introduction of data from ambient sensors expressively improved the accuracy results. [ABSTRACT FROM AUTHOR]