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Over the last decades, digital technologies have moved away from the personal computer (PC) into cloud computing, ubiquitous computing, intelligent robots and smart devices. From wearable technologies to remote-controlled household items and from sensors for crowd control to personal drones, there is a broad range of sources which can be exploited for artificial intelligence in games. While artificial intelligence (AI) is already a big part of the Internet of Things, raising concerns in terms of ethics and politics, games have been relatively partitioned away to PCs. Relevant work on wearable technologies as game controllers, mixed- or virtual-reality rendering, or technology-enhanced play in playgrounds, social robots for games or board games, has largely not taken advantage of artificial intelligence for controlling or mediating the experience. Using the term mixed-media to refer broadly to any media, digital or otherwise, outside the game data within a PC or a game-specific database, this working group attempted to map out the broad topic of mixed-media in terms of its applications for game AI., peer-reviewed

Introduction: In 2001, the Radiation Therapy Advisory Panel (RTAP) of the Australian Society of Medical Imaging and Radiation Therapy (ASMIRT) (formerly known as Australian Institute of Radiography) published a model for radiation therapist staffing in Australian radiation oncology departments. Between 2012–2013, the model was reviewed to ensure it reflected current radiation therapy practice, technology, and to facilitate forward planning of the radiation therapy workforce. Method: Twenty-four sites from all states participated and provided data on megavoltage simulation, planning and treatment delivery. For simulation and planning activity, the length of time to complete was collected against relevant Medicare Benefits Schedule (MBS) items. For treatment delivery, time to complete activities was collected against a common set of activities. Modelling assumptions are clearly identified in the methodology. Results: A new model was developed retaining the essential model parameter of full-time equivalent (FTE) radiation therapists (RTs) per linear accelerator operating hour as in the 2001 model but based on contemporary practice and data. The model also includes significant refinements that improve the model's overall utility and flexibility for both workforce planning purposes and for individual services to use the model according to their own organisational needs and service delivery profiles. Conclusion: The ASMIRT believes that the 2014 RT staffing model provides the utility and flexibility for radiation oncology services to best plan RT staffing establishments according to their needs and reflecting the diversity between services and within the sector. It should also provide a robust and valid basis for governments and service planners to use as a guide in workforce planning into the future. Refereed/Peer-reviewed