Towards Design Patterns for Augmented Reality Serious Games

For professional workers today, keeping up with knowledge and the continuous technology progress is challenging. Increased innovation speed and dynamic work situations shorten preparation times for new tasks significantly. Traditional professional training approaches preparing employees for new tasks are becoming inappropriate. Thus new educational means are needed. These would help employees get acquainted with new situations faster and more efficiently. According to learning theories such as action learning and situated learning, which embed the learning process in the application context and challenge the learner to be actively involved help to improve the learning process. These theories are the basis for mobile learning and serious games. From research in Serious Games we know that games have the potential to actively involve learners and to immerse them in a learning situation and increase their engagement. With Augmented Reality (AR) and wearable devices a new generation of tools and applications becomes available, which inherently are mobile, contextualized and personalized. First successful application scenarios show the potential of these new technologies for education and training. While the application of game-design patterns to learning processes help to systematically design learning games supporting specific learning outcomes, an empirically tested, systematic approach towards the design of AR-based learning solutions is still missing. Based on the state of the art in AR research and in applying design patterns for serious games, we consequently propose a research methodology to apply game design patterns to augmented reality-based learning games for the training of professionals in dynamic situations

Zooplankton-associated and free-living bacteria in the York River, Chesapeake Bay: comparison of seasonal variations and controlling factors

Trace caches deliver a high number of instructions per cycle to wide-issue superscalar processors. To overcome complex control flow, multiple branch predictors have to predict up to 3 conditional branches per cycle. These multiple branch predictors sometimes predict completely wrong paths of execution, degrading the average fetch bandwidth. This paper shows that such mispredictions can be detected by monitoring trace cache misses. Based on this observation, a new technique called trace substitution is introduced. On a trace cache miss, trace substitution overrides the predicted trace with a cached trace. If the substitution is correct, the fetch bandwidth increases. We show that trace substitution consistently improves the fetch bandwidth with 0.2 instructions per access. For inaccurate predictors, trace substitution can increase the fetch bandwidth with up to 2 instructions per access