A pro-active and dynamic prediction assistance using BaranC framework

Monitoring user interaction activities provides the basis for creating a user model that can be used to predict user behaviour and enable user assistant services. The BaranC framework provides components that perform UI monitoring (and collect all associated context data), builds a user model, and supports services that make use of the user model. In this case study, a Next-App prediction service is built to demonstrate the use of the framework and to evaluate the usefulness of such a prediction service. Next-App analyses a user's data, learns patterns, makes a model for a user, and finally predicts based on the user model and current context, what application(s) the user is likely to want to use. The prediction is pro-active and dynamic; it is dynamic both in responding to the current context, and also in that it responds to changes in the user model, as might occur over time as a user's habits change. Initial evaluation of Next-App indicates a high-level of satisfaction with the service.

Scaling up strategies of the chronic respiratory disease programme of the European Innovation Partnership on Active and Healthy Ageing (Action Plan B3: Area 5)

Action Plan B3 of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) focuses on the integrated care of chronic diseases. Area 5 (Care Pathways) was initiated using chronic respiratory diseases as a model. The chronic respiratory disease action plan includes (1) AIRWAYS integrated care pathways (ICPs), (2) the joint initiative between the Reference site MACVIA-LR (Contre les MAladies Chroniques pour un VIeillissement Actif) and ARIA (Allergic Rhinitis and its Impact on Asthma), (3) Commitments for Action to the European Innovation Partnership on Active and Healthy Ageing and the AIRWAYS ICPs network. It is deployed in collaboration with the World Health Organization Global Alliance against Chronic Respiratory Diseases (GARD). The European Innovation Partnership on Active and Healthy Ageing has proposed a 5-step framework for developing an individual scaling up strategy: (1) what to scale up: (1-a) databases of good practices, (1-b) assessment of viability of the scaling up of good practices, (1-c) classification of good practices for local replication and (2) how to scale up: (2-a) facilitating partnerships for scaling up, (2-b) implementation of key success factors and lessons learnt, including emerging technologies for individualised and predictive medicine. This strategy has already been applied to the chronic respiratory disease action plan of the European Innovation Partnership on Active and Healthy Ageing.

Spatial overlap of grey seals and fisheries in Irish waters, some new insights using telemetry technology and VMS

Seals and humans often target the same food resource, leading to competition. This is of mounting concern with fish stocks in global decline. Grey seals were tracked from southeast Ireland, an area of mixed demersal and pelagic fisheries, and overlap with fisheries on the Celtic Shelf and Irish Sea was assessed. Overall, there was low overlap between the tagged seals and fisheries. However, when we separate active (e.g. trawls) and passive gear (e.g. nets, lines) fisheries, a different picture emerged. Overlap with active fisheries was no different from that expected under a random distribution, but overlap with passive fisheries was significantly higher. This suggests that grey seals may be targeting the same areas as passive fisheries and/or specifically targeting passive gear. There was variation in foraging areas between individual seals suggesting habitat partitioning to reduce intra-specific competition or potential individual specialisation in foraging behaviour. Our findings support other recent assertions that seal/fisheries interactions in Irish waters are an issue in inshore passive fisheries, most likely at the operational and individual level. This suggests that seal population management measures would be unjustifiable, and mitigation is best focused on minimizing interactions at nets.