Intensive agricultural practices in Europe and Canada have led to high levels of non-point source nutrient pollution, threatening drinking water quality and contributing to the destruction of aquatic ecosystems. Despite widespread implementation of a range of conservation measures to mitigate the impacts of fertilizer-intensive agriculture, nitrogen (N) and phosphorous (P) concentrations of inland waters are in many cases remaining steady or continuing to increase. This lack of response to conservation measures is increasingly attributed to the presence of legacy nutrient stores, which cause the long-term release of N and P, hence delaying the expected water quality benefits in receiving water bodies. However, our current knowledge regarding the magnitudes and spatial distributions of legacy nutrients across the landscape, as well as the time scales over which these legacies may contribute to elevated nutrient concentrations in surface and groundwater, remains woefully inadequate. The proposed LEAP project will move beyond a simple focus on nutrient concentrations and fluxes, and instead work towards the explicit quantification of the spatio-temporal dynamics of non-point source nutrient legacies within watersheds and the ongoing and future impacts on water quality. The quantitative understanding of nutrient legacies and the associated legacy-related time lags to achieving improvements in water quality at the project's study sites will allow us to develop an integrated analysis framework and innovative modelling tools to predict agricultural N and P loadings. Due to the strong impacts of nutrient legacies on the time scales for recovery in at-risk landscapes, integration of legacy dynamics into a hydro-economic modelling framework will enable a more accurate assessment of the outcomes of alternative management approaches in terms of both short- and long-term costs and benefits, and the evaluation of temporal uncertainties associated with different intervention strategies. In addition, our mapping of legacy nutrient stores and attention to spatial variations in legacy accumulation will inform the development of targeted, and thus more cost-effective, nutrient mitigation strategies. At a larger scale, our analysis of similarities and differences in agricultural trajectories, and thus differences in legacy nutrient dynamics, across Europe and North America will facilitate the exchange of ideas and perspectives and create new synergies with ongoing EU and Canadian water research and policy development.

Quantum sensors use the properties of quantum physics, a theory that describes phenomena at the atomic scale. We now know how to perfectly manipulate photons, atoms and electrons and place them on demand in a given quantum state. These states can be extremely sensitive to the slightest disturbance. It is on this principle that quantum sensors are based to detect, with great precision, quantities such as acceleration, rotation, magnetic field ... Their unparalleled sensitivity allows them to detect tiny variations but also to make very precise recordings over very long periods, thus opening the way to applications ranging from the measurement of the gravitational attraction of a buried object to the mapping of the magnetic fields emitted by our brain. The VeQSeNse project focuses on laser-cooled atomic inertial quantum sensors. At a temperature below microKelvin, it is thus possible to create waves of matter. By then using a laser pulse, we can form copies of these waves which will move away from each other in the direction of the laser beams. We thus create a quantum superposition of matter waves whose trajectories we will control using a series of light pulses, to form an interferometer with the possibility of observing interference fringes in the probability of detecting atoms at the exit of the interferometer. These fringes will be sensitive to accelerations along the laser, and it is possible to detect tiny changes, on the order of a billionth of the acceleration, and record these variations over very long periods of time. All these properties open the field of new applications, such as positioning without GPS, resource management without drilling or monitoring with a view to preventing disasters such as earthquakes. These sensors have been studied extensively and are commercially available today, but they are also very limited. Only one direction is measurable, whereas three-dimensional vector-type measurements would be required. In addition, there is still a lot of “dead time” in the measurement which degrades the accuracy, especially over long term use. The VeQSeNse project will study and develop a new generation of vector quantum sensors which will be used via a network of correlated sensors to meet the two challenges of vector measurement and reduced “dead time”. Vector sensors already exist by sequentially interrogating atoms on three axes, but this only partially solves the problem as it increases “dead times”. A series of correlated measurements can also be created by interrogating many clouds of atoms with the same laser, but this can only be done at short scales and therefore cannot be deployed over large areas as surveillance would require. and disaster forecasting. Through close collaboration between European and Canadian research institutes, we propose to develop a quantum link between “classic” vector sensors in order to improve sensitivity and precision and to develop a new 3D quantum manipulation tool that will lead to to a new generation of multi-axis quantum sensors. The combination of these two methods will increase the ability of these networks to record an acceleration vector map for geosphere monitoring applications. The aim is to set up a consortium of experts on aspects of sensors, synchronization networks and geophysical application. The work established in this context will also make it possible to foreshadow a consortium led by industrial partners aiming to accelerate the commercial development of gravimetric sensors.

EnTimeMent aims at a radical change in scientific research and enabling technologies for human movement qualitative analysis, entrainment and prediction, based on a novel neuro-cognitive approach of the multiple, mutually interactive time scales characterizing human behaviour. Our approach will afford the development of computational models for the automated detection, measurement, and prediction of movement qualities from behavioural signals, based on multi-layer parallel processes at non-linearly stratified temporal dimensions, and will radically transform technology for human movement analysis. EnTimeMent new innovative scientifically-grounded and time-adaptive technologies operate at multiple time scales in a multi-layered approach: motion capture and movement analysis systems will be endowed with a completely novel functionality, achieving a novel generation of time-aware multisensory motion perception and prediction systems. The proposed model and technologies will be iteratively tested and refined, by designing and performing controlled and ecological experiments, ranging from action prediction in a controlled laboratory setting, to prediction in dyadic and small group interaction. EnTimeMent scenarios include health (healing and support of everyday life of persons with chronic pain and disability), performing arts (e.g. dance), sports, and entertainment group activities, with and without living architectures. EnTimeMent will create and support community-building and exploitation with concrete initiatives, including a community of users and stakeholders, innovation hubs and SME incubators, as premises for the consolidation beyond the end of the project in a broader range of market areas.