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57 Projects

  • Canada
  • 2022-2022
  • 2025

10
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  • Funder: EC Project Code: 101000302
    Overall Budget: 7,919,410 EURFunder Contribution: 7,919,410 EUR

    The EcoScope project will develop an interoperable platform and a robust decision-making toolbox, available through a single public portal, to promote an efficient, ecosystem-based fisheries management. It will be guided by policy makers and scientific advisory bodies, and address ecosystem degradation and the anthropogenic impact that are causing fisheries to be unsustainably exploited across European Seas. The EcoScope Platform will organise and homogenise climatic, oceanographic, biogeochemical, biological and fisheries datasets for European Seas to a common standard type and format that will be available through interactive mapping layers. The EcoScope Toolbox, a scoring system based on assessments of all ecosystem components, ecosystem and economic models, will operate as a decision-support tool for examining fisheries management and marine policy scenarios and spatial planning simulations. Groups of end-users and stakeholders will be involved in the design, development and operation of both the platform and the toolbox. Novel assessment methods for data-poor fisheries, including non-commercial species, as well as for biodiversity and the conservation status of protected megafauna, will be used to assess the status of all ecosystem components across European Seas and test new technologies for evaluating the environmental, anthropogenic and climatic impact on ecosystems and fisheries. A series of sophisticated capacity building tools (online courses, webinars and games) will be available to stakeholders through the EcoScope Academy. The EcoScope project will provide an effective toolbox to decision makers and end-users that will be adaptive to their capacity, needs and data availability. The toolbox will incorporate methods for dealing with uncertainty; thus, it will promote efficient, holistic, sustainable, ecosystem-based fisheries management that will aid towards restoring fisheries sustainability and ensuring balance between food security and healthy seas.

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  • Funder: UKRI Project Code: NE/W001233/1
    Funder Contribution: 647,247 GBP

    This project addresses how environmental change affects the movement of sediment through rivers and into our oceans. Understanding the movement of suspended sediment is important because it is a vector for nutrients and pollutants, and because sediment also creates floodplains and nourishes deltas and beaches, affording resilience to coastal zones. To develop our understanding of sediment flows, we will quantify recent variations (1985-present) in sediment loads for every river on the planet with a width greater than 90 metres. We will also project how these river sediment loads will change into the future. These goals have not previously been possible to achieve because direct measurements of sediment transport through rivers have only ever been made on very few (<10% globally) rivers. We are proposing to avoid this difficulty by using a 35+ years of archive of freely available satellite imagery. Specifically, we will use the cloud-based Google Earth Engine to automatically analyse each satellite image for its surface reflectance, which will enable us to estimate the concentration of sediment suspended near the surface of rivers. In conjunction with other methods that characterise the flow and the mixing of suspended sediment through the water column, these new estimates of surface Suspended Sediment Concentration (SSC) will be used to calculate the total movement of suspended sediment through rivers. We then analyse our new database (which, with a five orders of magnitude gain in spatial resolution relative to the current state-of-the-art, will be unprecedented in its size and global coverage) of suspended sediment transport using novel Machine Learning techniques, within a Bayesian Network framework. This analysis will allow us to link our estimates of sediment transport to their environmental controls (such as climate, geology, damming, terrain), with the scale of the empirical analysis enabling a step-change to be obtained in our understanding of the factors driving sediment movement through the world's rivers. In turn, this will allow us to build a reliable model of sediment movement, which we will apply to provide a comprehensive set of future projections of sediment movement across Earth to the oceans. Such future projections are vital because the Earth's surface is undergoing a phase of unprecedented change (e.g., through climate change, damming, deforestation, urbanisation, etc) that will likely drive large transitions in sediment flux, with major and wide reaching potential impacts on coastal and delta systems and populations. Importantly, we will not just quantify the scale and trajectories of change, but we will also identify how the relative contributions of anthropogenic, climatic and land cover processes drive these shifts into the future. This will allow us to address fundamental science questions relating to the movement of sediment through Earth's rivers to our oceans, such as: 1. What is the total contemporary sediment flux from the continents to the oceans, and how does this total vary spatially and seasonally? 2. What is the relative influence of climate, land use and anthropogenic activities in governing suspended sediment flux and how have these roles changed? 3. How do physiographic characteristics (area, relief, connectivity, etc.) amplify or dampen sediment flux response to external (climate, land use, damming, etc) drivers of change and thus condition the overall response, evolution and trajectory of sediment flux in different parts of the world? 4. To what extent is the flux of sediment driven by extreme runoff generating events (e.g. Tropical Cyclones) versus more common, lower magnitude events? How will projected changes in storm frequency and magnitude affect the world's sediment fluxes in the future? 5. How will the global flux of sediment to the oceans change over the course of the 21st century under a range of plausible future environmental change scenarios?

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  • Funder: EC Project Code: 945234
    Overall Budget: 8,971,950 EURFunder Contribution: 3,997,240 EUR

    The ECC-SMART is oriented towards assessing the feasibility and identification of safety features of an intrinsically and passively safe small modular reactor cooled by supercritical water (SCW-SMR), taking into account specific knowledge gaps related to the future licensing process and implementation of this technology. The main objectives of the project are to define the design requirements for the future SCW-SMR technology, to develop the pre-licensing study and guidelines for the demonstration of the safety in the further development stages of the SCW-SMR concept including the methodologies and tools to be used and to identify the key obstacles for the future SMR licencing and propose a strategy for this process. To reach these objectives, specific technical knowledge gaps were defined and will be assessed to achieve the future smooth licensing and implementation of the SCW-SMR technology (especially the behaviour of materials in the SCW environment and irradiation, validation of the codes and design of the reactor core will be developed, evaluated by simulations and experimentally validated). The ECC-SMART project consortium consists of EU, Canadian and Chinese partners to use the trans-continental synergy and knowledge developed separately by each partner. The project consortium and project scope were created according to the joint research activities under the International Atomic Energy Agency, Generation-IV International Forum umbrella and as much data as possible will be taken from the already performed projects. This project brings together the best scientific teams working in the field of SCWR using the best facilities and methods worldwide, to fulfil the common vision of building an SCW-SMR in the near future.

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  • Funder: NIH Project Code: 2R01DA028648-11
    Funder Contribution: 538,742 USD
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  • Funder: NSF Project Code: 2202340
    Funder Contribution: 100,000 USD
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  • Funder: EC Project Code: 101003575
    Overall Budget: 15,924,000 EURFunder Contribution: 5,000,000 EUR

    ERA-MIN3 comprises a progressive, pan-European public-public partnership of 25 public research funding organisations from 19 European countries/regions and 3 third countries, which aims to continue strengthening the mineral raw materials (RM) community through the coordination of research and innovation (R&I) programmes on non-energy, non-agricultural raw materials (metallic, construction, and industrial minerals). ERA-MIN3 will thus contribute to the objectives of the EIP on Raw Material’s Strategic Implementation Plan and the EU Circular Economy Action Plan, in support of the EU Raw Materials Initiative, the UN sustainable development goals and the European Green Deal. Built on the successes of the previous ERA-MIN and ERA-MIN 2, and to ensure the EU’s resource security and sustainable supply of strategic RM to the European society, ERA-MIN3 will achieve its goals of improving synergy, coordination and coherence between regional, national and EU funding in the RM sector by reducing fragmentation of RM funding across Europe and globally, as well as, improving the use of human and financial resources, the competitiveness and the environmental, social, health and safety issues of RM operations through supporting of transnational, excellent and translational R&I activities. This will be achieved through a EU co-funded joint transnational call for R&I proposals and, at least, one additional call with participation of invited partners, on demand-driven R&I on primary and secondary resources, covering the entire value chain, from exploration, extraction and processing technologies to recycling and substitution of CRM, as well as, environmental and societal impact, new business models and/or public perception. ERA-MIN3 will liaison with RM related initiatives to ensure alignment of research topics (e.g. batteries), promote synergies and complementarities thus avoiding duplication of efforts and contributing for the circular economy and the sustainable development.

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  • Funder: UKRI Project Code: EP/S028730/1
    Funder Contribution: 1,046,720 GBP

    Colour Imaging is part of every day life. Whether we watch TV, browse content on our tablets or phones or use apps and software in our work the content we see on our screens is the result of decades of colour & imaging research. In the future, the challenge is to understand more about the content images. As an example, in autonomous driving we wish to build a platform that sees the road independent of the atmospheric conditions, we don't want to crash when we are driving in fog. It is well known that an image that records the near-infrared signal is much sharper (compared to RGB) in foggy conditions. What is near infrared? The visible spectrum has a natural rainbow order: Violet, Indigo, Blue, Green, Yellow Orange and Red. Infrared is the 'next colour' after red that we can't quite see. Image fusion can be used to map the RGB+NIR signal to a fused RGB counterpart, that we can see. Through image fusion the same detail will be present in foggy or non-foggy conditions. Advantageously, Image Fusion is a tool that will allow non visible information to be incorporated and deployed in existing RGB-based AI scene interpretation systems with minimal retraining. Our project begins with the Spectral Edge Image fusion method, the current leading technique. This method - and most image fusion algorithms - works by combining edges from the 4 images (RGB+NIR) to make a fused RGB-only 3-channel edge map. The edges are then transformed (the technical term is reintegrated) back to form a colour image. Unfortunately, and necessarily, the reintegrated images often have defects such as bright halos round edges or smearing. We argue that the defects are a direct consequence of how 'edges' are defined. In our research we will - based on a surprising mathematical insight - develop a new definition of edge, quite a bold thing to do after 50 years of image processing research! By construction the reintegrated new edges will have much less halo and smearing artefacts. We will then use our improved edge representation and improved image fusion algorithm to make better looking images. These might be the fused images themselves: wouldn't it be great to have smart binoculars that allow us to see more detail in images when it is rainy or a landscape that is blurred by distance. However, we also believe the future of photography, in general, is content-based and that image fusion will help us determine the content in an image. As an example, when we take a picture at sunset, the shadows in the scene are very blue. But, outside of the shadow the light is very warm (orangish). The best image reproductions for these scenes involves manually and differentially processing shadow and non shadow regions. Here, we seek to find the illumination content in image automatically. Then in a second step we will develop a new content-based framework for manipulating images so that, for this sunset example, we don't need to edit the photos ourselves. In complementary work, we are also interested in helping people see better. Indeed, there is a lot of research that demonstrates that coloured filters can help mitigate visual stress. Coloured filters are used in Dyslexia (sometimes leading to dramatic improvements in reading speed) and there is now blue absorbing glass which will reduces the blue light coming from a tablet display (since blue light at night tends to keep you awake). Much of the prior art in this area is 'direct'. We find a filter to directly impact on how we see (simply, if we put a yellow filter in front of the eye then everything looks more yellow). Our idea is to deign filters that are related to the tasks we need to solve. For the problem of matching colours we will design filters so that if you suffer from colour-blindness you will be able to colour match as if you had normal colour vision. We will also develop indirect solutions for the 'blue light' problem and visual stress.

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  • Funder: UKRI Project Code: NE/T00648X/1
    Funder Contribution: 1,581,310 GBP

    Clouds containing a mixture of ice and water (mixed-phase clouds) are likely to change in response to climate change. It is expected that warming will cause an increase in the amount of water and a decrease in the amount of ice in these clouds. Because water droplets reflect more solar radiation than ice crystals (and cause less precipitation), the clouds are expected to become brighter, thereby causing a cooling effect (or negative feedback) on the climate system at mid- to high-latitudes. The magnitude of the cloud-phase feedback is very uncertain. If the feedback is strong then global temperatures will increase more slowly in future, but if it is weak then temperatures will increase more rapidly. It was recently shown that adjusting the ratio of ice and water in a climate model to match satellite observations could increase Earth's equilibrium climate sensitivity (warming with a doubling of CO2) by 1.5 degrees; hence, Earth may warm faster than thought. The feedback process is further complicated by the fact that the special particles, ice nucleating particles (INPs), which trigger ice production, may be more abundant in a warmer world where INP sources, such as glacial valleys, will be covered in ice and snow for less time. Increased INP concentrations would mean more ice in clouds and lead to a positive feedback. These two opposing feedbacks contribute to what we refer to as the cloud-phase feedback. This proposal will improve our understanding of how ice particles form in clouds and how this affects the cloud-phase feedback. Ice formation is the key process that controls this feedback. The problem can be broken down into two parts: First, we will address the open questions related to the chain of processes that link initial ice formation to the reflectivity of the clouds and how the reflectivity will change with warming. We have designed an aircraft campaign targeted at conditions of most relevance to the cloud feedback problem: moderately cold clouds that will be most sensitive to changes in temperature, and where high INP concentrations are likely to influence large regions of the N Atlantic. These cold-air outbreaks clouds provide an ideal meteorological situation for studying the formation and evolution of the kinds of shallow mixed-phase clouds which are important for cloud feedbacks. Second, we will address the paucity of knowledge on the sources, distribution and seasonal cycles of INPs at the mid- to high-latitudes. Our strategy is to use measurements to identify sources of INPs and use this information to inform the inclusion of mid- to high-latitude sources in our global model of INPs. We will perform new long-term measurements through a whole year and ship borne measurements through the key source regions in the Arctic. We have built a substantial network of Partners who will contribute INP data across the northern and southern mid- to high-latitudes which will allow us to expand our study to the globe. The new knowledge on cloud processes and INP will be used to improve the representation of mixed-phase clouds in the Met Office weather and climate model. The model will be run at very high spatial resolution so that the individual clouds in the cold-air outbreaks can be simulated. The model will be tested and improved by comparing it to our measurements as well as against satellite observations. We will then extend this study to contrasting cases from the Southern Ocean and the other side of the Atlantic. We anticipate the new knowledge will lead to a greatly improved representation of these climatically critical clouds. We will then perform a sensitivity analysis on selected cases in order to test how these cloud systems will respond to climate change. Finally, we will use the new knowledge to develop a plan for improving how mixed-phase clouds are treated in global climate models so that this work can be carried out in a follow-on project.

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  • Funder: UKRI Project Code: EP/T033568/1
    Funder Contribution: 1,796,880 GBP

    There is nothing quite like the magic of magnets. And yet even Richard Feynman, an incredibly gifted science communicator, struggled to explain just how magnetism works. (The video in question is easily found on YouTube. Feynman's slight tetchiness with the interviewer who raises the subject of magnetic forces is not entirely unrelated to the difficulty in explaining their fundamental origin at a level that a non-physicist -- or, indeed, a physicist -- can readily grasp.) Scientists are now at the point, however, where not only can we measure forces on an atom-by-atom basis, but we can harness and exploit those self-same forces to manipulate magnetism right down to the atomic level (and beyond). The instrument that allows this exquisite level of control of magnetic forces is the scanning probe microscope. A technique that will shortly reach its fortieth birthday, probe microscopy is conceptually rather straight-forward -- its experimental realisation rather less so. An exceptionally sharp tip, terminated in a single atom or molecule, is brought extremely close to a surface such that the tip-surface separation is of the order of the diameter of an atom or less. This atomically sharp probe can then be used in a number of modes to explore, interrogate, and modify the underlying sample surface on an atom-by-atom basis. Some of the most exciting and ground-breaking science ever carried out has involved the scanning probe microscope's unparalleled ability to not only image, but manipulate, matter at the single atom level. Probe microscopes are not just limited to the imaging and control of atoms; they can go much further. With an appropriately modified tip apex, even the quantum mechanical spin of electrons -- which, ultimately, is the source of magnetism -- is detectable either via the tiny electrical current that flows between the probe and the sample, or, incredibly, via measurement of the minuscule magnetic force between single atoms. Just a couple of months ago (in Oct. 2019), Chris Lutz' group at the IBM Almaden Research Centre reported that they have achieved, in collaboration with researchers in Korea and Oxford, the most precise and coherent control of the spin state of individual atoms ever attempted with SPM. (It's worth noting that IBM is the birthplace of both the scanning probe microscope itself, which was invented by Binnig, Rohrer and co-workers in the Ruschlikon, Zurich research labs, and of SPM-driven single atom manipulation, due to the inspiring efforts of Don Eigler and colleagues at IBM Almaden.) But the deep, dark secret of the probe microscopist is that a very large percentage of their time is spent coercing and cajoling the probe into providing atomic resolution. Yet even that's not enough -- when that resolution is achieved, the microscopist very often has to maintain the ability to image, move, and spectroscopically interrogate single atoms at the same time, while always being on the look-out for tip-derived artefacts. The component at the core of probe microscopy -- the probe itself -- therefore represents a major, and infuriating, bottleneck in the technique. This project integrates artificial intelligence, surface science, and nanoscience to take the pain out of probe microscopy. We will develop a machine learning framework that, in essence, "auto focuses" a probe microscope and then takes the SPM to the point where it can learn how to build magnetic nanostructures atom-by-atom and spin-by-spin. By itself. This AI-enabled probe microscope will then be used to carry out a programme of exceptionally challenging experiments whose common theme is the control of magnetism at the most fundamental levels: single domains, single molecules, single atoms, and single spins.

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  • Funder: NIH Project Code: 5R01DA028648-13
    Funder Contribution: 538,742 USD
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57 Projects
  • Funder: EC Project Code: 101000302
    Overall Budget: 7,919,410 EURFunder Contribution: 7,919,410 EUR

    The EcoScope project will develop an interoperable platform and a robust decision-making toolbox, available through a single public portal, to promote an efficient, ecosystem-based fisheries management. It will be guided by policy makers and scientific advisory bodies, and address ecosystem degradation and the anthropogenic impact that are causing fisheries to be unsustainably exploited across European Seas. The EcoScope Platform will organise and homogenise climatic, oceanographic, biogeochemical, biological and fisheries datasets for European Seas to a common standard type and format that will be available through interactive mapping layers. The EcoScope Toolbox, a scoring system based on assessments of all ecosystem components, ecosystem and economic models, will operate as a decision-support tool for examining fisheries management and marine policy scenarios and spatial planning simulations. Groups of end-users and stakeholders will be involved in the design, development and operation of both the platform and the toolbox. Novel assessment methods for data-poor fisheries, including non-commercial species, as well as for biodiversity and the conservation status of protected megafauna, will be used to assess the status of all ecosystem components across European Seas and test new technologies for evaluating the environmental, anthropogenic and climatic impact on ecosystems and fisheries. A series of sophisticated capacity building tools (online courses, webinars and games) will be available to stakeholders through the EcoScope Academy. The EcoScope project will provide an effective toolbox to decision makers and end-users that will be adaptive to their capacity, needs and data availability. The toolbox will incorporate methods for dealing with uncertainty; thus, it will promote efficient, holistic, sustainable, ecosystem-based fisheries management that will aid towards restoring fisheries sustainability and ensuring balance between food security and healthy seas.

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  • Funder: UKRI Project Code: NE/W001233/1
    Funder Contribution: 647,247 GBP

    This project addresses how environmental change affects the movement of sediment through rivers and into our oceans. Understanding the movement of suspended sediment is important because it is a vector for nutrients and pollutants, and because sediment also creates floodplains and nourishes deltas and beaches, affording resilience to coastal zones. To develop our understanding of sediment flows, we will quantify recent variations (1985-present) in sediment loads for every river on the planet with a width greater than 90 metres. We will also project how these river sediment loads will change into the future. These goals have not previously been possible to achieve because direct measurements of sediment transport through rivers have only ever been made on very few (<10% globally) rivers. We are proposing to avoid this difficulty by using a 35+ years of archive of freely available satellite imagery. Specifically, we will use the cloud-based Google Earth Engine to automatically analyse each satellite image for its surface reflectance, which will enable us to estimate the concentration of sediment suspended near the surface of rivers. In conjunction with other methods that characterise the flow and the mixing of suspended sediment through the water column, these new estimates of surface Suspended Sediment Concentration (SSC) will be used to calculate the total movement of suspended sediment through rivers. We then analyse our new database (which, with a five orders of magnitude gain in spatial resolution relative to the current state-of-the-art, will be unprecedented in its size and global coverage) of suspended sediment transport using novel Machine Learning techniques, within a Bayesian Network framework. This analysis will allow us to link our estimates of sediment transport to their environmental controls (such as climate, geology, damming, terrain), with the scale of the empirical analysis enabling a step-change to be obtained in our understanding of the factors driving sediment movement through the world's rivers. In turn, this will allow us to build a reliable model of sediment movement, which we will apply to provide a comprehensive set of future projections of sediment movement across Earth to the oceans. Such future projections are vital because the Earth's surface is undergoing a phase of unprecedented change (e.g., through climate change, damming, deforestation, urbanisation, etc) that will likely drive large transitions in sediment flux, with major and wide reaching potential impacts on coastal and delta systems and populations. Importantly, we will not just quantify the scale and trajectories of change, but we will also identify how the relative contributions of anthropogenic, climatic and land cover processes drive these shifts into the future. This will allow us to address fundamental science questions relating to the movement of sediment through Earth's rivers to our oceans, such as: 1. What is the total contemporary sediment flux from the continents to the oceans, and how does this total vary spatially and seasonally? 2. What is the relative influence of climate, land use and anthropogenic activities in governing suspended sediment flux and how have these roles changed? 3. How do physiographic characteristics (area, relief, connectivity, etc.) amplify or dampen sediment flux response to external (climate, land use, damming, etc) drivers of change and thus condition the overall response, evolution and trajectory of sediment flux in different parts of the world? 4. To what extent is the flux of sediment driven by extreme runoff generating events (e.g. Tropical Cyclones) versus more common, lower magnitude events? How will projected changes in storm frequency and magnitude affect the world's sediment fluxes in the future? 5. How will the global flux of sediment to the oceans change over the course of the 21st century under a range of plausible future environmental change scenarios?

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  • Funder: EC Project Code: 945234
    Overall Budget: 8,971,950 EURFunder Contribution: 3,997,240 EUR

    The ECC-SMART is oriented towards assessing the feasibility and identification of safety features of an intrinsically and passively safe small modular reactor cooled by supercritical water (SCW-SMR), taking into account specific knowledge gaps related to the future licensing process and implementation of this technology. The main objectives of the project are to define the design requirements for the future SCW-SMR technology, to develop the pre-licensing study and guidelines for the demonstration of the safety in the further development stages of the SCW-SMR concept including the methodologies and tools to be used and to identify the key obstacles for the future SMR licencing and propose a strategy for this process. To reach these objectives, specific technical knowledge gaps were defined and will be assessed to achieve the future smooth licensing and implementation of the SCW-SMR technology (especially the behaviour of materials in the SCW environment and irradiation, validation of the codes and design of the reactor core will be developed, evaluated by simulations and experimentally validated). The ECC-SMART project consortium consists of EU, Canadian and Chinese partners to use the trans-continental synergy and knowledge developed separately by each partner. The project consortium and project scope were created according to the joint research activities under the International Atomic Energy Agency, Generation-IV International Forum umbrella and as much data as possible will be taken from the already performed projects. This project brings together the best scientific teams working in the field of SCWR using the best facilities and methods worldwide, to fulfil the common vision of building an SCW-SMR in the near future.

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  • Funder: NIH Project Code: 2R01DA028648-11
    Funder Contribution: 538,742 USD
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  • Funder: NSF Project Code: 2202340
    Funder Contribution: 100,000 USD
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  • Funder: EC Project Code: 101003575
    Overall Budget: 15,924,000 EURFunder Contribution: 5,000,000 EUR

    ERA-MIN3 comprises a progressive, pan-European public-public partnership of 25 public research funding organisations from 19 European countries/regions and 3 third countries, which aims to continue strengthening the mineral raw materials (RM) community through the coordination of research and innovation (R&I) programmes on non-energy, non-agricultural raw materials (metallic, construction, and industrial minerals). ERA-MIN3 will thus contribute to the objectives of the EIP on Raw Material’s Strategic Implementation Plan and the EU Circular Economy Action Plan, in support of the EU Raw Materials Initiative, the UN sustainable development goals and the European Green Deal. Built on the successes of the previous ERA-MIN and ERA-MIN 2, and to ensure the EU’s resource security and sustainable supply of strategic RM to the European society, ERA-MIN3 will achieve its goals of improving synergy, coordination and coherence between regional, national and EU funding in the RM sector by reducing fragmentation of RM funding across Europe and globally, as well as, improving the use of human and financial resources, the competitiveness and the environmental, social, health and safety issues of RM operations through supporting of transnational, excellent and translational R&I activities. This will be achieved through a EU co-funded joint transnational call for R&I proposals and, at least, one additional call with participation of invited partners, on demand-driven R&I on primary and secondary resources, covering the entire value chain, from exploration, extraction and processing technologies to recycling and substitution of CRM, as well as, environmental and societal impact, new business models and/or public perception. ERA-MIN3 will liaison with RM related initiatives to ensure alignment of research topics (e.g. batteries), promote synergies and complementarities thus avoiding duplication of efforts and contributing for the circular economy and the sustainable development.

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  • Funder: UKRI Project Code: EP/S028730/1
    Funder Contribution: 1,046,720 GBP

    Colour Imaging is part of every day life. Whether we watch TV, browse content on our tablets or phones or use apps and software in our work the content we see on our screens is the result of decades of colour & imaging research. In the future, the challenge is to understand more about the content images. As an example, in autonomous driving we wish to build a platform that sees the road independent of the atmospheric conditions, we don't want to crash when we are driving in fog. It is well known that an image that records the near-infrared signal is much sharper (compared to RGB) in foggy conditions. What is near infrared? The visible spectrum has a natural rainbow order: Violet, Indigo, Blue, Green, Yellow Orange and Red. Infrared is the 'next colour' after red that we can't quite see. Image fusion can be used to map the RGB+NIR signal to a fused RGB counterpart, that we can see. Through image fusion the same detail will be present in foggy or non-foggy conditions. Advantageously, Image Fusion is a tool that will allow non visible information to be incorporated and deployed in existing RGB-based AI scene interpretation systems with minimal retraining. Our project begins with the Spectral Edge Image fusion method, the current leading technique. This method - and most image fusion algorithms - works by combining edges from the 4 images (RGB+NIR) to make a fused RGB-only 3-channel edge map. The edges are then transformed (the technical term is reintegrated) back to form a colour image. Unfortunately, and necessarily, the reintegrated images often have defects such as bright halos round edges or smearing. We argue that the defects are a direct consequence of how 'edges' are defined. In our research we will - based on a surprising mathematical insight - develop a new definition of edge, quite a bold thing to do after 50 years of image processing research! By construction the reintegrated new edges will have much less halo and smearing artefacts. We will then use our improved edge representation and improved image fusion algorithm to make better looking images. These might be the fused images themselves: wouldn't it be great to have smart binoculars that allow us to see more detail in images when it is rainy or a landscape that is blurred by distance. However, we also believe the future of photography, in general, is content-based and that image fusion will help us determine the content in an image. As an example, when we take a picture at sunset, the shadows in the scene are very blue. But, outside of the shadow the light is very warm (orangish). The best image reproductions for these scenes involves manually and differentially processing shadow and non shadow regions. Here, we seek to find the illumination content in image automatically. Then in a second step we will develop a new content-based framework for manipulating images so that, for this sunset example, we don't need to edit the photos ourselves. In complementary work, we are also interested in helping people see better. Indeed, there is a lot of research that demonstrates that coloured filters can help mitigate visual stress. Coloured filters are used in Dyslexia (sometimes leading to dramatic improvements in reading speed) and there is now blue absorbing glass which will reduces the blue light coming from a tablet display (since blue light at night tends to keep you awake). Much of the prior art in this area is 'direct'. We find a filter to directly impact on how we see (simply, if we put a yellow filter in front of the eye then everything looks more yellow). Our idea is to deign filters that are related to the tasks we need to solve. For the problem of matching colours we will design filters so that if you suffer from colour-blindness you will be able to colour match as if you had normal colour vision. We will also develop indirect solutions for the 'blue light' problem and visual stress.

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  • Funder: UKRI Project Code: NE/T00648X/1
    Funder Contribution: 1,581,310 GBP

    Clouds containing a mixture of ice and water (mixed-phase clouds) are likely to change in response to climate change. It is expected that warming will cause an increase in the amount of water and a decrease in the amount of ice in these clouds. Because water droplets reflect more solar radiation than ice crystals (and cause less precipitation), the clouds are expected to become brighter, thereby causing a cooling effect (or negative feedback) on the climate system at mid- to high-latitudes. The magnitude of the cloud-phase feedback is very uncertain. If the feedback is strong then global temperatures will increase more slowly in future, but if it is weak then temperatures will increase more rapidly. It was recently shown that adjusting the ratio of ice and water in a climate model to match satellite observations could increase Earth's equilibrium climate sensitivity (warming with a doubling of CO2) by 1.5 degrees; hence, Earth may warm faster than thought. The feedback process is further complicated by the fact that the special particles, ice nucleating particles (INPs), which trigger ice production, may be more abundant in a warmer world where INP sources, such as glacial valleys, will be covered in ice and snow for less time. Increased INP concentrations would mean more ice in clouds and lead to a positive feedback. These two opposing feedbacks contribute to what we refer to as the cloud-phase feedback. This proposal will improve our understanding of how ice particles form in clouds and how this affects the cloud-phase feedback. Ice formation is the key process that controls this feedback. The problem can be broken down into two parts: First, we will address the open questions related to the chain of processes that link initial ice formation to the reflectivity of the clouds and how the reflectivity will change with warming. We have designed an aircraft campaign targeted at conditions of most relevance to the cloud feedback problem: moderately cold clouds that will be most sensitive to changes in temperature, and where high INP concentrations are likely to influence large regions of the N Atlantic. These cold-air outbreaks clouds provide an ideal meteorological situation for studying the formation and evolution of the kinds of shallow mixed-phase clouds which are important for cloud feedbacks. Second, we will address the paucity of knowledge on the sources, distribution and seasonal cycles of INPs at the mid- to high-latitudes. Our strategy is to use measurements to identify sources of INPs and use this information to inform the inclusion of mid- to high-latitude sources in our global model of INPs. We will perform new long-term measurements through a whole year and ship borne measurements through the key source regions in the Arctic. We have built a substantial network of Partners who will contribute INP data across the northern and southern mid- to high-latitudes which will allow us to expand our study to the globe. The new knowledge on cloud processes and INP will be used to improve the representation of mixed-phase clouds in the Met Office weather and climate model. The model will be run at very high spatial resolution so that the individual clouds in the cold-air outbreaks can be simulated. The model will be tested and improved by comparing it to our measurements as well as against satellite observations. We will then extend this study to contrasting cases from the Southern Ocean and the other side of the Atlantic. We anticipate the new knowledge will lead to a greatly improved representation of these climatically critical clouds. We will then perform a sensitivity analysis on selected cases in order to test how these cloud systems will respond to climate change. Finally, we will use the new knowledge to develop a plan for improving how mixed-phase clouds are treated in global climate models so that this work can be carried out in a follow-on project.

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  • Funder: UKRI Project Code: EP/T033568/1
    Funder Contribution: 1,796,880 GBP

    There is nothing quite like the magic of magnets. And yet even Richard Feynman, an incredibly gifted science communicator, struggled to explain just how magnetism works. (The video in question is easily found on YouTube. Feynman's slight tetchiness with the interviewer who raises the subject of magnetic forces is not entirely unrelated to the difficulty in explaining their fundamental origin at a level that a non-physicist -- or, indeed, a physicist -- can readily grasp.) Scientists are now at the point, however, where not only can we measure forces on an atom-by-atom basis, but we can harness and exploit those self-same forces to manipulate magnetism right down to the atomic level (and beyond). The instrument that allows this exquisite level of control of magnetic forces is the scanning probe microscope. A technique that will shortly reach its fortieth birthday, probe microscopy is conceptually rather straight-forward -- its experimental realisation rather less so. An exceptionally sharp tip, terminated in a single atom or molecule, is brought extremely close to a surface such that the tip-surface separation is of the order of the diameter of an atom or less. This atomically sharp probe can then be used in a number of modes to explore, interrogate, and modify the underlying sample surface on an atom-by-atom basis. Some of the most exciting and ground-breaking science ever carried out has involved the scanning probe microscope's unparalleled ability to not only image, but manipulate, matter at the single atom level. Probe microscopes are not just limited to the imaging and control of atoms; they can go much further. With an appropriately modified tip apex, even the quantum mechanical spin of electrons -- which, ultimately, is the source of magnetism -- is detectable either via the tiny electrical current that flows between the probe and the sample, or, incredibly, via measurement of the minuscule magnetic force between single atoms. Just a couple of months ago (in Oct. 2019), Chris Lutz' group at the IBM Almaden Research Centre reported that they have achieved, in collaboration with researchers in Korea and Oxford, the most precise and coherent control of the spin state of individual atoms ever attempted with SPM. (It's worth noting that IBM is the birthplace of both the scanning probe microscope itself, which was invented by Binnig, Rohrer and co-workers in the Ruschlikon, Zurich research labs, and of SPM-driven single atom manipulation, due to the inspiring efforts of Don Eigler and colleagues at IBM Almaden.) But the deep, dark secret of the probe microscopist is that a very large percentage of their time is spent coercing and cajoling the probe into providing atomic resolution. Yet even that's not enough -- when that resolution is achieved, the microscopist very often has to maintain the ability to image, move, and spectroscopically interrogate single atoms at the same time, while always being on the look-out for tip-derived artefacts. The component at the core of probe microscopy -- the probe itself -- therefore represents a major, and infuriating, bottleneck in the technique. This project integrates artificial intelligence, surface science, and nanoscience to take the pain out of probe microscopy. We will develop a machine learning framework that, in essence, "auto focuses" a probe microscope and then takes the SPM to the point where it can learn how to build magnetic nanostructures atom-by-atom and spin-by-spin. By itself. This AI-enabled probe microscope will then be used to carry out a programme of exceptionally challenging experiments whose common theme is the control of magnetism at the most fundamental levels: single domains, single molecules, single atoms, and single spins.

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  • Funder: NIH Project Code: 5R01DA028648-13
    Funder Contribution: 538,742 USD
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