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The following results are related to Canada. Are you interested to view more results? Visit OpenAIRE - Explore.
51 Projects, page 1 of 6

  • Canada
  • UK Research and Innovation
  • UKRI|NERC
  • 2022

10
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  • Funder: UKRI Project Code: NE/X00662X/1
    Funder Contribution: 9,467 GBP
    Partners: UoC, University of Glasgow

    This research proposal aims to develop advanced power sources that can convert indoor light into electricity to operate electronic sensors for the internet of things (IoT) - an emerging trillion-dollar industry that impacts all human life. The proposed new technology is termed 'indoor photovoltaics'. The technology is based on current organic photovoltaics that can be made flexible, lightweight, rollable, semi-transparent and of different colours at an ultra-low dollar per-watt cost. Using new chemistry principles, photoactive materials design, device engineering, advanced printing and electrical connections, the project aims to deliver fully functional indoor power devices ready for market evaluation. The proposed concept is new and expected to have a broad impact on Canada's and the UK's energy, communication and manufacturing sectors. The proposed chemistries are unique and should lead to paradigm shifts in the view of molecular self-assembly of organic photoactive materials. The ability to fabricate fully printed devices and integrate them into circuits all at once is the key strength of this proposal and serves to immediately validate or invalidate specific materials and/or device designs to ensure objectives are met in a timely fashion. The development of prototypes at the University level enables faster innovations and will allow this technology to bridge the infamous "valley-of-death" laboratory to market transition. The iOPV technology embodies a new paradigm in photovoltaics fabrication using solution-processable materials that can be delivered under ambient conditions (much like ink printed on paper). The simple additive manufacturing process mitigates CO2 production by requiring significantly less energy than traditional lithography-based methods. In addition, the potential for large scale roll-to-roll processing requires only a small capital investment, allowing for localised manufacturing. Printing equipment can tremendously reduce human interaction and the labour required for mass production. Thus, this can promote cost-effective local manufacturing for electronic devices.

  • Funder: UKRI Project Code: NE/W001233/1
    Funder Contribution: 647,247 GBP
    Partners: University of Southampton, EA, Stantec, AUS (United States), University of Rennes 1, Unesco IHE, CECOAL, University of Cambridge, Dartmouth College, Arup Group Ltd...

    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?

  • Funder: UKRI Project Code: NE/X007650/1
    Funder Contribution: 9,533 GBP
    Partners: University of Glasgow, McMaster University

    MRC : Erik Igelström : MC_UU_00022/2 People who live in countries with low inequality (where the gap between the rich and the poor is small) tend to be healthier on average. Countries with low inequality (like Finland and Denmark) also tend to have a lot of social mobility across generations. In other words, people's chances of success don't depend on how well off their parents were. But this isn't always the case. For example, Canada has high social mobility, but also high inequality. We don't know much about how these two factors work together to affect health: Does Canada's high social mobility compensate for the negative impact of high inequality? To help understand this, I will compare parts of Canada, and look at whether inequality seems to matter less when there is a lot of social mobility. To do this, I will first need to calculate the amount of social mobility in different metropolitan areas. To make studies like this easier in the future, I will make the social mobility data available to other researchers.

  • Funder: UKRI Project Code: NE/X006662/1
    Funder Contribution: 11,268 GBP
    Partners: KCL, UBC

    MRC : Lydia Daniels Gatward : MR/N013700/1 Diabetes is a disease affecting around 10% of the population which is caused by an inability to regulate blood glucose levels. Sex differences exist in diabetes; men have increased incidence of type 2 diabetes compared to women, an effect which is lost after the menopause. Blood glucose levels are normally tightly controlled by insulin release from beta cells in the pancreas, a process which involves calcium signalling. Dysregulated calcium signalling in these cells disrupts insulin release and has been associated with the development of diabetes. Considering the sex differences in diabetes and findings that other tissues show sex differences in calcium handling, we want to investigate whether sex differences exist in calcium handling in beta cells. To do this, we plan to use state-of-the-art imaging technology to study calcium signalling in two of the main calcium storage compartments in beta cells isolated from non-diabetic and diabetic male and female mice. Understanding these pathways in more detail could help determine whether the sex of the patient should be more carefully considered when choosing treatments for diabetes.

  • Funder: UKRI Project Code: NE/X007472/1
    Funder Contribution: 10,449 GBP
    Partners: Carleton University, University of London

    BBSRC : Regan Harle : BB/T008709/1 Drugs act by binding to a target protein. Determining the three-dimensional shape the complex between a drug and a protein is challenging, particularly when the protein spans the membrane separating the interior and exterior of a cell. Cryo-electron microscopy allows these structures to be measured experimentally, but often the resolution is too low to aid the development of new drugs. We will combine this technique with artificial intelligence protein structure determination and computer simulations to determine the structures of drug-bound structures with minimal direction by the user but with a level of resolution that had not been possible in the past. I will be trained in machine learning, molecular simulation, and artificial-intelligence protein structure prediction.

  • Funder: UKRI Project Code: NE/X008347/1
    Funder Contribution: 7,747 GBP
    Partners: University of Montreal, University of London

    EPSRC : Max Hird : EP/T517793/1 Algorithms that learn and sample from probability distributions form an important part of machine learning, AI, and the natural sciences. One needn't look far to find such algorithms at the bleeding edge of methodology, and in everyday scientific pursuit. The Wang-Landau algorithm is an example. It combines a sampling step with a learning step, to learn a probability distribution about which our knowledge is limited. The probability distribution may be over physical states, so an efficiently running algorithm would allow the simulation of the dynamics of protein folding, for instance. The learning step incorporates information gained from the sampling step, forming a more complete picture of the distribution. The particular form of the learning step is foundational in many neural networks and is called stochastic approximation. Due to our incomplete knowledge of the distribution, we cannot apply standard sampling methods. We therefore need to employ a more exotic sampler. Coupling exotic samplers alongside stochastic approximation is underexplored, and potentially fruitful. We will try to assess the behaviour of such a coupling, an assessment not yet existing in the literature.

  • Funder: UKRI Project Code: NE/X00743X/1
    Funder Contribution: 10,902 GBP
    Partners: University of Edinburgh, University of Toronto

    EPSRC : Vesela Zarkina : EP/R513209/1 This project will develop new abundant-element systems for catalytically relevant chemistry. It will establish phosphorus/boron containing alternatives of a key class of compounds called allenes. The development of abundant-element chemistry, capable of the powerful synthetic transformations, traditionally enabled by precious metals, is key to reducing environmental damage and meeting the global economy requirements. This placement aims to overcome a central disadvantage of main-group element containing systems compared to precious metal ones: their limited coordination sites for chemical activation. This will be achieved by the synthesis of phosphorus/boron containing allenes which will be used for the chemical activation of small molecules such as hydrogen and carbon monoxide. This will provide fundamental knowledge required for further advances in metal-free catalysis and establish a new collaboration between two of the top main-group chemistry researchers leading the Stephan group at the University of Toronto and the Cowley group at the University of Edinburgh.

  • Funder: UKRI Project Code: NE/W00948X/1
    Funder Contribution: 937,801 GBP
    Partners: GT Energy, NERC British Geological Survey, Applied Seismology Consulting Ltd., BP International Limited, GFZ German Research, UoC, Imperial College London, University of Bergen, Geothermal Engineering Limited, Storegga...

    Green-energy transition technologies such as carbon storage, geothermal energy extraction, hydrogen storage, and compressed-air energy storage, all rely to some extent on subsurface injection or extraction of fluids. This process of injection and retrieval is well known to industry, as it has been performed all over the world, for decades. Fluid injection processes create mechanical disturbances in the subsurface, leading to local or regional displacements that may result in tremors. In the vast majority of cases, these tremors are imperceptible to humans, and have no effect on engineered structures. Nonetheless, in recent years, low magnitude induced seismic events have had profound consequences on the social acceptance of subsurface technologies, including the halting of natural gas production at the Groningen field in the Netherlands, halting of carbon storage experiments in Spain, halting of geothermal energy projects in Switzerland, and the moratorium on UK onshore gas extraction. In light of the seismic events of increasing severity recently measured during geothermal mining in Cornwall, the need to develop a rigorous fundamental understanding of induced seismicity is clear, significant, and timely, in order to prevent induced seismicity from jeopardising the ability to effectively develop the green energy transition. Most mathematical models that are used to predict and understand tremors rely on past observations of natural tremors and earthquakes. However, fluid-driven displacement in the subsurface is a controlled event, in which the properties of the injected fluids and the conditions of injection can be adjusted and optimised to avoid large events from happening. This project aims to develop a fundamental understanding of how the conditions of subsurface rocks, and the way in which fluid is injected in these rocks, affect the amount of seismicity that may be produced. We will analyse in detail the behaviour of fluid-driven seismic events, and will develop a physically realistic model based on computer simulations, novel laboratory experiments, and comprehensive field observations. Our model will characterise the relationships between specific subsurface properties, the nature of the fluid injection, and the severity of the seismic event. These findings will be linked to hazard analysis, to identify the conditions under which processes such as carbon storage, deep geothermal energy extraction, and compressed-air energy storage, are more or less likely to create tremors. We will also investigate how to best share our scientific findings with regulators and the general public, so as to maximise the impact of this work. This project will lead to an improved understanding of the processes and conditions that underpin the severity of induced seismic events, with a vision of developing strategies that will improve our ability to prevent and control these events. This project will also provide the scientific basis to improve precision and cost-effectiveness of scientific instruments that are used to monitor the subsurface, so that we can identify tremors as they occur, and better interpret what is causing them as we observe them. In the short term, we need to develop these models so that regulators and decision-makers can develop policies based on scientific evidence, using a variety of analysis tools that inter-validate each other, thereby ensuring that their predictions are robust. This is an important step in supporting the ability of developing a resilient, diversified, sustainable, and environmentally responsible energy security strategy for the UK. In the long term, by creating confidence in the understanding of these subsurface events, and demonstrating evidence of our ability to control them, we will lead the UK into an era where humans understand why certain seismic events have occurred, allowing them to potentially develop mechanisms to forecast their occurrence, and reduce their severity.

  • Funder: UKRI Project Code: NE/X006735/1
    Funder Contribution: 12,489 GBP
    Partners: Newcastle University, UBC

    NERC: James Carruthers: NE/S007512/1 Cities need to adapt to climate change in order to prevent extreme impacts. However, the way in which adaptation measures are put in place may have unintended consequences since they increase the value of land around them and may be targeted in currently inexpensive areas. Therefore, these measures could displace vulnerable people into areas which have fewer adaptation measures and are therefore more exposed to climate change, a process known as climate gentrification. This is an under-researched topic and the drivers and impacts of climate gentrification in terms of aggregate risk are unclear. This project will investigate these processes and develop scenarios of how climate gentrification could evolve in the future, using socio-economic and climate data. These scenarios will provide a better understanding of how vulnerabilities may change in the future and will inform climate impact studies.

  • Funder: UKRI Project Code: NE/X006557/1
    Funder Contribution: 9,075 GBP
    Partners: Ryerson University, University of Bath

    Unmanned systems are growing fast, and there is an urgent need to improve the robustness and efficiency of such systems. Quadrotors are one prime example, which can be used in a variety of different domains. This includes infrastructure inspection, disaster management, search and rescue, precise agriculture, and package delivery. The government has shown a huge interest in autonomous vehicles. The release of the Future of Transport: rural strategy highlights the opportunities for drones to make deliveries in rural or isolated towns and to help reduce pollution. Furthermore, reports have shown the self-driving vehicle industry to be worth nearly £42 billion by 2035. Autonomous vehicles rely on highly accurate localization and mapping techniques which can be very difficult in cluttered and dynamic scenes. Dead-reckoning based methods which rely on previous estimates work in these scenarios but fall victim to propagated error which leads to inaccuracies in the long run. This has led to research in the loop closure which utilizes previously seen landmarks to re-localize the vehicle. The most common form of self-localization within autonomous vehicles comes from Simultaneous Localization and Mapping, which is a technique that utilizes detected landmarks and control inputs to estimate the position and orientation of the vehicle within a generated map. The assumption of static landmarks however still provides an issue within the previously mentioned dynamic environments, as static landmarks are needed to be filtered from dynamic landmarks. Dynamic-SLAM methods modify the existing method by providing this filtering technique but still lack robustness when dynamic objects fill up the majority of the environment. We hope to tackle this problem using data-driven approaches. Reinforcement learning has been shown as a viable solution for navigation within mapless and dynamic environments. We hope to train the reinforcement learning agent, through a series of simulation environments, the ability to navigate in a dynamic and cluttered environment using onboard camera depth sensors. Building on work already done but that would not have been able to take place during the PhD. An experimental quadrotor has already been developed and we hope to utilize this within Ryerson University's drone arena to validate the proposed hypothesis. The key outputs of this project will be the development of reinforcement learning techniques to navigate within a mapless environment to aid with the mapping process in a dynamic scene. This novel technique provides an alternative solution to the current advances in dynamic-SLAM. We hope that reinforcement learning-based techniques will improve dynamic-SLAM's ability to be utilized. Furthermore, such a technical solution can be easily applied to industrial applications and is supposed to, in practice, fill the gap between autonomous control and popular artificial intelligence techniques We believe that the proposed research brings the strength of robotics research from our partners in Canada to significantly improve the accessibility of AI techniques in autonomous robotics, and further strengthen the UK's role as the global leader in the creation of industrial autonomy solutions. Such a role aligns with the current UK research roadmap, with at least £800 million to ensure the UK can gain a competitive advantage in the creation of artificial intelligence and industrial autonomy.

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The following results are related to Canada. Are you interested to view more results? Visit OpenAIRE - Explore.
51 Projects, page 1 of 6
  • Funder: UKRI Project Code: NE/X00662X/1
    Funder Contribution: 9,467 GBP
    Partners: UoC, University of Glasgow

    This research proposal aims to develop advanced power sources that can convert indoor light into electricity to operate electronic sensors for the internet of things (IoT) - an emerging trillion-dollar industry that impacts all human life. The proposed new technology is termed 'indoor photovoltaics'. The technology is based on current organic photovoltaics that can be made flexible, lightweight, rollable, semi-transparent and of different colours at an ultra-low dollar per-watt cost. Using new chemistry principles, photoactive materials design, device engineering, advanced printing and electrical connections, the project aims to deliver fully functional indoor power devices ready for market evaluation. The proposed concept is new and expected to have a broad impact on Canada's and the UK's energy, communication and manufacturing sectors. The proposed chemistries are unique and should lead to paradigm shifts in the view of molecular self-assembly of organic photoactive materials. The ability to fabricate fully printed devices and integrate them into circuits all at once is the key strength of this proposal and serves to immediately validate or invalidate specific materials and/or device designs to ensure objectives are met in a timely fashion. The development of prototypes at the University level enables faster innovations and will allow this technology to bridge the infamous "valley-of-death" laboratory to market transition. The iOPV technology embodies a new paradigm in photovoltaics fabrication using solution-processable materials that can be delivered under ambient conditions (much like ink printed on paper). The simple additive manufacturing process mitigates CO2 production by requiring significantly less energy than traditional lithography-based methods. In addition, the potential for large scale roll-to-roll processing requires only a small capital investment, allowing for localised manufacturing. Printing equipment can tremendously reduce human interaction and the labour required for mass production. Thus, this can promote cost-effective local manufacturing for electronic devices.

  • Funder: UKRI Project Code: NE/W001233/1
    Funder Contribution: 647,247 GBP
    Partners: University of Southampton, EA, Stantec, AUS (United States), University of Rennes 1, Unesco IHE, CECOAL, University of Cambridge, Dartmouth College, Arup Group Ltd...

    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?

  • Funder: UKRI Project Code: NE/X007650/1
    Funder Contribution: 9,533 GBP
    Partners: University of Glasgow, McMaster University

    MRC : Erik Igelström : MC_UU_00022/2 People who live in countries with low inequality (where the gap between the rich and the poor is small) tend to be healthier on average. Countries with low inequality (like Finland and Denmark) also tend to have a lot of social mobility across generations. In other words, people's chances of success don't depend on how well off their parents were. But this isn't always the case. For example, Canada has high social mobility, but also high inequality. We don't know much about how these two factors work together to affect health: Does Canada's high social mobility compensate for the negative impact of high inequality? To help understand this, I will compare parts of Canada, and look at whether inequality seems to matter less when there is a lot of social mobility. To do this, I will first need to calculate the amount of social mobility in different metropolitan areas. To make studies like this easier in the future, I will make the social mobility data available to other researchers.

  • Funder: UKRI Project Code: NE/X006662/1
    Funder Contribution: 11,268 GBP
    Partners: KCL, UBC

    MRC : Lydia Daniels Gatward : MR/N013700/1 Diabetes is a disease affecting around 10% of the population which is caused by an inability to regulate blood glucose levels. Sex differences exist in diabetes; men have increased incidence of type 2 diabetes compared to women, an effect which is lost after the menopause. Blood glucose levels are normally tightly controlled by insulin release from beta cells in the pancreas, a process which involves calcium signalling. Dysregulated calcium signalling in these cells disrupts insulin release and has been associated with the development of diabetes. Considering the sex differences in diabetes and findings that other tissues show sex differences in calcium handling, we want to investigate whether sex differences exist in calcium handling in beta cells. To do this, we plan to use state-of-the-art imaging technology to study calcium signalling in two of the main calcium storage compartments in beta cells isolated from non-diabetic and diabetic male and female mice. Understanding these pathways in more detail could help determine whether the sex of the patient should be more carefully considered when choosing treatments for diabetes.

  • Funder: UKRI Project Code: NE/X007472/1
    Funder Contribution: 10,449 GBP
    Partners: Carleton University, University of London

    BBSRC : Regan Harle : BB/T008709/1 Drugs act by binding to a target protein. Determining the three-dimensional shape the complex between a drug and a protein is challenging, particularly when the protein spans the membrane separating the interior and exterior of a cell. Cryo-electron microscopy allows these structures to be measured experimentally, but often the resolution is too low to aid the development of new drugs. We will combine this technique with artificial intelligence protein structure determination and computer simulations to determine the structures of drug-bound structures with minimal direction by the user but with a level of resolution that had not been possible in the past. I will be trained in machine learning, molecular simulation, and artificial-intelligence protein structure prediction.

  • Funder: UKRI Project Code: NE/X008347/1
    Funder Contribution: 7,747 GBP
    Partners: University of Montreal, University of London

    EPSRC : Max Hird : EP/T517793/1 Algorithms that learn and sample from probability distributions form an important part of machine learning, AI, and the natural sciences. One needn't look far to find such algorithms at the bleeding edge of methodology, and in everyday scientific pursuit. The Wang-Landau algorithm is an example. It combines a sampling step with a learning step, to learn a probability distribution about which our knowledge is limited. The probability distribution may be over physical states, so an efficiently running algorithm would allow the simulation of the dynamics of protein folding, for instance. The learning step incorporates information gained from the sampling step, forming a more complete picture of the distribution. The particular form of the learning step is foundational in many neural networks and is called stochastic approximation. Due to our incomplete knowledge of the distribution, we cannot apply standard sampling methods. We therefore need to employ a more exotic sampler. Coupling exotic samplers alongside stochastic approximation is underexplored, and potentially fruitful. We will try to assess the behaviour of such a coupling, an assessment not yet existing in the literature.

  • Funder: UKRI Project Code: NE/X00743X/1
    Funder Contribution: 10,902 GBP
    Partners: University of Edinburgh, University of Toronto

    EPSRC : Vesela Zarkina : EP/R513209/1 This project will develop new abundant-element systems for catalytically relevant chemistry. It will establish phosphorus/boron containing alternatives of a key class of compounds called allenes. The development of abundant-element chemistry, capable of the powerful synthetic transformations, traditionally enabled by precious metals, is key to reducing environmental damage and meeting the global economy requirements. This placement aims to overcome a central disadvantage of main-group element containing systems compared to precious metal ones: their limited coordination sites for chemical activation. This will be achieved by the synthesis of phosphorus/boron containing allenes which will be used for the chemical activation of small molecules such as hydrogen and carbon monoxide. This will provide fundamental knowledge required for further advances in metal-free catalysis and establish a new collaboration between two of the top main-group chemistry researchers leading the Stephan group at the University of Toronto and the Cowley group at the University of Edinburgh.

  • Funder: UKRI Project Code: NE/W00948X/1
    Funder Contribution: 937,801 GBP
    Partners: GT Energy, NERC British Geological Survey, Applied Seismology Consulting Ltd., BP International Limited, GFZ German Research, UoC, Imperial College London, University of Bergen, Geothermal Engineering Limited, Storegga...

    Green-energy transition technologies such as carbon storage, geothermal energy extraction, hydrogen storage, and compressed-air energy storage, all rely to some extent on subsurface injection or extraction of fluids. This process of injection and retrieval is well known to industry, as it has been performed all over the world, for decades. Fluid injection processes create mechanical disturbances in the subsurface, leading to local or regional displacements that may result in tremors. In the vast majority of cases, these tremors are imperceptible to humans, and have no effect on engineered structures. Nonetheless, in recent years, low magnitude induced seismic events have had profound consequences on the social acceptance of subsurface technologies, including the halting of natural gas production at the Groningen field in the Netherlands, halting of carbon storage experiments in Spain, halting of geothermal energy projects in Switzerland, and the moratorium on UK onshore gas extraction. In light of the seismic events of increasing severity recently measured during geothermal mining in Cornwall, the need to develop a rigorous fundamental understanding of induced seismicity is clear, significant, and timely, in order to prevent induced seismicity from jeopardising the ability to effectively develop the green energy transition. Most mathematical models that are used to predict and understand tremors rely on past observations of natural tremors and earthquakes. However, fluid-driven displacement in the subsurface is a controlled event, in which the properties of the injected fluids and the conditions of injection can be adjusted and optimised to avoid large events from happening. This project aims to develop a fundamental understanding of how the conditions of subsurface rocks, and the way in which fluid is injected in these rocks, affect the amount of seismicity that may be produced. We will analyse in detail the behaviour of fluid-driven seismic events, and will develop a physically realistic model based on computer simulations, novel laboratory experiments, and comprehensive field observations. Our model will characterise the relationships between specific subsurface properties, the nature of the fluid injection, and the severity of the seismic event. These findings will be linked to hazard analysis, to identify the conditions under which processes such as carbon storage, deep geothermal energy extraction, and compressed-air energy storage, are more or less likely to create tremors. We will also investigate how to best share our scientific findings with regulators and the general public, so as to maximise the impact of this work. This project will lead to an improved understanding of the processes and conditions that underpin the severity of induced seismic events, with a vision of developing strategies that will improve our ability to prevent and control these events. This project will also provide the scientific basis to improve precision and cost-effectiveness of scientific instruments that are used to monitor the subsurface, so that we can identify tremors as they occur, and better interpret what is causing them as we observe them. In the short term, we need to develop these models so that regulators and decision-makers can develop policies based on scientific evidence, using a variety of analysis tools that inter-validate each other, thereby ensuring that their predictions are robust. This is an important step in supporting the ability of developing a resilient, diversified, sustainable, and environmentally responsible energy security strategy for the UK. In the long term, by creating confidence in the understanding of these subsurface events, and demonstrating evidence of our ability to control them, we will lead the UK into an era where humans understand why certain seismic events have occurred, allowing them to potentially develop mechanisms to forecast their occurrence, and reduce their severity.

  • Funder: UKRI Project Code: NE/X006735/1
    Funder Contribution: 12,489 GBP
    Partners: Newcastle University, UBC

    NERC: James Carruthers: NE/S007512/1 Cities need to adapt to climate change in order to prevent extreme impacts. However, the way in which adaptation measures are put in place may have unintended consequences since they increase the value of land around them and may be targeted in currently inexpensive areas. Therefore, these measures could displace vulnerable people into areas which have fewer adaptation measures and are therefore more exposed to climate change, a process known as climate gentrification. This is an under-researched topic and the drivers and impacts of climate gentrification in terms of aggregate risk are unclear. This project will investigate these processes and develop scenarios of how climate gentrification could evolve in the future, using socio-economic and climate data. These scenarios will provide a better understanding of how vulnerabilities may change in the future and will inform climate impact studies.

  • Funder: UKRI Project Code: NE/X006557/1
    Funder Contribution: 9,075 GBP
    Partners: Ryerson University, University of Bath

    Unmanned systems are growing fast, and there is an urgent need to improve the robustness and efficiency of such systems. Quadrotors are one prime example, which can be used in a variety of different domains. This includes infrastructure inspection, disaster management, search and rescue, precise agriculture, and package delivery. The government has shown a huge interest in autonomous vehicles. The release of the Future of Transport: rural strategy highlights the opportunities for drones to make deliveries in rural or isolated towns and to help reduce pollution. Furthermore, reports have shown the self-driving vehicle industry to be worth nearly £42 billion by 2035. Autonomous vehicles rely on highly accurate localization and mapping techniques which can be very difficult in cluttered and dynamic scenes. Dead-reckoning based methods which rely on previous estimates work in these scenarios but fall victim to propagated error which leads to inaccuracies in the long run. This has led to research in the loop closure which utilizes previously seen landmarks to re-localize the vehicle. The most common form of self-localization within autonomous vehicles comes from Simultaneous Localization and Mapping, which is a technique that utilizes detected landmarks and control inputs to estimate the position and orientation of the vehicle within a generated map. The assumption of static landmarks however still provides an issue within the previously mentioned dynamic environments, as static landmarks are needed to be filtered from dynamic landmarks. Dynamic-SLAM methods modify the existing method by providing this filtering technique but still lack robustness when dynamic objects fill up the majority of the environment. We hope to tackle this problem using data-driven approaches. Reinforcement learning has been shown as a viable solution for navigation within mapless and dynamic environments. We hope to train the reinforcement learning agent, through a series of simulation environments, the ability to navigate in a dynamic and cluttered environment using onboard camera depth sensors. Building on work already done but that would not have been able to take place during the PhD. An experimental quadrotor has already been developed and we hope to utilize this within Ryerson University's drone arena to validate the proposed hypothesis. The key outputs of this project will be the development of reinforcement learning techniques to navigate within a mapless environment to aid with the mapping process in a dynamic scene. This novel technique provides an alternative solution to the current advances in dynamic-SLAM. We hope that reinforcement learning-based techniques will improve dynamic-SLAM's ability to be utilized. Furthermore, such a technical solution can be easily applied to industrial applications and is supposed to, in practice, fill the gap between autonomous control and popular artificial intelligence techniques We believe that the proposed research brings the strength of robotics research from our partners in Canada to significantly improve the accessibility of AI techniques in autonomous robotics, and further strengthen the UK's role as the global leader in the creation of industrial autonomy solutions. Such a role aligns with the current UK research roadmap, with at least £800 million to ensure the UK can gain a competitive advantage in the creation of artificial intelligence and industrial autonomy.