An inadequate blood supply to the heart, often causing chest pain during exercise, is typically caused by narrowing of the blood vessels that supply the heart muscle. However, in around half of these patients no severe narrowing is visible in pictures of the vessels and the pain is often due to disease in the microscopic blood vessels of the heart. This condition is known as microvascular dysfunction. We will develop a new type of MRI scan known as STEAM-tIVIM that provides information on both the blood flow in the microscopic vessels and the microscopic structure of the heart muscle. This method detects the random movement of water molecules as they move around inside and outside the cells of our bodies. As the microscopic vessels of the heart have many twists and turns in a short distance, the movement of blood appears random and can also be detected with STEAM-tIVIM. We will be able to detect the direction of blood flow and the direction that the brick-like muscle cells are pointing in. No other method exists that provides this information without removing a piece of the heart and studying it under a microscope. Our MRI technique uses no radiation or injection of dyes. We will assess how sensitive our MRI scan is to changes in blood flow by adding microscopic blood vessels to a computer model of the heart muscle on a microscopic scale that we have developed. This model will tell us what the smallest change in blood flow that we will be able to detect using our scans is and what the scanner settings for the highest sensitivity will be. We will programme the MRI scanner to collect the data and turn this stream of numbers into the pictures showing how measures such as the flow of blood within microscopic blood vessels, how much blood is in the vessels and the direction of the blood vessels vary across the heart. From these same scans, other pictures will show how the heart muscle cells are aligned. Our programmes will be tested using our computer model, then by scanning test objects (bottles of water-based liquids) and then 10 volunteers to check how well the scans work in a beating heart. These scans take around 1 hour, there is no radiation and we monitor the heartbeat using an ECG to allow us scan in the part of the heartbeat when the heart is moving least. To confirm how sensitive the MRI scan pictures are to changes in the flow of blood through the microscopic blood vessels we will scan pigs' hearts. The hearts come from butchered pigs and would otherwise be thrown away. We will pump blood-like liquid through the vessels of the pig hearts while we scan. By varying the flow of liquid we can check how sensitive our methods are. We will also add a medicine to the liquid which makes arteries wider in healthy hearts, mimicking exercise to check that we can detect the extra blood in the heart with our MRI scan pictures when we give this medicine. Our new MRI scan will be compared to another type of MRI scan that is available at the moment, but needs injection of a dye into the heart so is not possible in some patients. Scientists are also concerned about the build-up of this dye in the body when patients have many scans using it. Finally, we will check that the MRI scan can detect changes in blood flow in the heart muscle of patients with microvascular dysfunction. Many patients who doctors think have microvascular dysfunction have MRI scans as part of their normal care and we will invite them to come back for a second scan. In this second scan, we will run our new STEAM-tIVIM method twice, once while the patient is injected with the medicine used to simulate exercise. We will scan 20 patients and scan the same number of volunteers of a similar age and male to female ratio as the patients. We believe that when we simulate exercise, the increase in blood flow to the heart muscle measured in patients will be smaller than in the volunteers.

Enterprises and government entities have a growing need for systems that provide decision support based on descriptive and predictive analytics over large volumes of data. Examples include supporting decisions on pricing and promotions based on analyses of revenue and demand data; supporting decisions on the operation of complex equipment based on analyses of sensor data; and supporting decisions on website content based on analyses of user behaviour. Such support may be critical for safety and regulatory compliance as well as for competitiveness. Current data analytics technology and workflows are well-suited to settings where the data has a uniform structure and is easy to access. Problems can arise, however, when performing data analytics in real-world settings, where as well as being large, datasources are often distributed, heterogeneous, and dynamic. Consider, for example, the case of Siemens Energy Services, which runs over 50 service centres, each of which provides remote monitoring and diagnostics for thousands of gas/steam turbines and ancillary equipment located in hundreds of power plants. Effective monitoring and diagnosis is essential for maintaining high availability of equipment and avoiding costly failures. A typical descriptive analytics procedure might be: "based on sensor data from an SGT-400 gas turbine, detect abnormal vibration patterns during the period prior to the shutdown and compare them with data on similar patterns in similar turbines over the last 5 years". Such diagnostic tasks employ sophisticated data analytics tools, and operate on many TBs of current and historical data. In order to perform the analysis it is first necessary to identify, acquire and transform the relevant data. This data may be stored on-site (at a power-plant), at the local service centre or at other service centres; it comes in a wide range of different formats, ranging from flat files to XML and relational stores; access may be via a range of different interfaces, and incur a range of different costs; and it is constantly being augmented, with new data arriving at a rate of more than 30 GB per centre per day. Acquiring the relevant data is thus very challenging, and is typically achieved via a combination of complex queries and bespoke data processing code, with numerous variants being required in order to deal with distribution and heterogeneity of the data. Given the large number of different analytics tasks that service centres need to perform, the development and maintenance of such procedures becomes a critical bottleneck. In ED3 we will address this problem by developing an abstraction layer that mediates between analytics tools and datasources. This abstraction layer will adapt Ontology Based Data Access (OBDA) techniques, using an ontology to provide a uniform conceptual schema, declarative mappings to establish connections between ontological terms and data sources, and logic-based rewriting techniques to transform ontological queries into queries over the data sources. For OBDA to be effective in this new setting, however, it will need to be extended in several different directions. Firstly, it needs to provide greatly extended support for basic arithmetic and aggregation operations. Secondly, it needs to deal more effectively with heterogeneous and distributed data sources. Thirdly, it will be necessary to support the development, maintenance and evolution of suitable ontologies and mappings. In ED3 we will address all of these issues, laying the foundations for a new generation of data access middleware with the conceptual modelling, query processing, and rapid-development infrastructure necessary to support analytic tasks. Moreover, we will develop a prototypical implementation of a suitable abstraction layer, and will evaluate our prototype in real-life deployments with our industrial partners.

Hearing aids can profoundly transform the lives of people with hearing impairments but in the UK alone about 6 million such people do not use them. An important reason for this is that conventional hearing aids don't make it easy to distinguish meaningful signals such as speech from background noise. The combination of a fully functioning ear and the brain are fantastically good at this job and easily outperform the best computerized speech-recognition apps when even just a small amount of noise is present. A conventional hearing aid amplifies both the speech and noise indiscriminately, so even though the neural pathways of the brain may be unimpaired the task of distinguishing speech from noise becomes much harder. Various approaches to automatic speech enhancement have been tried but none comes close to what nature can do. Despite the demand for better solutions, recent progress in research and development has been slow and no breakthrough technology has yet emerged. Speech enhancement strategies have been generally developed on the basis of mathematical, but not physiological principles. These methods, although based on fundamentally different strategies, have two things in common: first, they operate on signal features that rely primarily on the signals' local energy, and second, they have not improved speech intelligibility. Here, we propose overcoming this conceptual barrier by developing engineering solutions to the speech-in-noise problem that are based on physiological principles. The same technology will also be of benefit for automatic speech recognition systems, since the problems of both are similar. We expect to see direct applications of our work to be implemented in hearing aids within the next 5 years. Two of the biggest companies in the world in their field (Siemens for hearing aids and Google for signal processing) demonstrate the interest, support and the confidence toward this approach. We have substantial experience with the whole development cycle: we have invented, designed, evaluated and implemented a noise reduction scheme that will be part of the next generation of Cochlea Ltd. cochlear implants. Our central hypothesis in this proposal is that the brain uses sparse coding when distinguishing meaningful signals from noise and it uses a dynamic dictionary for sound representation. We are going to investigate this coding mechanism in individual neurons in the auditory brainstem, and based on the results, will develop novel signal-processing strategies. We expect that these algorithms will be better than conventional algorithms and consequently can help hearing impaired. An animal model is essential to this project, because it is impossible to study responses of individual neurons from the auditory brainstem in humans. Sparse Neuron adapt their response because they have a limited dynamic rate which they constantly optimize in response to the environment in order to reduce redundancy and to maximise the information flow. In this project, we are going to extend the description of static neural response patterns to include a time varying and context sensitive components and we will measure these dynamic responses in single neurons in the brain stem. Knowing how neuronal responses change in noise will enable us to create a dynamic dictionary that can be used for sparsification. We expect that in this representation speech and noise is separable. We will use these dynamic response pattern as the basis of a novel transformation and sparsification in order to enhance the components of speech that are relevant for understanding, thus improving speech intelligibility without reducing the quality. We will evaluate the algorithm in substantial clinical trials.

The scale of the investment (in power generation, transmission and charging infrastructure) that is required to support the widespread adoption of Electric Vehicles (EVs) is massive. This, combined with natural delays associated with fleet turnover and consumer acceptance and adoption of new technology, suggests that the transition to a predominantly grid-supplied EV fleet will be gradual and often infrastructure-limited. This Prosperity Partnership proposes a new and faster route to full fleet electrification. We propose to develop a Thermal Propulsion System (TPS) that, combined with a matched hybrid energy recovery system, will be capable of powering an EV from an energy dense liquid fuel at the same or lower economic and environmental cost than would be incurred by importing electricity to the vehicle from the grid. By utilising a globally established refuelling network of proven capacity, the TPS technology that will be delivered by this partnership will enable the widespread adoption of zero-emissions capable, electrically driven, vehicles ahead of the required infrastructure developments of the grid-dependent Battery Electric Vehicle (BEV) and the hydrogen Fuel Cell Electric Vehicle (FCEV). This will lighten the burden on the UK's electricity generating capacity and distribution network as BEV and FCEV usage increases, allowing valuable time for the required development of grid and charging infrastructures while simultaneously providing an option for low carbon transport at times of low renewable input to the grid. This work is of substantial national importance to the UK's manufacturing sector. The research will protect the role of the TPS, and the UK's well-established engine manufacturing expertise, within the rapidly growing low-emission vehicle sector of the automotive market. The UK government predict that the global market for these low-emissions vehicles could be worth £1.0-2.0 trillion per year by 2030, and £3.6-7.6 trillion per year by 2050. The UK's automotive supply chain as a whole would benefit from the world leading technology that this Partnership seeks to provide. This Partnership combines the industry knowledge, design and manufacturing resources of Jaguar Land Rover (JLR), with the academic expertise of two of the UK's leading TPS research groups. The University of Oxford are world-leaders in the development of optical diagnostics and the study of in-cylinder phenomena: sprays, combustion and emissions. The University of Bath are similarly expert in the study of air handling, waste heat recovery and the systems-level analysis and modelling of vehicle powertrain. The research is divided into interrelated "Grand Challenges". Jaguar Land Rover will lead the TPS concept design and evaluation. The University of Oxford will perform fundamental experimental studies on mixing, ignition, combustion and emissions formation under extreme lean-burn and highly dilute conditions relevant to hybrid-focused TPS operation. The data from these experiments will be used at Oxford to develop and validate new predictive models that, in turn, will feed back into concept design process at JLR and systems models at the University of Bath. Oxford will also develop new and improved measurement tools and methods for the experiments. The University of Bath will investigate low-grade and high-grade heat recovery, air-handling and boosting systems--demonstrating and evaluating concepts on a prototype multi-cylinder TPS and feeding back in to JLR's concept design process. Bath will also perform extensive systems and vehicle modelling of the TPS system (using models validated against Oxford's data) in a hybrid powertrain to optimise system-level energy balance and demonstrate the target systems-level energy recovery in a virtual environment.

The gas turbine engine is an adaptable source of power and has been used for a wide variety of applications, ranging from the generation of electric power and jet propulsion to the supply of compressed air and heat. Competition within the industry and, more recently, environmental legislation from government have exerted pressure on engine manufacturers to produce ever more cleaner and efficient products.The most important parameter in governing engine performance and life cycle operating costs is the overall efficiency. High cycle efficiency depends on a high turbine entry temperature and an appropriately high pressure ratio across the compressor. The life of turbine components (vanes, blades and discs) at these hot temperatures is limited primarily by creep, oxidation or by thermal fatigue. It is only possible for the turbine to operate using these elevated mainstream gas temperatures (as hot as 1800 K) because its components are protected by relatively cool air (typically 800 K) taken from the compressor. However, this cooling comes at a cost: as much as 15-25% of the compressor air bypasses combustion to provide the required coolant to the combustor and turbine stages. Ingress is one of the most important of the cooling-air problems facing engine designers, and considerable international research effort has been devoted to finding acceptable design criteria. Ingress occurs when hot gas from the mainstream gas path is ingested into the wheel-space between the turbine disc and its adjacent casing. Rim seals are fitted at the periphery of the system, and a sealing flow of coolant is used to reduce or prevent ingress. However, too much sealing air reduces the engine efficiency, and too little can cause serious overheating, resulting in damage to the turbine rim and blade roots. It is proposed to build a new rotating-disc rig to measure the flow structure and heat transfer characteristics of hot gas ingress in an engine-representative model of a gas-turbine wheel-space. The rig will feature generic engine geometries; it will be fully-instrumented and specifically designed for optical access. An annular, single-stage turbine will create an unsteady circumferential distribution of pressure, which in turn will create the ingestion of hot air in the wheel-space. Fast-response thermocouples and thermochromic liquid crystal in conjunction with a stroboscopic light will be used in thermal transient experiments to measure the temperature of the rotating disc, the stator and the air inside the wheel-space of the rig. Miniature pressure transducers, pressure taps, pitot tubes, and concentration probes will also be used inside the seal annulus and in the wheel-space. In addition, a theoretical model of ingress will be developed and validated using the experimental data collected. This ingress model will be used to obtain correlations of cooling effectiveness and surface temperatures. More generally, the research will provide fundamental insight into the thermal effects of ingress in gas turbines and in turn inform the design of internal air systems.