Developing new health technologies is complicated and often fails to lead to improved patient care. Successfully taking an idea through the necessary research studies and developing it to the point of use in the NHS requires many different areas of expertise. These include; understanding patients' and health professionals' needs, medical and healthcare environments, engineering and digital technologies, design, manufacturing, legal and ethical regulation, business development, how to obtain funding, and many other topics (in our application we refer to these areas the "Innovation Curriculum"). Our Hub covers a region of 1.4 million people in a region that is affected by high levels of disease and health inequalities. Our team includes all regional NHS organisations including GPs, adult and children's hospitals, mental health services and the recently introduced South Yorkshire "Integrated Care System", hundreds of researchers from the University of Sheffield and Sheffield Hallam University, many large and small companies, and patient and public groups. These partners between them have all the necessary expertise and experience in developing new Digital Health technologies to the point of use in the NHS. We will help researchers develop Digital Health technologies by training them in all aspects of the Innovation Curriculum, and by supporting them to work together with the NHS and patients on real ideas and projects. We will hold Citizen's Juries to understand the public and patients' views of Digital Health and to help design our research. We will produce sixty hours of training in Digital Health for researchers, clinicians, patients and the public, freely available and accredited through our partnership with YouTube's authoritative health content programme. We will hold regular "Calls for Ideas" where we support project teams and train them in Digital Health, providing the most promising ideas with initial project funding to help take these towards potential commercialisation.

Autonomous systems promise to improve our lives; driverless trains and robotic cleaners are examples of autonomous systems that are already among us and work well within confined environments. It is time we work to ensure developers can design trustworthy autonomous systems for dynamic environments and provide evidence of their trustworthiness. Due to the complexity of autonomous systems, typically involving AI components, low-level hardware control, and sophisticated interactions with humans and the uncertain environment, evidence of any nature requires efforts from a variety of disciplines. To tackle this challenge, we gathered consortium of experts on AI, robotics, human-computer interaction, systems and software engineering, and testing. Together, we will establish the foundations and techniques for verification of properties of autonomous systems to inform designs, provide evidence of key properties, and guide monitoring after deployment. Currently, verifiability is hampered by several issues: difficulties to understand how evidence provided by techniques that focus on individual aspects of a system (control engineering, AI, or human interaction, for example) compose to provide evidence for the system as whole; difficulties of communication between stakeholders that use different languages and practices in their disciplines; difficulties in dealing with advanced concepts in AI, control and hardware design, software for critical systems; and others. As a consequence, autonomous systems are often developed using advanced engineering techniques, but outdated approaches to verification. We propose a creative programme of work that will enable fundamental changes to the current state of the art and of practice. We will define a mathematical framework that enables a common understanding of the diverse practices and concepts involved in verification of autonomy. Our framework will provide the mathematical underpinning, required by any engineering effort, to accommodate the notations used by the various disciplines. With this common understanding, we will justify translations between languages, compositions of artefacts (engineering models, tests, simulations, and so on) defined in different languages, and system-level inferences from verifications of components. With such a rich foundation and wealth of results, we will transform the state of practice. Currently, developers build systems from scratch, or reusing components without any evidence of their operational conditions. Resulting systems are deployed in constrained conditions (reduced speed or contained environment, for example) or offered for deployment at the user's own risk. Instead, we envisage the future availability of a store of verified autonomous systems and components. In such a future, in the store, users will find not just system implementations, but also evidence of their operational conditions and expected behaviour (engineering models, mathematical results, tests, and so on). When a developer checks in a product, the store will require all these artefacts, described in well understood languages, and will automatically verify the evidence of trustworthiness. Developers will also be able to check in components for other developers; equally, they will be accompanied by evidence required to permit confidence in their use. In this changed world, users will buy applications with clear guarantees of their operational requirements and profile. Users will also be able to ask for verification of adequacy for customised platforms and environment, for example. Verification is no longer an issue. Working with the EPSRC TAS Hub and other nodes, and our extensive range of academic and industrial partners, we will collaborate to ensure that the notations, verification techniques, and properties, that we consider, contribute to our common agenda to bring autonomy to our everyday lives.

Conditions such as long-gap oesophageal atresia (LGOA) and short bowel syndrome (SBS) are two examples of chronic paediatric cases of gastrointestinal tissue reconstruction where up to two thirds of the oesophagus and bowel, respectively, may be missing. These are among the most complex and devastating paediatric anomalies that have a life-long debilitating effect on patients. Their current treatments are not widely available, are complex, primitive, long-term, and have disputed outcome quality. Families and surgeons have long sought an effective treatment to improve these patients' quality of life. The proposed project aims to initiate an ambitious research agenda for a novel technology for the repair and reconstruction of soft tubular tissues inside the body using robotic and tissue regeneration principles. The underlying technology unifies the fields of tissue engineering, surgery and medical implants into a new concept of 'robotic implants'. The proposed robotic implants are one-size-fits-all linings for tubular tissues that enable autonomous tissue-responsive mechanical interaction with tissues to induce their growth. Based on evidence from cell biology studies and clinical practice showing how tissues respond to mechanical stimulation in vivo, the proposed robotic implant applies gentle force directly to tissues to induce growth through cell proliferation. Thus, these robotic implants deliver controlled, long-term, customisable and optimal reconstructive therapy for tissues in an unprecedented way. The proposed technology has the potential to restore patients' mobility and social activity, as well as reduce hospitalisation and post-surgery complications, treatment and costs. This proposal has a pioneering focus: to develop the design, fabrication and control of robotic implants that can physically and physiologically adapt to the changing properties of tissues and stimulate their growth. These robotic implants will consist of fundamental, compact and functional elastomeric strands that can be assembled into an architecture that can elongate with the growing tissue and apply controlled, directional mechanical stimulation to the tissue. This project is the basis of an exciting interdisciplinary research framework that will allow communities of surgeons, biologists, tissue engineers and tissue mechanics researchers to investigate basic mechanisms of tissue growth and understand the relationships among tissue strain, tissue regeneration and inflammatory responses. In particular, the technology to be developed in this project will be a precursor clinical device for LGOA and SBS. This project also launches an investigation into soft robots that physically adapt and perform inside the body, which is imperative for tissue regeneration and growth as well as for wearable technologies that need to adapt to children's developmental stages.

Imagine a future where autonomous systems are widely available to improve our lives. In this future, autonomous robots unobtrusively maintain the infrastructure of our cities, and support people in living fulfilled independent lives. In this future, autonomous software reliably diagnoses disease at early stages, and dependably manages our road traffic to maximise flow and minimise environmental impact. Before this vision becomes reality, several major limitations of current autonomous systems need to be addressed. Key among these limitations is their reduced resilience: today's autonomous systems cannot avoid, withstand, recover from, adapt, and evolve to handle the uncertainty, change, faults, failure, adversity, and other disruptions present in such applications. Recent and forthcoming technological advances will provide autonomous systems with many of the sensors, actuators and other functional building blocks required to achieve the desired resilience levels, but this is not enough. To be resilient and trustworthy in these important applications, future autonomous systems will also need to use these building blocks effectively, so that they achieve complex technical requirements without violating our social, legal, ethical, empathy and cultural (SLEEC) rules and norms. Additionally, they will need to provide us with compelling evidence that the decisions and actions supporting their resilience satisfy both technical and SLEEC-compliance goals. To address these challenging needs, our project will develop a comprehensive toolbox of mathematically based notations and models, SLEEC-compliant resilience-enhancing methods, and systematic approaches for developing, deploying, optimising, and assuring highly resilient autonomous systems and systems of systems. To this end, we will capture the multidisciplinary nature of the social and technical aspects of the environment in which autonomous systems operate - and of the systems themselves - via mathematical models. For that, we have a team of Computer Scientists, Engineers, Psychologists, Philosophers, Lawyers, and Mathematicians, with an extensive track record of delivering research in all areas of the project. Working with such a mathematical model, autonomous systems will determine which resilience- enhancing actions are feasible, meet technical requirements, and are compliant with the relevant SLEEC rules and norms. Like humans, our autonomous systems will be able to reduce uncertainty, and to predict, detect and respond to change, faults, failures and adversity, proactively and efficiently. Like humans, if needed, our autonomous systems will share knowledge and services with humans and other autonomous agents. Like humans, if needed, our autonomous systems will cooperate with one another and with humans, and will proactively seek assistance from experts. Our work will deliver a step change in developing resilient autonomous systems and systems of systems. Developers will have notations and guidance to specify the socio-technical norms and rules applicable to the operational context of their autonomous systems, and techniques to design resilient autonomous systems that are trustworthy and compliant with these norms and rules. Additionally, developers will have guidance to build autonomous systems that can tolerate disruption, making the system usable in a larger set of circumstances. Finally, they will have techniques to develop resilient autonomous systems that can share information and services with peer systems and humans, and methods for providing evidence of the resilience of their systems. In such a context, autonomous systems and systems of systems will be highly resilient and trustworthy.

Context: Testing how well the lung "functions" usually involves the use of breathing tests. However, these tests are extremely difficult to do reliably and accurately in newborn babies and infants. We need new imaging techniques that can help visualise the best and worst functioning areas of the lungs. In adults, x-ray and computed tomography (CT) imaging is often used to study the lungs. However, these methods pose an increased harmful radiation risk to newborns and infants. In addition, the function of the heart is normally measured by invasive methods that are not safe for newborns and infants, or echocardiography, which is technically challenging in these populations. As a result, our knowledge of newborn and infant diseases of the lung and heart is collectively poor compared to that of adolescents and adults. In particular, lung and heart problems in babies born pre-term are the major cause of death, yet remain not well understood. Objectives: The main purpose of this research is to develop safe, robust methods for imaging the lungs and heart in newborns and infants to better understand and manage debilitating diseases, in particular those related to pre-term birth. We will use magnetic resonance imaging (MRI); a safe imaging method that poses no harmful radiation risk to newborns and infants. The main objectives are as follows: - Develop MRI methods to investigate how diseases affect the lungs and heart in newborns and infants: -- Develop software to control the MRI scanner to obtain the best quality images that inform us about the structure and function of the lungs and heart in newborns and infants. -- Develop MRI hardware that is comfortable for newborns and infants and helps to improve image quality. - Test how well our developed methods and technology can detect changes to the structure and function of the lungs and heart in newborns and infant lung diseases, including diseases related to premature birth. -- Measure how well these methods can detect the causes for changes in patient's health over time as disease progresses. My research will be carried out at the University of Sheffield, a world-leading institution in MRI technique development with a unique interdisciplinary balance of scientists and clinicians to ensure that technological developments lead directly to NHS and patient benefit. Potential Applications & Benefits: The long-term benefit of this research is the potential to change the way lung and cardiac disease is managed in newborns and infants and improve patient quality-of-life. In particular, the methods we develop will help identify early signs of disease that cannot easily be identified by other methods. In addition, MRI is safe, and scanning can be repeated often to monitor disease progress or visualise the changes due to treatment. This cannot be done with CT, and will aid our understanding of diseases and help identify new ways they can be treated. We will develop these techniques for whole-body MRI scanners, of the sort available in most hospitals, which will increase accessibility of the technique to NHS clinicians nationally.