Our ancestors communicated by scratching on the walls of caves, took navigational decisions by looking at the stars and made medical diagnoses simply by listening to patients. A great deal of information is captured in these simple data streams; our ability to capture, process, and decide actions based on information pervades all aspects of human life. Today, one has the same challenges but the information is much more voluminous and the expectations for the outcomes far higher. When we write using our finger on an iphone, as our voice is recorded for doctors to assess our mood, when video is analysed for abnormal actions, or as telescopes look deep into the galaxies for black holes, stars, planets,... technically sophisticated systems translate streams of sequential data into processed and recognised patterns that can be actioned. Our relatively new ability to offload data analysis onto massive digital systems is transforming our world. However huge challenges remain. Groundbreaking mathematical innovation is rapidly expanding our depth of understanding in one area. This project aims to build on successful pilot collaborations to create tools that really merge this new maths with the existing data science, and then apply them to exemplar challenges to produce a more effective abstraction of the "capture, process, and decide" process. The evidence is now overwhelming that dimension reduction and high order methods can capture sequential data very effectively. The maths underpinning this provided the crucial step that resulted in the extension of Newton's calculus beyond Itô's theory to rough paths; its mathematical articulation, the signature of a stream, has significantly enhanced deep learning methods to develop online handwriting recognition with state-of-the-art accuracy. This project has the goal of developing and embedding the abstract mathematics around rough paths and complex streamed data into a few of the richest challenges involved in the "capture, process, and decide" task. The investigators and the world-leading project partners are connected by the shared challenge of improving this task with complex datasets of importance in four contexts: * Health * Human interfaces * Human Actions * Observing the Universe The specific base challenges we start from are: 1) Use face, speech data, with other self-reported mood data to better detect when an intervention to support someone with mental illness is or is not working. 2) When a person writes (in Chinese) with their finger on a sat-nav device or mobile phone, to better transcribe this signal into digital characters accurately and economically, and to recognise who wrote it. 3) By observing evolving images in video data, develop tools that can classify the human actions. 4) Develop measurement instruments, and nonlinear processing techniques for astronomical data that improve detection sensitivity for transients and make new observations, e.g. for planets orbiting stars. The technical challenges are deeply interconnected. This project is a near unique opportunity to bring these together to produce a validated common methodology, and to create substantial cross-fertilization. One recent example of how this can happen is worth highlighting. In 2013, Ben Graham (then University of Warwick, now Facebook) used the signature to quantify strokes from Chinese hand-written characters parsimoniously and efficiently. The capture stage is subtle and has appreciably improved the accuracy of the recognition process; the China-based partners on this project subsequently created an app which has been downloaded millions of times. While the handwriting context for rough paths is very well defined and successful, understanding motion of people in videos is at a successful but early stage! The contexts are clearly related, and link through faces with the mental health challenge, and through occlusion with transients in astronomy. It is all joined up!

Decision-making and policy formulation in sectors such as health, emergency/crisis response and national security, ideally require accurate dynamic information on the number of people in specific places at specific times of the day, week, season or year. Traditional census data do not provide this level of detail but are often used for such policy and planning purposes. The ESRC-funded Population247 programme of research (Martin et al, 2015) developed a framework, methodology and software tool (SurfaceBuilder247) for integrating diverse contemporary data sources to produce enhanced time-specific population estimates for small geographical areas. Its usefulness has since been demonstrated for flooding and radiation emergency response/planning, through collaborations with HR Wallingford and Public Health England. These models have primarily involved the integration of open administrative data for activities such as place of residence, work, education and health. Now, new and emerging forms of data, such as sensor data, live and static data feeds provided via the internet, and various commercial datasets which were not previously available, provide exciting opportunities to enhance these population estimates. Such new and emerging datasets are useful because they provide near real-time information on population activity in sectors which are particularly dynamic and have previously been difficult to model, such as retail, leisure and transport. However, extracting useful intelligence from these sources, and integrating and calibrating them with existing data sources, poses significant challenges for researchers and practitioners seeking to employ them in the creation of time-specific population estimates. This project will combine new, emerging and existing datasets in order to produce enhanced time-specific population estimates for more informed decision-making and policy formulation in the health, emergency/crisis response and national security sectors. It is a collaborative project between University of Southampton, Public Health England (PHE), Health and Safety Executive (HSE) and Defence Science and Technology Laboratory (Dstl). The project will enhance existing methods and tools for harvesting, processing, integrating and calibrating new, emerging and existing data sources in order to produce time-specific population estimates. It will deliver two substantive policy demonstrator case studies with the project partners. The first case study will demonstrate the potential for using time-specific population estimates for near real-time response in emergencies; the second will explore their usefulness for modelling variation in 'normal' population distributions through space and time in order to inform longer-term planning and policy formulation. Importantly, the project will also encourage the sharing of knowledge and expertise between academia and the public sector through joint design and implementation of the case studies, internal seminars and a jointly organised stakeholder workshop. Invitees to the workshop will be key stakeholders in policy and practice from within and beyond the partners' sectors. The workshop will showcase the data, methods and tools developed by the project, discuss the opportunities and challenges involved in implementing these for decision-making and policy formulation, and identify how such methods might realistically be scaled up within these sectors. Ultimately, the aim of the project is to help partners such as PHE, HSE and Dstl carry out their remits more effectively and efficiently through the provision of better time-specific population estimates.

The Ocean-REFuel project brings together a multidisciplinary, world-leading team of researchers to consider at a fundamental level a whole-energy system to maximise ocean renewable energy (Offshore wind and Marine Renewable Energy) potential for conversion to zero carbon fuels. The project has transformative ambition addressing a number of big questions concerning our Energy future: How to maximise ocean energy potential in a safe, affordable, sustainable and environmentally sensitive manner? How to alleviate the intermittency of the ocean renewable energy resource? How ocean renewable energy can support renewable heat, industrial and transport demands through vectors other than electricity? How ocean renewable energy can support local, national and international whole energy systems? Ocean-REFuel is a large project integrating upstream, transportation and storage to end use cases which will over an extended period of time address these questions in an innovative manner developing an understanding of the multiple criteria involved and their interactions.

The COVID-19 pandemic has exposed how significant a role the indoor environment plays in the transmission of infection. The virus has highlighted how there are substantial gaps in knowledge relating to how microorganisms in aerosols and droplets are generated and dispersed in our buildings, how effectively we can measure and monitor risks in indoor environments, and how the design of the environment and the technologies within it can be used to control exposure to pathogens. While there is an immediate focus on respiratory infections, this challenge applies to a very wide range of microorganisms including gastroenteric pathogens and environmental microorganisms where exposure risks are driven by human interactions with the building layout, ventilation, heating and water systems. Understanding and tackling these challengers requires new knowledge about the interactions between microorganisms and the physical environment. Microbial aerosols in buildings are known to be released from human sources (respiratory aerosols, skin squame), building systems (aerosols from water, drainage and ventilation systems), industry processes (waste and waste water treatment, agricultural activities), the natural environment (sea, animals, plant pathogens) and medical procedures (dentistry, intubation). However we know very little about how the engineering design of the environment determines the generation, transport, deposition and control of microorganisms. Beyond microorganisms, there is growing awareness that human health is significantly affected by exposure to pollutants in indoor spaces and that many buildings are inadequately ventilated to provide healthy conditions for occupants. The CECAM (Chamber for Environmental Control of Airborne Microorganisms) facility will provide a new, multi-user research environment that can enable controlled experiments with aerosolised microorganisms under different indoor environmental conditions. The facility will enable key research questions to be addressed relating to sources and survival of microbial aerosols, methods for measuring and monitoring microbial aerosols and pollutants, the role of ventilation and room layouts on the dispersion and deposition of microbial aerosols and other pollutants, the development of effective engineering solutions including personal protective equipment, air cleaning and disinfection devices, and better designs of key components such as showers, hot air dryers, air conditioning units and drainage systems. The facility will enable research at the interfaces of fluid dynamics and aerosol sciences with microbiology and indoor air chemistry that is driven by clinical challenges and the need for improved indoor environmental quality in buildings across just about every sector of society. The CECAM facility will provide an integrated user environment that combines a controlled biocontainment chamber with dedicated air handling systems with a suite of environmental sensors and bioaerosol samplers including real-time bioaerosol sampling. Through location within a well-equipped microbiology laboratory and managed by a dedicated experimental officer, the CECAM facility will enable robust and safe experiments to be carried out by academic users, research organisations, NHS users and industry. This will include the ability for experiments to be carried out using human participants.

Submarine landslides can be far larger than terrestrial landslides, and many generate destructive tsunamis. The Storegga Slide offshore Norway covers an area larger than Scotland and contains enough sediment to cover all of Scotland to a depth of 80 m. This huge slide occurred 8,200 years ago and extends for 800 km down slope. It produced a tsunami with a run up >20 m around the Norwegian Sea and 3-8 m on the Scottish mainland. The UK faces few other natural hazards that could cause damage on the scale of a repeat of the Storegga Slide tsunami. The Storegga Slide is not the only huge submarine slide in the Norwegian Sea. Published data suggest that there have been at least six such slides in the last 20,000 years. For instance, the Traenadjupet Slide occurred 4,000 years ago and involved ~900 km3 of sediment. Based on a recurrence interval of 4,000 years (2 events in the last 8,000 years, or 6 events in 20,000 years), there is a 5% probability of a major submarine slide, and possible tsunami, occurring in the next 200 years. Sedimentary deposits in Shetland dated at 1500 and 5500 years, in addition to the 8200 year Storegga deposit, are thought to indicate tsunami impacts and provide evidence that the Arctic tsunami hazard is still poorly understood. Given the potential impact of tsunamis generated by Arctic landslides, we need a rigorous assessment of the hazard they pose to the UK over the next 100-200 years, their potential cost to society, degree to which existing sea defences protect the UK, and how tsunami hazards could be incorporated into multi-hazard flood risk management. This project is timely because rapid climatic change in the Arctic could increase the risk posed by landslide-tsunamis. Crustal rebound associated with future ice melting may produce larger and more frequent earthquakes, such as probably triggered the Storegga Slide 8200 years ago. The Arctic is also predicted to undergo particularly rapid warming in the next few decades that could lead to dissociation of gas hydrates (ice-like compounds of methane and water) in marine sediments, weakening the sediment and potentially increasing the landsliding risk. Our objectives will be achieved through an integrated series of work blocks that examine the frequency of landslides in the Norwegian Sea preserved in the recent geological record, associated tsunami deposits in Shetland, future trends in frequency and size of earthquakes due to ice melting, slope stability and tsunami generation by landslides, tsunami inundation of the UK and potential societal costs. This forms a work flow that starts with observations of past landslides and evolves through modelling of their consequences to predicting and costing the consequences of potential future landslides and associated tsunamis. Particular attention will be paid to societal impacts and mitigation strategies, including examination of the effectiveness of current sea defences. This will be achieved through engagement of stakeholders from the start of the project, including government agencies that manage UK flood risk, international bodies responsible for tsunami warning systems, and the re-insurance sector. The main deliverables will be: (i) better understanding of frequency of past Arctic landslides and resulting tsunami impact on the UK (ii) improved models for submarine landslides and associated tsunamis that help to understand why certain landslides cause tsunamis, and others don't. (iii) a single modelling strategy that starts with a coupled landslide-tsunami source, tracks propagation of the tsunami across the Norwegian Sea, and ends with inundation of the UK coast. Tsunami sources of various sizes and origins will be tested (iv) a detailed evaluation of the consequences and societal cost to the UK of tsunami flooding , including the effectiveness of existing flood defences (v) an assessment of how climate change may alter landslide frequency and thus tsunami risk to the UK.