It is evident that service robotics has the potential to improve people's quality of life and it holds the key to a number of unmet applications related to health care and rehabilitation. According to the prediction of International Federation of Robotics, the global market for intelligent service robots is forecast to reach 24.3 billion USD worldwide by 2010. A multi-fingered robotic hand is the most complex and dexterous robotic system, whose development represents frontiers in service robotics research. Recent innovations in motor technology and robotics have achieved impressive results in the hardware of robotic hands such as Robonaut hand. However, the manipulation systems of robotic hands are hardcoded to handle specific objects in specific ways, which significantly limits their transfer to a range of different situations and applications. The control and optimisation problems involved in robot hand manipulation are very difficult to solve in mathematical terms, however humans solve their hand manipulation related tasks easily using skill and experience. Object manipulation algorithms are required to meet the market requirement that robot hand systems should have human-like manipulation capabilities and be independent of robot hand hardware. Hence, the main challenge that researchers now face is how to enable robot hands to use what can be learned from human hands, to manipulate objects, with the same degree of skill and delicacy as human hands. The proposed work aims to investigate artificial intelligence (AI) methodologies and practical solutions which will allow robotic hands to automatically adapt to human environments and thus to enable them to autonomously perform useful manipulation tasks involved in daily living, pontentially for health care and rehabilitation applications. The investigation will focus on the following areas. 1) To generate a series of responsive human-like finger gaits for a robotic hand given an object to manipulate. This will have the capability to iteratively build a knowledge base representing the features of human hand manipulation behaviour and to efficiently provide corresponding robot hand gaits and manipulation strategies for a given manipulation task in a human environment.2) To develop feasible friction models for the interaction of objects and a robot/human hand. This will enable the application of existing mathematical research findings in multifingered robot manipulation to realworld applications in human environments and will integrate related methods in engineering and AI domains. 3) To develop an AI-based control architecture to ensure robust object manipulation of multifingered robots in terms of manipulation feasibility and efficiency. This will allow robot hands to perform stable human-like object grasping and manipulation and will also provide an open architecture which has the potential to introduce human brain (EEG/MRI signals) and human muscles (EMG signals) information into robotic hand systems.4) To validate the proposed algorithms by implementing these into a set of defined scenarios with a set of simulated multifingered robot hands and three different types of physical robot hands.

Explosive volcanic eruptions have devastating impacts in near-vent areas where pyroclastic density currents can cause significant loss of life, yet the injection of large volumes of ash into the atmosphere and its subsequent dispersal over hundreds to thousands of kilometres, pose significant and far-reaching hazards. Ash fall is a severe and wide-ranging volcanic hazard; causing roof collapse, health (respiratory) and agricultural issues and wide-scale interruptions to essential infrastructure (e.g., electricity, food/water supplies; roads and rail closures). Even ash emitted during moderately explosive eruptions can ground air traffic as was demonstrated by the 2010 Eyjafjallajökull eruption (Iceland). As such widespread volcanic ash dispersals present huge economic and societal costs. Disturbingly, 800 million people live within 100 km of active volcanoes globally, yet statistical studies of detailed eruption databases (e.g., Japan) reveal significant under-recording of past volcanic eruptions deeper in time. Our understanding of the magnitude and frequency of eruptions at a particular volcano is typically skewed to recent activities, because records of older eruptions are fragmentary often owing to erosion and/or burial by more recent eruptions. The better-preserved, shorter-term records, however, do not necessarily reflect the full range of volcanic activity, or variations in the tempo of activity. This is a major obstacle for long-term volcanic hazard assessments and hampers our ability to: i) determine changing eruption-rates through time, ii) evaluate magnitude-frequency relationships and iii) project the recurrence intervals of hazardous ash dispersals. This research will overcome this impasse and reconstruct comprehensive long-term records of explosive volcanism for productive arc volcanoes. It will exploit the under-utilised record of ash layers preserved in dense networks of marine sediment cores. These continuous sequences represent unprecedented repositories of ash fall (preserved as visible and microscopic deposits), which are not susceptible to destructive near-source processes. Using state-of-the-art geochemical 'fingerprinting' techniques, it is possible to pinpoint the volcanic source of the marine ash layers, whilst tracing these ash fall events across a network of cores provides a unique opportunity to computationally model and map ash dispersals, and calculate eruption magnitudes. Cutting-edge argon-argon dating techniques to directly date the ash deposits, will reveal the tempo of past explosive eruptions at an individual volcano, and importantly determine the recurrence intervals of widespread hazardous volcanic ash dispersals from these volcanoes. With evidence for near-source under-reporting of explosive volcanic activity emanating from Japanese eruption records, this research will begin by utilising a wealth of marine sediment records from around the Japanese Islands, including those of the International Ocean Discovery Programme (IODP) and the Geological Survey of Japan (AIST). This research will then look to expand into other productive volcanic arc settings, particularly those that are vulnerable owing to inadequate records of explosive volcanism (e.g., circum-Pacific volcanic arcs). These new offshore volcanological records will be examined in partnership with those directly responsible for hazard/risk assessments at individual volcanoes and policy-makers working in this field.

Explosive volcanic eruptions have devastating impacts in near-vent areas where pyroclastic density currents can cause significant loss of life, yet the injection of large volumes of ash into the atmosphere and its subsequent dispersal over hundreds to thousands of kilometres, pose significant and far-reaching hazards. Ash fall is a severe and wide-ranging volcanic hazard; causing roof collapse, health (respiratory) and agricultural issues and wide-scale interruptions to essential infrastructure. Even ash emitted during moderately explosive eruptions can ground air traffic as was demonstrated by the 2010 Eyjafjallajökull eruption (Iceland). As such widespread volcanic ash dispersals present huge economic and societal costs. Disturbingly, 800 million people live within 100 km of active volcanoes globally, yet statistical studies of global eruption databases indicate significant under-recording of past volcanic eruptions deeper in time. For instance, this analysis would indicate up to 66% of VEI 5 eruptions, equivalent in scale to the 1980 Mount St Helens eruption, are missing within the geological record spanning the last 200,000 years. Our understanding of the magnitude and frequency of eruptions at a particular volcano is typically skewed to recent activities, because records of older eruptions are fragmentary often owing to erosion and/or burial by more recent eruptions. The better-preserved, shorter-term records, however, do not necessarily reflect the full range of volcanic activity, or variations in the tempo of activity. This is a major obstacle for long-term volcanic hazard assessments and hampers our ability to: i) determine changing eruption-rates through time, ii) evaluate magnitude-frequency relationships and iii) project the recurrence intervals of hazardous ash dispersals. This research has overcome this impasse by reconstruct comprehensive long-term records of explosive volcanism for volcanoes in southern and central Japan. This research exploits the under-utilised record of volcanic ash layers preserved in dense networks of marine and lake sediment cores away from the volcano. These continuous sediment sequences present unprecedented repositories of ash fall (preserved as visible and microscopic deposits), which are not susceptible to destructive near-source volcanic processes. Using state-of-the-art chemical 'fingerprinting' techniques, it is possible to pinpoint the volcanic source of the distal ash layers, whilst tracing these ash fall events across a network of cores provides the opportunity to computationally model and map past ash dispersals, and calculate eruption magnitudes. Integrating cutting-edge dating techniques (40Ar/39Ar/14C) to date the ash deposits, enables us to reveal the timing and tempo of past explosive eruptions at an individual volcano, and importantly determine the recurrence intervals of widespread hazardous volcanic ash dispersals from these volcanoes. Our research in south and central Japan has been successfully tackling eruption un-reporting and plugged the gaps in the eruption records of numerous volcanoes. In the next phase of our research we will expand the application of our methods to address the volcanoes of NE Japan and the Kurile Arc, utilising newly available marine cores from International Ocean Discovery Programme (IODP) and Japan Agency For Marine-Earth Sciences and Technology (JAMSTEC). In addition, using our ability to produce comprehensive eruption records, we will explore volcano-climate interactions. The distal volcanological records generated in this project will continue to be examined in partnership with those directly responsible for volcanic hazard assessments at individual volcanoes, and policy-makers working in the field.

Structural biology concerns the study of the three-dimensional structure of biological macromolecules and their interactions. Biomolecular Nuclear Magnetic Resonance (NMR) spectroscopy is one of three core techniques in structural biology, and highly complementary to the other two, i.e. X-ray crystallography and Cryo EM. Extracting information from NMR data has traditionally been complex and non-intuitive. Many smart and innovative tools have been developed, which unfortunately often have been disconnected and not well integrated and sometimes hard to use. For nearly two decades, the Collaborative Computational Project for NMR (CCPN) has been central in providing a connecting interface between the NMR data and many of these tools. CCPN also actively promotes the sharing and exchange of knowledge and best practices. CCPN also actively engages with the UK and international research communities in all matters relating to research funding and policies. The CCPN aims to continue its immense value to the scientific community over the next 5 years by pursuing the following specific objectives: 1. Development of software relevant for NMR CCPN will improve, maintain and expand its programmes, to provide for new functionalities, improved handling, and better speeds. Through fortnightly updates and regular new releases, we will ensure the proper functioning of the software across multiple platforms. We will continuously work on interoperability of our software with other NMR programmes and implement relevant tools for reporting and research data management. 2. NMR in support of Biological Sciences We will facilitate and implement the latest computational tools and developments for NMR data analysis, automation, structure generation and validation. 3. NMR in support of Medicine NMR metabolomics is a thriving field that generates crucial knowledge on metabolic pathways from cells to organisms, including humans. We have designed AnalysisMetabolomics to leverage its power and we will focus on tools for non-expert users, streamlined annotation, assignment, metadata and deposition in public repositories. 4. NMR in support of Industry In collaboration with industrial and academic partners, we will test and enhance applications that are useful to industry. Examples are AnalysisScreen, small-molecule (NMR-assisted) docking procedures to optimise workflows and efficiency and ChemBuild to assist with fragment-based drug discovery. 5. Outreach and training Through its active outreach programme, engaging with all stakeholders including national and major international NMR facilities, CCPN will promote the continuous exchange of knowledge, provide training and support the adoption of best practices in NMR. There is a growing body of "how to" videos available on the website. CCPN will actively continue to promote and develop community data standards (NEF), and will take a leading role in discussions on research funding and policies. CCPN will continue its crucial role as intermediary between the UK and international NMR community, by fostering contacts with (inter-)national NMR facilities, other CCPs, the international wwPDB and NEF efforts. To strengthen the UK NMR community, we will continue the successful series of UK CCPN conferences and teaching programs, our comprehensive help and support for CCPN users, participate in international efforts in knowledge sharing and exchange of best practices, and engage in training and teaching through workshops, papers and (video) tutorials. In short, CCPN will continue to make a crucial difference to the biological NMR community in different fields such as Medicine and Industry as well as its traditional base of the biological sciences by acting as a focal point for technology development, collaboration and sharing best practices. Ultimately, researchers who are empowered with the best tools have more time to make new discoveries.