Software systems pervade modern life; they control everything from fly-by-wire aircraft and financial transfer systems to ABS breaking systems in cars and cooking modes in microwaves. It is an accepted fact that, as software systems evolve and their requirements change, they become increasingly complex and difficult to maintain. Different developers carry out (sometimes conflicting) changes to address different bugs or add different features, and before long the original design of the system is neglected and it becomes impossible to understand exactly how the system operates.One way around this problem is to keep a high-level design document that specifies exactly how the system is supposed to behave. A major benefit of developing such specifications along with the actual software itself is the fact that powerful testing and verification techniques exist that can be used to determine whether the actual system conforms to the specification. However, one major problem hampers the routine use of such specifications: as the software system evolves, they often tedious and time-consuming to generate and maintain separately from the code. With this project we will produce a technique that addresses the above problem by automating the process of generating a specification. Given a system that has no specification (or only a partial one), our technique will analyse and probe the system by both running it and looking at its static structure. It will produce a complete document specifying the behaviour of the system as a collection of state machines. Often, depending on the facet of behaviour that is of interest, it is useful to display certain aspects at a greater level of detail than others (for a financial system for example, the developer might be interested in details sub-prime loan management and not the transaction processing system). For this reason, state machines that are generated will be presented as a hierarchy. Devising a practical technique to automatically reverse engineer such specifications is challenging; the system in question needs to be suitably sampled to identify relevant behaviour, and the final specification will have to be a valid generalisation of these samples. How do we identify the set(s) of samples that can be used as a basis for generating the specification? How do we go about building an accurate specification of the system from this set of samples? One key realisation that will be investigated in this work is the fact that identical challenges to these arise in the field of grammar inference, where the challenge is to build a grammar (which can be represented by a state machine) from a sample of sentences in the target language. Grammar inference is a very mature field, with many very powerful techniques, but has never been linked to the similar problem of reverse-engineering specifications from software systems.This work will explore the relationship between the two fields of reverse-engineering and grammar inference, and will produce a set of approaches based on and extending existing techniques that, when combined, will produce a practical technique that generates complete and accurate hierarchy of state machines of a software system.

In the UK, musculoskeletal disorders (joint and back problems) affect one in five people long term. While joint replacements are successful, they are challenged by demands of an active and younger population presenting with disorders due to trauma, obesity, or other lifestyle choices. One of the causes for joint and back pain is the deterioration of the different soft tissues acting as cushions in the joints. New surgical interventions are being developed to repair or locally replace those soft tissues in order to delay or prevent a total joint replacement. There is no clear indication yet on which patients benefit the most from them. There is an urgent need to define the type of patients for which new and existing interventions are most beneficial. The local anatomy or level of tissue deterioration differs greatly between patients, and there is currently a lack of biomechanical evidence that takes into account these large variations to help matching patients to interventions. To tackle these issues, this Fellowship, MSKDamage, will develop novel testing methods and tools combining laboratory simulation with computer modelling and imaging. MSKDamage methods will be used to predict the variation in the mechanical performance of a series of treatments at various levels of joint deterioration. This will enable the different interventions to be matched to different patient's characteristics. I will focus on three musculoskeletal disorders and associated repairs: 1. Emerging treatments involving the injection of biomaterials in the intervertebral disc: I will produce realistic testing conditions that can be personalised to a specific patient, assessing each patient's chance of success and identifying areas for treatment optimisation. 2. Evaluation of meniscus repairs in the knee and their interaction with cartilage defects: I will provide new information on the type of cartilage defect that reduces the chances of success of a meniscus replacement in the knee. The research will develop guidance on the type of cartilage defects that need repair for a meniscus replacement to be successful. 3. Optimisation of custom wrist repair: I will help optimise patient-specific wrist repairs so that they reduce the damage in tendons and ligaments in the wrist. MSKDamage builds on my strong track-record in the field and network of industry, clinical and academic collaborators, as well as my recent work that demonstrates the specific information which need to be included in models of degraded tissues in the spine and the knee. MSKDamage aims to (1) develop a methodology to test interventions for a specific patient and its specific tissue degradation, (2) carry out a series of case studies which demonstrate the capacity to test a range tissues disorders and repairs. This work is a particularly suitable for a Fellowship, as it will allow me to develop fundamental engineering tools and methods while engaging with end users for significant economic and societal impact. I will also develop as a leader in the field, leading a growing research group and taking actions for the research community, directly related to the research, with advocacy on sharing more research outcomes openly for creation of more impact, and indirectly related to act as an ambassador for public and patient involvement for research related to computer simulations in healthcare.

This proposal identifies an opportunity to bring together leading international experts to consider the dispersal and impacts of volcanic ash. A key theme emerging from one of our existing research project (STREVA) is the role that volcanic ash plays in disrupting lives and livelihoods across all scales: from major disruption of international air traffic to the destruction of individual livelihoods via irreparable damage to crops and livestock or health problems. Another (VANAHEIM) is uncovering new insights into the ash loading and subsequent dispersal from eruptive columns. Globally other researchers have started to systematically examine the impacts of ash fall-out on critical infrastructure, buildings, communication, vegetation, soil and human or animal health However we currently do not fully understand several things: (i) localised variance in ash dispersal on the kilometre scale and regional (cross-border) dispersal; (ii) thresholds and timings of the ways in which soils and plants are impacted by ash concentrations; (iii) the impact of ash on human and animal health over both short and long time-scales; and (iv) the role that ash concentration plays in disrupting transportation and communication networks during an acute volcanic crisis. Even more importantly, the impact of these processes on communities affected by eruptions lies in their cumulative effects and interacting processes. We want to consider how to tackle this more effectively, by developing andapplying the very best scientific approaches. Through this International Opportunities Fund we will establish a new team of experts to start to tackle these problems with a multi-disciplinary approach which engages key stakeholders and end users, and paves the way for future long-term collaborations. We are taking a 'problem-based' approach to this issue and will focus on one particular island, but use it to consider general problems. This will help us to focus on the most critical scientific issues and provide a new group of researchers with a common problem on which to build an analysis of future research need. The information from the specific setting (St. Vincent) can be immediately applied in disaster planning and regional contingencies for ash disruption. The network built by this project intends to not only report on its findings relevant to St. Vincent but to use these to apply for research finding that enables a diverse group of experts to make real progress in understanding, anticipating and mitigating against the risks from ash fall.

Software systems pervade modern life; they control everything from fly-by-wire aircraft and financial transfer systems to ABS breaking systems in cars and cooking modes in microwaves. The ability to understand these complex systems, and to make sure that they behave as expected, is crucial. State machines are a formal, diagrammatic notation that can be used to visualise behaviour of these systems in an accessible way. They can also be used as a basis for several rigorous and automated testing and verification techniques.Currently, state machines have to be designed and maintained by hand. This is an expensive and error-prone task, particularly when the system in question is constantly subject to change. Faced with this challenge, a substantial amount of research has been devoted to solving this problem with automated techniques; to automatically infer state machines of software systems, usually from samples of their behaviour. This has resulted in a multitude of proposed solutions from groups around the world.Although these advances are welcome, they have given rise to an important problem: There is no accepted process by which these techniques can be evaluated and compared against each other. There is no evidence to indicate which technique is better than the others, and why certain techniques excel. This in turn hampers further research in the area.With this project, we will address the above problems by organising an international competition to thoroughly compare and evaluate a diverse range of state machine inference techniques. The competition is especially novel because it will employ a range of techniques to compare the results of different techniques against each other. This will identify (a) which techniques are the most effective ones and (b) shed light on the possible reasons for their effectiveness. It is envisaged that this competition will become a regular event, driving research in the area.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is an outstanding method of chemical analysis, used extensively in academia and industry to characterise complex samples in 2D/3D. Application areas include materials science, biology, healthcare, energy etc. In the analysis the high-energy 'primary' ion projectile impact on a sample surface, causes ejection of 'secondary' molecular ions which are analysed by a mass spectrometer to provide chemically-rich material characterisation. Scanning the primary beam across the sample provides 2D surface imaging (>100 nm lateral resolution) and by sequentially collecting images while the sample is eroded, 3D sub-surface imaging (>3 nm depth resolution). This unique combination of analytical capabilities means ToF-SIMS is unmatched in its potential to determine, in a single analysis, the composition and detailed distribution of multiple, chemicals in complex samples. Importantly, this technology supports 'discovery mode' research, where the analysis is not biased towards pre-selected, labelled compounds, and therefore leads to hypothesis generation. The analysis is highly-multiplexed and comprehensive - hundreds of species can be potentially detected in a single measurement, limited only by the sensitivity of the process, which here we seek to enhance 100-fold. This proposal addresses critical challenges from next-generation samples demanding greater sensitivity, broader chemical coverage and reliable quantification to address issues including sub-cellular drug localisation and nanoscale molecular materials. It builds on our internationally-leading reputation for innovative ToF-SIMS instrumentation. The characteristics of the primary ion are fundamental in determining impact dynamics at the sample surface and the success of the resulting measurement. The challenge of producing intact secondary molecules from the sample has been largely solved using polyatomic cluster projectiles e.g. C60 and Ar2000 which produce ~100 sputtered molecules per impact. However, only ~0.001-0.1% of these molecules are produced as charged ions, which is necessary for their detection. Clearly there is huge room for improvement in the ionisation efficiency. The principle of projectile-initiated chemical reactions promoting ionisation of sputtered species has recently been firmly established by our work and that of others. We must now build on this knowledge and develop complementary approaches to meet the ionisation challenge and deliver quantitative compositional information. We have assembled a multidisciplinary team of international experts from academia and industry, which is uniquely positioned to pursue this important project. Building on >20 years' experience in innovation of SIMS instrumentation, enabled through EPSRC support and close collaboration with UK Industry, we will develop next-generation reactive ion beams and analytical methodology. This will deliver further transformative gains in performance which are critical to meet future application needs. Our novel results will be framed within the context of emerging theory to understand mechanisms of enhanced ionisation and to underpin the optimisation of projectile parameters. They will stimulate further development of theoretical models of the physical processes underlying SIMS and related techniques. The project is highly-adventurous, providing beyond state-of-the-art analytical capability underpinned with new fundamental understanding. We are ideally placed to exploit this through the interdisciplinary research collaborations at the Manchester Institute of Biotechnology and the Sir Henry Royce Institute for Advanced Materials. The vastly increased quality of data will result in new understanding in a wide range of applications spanning many areas of science and technology.