The safe operation of engineering structures is vital in safety-critical industries such as power generation, nuclear, aerospace and oil and gas. Structural failures can have catastrophic consequences in terms of loss of life and financial circumstances. Meanwhile there is a strong interest in reducing costs, light-weighting, increasing design life and life extension. To this end, reliable structural integrity assessments are essential at the design stage and through in-service life to ensure continuous profitable operation of assets. Residual stresses are inevitably introduced in engineering structures during manufacturing processes. Their presence can have adverse effects on the behaviour of structures and contribute to driving force promoting various degradation mechanisms. Therefore, it is of paramount importance that the state of residual stresses in engineering structures is carefully and reliably characterised so that remedial actions could be taken to enhance the lifetime of current materials or novel designs and manufacturing methods developed and optimised. The contour method, first presented in 2000, is emerging as a powerful technique for the measurement of residual stresses in bulky parts. The method involves making a straight cut in the component of interest along a nominally flat plane where residual stresses are desired to be determined. The created cut surfaces deform due to the relaxation of residual stresses. The deformation of the cut surfaces are measured and then used to back-calculate 2D distribution of residual stress that was present along the flat plane prior to the cut. Nevertheless, there are still several limitations associated with application of the contour method: a) only a straight cut over a flat plane is used to section components for contour measurements; b) the standard method can only measure 2D distribution of one component of the residual stress tensor over a flat plane; c) the method is limited to symmetric sectioning of the cut parts, d) like other mechanical strain relief techniques, the contour method is prone to plasticity-induced errors where the magnitude of stresses or level of triaxiality is very high and e) most historical measurements using the contour method have concerned simple geometries such as welded rectilinear plates. For the first time, the "Complex Contour Method" proposes to develop the use of complex cutting paths, for example non-planar and closed complex cutting paths instead of cutting along a flat plane. This innovative approach will radically bring new capabilities for the contour method in several ways: it will unlock map of residual stress in multiple directions simultaneously. Of a true step change is extending the application of the technique to measure 3D maps of residual stress. Enabling the technique to deal with complex cutting paths will inherently deal with limitations of the standard method regarding symmetry of the cut parts. Moreover, removing the constraint of a symmetric planar cut opens the potential to mitigate plasticity-induced errors that can accompany standard contour method cuts. Of another radical step change of the application of the complex cutting paths is that it enables the technique to be implemented on complex engineering structures. For example, the conventional contour method confined to symmetric planar cuts cannot be applied to complex components such as tube penetration welds for pressure vessel heads. The proposed research has the potential to provide far more complete residual stress information about safety critical components of high interest to engineers in the aerospace, petrochemical, power generation and nuclear industries. In addition for industrial applications, a single complex contour cut offers a far more cost effective tool compared to the cumbersome and time consuming conventional contour method using multiple-method and multiple-cut approaches.

The EPSRC Centre for Doctoral Training in Statistics and Machine Learning (StatML) will address the EPSRC research priority of the 'physical and mathematical sciences powerhouse' through an innovative cohort-based training program. StatML harnesses the combined strengths of Imperial and Oxford, two world-leading institutions in statistics and machine learning, in collaboration with a broad spectrum of industry partners, to nurture the next generation of leaders in this field. Our students will be at the forefront of advancing the core methodologies of data science and AI, crucial for unlocking the value inherent in data to benefit industry and society. They will be equipped with advanced research, technical, and practical skills, enabling them to make tangible real-world impacts. Our students will be ethical and responsible innovators, championing reproducible research and open science. Collaborating with students, charities and equality experts, StatML will also pioneer a comprehensive strategy to promote inclusivity, attract individuals from diverse backgrounds and eliminate biases. This will help diversify the UK's future statistics and machine learning workforce, essential for ensuring data science is used for public good. Data science and AI are now part of our everyday lives, transforming all sectors of the economy. To future-proof the UK's prosperity and security, it is essential to develop new methodology, specifically tailored to meet the big societal challenges of the future. The techniques underpinning such methods are founded in statistics and machine learning. Through close collaboration with a broad range of industry partners, our cohort-based training will support the UK in producing a critical mass of world-leading researchers with expertise in developing cutting-edge, impactful statistical and machine learning methodology and theory. It is well documented in government and learned society reports that the UK economy has an urgent need for these people. The significant level of industry support for our proposal also highlights the necessity of filling this gap in the UK data science ecosystem. StatML will learn from and build upon our previous successful experiences in cohort training of doctoral students (our existing StatML CDT funded in 2018, as well as other CDTs at Imperial and Oxford). Our students will continue to produce impactful, internationally leading research in statistics and machine learning (as evidenced by our students' impressive publication record and our world-leading research environment, as rated by the REF 2021 evaluation), while complementing this with a bespoke cohort-based Advanced Training program in Statistics and Machine Learning (StatML-AT). StatML-AT has been developed from our experience and in partnership with industry. It will be responsive to emerging technologies and equip our students with the practical skills required to transform how data is used. It will be delivered by our outstanding academics from both institutions alongside with industry leaders to ensure that students receive training in cutting edge technologies, along with the latest ideas in ethics, responsible innovation, sustainability and entrepreneurship. This will be complemented by industrial and academic placements to allow the students to develop their own international network and produce high-impact research. Together, StatML and its partners will train 90+ students over 5 cohorts. More than half of these will be funded from external sources, including 25+ by industry, representing excellent value for money. Our diverse cohorts will benefit from a unique and responsive training program combining academic excellence, industry engagement, and interdisciplinary culture. This will make StatML a vibrant research environment inspiring the next methodological advancements to transform the use of data and AI across industry and society.

The most fundamental task in information security is to establish what we mean by saying that information is secure: what is it that we are trying to achieve? One subfield of information security that takes great care in tending to its definitions is cryptography. Indeed, finding the correct security definition for a cryptographic primitive or protocol is a critical part of cryptographic work. However, these security notions -- and everything that depends on them -- do not exist in a vacuum. While the immediate objects of cryptography are not social relations, it presumes and models them. This fact is readily acknowledged in the introductions of cryptographic works where authors illustrate the utility of their proposed constructions by reference to some social situation where several parties have conflicting ends but a need or desire to interact. Yet, this part of the definitional work has not received the same rigour from the cryptographic community as complexity-theoretic and mathematical questions. The broader social sciences offer a wealth of approaches to answering questions about social situations, relations, (collective) needs, imaginations and desires. However, they are often relegated to a service role in information security, e.g. to perform usability testing of existing security technologies after those have been designed. In contrast, in this project we ask social science to establish core notions for technology. To establish what security means within social settings -- to identify and understand security concerns -- one approach stands out in promising deep and detailed insights: ethnography. Ethnography is uniquely placed to "unearth what the group (under study) takes for granted". A key challenge in engaging those who depend on security technology is that they are not trained information security professionals. They do not know and, indeed, should not need to know, for example, that confidentiality requires integrity, that existing onboarding practices can be phrased in the language of information security, which different security notions cannot be achieved simultaneously and what guarantees, say, cryptography, can give if asked. Therefore, to know exactly what is taken for granted, or put otherwise, expected or desired, in social interactions, social and technical protocols and, indeed, cryptography is of critical import. Some more commonly relied upon social science methods in information security, while much more practical and less time consuming than ethnography, are therefore less suitable research approaches in this context. For example, questionnaires and surveys, both the qualitative and quantitative kind, are limited means of inquiry here. While interviews provide some opportunity for deeper engagement, ethnography allows us to learn that which people do not know themselves. Through close observations and analysis of everyday activities and relations, ethnography reveals "the knowledge and meaning structures that provide the blueprint for social action" within the group under study. The exploratory nature of ethnographic inquiry, rooted in fieldwork with the group it aims to understand, is thus a key enabler in unlocking an understanding of individual and collective security needs and practices. The inherently reflexive and embedded nature of ethnography enables such insights. In this project we adopt this approach to the specific example settings of large-scale protests. These, on the one hand, offer rich and diverse settings where security needs are paramount, while also being sufficiently different from standard cryptographic use-cases (e.g. in e-commerce) to promise novel insights. Based on our ethnographic findings, we will study existing technologies on whether they satisfy the security needs identified and will design novel cryptographic notions and solutions to satisfy these identified needs.

Title: novel routes to the activation of gene transcription by synaptic activity: Brain cells (neurons) communicate with each other by releasing chemical messengers (neurotransmitters) onto each other at structures called synapses, a process called 'synaptic activity'. These messengers are detected by special channels on the cell surface, which then open and allows calcium and sodium ions to flow into the cell. This triggers the release of neurotransmitter onto yet more neurons. This means of neuron-to-neuron communication is the way by which information flows round the brain. However, 'synaptic activity' also triggers changes inside neurons. The calcium ions which flow into the neuron activate signal pathways, which in turn activate the transcription of genes. Transcription is a crucial step in the process whereby genes (made of DNA and located in the nucleus) are read by the cell's machinery and decoded into new proteins. These new proteins are crucial for many fundamental processes in the neuron. For example, learning and memory involves changes in the way neurons communicate with each other, and this process relies on these new proteins made in response to 'synaptic activity'. These new proteins also control how neurons in the brain develop from the foetus, through infancy and on to adulthood. Equally importantly, these new proteins also make individual neurons healthier and more likely to survive for longer than neurons that don't experience synaptic activity. Therefore, an understanding of how synaptic activity activates gene transcription is an important problem for scientists studying the brain. Our proposed research will characterise a completely new way by which genes can be activated by synaptic activity. The transcription of many genes is suppressed by special molecules called corepressors. One particularly important one is called SMRT, which represses many different genes in the nucleus by blocking the action of the cell's transcription machinery. We have recently discovered that when calcium ions flow into neurons following synaptic activity, signals in the neuron are activated which cause SMRT to leave the nucleus and go into the cytoplasm. Once in the cytoplasm, SMRT is unable to suppress transcription because the genes and transcription machinery are all in the nucleus. Therefore these genes become much easier to activate. Our work will uncover the exact signalling events that take place that make SMRT stop repressing transcription in the nucleus, and go into the cytoplasm. In addition, we will identify exactly what type of genes are likely to be influenced by this 'export' of SMRT. We will also determine the effect that SMRT export has on the way in which a neuron develops, looking particularly at the way a neuron changes shape as it matures. Because SMRT is known to repress the transcription of so many types of gene, signals that stop SMRT from working have the potential to have a big effect on the neuron. As mentioned earlier, the activation of gene transcription by synaptic activity controls many very important processes. SMRT export triggered by synaptic activity is a previously undiscovered route by which transcription of many genes can be turned on. Therefore understanding the mechanism and consequences of this process is of utmost importance. While this work is centred on the study of neurons, SMRT represses genes in many cell types, so the relevance of this work is not restricted to neurons. Furthermore, calcium ions don't just have effects in neurons, they are able to activate signalling pathways in all types of cell, from white blood cells to egg cells. The gene transcription that calcium ions activate in these cells are important for other processes, such as for white blood cells to fight infection. therefore our discoveries regarding how calcium activates gene transcription in neurons will be of benefit to scientists researching a wide variety of problems.

Statistical theory and methods play a fundamental role in scientific discovery and advancement, including in modern astronomy, where data are collected on increasingly massive scales and with more varieties and complexity. New technology and instrumentation are spawning a diverse array of emerging data types and data analytic challenges, which in turn require and inspire ever more innovative statistical methods and theories. This proposal is guided by the dual aims of advancing statistical foundations and frontiers, motivated by astronomical problems and providing principled data analytic solutions to challenges in astronomy. The CHASC International Center for Astrostatistics has an extensive track record in accomplishing both tasks. This NSF-EPSRC project leverages CHASC's track record to make progress in several new projects. Fitting sophisticated astrophysical models to complex data that were collected with high-tech instruments, for example, often involves a sequence of statistical analyses. Several UK-led projects center on developing new statistical methods that properly account for errors and carry uncertainty forward within such sequences of analyses. Additional US-led work will focus on developing theoretical properties of novel statistical estimation procedures to address data-analytic challenges associated with solar flares and X-ray observations. Other US-led projects involve fast and automatic detection of astronomical objects such as galaxies from 2D or even 4D data. The PIs will develop statistical theory and methods in the context of these projects, building statistical foundations and pushing the frontiers of statistics forward for broad impact that will extend well beyond astrostatistics. The PIs plan to offer effective methods and algorithms for tackling emerging challenges in astronomy, with the aspiration of promoting such principled data-analytic methods among researchers in astronomy. Its provision of free software via the CHASC GitHub Software Library will enable the distribution and impact of the proposed methods and algorithms.