Electronic properties of metal oxides such as superconductivity and colossal magnetoresistance are important both for fundamental science and for applications. We aim to discover new materials with notable properties by preparing new or ill-characterised perovskites at high temperatures (1000 C) and pressures (5-20 GPa) - perovskites are dense phases and so are favoured under such conditions. Target materials include cubic and layered Cr4+ perovskites, new Bi materials with multiferroic properties, and new magnetic cuprate superconductors. A press and a Walker multianvil module are requested for synthesis. The materials will be characterised by X-ray and neutron diffraction and electron microscopy, and conducting, magnetic and ferroelectric properties will be measured. The project will benefit from collaborations with other UK groups for measurements and with leading European high pressure synthesis laboratories (through an EU COST network).

Micromegas detectors are used in a variety of physics projects including in low-energy nuclear physics and neutron-beam related detectors. With this proposal we intend to develop a “transparent” orthogonal strip Micromegas neutron detector with unprecedented position and time resolving data acquisition capabilities, for use at the major neutron time-of-flight facilities. Typical usages are neutron flux and reaction cross section measurements, and neutron beam imaging. A very thin detector with both a segmented anode and segmented mesh, coupled to the dedicated VMM3 chip developed for Micromegas detectors, will lead to an innovative detection device. The new detector will also be used as a time-projection chamber (TPC) to investigate angular distribution measurements of reaction particles of interest for nuclear reaction studies and nuclear data. The TPC mode will be tested in the neutron beam of GELINA with light charged particles and fission fragments.

Heart Failure (HF) is defined by the heart's reduced ability to pump blood due to a drop in cellular contractility, enlarged anatomy and increased coronary micro-vascular resistance. This loss of pump function accounts for a significant increase in both mortality and morbidity in western society. With the U.K.'s elderly population expanding, HF is rapidly becoming an epidemic. There is currently a 1 in 5 life-time risk of HF and costs associated with acute and long term hospital treatments are accelerating. The significance of the disease has motivated the application of state of the art clinical imaging techniques to aid diagnosis and clinical planning. Measurements of cardiac wall motion, chamber flow patterns and coronary perfusion currently provide high resolution data sets for characterising HF patients. However, the clinical practice of using population-based metrics derived from separate image sets often indicates contradictory treatments plans due to inter-individual variability in pathophysiology. Thus, despite imaging advances, determining optimal treatment strategies for HF patients remains problematic. To exploit the full value of imaging technologies, and the combined information content they produce, requires the ability to integrate multiple types of functional data into a consistent framework. This in turn will support a paradigm shift away from predefined clinical indices determining treatment options and a move towards true personalisation of care based on an individual's physiology.An exciting and highly promising strategy for underpinning this shift is the assimilation of multiple image sets into personalised and biophysically consistent mathematical models. The development of such models provides the ability to capture the multi-factorial cause and effect relationships which link the underlying pathophysiological mechanisms. Furthermore, using a biophysical basis presents unique opportunities to assist with treatment decisions through the derivation of quantities that cannot be imaged but are likely to play a key mechanistic role in HF e.g. tissue stress and pump efficiency.In parallel with imaging advances the approach is also underpinned by the ongoing development of complementary technologies, including improved numerical methods and increased performance per unit cost of computing. This computational progress has accelerated the addition of multi-physics functionality to a range of organ models which have recently been organized into international initiatives such as the IUPS sponsored Physiome and VPH projects. Within these programmes the heart is arguably the most advanced current exemplar of an integrated organ model. As such it represents a promising first candidate with which to focus on an important human disease.My goal during this fellowship will be to focus on personalising and applying these models in clinical and industrial settings for treating HF patients. Model simulations will be focused on quantifying diagnosis, aiding patient selection and guiding interventional planning for specific treatments carried out by leading clinicians based in the cardio-vascular imaging group at Kings College London (KCL). In addition to this direct clinical application of the model, the research will also be focused on the tuning of Left Ventricular Assist Devices (LVADs) which are often connected to the heart in HF to reduce mechanical load by pumping blood from the left ventricle directly into the aorta. Through these applications my aim is to both improve our understanding of this significant cardiovascular disease and demonstrate the potential of biophysical models for improving human healthcare.

Heart Failure (HF) is defined by the heart's reduced ability to pump blood due to a drop in cellular contractility, enlarged anatomy and increased coronary micro-vascular resistance. This loss of pump function accounts for a significant increase in both mortality and morbidity in western society. With the U.K.'s elderly population expanding, HF is rapidly becoming an epidemic. There is currently a 1 in 5 life-time risk of HF and costs associated with acute and long term hospital treatments are accelerating. The significance of the disease has motivated the application of state of the art clinical imaging techniques to aid diagnosis and clinical planning. Measurements of cardiac wall motion, chamber flow patterns and coronary perfusion currently provide high resolution data sets for characterising HF patients. However, the clinical practice of using population-based metrics derived from separate image sets often indicates contradictory treatments plans due to inter-individual variability in pathophysiology. Thus, despite imaging advances, determining optimal treatment strategies for HF patients remains problematic. To exploit the full value of imaging technologies, and the combined information content they produce, requires the ability to integrate multiple types of functional data into a consistent framework. This in turn will support a paradigm shift away from predefined clinical indices determining treatment options and a move towards true personalisation of care based on an individual's physiology.An exciting and highly promising strategy for underpinning this shift is the assimilation of multiple image sets into personalised and biophysically consistent mathematical models. The development of such models provides the ability to capture the multi-factorial cause and effect relationships which link the underlying pathophysiological mechanisms. Furthermore, using a biophysical basis presents unique opportunities to assist with treatment decisions through the derivation of quantities that cannot be imaged but are likely to play a key mechanistic role in HF e.g. tissue stress and pump efficiency.In parallel with imaging advances the approach is also underpinned by the ongoing development of complementary technologies, including improved numerical methods and increased performance per unit cost of computing. This computational progress has accelerated the addition of multi-physics functionality to a range of organ models which have recently been organized into international initiatives such as the IUPS sponsored Physiome and VPH projects. Within these programmes the heart is arguably the most advanced current exemplar of an integrated organ model. As such it represents a promising first candidate with which to focus on an important human disease.My goal during this fellowship will be to focus on personalising and applying these models in clinical and industrial settings for treating HF patients. Model simulations will be focused on quantifying diagnosis, aiding patient selection and guiding interventional planning for specific treatments carried out by leading clinicians based in the cardio-vascular imaging group at Kings College London (KCL). In addition to this direct clinical application of the model, the research will also be focused on the tuning of Left Ventricular Assist Devices (LVADs) which are often connected to the heart in HF to reduce mechanical load by pumping blood from the left ventricle directly into the aorta. Through these applications my aim is to both improve our understanding of this significant cardiovascular disease and demonstrate the potential of biophysical models for improving human healthcare.

The problem: Building climate change resilience necessarily means building urban resilience. Africa's future is dominated by a rapidly increasing urban population with complicated demographic, economic, political, spatial and infrastructural transitions. This creates complex climate vulnerabilities of critical consequence in the co-dependent city-regions. Climate change substantially complicates the trajectories of African development, exacerbated by climate information that is poorly attuned to the needs of African decision makers. Critical gaps are how climate processes interact at the temporal and spatial scales that matter for decision making, limited institutional capacity to develop and then act on climate information, and inadequate means, methods, and structures to bridge the divides. Current modalities in climate services are largely supply driven and rarely begin with the multiplicity of climate sensitive development challenges. There is a dominant need to address this disconnect at the urban scale, yet climate research in Africa is poorly configured to respond, and the spatial scale and thematic foci are not well attuned to urban problems. Most climate-related policies and development strategies focus at the national scale and are sectorally based, resulting in a poor fit to the vital urban environments with their tightly interlocking place-based systems. Response: FRACTAL's aim is to advance scientific knowledge about regional climate responses to anthropogenic forcings, enhance the integration of this knowledge into decision making at the co-dependent city-region scale, and thus enable responsible development pathways. We focus on city-region scales of climate information and decision making. Informed by the literature, guided by co-exploration with decision makers, we concentrate on two key cross-cutting issues: Water and Energy, and secondarily their influence on food security. We work within and across disciplinary boundaries (transdisciplinarity) and develop all aspects of the research process in collaboration with user groups (co-exploration).The project functions through three interconnected work packages focused on three Tier 1 cities (Windhoek, Maputo and Lusaka), a secondary focus on three Tier 2 cities (Blantyre, Gaborone and Harare), and two self-funded partner cities (Cape Town and eThekwini). Work Package 1 (WP1) is an ongoing and sustained activity operating as a learning laboratory for pilot studies to link research from WP2 and 3 to a real world iterative dialogue and decision process. WP1 frames, informs, and steers the research questions of WP2 and 3, and so centres all research on needs for responsible development pathways of city-region systems. WP2 addresses the decision making space in cities; the political, economic, technical and social determinants of decision making, and seeks to understand the opportunities for better incorporation of climate information into local decision making contexts. WP3, the majority effort, focuses on advancing understanding of the physical climate processes that govern the regional system, both as observed and simulated. This knowledge grounds the development of robust and scale relevant climate information, and the related analysis and communication. This is steered explicitly by WP1's perspective of urban climate change risk, resilience, impacts, and decisions for adaptation and development. The project will frame a new paradigm for user-informed, knowledge-based decisions to develop pathways to resilience for the majority population. It will provide a step change in understanding the cross-scale climate processes that drive change and so enable enhanced uptake of climate information in near to medium-term decision making. The project legacy will include improved scientific capacity and collaboration, provide transferable knowledge to enhance decision making on the African continent, and in this make significant contribution to academic disciplines.