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In situ muon spin relaxation is demonstrated as an emerging technique that can provide a volume-averaged local probe of the ionic diffusion processes occurring within electrochemical energy storage devices as a function of state of charge. Herein, we present work on the conceptually interesting NASICON-type all-solid-state battery LiM2(PO4)3, using M = Ti in the cathode, M = Zr in the electrolyte, and a Li metal anode. The pristine materials are studied individually and found to possess low ionic hopping activation energies of ∼50−60 meV and competitive Li+ self-diffusion coefficients of ∼10^–10–10^–9 cm2 s^–1 at 336 K. Lattice matching of the cathode and electrolyte crystal structures is employed for the all-solid-state battery to enhance Li+ diffusion between the components in an attempt to minimize interfacial resistance. The cell is examined by in situ muon spin relaxation, providing the first example of such ionic diffusion measurements. This technique presents an opportunity to the materials community to observe intrinsic ionic dynamics and electrochemical behavior simultaneously in a nondestructive manner.

This report aims to provide built environment professionals with case study insights into the nature of GI decision making. Insights are shared from the project teams and stakeholders involved. These cover the GI benefits attained, the decision making process followed and the barriers, solutions and lessons learned. This report explores five Green Infrastructure (GI) case studies; Marks & Spencer Newcastle, Wild West End, Victory Oak, Kingsbrook and City of Trees. \ud \ud \ud \ud This report was prepared with funding from the Natural Environment Research Council (NERC) by the UK Green Building Council (UKGBC) with the University of Manchester and the University of Sheffield as part of project NE/N01748/1. The report compliments other outputs produced as part of the project. These include:\ud \ud • Practical how-to guide: Developing and implementing a green infrastructure strategy \ud \ud • Understanding Green Infrastructure at Different Scales\ud \ud \ud \ud The reports also features a ‘Green Infrastructure Best Practice: Route Map’. This provides a synthesis of the current GI best practice which applies to different decision making stages in the development life cycle. This was created based on interview centred research conducted for the Green Growth Project and follows the key steps set out in the Practical How-to-Guide.

Neural embeddings have been used with great success in Natural Language Processing (NLP). They provide compact representations that encapsulate word similarity and attain state-of-the-art performance in a range of linguistic tasks. The success of neural embeddings has prompted significant amounts of research into applications in domains other than language. One such domain is graph-structured data, where embeddings of vertices can be learned that encapsulate vertex similarity and improve performance on tasks including edge prediction and vertex labelling. For both NLP and graph based tasks, embeddings have been learned in high-dimensional Euclidean spaces. However, recent work has shown that the appropriate isometric space for embedding complex networks is not the flat Euclidean space, but negatively curved, hyperbolic space. We present a new concept that exploits these recent insights and propose learning neural embeddings of graphs in hyperbolic space. We provide experimental evidence that embedding graphs in their natural geometry significantly improves performance on downstream tasks for several real-world public datasets.

The most fundamental struggle for realizing a sustainable built environment still lies in the use of non-renewable resources in its articulation. Although effort has been taken to increase the use of sustainable materials (bio based, C2C, etc.) the vast majority of the building sector still relies heavily on steel and concrete. This article debates that the most fundamental contributors to sustainable development are the evaluation and incorporation of inhabitant capacities. Evaluating the proximity of available natural materials, inhabitant skills and tools could play a fundamental role in creating (social) sustainable societies and environments. However, inhabitant capacity models insufficiently cover the various capacities into one model (both inhabitant and community). Therefore, this article describes: a framework for evaluating inhabitant capacities; how to map available resource capacities; how these capacities can be incorporated into sustainable housing development and planning. The framework was developed as a part of a support tool, which helps designers and engineers to evaluate inhabitant capacities. To describe the framework and support tool a rural Sub- Saharan community is used- their capacities are relatively less complex compared to a ‘western’ or urban context. Moreover the articulated built environment is relatively uncomplicated. The article concludes that the framework shows great potential in reducing the use of unsustainable materials. Moreover, that it could enable