Stress-strain relationships for plastics

Imperial Users onl

Carrier-controlled ferromagnetism in SrTiO3

Magnetotransport and superconducting properties are investigated for uniformly La-doped SrTiO3 films and GdTiO3/SrTiO3 heterostructures, respectively. GdTiO3/SrTiO3 interfaces exhibit a high-density two-dimensional electron gas on the SrTiO3-side of the interface, while for the SrTiO3 films carriers are provided by the dopant atoms. Both types of samples exhibit ferromagnetism at low temperatures, as evidenced by a hysteresis in the magnetoresistance. For the uniformly doped SrTiO3 films, the Curie temperature is found to increase with doping and to coexist with superconductivity for carrier concentrations on the high-density side of the superconducting dome. The Curie temperature of the GdTiO3/SrTiO3 heterostructures scales with the thickness of the SrTiO3 quantum well. The results are used to construct a stability diagram for the ferromagnetic and superconducting phases of SrTiO3.Comment: Revised version that is closer to the published version; Fig. 2 correcte

Modelling trade offs between public and private conservation policies

To reduce global biodiversity loss, there is an urgent need to determine the most efficient allocation of conservation resources. Recently, there has been a growing trend for many governments to supplement public ownership and management of reserves with incentive programs for conservation on private land. At the same time, policies to promote conservation on private land are rarely evaluated in terms of their ecological consequences. This raises important questions, such as the extent to which private land conservation can improve conservation outcomes, and how it should be mixed with more traditional public land conservation. We address these questions, using a general framework for modelling environmental policies and a case study examining the conservation of endangered native grasslands to the west of Melbourne, Australia. Specifically, we examine three policies that involve: i) spending all resources on creating public conservation areas; ii) spending all resources on an ongoing incentive program where private landholders are paid to manage vegetation on their property with 5-year contracts; and iii) splitting resources between these two approaches. The performance of each strategy is quantified with a vegetation condition change model that predicts future changes in grassland quality. Of the policies tested, no one policy was always best and policy performance depended on the objectives of those enacting the policy. This work demonstrates a general method for evaluating environmental policies and highlights the utility of a model which combines ecological and socioeconomic processes.Comment: 20 pages, 5 figure

Deep Learning with Photonic Neural Cellular Automata

Rapid advancements in deep learning over the past decade have fueled an insatiable demand for efficient and scalable hardware. Photonics offers a promising solution by leveraging the unique properties of light. However, conventional neural network architectures, which typically require dense programmable connections, pose several practical challenges for photonic realizations. To overcome these limitations, we propose and experimentally demonstrate Photonic Neural Cellular Automata (PNCA) for photonic deep learning with sparse connectivity. PNCA harnesses the speed and interconnectivity of photonics, as well as the self-organizing nature of cellular automata through local interactions to achieve robust, reliable, and efficient processing. We utilize linear light interference and parametric nonlinear optics for all-optical computations in a time-multiplexed photonic network to experimentally perform self-organized image classification. We demonstrate binary classification of images in the fashion-MNIST dataset using as few as 3 programmable photonic parameters, achieving an experimental accuracy of 98.0% with the ability to also recognize out-of-distribution data. The proposed PNCA approach can be adapted to a wide range of existing photonic hardware and provides a compelling alternative to conventional photonic neural networks by maximizing the advantages of light-based computing whilst mitigating their practical challenges. Our results showcase the potential of PNCA in advancing photonic deep learning and highlights a path for next-generation photonic computers

Paper Session II-A - Polyimide Foam Insulation Materials for Aerospace Vehicles and Spaceport Applications

Advancements in high temperature materials by NASA have led to the development of polyimide foam systems with very attractive properties. The properties generated demonstrate the suitability of these materials for use as insulation for cryogenic fuel tanks on next generation vehicles, commercial and military ships, and potentially commercial aircraft. The significance of structural polyimide foams can be realized with a reduction in the overall weight of a launch vehicle. Due to a polyimide\u27 s high operating temperature ( \u27 \u27 260°C) structural polyimide foams can potentially reduce the amount of Thermal Protection System (TPS) and TPS integration structure that is required on launch vehicles. The lowtemperature elasticity of other polyimide foams is an enabling feature for many new cryogenic applications. These high performance materials also have properties that fulfill the demanding upcoming needs in ground support equipment for a Spaceport Technology Center. In a research study performed by Kennedy Space Center (KSC) and Langley Research Center (LaRC), polyimide foams were investigated for their physical, mechanical, thermal, and flammability properties. Variations in chemical structure, cell surface area, cell content and density on the resultant physical properties of the foams were studied. Data generated from this research revealed vital information involving foam technology and the interplay of factors such as foam density, open-closed cell content, surface area, and cell structure on the overall performance of the material. By controlling these parameters, new thermal insulation systems based on polyimide foam materials can be designed to meet demanding applications for spaceports and space vehicles

Full-field quantitative phase and polarisation-resolved imaging through an optical fibre bundle.

Flexible optical fibres, used in conventional medical endoscopy and industrial inspection, scramble phase and polarisation information, restricting users to amplitude-only imaging. Here, we exploit the near-diagonality of the multi-core fibre (MCF) transmission matrix in a parallelised fibre characterisation architecture, enabling accurate imaging of quantitative phase (error <0.3 rad) and polarisation-resolved (errors <10%) properties. We first demonstrate accurate recovery of optical amplitude and phase in two polarisations through the MCF by measuring and inverting the transmission matrix, and then present a robust Bayesian inference approach to resolving 5 polarimetric properties of samples. Our method produces high-resolution (9.0±2.6μm amplitude, phase; 36.0±10.4μm polarimetric) full-field images at working distances up to 1mm over a field-of-view up to 750×750μm 2 using an MCF with potential for flexible operation. We demonstrate the potential of using quantitative phase for computational image focusing and polarisation-resolved properties in imaging birefringence