2,505 research outputs found
Generation of internal stress and its effects
Internal stresses may be generated continually in many polycrystalline materials. Their existence is manifested by changes in crystal defect concentration and arrangement, by surface observations, by macroscopic shape changes and particularly by alteration of mechanical properties when external stresses are simultaneously imposed
Automatic annotation of bioinformatics workflows with biomedical ontologies
Legacy scientific workflows, and the services within them, often present
scarce and unstructured (i.e. textual) descriptions. This makes it difficult to
find, share and reuse them, thus dramatically reducing their value to the
community. This paper presents an approach to annotating workflows and their
subcomponents with ontology terms, in an attempt to describe these artifacts in
a structured way. Despite a dearth of even textual descriptions, we
automatically annotated 530 myExperiment bioinformatics-related workflows,
including more than 2600 workflow-associated services, with relevant
ontological terms. Quantitative evaluation of the Information Content of these
terms suggests that, in cases where annotation was possible at all, the
annotation quality was comparable to manually curated bioinformatics resources.Comment: 6th International Symposium on Leveraging Applications (ISoLA 2014
conference), 15 pages, 4 figure
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Balancing macronutrient stoichiometry to alleviate eutrophication
Reactive nitrogen (N) and phosphorus (P) inputs to surface waters modify aquatic environments and affect public health and recreation. Until now, source control is the dominating measure of eutrophication management, and biological regulation of nutrients is largely neglected, although aquatic microbial organisms have huge potential to process nutrients. The stoichiometric ratio of organic carbon (OC) to N to P atoms should modulate heterotrophic pathways of aquatic nutrient processing, as high OC availability favours aquatic microbial processing. Such microbial processing removes N by denitrification and captures N and P as organically-complexed, less eutrophying forms. With a global data synthesis, we show that the atomic ratios of bioavailable dissolved OC to either N or P in rivers with urban and agricultural land use are often distant from a ‘microbial optimum’. This OC-deficiency relative to high availabilities of N and P likely overwhelms within-river heterotrophic processing and we propose that the capability of streams and rivers to retain N and P may be improved by active stoichiometric rebalancing. This rebalancing should be done by reconnecting appropriate OC sources such as wetlands and riparian forests, many of which have become disconnected from rivers concurrent to the progress of agriculture and urbanization. However, key knowledge gaps leave questions in the safe implementation of this approach in management: Mechanistic research is required to (i) evaluate system responses to catchment inputs of dissolved OC forms and amounts relative to internal-cycling controls of dissolved OC from aquatic production and particulate OC from aquatic and terrestrial sources and (ii) evaluate risk factors in anoxia-mediated P desorption with elevated OC scenarios. Still, we find this to be an approach with high potential for river management and we recommend to evaluate this stoichiometric approach for alleviating eutrophication, improving water quality and aquatic ecosystem health
Macro-, meso- and microstructural characterization of metallic lattice structures manufactured by additive manufacturing assisted investment casting
Cellular materials are recognized for their high specific mechanical properties, making them desirable in ultra-lightweight applications. Periodic lattices have tunable properties and may be manufactured by metallic additive manufacturing (AM) techniques. However, AM can lead to issues with un-melted powder, macro/micro porosity, dimensional control and heterogeneous microstructures. This study overcomes these problems through a novel technique, combining additive manufacturing and investment casting to produce detailed investment cast lattice structures. Fused filament fabrication is used to fabricate a pattern used as the mold for the investment casting of aluminium A356 alloy into high-conformity thin-ribbed (~ 0.6 mm thickness) scaffolds. X-ray micro-computed tomography (CT) is used to characterize macro- and meso-scale defects. Optical and scanning electron (SEM) microscopies are used to characterize the microstructure of the cast structures. Slight dimensional (macroscale) variations originate from the 3D printing of the pattern. At the mesoscale, the casting process introduces very fine (~ 3 µm) porosity, along with small numbers of (~ 25 µm) gas entrapment defects in the horizontal struts. At a microstructural level, both the (~ 70 μm) globular/dendritic grains and secondary phases show no significant variations across the lattices. This method is a promising alternative means for producing highly detailed non-stochastic metallic cellular lattices and offers scope for further improvement through refinement of filament fabrication.This work was supported by Portuguese FCT, under the reference project UIDB/04436/2020. We are grateful
to the funding from the European Research Council through the ERC grant CORREL-CT, number 695638 to
enable VHC to visit the Henry Royce Institute to undertake the X-ray CT studies. Tis work was supported by
the Henry Royce Institute for Advanced Materials, funded through EPSRC grants EP/R00661X/1, EP/S019367/1, EP/P025021/1 and EP/P025498/1 and the Henry Moseley X-ray Imaging Facility funded by EP/T02593X/1
Digital Fingerprinting of Microstructures
Finding efficient means of fingerprinting microstructural information is a
critical step towards harnessing data-centric machine learning approaches. A
statistical framework is systematically developed for compressed
characterisation of a population of images, which includes some classical
computer vision methods as special cases. The focus is on materials
microstructure. The ultimate purpose is to rapidly fingerprint sample images in
the context of various high-throughput design/make/test scenarios. This
includes, but is not limited to, quantification of the disparity between
microstructures for quality control, classifying microstructures, predicting
materials properties from image data and identifying potential processing
routes to engineer new materials with specific properties. Here, we consider
microstructure classification and utilise the resulting features over a range
of related machine learning tasks, namely supervised, semi-supervised, and
unsupervised learning.
The approach is applied to two distinct datasets to illustrate various
aspects and some recommendations are made based on the findings. In particular,
methods that leverage transfer learning with convolutional neural networks
(CNNs), pretrained on the ImageNet dataset, are generally shown to outperform
other methods. Additionally, dimensionality reduction of these CNN-based
fingerprints is shown to have negligible impact on classification accuracy for
the supervised learning approaches considered. In situations where there is a
large dataset with only a handful of images labelled, graph-based label
propagation to unlabelled data is shown to be favourable over discarding
unlabelled data and performing supervised learning. In particular, label
propagation by Poisson learning is shown to be highly effective at low label
rates
Polarization Selection Rules and Superconducting Gap Anisotropy in
We discuss polarization selection rules for angle-resolved photoemission
spectroscopy in Bi2212. Using these we show that the ``hump'' in the
superconducting gap observed in the quadrant in our earlier work is not on
the main band, but rather on an umklapp band arising from the
structural superlattice. The intrinsic gap is most likely quite small over a
range of about the diagonal directions.Comment: 3 pages, revtex, 3 uuencoded postscript figure
The Debrisoft ® monofilament debridement pad for use in acute or chronic wounds: A NICE medical technology guidance
As part of its Medical Technology Evaluation Programme, the National Institute for Health and Care Excellence (NICE) invited a manufacturer to provide clinical and economic evidence for the evaluation of the Debrisoft ® monofilament debridement pad for use in acute or chronic wounds. The University of Birmingham and Brunel University, acting as a consortium, was commissioned to act as an External Assessment Centre (EAC) for NICE, independently appraising the submission. This article is an overview of the original evidence submitted, the EAC’s findings and the final NICE guidance issued. The sponsor submitted a simple cost analysis to estimate the costs of using Debrisoft® to debride wounds compared with saline and gauze, hydrogel and larvae. Separate analyses were conducted for applications in home and applications in a clinic setting. The analysis took an UK National Health Service (NHS) perspective. It incorporated the costs of the technologies and supplementary technologies (such as dressings) and the costs of their application by a district nurse. The sponsor concluded that Debrisoft® was cost saving relative to the comparators. The EAC made amendments to the sponsor analysis to correct for errors and to reflect alternative assumptions. Debrisoft® remained cost saving in most analyses and savings ranged from £77 to £222 per patient compared with hydrogel, from £97 to £347 compared with saline and gauze, and from £180 to £484 compared with larvae depending on the assumptions included in the analysis and whether debridement took place in a home or clinic setting. All analyses were severely limited by the available data on effectiveness, in particular a lack of comparative studies and that the effectiveness data for the comparators came from studies reporting different clinical endpoints compared with Debrisoft®. The Medical Technologies Advisory Committee made a positive recommendation for adoption of Debrisoft® and this has been published as a NICE medical technology guidance (MTG17).The Birmingham and Brunel Consortium is funded by NICE to act as an External Assessment Centre for the Medical Technologies Evaluation Programme
Residual stress control of multipass welds using low transformation temperature fillers
Low transformation temperature (LTT) weld fillers can be used to replace tensile weld residual stresses with compressive ones and reduce the distortion of single-pass welds in austenitic plates. By contrast, weld fillers in multipass welds experience a number of thermal excursions, meaning that the benefit of the smart LTT fillers may not be realised. Here, neutron diffraction and the contour method are used to measure the residual stress in an eight pass groove weld of a 304 L stainless steel plate using the experimental LTT filler Camalloy 4. Our measurements show that the stress mitigating the effect of Camalloy 4 is indeed diminished during multipass welding. We propose a carefully selected elevated interpass hold temperature and demonstrate that this restores the LTT capability to successfully mitigate residual tensile stresses
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