126 research outputs found

    Machine learning outperforms clinical experts in classification of hip fractures

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    Hip fractures are a major cause of morbidity and mortality in the elderly, and incur high health and social care costs. Given projected population ageing, the number of incident hip fractures is predicted to increase globally. As fracture classification strongly determines the chosen surgical treatment, differences in fracture classification influence patient outcomes and treatment costs. We aimed to create a machine learning method for identifying and classifying hip fractures, and to compare its performance to experienced human observers. We used 3659 hip radiographs, classified by at least two expert clinicians. The machine learning method was able to classify hip fractures with 19% greater accuracy than humans, achieving overall accuracy of 92%

    Towards Symbolic Model-Based Mutation Testing: Combining Reachability and Refinement Checking

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    Model-based mutation testing uses altered test models to derive test cases that are able to reveal whether a modelled fault has been implemented. This requires conformance checking between the original and the mutated model. This paper presents an approach for symbolic conformance checking of action systems, which are well-suited to specify reactive systems. We also consider nondeterminism in our models. Hence, we do not check for equivalence, but for refinement. We encode the transition relation as well as the conformance relation as a constraint satisfaction problem and use a constraint solver in our reachability and refinement checking algorithms. Explicit conformance checking techniques often face state space explosion. First experimental evaluations show that our approach has potential to outperform explicit conformance checkers.Comment: In Proceedings MBT 2012, arXiv:1202.582

    Mechanical characterisation of polymer of intrinsic microporosity PIM-1 for hydrogen storage applications

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    Polymers of intrinsic microporosity (PIMs) are currently attracting interest due to their unusual combination of high surface areas and capability to be processed into free-standing films. However, there has been little published work with regards to their physical and mechanical properties. In this paper, detailed characterisation of PIM-1 was performed by considering its chemical, gas adsorption and mechanical properties. The polymer was cast into films, and characterised in terms of their hydrogen adsorption at −196 °C up to much higher pressures (17 MPa) than previously reported (2 MPa), demonstrating the maximum excess adsorbed capacity of the material and its uptake behaviour in higher pressure regimes. The measured tensile strength of the polymer film was 31 MPa with a Young’s modulus of 1.26 GPa, whereas the average storage modulus exceeded 960 MPa. The failure strain of the material was 4.4%. It was found that the film is thermally stable at low temperatures, down to −150 °C, and decomposition of the material occurs at 350 °C. These results suggest that PIM-1 has sufficient elasticity to withstand the elastic deformations occurring within state-of-the-art high-pressure hydrogen storage tanks and sufficient thermal stability to be applied at the range of temperatures necessary for gas storage applications
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