18 research outputs found

    International Lower Limb Collaborative (INTELLECT) study: a multicentre, international retrospective audit of lower extremity open fractures

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    International lower limb collaborative (INTELLECT) study: a multicentre, international retrospective audit of lower extremity open fractures

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    Trauma remains a major cause of mortality and disability across the world1, with a higher burden in developing nations2. Open lower extremity injuries are devastating events from a physical3, mental health4, and socioeconomic5 standpoint. The potential sequelae, including risk of chronic infection and amputation, can lead to delayed recovery and major disability6. This international study aimed to describe global disparities, timely intervention, guideline-directed care, and economic aspects of open lower limb injuries

    International Lower Limb Collaborative (INTELLECT) study : a multicentre, international retrospective audit of lower extremity open fractures

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    Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

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    Contains fulltext : 172380.pdf (publisher's version ) (Open Access

    Adaptively Combined LMS and Logistic Equalizers

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    Operation and control of HVDC stations using continuous mixed p-norm-based adaptive fuzzy technique

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    © The Institution of Engineering and Technology. The power system is purely non-linear and there exist a lot of uncertainties in generation, transmission, and distribution sectors. This study focuses on the operation and control of a voltage source converter (VSC)-based high voltage direct current (HVDC) transmission system, which can cope up with system non-linearity and parameter uncertainties. The HVDC system connects an offshore wind farm to the onshore power grid. A new adaptive fuzzy logic controller is proposed for both onshore and offshore HVDC stations to control the real and reactive power flow in normal and undertrain parameter conditions. The adaptive technique is based on the continuous mixed p-norm algorithm, which updates the fuzzy inference system automatically at a high convergence speed. The performance of the proposed adaptive controller is tested under different parameter uncertainties and the results are compared with Taguchi optimisation-based proportional-integral controllers
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