University of South Australia

About: University of South Australia is a education organization based out in Adelaide, South Australia, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 10086 authors who have published 32587 publications receiving 913683 citations. The organization is also known as: The University of South Australia & UniSA.

Interventions to prevent back pain and back injury in nurses: a systematic review

Abstract: A systematic literature review was undertaken to assess the effectiveness of interventions that aim to prevent back pain and back injury in nurses. Ten relevant databases were searched; these were examined and reference lists checked. Two reviewers applied selection criteria, assessed methodological quality and extracted data from trials. A qualitative synthesis of evidence was undertaken and sensitivity analyses performed. Eight randomised controlled trials and eight non-randomised controlled trials met eligibility criteria. Overall, study quality was poor, with only one trial classified as high quality. There was no strong evidence regarding the efficacy of any interventions aiming to prevent back pain and injury in nurses. The review identified moderate level evidence from multiple trials that manual handling training in isolation is not effective and multidimensional interventions are effective in preventing back pain and injury in nurses. Single trials provided moderate evidence that stress management programs do not prevent back pain and limited evidence that lumbar supports are effective in preventing back injury in nurses. There is conflicting evidence regarding the efficacy of exercise interventions and the provision of manual handling equipment and training. This review highlights the need for high quality randomised controlled studies to examine the effectiveness of interventions to prevent back pain and injury in nursing populations. Implications for future research are discussed.

Adaptive filtering algorithms with selective partial updates

Abstract: In some applications of adaptive filtering such as active noise reduction, and network and acoustic echo cancellation, the adaptive filter may be required to have a large number of coefficients in order to model the unknown physical medium with sufficient accuracy. The computational complexity of adaptation algorithms is proportional to the number of filter coefficients. This implies that, for long adaptive filters, the adaptation task can become prohibitively expensive, ruling out cost-effective implementation on digital signal processors. The purpose of partial coefficient updates is to reduce the computational complexity of an adaptive filter by adapting a block of the filter coefficients rather than the entire filter at every iteration. In this paper, we develop a selective-partial-update normalized least-mean-square (NLMS) algorithm, and analyze its stability using the traditional independence assumptions and error-energy bounds. Selective partial updating is also extended to the affine projection (AP) algorithm by introducing multiple constraints. The new algorithms appear to have good convergence performance as attested to by computer simulations with real speech signals.