Manual therapy for chronic migraine: a pragmatic randomised controlled trial study protocol

Introduction Chronic migraine is a largely refractory condition affecting between 1 and 2.2% of the overall population worldwide, with females more affected than males. There are also high health and socioeconomic costs associated both for the individual and society. The mainstay of chronic migraine management is pharmacological, but the options available have limited efficacy and there are often unwanted side effects. There is some evidence for manual therapy as a treatment option for migraine, but its effectiveness for chronic migraine is unknown. Therefore, we have designed a pragmatic randomised control trial to investigate whether adding manual therapy to the tertiary specialist treatment of chronic migraine improves patient-reported outcomes. Methods A pragmatic, randomised controlled trial in a hospital tertiary headache clinic. Participants will be randomised into one of two groups: treatment as usual or treatment as usual plus manual therapy. The primary outcome measure will be a change in the Headache Impact Test score. Secondary outcomes will also be measured over the 12-week study period including changes in headache frequency, migraine specific quality of life and reductions in relevant medicine consumption. The manual therapy group will have five treatment sessions each lasting 30 min. The recruitment target of 64 participants will allow power at 80% with p = 0.05 using minimal clinical difference for Headache Impact Test of 3.7 and includes provision for a 10% dropout rate. Recruitment will take place between August 2018 and February 2019. The results will form part of a doctoral study and be published in peer-reviewed journals and presented at national/international conferences. Discussion Current pharmacological approaches have limited effects in the management of chronic migraine and there is a requirement to improve treatment options and reduce the health and economic burden of the condition. Manual therapy has been shown to be effective in other chronic pain conditions as well as other primary headaches. This study will explore the effectiveness of manual therapy as an adjunctive approach to the management of chronic migraine. Trial registration The trial has received a favourable opinion from the UK Health Research Authority (IRAS 228901) and is registered at ClinicalTrials.gov.number NCT03395457. Registered 1st March 2018

A sub-stepping iterative constitutive model for cyclic cracking-crushing-shearing in masonry interface elements

This paper proposes a sub-step based iterative constitutive model for line interface elements used to analyse masonry structures loaded in-plane. Based on a total deformation theory, the model adopts characteristics of multi-surface plasticity, including a Coulomb friction failure surface for shear, with tension and compression cut-off and softening for all three domains. The model is driven by two damage parameters, one for compression and one that couples tension and shear. The sub-stepping technique is demonstrated to be numerically stable and is used as an alternative to the traditional return-mapping algorithms, which are prone to convergence issues and instability. The proposed model has been validated against experimental tests performed on masonry walls subjected to cyclic, in-plane loading. The numerical simulations adequately identify the failure mode, the hysteretic behaviour and the crack pattern. When toe crushing is governing, the results appear to be sensitive to the assumed masonry compressive strength. It is shown that calibration of the lumped compressive strength makes possible to fully describe the damage evolution in walls that exhibit a mix of flexural crack-crush failure and shear failure. Overall, the model is demonstrated to be an efficient and robust tool for analysing the cyclic, in-plane behaviour of masonry walls.Applied Mechanic

Artificial Neural Network-Based Clutter Reduction Systems for Ship Size Estimation in Maritime Radars

The existence of clutter in maritime radars deteriorates the estimation of some physical parameters of the objects detected over the sea surface. For that reason, maritime radars should incorporate efficient clutter reduction techniques. Due to the intrinsic nonlinear dynamic of sea clutter, nonlinear signal processing is needed, what can be achieved by artificial neural networks (ANNs). In this paper, an estimation of the ship size using an ANN-based clutter reduction system followed by a fixed threshold is proposed. High clutter reduction rates are achieved using 1-dimensional (horizontal or vertical) integration modes, although inaccurate ship width estimations are achieved. These estimations are improved using a 2-dimensional (rhombus) integration mode. The proposed system is compared with a CA-CFAR system, denoting a great performance improvement and a great robustness against changes in sea clutter conditions and ship parameters, independently of the direction of movement of the ocean waves and ships