5,847 research outputs found

    Optimisation of anticoagulation in patients with atrial fibrillation

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    Atrial fibrillation is a common cardiac arrhythmia associated with debilitating complications, one of which is stroke. Anticoagulants (warfarin and the non-vitamin K antagonist oral anticoagulants) are recommended for stroke prophylaxis, their utilisation however requires stroke risk reduction to be balanced against hemorrhage risk. Current review of the literature suggests that despite the presence of risk stratification tools such as the CHADS2 and the newer CHA2DS2-VASc, clinicians often find it challenging to anticipate the risk-benefit ratio of anticoagulation. This results in both the underuse and overuse of anticoagulation in patients as well as uncertainty over whether to use anticoagulation in paroxysmal AF. This review looks at optimising anticoagulation by improving the assessment of bleeding risk and by improving the assessment of stroke risk. The percutaneous occlusion of the left atrial appendage is an emerging alternative to oral anticoagulation therapy.peer-reviewe

    Jet Identification with Zest

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    We present a new observable zest and demonstrate its potential to differentiate between jets originated by gluons, top quark and vector bosons. Zest has salient properties such as boost invariance, stability against global color flow of partons and inclusion or exclusion of a few soft particles to the jet. For a gluon jet, zest distribution is also insensitive to the jet mass. We show that when zest is used in conjunction with other observables, it can yield high gluon rejection while retaining high signal sample.Comment: 3 pages, 5 figures, XXII DAE-BRNS Symposium Proceeding

    Using Big Data to Enhance the Bosch Production Line Performance: A Kaggle Challenge

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    This paper describes our approach to the Bosch production line performance challenge run by Kaggle.com. Maximizing the production yield is at the heart of the manufacturing industry. At the Bosch assembly line, data is recorded for products as they progress through each stage. Data science methods are applied to this huge data repository consisting records of tests and measurements made for each component along the assembly line to predict internal failures. We found that it is possible to train a model that predicts which parts are most likely to fail. Thus a smarter failure detection system can be built and the parts tagged likely to fail can be salvaged to decrease operating costs and increase the profit margins.Comment: IEEE Big Data 2016 Conferenc

    Multi Dimensional Deprivation in India during and after the Reforms: Do the Household Expenditure and the Family Health Surveys Present Consistent Evidence?

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    This paper uses the recent approach of multidimensional deprivation measures to provide a comprehensive and wide ranging assessment of changes to living standards in India during the period, 1992/93-2004/5.This covers the reforms and the immediate post reforms time periods. The study is based on the simultaneous use of two parallel data sets, namely the NSS and NFHS data sets covering proximate rounds and near identical time periods. The study is conducted both at regionally disaggregated levels and by socio economic groups. The deprivation dimensions range widely from the conventional expenditure dimensions to non expenditure dimensions such as access to drinking water and clean fuel, to health dimensions such as child stunting and the mother’s BMI. The use of decomposable deprivation measures allows the identification of regions, socio economic groups and deprivation dimensions that are contributing more than others to total deprivation.Multidimensional Deprivation, Social Exclusion, Decomposable Deprivation Measures, Scheduled Classes and Tribes, Clean Fuel, Stunted Children.