Brunel University London

About: Brunel University London is a education organization based out in London, United Kingdom. It is known for research contribution in the topics: Context (language use) & Large Hadron Collider. The organization has 10918 authors who have published 29515 publications receiving 893330 citations. The organization is also known as: Brunel & University of Brunel.

Locating the coaching process in practice: models ‘for’ and ‘of’ coaching

Abstract: Background: Despite an increasing recognition of the existence of a process of coaching, and a resulting increase in research activity, there remains a lack of a clear conceptual base for sports coaching This situation has left coaching without a clear set of concepts and principles that reflect coaching practice Purpose: The aim of this paper is to critically examine current conceptualisations of the coaching process, principally in terms of how they have been generated and their contribution to coaching knowledge By exploring models for (idealistic representations) and of (empirically based) the coaching process, this paper examines the model's nature and conceptual underpinnings, in an attempt to position them within a broader framework of understanding coaching and the coaching process Conclusions: The analysis suggests that the current set of models result in a representation of the coaching process that is often reduced in complexity and scale, and the essential social and cultural elements of t

A model and a performance measurement system for collaborative supply chains

Abstract: Modeling the constituents of a collaborative supply chain, the key parameters they influence, and the appropriate performance measures in a decision support environment enables prior understanding of the impact on the performance of a collaborative supply chain as a result of changes in the constituents and key parameters. In turn, this allows pinpointing of those areas where the actual supply chain can be improved and hence manage the chain's performance. This paper shows how the constituents, key parameters and performance indicators are modelled into the environment and through a case study illustrates how the decision support environment may be used to improve the performance of a collaborative supply chain by pinpointing areas for improvement.

Search for a Higgs boson in the mass range from 145 to 1000 GeV decaying to a pair of W or Z bosons

Abstract: A search for a heavy Higgs boson in the H to WW and H to ZZ decay channels is reported. The search is based upon proton-proton collision data samples corresponding to an integrated luminosity of up to 5.1 inverse femtobarns at sqrt(s)=7 TeV and up to 19.7 inverse femtobarns at sqrt(s)=8 TeV, recorded by the CMS experiment at the CERN LHC. Several final states of the H to WW and H to ZZ decays are analyzed. The combined upper limit at the 95% confidence level on the product of the cross section and branching fraction exclude a Higgs boson with standard model-like couplings and decays in the range 145 < m[H] < 1000 GeV. We also interpret the results in the context of an electroweak singlet extension of the standard model.

Nonparallel Support Vector Machines for Pattern Classification

Abstract: We propose a novel nonparallel classifier, called nonparallel support vector machine (NPSVM), for binary classification. Our NPSVM that is fully different from the existing nonparallel classifiers, such as the generalized eigenvalue proximal support vector machine (GEPSVM) and the twin support vector machine (TWSVM), has several incomparable advantages: 1) two primal problems are constructed implementing the structural risk minimization principle; 2) the dual problems of these two primal problems have the same advantages as that of the standard SVMs, so that the kernel trick can be applied directly, while existing TWSVMs have to construct another two primal problems for nonlinear cases based on the approximate kernel-generated surfaces, furthermore, their nonlinear problems cannot degenerate to the linear case even the linear kernel is used; 3) the dual problems have the same elegant formulation with that of standard SVMs and can certainly be solved efficiently by sequential minimization optimization algorithm, while existing GEPSVM or TWSVMs are not suitable for large scale problems; 4) it has the inherent sparseness as standard SVMs; 5) existing TWSVMs are only the special cases of the NPSVM when the parameters of which are appropriately chosen. Experimental results on lots of datasets show the effectiveness of our method in both sparseness and classification accuracy, and therefore, confirm the above conclusion further. In some sense, our NPSVM is a new starting point of nonparallel classifiers.