Study of e+e- -> H+H- at a 800 GeV Linear Collider

The production and decay of heavy charged Higgs bosons at a 800 GeV e+e- linear collider have been studied. The analysis of the H+H- -> tb tb, expected to be dominant in the MSSM, and H+H- -> W+h0 W-h0 decay modes leading to the same final state consisting of two W bosons and four b quarks, provides with a determination of the boson mass to 1 GeV and of the production cross section with 10% accuracy for 500 fb-1 of data.Comment: 4 pages, 1 figure, to appear in the Proceedings of the 5th Linear Collider Workshop Fermilab, October 200

Effects of a progressive aquatic resistance exercise program on the biochemical composition and morphology of cartilage in women with mild knee osteoarthritis: protocol for a randomised controlled trial

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Study of e+e−→H+H−e^+ e^- \to H^+ H^- at a 800-GeV linear collider

The production and decay of heavy charged Higgs bosons at a 800 GeV $e^+e^−$ linear collider have been studied. The analysis of the $H^+H^−→tb̄t̄b$, expected to be dominant in the MSSM, and $H^+H^−→W^+h^0W^−h^0$ decay modes leading to the same final state consisting of two $W$ bosons and four $b$ quarks, provides with a determination of the boson mass to 1 GeV/c$^2$ and of the production cross section with 10% accuracy for 500 fb$^{−1}$ of data

INSULATION DEFECT LOCALIZATION THROUGH PARTIAL DISCHARGE MEASUREMENTS AND NUMERICAL CLASSIFICATION

In this paper, PD signals are analyzed to localize defects in insulation systems. The task of automatic defect localization with respect to electrodes has a wide range of industrial applications. In fact, depending on the apparatus type, risk assessment is remarkably affected by defect location with respect to the electrodes. In this study, various parameters are first extracted from PD distributions, and statistical analysis is performed to select the most significant parameters concerning localization. Then, the localization process is carried out through numerical classification. Three different classification methods are compared to find the best approach for this application. Comparing a k-nearest neighbour classifier, a probabilistic neural network and a support vector machine (SVM) based classifier, the best results are gained with SVM, although the former two are simpler to implement and easier to tune. SVM based classification has not been applied in PD analysis before this approach