1,797 research outputs found

    Synchronization methods for the PAC RPC trigger system in the CMS experiment

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    The PAC (pattern comparator) is a dedicated muon trigger for the CMS (Compact Muon Solenoid) experiment at the LHC (Large Hadron Collider). The PAC trigger processes signals provided by RPC (resistive plate chambers), a part of the CMS muon system. The goal of the PAC RPC trigger is to identify muons, measure their transverse momenta and select the best muon candidates for each proton bunch collision occurring every 25 ns. To perform this task it is necessary to deliver the information concerning each bunch crossing from many RPC chambers to the trigger logic at the same moment. Since the CMS detector is large (the muon hits are spread over 40 ns), and the data are transmitted through thousands of channels, special techniques are needed to assure proper synchronization of the data. In this paper methods developed for the RPC signal synchronization and synchronous transmission are presented. The methods were tested during the MTCC (magnet test and cosmic challenge). The performance of the synchronization methods is illustrated by the results of the tests

    Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

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    Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

    Juxtaposing BTE and ATE – on the role of the European insurance industry in funding civil litigation