RESUMO
The TOTEM collaboration has measured the proton-proton total cross section at âs=8 TeV using a luminosity-independent method. In LHC fills with dedicated beam optics, the Roman pots have been inserted very close to the beam allowing the detection of ~90% of the nuclear elastic scattering events. Simultaneously the inelastic scattering rate has been measured by the T1 and T2 telescopes. By applying the optical theorem, the total proton-proton cross section of (101.7±2.9) mb has been determined, well in agreement with the extrapolation from lower energies. This method also allows one to derive the luminosity-independent elastic and inelastic cross sections: σ(el)=(27.1±1.4) mb; σ(inel)=(74.7±1.7) mb.
RESUMO
The first double diffractive cross-section measurement in the very forward region has been carried out by the TOTEM experiment at the LHC with a center-of-mass energy of sqrt[s]=7 TeV. By utilizing the very forward TOTEM tracking detectors T1 and T2, which extend up to |η|=6.5, a clean sample of double diffractive pp events was extracted. From these events, we determined the cross section σDD=(116±25) µb for events where both diffractive systems have 4.7<|η|min<6.5.
RESUMO
Background and Objective. Needle electromyography can be used to detect the number of changes and morphological changes in motor unit potentials of patients with axonal neuropathy. General mathematical methods of pattern recognition and signal analysis were applied to recognize neuropathic changes. This study validates the possibility of extending and refining turns-amplitude analysis using permutation entropy and signal energy. Methods. In this study, we examined needle electromyography in 40 neuropathic individuals and 40 controls. The number of turns, amplitude between turns, signal energy, and "permutation entropy" were used as features for support vector machine classification. Results. The obtained results proved the superior classification performance of the combinations of all of the above-mentioned features compared to the combinations of fewer features. The lowest accuracy from the tested combinations of features had peak-ratio analysis. Conclusion. Using the combination of permutation entropy with signal energy, number of turns and mean amplitude in SVM classification can be used to refine the diagnosis of polyneuropathies examined by needle electromyography.