NA62 is a fixed-target experiment at the CERN SPS dedicated to measurements of rare kaon decays. Such measurements, like the branching fraction of the K+ → π+ ν ν̄ decay, have the potential to bring significant insights into new physics processes when comparison is made with precise theoretical predictions. For this purpose, innovative techniques have been developed, in particular, in the domain of low-mass tracking devices. Detector construction spanned several years from 2009 to 2014. The collaboration started detector commissioning in 2014 and will collect data until the end of 2018. The beam line and detector components are described together with their early performance obtained from 2014 and 2015 data.
Echocardiographic parameters provide additional information compared to other variables routinely used in clinical practice to identify patients at higher risk of hemodynamic deterioration and poor in-hospital outcome, allowing prompt institution of appropriate pharmacological treatment and adequate mechanical support.
BackgroundThere is consensus that Heart Rate Variability is associated with the risk of vascular events. However, Heart Rate Variability predictive value for vascular events is not completely clear. The aim of this study is to develop novel predictive models based on data-mining algorithms to provide an automatic risk stratification tool for hypertensive patients.MethodsA database of 139 Holter recordings with clinical data of hypertensive patients followed up for at least 12 months were collected ad hoc. Subjects who experienced a vascular event (i.e., myocardial infarction, stroke, syncopal event) were considered as high-risk subjects. Several data-mining algorithms (such as support vector machine, tree-based classifier, artificial neural network) were used to develop automatic classifiers and their accuracy was tested by assessing the receiver-operator characteristics curve. Moreover, we tested the echographic parameters, which have been showed as powerful predictors of future vascular events.ResultsThe best predictive model was based on random forest and enabled to identify high-risk hypertensive patients with sensitivity and specificity rates of 71.4% and 87.8%, respectively. The Heart Rate Variability based classifier showed higher predictive values than the conventional echographic parameters, which are considered as significant cardiovascular risk factors.ConclusionsCombination of Heart Rate Variability measures, analyzed with data-mining algorithm, could be a reliable tool for identifying hypertensive patients at high risk to develop future vascular events.
The NA62 experiment reports the branching ratio measurement $$ \mathrm{BR}\left({K}^{+}\to {\pi}^{+}\nu \overline{\nu}\right)=\left({10.6}_{-3.4}^{+4.0}\left|{}_{\mathrm{stat}}\right.\pm {0.9}_{\mathrm{syst}}\right)\times {10}^{-11} $$ BR K + → π + ν ν ¯ = 10.6 − 3.4 + 4.0 stat ± 0.9 syst × 10 − 11 at 68% CL, based on the observation of 20 signal candidates with an expected background of 7.0 events from the total data sample collected at the CERN SPS during 2016–2018. This provides evidence for the very rare K+→$$ {\pi}^{+}\nu \overline{\nu} $$ π + ν ν ¯ decay, observed with a significance of 3.4σ. The experiment achieves a single event sensitivity of (0.839 ± 0.054) × 10−11, corresponding to 10.0 events assuming the Standard Model branching ratio of (8.4 ± 1.0) × 10−11. This measurement is also used to set limits on BR(K+→ π+X), where X is a scalar or pseudo-scalar particle. Details are given of the analysis of the 2018 data sample, which corresponds to about 80% of the total data sample.
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