Measurements of differential production cross sections for a Z boson in association with jets in pp collisions at root s=8 TeV

Peer reviewe

Charged-particle nuclear modification factors in PbPb and pPb collisions at √=sNN=5.02 TeV

The spectra of charged particles produced within the pseudorapidity window |η| < 1 at √ sNN = 5.02 TeV are measured using 404 µb −1 of PbPb and 27.4 pb−1 of pp data collected by the CMS detector at the LHC in 2015. The spectra are presented over the transverse momentum ranges spanning 0.5 < pT < 400 GeV in pp and 0.7 < pT < 400 GeV in PbPb collisions. The corresponding nuclear modification factor, RAA, is measured in bins of collision centrality. The RAA in the 5% most central collisions shows a maximal suppression by a factor of 7–8 in the pT region of 6–9 GeV. This dip is followed by an increase, which continues up to the highest pT measured, and approaches unity in the vicinity of pT = 200 GeV. The RAA is compared to theoretical predictions and earlier experimental results at lower collision energies. The newly measured pp spectrum is combined with the pPb spectrum previously published by the CMS collaboration to construct the pPb nuclear modification factor, RpA, up to 120 GeV. For pT > 20 GeV, RpA exhibits weak momentum dependence and shows a moderate enhancement above unity

Dwelling house construction

xi+396hlm.;23c

Dwelling House Construction

xvi;p 396;ill.;index;23 c

Dwelling house construction

xi,446hlm.;bib.;ill.;indek

Predicting outcomes of pelvic exenteration using machine learning

Aim: We aim to compare machine learning with neural network performance in predicting R0 resection (R0), length of stay &gt;&nbsp;14&nbsp;days (LOS), major complication rates at 30&nbsp;days postoperatively (COMP) and survival greater than 1 year (SURV) for patients having pelvic exenteration for locally advanced and recurrent rectal cancer. Method: A deep learning computer was built and the programming environment was established. The PelvEx Collaborative database was used which contains anonymized data on patients who underwent pelvic exenteration for locally advanced or locally recurrent colorectal cancer between 2004 and 2014. Logistic regression, a support vector machine and an artificial neural network (ANN) were trained. Twenty per cent of the data were used as a test set for calculating prediction accuracy for R0, LOS, COMP and SURV. Model performance was measured by plotting receiver operating characteristic (ROC) curves and calculating the area under the ROC curve (AUROC). Results: Machine learning models and ANNs were trained on 1147 cases. The AUROC for all outcome predictions ranged from 0.608 to 0.793 indicating modest to moderate predictive ability. The models performed best at predicting LOS &gt;&nbsp;14&nbsp;days with an AUROC of 0.793 using preoperative and operative data. Visualized logistic regression model weights indicate a varying impact of variables on the outcome in question. Conclusion: This paper highlights the potential for predictive modelling of large international databases. Current data allow moderate predictive ability of both complex ANNs and more classic methods