Long-range Angular Correlations On The Near And Away Side In P-pb Collisions At √snn=5.02 Tev

7191/Mar294

Dynamics of water exchange and salt flux in the Macuse Estuary, central Mozambique, southern Africa

Studies of water-particle flow dynamics in shallow estuarine systems show that tidal currents control water exchange, salt flux and residence time. We used the 3D Estuary, Lake and Coastal Ocean Model (ELCOM) to estimate the dynamics of tidal currents, salt flux and residence time in the Macuse Estuary, central Mozambique. The model was calibrated using data acquired from field-data measurements obtained in 2014 with an acoustic Doppler current profiler and conductivity-temperature-depth (CTD) casts and a tide-gauge. The water flow dynamics that were tracked indicated that tidal currents of 100 cm s–1 were dissipated by friction caused by bathymetric morphology during both the spring and neap tides. The water dissipation was translated into a phase-difference delay of ~15 min between the catchment zone and the outlet during flood and ebb tides. The study showed that tidal currents and river discharges controlled the salt flux (2.07 × 105 g kg–1 m3 s–1) and the variation in residence time from hours to 40 days. The water ages were mostly driven by U-velocity tidal currents, bathymetric gradients and seasonal river discharges of ~500 m3 s–1. River discharges seasonally affected salinity changes between 15 and 30, and changes in the concentration of suspended sediments of ~300 mg l–1. In addition, it was shown that ELCOM was able to track the water-particle dynamics well, indicating the model’s suitability. These results contribute to our understanding of the effect of water-exchange dynamics on residence time and salt flux in shallow coastal inlet systems. Keywords: bathymetry, bottom morphology, coastal inlet, ELCOM, hydrodynamics, residence time, water qualit

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 > 14 days (LOS), major complication rates at 30 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 > 14 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

Underlying Event Measurements In Pp Collisions At √s = 0:9 And 7 Tev With The Alice Experiment At The Lhc

2012