Flare Activity and Magnetic Feature Analysis of the Flare Stars II: Sub-Giant Branch

We present an investigation of the magnetic activity and flare characteristics of the sub-giant stars mostly from F and G spectral types and compare the results with the main-sequence (MS) stars. The light curve of 352 stars on the sub-giant branch (SGB) from the Kepler mission is analyzed in order to infer stability, relative coverage and contrast of the magnetic structures and also flare properties using three flare indexes. The results show that: (i) Relative coverage and contrast of the magnetic features along with rate, power and magnitude of flares increase on the SGB due to the deepening of the convective zone and more vigorous magnetic field production (ii) Magnetic activity of the F and G-type stars on the SGB does not show dependency to the rotation rate and does not obey the saturation regime. This is the opposite of what we saw for the main sequence, in which the G-, K- and M-type stars show clear dependency to the Rossby number; (iii) The positive relationship between the magnetic features stability and their relative coverage and contrast remains true on the SGB, though it has lower dependency coefficient in comparison with the MS; (iv) Magnetic proxies and flare indexes of the SGB stars increase with increasing the relative mass of the convective zone

A Surrogate Model Based on a Finite Element Model of Abdomen for Real-Time Visualisation of Tissue Stress during Physical Examination Training

Robotic patients show great potential for helping to improve medical palpation training, as they can provide feedback that cannot be obtained in a real patient. They provide information about internal organ deformation that can significantly enhance palpation training by giving medical trainees visual insight based on the pressure they apply for palpation. This can be achieved by using computational models of abdomen mechanics. However, such models are computationally expensive, and thus unable to provide real-time predictions. In this work, we proposed an innovative surrogate model of abdomen mechanics by using machine learning (ML) and finite element (FE) modelling to virtually render internal tissue deformation in real time. We first developed a new high-fidelity FE model of the abdomen mechanics from computerized tomography (CT) images. We performed palpation simulations to produce a large database of stress distribution on the liver edge, an area of interest in most examinations. We then used artificial neural networks (ANNs) to develop the surrogate model and demonstrated its application in an experimental palpation platform. Our FE simulations took 1.5 h to predict stress distribution for each palpation while this only took a fraction of a second for the surrogate model. Our results show that our artificial neural network (ANN) surrogate has an accuracy of 92.6%. We also showed that the surrogate model is able to use the experimental input of palpation location and force to provide real-time projections onto the robotics platform. This enhanced robotics platform has the potential to be used as a training simulator for trainees to hone their palpation skills