Rapid assessment of cosmic radiation exposure in aviation based on BP neural network method.

Cosmic radiation exposure is one of the important health concerns for aircrews. In this work, we constructed a back propagation neural network model for the real-time and rapid assessment of cosmic radiation exposure to the public in aviation. The multi-dimensional dataset for this neural network was created from modeling the process of cosmic ray transportation in magnetic field by geomagnetic cutoff rigidity method and air shower simulation by a Monte Carlo based Geant4 code.

Electric manipulation on deformation of ionic ferrofluid sessile droplets.

Active electric-driven droplet manipulation in digital microfluidics constitutes a promising domain owing to the unique and programmable wettability inherent in sessile ionic droplets. The coupling between the electric field and flow field enables precise control over wetting characteristics and droplet morphology. This study delves into the deformation phenomena of ionic sessile ferrofluid droplets in ambient air induced by uniform electric fields.

Prediction model of liver metastasis risk in patients with gastric cancer: A population-based study.

Liver was the most common site of distant metastasis in patients with gastric cancer (GC). The prediction model of the risk of liver metastasis was rarely proposed. Therefore, we aimed to establish a prediction model for liver metastasis in patients with GC.