Collective motion of polar active particles on a sphere

Collective motion of active particles with polar alignment is investigated on a sphere.We discussed the factors that affect particle swarm motion and define an order parameter that can show the degree of particle swarm motion.In the model,we added a polar alignment strength,along with Gaussian curvature,affecting particles swarm motion.We find that when the force exceeds a certain limit,the order parameter will decrease with the increase of the force.Combined with our definition of order parameter and observation of the model,the reason is that particles begin to move side by side under the influence of polar forces.In addition,the effects of velocity,rotational diffusion coefficient,and packing fraction on particle swarm motion are discussed.It is found that the rotational diffusion coefficient and the packing fraction have a great influence on the clustering motion of particles,while the velocity has little influence on the clustering motion of particles.

Preoperative prediction of hepatocellular carcinoma microvascular invasion based on magnetic resonance imaging feature extraction artificial neural network

BACKGROUND Hepatocellular carcinoma(HCC)recurrence is highly correlated with increased mortality.Microvascular invasion(MVI)is indicative of aggressive tumor biology in HCC.AIM To construct an artificial neural network(ANN)capable of accurately predicting MVI presence in HCC using magnetic resonance imaging.METHODS This study included 255 patients with HCC with tumors<3 cm.Radiologists annotated the tumors on the T1-weighted plain MR images.Subsequently,a three-layer ANN was constructed using image features as inputs to predict MVI status in patients with HCC.Postoperative pathological examination is considered the gold standard for determining MVI.Receiver operating characteristic analysis was used to evaluate the effectiveness of the algorithm.RESULTS Using the bagging strategy to vote for 50 classifier classification results,a prediction model yielded an area under the curve(AUC)of 0.79.Moreover,correlation analysis revealed that alpha-fetoprotein values and tumor volume were not significantly correlated with the occurrence of MVI,whereas tumor sphericity was significantly correlated with MVI(P<0.01).CONCLUSION Analysis of variable correlations regarding MVI in tumors with diameters<3 cm should prioritize tumor sphericity.The ANN model demonstrated strong predictive MVI for patients with HCC(AUC=0.79).