Temporal representation learning enhanced dynamic adversarial graph convolutional network for traffic flow prediction.

Accurate traffic flow prediction serves as the foundation for urban traffic guidance and control, playing a crucial role in intelligent transportation management and regulation. However, current methods fail to fully capture the complex patterns and periodic characteristics of traffic flow, leading to significant discrepancies between predicted and actual values. This gap hampers the achievement of high-precision forecasting.

Abnormalities of interhemispheric functional connectivity in individuals with acute eye pain: a resting-state fMRI study.

Aim: To study the changes of the resting state functional connectivity (rsFC) between acute eye pain (EP) subjects and healthy controls (HCs) in the two hemispheres by using voxel-mirrored homotopic connectivity (VMHC) method.

Methods: Totally 20 patients with EP and 20 HCs were enrolled, sex, age, and education were matched, and all subjects were examined by functional magnetic resonance imaging (fMRI) scans at resting-state. The changes of rsFC between the hemispheres were evaluated by the VMHC method according to Gaussian random field (GRF) theory.