As part of airport behavior decisions, the accurate prediction of airport delay is highly significant in optimizing flight takeoff and landing sequences. However, the combination of various influencing factors affects airport delay prediction strongly, which would bring severe challenges in prediction. This paper introduces the sequence-to-sequence network and proposes a multi-step regression prediction method for the airport delay (DA-BILSTM) to accurately predict the airport delay. Rather than only considering a single kind of airport delay influencing factors, we design an attention fusion network for learning the sequence and condition correlation features adaptively. Moreover, the Bayesian optimization algorithm is introduced to optimize DA-BILSTM’s hyperparameters. The method is applied individually to two datasets for predicting the airport’s delays. The experiment results show that the prediction performance of DA-BILSTM is better than many state-of-the-art methods including the autoregressive integrated moving average model (ARIMA), long short-term memory (LSTM), gated recurrent unit(GRU), CNN-BILSTM, and TS-LSTM. When using DA-BILSTM in the two datasets, the average MAE of airport delay prediction in the next 5 hours is about 10 minutes, and the average RMSE is 20 minutes.

Diffusion weighted magnetic resonance (MR) imaging is a powerful tool that can be employed to study white matter microstructure by examining the 3D displacement profile of water molecules in brain tissue. By applying diffusion-sensitized gradients along a minimum of 6 directions, second-order tensors can be computed to model dominant diffusion processes. However, conventional DTI is not sufficient to resolve crossing fiber tracts. A number of high-angular resolution schemes with greater than 6 gradient directions have been employed to address this issue. In this paper, we introduce the tensor distribution function (TDF), a probability function defined on the space of symmetric positive definite matrices. Here, fiber crossing is modeled as an ensemble of Gaussian diffusion processes with weights specified by the TDF once this optimal TDF is determined, the diffusion orientation distribution function (ODF) can easily be computed by analytic integration of the resulting displacement probability function.