Ting Hu

With the expansion of the digital business line, the network flow behind the digital power grid is also exploding. To prevent network congestion, this article proposes a novel network flow forecasting model, which is composed of variational mode decomposition, GRU-xgboost block, and a forecasting adjustment block, to grasp the changing patterns and trends of network flow in advance, and to formulate reasonable and effective flow management strategies and meet the requirements of users for network service quality. The network flow series in power grid enterprise always contain complex patterns and outliers, and VMD is applied to adaptively process complex net flow time series into several subseries with simpler patterns. A GRU-xgboost block is designed to reconstruct the features of historical series. Then, xgboost model is applied to generate predictions for all decomposed subsignals. For the final predictions, we design a forecasting adjustment block to further remove the influence of random noise. Finally, the empirical results show the superior performance of the proposed model on network flow forecasting task.

It has been four decades since eye-tracking was first used in interpreting studies, and recent years has witnessed a growing interest in the application of this method, which holds great potential for offering a look into the “black box” of interpreting processing. However, little attention has been paid to comprehensively illustrating what has been done, what can be done, and what needs to be done with this method in this discipline. With this in view, this paper sets out to understand contributions of previous studies—key themes discussed, eye-tracking measures used, their limitations and implications, and future directions. To this end, we conduct a review of a total of 26 empirical papers from peer-reviewed journals within a time span of 4 decades ranging from 1981 to 2021. This study, as the first attempt of its kind at a comprehensive review on using eye-tracking in interpreting studies, should have implications for researchers, educators, and practitioners.