Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN) for Travel Demand Forecasting During Wildfires

no code implementations • 13 Apr 2023 • Xiaojian Zhang, Xilei Zhao, Yiming Xu, Ruggiero Lovreglio, Daniel Nilsson

Based on the travel demand inferred from the GPS data, we develop a new deep learning model, i. e., Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN), along with a model updating scheme to achieve real-time forecasting of travel demand during wildfire evacuations.

Spliced Leader Trapping Reveals Widespread Alternative Splicing Patterns in the Highly Dynamic Transcriptome of Trypanosoma brucei

no code implementations • 27 Aug 2021 • Daniel Nilsson, Kapila Gunasekera, Jan Mani, Magne Osteras, Laurent Farinelli, Loic Baerlocher, Isabel Roditi, Torsten Ochsenreiter

Trans-splicing of leader sequences onto the 59ends of mRNAs is a widespread phenomenon in protozoa, nematodes and some chordates.