Modeling differential car-following behavior under normal and rainy conditions: A memory-based deep learning method with attention mechanism

In order to analyze and learn the difference in car-following behavior between normal and rainy days, we first collect car-following trajectory data of an urban elevated road on normal and rainy days by microwave radar and analyze the differences in speed, relative speed, acceleration, space headway, and time headway among data through statistics. Secondly, owing to the time-series characteristics of car-following data, we use the long short-term memory(LSTM) neural network optimized by attention mechanism(AM) and sparrow search algorithm(SSA) to learn the different car-following behaviors under different weather conditions and build corresponding models(ASL-Normal, ASL-Rain, where ASL stands for AM-SSA-LSTM), respectively. Finally, the simulation test shows that the mean square error(MSE) and reciprocal of time-to-collision(RTTC) of the ASL model are better than those of LSTM and intelligent diver model(IDM), which is closer to the real data. The ASL model can better learn different driving behaviors on normal and rainy days. However,it has a higher sensitivity to weather conditions from cross test on normal and rainy data-sets which need classification training or sample diversification processing. In the car-following platoon simulation, the stability performances of two models are excellent, which can describe the basic characteristics of traffic flow on normal and rainy days. Comparing with ASL-Rain model, the convergence time of ASL-Normal is shorter, reflecting that cautious driving behavior on rainy days will reduce traffic efficiency to a certain extent. However, ASL-Normal model produces a more severe and frequent traffic oscillation within a shorter period because of aggressive driving behavior on normal days.

Therapeutic dendritic cell vaccines engineered with antigen-biomineralized Bi_(2)S_(3) nanoparticles for personalized tumor radioimmunotherapy

Therapeutic vaccines,an exciting development in cancer immunotherapy,share the goal of priming of personalized antigen-specific T-cell response by precise antigen presentation of dendritic cells(DCs),but major obstacles include insufficient antigen loading and off-target to DCs remain to their success.Here,we developed an imageable therapeutic vaccine with whole-antigen loading and target delivery constructed by ovalbumin(OVA)-biomineralized Bi_(2)S_(3) nanoparticles-pulsed DCs.Relying on the strong X-ray absorption and fluorescence labeling performance of Bi_(2)S_(3)@OVA nanoparticles,the in vivo spatiotemporal fate of the vaccine(Bi_(2)S_(3)@OVA@DC)can be noninvasively monitored by computed tomography and near-infrared fluorescence imaging in real time.The Bi_(2)S_(3)@OVA@DC can rapidly and durably accumulate in draining lymph nodes and thus trigger stronger T-cell responses compared to OVA-pulsed DCs.Meanwhile,Bi_(2)S_(3)@OVA@DC can further achieve in vivo antitumor effects against OVA-expressing B16F10 melanoma when combined with fractionated radiotherapy,resulting from the upregulation of cytotoxic CD8^(+)T cells and restraint of regulatory T cells in the tumor microenvironment,and the systemical secretion of OVA-specific IgG1/IgG2α antibody.Overall,we successfully fabricated an engineered DC vaccine featured in high whole-antigen loading capacity that can be precisely delivered to the lymphatic system for visualization,serving as a powerful therapeutic platform for cancer radioimmunotherapy.