A prediction method of missing vehicle position information based on least square support vector machine

The continuous development of VANET has accelerated the development of V2X communication. In the DSRC communication mode of VANET, the location information of the vehicles is interfered by factors such as high-density broadcasting and electromagnetic radiation, which can lead to the loss of the original vehicle information data collected by GPS easily. To solve it, this paper proposed the Least Squared SVM based Beacon Data Complete Algorithm. Unlike previous studies that historical trends of vehicle operation were mainly used to predict vehicle location., this method attempts to find a function, which is used to establish the relationship between the lost value and the past value of the vehicle. On this basis, a nonlinear function approximation strategy is used to predict the position of the missing vehicle. Part of the original data was lost artificially to complete checking calculation and to verify the effectiveness of it. The results show that the average relative error between the complemented vehicle position data and the real data is 0.45% and the maximum absolute relative error is 8.25%. This method has the advantage of not needing to extract historical trend data and high calculation accuracy compared with the methods such as PWHOG algorithm, difference matrix, and moving average data preprocessing. It is suitable for real-time acquisition of vehicle position of VANET and can reduce the complexity of detection time

NiFeMn-Layered Double Hydroxides Linked by Graphene as High-Performance Electrocatalysts for Oxygen Evolution Reaction

Currently, precious metal group materials are known as the efficient and widely used oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) catalysts. The exorbitant prices and scarcity of the precious metals have stimulated scale exploration of alternative non-precious metal catalysts with low-cost and high performance. Layered double hydroxides (LDHs) are a promising precursor to prepare cost-effective and high-performance catalysts because they possess abundant micropores and nitrogen self-doping after pyrolysis, which can accelerate the electron transfer and serve as active sites for efficient OER. Herein, we developed a new highly active NiFeMn-layered double hydroxide (NFM LDH) based electrocatalyst for OER. Through building NFM hydroxide/oxyhydroxide heterojunction and incorporation of conductive graphene, the prepared NFM LDH-based electrocatalyst delivers a low overpotential of 338 mV at current density of 10 mA cm−2 with a small Tafel slope of 67 mV dec−1, which are superior to those of commercial RuO2 catalyst for OER. The LDH/OOH heterojunction involves strong interfacial coupling, which modulates the local electronic environment and boosts the kinetics of charge transfer. In addition, the high valence Fe3+ and Mn3+ species formed after NaOH treatment provide more active sites and promote the Ni2+ to higher oxidation states during the O2 evolution. Moreover, graphene contributes a lot to the reduction of charge transfer resistance. The combining effects have greatly enhanced the catalytic ability for OER, demonstrating that the synthesized NFM LDH/OOH heterojunction with graphene linkage can be practically applied as a high-performance electrocatalyst for oxygen production via water splitting

Therapeutic paradigm of dual targeting VEGF and PDGF for effectively treating FGF-2 off-target tumors

Anti-VEGF therapy has many limitations that might be resolved by using combination treatment approaches. Here, the authors demonstrate that the dual-targeting of VEGF and PDGF is required for targeting resistant FGF2+ tumors which depend on the recruitment of pericytes on tumor microvessels