A Novel A-ECMS Energy Management Strategy Based on Dragonfly Algorithm for Plug-in FCEVs.

The mechanical coupling of multiple powertrain components makes the energy management of 4-wheel-drive (4WD) plug-in fuel cell electric vehicles (PFCEVs) relatively complex. Optimizing energy management strategies (EMSs) for this complex system is essential, aiming at improving the vehicle economy and the adaptability of operating conditions. Accordingly, a novel adaptive equivalent consumption minimization strategy (A-ECMS) based on the dragonfly algorithm (DA) is proposed to achieve coordinated control of the powertrain components, front and rear motors, as well as the fuel cell system and the battery.

An Equivalent Consumption Minimization Strategy for a Parallel Plug-In Hybrid Electric Vehicle Based on an Environmental Perceiver.

An energy management strategy is a key technology used to exploit the energy-saving potential of a plug-in hybrid electric vehicle. This paper proposes the environmental perceiver-based equivalent consumption minimization strategy (EP-ECMS) for parallel plug-in hybrid vehicles. In this method, the traffic characteristic information obtained from the intelligent traffic system is used to guide the adjustment of the equivalence factor, improving the environmental adaptiveness of the equivalent consumption minimization strategy (ECMS).

SGGformer: Shifted Graph Convolutional Graph-Transformer for Traffic Prediction.

Accurate traffic prediction is significant in intelligent cities' safe and stable development. However, due to the complex spatiotemporal correlation of traffic flow data, establishing an accurate traffic prediction model is still challenging. Aiming to meet the challenge, this paper proposes SGGformer, an advanced traffic grade prediction model which combines a shifted window operation, a multi-channel graph convolution network, and a graph Transformer network.

Dual-Input and Multi-Channel Convolutional Neural Network Model for Vehicle Speed Prediction.

With the development of technology, speed prediction has become an important part of intelligent vehicle control strategies. However, the time-varying and nonlinear nature of vehicle speed increases the complexity and difficulty of prediction. Therefore, a CNN-based neural network architecture with two channel input (DICNN) is proposed in this paper.

Liver Metabolomics Reveals the Effect of on Alcoholic Liver Disease.

Alcoholic liver disease (ALD), a type of chronic liver disease that is prevalent worldwide, is still identified to have a poor prognosis despite many medical treatment protocols. Thus, it is urgent to develop and test new treatment protocols for ALD. () has been widely used in the clinical treatment of digestive system diseases, but studies on the protective effect of on ALD are considered to be rare.

Aloin Preconditioning Attenuates Hepatic Ischemia/Reperfusion Injury via Inhibiting TLR4/MyD88/NF-B Signal Pathway and .

Background: Aloin exerts considerable protective effects in various disease models, and its effect on hepatic ischemia-reperfusion (HIR) injury remains unknown. This research is aimed at conducting an in-depth investigation of the antioxidant, anti-inflammatory, and antiapoptosis effects of aloin in HIR injury and explain the underlying molecular mechanisms.

Methods: , different concentrations of aloin were intraperitoneally injected 1 h before the establishment of the HIR model in male mice.

Polyaspartic acid (PASP)-urea and optimised nitrogen management increase the grain nitrogen concentration of rice.

Increase in grain nitrogen concentration (GNC), which is directly affected by nitrogen (N) application, can help overcome the issues of malnutrition. Here, the effects of urea type (polyaspartic acid (PASP) urea and conventional urea) and N management method (two splits and four splits) on GNC and N concentration of head rice were investigated in field experiments conducted in Sichuan, China, in 2014 and 2015. N concentration of grain and head rice were significantly (P < 0.