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. To begin with, the equivalent consumption minimization strategy (ECMS) with extraordinary instantaneous optimization ability is used to distribute the vehicle demand power into the front and rear motor power, considering the different motor characteristics. Subsequently, under the proposed novel hierarchical energy management framework, the well-designed A-ECMS based on DA empowers PFCEVs with significant energy-saving advantages and adaptability to operating conditions, which are achieved by precise power distribution considering the operating characteristics of the fuel cell system and battery. These provide state-of-the-art energy-saving abilities for the multi-degree-of-freedom systems of PFCEVs. Lastly, a series of detailed evaluations are performed through simulations to validate the improved performance of A-ECMS. The corresponding results highlight the optimal control performance in the energy-saving performance of A-ECMS.

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). Two main works have been completed. First, a high-accuracy environmental perceiver was developed based on a graph convolutional network (GCN) and attention mechanism to complete the traffic state recognition of all graph regions based on historical information. Moreover, it provides the grade of the corresponding region where the vehicle is located (for the ECMS). Secondly, in the offline process, the search for the optimal equivalent factor is completed by using the Harris hawk optimization algorithm based on the representative working conditions under various grades. Based on the identified traffic grades in the online process, the optimized equivalence factor tables are checked for energy management control. The simulation results show that the improved EP-ECMS can achieve 7.25% energy consumption optimization compared with the traditional 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. Firstly, the shifted window operation is used for coarsening the time series data, thus, the computational complexity can be reduced. Then, a multi-channel graph convolutional network is adopted to capture and aggregate the spatial correlations of the roads in multiple dimensions. Finally, the improved graph Transformer based on the advanced Transformer model is proposed to extract the long-term temporal correlation of traffic data effectively. The prediction performance is evaluated by using actual traffic datasets, and the test results show that the SGGformer proposed exceeds the state-of-the-art baseline.

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. With two inputs and four channels, DICNN can predict the speed changes in the next 5 s by extracting the temporal information of 10 vehicle signals and the driver's intention. The prediction performances of DICNN are firstly examined. The best RMSE, MAE, ME and R2 are obtained compared with a Markov chain combined with Monte Carlo (MCMC) simulation, a support vector machine (SVM) and a single input CNN (SICNN). Secondly, equivalent fuel consumption minimization strategies (ECMS) combining different vehicle speed prediction methods are constructed. After verification by simulation, the equivalent fuel consumption of the simulation increases by only 4.89% compared with dynamic-programming-based energy management strategy and decreased by 5.40% compared with the speed prediction method with low accuracy.

Liver Metabolomics Reveals the Effect of Lactobacillus reuteri 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. Lactobacillus reuteri (L. reuteri) has been widely used in the clinical treatment of digestive system diseases, but studies on the protective effect of L. reuteri on ALD are considered to be rare. Therefore, in the present study, we examined the effect of L. reuteri on ALD and provide data that are significant in the development of new treatment protocols for ALD. An ALD model has been established in C57BL/6J mice treated according to the Gao-binge modeling method. Mice in the treatment group were administered with L. reuteri. Hematoxylin and eosin (H&E) staining, oil red O staining, immunohistochemistry, and biochemical analyses were performed to detect the phenotypic changes in the liver among mice in the different treatment groups. L. reuteri treatment reversed inflammatory cell infiltration and lipid accumulation. Moreover, AST, ALT, TG, and TCH levels were also reduced in the probiotics-treatment group. Five candidate biomarkers were found in the liver metabolites of different treatment groups by UPLC/QTOF-MS and a multivariate analysis. Several fatty acid metabolic pathways such as linoleic acid metabolism and glycerolipid metabolism were involved. All these findings suggested that L. reuteri treatment reversed the phenotype of ethanol-induced hepatitis and metabolic disorders. These findings provide evidence that L. reuteri might serve as a new therapeutic strategy for ALD.

Aloin Preconditioning Attenuates Hepatic Ischemia/Reperfusion Injury via Inhibiting TLR4/MyD88/NF-κB Signal Pathway In Vivo and In Vitro.

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: In vivo, different concentrations of aloin were intraperitoneally injected 1 h before the establishment of the HIR model in male mice. The hepatic function, pathological status, oxidative stress, and inflammatory and apoptosis markers were measured. In vitro, aloin (AL, C21H22O9) or lipopolysaccharide (LPS) was added to a culture of mouse primary hepatocytes before it underwent hypoxia/reoxygenation (H/R), and the apoptosis in the mouse primary hepatocytes was analyzed. RESULTS: We found that 20 mg/kg was the optimum concentration of aloin for mitigating I/R-induced liver tissue damage, characterized by decreased serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST). Aloin pretreatment substantially suppressed the generation of hepatic malondialdehyde (MDA), tumor necrosis factor alpha (TNF-α), and IL-6 and enhanced the hepatic superoxide dismutase (SOD) activities as well as glutathione (GSH) and IL-10 levels in the liver tissue of I/R mice; this indicated that aloin ameliorated I/R-induced liver damage by reducing the oxidative stress and inflammatory response. Moreover, aloin inhibited hepatocyte apoptosis and inflammatory response that was caused by the upregulated expression of Bcl-2, the downregulated expression of cleaved caspase3(C-caspase3), Bax, Toll-like receptor 4 (TLR4), FADD, MyD88, TRAF6, phosphorylated IKKα/ß (p-IKKα/ß), and phosphorylated nuclear factor κB p65 (p-NF-κB p65).

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 < 0.05) increased by N redistribution from the leaf lamina, activities of glutamine synthetase (GS), and glutamate synthase (GOGAT) at the heading stage, and N concentration and GOGAT activity in the leaf lamina at the maturity stage. Compared to conventional urea, PASP-urea significantly improved N concentration of grain and head rice by improving the activities of GS and GOGAT, thereby increasing N distribution in the leaf lamina. The four splits method, unlike the two splits method, enhanced N concentration and activities of key N metabolism enzymes of leaf lamina, leading to increased GNC and N concentration in head rice too. Overall, four splits is a feasible method for using PASP-urea and improving GNC.