Macrophyte identity shapes water column and sediment bacterial community

By assembling mesocosms and utilizing high-throughput sequencing, we aim to characterize the shifts of the bacterial community in freshwaters driven by two contrasting submerged macrophyte species, Ceratophyllum demersum L. and Vallisneria spiralis L. Although the microbe in both the water column and sediment were largely modulated by the macrophyte, the effect varied considerably depending on bacterial locations and macrophyte species. Actinobacteria was the most abundant taxa in the water column of all the three treatments, but its abundances were significantly higher in the two planted treatments. Moreover, Alphaproteobacteria showed high abundance only in the unplanted control. For bacterial taxa in the sediment, C. demersum significantly increased the relative abundance of Anaerolineae but reduced the relative abundance of Betaproteobacteria and Gammaproteobacteria, while V. spiralis increased the relative abundance of Deltaproteobacteria and Gammaproteobacteria. Additionally, in the C. demersum treatment, the water column bacterial community increased more dramatically in richness, alpha diversity, and the relative abundance of the dominant taxa than those in the V. spiralis treatment. Taken together, the findings from this study reveal that the two species of submerged macrophyte modified the bacterial community in waters, despite the obvious interspecific performance differences

Fine Mapping of the Bsr1 Barley Stripe Mosaic Virus Resistance Gene in the Model Grass Brachypodium distachyon

The ND18 strain of Barley stripe mosaic virus (BSMV) infects several lines of Brachypodium distachyon, a recently developed model system for genomics research in cereals. Among the inbred lines tested, Bd3-1 is highly resistant at 20 to 25°C, whereas Bd21 is susceptible and infection results in an intense mosaic phenotype accompanied by high levels of replicating virus. We generated an F6∶7 recombinant inbred line (RIL) population from a cross between Bd3-1 and Bd21 and used the RILs, and an F2 population of a second Bd21 × Bd3-1 cross to evaluate the inheritance of resistance. The results indicate that resistance segregates as expected for a single dominant gene, which we have designated Barley stripe mosaic virus resistance 1 (Bsr1). We constructed a genetic linkage map of the RIL population using SNP markers to map this gene to within 705 Kb of the distal end of the top of chromosome 3. Additional CAPS and Indel markers were used to fine map Bsr1 to a 23 Kb interval containing five putative genes. Our study demonstrates the power of using RILs to rapidly map the genetic determinants of BSMV resistance in Brachypodium. Moreover, the RILs and their associated genetic map, when combined with the complete genomic sequence of Brachypodium, provide new resources for genetic analyses of many other traits

Soft Actor-Critic Algorithm-Based Energy Management Strategy for Plug-In Hybrid Electric Vehicle

Plug-in hybrid electric vehicles (PHEVs) are equipped with more than one power source, providing additional degrees of freedom to meet the driver’s power demand. Therefore, the reasonable allocation of the power demand of each power source by the energy management strategy (EMS) to keep each power source operating in the efficiency zone is essential for improving fuel economy. This paper proposes a novel model-free EMS based on the soft actor-critic (SAC) algorithm with automatic entropy tuning to balance the optimization of energy efficiency with the adaptability of driving cycles. The maximum entropy framework is introduced into deep reinforcement learning-based energy management to improve the performance of exploring the internal combustion engine (ICE) as well as the electric motor (EM) efficiency interval. Specifically, the automatic entropy adjustment framework improves the adaptability to driving cycles. In addition, the simulation is verified by the data collected from the real vehicle. The results show that the introduction of automatic entropy adjustment can effectively improve vehicle equivalent fuel economy. Compared with traditional EMS, the proposed EMS can save energy by 4.37%. Moreover, it is able to adapt to different driving cycles and can keep the state of charge to the reference value

Soft Actor-Critic Algorithm-Based Energy Management Strategy for Plug-In Hybrid Electric Vehicle

Plug-in hybrid electric vehicles (PHEVs) are equipped with more than one power source, providing additional degrees of freedom to meet the driver’s power demand. Therefore, the reasonable allocation of the power demand of each power source by the energy management strategy (EMS) to keep each power source operating in the efficiency zone is essential for improving fuel economy. This paper proposes a novel model-free EMS based on the soft actor-critic (SAC) algorithm with automatic entropy tuning to balance the optimization of energy efficiency with the adaptability of driving cycles. The maximum entropy framework is introduced into deep reinforcement learning-based energy management to improve the performance of exploring the internal combustion engine (ICE) as well as the electric motor (EM) efficiency interval. Specifically, the automatic entropy adjustment framework improves the adaptability to driving cycles. In addition, the simulation is verified by the data collected from the real vehicle. The results show that the introduction of automatic entropy adjustment can effectively improve vehicle equivalent fuel economy. Compared with traditional EMS, the proposed EMS can save energy by 4.37%. Moreover, it is able to adapt to different driving cycles and can keep the state of charge to the reference value

Proximal Policy Optimization Based Intelligent Energy Management for Plug-In Hybrid Electric Bus Considering Battery Thermal Characteristic

As the performances of energy management strategy (EMS) are essential for a plug-in hybrid electric bus (PHEB) to operate in an efficient way. The proximal policy optimization (PPO) based multi-objective EMS considering the battery thermal characteristic is proposed for PHEB, aiming to improve vehicle energy saving performance while ensuring the battery State of Charge (SOC) and temperature within a rational range. Since these three objectives are contradictory to each other, the optimal tradeoff between multiple objectives is realized by intelligently adjusting the weights in the training process. Compared with original PPO-based EMSs without considering battery thermal dynamics, simulation results demonstrate the effectiveness of the proposed strategies in battery thermal management. Results indicate that the proposed strategies can obtain the minimum energy consumption, fastest computing speed, and lowest battery temperature in comparison with other RL-based EMSs. Regarding dynamic programming (DP) as the benchmark, the PPO-based EMSs can achieve similar fuel economy and outstanding computation efficiency. Furthermore, the adaptability and robustness of the proposed methods are confirmed in UDDS, WVUSUB and real driving cycle

An adaptive estimation scheme for open-circuit voltage of power Lithium-Ion battery”, Research article in Abstract and applied analysis, Hindawi publishing corp

Open-circuit voltage (OCV) is one of the most important parameters in determining state of charge (SoC) of power battery. The direct measurement of it is costly and time consuming. This paper describes an adaptive scheme that can be used to derive OCV of the power battery. The scheme only uses the measurable input (terminal current) and the measurable output (terminal voltage) signals of the battery system and is simple enough to enable online implement. Firstly an equivalent circuit model is employed to describe the polarization characteristic and the dynamic behavior of the lithium-ion battery; the state-space representation of the electrical performance for the battery is obtained based on the equivalent circuit model. Then the implementation procedure of the adaptive scheme is given; also the asymptotic convergence of the observer error and the boundedness of all the parameter estimates are proven. Finally, experiments are carried out, and the effectiveness of the adaptive estimation scheme is validated by the experimental results