E nergy management is a critical technology in plug-in hybrid-electric vehicles (PHEVs) for maximizing efficiency, fuel economy, and range, as well as reducing pollutant emissions. At the same time, deep reinforcement learning (DRL) has become an effective and important methodology to formulate model-free and realtime energy-management strategies for HEVs and PHEVs. In this article, we describe the energy-management issues of HEVs/PHEVs and summarize a variety of potential DRL applications for onboard energy management. We also discuss the prospects for various DRL approaches in the energy-management field.

Heavy summer rainfall induces significant soil erosion and shallow landslide activity on the loess hillslopes of the Xining Basin at the northeast margin of the Qinghai-Tibet Plateau. This study examines the mechanical effects of five native shrubs that can be used to reduce shallow landslide activity. We measured single root tensile resistance and shear resistance, root anatomical structure and direct shear and triaxial shear for soil without roots and five rootsoil composite systems. Results show that Atriplex canescens (Pursh) Nutt. possessed the strongest roots, followed by Caragana korshinskii Kom., Zygophyllum xanthoxylon (Bunge) Maxim., Nitraria tangutorum Bobr. and Lycium chinense Mill. Single root strength and shear resistance relationships with root diameter are characterized by power or exponential relations, consistent with the MohrCoulomb law. Root mechanical strength reflects their anatomical structure, especially the percentage of phloem and xylem cells, and the degree and speed of periderm lignifications. The cohesion force of rootsoil composite systems is notably higher than that of soil without roots, with increasing amplitudes of cohesion force for A. canescens, C. korshinskii, Z. xanthoxylon, N. tangutorum and L. chinense of 75.9%, 75.1%, 36.2%, 24.6% and 17.0 % respectively. When subjected to shear forces, the soil without root samples show much greater lateral deformation than the root-soil composite systems, reflecting the restraining effects of roots. Findings from this paper indicate that efforts to reduce shallow landslides in this region by enhancing root reinforcement will be achieved most effectively using A. canescens and C. korshinskii.

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Identifying the relative contributions of climate change and human activities to alpine grassland dynamics is critical for understanding grassland degradation mechanisms. In this study, first, the actual NPP (NPPa) was obtained by MOD17A3. Second, we used the Zhou Guangsheng model to simulate the potential met net primary productivity (NPPp). Finally, the NPP generated by anthropogenic activities (NPPh) was estimated by calculating the difference between NPPp and NPPa. Then, the relative contributions of climate change and human activities to NPP changes in grasslands were quantitatively assessed by analyzing trends in NPPp and NPPa. Thereby, the drivers of NPP change in the Yellow River source grassland were identified. The results showed that the temperature and precipitation in the study area showed a warm-humid climate trend from 2000 to 2020. The NPPp and NPPa increased at a rate of 1.07 g C/m2 and 1.51 g C/m2 per year, respectively, while the NPPh decreased at a rate of 0.46 g C/m2 per year. It can be seen that human activities had a positive effect on the change of NPP in the Yellow River source grassland from the change rate. The relative contribution analysis showed that 55.90% of grassland NPP increased due to climate change, 40.16% of grassland NPP increased due to human activities, and the grassland degradation was not significant. The research results can provide a theoretical basis and technical support for the next step of the Yellow River source grassland ecological protection project.

The outer banks of meadow-type meandering river bends in the source zone of the Yellow River are especially vulnerable to bank failure. This study aims to understand how vegetation affects bank stability and the mechanism of bank failure, especially via a prediction of the width of a collapsed block of small rivers through a proposed bank stability equilibrium as well as field sampling. Soil and vegetation properties were surveyed at four sites near the riverbank in 2013-2016. It was found that the failed blocks had, on average, a dimension of 0.865 m (width) by 0.817 m (thickness) by 2.228 m (length). The variability in the size of all the failed blocks was attributed predominantly to the roots of plants. Block thickness could be logarithmically predicted by root length at R 2 ≥ 0.76. The block width predicted from the proposed equilibrium equation deviated from in situ measurements by approximately 22.1%, a discrepancy highly subject to the overestimation of root reinforcement using Wu's model. By reducing the coefficient of Wu's model from 1.2 to 0.85, the proposed equilibrium equation was reliable to predict the width of bank collapse. However, its applicability to other study areas needs to be verified in further studies.

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