Distributed Cooperative Autonomous Driving of Intelligent Vehicles Based on Spring-Damper Energy System

Distributed cooperative control of autonomous vehicle platoons has been widely considered as a potential solution for reducing traffic congestion, increasing road capacity and improving traffic safety. However, in the real-world implementation, sudden communication loss will degrade cooperative adaptive cruise control to adaptive cruise control, which may bring negative influences on safety (i.e., increase the risk of collisions). To overcome this limitation, this paper innovatively applies a spring-damper energy system to construct a robust leader-following vehicle platoon system. The special design of the energy system ensures that the stability and safety of the platoon system are maintained in the event of a sudden degradation. Based on the proposed energy model, a distributed control protocol is developed. The distributed control protocol achieves speed synchronisation of vehicle platoon and ensures that the following distance is safe over dynamic communication networks. Finally, the effectiveness of the proposed control strategy is validated by simulation experiments

Long-run dynamics of sulphur dioxide emissions, economic growth and energy efficiency in China

This paper estimates the linkages among total Sulphur dioxide (SO2) emissions, total GDP and energy efficiency using China’s provincial panel data from 2002 to 2015. We investigate total emissions rather than per capita emissions or ambient concentrations, since it is total emissions that the environment cares about. Energy efficiency is estimated using stochastic frontier analysis and decomposed into both persistent and transient efficiency. We then investigate the long-run dynamics among SO2 emissions, economic growth and energy efficiency by employing the panel-based error correction model and taking the effects of cyclical variations into account. Our analysis shows that GDP has a positive impact on total SO2 emissions in the short run and gains in energy efficiency have a significant negative effect on emissions in the long run. By controlling the effects of business cycle, the effects of GDP on emissions remain positive in both short and long run. Cross-sectional analysis provides similar insights. We argue that economic growth itself is an emission generator. Therefore, the government needs to establish a long-run strategy to curb the emissions by improving energy efficiency

High-Power Electromagnetic Pulse Exposure of Healthy Mice: Assessment of Effects on Mice Cognitions, Neuronal Activities, and Hippocampal Structures

Electromagnetic pulse (EMP) is a high-energy pulse with an extremely rapid rise time and a broad bandwidth. The brain is a target organ sensitive to electromagnetic radiation (EMR), the biological effects and related mechanisms of EMPs on the brain remain unclear. The objectives of the study were to assess the effects of EMP exposure on mouse cognitions, and the neuronal calcium activities in vivo under different cases of real-time exposure and post exposure. EMP-treated animal model was established by exposing male adult C57BL/6N mice to 300 kV/m EMPs. First, the effects of EMPs on the cognitions, including the spatial learning and memory, avoidance learning and memory, novelty-seeking behavior, and anxiety, were assessed by multiple behavioral experiments. Then, the changes in the neuronal activities of the hippocampal CA1 area in vivo were detected by fiber photometry in both cases of during real-time EMP radiation and post-exposure. Finally, the structures of neurons in hippocampi were observed by optical microscope and transmission electron microscope. We found that EMPs under this condition caused a decline in the spatial learning and memory ability in mice, but no effects on the avoidance learning and memory, novelty-seeking behavior, and anxiety. The neuron activities of hippocampal CA1 were disturbed by EMP exposure, which were inhibited during EMP exposure, but activated immediately after exposure end. Additionally, the CA1 neuron activities, when mice entered the central area in an Open field (OF) test or explored the novelty in a Novel object exploration (NOE) test, were inhibited on day 1 and day 7 after radiation. Besides, damaged structures in hippocampal neurons were observed after EMP radiation. In conclusion, EMP radiation impaired the spatial learning and memory ability and disturbed the neuronal activities in hippocampal CA1 in mice

A Spin-dependent Machine Learning Framework for Transition Metal Oxide Battery Cathode Materials

Owing to the trade-off between the accuracy and efficiency, machine-learning-potentials (MLPs) have been widely applied in the battery materials science, enabling atomic-level dynamics description for various critical processes. However, the challenge arises when dealing with complex transition metal (TM) oxide cathode materials, as multiple possibilities of d-orbital electrons localization often lead to convergence to different spin states (or equivalently local minimums with respect to the spin configurations) after ab initio self-consistent-field calculations, which causes a significant obstacle for training MLPs of cathode materials. In this work, we introduce a solution by incorporating an additional feature - atomic spins - into the descriptor, based on the pristine deep potential (DP) model, to address the above issue by distinguishing different spin states of TM ions. We demonstrate that our proposed scheme provides accurate descriptions for the potential energies of a variety of representative cathode materials, including the traditional Li$_x$TMO$_2$ (TM=Ni, Co, Mn, $x$=0.5 and 1.0), Li-Ni anti-sites in Li$_x$NiO$_2$ ($x$=0.5 and 1.0), cobalt-free high-nickel Li$_x$Ni$_{1.5}$Mn$_{0.5}$O$_4$ ($x$=1.5 and 0.5), and even a ternary cathode material Li$_x$Ni$_{1/3}$Co$_{1/3}$Mn$_{1/3}$O$_2$ ($x$=1.0 and 0.67). We highlight that our approach allows the utilization of all ab initio results as a training dataset, regardless of the system being in a spin ground state or not. Overall, our proposed approach paves the way for efficiently training MLPs for complex TM oxide cathode materials