We constructed an intelligent cloud lab that integrates lab automation with cloud servers and artificial intelligence (AI) to detect chirality in perovskites. Driven by the materials acceleration ...operating system in cloud (MAOSIC) platform, on-demand experimental design by remote users was enabled in this cloud lab. By employing artificial intelligence of things (AIoT) technology, synthesis, characterization, and parameter optimization can be autonomously achieved. Through the remote collaboration of researchers, optically active inorganic perovskite nanocrystals (IPNCs) were first synthesized with temperature-dependent circular dichroism (CD) and inversion control. The inter-structure (structural patterns) and intra-structure (screw dislocations) dual-pattern-induced mechanisms detected by MAOSIC were comprehensively investigated, and offline theoretical analysis revealed the thermodynamic mechanism inside the materials. This self-driving cloud lab enables efficient and reliable collaborations across the world, reduces the setup costs of in-house facilities, combines offline theoretic analysis, and is practical for accelerating the speed of material discovery.
The formulation of high-efficient energy management strategy (EMS) for hybrid electric vehicles (HEVs) becomes the most crucial task owing to the variation of electrified powertrain topology and ...uncertainty of driving scenarios. In this study, a deep reinforcement learning (DRL) algorithm, namely TD3, is leveraged to derivate intelligent EMS for HEV. A heuristic rule-based local controller (LC) is embedded within the DRL loop to eliminate irrational torque allocation with considering the characteristics of powertrain components. In order to resolve the influence of environmental disturbance, a hybrid experience replay (HER) method is proposed based on a mixed experience buffer (MEB) consisting of offline computed optimal experience and online learned experience. The results indicate that improved TD3 based EMS obtained the best fuel optimality, fastest convergence speed and highest robustness in comparison to typical value-based and policy-based DRL EMSs under various driving cycles. LC leads to a boosting effect on the convergence speed of TD3-based EMS wherein a “warm” start of exploring is exhibited. Meanwhile, by incorporating HER coupled with MEB, the impact of environmental disturbance including load mass and road gradient, as an increase of input observations, can be negligible to the performance of TD3-based EMS.
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•A novel DRL algorithm TD3 is leveraged to formulate intelligent HEV EMS.•A heuristic rule-based local controller is embedded in the DRL loop to eliminate irrational exploration.•A hybrid experience replay method is proposed through mixed experience buffer consisting of environmental disturbances.•Comparison analysis is systematically conducted among DDPG, double DQN, dueling DQN with proposed DRL-based EMS.
Solar eruptions are the main driver of space-weather disturbances at Earth. Extreme events are of particular interest, not only because of the scientific challenges they pose, but also because of ...their possible societal consequences. Here we present a magnetohydrodynamic (MHD) simulation of the 2000 July 14 "Bastille Day" eruption, which produced a very strong geomagnetic storm. After constructing a "thermodynamic" MHD model of the corona and solar wind, we insert a magnetically stable flux rope along the polarity inversion line of the eruption's source region and initiate the eruption by boundary flows. More than 1033 erg of magnetic energy is released in the eruption within a few minutes, driving a flare, an extreme-ultraviolet wave, and a coronal mass ejection (CME) that travels in the outer corona at 1500 km s−1, close to the observed speed. We then propagate the CME to Earth, using a heliospheric MHD code. Our simulation thus provides the opportunity to test how well in situ observations of extreme events are matched if the eruption is initiated from a stable magnetic equilibrium state. We find that the flux-rope center is very similar in character to the observed magnetic cloud, but arrives 8.5 hr later and 15° too far to the north, with field strengths that are too weak by a factor of 1.6. The front of the flux rope is highly distorted, exhibiting localized magnetic field concentrations as it passes 1 au. We discuss these properties with regard to the development of space-weather predictions based on MHD simulations of solar eruptions.
The role of cosmic rays generated by supernovae and young stars has very recently begun to receive significant attention in studies of galaxy formation and evolution due to the realization that ...cosmic rays can efficiently accelerate galactic winds. Microscopic cosmic-ray transport processes are fundamental for determining the efficiency of cosmic-ray wind driving. Previous studies modeled cosmic-ray transport either via a constant diffusion coefficient or via streaming proportional to the Alfvén speed. However, in predominantly cold, neutral gas, cosmic rays can propagate faster than in the ionized medium, and the effective transport can be substantially larger; i.e., cosmic rays can decouple from the gas. We perform three-dimensional magnetohydrodynamical simulations of patches of galactic disks including the effects of cosmic rays. Our simulations include the decoupling of cosmic rays in the cold, neutral interstellar medium. We find that, compared to the ordinary diffusive cosmic-ray transport case, accounting for the decoupling leads to significantly different wind properties, such as the gas density and temperature, significantly broader spatial distribution of cosmic rays, and higher wind speed. These results have implications for X-ray, γ-ray, and radio emission, and for the magnetization and pollution of the circumgalactic medium by cosmic rays.
Abstract
The revolutionary 5G cellular systems represent a breakthrough in the communication network design to provide a single platform for enabling enhanced broadband communications, virtual ...reality, autonomous driving, and the internet of everything. However, the ongoing massive deployment of 5G networks has unveiled inherent limitations that have stimulated the demand for innovative technologies with a vision toward 6G communications. Terahertz (0.1-10 THz) technology has been identified as a critical enabler for 6G communications with the prospect of massive capacity and connectivity. Nonetheless, existing terahertz on-chip communication devices suffer from crosstalk, scattering losses, limited data speed, and insufficient tunability. Here, we demonstrate a new class of phototunable, on-chip topological terahertz devices consisting of a broadband single-channel 160 Gbit/s communication link and a silicon Valley Photonic Crystal based demultiplexer. The optically controllable demultiplexing of two different carriers modulated signals without crosstalk is enabled by the topological protection and a critically coupled high-quality (
Q
) cavity. As a proof of concept, we demultiplexed high spectral efficiency 40 Gbit/s signals and demonstrated real-time streaming of uncompressed high-definition (HD) video (1.5 Gbit/s) using the topological photonic chip. Phototunable silicon topological photonics will augment complementary metal oxide semiconductor (CMOS) compatible terahertz technologies, vital for accelerating the development of futuristic 6G and 7G communication era driving the real-time terabits per second wireless connectivity for network sensing, holographic communication, and cognitive internet of everything.
Using a general model of opinion dynamics, we conduct a systematic investigation of key mechanisms driving elite polarization in the United States. We demonstrate that the self-reinforcing nature of ...elite-level processes can explain this polarization, with voter preferences accounting for its asymmetric nature. Our analysis suggests that subtle differences in the frequency and amplitude with which public opinion shifts left and right over time may have a differential effect on the self-reinforcing processes of elites, causing Republicans to polarize more quickly than Democrats. We find that as self-reinforcement approaches a critical threshold, polarization speeds up. Republicans appear to have crossed that threshold while Democrats are currently approaching it.
•Automated Vehicles (AVs) need to be driven for 11 billion miles to prove their safety•Number of test miles is not, by itself, a meaningful metric for judging the safety•Types of scenarios ...encountered by the AVs during testing are critically important•STPA inspired Hazard Based Testing creates ‘smart miles’ which uncover AV failures•Using STPA and its proposed extension, test scenarios for SAE Level 4 AV are proposed
Increased safety has been advocated as one of the major benefits of the introduction of Automated Driving Systems (ADSs). Incorporation of ADSs in vehicles means that associated software has safety critical application, thus requiring exhaustive testing. To prove ADSs are safer than human drivers, some work has suggested that they will need to be driven for over 11 billion miles. The number of test miles driven is not, by itself, a meaningful metric for judging the safety of ADSs. Rather, the types of scenarios encountered by the ADSs during testing are critically important.
With a Hazard Based Testing approach, this paper proposes that the extent to which testing miles are ‘smart miles’ that reflect hazard-based scenarios relevant to the way in which an ADS fails or handles hazards is a fundamental, if not pivotal, consideration for safety-assurance of ADSs. Using Systems Theoretic Process Analysis (STPA) method as a foundation, an extension to the STPA method has been developed to identify test scenarios. The approach has been applied to a real-world case study of a SAE Level 4 Low-Speed Automated Driving system (a.k.a. a shuttle). This paper, discusses the STPA analysis and a newly-developed test scenarios creation method derived from STPA.
Electric Vehicles (EVs) are known as the future vehicles that have the potential to provide environmental benefits all over the world. The Greenhouse Gas (GHG) emissions of EVs have already been ...estimated for each phase in the life cycle. However, the dedicated estimations in China are not complete enough to reveal the systematic impacts of real manufacturing technologies, driving cycle and recycling processes. This study has analyzed the GHG emissions of the Cradle-to-Gate (CTG) phase, Well-to-Wheel (WTW) phase and Grave-to-Cradle (GTC) phase for different vehicles in different time to figure out the key drivers and reduction opportunities, which are based on the well-selling A0-A class compact sedan model currently in China. The results indicate that the life cycle GHG emissions of an EV are about 41.0 t CO2eq in 2015, 18% lower than those of an Internal Combustion Engine Vehicle (ICEV). This value will decrease to only 34.1 t CO2eq in 2020 due to the reduction of GHG emission factor of electricity. Although the WTW phase is the largest contributor of GHG emissions for both vehicles, the proportions of each phase are quite different. The GHG emissions of the WTW phase of an EV are decreasing rapidly, but the CTG phase will not be improved at the same speed, which may become a barrier to fully take the environmental benefits of an EV. There are two major opportunities for reduction in the entire life cycle besides fuel economy development. One is EV recycling that can reduce the GHG emissions of the CTG phase by about a half. The other is the improvement of clean power grid that can further reduce the GHG emissions of the WTW phase.
•The complete life cycle of electric vehicle is identified in detail.•The greenhouse gas emissions of vehicle cycle and fuel cycle are both evaluated.•The space for improvement is analyzed in each phase.•Major opportunities for reduction such as electric vehicle recycling and clean power grid are discussed.
This research proposes a reinforcement learning-based algorithm and a deep reinforcement learning-based algorithm for energy management of a series hybrid electric tracked vehicle. Firstly, the ...powertrain model of the series hybrid electric tracked vehicle (SHETV) is constructed, then the corresponding energy management formulation is established. Subsequently, a new variant of reinforcement learning (RL) method Dyna, namely Dyna-H, is developed by combining the heuristic planning step with the Dyna agent and is applied to energy management control for SHETV. Its rapidity and optimality are validated by comparing with DP and conventional Dyna method. Facing the problem of the “curse of dimensionality” in the reinforcement learning method, a novel deep reinforcement learning algorithm deep Q-learning (DQL) is designed for energy management control, which uses a new optimization method (AMSGrad) to update the weights of the neural network. Then the proposed deep reinforcement learning control system is trained and verified by the realistic driving condition with high-precision, and is compared with the benchmark method DP and the traditional DQL method. Results show that the proposed deep reinforcement learning method realizes faster training speed and lower fuel consumption than traditional DQL policy does, and its fuel economy quite approximates to global optimum. Furthermore, the adaptability of the proposed method is confirmed in another driving schedule.
•The powertrain model of the series hybrid electric tracked vehicle is constructed.•A novel reinforcement learning-based energy management strategy is proposed.•The rapidity and optimality of the reinforcement learning method are validated.•A new optimization method is applied to update the weights of the neural network.•The proposed deep reinforcement learning method realizes better performance.
Abstract METTL3 is the catalytic subunit of the methyltransferase complex, which mediates m 6 A modification to regulate gene expression. In addition, METTL3 regulates transcription in an enzymatic ...activity-independent manner by driving changes in high-order chromatin structure. However, how these functions of the methyltransferase complex are coordinated remains unknown. Here we show that the methyltransferase complex coordinates its enzymatic activity-dependent and independent functions to regulate cellular senescence, a state of stable cell growth arrest. Specifically, METTL3-mediated chromatin loops induce Hexokinase 2 expression through the three-dimensional chromatin organization during senescence. Elevated Hexokinase 2 expression subsequently promotes liquid-liquid phase separation, manifesting as stress granule phase separation, by driving metabolic reprogramming. This correlates with an impairment of translation of cell-cycle related mRNAs harboring polymethylated m 6 A sites. In summary, our results report a coordination of m 6 A-dependent and -independent function of the methyltransferase complex in regulating senescence through phase separation driven by metabolic reprogramming.
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