5-Aminolevulinic acid (ALA) at low concentrations increased cold resistance in rice seedlings. The pretreatment of rice seedlings by root-soaking with ALA solution at 0.1-1 ppm reduced the ratio of leaf rolling and electrolyte leakage from leaf tissue after cold treatment. Thirty days after cold treatment at 5C for 5 days, the plants treated with 1 ppm ALA resulted in 85% survival ratio, 6.1 leaves per plant and 111.8 mg dry weight per aerial part of seedling, while 65%, 5.9 and 65.0 mg respectively in the control plants. The dry weight of seedlings treated with ALA increased 1.7 folds as compared to the control plants. These results are the first evidence that ALA has protective effects against cold stress in rice seedlings. Abscisic acid (ABA) and brassinolide (BR) also increased the cold resistance in our bioassay system. Protective effect of BR at 0.001 ppm against cold stress was similar to that of ALA. However, protective effect of ABA was different from that of ALA in terms of the ratio of leaf rolling after cold stress. ABA protected young leaves rather than old ones, while ALA and BR were more effective on the protection of old leaves.
Scene perception and trajectory forecasting are two fundamental challenges that are crucial to a safe and reliable autonomous driving (AD) system. However, most proposed methods aim at addressing one of the two challenges mentioned above with a single model. To tackle this dilemma, this paper proposes spatio‐temporal semantics and interaction graph aggregation for multi‐agent perception and trajectory forecasting (ST‐SIGMA), an efficient end‐to‐end method to jointly and accurately perceive the AD environment and forecast the trajectories of the surrounding traffic agents within a unified framework. ST‐SIGMA adopts a trident encoder–decoder architecture to learn scene semantics and agent interaction information on bird’s‐eye view (BEV) maps simultaneously. Specifically, an iterative aggregation network is first employed as the scene semantic encoder (SSE) to learn diverse scene information. To preserve dynamic interactions of traffic agents, ST‐SIGMA further exploits a spatio‐temporal graph network as the graph interaction encoder. Meanwhile, a simple yet efficient feature fusion method to fuse semantic and interaction features into a unified feature space as the input to a novel hierarchical aggregation decoder for downstream prediction tasks is designed. Extensive experiments on the nuScenes data set have demonstrated that the proposed ST‐SIGMA achieves significant improvements compared to the state‐of‐the‐art (SOTA) methods in terms of scene perception and trajectory forecasting, respectively. Therefore, the proposed approach outperforms SOTA in terms of model generalisation and robustness and is therefore more feasible for deployment in real‐world AD scenarios.
Dielectric elastomer (DE) is a soft material that can deform to a large degree under the action of an electric field. In this paper, multilayer DE films were stacked in parallel to prepare a 20-layer dielectric elastomer actuator (DEA). This DEA could provide a peak output force of 30 N, which significantly improves the driving performance of the DEA and provides conditions for large load driving of the DEA. As a new driving method, the DEA was applied to a jumping robot, and the heavy-weight robot accomplished jumping motion after several cycles of energy storage.
The electromechanical properties of ionic polymer-metal composites (IPMC) are affected by many factors, including resistivity of surface electrodes, bending stiffness and dielectric modulus, etc, which are closely related to physical and chemical preparation steps. This paper focuses on the effects of preparation steps on these physical parameters and electromechanical properties of IPMC actuators. The mechanisms of electrode formation in the preparation steps are also clarified and investigated. To obtain samples with different features, one or more of the crucial process steps, including pretreatment, impregnation–reduction and chemical plating, were selected to fabricate IPMC. The experimental observations revealed that the physical parameters of IPMC strongly depend on their electrode morphologies caused by different steps, which were reasonable from the standpoint of physics. IPMC with the characteristics of low surface resistance and low bending stiffness, and a large area of interface electrode exhibits a perfect performance. The improvements were considered to be attributed to the double-layer electrostatic effect, induced by the broad dispersion of penetrated electrode nanoparticles. An electrical component, consisting of an equivalent circuit of a parallel combination of the serial circuit of the resistance and the electric double-layer capacitance, is introduced to qualitatively explain the deformation behaviors of IPMC. This research helps to improve the preparation steps and promote the understanding of IPMC.
As a typical smart material, ionic polymer metal composite (IPMC) has a sandwich structure, which consists of a base membrane and two thin metallic electrodes on both sides of the base membrane. The properties of the base membrane, Nafion as the most used base material, strongly affect the performance of IPMC actuator. This paper reports the effects of different additives, such as ethylene glycol (EG), dimethyl sulfoxide (DMSO), N, N 0 -dimethyl formamide (DMF), and N-methyl formamide (NMF), on the performances of the casting membranes and SO-based IPMC actuators. Studies have shown that the microstructures of the casting membranes with EG and DMSO as additives are more loose and amorphous, leading to higher water contents and thus higher conductivity than those with DMF, NMF, and Nafion 117. Among the casting membrane-based IPMC actuators, EG-based IPMC actuator has larger deformation and blocking force, higher strain energy density and conversion efficiency at 2 V DC voltage, whose electromechanical properties are most close to that based on Nafion 117. POLYM. ENG. SCI., 823 FIG. 12. DCF and ACF of SO-based IPMCs: (a) The dynamic energy conversion factor (DFC) results and (b) The average energy conversion factors (AFC) results. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
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