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Abstract Actuators based on shape memory polymers and composites incorporating nanomaterial additives have been extensively studied; achieving both high output stress and precise shape change by low‐cost, scalable methods is a long‐term‐desired yet challenging task. Here, conventional polymers (polyurea) and carbon nanotube (CNT) fillers are combined to fabricate reinforced composite fibers with exceptional actuation performance, by a wet‐spinning method amenable for continuous production. It is found that a thermal‐induced shrinkage step could obtain densified strong fibers, and the presence of CNTs effectively promotes the tensile orientation of polymer molecular chains, leading to much improved mechanical properties. Consequently, the CNT/ polyurea composite fibers exhibit stresses as high as 33 MPa within 0.36 s during thermal actuation, and stresses up to 22 MPa upon electrical stimulation enabled by the built‐in conductive CNT networks. Utilizing the flexible thin fibers, various shape change behavior are also demonstrated including the conversion between different structures/curvatures, and recovery of predefined simple patterns. This high‐performance composite fibers, capable of both thermal and electrical actuation and produced by low‐cost materials and fabrication process, may find many potential applications in wearable devices, robotics, and biomedical areas.

Abstract Accurate offshore surface wind forecasting is crucial for navigation safety and disaster prevention. However, significant biases exist in forecasting sea surface winds due to the uncertainties in estimating sea surface roughness. In this study, we propose a deep learning‐based scheme (DL2023) for estimating sea surface roughness and integrate it into a regionally coupled ocean‐atmosphere‐wave model. Single‐point experiments demonstrate that DL2023 achieves a remarkable 50% reduction in the Root Mean Square Error (RMSE) compared to the four traditional schemes. During five typhoon cases in August 2020, compared to the four traditional schemes, the RMSEs of forecasted surface winds using DL2023 are reduced by 6.02%–14.75%, 11.17%–18.30%, and 11.91%–19.46% at lead times of 24, 48, and 72 hr, respectively. Thus, the DL2023 scheme, trained using data from the Atlantic Ocean, successfully improves the forecast of surface winds over the Northwest Pacific Ocean.

Abstract Wintertime precipitation, especially snowstorms, significantly impacts people's lives. However, the current forecast skill of wintertime precipitation is still low. Based on data augmentation (DA) and deep learning, we propose a DABU‐Net which improves the Global Forecast System wintertime precipitation forecast over southeastern China. We build three independent models for the forecast lead times of 24, 48, and 72 hr, respectively. After using DABU‐Net, the mean Root Mean Squared Errors (RMSEs) of the wintertime precipitation at the three lead times are reduced by 19.08%, 25.00%, and 22.37%, respectively. The threat scores (TS) are all significantly increased at the thresholds of 1, 5, 10, 15, and 20 mm day−1 for the three lead times. During heavy precipitation days, the RMSEs are decreased by 14% and TS are increased by 7% at the lead times within 48 hr. Therefore, combining DA and deep learning has great prospects in precipitation forecasting.