With improvements in energy conversion efficiency and diversification of conversion manner, devices through natural evaporation from water reservoirs have potential to become an avenue to harvest energy. Using green, sustainable and biocompatible components is ever greater interesting for construction of humidity-induced actuator and generator. Here we demonstrate that green nanoarchitectonics with the bio-friendly poly(3, 4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS)–gelatin have good mechanical and humidity responsive properties which can use to construct moisture-responsive actuator and generator. The PEDOT:PSS–gelatin film actuator is fabricated by simple spin-coating, in which PEDOT:PSS is introduced into natural gelatin to increase the mechanical intensity due to hydrogen bond and interpenetrated network between rigid PEDOT:PSS chains and protein molecules. The PEDOT:PSS–gelatin film combined with piezoelectric poly(vinylidene fluoride) (PVDF) film can be driven into the movement by humidity. The PEDOT:PSS–gelatin film and the driven PVDF film as actuator and generator generates piezoelectric signal. The bio-friendly evaporation-driven generator may have applications in self-powering biomedicine robotic system and sensors.

Aiming at the drift problem that the tracking control of the actual load relative to the target load during the electromagnetic excitation biaxial fatigue test of wind turbine blades is easy to drift, a biaxial fatigue testing machine for electromagnetic excitation is designed, and the following strategy of the actual load and the target load is studied. A Fast Transversal Recursive Least Squares algorithm based on fuzzy logic (Fuzzy FTRLS) is proposed to develop a fatigue loading following dynamic strategy, which adjusts the forgetting factor in the algorithm through fuzzy logic to overcome the contradiction between convergence accuracy and convergence speed and solve the phenomenon of amplitude overshoot and phase lag of the actual load relative to the target load. Combined with the previous research results, a simulation model was constructed to verify the strategy’s effectiveness. Field tests were carried out to verify its follow-up effect. The results show that the tracking error of flapwise and edgewise direction is within 4%, which has better robustness and dynamic and static performance than the traditional Recursive Least Squares (RLS) algorithm.

Incomplete remote sensing image observation data require proper processing to restore the original data. Nevertheless, the lack of information, due to noise and dead pixels, along with the quality of the acquired images that may be severely degraded, limits the accuracy of later processing. Therefore, this paper proposes a remote sensing image restoration scheme that employs the low-rank structure of the Bayesian probability tensor latent space and a new tensor completion method, which has strong robustness for the model selection process of the following remote sensing image data analysis. Considering the data space, we incorporate an adaptive nuclear norm regularization into the processing of latent factors that performs singular value decomposition on a more accurate scale. The proposed model adopts two algorithms: Augmented Lagrange Multiplier (ALM) and Alternating Least Squares (ALS). Several synthetic and real-data-based experiments illustrate the inherent ability of our method in restoring original information and preventing overfitting problems, even when a vast number of items are missing. In addition, the combination of adaptive nuclear norm regularization and tensor decomposition is stable, settling a high sensitivity of rank selection and low computational efficiency in traditional tensor reconstruction methods.