A three-degrees-of-freedom model, including surge, sway and yaw motion, with differential thrusters is proposed to describe unmanned surface vehicle (USV) dynamics in this study. The experiment is carried out in the Qing Huai River and the data obtained from different zigzag trajectories are filtered by a Gaussian filtering method. A physics-informed neural network (PINN) is proposed to identify the dynamic models of the USV. PINNs combine the advantages of data-driven machine learning and physical models. They can also embed the speed and steering models into the loss function, which can significantly retain all types of information. Compared with traditional neural networks, the results show that the PINN has better generalization ability in predicting the surge and sway velocities and rotation speed with only limited training data.

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AUV (autonomous underwater vehicles) are required to have long-term and high-precision positioning capability relative to seabed targets in most deep-sea exploration tasks. However, acoustic positioning error is positively correlated with its operating range and inertial navigation has inevitable accumulated time errors, neither of which provide precise AUV positions. TAN (terrain aided navigation) directly calculates the AUV position to the seabed terrain coordinate system by tracking the seabed topographic characteristics, which can guide the AUV to seabed target accurately. However, the initial TAN positioning error will increase with the AUV operation depth, which causes a large PF (particle filter) initialization error and particle coverage interval, and will affect the convergence and stability of TAN. To solve this problem, we first propose a TAP (terrain aided position) confidence interval model. We then use the confidence interval to constrain the initial particles to a smaller range. Finally, the validity of the algorithm is verified by playback simulation with ship-borne multi-beam sonar sensor measured data. The results show that the TAP confidence interval can reduce the coverage of the initial particle, and can improve the convergence speed and filtering accuracy of the TAN.

Brassica crops include various edible vegetable and plant oil crops, and their production is limited by low temperature beyond their tolerant capability. The key regulators of low‐temperature resistance in Brassica remain largely unexplored. To identify posttranscriptional regulators of plant response to low temperature, we performed small RNA profiling, and found that 16 known miRNAs responded to cold treatment in Brassica rapa. The cold response of seven of those miRNAs were further confirmed by qRT‐PCR and/or northern blot analyses. In parallel, a genome‐wide association study of 220 accessions of Brassica napus identified four candidate MIRNA genes, all of which were cold‐responsive, at the loci associated with low‐temperature resistance. Specifically, these large‐scale data analyses revealed a link between miR1885 and the plant response to low temperature in both B. rapa and B. napus. Using 5′ rapid amplification of cDNA ends approach, we validated that miR1885 can cleave its putative target gene transcripts, Bn.TIR.A09 and Bn.TNL.A03, in B. napus. Furthermore, overexpression of miR1885 in Semiwinter type B. napus decreased the mRNA abundance of Bn.TIR.A09 and Bn.TNL.A03 and resulted in increased sensitivity to low temperature. Knocking down of miR1885 in Spring type B. napus led to increased mRNA abundance of its targets and improved rapeseed tolerance to low temperature. Together, our results suggested that the loci of miR1885 and its targets could be potential candidates for the molecular breeding of low temperature‐tolerant Spring type Brassica crops.

Fixed-point underwater hovering is a key technology for the reliable operation of a remotely operated vehicle in the ocean to inspect the surfaces of a variety of underwater structures, such as ports and offshore wind power facilities. This study proposes an underwater wall absorber that can be used in remotely operated vehicles. First, we explain the working principle of the underwater absorber. Second, we analyze the main factors affecting its adsorption performance by using numerical simulations. Finally, we show the results of a tested prototype of the proposed absorber, whose performance was consistent with the results of numerical calculations. The proposed absorber may have important technical prospects for use in remotely operated underwater vehicles.