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Bionic joints are crucial for robotic motion and are a hot topic in robotics research. Among various actuators for joints, shape memory alloys (SMAs) have attracted significant interest due to their similarity to natural muscles. SMA exhibits the shape memory effect (SME) based on martensite-to-austenite transformation and its inverse, which allows for force and displacement output through low-voltage heating. However, one of the main challenges with SMA is its limited axial stroke. In this article, a bionic joint based on SMA wires and a differential pulley set structure was proposed. The axial stroke of the SMA wires was converted into rotational motion by the stroke amplification of the differential pulley set, enabling the joint to rotate by a sufficient angle. We modeled the bionic joint and designed a proportional–integral (PI) controller. We demonstrated that the bionic joint exhibited good position control performance, achieving a rotation angle range of −30° to 30°. The proposed bionic joint, utilizing SMA wires and a differential pulley set, offers an innovative solution for enhancing the range of motion in SMA actuated bionic joints.

Inspired by the natural skeletal muscles, this paper presents a novel shape memory alloy-based artificial muscle matrix (AMM) with advantages of a large output force and displacement, flexibility, and compactness. According to the composition of the AMM, we propose a matrix control strategy to achieve independent control of the output force and displacement of the AMM. Based on the kinematics simulation and experiments, we obtained the output displacement and bearing capacity of the smart digital structure (SDS) and confirmed the effectiveness of the matrix control strategy to achieve force and displacement output independently and controllably. A bionic mechanical ankle actuated by AMM was proposed to demonstrate the actuating capability of the AMM. Experimental results show that the angle and force of the bionic mechanical ankle are output independently and have a significant gradient. In addition, by using a self-sensing method (resistance self-feedback) and PD control strategy, the output angle and force of the bionic mechanical ankle can be maintained for a long time without overheating of the AMM.

Existing thruster fault diagnosis methods for AUV (autonomous underwater vehicle) usually need sufficient labeled training data. However, it is unrealistic to get sufficient labeled training data for each working condition in practice. Based on this challenge, a transferable thruster fault diagnosis approach is proposed. In the approach, an IPSE (instantaneous power spectrum entropy) and a STNED (signal-to-noise energy difference) are added to SPWVD (smoothed pseudo Wigner-Ville distribution) to identify time and frequency boundaries of the local region in the time-frequency power spectrum caused by thruster fault, forming a TFE (time-frequency energy) method for feature extraction. In addition, the RCQFFV (relative change quantity of the fault feature value), an MSN (multiple scale normalization) and a LSP (least square prediction) are added to SVDD (support vector data description) to align distributions of fault samples, contributing a TSVDD (transferable SVDD) for classification of fault samples. The experimental results of a prototype AUV indicate that the fault feature is monotonic to the percentage of thrust loss for the proposed TFE but not for the SPWVD. The TSVDD has a higher overall classification accuracy in comparison to conventional SVDD under working conditions with no labeled training data.

Amino acid decarboxylases convert amino acids into different biogenic amines which regulate diverse biological processes. Therefore, identifying the substrates of amino acid decarboxylases is critical for investigating the function of the decarboxylases, especially for the new genes predicted to be amino acid decarboxylases. In the present work, we have established a simple and efficient method to identify the substrates and enzymatic activity of amino acid decarboxylases based on LC-MS methods. We chose GAD65 and AADC as models to validate our method. GAD65 and AADC were expressed in HEK 293T cells and purified through immunoprecipitation. The purified amino acid decarboxylases were subjected to enzymatic reaction with different substrate mixtures in vitro. LC-MS analysis of the reaction mixture identified depleted or accumulated metabolites, which corresponded to candidate enzyme substrates and products, respectively. Our method successfully identified the substrates and products of known amino acid decarboxylases. In summary, our method can efficiently identify the substrates and products of amino acid decarboxylases, which will facilitate future amino acid decarboxylase studies.