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Hadrotreta is a worldwide acrotretoid brachiopod reported from the Cambrian Series 2 to Miaolingian. Here, a number of well-preserved fossils of Hadrotreta, identified as Hadrotreta cf. H. timchristiorum, were found in the Protoryctocephalus arcticus Zone of the Tsinghsutung Formation of Cambrian Series 2, Stage 4 in Jianhe, Guizhou, south China. This is the first report of Hadrotreta in China, which enriches its global palaeogeographical distribution. Hadrotreta is very similar to acrotretoids such as Kostjubella, Vandalotreta, Linnarssonia, and Eohadrotreta. It differs from them with its well-developed ventral boss-like apical process, apical pits, and dorsal median sulcus. In view of the palaeogeography of Hadrotreta, this genus was mainly distributed in low-latitude regions. Hadrotreta was only found in south China and Laurentia during the Cambrian Age 4, then expanded its distribution to other regions such as Siberia, Baltica, the Kazakh Terranes, the Far East, and Gondwana Pange during the Miaolingian Epoch. Hadrotreta seems to have shifted from deeper water to shallow-water environments during the period from the Cambrian Series 2 to the Miaolingian.

Gearbox is an important structure of rotating machinery, and the accurate fault diagnosis of gearboxes is of great significance for ensuring efficient and safe operation of rotating machinery. Aiming at the problem that there is little common compound fault data of gearboxes, and there is a lack of an effective diagnosis method, a gearbox fault simulation experiment platform is set up, and a diagnosis method for the compound fault of gearboxes based on multi-feature and BP-AdaBoost is proposed. Firstly, the vibration signals of six typical states of gearbox are obtained, and the original signals are decomposed by empirical mode decomposition and reconstruct the new signal to achieve the purpose of noise reduction. Then, perform the time domain analysis and wavelet packet analysis on the reconstructed signal, extract three time domain feature parameters with higher sensitivity, and combine them with eight frequency band energy feature parameters obtained by wavelet packet decomposition to form the gearbox state feature vector. Finally, AdaBoost algorithm and BP neural network are used to build the BP-AdaBoost strong classifier model, and feature vectors are input into the model for training and verification. The results show that the proposed method can effectively identify the gearbox failure modes, and has higher accuracy than the traditional fault diagnosis methods, and has certain reference significance and engineering application value.