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Simple Summary: A new species of odd-scaled snake in the genus Achalinus is described from Dabie Mountains Luan City, Anhui Province, China, based on one male and two female specimens. Bayesian inference and maximum likelihood analyses based on a mitochondrial DNA fragment (CO1) indicated the new taxon is different from its congeners (p–distance ≥ 9.4%). Morphologically, the new species can be diagnosed from the other species by a combination of 12 characters. The recognition of the new species brings the number of described Achalinus species to 22. A new species of Xenodermid snake, Achalinus dabieshanensis sp. nov., was described based on three specimens (two female and one male) collected from the Dabie Mountains of western Anhui Province. It can be distinguished from known congeners by a significant genetic divergence in the mitochondrial gene fragment COI (p-distance ≥ 9.4%) and the following combination of characteristics: (1) length of the suture between the internasals being distinctly shorter than between the prefrontals; (2) a single loreal; (3) dorsal scales strongly keeled, in 23 rows throughout the body; (4) two pairs of prefrontals; (5) six supralabials; (6) five infralabials; (7) temporals 2 + 2 + 3 (or 2 + 2 + 4); (8) 141–155 ventrals; (9) 45–55 subcaudals, unpaired; (10) anal entire; (11) weakly iridescent tinged, uniform, brown to black dorsum with vertebral scales and about three adjacent dorsal scales dark brown forming a longitudinal vertebral line from posterior margin of parietals to tail tip; (12) light brown venter, ventral shields wide, visible on both sides, light brown flanks, giving the appearance of a black subcaudal streak. The recognition of the new species increases the number of described Achalinus species to 22. [ABSTRACT FROM AUTHOR]

Bearings, as the key mechanical components of rotary machinery, are widely used in modern aerospace equipment, such as helicopters and aero-engines. Intelligent fault diagnosis, as the main function of prognostic health management systems, plays a critical role in maintaining equipment safety in aerospace applications. Recently, data-driven intelligent diagnosis approaches have achieved great success due to the availability of large-scale, high-quality, and complete labeled data. However, in a real application, labeled data is often scarce because it requires manual labeling, which is time-consuming and labor-intensive. Meanwhile, health monitoring data are usually scattered in different regions or equipment in the form of data islands. Traditional fault diagnosis techniques fail to gather enough data for model training due to data security, economic conflict, relative laws, and other reasons. Therefore, it is a challenge to effectively combine the data advantages of different equipment to develop an intelligent diagnosis model with better performance. To address this issue, a novel clustering federated learning (CFL) method with a self-attention mechanism is proposed for bearing fault diagnosis. Firstly, a deep neural network with a self-attention mechanism is developed in a convolutional pipe for feature extraction, which can capture local and global information from raw input. Then, the CFL is further constructed to gather the data from different equipment with similar data distribution in an unsupervised manner. Finally, the CFL-based diagnosis model can be well trained by fully utilizing the distributed data, while ensuring data privacy safety. Experiments are carried out with three different bearing datasets in aerospace applications. The effectiveness and the superiority of the proposed method have been validated compared with other popular fault diagnosis schemes. [ABSTRACT FROM AUTHOR]