Bearing fault diagnosis is essential for ensuring the safety and reliability of industrial systems. Recently, deep learning approaches, especially the convolutional neural network, have demonstrated exceptional performance in bearing fault diagnosis. However, the limited availability of training samples has been a persistent issue, leading to a significant reduction in diagnostic accuracy. Additionally, noise interference or load variation during bearing operation pose significant challenges for fault diagnosis. To tackle the above issues, this paper explores the application of quadratic neuron with attention-embedded for fault diagnosis networks and introduces a trusted multi-scale learning strategy that fully considers the characteristics of bearing vibration signals. Building upon these concepts, a trusted multi-scale quadratic attention-embedded convolutional neural network is proposed for bearing faults diagnosis. Experimental results indicate that the proposed network outperforms six stateof-the-art networks under noise interference or load variation superimposed on small samples.

Objective. Obesity causes a variety of metabolic alterations that may contribute to abnormalities of the cardiac structure and function (obesity cardiomyopathy). In previous works, we have shown that pentamethylquercetin (PMQ) significantly improved metabolic disorders in obese mice and it inhibited pressure overload‐induced cardiac remodeling in mice. However, its potential benefit in obesity cardiomyopathy remains unclear. The aim of this study was to investigate the effects of PMQ on cardiac remodeling in obese mice. Methods. We generated a monosodium glutamate‐induced obese (MSG‐IO) model in mice, which were treated with PMQ (5, 10, and 20 mg/kg) for 16 weeks consecutively. We examined the metabolic parameters and observed cardiac remodeling by performing cardiac echocardiography and Masson’s staining. The expression levels of molecules associated with the endogenous antioxidant system, including the sestrins/kelch‐like ECH‐associated protein 1 (Keap1)/Nuclear factor (erythroid‐derived 2)‐like 2 (Nrf2) signaling pathway, were analyzed by western blotting and immunofluorescent staining. Results. We found that PMQ treatment significantly ameliorated obesity phenotypes and improved metabolic disorders in MSG‐IO mice. PMQ decreased the heart wall thickness and attenuated cardiac fibrosis. Further study revealed that the protective effects of PMQ might be mediated by promoting Keap1 degradation and augmenting sestrins expression and Nrf2 nuclear translocation. Conclusion. Our findings indicated that PMQ ameliorated cardiac remodeling in obese mice by targeting the sestrins/Keap1/Nrf2 signaling pathway.

Weld tracking systems based on structured light vision have been widely used in robotic welding. The precise extraction of laser stripes is key to achieving automatic weld seam tracking based on laser vision. However, traditional image-based laser stripe extraction methods are often susceptible to welding-induced interference, including smoke, spatter, and arc light, which greatly limits their practical applications in industry. To solve this problem, this paper proposes a U-shaped laser stripe segmentation network, named CP-PS-Unet, which combines the copy-paste method, a lightweight pyramid pooling module, and a spatial attention mechanism. Furthermore, an image pre-processing method with addition of Gaussian noise, salt-pepper noise, random noise is also employed. The proposed CP-PS-Unet can effectively extract laser stripes from seam images captured by a laser structured-light vision sensor, realize the adaptive extraction of laser stripe features, and improve the real-time performance and robustness of the system. According to the experimental results, the Mean Intersection over Union, Kappa coefficient, and dice coefficient of the proposed CP-PS-Unet algorithm are 86.01%, 83.79% and 91.90%, respectively, representing improvements of 7.81%, 11.51% and 5.76% compared to the traditional Unet, and the accuracy is increased to 99.71%. Overall, the proposed CP-PS-Unet shows good segmentation performance and high robustness to seam images affected by strong welding interference. It can be used for the automatic extraction of laser stripes and provides an effective method for real-time laser vision seam tracking in robotic welding.