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The latest developments combining deep learning technology and medical image data have attracted wide attention and provide efficient noninvasive methods for the early diagnosis of breast cancer. The success of this task often depends on a large amount of data annotated by medical experts, which is time-consuming and may not always be feasible in the biomedical field. The lack of interpretability has greatly hindered the application of deep learning in the medical field. Currently, deep stable learning, including causal inference, make deep learning models more predictive and interpretable. In this study, to distinguish malignant tumors in Breast Imaging-Reporting and Data System (BI-RADS) category 3–4A breast lesions, we propose BD-StableNet, a deep stable learning model for the automatic detection of lesion areas. In this retrospective study, we collected 3103 breast ultrasound images (1418 benign and 1685 malignant lesions) from 493 patients (361 benign and 132 malignant lesion patients) for model training and testing. Compared with other mainstream deep learning models, BD-StableNet has better prediction performance (accuracy = 0.952, area under the curve = 0.982, precision = 0.970, recall = 0.941, F 1-score = 0.955 and specificity = 0.965). The lesion area prediction and class activation map results both verify that our proposed model is highly interpretable. The results indicate that BD-StableNet significantly enhances diagnostic accuracy and interpretability, offering a promising noninvasive approach for the diagnosis of BI-RADS category 3–4A breast lesions. Clinically, the use of BD-StableNet could reduce unnecessary biopsies, improve diagnostic efficiency, and ultimately enhance patient outcomes by providing more precise and reliable assessments of breast lesions. [ABSTRACT FROM AUTHOR]

This study investigates whether and how federal judge ideology affects firm‐specific stock price crash risk. Using a comprehensive sample of US firms, we find a decline in the likelihood of future stock price crashes for firms headquartered in more liberal circuits. In identifying potential mechanisms, we show that liberal judge ideology reduces information opacity, risk‐taking behaviours and overinvestment, and thus curbs stock price crash risk. Furthermore, the curbing effect is more salient for firms with poor monitoring quality and those in low social capital areas. Overall, this study elucidates how federal judge ideology affects capital markets. [ABSTRACT FROM AUTHOR]

As one of the most destructive, hazardous, and frequent marine geohazards, correctly recognizing submarine landslides holds substantial importance for regional risk assessment, disaster prevention, and marine resource development. Many conventional approaches to prediction and mapping necessitate the involvement of expert insights, oversight, and extensive field investigations, which can result in significant time and effort invested in the prediction process. This paper focuses on employing a deep neural network semantic segmentation technique to detect submarine landslides to replace previous methods, such as numerical analysis and physical modeling, to predict and identify the landslide areas quickly. The peripheral zone of the western Iberian Sea is selected as the study area. Since the neural network image recognition task usually requires RGB images as input data, factors such as slope, hillshade, and elevation extracted from digital elevation model (DEM) data are used to synthesize RGB images through band synthesis methods, and the number and diversity of data are increased utilizing data enhancement. Based on the classical semantic segmentation model DeepLabV3, this paper proposes an improved deep learning method, which strengthens the ability of model feature extraction for complex situations by adding an attention mechanism module, improving the spatial pyramid pooling module, and improving the landslide intersection over union metric from 0.4257 to 0.5219 and the F1-score metric from 0.609 to 0.6631 to achieve effective identification of submarine landslides. [ABSTRACT FROM AUTHOR]

Multi‐bolted composite laminates are popular in various fields. Under different working conditions, it is a serious problem to produce different kinds and extent of damage at the joints. Real‐time monitoring of it is a challenging task. The objective of this artical is to use ultrafiltration membrane/buckypaper sensor to monitor the health status of the structure at different positions in the tensile environment. Firstly, the aperture ratio is selected by tensile test, and the feasibility of ultrafiltration membrane/buckypaper sensor to monitor the bolt structure and the different response degree to different strains are verified. Then, ultrafiltration membrane/buckypaper sensor layout position is designed, and the signal response of the sensor at different positions is analyzed. The damage degree and form of different bolts are visualized by ΔR/R0 change, and the SEM scanning diagram is used for auxiliary verification. The results show that The sensor can monitor the damage condition and damage form at different positions of the double bolt structure laminate in real time. Based on this method, it can be used to study the damage behavior of more complex multi‐bolt structure under tensile environment. Highlights: Ultrafiltration membrane/buckypaper sensor was prepared by a simple and efficient process.The pore structure of the ultrafiltration membrane integrates the sensor with the double bolt structure.Monitor the different damage forms of double bolt structure caused by external load.Monitor the different degrees of strain and crack propagation stage of the double bolt structure. [ABSTRACT FROM AUTHOR]

In this paper, a practical maintenance algorithm is proposed to improve the reliability of actuation systems and their components, specifically addressing the consistency degradation caused by faults in the symmetric actuation system components of more electric aircraft (MEA). By integrating important measures with traditional genetic algorithms, the accuracy of the algorithm is improved. Prior to maintenance, a reasonable classification of components is built to mitigate the adverse effects of extreme fault conditions on the algorithm. This approach improves both the effectiveness and efficiency of the algorithm, rendering the overall maintenance strategy better suited for real-world needs. Finally, comparative simulations confirm the algorithm's superior performance in reliability improvement, demonstrating its substantial contribution to the field of MEA maintenance and reliability. [ABSTRACT FROM AUTHOR]

Typical wind tunnel testing involves a series of configuration changes (to the angles of control surfaces) to simulate the lift and resistance characteristics of control surfaces in different flight conditions. It is very time-consuming and labor-intensive to manually change the angles of control surfaces, especially in the large continuous reflux wind tunnel. Thus, there is a demand for a morphing system design within the wind tunnel model that can deflect the control surfaces remotely and automatically. The basic design flow and characteristics of different actuator techniques for the morphing systems were summarized in this paper, including electromechanical actuator, pneumatic actuator, shape memory material actuator and piezoelectric actuator. In the case study, the accuracy of the control surface angle and aerodynamic performance of the ultrasonic-driven automatic control surface system reached the level of traditional fixed control surface systems, while its efficiency was much higher than that of the traditional fixed control surface systems. [ABSTRACT FROM AUTHOR]