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The rolling bearing condition monitoring signal under strong noise interference is characterized by non-stationary multi-component signals, and the fault information contained in a single sensor signal is limited, which cannot fully characterize the operating state of the equipment. This study proposed a multi-sensor fusion fault diagnosis method based on the adaptive residual graph attention convolutional neural network (ResGAT), which uses multiple sensor monitoring signals to accurately identify the rolling bearing fault information under different working conditions. Firstly, the vibration signals collected by multiple sensors were decomposed into wavelet coefficient matrices by variational mode decomposition (VMD) and wavelet packet decomposition (WPD), and the graph structure data containing multi-sensor network information was constructed based on the radius graph strategy. Secondly, based on the short-circuit characteristics of the residual network, an adaptive ResGAT was designed, which used the output and residual of the network to deeply mine the redundant fault information of multi-sensor fusion data. Finally, the proposed ResGAT model was applied to rolling bearing fault diagnosis datasets under three different working conditions: constant speed, variable speed, and composite fault. The research results show that compared with existing methods, the proposed method has higher classification accuracy and robustness under three working conditions.

The carbon peaking and carbon neutralization goals have brought significant pressure and challenges to China’s civil aviation industry. Research on carbon reduction strategies for the industry has attracted great attention from China and abroad. This study summarizes the development status of and requirements for the low-carbon development of the global aviation industry (particularly commercial aircrafts), analyzes the carbon reduction status and problems of China’s civil aviation industry, and proposes corresponding development goals and basic ideas. Through industrial planning, product innovation, low-carbon energy utilization, and airline operation, carbon neutralization of China’s civil aviation industry is expected to be achieved by 2060, thus to form a green and low-carbon development pattern. Technological innovation is the key driving force for promoting the low-carbon transformation of the civil aviation industry. Therefore, we propose the following suggestions: planning the development and key technology research of low-carbon commercial aircrafts, accelerating the commercial application of sustainable aviation fuels, and developing intelligent aviation to promote low-carbon operations.

To address the limitations of existing optimization methods that focus on single objectives or neglect stiffener features, a multi-objective topology optimization (MOTO) method is proposed based on the stiffener size and layout. By constraining the initial structural performance parameters, the optimal stiffener height is determined through size optimization. Based on the stiffener height, single-objective topology optimization is used to achieve the best material distribution. The stiffener width is treated as a design variable, while MOTO is performed on the load point displacement, first natural frequency, and mass, thereby yielding an optimal stiffener width and performance. Finally, a multi-dimensional analysis of the stiffener height, width, and dynamic and static characteristics of the stiffened thin-plate structure is conducted. The results indicate that the optimized stiffener layout is considerably improved. Compared to the initial structure, the maximum and average displacements of the load point are reduced by 23.26% and 8.62%, respectively. The first natural frequency increases by 3.81%, while the maximum resonance amplitude and overall structural mass decrease by 39.97% and 1.99%, respectively. The results indicate that the optimized structure achieves a lightweight design while maintaining better stiffness and low-frequency vibration resistance. The feasibility and effectiveness of the proposed method are validated. [ABSTRACT FROM AUTHOR]

In the pilot project of the substantive reform of court trials, “unexpected” judgments appeared constantly because the traditional documentary investigation mode has been replaced by an investigation mode under the principle of directness and verbalism. This change in the logic of factual judgments has caused a change in the methods of evidence examination. In traditional evidence examination, evidence obtained from investigations is supposed to be genuine and reliable. In the substantive reform of court trials, it is presumed that evidence obtained from investigations cannot be fully trusted and it is easier to ascertain the facts of the case by investigating using the principles of directness, verbalism and individualized judging methods for evidence examinations. In practice, there are three main factors affecting the genuineness of evidence: the cognitive rules of testifiers, the motivation of the subjects who provide evidence, and the methods used by investigators to obtain evidence. Based on any one of these three factors, it cannot be concluded that evidence obtained from investigations is superior to evidence presented in court. The substantive court investigation is more advantageous to establishing the facts of a case than the traditional court investigation. The essential characteristics of the substantive reform of court trials are pursuing reality in essence instead of in form, and using the files of the court trials instead of the files of the investigation to avoid the evidence obtained through investigations from playing a decisive role in the adjudication thus making criminal procedures trial-centered rather than investigation-centered.