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The aluminum alloy performance gradient plate was prepared by friction stir joining. Analysis results of the macro morphology, microstructure, and hardness of the aluminum alloy performance gradient plate prepared under various parameters show that when the feed speed of the stirring tool is 250 mm/min, the downward pressure of the stirring tool is 6.6 mm, and the rotation speed of the stirring tool changes from 200 rpm to 800 rpm. The macroscopic morphology of the aluminum alloy gradient plates prepared by the method first changed from burr to smooth, and vice versa. There is a different cross-section morphology of the prepared aluminum alloy gradient plates, however, the aluminum alloy plates are stirred and involved with each other, and the grains of the prepared plates are refined. The hardness of the upper and lower surfaces of the aluminum alloy gradient sheet decreases, whereas that of the upper surface of the side increases, and that of the middle and bottom sides also decreases. However, the hardness of the middle side of the sheet prepared with the rotation speed of the stirring tool at 800 rpm increases, but that of the bottom side still decreases. Obtained through analysis that the performance of the aluminum alloy gradient sheet prepared at the stirring tool rotation speed of 500 rpm increases in equal proportion to achieve a good performance gradient change.

Acupuncture and moxibustion are increasingly recognized as effective and safe modalities for the management of neurological disorders, garnering substantial interest from both neurologists and patients. This study systematically reviews literature from major databases spanning the years 2012 to 2024 to construct a comprehensive dataset on the acupuncture treatment of neurological diseases. The data were meticulously compiled through a detailed search and screening process, adhering to established principles for data extraction and segmentation. The resultant acupuncture treatment database served as the foundation for subsequent analyses utilizing the association rule algorithm. This advanced computational approach facilitated a deep investigation into the relationships among acupuncture points used in neurological treatments. By employing a complex network analysis, the study identified the optimal distribution of acupuncture points, and cluster analysis further categorized these points for diverse neurological conditions. Our findings indicate that a total of 206 acupuncture points are prevalently used in neurological therapies. The regions of the head, face, and neck are most frequently targeted, with these areas collectively accounting for 1,048 instances of point utilization. Specifically, 124 unique acupuncture points have been identified as particularly effective for neurological applications. The association pairs “Yangbai-Zusanli” and “Sanyinjiao-Zusanli” demonstrated the highest levels of support and confidence, at 96.29% and 48.59% respectively. The core constellation of acupuncture points, comprising “Zhuzhu,” “Yangbai,” “Sizhukong,” “Yuyin,” “Sibai,” “Zusanli,” and “Baihui,” encapsulates the strategic focal areas for enhancing therapeutic outcomes in neurology. The integration of data mining techniques in this research not only underscores the specific patterns in the distribution of acupuncture points but also suggests a potential framework for augmenting the efficacy of treatments for neurological conditions. Through such analytical methodologies, the study contributes to a nuanced understanding of acupuncture’s role in neurology, paving the way for targeted and optimized therapeutic strategies.

To quickly manufacture multi-variety and low-volume products, manufacturing factories are increasingly sharing resources on collaborative production networks. However, the reliability of communication between factories cannot be fully guaranteed using traditional centralised approaches. Emerging blockchain technology can solve this problem due to its characteristics such as decentralisation and security. In this context, an integrated optimisation problem of multi-factory production and blockchain-secured collaborative maintenance is studied in this paper. Two scenarios are introduced with respective Q learning-based solution frameworks to solve the integrated problem. In the simulation scenario, preventive maintenance (PM) with flexible time windows is integrated with multi-factory production scheduling for reducing the probability of machine failures, and an initial integrated optimisation scheme is obtained. To make it more realistic, inevitable failures are considered in the actual production scenario, and the proposed collaborative maintenance strategy is triggered. Specifically, a corrective maintenance (CM) strategy is carried out immediately on the failed machine in case of a failure, followed by the PM on machines of the same type as the failed machine in other factories and the rescheduling of unprocessed jobs. Through a series of numerical studies, the effectiveness of the proposed optimisation approach and maintenance strategy is validated, and some interesting managerial implications also rise. [ABSTRACT FROM AUTHOR]

The development of automated tools using advanced technologies like deep learning holds great promise for improving the accuracy of lung nodule classification in computed tomography (CT) imaging, ultimately reducing lung cancer mortality rates. However, lung nodules can be difficult to detect and classify, from CT images since different imaging modalities may provide varying levels of detail and clarity. Besides, the existing convolutional neural network may struggle to detect nodules that are small or located in difficult-to-detect regions of the lung. Therefore, the attention pyramid pooling network (APPN) is proposed to identify and classify lung nodules. First, a strong feature extractor, named vgg16, is used to obtain features from CT images. Then, the attention primary pyramid module is proposed by combining the attention mechanism and pyramid pooling module, which allows for the fusion of features at different scales and focuses on the most important features for nodule classification. Finally, we use the gated spatial memory technique to decode the general features, which is able to extract more accurate features for classifying lung nodules. The experimental results on the LIDC-IDRI dataset show that the APPN can achieve highly accurate and effective for classifying lung nodules, with sensitivity of 87.59%, specificity of 90.46%, accuracy of 88.47%, positive predictive value of 95.41%, negative predictive value of 76.29% and area under receiver operating characteristic curve of 0.914. [ABSTRACT FROM AUTHOR]