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This study explores the performance of asphalt mixtures modified with North American rock asphalt and desulfurized rubber particles at varying rubber-to-asphalt ratios ranging from 18% to 36% by weight. A comprehensive set of laboratory tests, including high-temperature rutting tests, low-temperature bending tests, indirect tensile tests, and freeze–thaw splitting tests, were conducted to evaluate the modified mixtures. The results indicate that both wet and dry blending methods produce mixtures that meet technical requirements, with the optimal asphalt-to-aggregate ratio determined to be 7.1%. At a rubber-to-asphalt ratio of 18%, the wet blending method slightly improves high-temperature rutting resistance compared to the dry method. However, an increase in rubber content generally enhances rutting resistance regardless of the blending technique. The wet blending method excels in low-temperature crack resistance, possibly due to better rubber dispersion, while an increase in rubber content diminishes crack resistance due to a thinning asphalt film. In terms of fatigue performance, the dry blending method results in significantly longer fatigue life, with a 27% rubber-to-asphalt ratio exhibiting optimal balance. The dry method consistently outperforms the wet method in water stability, and the resistance to water damage increases with rubber content. In conclusion, this study provides valuable insights into optimizing rubber-to-asphalt ratios and blending methods for various application needs, showcasing the benefits of rock asphalt and desulfurized rubber particles in asphalt modification. [ABSTRACT FROM AUTHOR]

Drainage tunnel stability is crucial for engineering project safety (e.g., mine engineering and dams), and rockfall events and water release are key indicators of drainage tunnel stability. To address this, we developed a monitoring system to simulate drainage tunnel intrusions based on distributed acoustic sensing (DAS), and we obtained typical characteristics of events like rockfall events and water release. Given the multitude of DAS signal feature parameters and challenges, such as high-dimensional features impacting the classification accuracy of machine learning, we proposed an identification method for drainage tunnel intrusion events using principal component analysis (PCA) and neural networks. PCA reveals that amplitude-related parameters—amplitude, mean amplitude, and energy—significantly contribute to DAS signal classification, reducing the feature parameter dimensions by 54.8%. The accuracy of intrusion event classification improves with PCA-processed data compared to unprocessed data, with overall accuracy rates of 79.1% for rockfall events and 72.7% for water release events. Additionally, the artificial neural network model outperforms the Bayesian and logistic regression models, demonstrating that ANN has advantages in handling complex models for intrusion event classification. [ABSTRACT FROM AUTHOR]

The geometry of the shroud cavity, which accommodates the rotor shroud leakage flow, is very important due to its impact on the leakage flow path as well as the interaction between the leakage flow and mainstream. The research in this paper numerically investigates the potential for reducing the leakage loss by optimization to the casing. The spatial loss audit and the loss breakdown based on loss mechanisms undertaken in the first part of this study are the foundation to develop promising strategies for minimizing the leakage loss. The profiled casing in the exit cavity effectively deflects the leakage jet into the axial direction when it re-enters the mainstream. The mixing loss is significantly reduced due to the notable decrease in axial velocity disparity of the two streams. On this basis, the exit and inlet cavity are further shaped to both form a step. Reductions in the shroud leakage loss are achieved by alleviating the flow nonuniformity in the exit cavity and reducing the leakage flow rate, respectively. The work in this study contributes to the refinement of control methods for the shroud leakage loss. [ABSTRACT FROM AUTHOR]

Brain-inspired computing, drawing inspiration from the fundamental structure and information-processing mechanisms of the human brain, has gained significant momentum in recent years. It has emerged as a research paradigm centered on brain–computer dual-driven and multi-network integration. One noteworthy instance of this paradigm is the hybrid neural network (HNN), which integrates computer-science-oriented artificial neural networks (ANNs) with neuroscience-oriented spiking neural networks (SNNs). HNNs exhibit distinct advantages in various intelligent tasks, including perception, cognition and learning. This paper presents a comprehensive review of HNNs with an emphasis on their origin, concepts, biological perspective, construction framework and supporting systems. Furthermore, insights and suggestions for potential research directions are provided aiming to propel the advancement of the HNN paradigm. [ABSTRACT FROM AUTHOR]