Underwater tunnels beneath rivers and lakes are frequently subjected to leakage issues. However, the instability mechanism caused by particle loss remains insufficiently understood, particularly in pebble formations. This study combines experimental and numerical approaches to investigate the influence of particle loss on formation instability. The results indicate that the process of particle loss can be divided into intense erosion, erosion mitigation and stable seepage, and the anti-erosion capacity of gap-gradation pebble soils is the worst. When leakage occurs at the tunnel vault or sidewall, a continuous seepage erosion channel eventually propagates from the leakage location toward the ground surface. For leakage at the tunnel bottom, the particle disturbance zone is confined within the tunnel contour. As the leakage width increases, the disturbance zone continuously expands, and the instability evolution accelerates. The particle loss illustrates an initial rapid increase, followed by a trend toward stabilization after a certain step. Moreover, the calculation step required to reach the steady state decreases with increasing hydraulic pressure. The microscopic contact force above the vault deflects horizontally to form temporary arching effect near the leakage, determining the evolution of particle loss and formation deformation. These finds can provide references for the early warning and risk assessment of tunnel leakage disasters in pebble formations.
To achieve stable and efficient sludge reduction, this study employed an oxidant-microbial combined process. Sludge was pretreated with Oxone, followed by inoculation of strain YH14 (lysozyme-producing) and newly isolated strain XSC8 (biosurfactant-producing) for reduction treatment. The optimal sludge reduction efficiency was achieved when Oxone and FeSO4 dosages were 0.70 and 0.35 mmol/g SS, with inoculation amounts of strains YH14 and XSC8 at 3 and 6% (v/v). Compared with untreated original sludge, significant improvements were observed: there was a 25.9% increase in suspended solids (SS) reduction efficiency, while that of volatile suspended solids (VSS) rose by 23.7%, while the concentration of soluble chemical oxygen demand (SCOD) rose by 2135.06 mg/L. Additionally, variations in the sludge other physicochemical properties were indicative of both extracellular polymeric substances (EPS) disassembly and cellular disruption. High-throughput sequencing analysis revealed enhanced capacities of membrane transport and lipid metabolism in the sludge.
As critical load-bearing structures for offshore energy exploitation, jacket platforms confront severe challenges of non-uniform degradation during long-term service. To address the lack of physical interpretability in conventional data-driven methods and their high false-alarm rates under complex operational conditions, a damage identification framework is proposed in this study that integrates physics-informed constraints with a hierarchical cascaded optimization strategy. Firstly, Sobol global sensitivity analysis is employed to identify the core physical parameters governing the natural frequencies. Subsequently, a Physics-Constrained Regularized Extreme Learning Machine (PC-RELM) surrogate model is constructed. By embedding monotonic physical constraints, this model achieves high-fidelity frequency predictions while rigorously ensuring the physical consistency of gradients. Finally, leveraging the non-uniform sensitivity of modal frequencies to structural stiffness, a three-stage hierarchical cascaded optimization scheme is designed. By sequentially releasing optimization variables and incorporating inter-layer smoothing priors, the challenge of spatial ambiguity in multi-layer damage identification is effectively resolved. Experimental results demonstrate that the proposed model exhibits exceptional robustness across diverse and complex scenarios, including spatially isolated, adjacent regional, and mixed gradient damages, which precisely localizes subtle impairments and significantly suppresses false alarms in non-damaged zones.
In highly automated transport operations, operator recovery during automation-to-human control transitions is critical to mission continuity and system safety. Existing studies have not fully explained how takeover scenarios, human-related performance-shaping factors (HR-PSFs), and their coupling mechanisms affect takeover reliability. This study proposes an explainable human–system reliability framework integrating statistical inference, interpretable machine learning, global sensitivity analysis, and nonlinear threshold identification. Linear mixed models were used to examine the effects of takeover scenarios and HR-PSFs. A Tabular Prior-Data Fitted Network was then developed to predict takeover reliability, followed by Sobol sensitivity analysis, SHAP interpretation, and generalized additive modeling to identify key factors, interaction mechanisms, and operational thresholds. The results indicate that takeover trigger type was the dominant scenario factor, with unprompted pattern anomalies producing the most pronounced reliability degradation. External scenario conditions did not affect reliability uniformly. Instead, they amplified reliability fluctuations by changing task constraints and operator functional states. HR-PSFs did not act as independent linear predictors but formed context-dependent reliability-modification mechanisms and exhibited nonlinear threshold effects. These findings indicate that reliability degradation arises from dynamic mismatches among automation boundaries, operational contexts, and HR-PSFs. The proposed framework supports reliability monitoring, adaptive warning, and intervention design in safety-critical human–automation systems.
This review provides a critical and up-to-date assessment of additive manufacturing for zinc-based energy storage, with particular focus on zinc-air and aqueous zinc-ion batteries. The review examines how the leading AM routes, namely DIW, FDM and SLS, address major limitations of conventional battery fabrication, including limited architectural control, weak high-rate performance, short cycling life and difficulties in scalable production. Printable filaments, inks and materials reported for air cathodes, zinc anodes, current collectors, separators and functional interlayers are reviewed, covering carbon frameworks, metal oxides, polymeric binders, solid electrolytes and emerging conductive additives such as MXenes and graphene derivatives. By linking printing parameters and post-processing steps to microstructure, conductivity and wetting behavior, the review clarifies how designed porosity and tortuosity improve oxygen transport in ZABs and ion/electron pathways in ZIBs. Comparative findings indicate that DIW offers the strongest electrode performance, with FeVO/rHGO cathodes reaching 344.8 mAh g⁻1 and 7.04 mAh cm⁻2, while SLS provides stable zinc-anode architectures for up to 420 h at 7.5 mA cm⁻2. Overall, DIW appears most promising for high-performance electrodes, SLS for robust porous metallic structures and FDM for low-cost structural battery components.
