Introduction A substantial proportion of e-bike injuries and fatalities occur in motor-vehicle lanes, where riders are exposed to mixed traffic and higher crash risk, potentially offsetting the health benefits of e-bike use. Notably, many riders still enter motor-vehicle lanes even when dedicated bicycle lanes are available. Such lane intrusion increases exposure to hazardous interactions with vehicles and diminishes the safety benefits of cycling infrastructure. Therefore, identifying psychological factors associated with lane intrusion is important for informing injury-prevention interventions. Methodology An online survey of 729 e-bike riders (650 valid for analysis) assessed self-reported e-bike lane intrusion behaviour (e-bike LIB) using a newly developed Riding Lane Intrusion Behaviour Questionnaire, and assessed infrastructure perception, perceived safety, perceived risk, and Theory of Planned Behaviour constructs. We combined multi-group structural equation modelling with explainable machine learning to examine theory-informed associations among infrastructure perception, key psychological constructs, and e-bike LIB, and to explore potential non-linear patterns. Results Attitude, perceived behavioural control, and subjective norms were positively associated with self-reported lane intrusion, with attitude showing the strongest association. Infrastructure perception was negatively associated with self-reported lane intrusion and exhibited a possible non-linear pattern, with larger marginal reductions at higher levels of infrastructure perception. As infrastructure perception increased from low levels, perceived safety increased while perceived risk decreased, and these perceptions were further associated with e-bike LIB primarily indirectly through attitude and perceived behavioural control. Conclusions E-bike LIB should be understood as a preventable mixed-traffic exposure rather than a neutral lane-choice decision. The findings suggest that infrastructure provision should be complemented by behavioural measures targeting favourable attitudes, overconfidence, and permissive social norms. In poorer riding environments, stricter enforcement and improvements to the perceived usability and visibility of existing facilities may help discourage non-compliance with available bicycle lanes.
Tailoring the ordered structures within chemically complex alloys (CCAs), represents an effective strategy for addressing the strength-ductility trade-off dilemma in conventional metals, such as steels, aluminum alloys, and titanium alloys. In this study, a novel strategy is proposed in a CoCrNiAl dual-phase CCA, where prolonged low-temperature annealing is utilized to tailor local chemical fluctuations (LCF). This process promotes enhanced ordering degree of the matrix, resulting in an enhancement in strength without loss of uniform elongation. Comprehensive microstructural analysis reveals that LCF modulates dislocation activity and deformation mechanisms by altering local energy fluctuations (LEF). Specifically, the enhancement of atomic ordering increases the antiphase boundary (APB) energy, thereby increasing the resistance to dislocation glide. Meanwhile, elemental segregation reduces the stacking fault energy (SFE) of the matrix, enhancing its work-hardening capacity. These novel findings provide deeper insight into the influence of ordered structures on both the strength and work-hardening capacity of face-centered cubic CCAs at room temperature. (c) 2026 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
Hydrogenated diamond-like carbon (H-DLC) films are promising solid lubricants, yet their tribological performance changes unpredictably with temperature, making optimal material selection challenging. Here, we perform high‑vacuum tribological experiments to reveal the synergistic effects of temperature and hydrogen concentration on the lubrication and wear performance of H‑DLC films. We demonstrate that increasing the hydrogen content can reverse the temperature dependence of the lubrication lifetime, because the increased hydrogen content is associated with a shift in the dominant wear mechanism from adhesive wear to chemical wear. Our combined simulations and experiments further elucidate the contrasting temperature responses of these two mechanisms. Elevated temperatures promote adhesive wear by enhancing the formation of interfacial bridge bonds that pull off surface atoms during sliding. Conversely, increased temperature within the tested range may suppress chemical wear by reducing Young’s modulus, which enlarges the real contact area and lowers local stress, thereby hindering hydrocarbon emission. Overall, these findings reveal how hydrogen content governs the temperature‑dependent tribological behavior of H‑DLC, providing a rational basis for designing robust solid lubricants with predictable performance within the tested temperature range.
To establish a quantitative basis for high-confidence load spectrum acquisition, this study proposes a method for calculating the minimum acquisition mileage by integrating Bayesian theory with stratified sampling. First, a sample-size determination procedure that accounts for driving speed is introduced. The speed range is divided into equidistant intervals, and principal component analysis is applied to reduce the dimensionality of multivariate operating features to facilitate interval partitioning within a compact principal-component space. The optimal speedinterval width was determined by combining the Calinski-Harabasz index and the elbow method. Stratified sampling is then conducted using the accumulated power spectral density feature parameter. A two-parameter Weibull distribution is fitted via maximum likelihood estimation, with the goodness of fit verified by using percentile relative error to obtain the distribution parameters and required sample size for each speed interval. Subsequently, a Bayesian minimum sample-size model is constructed. Using the posterior variance of the Weibull scale parameter as the risk metric, analytical expressions for the minimum sample size of each speed interval are derived, and the minimum acquisition mileage is calculated. On-road tests conducted under representative driving conditions validate the proposed method, demonstrating that the minimum acquisition mileage decreases as the allowable maximum risk increases. Furthermore, as additional test segments are incorporated, the calculated minimum mileage converges rapidly and stabilizes, further validating the effectiveness of the proposed method. The proposed approach enhances the reliability of load spectrum acquisition while reducing test costs, demonstrating potential for practical engineering applications.
The chamber pressure in shield tunnelling is bounded by the lower-limit collapse pressure and the upper-limit blowout pressure, and its accurate determination is essential for construction safety and efficiency. However, existing approaches either neglect the effects of dynamic cutterhead–soil interaction or fail to account for spatial variability in soil. This study develops a three-dimensional probabilistic large-deformation framework for defining reliability-based chamber-pressure windows. By combining coupled Eulerian–Lagrangian modelling, lognormal random fields, and Monte Carlo simulations, the framework accounts simultaneously for dynamic cutterhead–soil interaction, large deformation, and spatial variability in soil strength, allowing both collapse and blowout limits to be evaluated within a unified design approach. Deterministic analyses show that cutterhead configuration strongly modifies the stress-transfer mechanism at the tunnel face. As the opening ratio increases from 15 % to 50 %, the lower-bound pressure required to prevent collapse increases, while the upper-bound pressure associated with blowout decreases, leading to a progressively narrower chamber-pressure window. Stochastic analyses further show that spatial variability in soil strength reduces the stability margin because failure preferentially localizes through weak zones near the tunnel face. Based on these results, a reliability-based procedure is proposed to calibrate collapse and blowout pressure limits for a prescribed target probability of failure. Comparison with chamber-pressure and settlement records from the Qianjiang Tunnel project indicates that field pressure control is governed primarily by collapse risk, and that incorporating both dynamic cutterhead–soil interaction and soil spatial variability provides a more conservative and realistic basis for chamber-pressure design in soft ground.
