In this paper, we establish the existence and multiplicity of normalized solutions for a class of Hamiltonian elliptic systems. Our work complements and extends the previous results on normalized solutions for strongly indefinite problems, especially for the Hamiltonian elliptic system with general nonlinearity and the potential to be nonvanishing at infinity.
Abstract The tunneling parameters of tunnel boring machines (TBMs) exhibit distinct spatiotemporal characteristics under different rock mass conditions, particularly their non-Euclidean spatial properties. This paper developed a real-time rock mass identification model based on the spatial-temporal graph convolutional network (STGCN) to simultaneously capture the spatiotemporal characteristics of tunneling parameters. TBM tunneling data and geological data were collected from an 8.41-km-long tunneling section of the YinChaoJiLiao (YCJL) project in China. By matching the data based on tunnel mileage, 2,367 valid samples were obtained. A downsampling strategy was applied to reduce data redundancy and computational cost while maintaining model performance. The optimized model achieved an accuracy of 93.04% and an F 1 -score of 89.75%, and comparative experiments verified its superior performance compared with the mainstream model in this task. A statistical analysis of the adjacency matrix derived from the graph convolutional network (GCN) module was conducted and the influence of input time length on model performance was analyzed to interpret the spatial and temporal characteristics of the tunneling parameters, respectively. Furthermore, the model was validated on a extension section of the same tunnel, confirming its accuracy and efficiency for practical application. The results indicate that the STGCN model can simultaneously capture the complex spatiotemporal characteristics of TBM data and provide accurate real-time rock mass identification results, showing great potential for engineering application in intelligent tunneling.
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.
Existing underwater acoustic black hole (ABH) plate models assume one-sided fluid loading or a rigid baffle, overlooking communication between upper and lower water regions and acoustic interactions among wetted surfaces. This study develops a coupled Gaussian–Mindlin semi analytical and boundary element model for a fully immersed unbaffled ABH plate. Boundary constraints are imposed through residual-driven adaptive collocation and nullspace projection based on singular value decomposition; a Helmholtz boundary integral over the closed wetted surface solves the exterior field. For all three boundary conditions, the mean error in the first 100 dry natural frequencies relative to finite element results is small, and broadband responses and radiation directivities agree with finite element results. Full immersion strengthens added inertia, while vibration and far field sound power do not vary in parallel. Boundary effects are strongest below the ABH cut-on frequency and weaken with frequency. The ABH taper increases the fraction of input power dissipated by the local damping layer, while material dissipation sustains broadband vibration reduction. Far field sound power is dominated by self-radiation from both faces and their coherent interference; finite-thickness sides contribute little directly but remain involved in local fluid–structure coupling. Underwater vibroacoustic performance should therefore be assessed jointly in terms of structural vibration, radiated sound power and radiation efficiency.
Understanding the socioeconomic performance of transport hubs and corridors is essential for designing resilient transportation systems and effective regional development policies. Existing assessments, however, often focus on isolated infrastructure components or rely on weakly validated indicators. This study develops a hub-corridor evaluation framework that links transportation structure with regional economic dynamics and household income. Indicator weights are derived using the Type-1 Fuzzy Best-Worst Method with triangular fuzzy numbers. A panel of 12 experts provides individual pairwise comparisons. Each expert independently identifies their best and worst indicators. Individual consistency is verified through the Fuzzy Consistency Ratio with a feedback revision cycle ensuring R(FCR) ≤ 0.1. Individual fuzzy weights are aggregated using the weighted geometric mean, and a Group Consensus Index diagnoses the level of expert agreement. The framework is empirically applied to four transport hubs in China’s Chengdu-Chongqing urban agglomeration. The ranking places Hub 1 first, followed by Hub 2, Hub 3, and Hub 4. Notably, Hub 1 outperforms the physically larger Hub 2 due to its balanced strengths in household transport affordability and spatial efficiency. Monte Carlo simulations with 1,000 weight perturbations confirm a completely stable ranking. The analysis reveals three resilience profiles: balanced hubs achieving synergies across multiple dimensions, economic-driven hubs generating strong agglomeration benefits but facing socio-ecological vulnerabilities, and latent hubs with infrastructure capacity underutilized by weak regional demand. Socioeconomic resilience depends less on transport scale than on alignment between hub capacity, corridor operation, and regional economic structure. These findings offer actionable guidance for policymakers to diagnose structural mismatches and prioritize resilience-enhancing investments. From a policy perspective, the proposed framework provides a transparent and replicable tool for identifying vulnerabilities and supporting context-sensitive strategies in rapidly urbanizing regions.
