This study investigates the nonlinear dynamic behavior and electromechanical response of an acoustic black hole (ABH)-assisted double-beam system through analytical modeling, numerical simulations, and experimental validation. A nonlinear dynamic model is developed to describe the interaction between a primary beam on an elastic foundation and an ABH-based beam connected through a spring. The governing equations are derived based on nonlinear beam theory, and the method of multiple scales is employed to investigate modal interactions and internal resonance behavior. The results reveal the possibility of a 1:3 internal resonance between the first and second vibration modes. Analytical mode shape functions for the coupled uniform-ABH beam system are derived, providing a theoretical formulation for coupled uniform-ABH beam configurations for which closed-form solutions are rarely available. The analytical results are further validated using both a self-consistent iterative method and a finite-difference approach. The analyses demonstrate that the ABH structure induces significant energy localization, leading to reduced vibration amplitude and enhanced electromechanical response. Parametric studies are conducted to examine the influence of the coupling location, showing that the mid-span configuration yields the most effective response. Numerical simulations are performed to verify the analytical predictions, and experimental validations are conducted under both near-internalresonance (frequency ratio approximate to 1:3.03) and non-internal-resonance (frequency ratio approximate to 1:2.08) conditions, demonstrating that the proposed ABH-assisted system remains effective and robust under practically achievable operating environments. Overall, the proposed analytical framework provides a physically consistent and computationally efficient methodology for modeling nonlinear dynamics and electromechanical behavior in ABH-assisted electromechanical beam systems.
Sustainability is increasingly vital for hospitality firms whose operations depend on natural and social resources. However, the financial consequences of environmental, social, and governance (ESG) practices—particularly their effect on the cost of debt (COD)—remain inconclusive and even contradictory. Using panel regression based on Chinese listed hospitality firms (2010–2023), this article proposes and empirically confirms a U-shaped ESG–COD nexus. The robustness of this U-shaped pattern demonstrates the double-edged nature of corporate sustainability initiatives: debt costs initially decline with ESG engagement; however, beyond a critical threshold, the effect turns upwards as ESG over-investment entails potential trade-offs. Further, mechanism analyses reveal the information asymmetry (IA) mediates this nonlinear relationship, while internal control quality (ICQ) moderates it. Practically, hospitality managers should recognize the U-shaped nature of ESG investments and the underlying mechanisms, through which lower information asymmetry and higher internal control quality weaken the adverse effects of ESG over-investing.
Why does the same unmanned retail technology produce different emotional responses across formats? Integrating the Stimulus-Organism-Response framework with Cognitive Appraisal Theory and the Technology Readiness Index 2.0, we test a dual-pathway model in which automation stimuli are associated with parallel emotional burden (via threat appraisal) and emotional uplift (via benefit appraisal), with retail format positioned as a categorical moderator capturing format-level automation match. On-site intercept data from 483 consumers across three unmanned formats in Taiwan (convenience stores, laundromats, unstaffed gyms) were analyzed using PLS-SEM, NCA, and fsQCA. Positive design features predict benefit appraisals and uplift but do not attenuate threat appraisals, which are primarily associated with perceived human absence. Consistent with our format-moderation hypotheses (H8a, H8b, and H8c), only the threat pathway is significantly moderated by retail format, being strongest in convenience stores and weakest in laundromats; the benefit pathway is largely format-invariant. Benefit appraisal and uplift are necessary conditions for behavioral loyalty, whereas the mere absence of burden is insufficient.
This study addresses the critical challenge of harmonizing circular economy goals with stringent fire safety requirements in additive manufacturing. We investigate geometry-induced melt-plug sealing in 3D-printed high-recycled-content flame-retardant polycarbonate (SORPLAS) composite lattices. By integrating mechanical testing, ISO 5660-1 cone calorimetry with gas analysis, and COMSOL thermal-flow simulations, we evaluated three nominally equal-porosity lattice architectures (circular, rhombic, and triangular) fabricated from PC, pure SORPLAS, and carbon-filled SORPLAS. Results demonstrate a dual dominance pattern: for mechanical performance, lattice geometry is the primary determinant (η²p = 0.948 for UTS), whereas for fire-response endpoints, material formulation is the primary driver (η²p = 0.40–0.50 for pHRR and Peak-MLR). Lattice geometry acts as a kinetic regulator governing the integrity and temporal evolution of melt plugging. Circular and triangular lattices promote molten polymer backfilling to delay peak heat release, whereas rhombic lattices facilitate ventilation and accelerate post-peak decay. Two critical trade-offs are revealed: first, mechanical performance is reduced by the addition of 1 wt
Long-term target coverage in solar-powered wireless rechargeable sensor networks (WRSNs) is fundamentally challenged by sensing uncertainty, weather-driven energy variability, and the strong coupling between adjustable sensing ranges and energy consumption. Existing approaches often rely on simplified sensing or harvesting models, which may lead to unstable schedules and degraded coverage at vulnerable targets. This paper proposes Physics-aware Bottleneck-first Target Coverage Scheduling (PBTCS), a unified framework for sustainable target coverage in WRSNs under energy-neutral operation constraints. PBTCS integrates a physics-prior, interpretable day-ahead photovoltaic (PV) forecasting model to derive feasible and auditable energy budgets, and employs a budget-driven time partitioning mechanism to stabilize day-night operations. Based on the probabilistic sensing model, a bottleneck-first scheduling principle is introduced to explicitly prioritize the weakest space-time points, rather than optimizing average coverage metrics. To efficiently realize this objective under adjustable sensing radii, a closed-form marginal-gain decomposition and a budgeted dynamic programming scheme are developed for per-sensor schedule construction. Extensive simulations using real PV and meteorological data demonstrate that PBTCS consistently outperforms state-of-the-art methods in surveillance quality, coverage fairness, and long-term network sustainability across different seasons and network scales.
