Automated driving system (ADS) deployment requires rigorous validation across safety-critical vehicle-pedestrian interactions, yet real-world datasets rarely capture high-risk scenarios while simulation platforms lack realistic behavior. In response, this study proposes a three-stage framework that combines real-world grounding with adaptive simulation to generate behaviorally realistic safety-critical scenarios at scale. Stage 1 pre-trains multi-agent state-space Transformer-enhanced DDPG (MA-SST-DDPG) agents on real-world safety-critical data to learn human-like interactive evasive behaviors through data-driven learning. Stage 2 deploys pretrained multi-agents in CARLA for online reinforcement learning to generalize across diverse scenarios, integrating real-world knowledge with simulation experience to produce a refined MASST-DDPG model. Stage 3 uses CARLA with the refined model to generate over 198,000 highresolution interaction episodes from eight intersection scenarios, culminating in the Vehicle-Pedestrian Safety-Critical Interaction (VPSCI) dataset. The Refined MA-SST-DDPG model outperformed baseline methods in reproducing realistic evasive behaviors, achieving the lowest trajectory errors (ADE = 0.072 m, FDE = 0.142 m). Statistical comparison confirmed distributional equivalence between the generated and real-world data in both conflict severity and behavioral response. A Turing test confirmed that the three-stage framework generated evasive behaviors were indistinguishable from real-world interactions. The generated data revealed realistic behavioral patterns: conflict rates rose with speed, and pedestrian yielding increased with vehicle proximity and speed-trends matching real-world observations. These results demonstrate the framework's effectiveness in producing high-fidelity safety-critical data, offering valuable sources for the development of ADS and simulation-based safety evaluations.
Coastal squeeze, the loss of intertidal habitat caused by sea level rise and constrained landward migration, occurs when human development restricts tidal marsh persistence in coastal environments. The resulting coastal squeeze pressure reflects the intensity of these coupled natural and human constraints across space and over time. However, few studies have examined this phenomenon using high-resolution temporal data and fine-scale spatial analyses at the municipal scale. To address this gap, we developed a new spatiotemporal analytical framework, the Municipality Coastal Squeeze Index (MCSI), and applied it to 17 watersheds in coastal Virginia, US. Annual MCSI values were calculated from 2020 to 2100 across 1-km2 hexagons by integrating built-environment constraints, marsh migration potential, and elevation thresholds. Time-series clustering identified four groups with distinct trajectories of increasing coastal squeeze pressure, characterized by similar growth patterns but differing onset times. These clusters range from areas with persistent high pressure to locations where exposure remains limited until mid- or late-century before rising rapidly. Spatial analysis shows that the earliest and most persistent high-pressure clusters are concentrated in low-lying and flood-vulnerable areas, while later-emerging pressures are more dispersed inland. Hot Spot Analysis further identifies statistically significant clusters where coastal squeeze pressures are expected to intensify first. By linking spatial concentration patterns with temporal trajectories, the MCSI framework provides a scalable tool for municipal-scale coastal management, helping planners identify where and when coastal squeeze will escalate and supporting phased adaptation strategies that incorporate intertidal marsh conservation as a nature-based approach to flood risk reduction.
Background The increased frequency and severity of natural disasters underscore the need for a resilient, disaster-prepared nursing workforce. Resilience is one factor that may shape how nursing students respond to disasters. This study examines the relationship between nursing students’ resilience, as measured by the Brief Resilience Scale (BRS), and disaster preparedness. Method A cross-sectional, correlational design was used. Data were collected via an anonymous online survey. Results Higher resilience scores were associated with greater confidence in disaster preparedness, feeling prepared, and having received disaster training. Most participants were willing to respond to a disaster but felt unprepared and were not trained or registered to respond. Most did not know about emergency plans and were unaware of evacuation routes and shelter locations. Those with prior disaster experience were more prepared. Conclusion Prioritizing disaster preparedness and resilience-building content in nursing curricula is essential to prepare a disaster-ready future nursing workforce.
The growing demand for rapid, on-site decision-making has accelerated the development of decentralized analytical tools capable of reliable measurements outside conventional laboratories. Stimuli-responsive molecularly imprinted polymers (SR-MIPs) are adaptive recognition materials that combine the selectivity of molecular imprinting with stimulus-dependent changes in polymer conformation, charge, volume, optical properties, or other physicochemical characteristics. These features enable controlled recognition, signal modulation, and stimulus-triggered regeneration, offering advantages over conventional passive MIPs. This review critically evaluates how different SR-MIP strategies translate adaptive recognition into sensing platforms, with an emphasis on decentralized applications. The systems are compared in terms of material design, signal transduction, analytical performance, operational simplicity, regeneration, portability, and performance in complex samples. Key challenges affecting practical deployment, including reproducibility, matrix interference, stability, quantitative readout, manufacturing, and commercialization, are discussed. Emerging portable and integrated technologies, together with sustainability and greener appraoches, are highlighted. Finally, current gaps and future directions toward robust, sustainable, and deployable SR-MIP-based analytical systems are identified.
We examine whether and how bidder complexity influences investor reactions to merger and acquisition (M&A) announcements. Using an established measure of complexity, we find a significant positive relationship between acquiring firm complexity and cumulative abnormal returns (CAR). This suggests that investors perceive more complex firms as capable and value-enhancing participants in M&A activities. The association is particularly strong for bidders with high operating risk, greater R&D intensity, and larger firm size. We also find that complex bidders tend to offer higher takeover premiums. Overall, our study contributes to the literature by demonstrating that bidder complexity is an important determinant of market reactions to M&A announcements.
