662 papers
This study utilizes Cluster Correspondence Analysis on multiyear FARS data to identify key factors contributing to fatal transit bus crashes, such as intersection conflicts and inadequate traffic control, and proposes targeted policy recommendations to improve infrastructure and safety.
This paper presents the implementation and experimental optimization of a robotic arm-assisted, tank-free additive manufacturing system that utilizes acoustic levitation for multi-directional, contactless deposition of photocurable resin, demonstrating its potential to overcome conventional 3D printing limitations through process characterization and Taguchi-based parameter optimization.
This study utilizes a GIS-based Multi-Criteria Decision Support System integrating network analysis, TOPSIS, and spatial autocorrelation to reveal significant spatial injustices in Mashhad's healthcare accessibility, demonstrating that proximity alone is insufficient and that pilgrimage-driven urban dynamics create a stark divide between central hotspots and peripheral cold spots.
This study proposes a novel Rainbow-based Deep Reinforcement Learning ramp metering controller that outperforms existing baselines in managing compound bridge-and-ramp bottlenecks under human-driven conditions, while identifying a specific Automated Vehicle penetration threshold beyond which centralized control becomes unnecessary due to the self-regulating capabilities of mixed traffic.
This paper presents a validated integrated finite element model that combines wire arc additive manufacturing with strain hardening to analyze the relationship between deposited material volume and temperature fields, demonstrating how geometric optimization and adaptive interlayer dwell time control can reduce peak temperatures and build times while minimizing defects in large-scale component production.
This paper, which originally investigated the effect of welding heat input on residual stresses at the corrugated interface of aluminum/steel composite panels, has been withdrawn by the authors due to lack of full consent from all co-authors and should not be cited.
This study proposes a simple and robust rooftop segmentation method for LiDAR point clouds that utilizes elevation change features and the Partitioning Around Medoids algorithm to achieve high accuracy (95.9% completeness and 98.1% correctness) while effectively identifying edges and corners for 3D reconstruction.
This paper proposes a precipitation forecast downscaling method based on a Correlation Diffusion Network (Diffusion-U-Net) that integrates channel-spatial dual attention mechanisms to effectively convert low-resolution Fuxi model outputs into high-resolution data, outperforming traditional and deep learning approaches in restoring fine-scale precipitation details.
This study introduces a novel probabilistic framework that utilizes Monte Carlo simulations of historical surplus electricity data to dynamically assess the operational success of high-temperature aquifer thermal energy storage (HT-ATES) systems, demonstrating their ability to meet seasonal heat targets in Neubrandenburg, Germany, under realistic variable conditions.
This paper proposes an energy-efficient measurement strategy for structural health monitoring in energy harvesting wireless sensor networks that utilizes indirect environmental parameters to update predictive models, thereby reducing measurement frequency by up to 92.18% and total energy consumption by up to 58% by triggering new measurements only when prediction errors exceed a threshold and sufficient energy is available.