1710 papers
By integrating AI-enabled social sensing with high-resolution energy modeling, this study reveals that least-regret variable renewable energy pathways for China require a spatially distributed layout and a 70–75% penetration level to better balance climate goals with equity and grid stability, challenging conventional cost-minimization approaches.
This paper presents a transparent, flexible, and self-powered trilayer graphene–InP Schottky photodetector that achieves high-performance near-infrared sensing at 1.55 µm and has been successfully validated for autonomous corona discharge monitoring on live high-voltage power lines.
This paper introduces an Outcome-as-a-Service framework for Dask-based Earth Observation workflows that utilizes a weighted multi-objective optimization approach to automatically select cluster configurations, achieving significant reductions in execution time, cost, energy consumption, and resource inefficiency compared to traditional fixed-heuristic provisioning strategies.
This randomized controlled trial demonstrates that an extended reality-based remote training system, which visualizes brushing force and motion in real time, is more effective than conventional instruction in helping learners acquire the critical skill of controlling brushing force for oral hygiene.
This study develops a highly accurate predictive model for estimating scour depth downstream of ski-jump spillways by optimizing key parameters with the Electric Eel Foraging Optimization (EEFO) algorithm, demonstrating superior performance compared to established empirical methods.
This study demonstrates that untreated sugarcane field drainage water serves as a sustainable and effective low-salinity injection brine for enhanced oil recovery, significantly outperforming distilled water by altering wettability and reducing interfacial tension to improve oil displacement in both pore-scale micromodels and coreflooding experiments.
This paper proposes a static plastic design methodology for ductile reinforced concrete frame–shear wall dual systems that ensures a global beam-sway collapse mechanism by integrating lateral load-sharing parameters and systematic overstrength factors, thereby achieving performance-based seismic design goals compatible with current code provisions.
This study proposes a novel optimization framework that integrates Karhunen–Loève expansion with robust multi-objective evolutionary algorithms to effectively incorporate ensemble streamflow forecast uncertainty into reservoir operation, thereby generating Pareto-optimal strategies that balance expected hydropower benefits against operational risks.
This study experimentally demonstrates that while increasing sulfur content enhances the mechanical performance of sulfur-based mortars at elevated temperatures, the addition of bitumen significantly reduces strength despite improving ductility, leading to a recommended safe service temperature limit of approximately 75°C.
This study presents a novel downscaling framework that integrates Pattern Recognition Neural Networks, radial basis function-based residual correction, and uncertainty-aware linear regression to generate high-resolution, spatially consistent, and bias-corrected soil moisture estimates with improved accuracy and temporal robustness.