626 papers
This study demonstrates that incorporating SiO₂ nanoparticles into a furan-based polymeric fluid significantly enhances the unconfined compressive strength and thermal stability of chemical sand consolidation in unconsolidated petroleum wells while preserving reservoir permeability, offering a robust and cost-effective alternative to conventional mechanical sand-control methods.
This study investigates the axial and seismic performance of aluminum alloy tube–concrete long columns through validated finite element analysis and cyclic loading simulations, revealing distinct failure mechanisms for different cross-sections and proposing a machine-learning-enhanced damage model that outperforms the conventional Park–Ang approach for accurate seismic assessment.
HipeX is a fast, lightweight aerothermodynamic analysis tool that utilizes local surface inclination methods and blended boundary-layer models to reliably predict aerodynamic coefficients and surface heat fluxes for hypersonic vehicle design, as validated against CFD data for the HEXAFLY-INT configuration.
This study proposes an integrated multi-horizon groundwater forecasting framework that combines optimal missing-data reconstruction with hyperbolic deep learning to effectively address observational gaps and reduce error accumulation in long-term predictions, demonstrating superior stability and accuracy compared to conventional deep learning architectures.
This study utilizes FLAC3D numerical simulations to determine that a 1.0m thick, C25-strength sealing wall offers the optimal balance of displacement control and strain management for shallow-buried connecting roadways under intensive mining pressure, leading to the development and application of a rapid, vehicle-mounted flexible formwork construction technology.
This study proposes a green wave coordinated signal optimization method based on a localized VISSIM-MOVES simulation platform and a multi-parameter collaborative model to simultaneously minimize carbon emissions and improve arterial traffic efficiency, demonstrating significant reductions in vehicle delay, stops, and total emissions.
This paper presents a DICOM RT-Plan-guided super-resolution framework that leverages a UNet++ model augmented with geometric aperture moments to reconstruct high-resolution 1 mm³ dose distributions from coarse 1 cm³ phantom measurements, thereby enabling accurate radiotherapy verification without requiring denser hardware or computationally expensive simulations.
This paper demonstrates a scalable hybrid quantum control architecture that integrates cryogenic CMOS ASICs for two-qubit flux control with room-temperature electronics for single-qubit operations, achieving high-fidelity gate performance on a 156-qubit processor while significantly reducing system footprint and overcoming wiring constraints for large-scale superconducting quantum computing.
This study proposes a novel hybrid framework combining Finite Element Modeling (FEM) and Artificial Intelligence, specifically utilizing a Particle Swarm Optimization-tuned Gradient Boosting Regressor and Evolutionary Polynomial Regression, to accurately predict the lateral displacement of single and grouped piles under quasi-static loads, thereby overcoming the limitations of traditional methods in capturing complex non-linear soil-structure interactions.
This paper proposes a novel "structure–function–equity" evaluation framework combining topological and functional indicators with machine learning to classify Guangzhou's bus routes, identifying significant inefficiencies and using a bi-objective optimization model to generate targeted strategies for reducing redundancy, enhancing metro integration, and improving rural equity.