1666 papers
This paper proposes a four-phase implementation protocol for deploying solid-state battery LHD loaders in Peruvian underground mines to achieve significant ventilation energy reductions by adapting Canadian Shield technologies, while also extending this electrification framework to Peru's public transport sector.
This paper demonstrates that a unified Physics-Informed Neural Network framework, which embeds the 2D shallow water equations directly into its loss function, achieves high accuracy (R² > 0.98) across four complex dam-break benchmarks, establishing it as a viable mesh-free alternative to traditional solvers for rapid flood risk assessment.
This study utilizes a refined three-dimensional dynamic mesh simulation to elucidate the defect formation mechanisms in a novel highly integrated die-casting (HIDC) process, revealing that a 60% filling percentage and an increased slow injection speed of 0.16 m/s are optimal for minimizing porosity defects in ADC12 aluminum alloy castings.
This paper proposes an event-triggered virtual-leader formation control method for UAV swarms using artificial potential fields and adaptive strategies to reduce communication overhead while maintaining robust formation tracking and obstacle avoidance in complex environments.
This paper proposes a semantic-constrained, multi-stage digital restoration framework for traditional silk paintings that prioritizes the protection of cultural heritage elements by integrating interpretable damage detection, context-aware restoration routing, and quality-gated rollback mechanisms to balance effective damage removal with the preservation of historical evidence and material texture.
This study employs bibliometric and main path analysis to map the technological evolution of geothermal energy systems, highlighting their critical role in providing stable baseload power and advancing sustainable low-carbon transitions through innovations like organic Rankine cycles, shallow geothermal applications, CO₂-based extraction, and hybrid renewable integration.
This paper presents a novel numerical model and moment-balance sanding criterion to predict sand production in loosely consolidated sandstones, demonstrating that the approach accurately matches experimental data and reveals that reducing production rates can significantly mitigate sand erosion and its impact on near-wellbore porosity and permeability.
This study proposes a novel tool wear prediction framework that integrates a mechanistic cutting bending moment model with a diffusion model via Feature-wise Linear Modulation to generate physically consistent synthetic signals, thereby significantly improving prediction accuracy under unseen conditions while drastically reducing the need for extensive real-world training data.
This paper presents a low-power transimpedance amplifier designed for 40 Gbps optical receivers using 32 nm CNTFET technology, which employs a regulated cascode structure with gain boosting and a diode-connected output to achieve a 51.4 dBΩ transimpedance gain, 28.3 GHz bandwidth, and 368 µW power consumption.
This study proposes a graph neural network-based framework using multi-source data to identify and classify fine-grained rural settlement units in Dangyang, China, revealing their spatial network structure and key drivers to enable differentiated governance beyond traditional administrative village scales.