1687 papers
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.
This study experimentally demonstrates that for thick-walled PP-R injection molding, conventionally machined CuBe alloy molds outperform selective laser melting (SLM) molds with conformal cooling across all economic metrics, offering superior cycle time reduction and cost savings despite a higher initial investment, while both advanced concepts successfully produce parts meeting functional qualification standards.
This paper presents a parameterized, physics-informed neural network that efficiently predicts transient thermal fields and melt pool geometry in SS316L Laser Powder Bed Fusion by combining FEM-generated data with heat conduction regularization to overcome the computational limitations of conventional simulations.
This paper analyzes the channel characteristics and performance of long-distance magnetic induction-based through-the-earth communication by establishing an equivalent circuit model, evaluating noise and frequency responses under various conditions, and determining optimal modulation schemes to enhance system reliability.
This paper proposes a position-weighted sequence-kernel Bayesian optimization framework that efficiently designs composite laminate stacking sequences under manufacturing constraints by leveraging a specialized distance metric for Gaussian process surrogates to maximize buckling performance while minimizing candidate redundancy.
This paper proposes FSMSN, a lightweight multi-scale network that integrates physical fault frequency priors into channel attention mechanisms to achieve robust, high-accuracy bearing fault diagnosis under severe noise interference with significantly fewer parameters than existing deep learning baselines.