1616 papers
This study experimentally validates the feasibility of a compact microwave-assisted freeze-drying prototype, demonstrating that it achieves stable temperature control, comparable product quality with enhanced polyphenol retention, and significant process efficiency improvements over conventional shelf-heated methods.
This paper proposes an energy-guided signal-level augmentation method to overcome data scarcity in microwave breast imaging, achieving 94.7% accuracy in classifying benign and malignant tumors by converting augmented S-parameter responses into CWT Morlet scalograms and fine-tuning an EfficientNet-B5 classifier.
This study demonstrates that Abrasive Flow Machining (AFM) effectively enhances the surface integrity and tribological performance of additively manufactured Inconel 718 conformal cooling channels by removing unmelted particles, reducing surface roughness by 98%, and achieving optimal friction reduction through specific process parameters and heat treatment.
This study evaluates the Japan Meteorological Agency's global data assimilation system to demonstrate that while clear-sky reconstructed radiance offers neutral forecast impacts compared to original radiance, assimilating all-sky reconstructed radiance for water vapor channels yields significant positive improvements similar to those achieved with original radiance.
This study develops a CPO-XGBoost model to accurately predict and optimize the compressive strength of limestone calcined clay cement, identifying optimal mix proportions that reduce carbon emissions by 41.9–53.2% compared to ordinary Portland cement and implementing these findings into a real-time intelligent design system.
This paper proposes a formal verification framework that utilizes mathematical induction to map discrete, acyclic fluid networks into directed graphs, enabling an efficient O(V+E) algorithm for detecting energy conservation anomalies in cyber-physical systems while significantly reducing computational complexity compared to traditional numerical solvers.
This paper presents an open-source, AI-driven pipeline that synthesizes realistic physiological stress facial expressions (pain, choking, and coughing) on 3D medical mannequins using the Facial Action Coding System, demonstrating that visual-only dynamic animations significantly improve recognition accuracy among nursing students compared to audio-visual presentations.
This paper proposes Trans-SAC, a novel gamification-aware dynamic Demand Response framework that integrates psychological fatigue modeling with Transformer-based engagement prediction and Soft Actor-Critic reinforcement learning to sustain long-term user participation and economic efficiency by dynamically adjusting task difficulties and rewards.
This paper presents a Sim-to-Real transfer learning framework that leverages a multi-input hybrid CNN pretrained on physics-based simulations and fine-tuned on limited experimental data to achieve accurate, real-time prediction of melt pool width and depth in Directed Energy Deposition, thereby overcoming the long-standing barrier of indirect depth measurement for closed-loop control.
This paper presents a unified analytical framework in Python that integrates quantum capacitance with high-κ dielectric engineering to demonstrate that ZrO₂-gated Black Phosphorus MOSFETs offer superior electrostatic control and performance metrics compared to SiO₂ and HfO₂ alternatives.