- Research Article
- 10.1016/j.foodres.2026.118570
Reheating strategies for crispy pork: A comparative study of microwave and steaming treatments.
- May 01, 2026
- Food research international (Ottawa, Ont.)
- Xueliang Sun + 9 more +9
Publications from 2021 to 2026
Showing 10 of 94 papers
Reheating strategies for crispy pork: A comparative study of microwave and steaming treatments.
Study on contact angle, adhesion force, diffusion coefficient and thermal conductivity of silica aerogels-water molecules based on molecular dynamics
Effects of local grazing and cross-diffusion on vegetation-water systems and modeling for desertification prevention
Numerical investigation of near-wall bubble collapse under acoustic intervention using the thermal Lattice Boltzmann Method
Early warning and safety state assessment of lithium iron phosphate batteries under thermal abuse using the technique for order preference by similarity to ideal solution method
Dual-objective prediction of garment pressure and body contouring efficacy in shapewear: a hybrid GA-BP framework for sizing systems
Synthesis or preparation, physicochemical characterization, and H460 cell inhibition of selenium nanoparticles stabilized by Marsdenia tenacissima residue polysaccharide.
Enhanced feature learning and model lightweighting for real-time UAV detection using FEGP-YOLOv8
In realistic scenarios, unmanned aerial vehicles (UAVs) often present as small targets within complex environmental backgrounds, posing significant challenges for detection. The YOLO series of algorithms have demonstrated proficiency in UAV target detection due to their computational efficiency; however, achieving a balance between accuracy, model size, and detection speed remains challenging. This paper introduces Feature Enhancement and Gradient Propagation-YOLOv8 (FEGP-YOLOv8), a lightweight and high-performance UAV detection model based on YOLOv8. The model optimizes feature representation quality and computational efficiency through a convolutional feature enhancement module, integrating FasterNet and efficient multi-scale attention. The neck network is redesigned to accommodate small target detection tasks, and a Partial Convolution-based Detect head (PDetect) eliminates ineffective computations. Tested on three UAV image datasets, FEGP-YOLOv8 achieves 95.4% mAP50 and 107.9 FPS on the DUT-Anti-UAV dataset with only 3.4 MB model size (reduced by 43% compared with YOLOv8n), higher mAP50, and lower GFLOPs/model size with competitive FPS. This research provides an innovative technical solution for the real-time upgrade of UAV intelligent inspection systems.
Read moreInvestigation on the interaction mechanism of multi-components during the co-combustion process of coal gangue and torrefied agricultural solid waste
Regulating the interfacial microstructure and mechanical properties of Inconel 718 coatings via Dual-Arc Modulated plasma arc cladding