Fruit ripening determines shelf life and consumer appeal, yet the molecular switches that control this process remain partially understood. 

Researchers extend classical CNOP method for deep learning forecasting models with multi-time-slice-input structure.  It reveals when—not just where—input errors matter most in targeted observations. This improves forecasts for ocean-atmospheric variables, especially high-impact environmental events.

Saline–alkali stress disrupts the delicate chemistry of rose petals, reshaping both their color and aroma profiles. 

As climate change fuels extreme weather events, ensuring crops can maintain disease resistance under shifting temperatures is critical. 

As global airspace regulations evolve and drone usage surges, accurately tracking multiple UAVs in dynamic swarm formations has become a pressing challenge for aerial safety, urban air mobility, and counter-drone operations. A research team from Beijing Institute of Technology has developed a novel visual tracking framework that significantly improves the identification and tracking of visually similar drones with nonlinear motion in air-to-air scenarios. Their work marks a major step toward scalable, intelligent swarm drone monitoring in real-world applications.

With the increasing focus on the pursuit-evasion game, the guidance law capturability analysis has been widely studied recently to theoretically assess the performance of different guidance laws and reveal the impact of the physical constraints on capture zones. In a recent study, the capture zones of the continuous and pulsed guidance laws in the pursuit-evasion game are analytically discussed to provide deep insights into the capturability distinction between the continuous guidance law and the pulsed guidance law.

Moving mesh adaptation provides optimal resource allocation to computational fluid dynamics for the capture of different key physical features, i.e., high-resolution flow field solutions on low-resolution meshes. Although many moving mesh methods are available, they require artificial experience as well as computation of a posteriori information about the flow field, which poses a significant challenge for practical applications. Para2Mesh uses a double-diffusion framework to accomplish accurate flow field reconstruction through iterative denoising to provide flow field features as supervised information for fast and reliable mesh movement, thus enabling adaptive mesh prediction from design parameters.

Aircraft safety faces a critical challenge: “stall,” where wings lose lift at high angles, risking crashes. Researchers from the Civil Aviation University of China have developed a bio-inspired solution—microscopic herringbone grooves mimicking bird feathers—that delays stalls by 28.57%. This passive, low-cost technology reduces flow separation on wings, outperforming traditional methods while minimizing drag.