A study published by Associate Professor Yanfei Li’s team from Hunan Agricultural University in Frontiers of Agricultural Science and Engineering provides an innovative solution to this challenge (DOI: 10.15302/J-FASE-2025613).

A study led by Professor Hui Liu from Central South University, published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2024599), introduces a hybrid deep learning model named “ICEEMDAN-LZC-BVMD-SSA-DELM”, which significantly enhances the prediction accuracy of soybean futures prices. By integrating multi-stage data preprocessing and intelligent optimization algorithms, the model addresses limitations of traditional methods in noise handling, parameter tuning, and generalization capabilities, offering new insights for risk management in agricultural financial markets.

Inspired by a simple children's toy, a jumping popper toy, researchers have unlocked a key to designing more agile and predictable soft robots. Soft robots, made from flexible materials, hold immense promise for delicate tasks, but their complex movements have been difficult to predict and control, especially dynamic actions like jumping. Now, a team from Keio University and The University of Osaka has published a study in Advanced Robotics Research detailing the physics behind the jump of thin hemispherical shells, a fundamental building block for jumping soft robots.

Tokyo, Japan – A team led by a researcher from Tokyo Metropolitan University has devised a new way of coating magnesium alloys to improve their corrosion resistance. Instead of costly, unwieldy, and slower coating techniques under vacuum, they used liquid-based chemical conversion coating with the addition of cavitation bubbles. The resulting thick coating helped improve corrosion resistance to chlorides and mechanical properties. The team’s new technology is aimed at reinforcing lightweight materials in electric cars.