Forest fires cause irreversible ecological and economic losses worldwide, often exacerbated by delayed or inefficient rescue efforts. A new study presents a groundbreaking data-driven framework to revolutionize aviation emergency networks. By integrating fire probability predictions with multi-objective optimization, the research enables faster, cost-effective rescue planning tailored to real-world fire risks. Tested in China’s Hainan Province, the model reduces response times while balancing ecological and operational costs—offering a scalable solution for global forest protection.

Researchers at the University of Electronic Science and Technology of China have developed a novel quantum algorithm that extends Grover's quadratic speedup to continuous search problems, including optimization and spectral analysis over infinite-dimensional spaces. The team rigorously proved the algorithm's optimality by establishing a matching lower bound on query complexity. They also proposed a general framework for constructing the required quantum oracle, enhancing adaptability to diverse applications.