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An autonomous drone carrying water to help extinguish a wildfire in the Sierra Nevada might encounter swirling Santa Ana winds that threaten to push it off course. Rapidly adapting to these unknown disturbances inflight presents an enormous challenge for the drone’s flight control system.

To help such a stay on target, MIT researchers developed a new, machine learning-based adaptive control algorithm that could minimize its deviation from its intended trajectory in the face of unpredictable forces like gusty winds.

The study is published on the arXiv preprint server.

Breast cancer is the most prevalent malignancy among women worldwide. Phototheranostics—an approach that uses light both to detect and treat cancerous lesions—has drawn growing attention due to its potential advantages, including light-triggered, non-invasive real-time diagnosis and simultaneous in situ therapy.

One promising strategy in light-based cancer treatment is (PTT), which employs photothermal agents—ideally with tumor-targeting capability—to convert light irradiation into localized heat. However, challenges remain in the clinical translation of PTT, particularly the risks of overheating and damaging , as well as the potential failure to effectively ablate tumors.

In a study published in PNAS, a team led by Zhang Pengfei from the Shenzhen Institutes of Advanced Technology (SIAT) of the Chinese Academy of Sciences, in collaboration with Jong Seung Kim from Korea University, Jonathan L. Sessler from the University of Texas at Austin, and Zhou Hui from the Nanjing University of Posts and Telecommunications, developed a dual-laser PTT (DLPTT) strategy for therapy.