At VivaTech in Paris, research center coordinators showcase the advances made at the University of São Paulo in agriculture, climate, energy, computing, artificial intelligence, and health.

A collaborative team led by Ravi Radhakrishnan of the School of Engineering and Applied Science and Jake Brenner and Jacob Myerson of the Perelman School of Medicine at the University of Pennsylvania has unraveled the mathematics of a 500-million-year-old protein network that acts like the body’s bouncer, “deciding” which foreign materials get degraded by immune cells and which are allowed entry. “This discovery enables us to design therapeutics the way you’d design a car or a spaceship—using the principles of physics to guide how the immune system will respond—rather than relying on trial and error,” says Brenner.

A new cooling technology could significantly improve the energy efficiency of data centers and high-powered electronics while reducing water use associated with cooling. By passively removing heat through evaporation, it offers a promising alternative to traditional cooling systems.

University of São Paulo researchers are developing an aircraft equipped with sensors that can detect and measure the concentrations of carbon dioxide and methane in the environment; the project was presented at FAPESP Week France in Toulouse.

Researchers from Tsinghua University and Henan Normal University developed a dedicated data analysis framework for the Silicon Strip Detector Telescopes (SSDTs) of the Compact Spectrometer for Heavy-IoN Experiments (CSHINE). Based on a modular architecture design, the framework integrates core analysis steps—including detector calibration, particle identification, and track reconstruction—into a unified system through C++ classes, effectively addressing the technical challenges of processing complex SSDT signals. Its robust performance was demonstrated through the successful analysis of light-charged particles in the 25 MeV/u ⁸⁶Kr + ¹²⁴Sn experiment conducted at the first Radioactive Ion Beam Line in Lanzhou, allowing for precise extraction of physical observables, including energy, momentum, and particle type. Through the optimization of track recognition algorithms with the utilization of reconstructed physical data, including effective physical event counts and energy spectra, the research team significantly enhanced the track recognition efficiency, achieving a remarkable rate of approximately 90%. This framework provides a standardized and reusable technical solution for SSDT-based detector systems.