Scalable Top-Down Approach for Recycling Highly Degraded Spent LiFePO via Lattice Fragmentation-Regeneration.

Designing efficient, scalable, and eco-friendly recycling technologies is crucial for addressing the widespread decommissioning of spent lithium-ion batteries. Here, an innovative top-down regeneration method is introduced to rejuvenate highly degraded LiFePO. Initially, the crystal structure of spent LiFePO is destroyed via the oxidation process, followed by the reconstruction of the LiFePO lattice through the reduction process.

Automated Detection of Clinically Significant Prostate Cancer in mp-MRI Images Based on an End-to-End Deep Neural Network.

Automated methods for detecting clinically significant (CS) prostate cancer (PCa) in multi-parameter magnetic resonance images (mp-MRI) are of high demand. Existing methods typically employ several separate steps, each of which is optimized individually without considering the error tolerance of other steps. As a result, they could either involve unnecessary computational cost or suffer from errors accumulated over steps.