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The paper proposes a physics-informed deep learning approach for lithium-ion battery state-of-health estimation using incomplete discharge curves, achieving high accuracy and enabling real-time degradation trend prognosis.
EHRBench is an automated and reliable benchmark for evaluating LLMs on clinical decision-making tasks using real-world electronic health records, covering nearly 1M QA items across diagnosis, treatment, and prognosis tasks.