NaTi2(PO4)3/N‐Doped Hard Carbon Nanocomposites with Sandwich Structure for High‐Performance Na‐Ion Full Batteries

The well‐matched technology of cathode and anode in Na‐ion full batteries is highly challenging yet critically important in practical applications. Here, the high‐performance Na‐ion full batteries are developed by using NaTi2(PO4)3/N‐doped mesoporous hard carbon hybrid anode and porous Na3V2(PO4)3 cathode. The different anodes are designed for well‐matched Na‐ion full batteries. The unique sandwich and mesoporous structural features endow the hybrid anode with a high reversible capacity (240 mAh g−1 at 1 C), high rate performance (109.7 mAh g−1 at 100 C), ultrahigh energy/power densities (76.56 Wh kg−1/5104 W kg−1) and a long cycle‐life (capacity retention of 92.1 % after 1000 cycles at 100 C) in a half cell. In a full battery this hybrid anode can also deliver a higher capacitive contribution (79.5–87.7 %) and high energy/power densities (104 Wh kg−1/5256 W kg−1). This design provides a promising pathway for developing high performance and low‐cost Na‐ion full batteries

Gout Staging Diagnosis Method Based on Deep Reinforcement Learning

In clinical practice, diseases with a prolonged course and disease characteristics at the time of diagnosis are often classified into specific stages. The precision of disease staging significantly impacts the therapeutic and curative outcomes for patients, and the diagnosis of multi-clinical-stage diseases based on electronic medical records is a problem that needs further research. Gout is a multi-stage disease. This paper focuses on the research of gout and proposes a staging diagnosis method for gout based on deep reinforcement learning. This method firstly uses the candidate binary classification model library for accurate diagnosis of gout, and then corrects the results of the binary classification through the set medical rules for diagnosis of gout, and then uses the machine learning model to diagnose different stages of corrected accurate data. In the course of the experiment, deep reinforcement learning was introduced to solve the hyperparameter tuning problem of the staging model. Through experiments conducted on 24,872 electronic medical records, the accuracy rate of gout diagnosis was found to be 90.03%, while the accuracy rate for diagnosing different stages of gout disease reached 86.85%. These findings serve as a valuable tool in assisting clinicians with accurate staging and diagnosis of gout. The application of deep reinforcement learning in gout staging diagnosis demonstrates a significant enhancement in diagnostic accuracy, thereby validating the effectiveness and feasibility of this method