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The electromagnet module, as a fundamental component providing levitation force for maglev trains, plays a crucial role in ensuring the stability of train operation. However, vibrations can easily occur due to the mechanical coupling between the two suspension points of the electromagnet module. To reveal the inherent instability of the system and the coupling relationship between the state variables, a state-space equation that considers the mechanical coupling between the two suspension points is established. Furthermore, a differential control algorithm based on geometric feature transformation is proposed to mitigate the structural coupling vibration. Simulation experiments are conducted to compare the dynamic characteristics of the system before and after implementing the improvement algorithm under complex conditions. At the same time, the influence of control parameters on electromagnetic vibration was analyzed, focusing particularly on vibrations resulting from parameter mismatch, offering crucial insights for enhancing system stability. Additionally, suspension tests are carried out on the high-speed double bogie test platform in the Key Laboratory of Hunan Province to further validate the effectiveness of the proposed algorithm. The proposed control framework is both effective and concise, making it easy to implement in engineering applications. This research holds significant practical value in improving the stability of maglev trains.

Objective To establish a model that can predict the occurrence of acute kidney injury (AKI) in elderly patients with HBV-related acute-on-chronic liver failure (HBV-ACLF), and to conduct a preliminary evaluation of the performance of the model. Methods A total of 276 patients with HBV-ACLF hospitalised in The First Affiliated Hospital of Harbin Medical University from January 2020 to January 2023 were retrospectively included and divided into 72 (26.09%) in the AKI group and 204 (73.91%) in the non AKI group, according to whether AKI occurred during hospitalisation. The clinical data of all patients were extracted and screened for independent risk factors for AKI during hospitalisation using multivariate logistic regression. A prediction model was constructed accordingly, and then the efficacy of the prediction model was evaluated using ROC curves. Results Multivariate logistic regression analysis showed that age ＞70 years (OR=1.404, 95%CI: 1.134～1.737), procalcitonin＞1 ng/L (OR=1.473, 95%CI: 1.074～2.019), model for end stage liver disease (MELD) score ＞34 (OR=1.702, 95%CI: 1.254～2.311), and combined upper gastrointestinal bleeding (OR=1.516, 95%CI: 1.123～2.047) were independent risk factors for the occurrence of AKI in HBV-ACLF patients during hospitalisation (P＜0.05). A prediction model for the occurrence of AKI in HBV-ACLF patients during hospitalisation was established based on the above four parameters, and the ROC curve showed that the AUC predicted by the model was 0.882 (95%CI: 0.838～0.917), with a sensitivity of 66.67%, a specificity of 93.14% and an accuracy of 86.23%. Conclusion Elderly, high MELD score and procalcitonin level, and concomitant upper gastrointestinal bleeding are independent risk factors for AKI in patients with HBV-ACLF during hospitalisation, and the model constructed accordingly can predict the risk of AKI, thus assisting clinical disease management.