Fuyao Group (China)

Polarforming emerges as a promising technique for manipulating the polarization of electromagnetic (EM) waves by shaping the polarization of an antenna into a desired state. By dynamically adjusting antenna polarization, polarforming enables real-time polarization matching or mismatching with received EM waves, thereby leveraging polarization degrees of freedom (DoFs) to enhance wireless communication performance. In this article, we first present an overview of the fundamental principles and design approaches underlying the polarforming technique. Then, we analyze the key advantages of polarforming, including hardware cost reduction, depolarization mitigation, channel adaptation, and signal quality improvement. Furthermore, we explore promising applications of polarforming for next-generation wireless networks. Finally, we discuss the implementation challenges and their potential solutions, motivating future research.

This retrospective study aims to develop and evaluate machine learning models for predicting blood glucose levels and insulin doses in hospitalized patients. Using a dataset of 1,161,779 glucose records collected from Tungs’ Taichung MetroHarbor Hospital between February 2022 and January 2024, we focused on two tasks: (1) predicting the next glucose measurement and (2) forecasting the next insulin dose. Multiple algorithms—including ensemble trees (Random Forest, ExtraTrees, XGBoost), support vector regression (SVR), and deep learning architectures (RNN, LSTM, GRU, TCN)—were trained and evaluated using mean squared error (MSE) and root mean squared error (RMSE). The temporal convolutional network (TCN) achieved the best performance in glucose prediction (RMSE = 0.0038), followed by LSTM. In contrast, insulin prediction remained challenging, with the best RMSE (~10.5 units) attained by ExtraTrees and Random Forest. Feature importance analysis showed that the most recent glucose reading was the strongest predictor of future glucose, while body weight was the most influential feature for insulin dosing. These findings highlight the potential of machine learning for short-term glucose forecasting, although further work is needed to improve insulin dose prediction accuracy for clinical integration.

To improve the path tracking performance of intelligent hybrid articulated tractors in all working conditions in unmanned operation, a path-tracking control method based on corrected model predictive control is proposed. The kinematic model of the tractor is established by analyzing the tractor’s kinematics. Taking the lateral and longitudinal errors as the target and the speed and articulation angular acceleration as the constraints, a tracking control algorithm based on model predictive control is proposed. In addition, to improve the transient performance of the tractor in the path tracking process, the proportional-integral-derivative controller and fuzzy controller are used to correct the model-predicted output articulation angular acceleration, forming a corrected model predictive control path tracking control method. To verify the effectiveness of the control method, model predictive control is used as a comparison method, and the effectiveness of the proposed method is verified based on the MATLAB 2024a simulation platform. The results show that compared with the MPC algorithm, the speed standard deviation is reduced by 2%, the longitudinal tracking error is reduced by 8%, and the lateral tracking error is reduced by 50%. The proposed method can effectively improve the path-tracking accuracy of the intelligent hybrid tractor.

To solve the problems of the low driving efficiency of a fuel cell tractor power source and the high hydrogen consumption caused by the irrational power allocation of the energy source, the power system was divided into two parts, power source and energy source, and a dual-source cooperative optimization energy management strategy was proposed. Firstly, a general energy efficiency optimization method was designed for the power source composed of a traction motor and PTO motor, and the energy source was composed of a fuel cell and power battery. Secondly, the unified objective function and constraint conditions were established, and the instantaneous optimization algorithm was used to construct the weight factor. The instantaneous optimal drive efficiency energy management strategy and the instantaneous optimal equivalent hydrogen consumption energy management strategy were designed, respectively. Finally, with the demand power as the transfer parameter, the instantaneous optimal drive efficiency energy management strategy and the instantaneous optimal equivalent hydrogen consumption energy management strategy were integrated to form a dual-source collaborative optimal energy management strategy. In order to verify the effectiveness of the proposed strategy, a rule-based energy management strategy was developed as a comparison strategy and tested in an HIL test under plowing and rotary plowing conditions. The results show that the average fuel cell efficiency of the proposed strategy increased by 7.86% and 8.17%, respectively, and the proposed strategy’s equivalent hydrogen consumption decreased by 24.21% and 9.82%, respectively, compared with the comparison strategy under the two conditions. It can significantly reduce the SOC fluctuation of the power battery and extend the service life of the power battery.

Due to its rich nutritional composition, biogas slurry can serve as a special liquid fertilizer. However, the application of slurry in agricultural fields currently faces challenges such as reliance on skilled famers’ experience, low precision, and difficulty in accurately controlling the irrigation dosage. To address these issues, an agricultural biogas slurry mixing agricultural machinery and its system has been designed and developed with the aim of enhancing the precision and safety of slurry application. The structure of the device has been designed, filter components have been selected, and improvements have been made to the structure of traditional connectors. Taking into account factors such as soil and humidity, an algorithm based on biogas slurry conductivity for slurry mixing decisions and the feedback control mechanism has been designed. After assembling the prototype, experiments were conducted, and the results showed that after processing by the system, compared to the calculated theoretical optimum, the concentration error of each component in the mixed fertilizer was controlled within 10%, and the conductivity fluctuation range was within 5%. This indicates that the overall ratio accuracy and stability of the biogas slurry mixing system are high. The biogas slurry mixing agricultural machinery and its system provide a novel intelligent equipment solution for the precise application of slurry, effectively enhancing the accuracy and safety of slurry application, reducing the use of chemical fertilizers during agricultural irrigation, and minimizing pollution to the environment and soil.

Dédiée aux sciences de l'atmosphère, au climat et à d'autres domaines connexes, tels que l'océanographie ou la glaciologie, La Météorologie, révisée par des pairs et publiée en français, s'adresse aux professionnels de la météo et du climat, aux enseignants, aux étudiants, aux amateurs et aux utilisateurs. La Météorologie a succédé en 1925 à l'Annuaire de la Société météorologique de France (1852-1924) qui avait lui-même succédé à l'Annuaire météorologique de la France (1849-1851).