An accurate assessment of the state of health (SOH) is the cornerstone for guaranteeing the long-term stable operation of electrical equipment. However, the noise the data carries during cyclic aging poses a severe challenge to the accuracy of SOH estimation and the generalization ability of the model. To this end, this paper proposed a novel SOH estimation model for lithium-ion batteries that incorporates advanced signal-processing techniques and optimized machine-learning strategies. The model employs a whale optimization algorithm (WOA) to seek the optimal parameter combination (K, α) for the variational modal decomposition (VMD) method to ensure that the signals are accurately decomposed into different modes representing the SOH of batteries. Then, the excellent local feature extraction capability of the convolutional neural network (CNN) was utilized to obtain the critical features of each modal of SOH. Finally, the support vector machine (SVM) was selected as the final SOH estimation regressor based on its generalization ability and efficient performance on small sample datasets. The method proposed was validated on a two-class publicly available aging dataset of lithium-ion batteries containing different temperatures, discharge rates, and discharge depths. The results show that the WOA-VMD-based data processing technique effectively solves the interference problem of cyclic aging data noise on SOH estimation. The CNN-SVM optimized machine learning method significantly improves the accuracy of SOH estimation. Compared with traditional techniques, the fused algorithm achieves significant results in solving the interference of data noise, improving the accuracy of SOH estimation, and enhancing the generalization ability.

Late blight remains one of the most devastating diseases threatening global potato production. 

Protonic ceramic cells (PCCs) are emerging as highly efficient devices for power generation, hydrogen production, and chemical synthesis at intermediate temperatures. However, their advancement depends on a deeper understanding of proton transport, hydration mechanisms, and surface catalytic reactions. This review highlights how diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) provides a powerful, surface-sensitive approach to uncover these mechanisms in real time. By probing hydroxyl formation, carbonate species, reaction intermediates, and proton migration pathways, DRIFTS enables researchers to decode key phenomena that govern PCC performance. The study also outlines major challenges and proposes strategies to expand DRIFTS capabilities for improving materials design and accelerating PCC development.