RESUMEN
Proton-exchange membrane fuel cells (PEMFCs) play a crucial role in the transition to sustainable energy systems. Accurately estimating the state of health (SOH) of PEMFCs under dynamic operating conditions is essential for ensuring their reliability and longevity. This study designed dynamic operating conditions for fuel cells and conducted durability tests using both crack-free fuel cells and fuel cells with uniform cracks. Utilizing deep learning methods, we estimated the SOH of PEMFCs under dynamic operating conditions and investigated the performance of long short-term memory networks (LSTM), gated recurrent units (GRU), temporal convolutional networks (TCN), and transformer models for SOH estimation tasks. We also explored the impact of different sampling intervals and training set proportions on the predictive performance of these models. The results indicated that shorter sampling intervals and higher training set proportions significantly improve prediction accuracy. The study also highlighted the challenges posed by the presence of cracks. Cracks cause more frequent and intense voltage fluctuations, making it more difficult for the models to accurately capture the dynamic behavior of PEMFCs, thereby increasing prediction errors. However, under crack-free conditions, due to more stable voltage output, all models showed improved predictive performance. Finally, this study underscores the effectiveness of deep learning models in estimating the SOH of PEMFCs and provides insights into optimizing sampling and training strategies to enhance prediction accuracy. The findings make a significant contribution to the development of more reliable and efficient PEMFC systems for sustainable energy applications.
RESUMEN
BACKGROUND: Vernalization is one of the pivotal ways for plants to flower. The twodimensional gel electrophoresis (2-DE) and matrix-assisted laser desorption/ionization time-offlight/ time-of-flight mass spectrometry (MALDI-TOF/TOF MS) were applied to analyze the changes in protein expression profiles in responding to vernalization in leaves of wheat seedling before (0d) and after (30d) of vernalization. OBJECTIVE: The main objective of this study was to analyze the vernalization-responsive proteins in winter wheat after vernalization. METHODS: Winter wheat seedling leaf proteins were extracted by phenol extraction coupled with ammonium acetate in methanol. 2-DE was conducted according to procedures described in the manual given by the GE manufacture. The selected protein spots were identified by MALDITOF/ TOF MS. Gene ontology (GO) classification was applied to classify the functions of the differentially expressed proteins. Pathway enrichment analysis identified significantly enriched metabolic pathways or signal transduction pathways relative to the whole proteins background. RESULTS: The results of 2-DE and MALDI-TOF/TOF MS showed that among the 65 differentially expressed proteins that were successfully identified under vernalization, 30 were up-regulated whereas 35 were down-regulated after vernalization, respectively. These vernalization-responsive proteins were found to play roles in carbohydrate metabolism, protein metabolism, photosynthesis, defense and stress-resistance and may therefore participate in many biological processes in responding to vernalization. The enhanced accumulation of proteins after vernalization, such as thiamine thiazole synthase, late embryogenesis abundant protein, and glutathione-S-transferase, probably play vital roles in the mechanisms underlying vernalization response in wheat. CONCLUSION: Our results indicated these vernalization-responsive proteins were found to be involved in protein metabolism, carbohydrate metabolism, photosynthesis, and stress resistance/ defense. The responses of plants to low temperature were very complex, involving in a wide range of cellular pathways for signal transduction, gene regulation, protein modifications, and metabolic regulation. Studying on wheat proteomic profiles in response to vernalization can improve our understanding the molecular mechanisms underlying vernalization in cereals. The results obtained in this study have provided a novel insight into the mechanisms underlying vernalization in cereal crops.