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This paper introduces the DeNet-Mamba-DC-SCSSA network, an advanced solution for predicting the Remaining Useful Life (RUL) of lithium-ion batteries, crucial for the safety and efficiency management of electric vehicles. Combining the robust Denoising Enhancement Network (DeNet), the Improved Sparrow Optimization Algorithm (SCSSA), the adept Mamba time-series model, and the proficient Dilated Convolution (DC), this model excels in precise noise handling and sophisticated feature extraction. DeNet diligently refines input data, mitigating noise interference, while Mamba skillfully captures sequential intricacies. DC, on the other hand, adeptly extracts features over varying time scales, ensuring meticulous RUL predictions.The model's efficacy was rigorously tested on NASA and CALCE datasets and was benchmarked against cutting-edge algorithms. Remarkably, it reduced average RE and RMSE by 48.59% and 21.45%, respectively, showcasing its superior performance and accuracy. Further evaluation on the CALCE dataset against the latest methods affirmed its leading predictive precision and stability.The model's robustness and practical applicability were further validated using real vehicle data from a new energy vehicle platform. In a challenging test, it accurately predicted the charging capacities corresponding to the mileage of four vehicles with minimal errors: 0.52 Ah, 1.03 Ah, 0.84 Ah, and 0.71 Ah. These results significantly surpassed those of other recent methods, highlighting the model's exceptional generalizability and potential for real-world applications in electric vehicle battery management. [ABSTRACT FROM AUTHOR]

This study delves deeply into exploring the artistic value of traditional Chinese painting (TCP) and aims to bridge the gap between its fundamental characteristics and the realm of human emotions. To achieve this, a novel convolutional neural network (CNN)-based classification model for TCP emotions is proposed. By thoroughly analyzing the distinct emotional mapping relationships inherent in TCP, a comprehensive framework is developed. Notably, emotional feature regions are accurately extracted using image saliency, and a multi-layer aggregation recalibrated emotional feature module is seamlessly integrated into the CNN network structure. This integration strengthens the activation of features that significantly influence TCP emotions. Moreover, a method of multi-category weighted activation localization is skillfully employed to classify Chinese painting emotions within the CMYK (cyan magenta yellow black) color space. The empirical results convincingly demonstrate that our algorithm surpasses existing approaches such as VGG-19, GoogLeNet, ResNet-50, and WSCNet in the task of TCP emotion recognition, achieving an impressive accuracy of 92.36%, the largest error is 0.191. This improvement signifies the advancements made by our model in accurately capturing and understanding the emotional nuances within TCP. By outperforming previous methods, our research contributes to the convergence of multimedia technology and cultural education. [ABSTRACT FROM AUTHOR]

Litter decomposition is dependent on the requirements of decomposer communities and their ability to acquire energy and nutrients from their substrates (i.e. litter) and the surrounding environment (i.e. soil). However, knowledge about whether and how stoichiometric imbalance (i.e. the differences in C:N:P ratios between microorganisms and their substrates) regulate litter decomposition rates and whether it can be compensated by soil resources have rarely been evaluated, and even less across different decomposition stages over time.In this study, we conducted a reciprocal litter transplantation experiment using a stoichiometric gradient along the forest‐steppe ecotone to evaluate mechanisms underlying litter‐microbe‐soil interactions at different moments during litter breakdown. We measured the C:N:P stoichiometry of litter, soil, microbes, enzyme ratios and soil microbial community composition (via metabarcoding) after 6 and 12 months of litter decomposition.We found that the stoichiometric imbalances between soil microorganisms and litter substrate controlled decomposition rates directly during the early phase of decomposition. In contrast, the stoichiometric imbalances between soil microorganisms and soil substrate regulated decomposition rates during the later phase of decomposition, but this was an indirect effect mediated via shifts in the saprophytic fungal community composition and enzyme allocation.These findings highlight that the stoichiometric imbalance between soil microorganisms and litter substrates can be partly compensated by the local soil resources over the course of the decomposition process. We conclude that the stoichiometric imbalance between soil microorganisms and their resources is a key mechanism that should not be ignored when predicting soil C and nutrient cycling in terrestrial ecosystems. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]