Nanocellulose has emerged as a highly promising and sustainable nanomaterial due to its unique structures, exceptional properties, and abundance in nature. In this comprehensive review, we delve into current research activities focused on harnessing the potential of nanocellulose for advanced electrochemical energy storage applications. We commence with a brief introduction to the structural features of cellulose nanofibers found within the cellulose resources’ cell walls. Subsequently, we explore various processes that have been investigated for utilizing cellulose in the realm of energy storage. In contrast to traditional binders, we place significant emphasis on the utilization of solid electrolytes and 3D printing techniques. Additionally, we examine different application areas, including supercapacitors, lithium-ion batteries, and Zn-ion batteries. Within this section, our primary focus lies in integrating nanocellulose with other active materials to develop flexible substrates such as films and aerogels. Lastly, we present our perspectives on several key areas that require further exploration in this dynamic research field in the future.

COVID-19 has produced significant fluctuations and impacts on the Chinese stock market, and the sentiment analysis of stock reviews is important for the study of economic recovery. Owing to the shortage of well-annotated Chinese stock reviews, and the more emotional complexity and obscurity of Chinese stock review text, this paper proposes an innovative Chinese stock review sentiment analysis model BERT-BiLSTM-Attention, which encodes the stock review text by BERT to enhance the semantic feature representation of the text and the ability to understand the context, BiLSTM is then utilized to enhance the contextual information of the overall context of the review as well as the model’s comprehension of the text sequences, and then Attention mechanism is utilized to obtain important textual information and get the most effective information quickly. Experiments show that the model is effective in sentiment analysis of Chinese stock reviews, with an accuracy of 93.98%. It can be proved that the proposed model well enhances the performance of stock review text classification, and has a strong generalization ability, which can be used for sentiment analysis in many fields.

Blockchain technology has witnessed remarkable growth, especially with the advent of Ethereum and its smart contract capabilities, spurring widespread applications across diverse industries. However, the scalability issue remains a significant bottleneck, primarily due to the prevalent serial transaction execution approach in many blockchain systems, leading to high latency and low throughput. Deterministic parallelism and optimistic parallelism are currently the two types of excellent parallel schemes to improve transaction execution efficiency. But there are still some problems and challenges in parallel execution. To address these challenges, this paper presents a novel parallel execution model TCHAO, which leverages historical access objects to classify and group transactions. It allows for serial execution within groups and parallel execution between groups, thereby reducing transaction execution time. The model eliminates the need for pre-defining read–write sets or pre-executing transactions, which are common limitations in existing parallelism schemes. Through the implementation of a prototype system and comprehensive experiments, TCHAO demonstrates superior performance compared to SE (serial execution), EV (Execute-Validate) architecture and sharding schemes in terms of applicable blockchain types, throughput, latency, condition and limitation. This research provides a new perspective and solution for enhancing the scalability of blockchain systems.

Image super-resolution (SR) models based on the generative adversarial network (GAN) face challenges such as unnatural facial detail restoration and local blurring. This paper proposes an improved GAN-based model to address these issues. First, a Multi-scale Hybrid Attention Residual Block (MHARB) is designed, which dynamically enhances feature representation in critical face regions through dual-branch convolution and channel-spatial attention. Second, an Edge-guided Enhancement Block (EEB) is introduced, generating adaptive detail residuals by combining edge masks and channel attention to accurately recover high-frequency textures. Furthermore, a multi-scale discriminator with a weighted sub-discriminator loss is developed to balance global structural and local detail generation quality. Additionally, a phase-wise training strategy with dynamic adjustment of learning rate (Lr) and loss function weights is implemented to improve the realism of super-resolved face images. Experiments on the CelebA-HQ dataset demonstrate that the proposed model achieves a PSNR of 23.35 dB, a SSIM of 0.7424, and a LPIPS of 24.86, outperforming classical models and delivering superior visual quality in high-frequency regions. Notably, this model also surpasses the SwinIR model (PSNR: 23.28 dB → 23.35 dB, SSIM: 0.7340 → 0.7424, and LPIPS: 30.48 → 24.86), validating the effectiveness of the improved model and the training strategy in preserving facial details.

To resolve the conflict between global structure modeling and local detail preservation in image super-resolution, we propose SwinT-SRGAN, a novel framework integrating Swin Transformer with GAN. Key innovations include: (1) A dual-path generator where Transformer captures long-range dependencies via window attention while CNN extracts high-frequency textures; (2) An end-to-end Detail Recovery Block (DRB) suppressing artifacts through dual-path attention; (3) A triple-branch discriminator enabling hierarchical adversarial supervision; (4) A dynamic loss scheduler adaptively balancing six loss components (pixel/perceptual/high-frequency constraints). Experiments on CelebA-HQ and Flickr2K demonstrate: (1) Very good performance (max gains: 0.71 dB PSNR, 0.83% SSIM, 4.67 LPIPS reduction vs. Swin-IR); (2) Ablation studies validate critical roles of DRB. This work offers a robust solution for high-frequency-sensitive applications.

COVID-19 has produced significant fluctuations and impacts on the Chinese stock market, and the sentiment analysis of stock reviews is important for the study of economic recovery. Due to the lack of a large amount of labeled data in the existing Chinese stock review data, and the currently popular Bert model mostly failed to consider contextual information according to different contextual backgrounds when extracting features, resulting in the lack of contextual information in the modeled features. To address the above problems, this paper proposes an innovative Chinese stock review sentiment analysis model BERT-BiLSTM-Attention, which encodes the stock review text by BERT to enhance the semantic feature representation of the text, BiLSTM is then utilized to enhance the contextual information of the overall context of the review as well as the model’s comprehension of the text sequences, and then Attention mechanism is utilized to obtain important textual information and get the most effective information quickly. Experiments show that the model is effective in sentiment analysis of Chinese stock reviews, with an accuracy of 93.98%, It can be proved that the proposed model well enhances the performance of stock review text classification, and has a strong generalization ability, which can be used for sentiment analysis in many fields.