The ability of transformers to capture global context information is highly beneficial for recognizing subtle differences in facial expressions. However, compared to convolutional neural networks, transformers require the computation of dependencies between each element and all other elements, leading to high computational complexity. Additionally, the large number of model parameters need extensive data for training so as to avoid overfitting. In this paper, according to the characteristics of facial expression recognition tasks, we made targeted improvements to the Swin transformer network. The proposed Swin-Fer network adopts the fusion strategy from the middle layer to deeper layers and employs a method of data dimension conversion to make the network perceive more spatial dimension information. Furthermore, we also integrated a mean module, a split module, and a group convolution strategy to effectively control the number of parameters. On the Fer2013 dataset, an in-the-wild dataset, Swin-Fer achieved an accuracy of 71.11%. On the CK+ dataset, an in-the-lab dataset, the accuracy reached 100%.

The major challenges for medical image segmentation tasks are complex backgrounds and fuzzy boundaries. In order to reduce their negative impacts on medical image segmentation tasks, we propose an enhanced feature extraction network (EFEN), which is based on U-Net. Our network is designed with the structure of feature re-extraction to strengthen the feature extraction ability. In the process of decoding, we use improved skip-connection, which includes positional encoding and a cross-attention mechanism. By embedding positional information, absolute information and relative information between organs can be captured. Meanwhile, useful information will be strengthened and useless information will be weakened by using the cross-attention mechanism. Our network can finely identify the features of each skip-connection and cause the features in the process of decoding to have less noise in order to reduce the effect of fuzzy object boundaries in medical images. Experiments on the CVC-ClinicDB, the task1 from ISIC-2018, and the 2018 Data Science Bowl challenge dataset demonstrate that EFEN outperforms U-Net and some recent networks. For example, our method obtains 5.23% and 2.46% DSC improvements compared to U-Net on CVC-ClinicDB and ISIC-2018, respectively. Compared with recent works, such as DoubleU-Net, we obtain 0.65% and 0.3% DSC improvements on CVC-ClinicDB and ISIC-2018, respectively.

Amidst the dual global pressures of plastic pollution and resource scarcity, the transition to a circular economy has become an imperative. The valorization of biomass waste from agricultural, food, and animal processing into biodegradable packaging materials presents a key strategy to address this challenge. This review aims to systematically construct a comprehensive knowledge framework for the field, addressing the thematic fragmentation and methodological limitations of existing literature through integrated cross-stream analysis, combined bibliometric and technological assessment, and identification of emerging research frontiers. We begin with a bibliometric analysis to delineate the field’s evolutionary trajectory since 2008, global collaboration networks, core research themes, and emerging frontiers, revealing a clear progression from environmental impact assessment to functional material innovation. Subsequently, this review delves into the complete technological chain, from the green extraction of bio-based materials from three major waste streams to the comparison of traditional and advanced film fabrication methods. We then elaborate on the critical performance evaluation dimensions, including mechanical, barrier, biodegradable, safety, and functional properties, and summarize current applications in sectors such as food and medicine. Finally, we critically assess the core challenges related to cost, performance stability, and large-scale production, and provide a systematic outlook on future research directions, particularly the development of high-performance, multifunctional, and intelligent materials. This review offers a comprehensive and data-driven reference framework for researchers and industry stakeholders in the field.

With advances in computer vision and artificial intelligence technology, facial expression recognition research has become a prominent topic. Current research is grappling with how to enable computers to fully understand expression features and improve recognition rates. Most single face image datasets are based on the psychological classification of the six basic human expressions used for network training. By outlining the problem of facial recognition by comparing traditional methods, deep learning, and broad learning techniques, this review highlights the remaining challenges and future directions of deep learning and broad learning research. The deep learning method has made it easier and more effective to extract expression features and improve facial expression recognition accuracy by end-to-end feature learning, but there are still many difficulties in robustness and real-time performance. The broad learning system (BLS) is a broad network structure that is expanded by increasing the number of feature nodes and enhancement nodes appropriately to reinforce the structure and is also effective in facial expression recognition. However, outliers and noises in unbalanced datasets need BLS to solve in the future. Finally, we present several problems that still need to be addressed in facial expression recognition.

Facial expression recognition (FER) is an important field in computer vision with many practical applications. However, one of the challenges in FER is dealing with small sample data, where the number of samples available for training machine learning algorithms is limited. To address this issue, a domain adaptive learning strategy is proposed in this paper. The approach uses a public dataset with sufficient samples as the source domain and a small sample dataset as the target domain. Furthermore, the maximum mean discrepancy with kernel mean embedding is utilized to reduce the disparity between the source and target domain data samples, thereby enhancing expression recognition accuracy. The proposed Domain Adaptive Facial Expression Recognition (DA-FER) method integrates the SSPP module and Slice module to fuse expression features of different dimensions. Moreover, this method retains the regions of interest of the five senses to accomplish more discriminative feature extraction and improve the transfer learning capability of the network. Experimental results indicate that the proposed method can effectively enhance the performance of expression recognition. Specifically, when the self-collected Selfie-Expression dataset is used as the target domain, and the public datasets RAF-DB and Fer2013 are used as the source domain, the performance of expression recognition is improved to varying degrees, which demonstrates the effectiveness of this domain adaptive method.