Change detection (CD) in hyperspectral images (HSIs) has become an increasingly vital research field in remote sensing. Over the past few years, the adoption of deep learning approaches, particularly convolutional neural network (CNN) and transformer-based architectures have significantly advanced performance in this field. While these models effectively capture spectral-spatial features, they may also introduce redundant or irrelevant spatial information, potentially degrading the accuracy of HSI CD. To address this challenge, a center-pixel and gated mechanism-based attention network (CGMNet) is proposed for HSI CD, leveraging the central pixel's significance to enhance accuracy and robustness. First, a gated-based center spatial attention (GCSA) module is designed to emphasize spatial relationships surrounding the central pixel. By incorporating gating mechanisms, GCSA selectively enhances relevant spatial features while suppressing irrelevant information. Second, a gated-based spectral attention (GSA) module is proposed to dynamically highlight the most significant spectral features, ensuring an effective spectral representation. Finally, a global transform fusion (GTF) module is proposed to capture global contextual information and to fuse it with the extracted spatial and spectral features. Moreover, we introduce a novel benchmark dataset, named the Hangzhou Bay (HZB), specifically designed to advance coastal remote sensing research. Experimental evaluations conducted on three publicly available datasets, as well as the HZB dataset, show that our CGMNet consistently outperforms some state-of-the-art methods in the HSI CD task. The source code of the proposed CGMNet, along with the HZB dataset, will be made publicly available at https://github.com/creativeXin/CGMNet.

Allergic rhinitis (AR) affects hundreds of millions globally and presents diverse, recurring symptoms that impair quality of life. Despite the widespread clinical use of nasal endoscopy, current evaluations remain largely subjective, lacking structured image-based assessment. Clear nasal discharge (CND), as a key visual indicator of AR, reflects inflammation and provides critical cues for grading and subtyping through its anatomical distribution and morphology. However, CND often presents with high visual heterogeneity and irregular anatomical distribution in nasal endoscopic images, making its segmentation particularly challenging—especially in the presence of optical artifacts, blurred anatomical boundaries, and structural diversity. To address these challenges, we propose VRSegNet, a segmentation framework that integrates anatomical structure information with inter-regional visual relations, aiming to enhance segmentation robustness and accuracy in complex scenarios. To accommodate structural and morphological variability, VRSegNet decomposes the nasal cavity into three functional zones: the Central Lumen Zone (CLZ), Nasal Groove Zone (NGZ), and Turbinate Sidewall Zone (TSZ). Based on this anatomical prior, the framework further incorporates a Regional Dynamic Perception (RDP) module and Visual-Relation Contrastive Learning (VR-CL) to improve the model's ability to identify spatially fragmented fluid anomalies within the same region, particularly under challenging conditions such as boundary ambiguity and partial occlusion. Evaluated per subset on RhinoMucus, VRSegNet attains mIoU of$\mathbf{7 3. 3 8 \%}$on MucusC (CLZ),$\mathbf{5 8. 8 7 \%}$on MucusG (NGZ), and 50.57 % on MucusW (TSZ), confirming robust performance across heterogeneous anatomical zones.

With the development of the Internet and the explosive expansion of information volume, it becomes arduous for users to make a choice in the face of overwhelming information. This makes the recommendation system an effective solution for the problem of information overload. Recommendation systems can acquire users' personalized preferences by comprehending their interactive behavior, thereby providing accurate recommendations. However, recommendation systems invariably encounter issues such as data sparsity and cold start. The introduction of auxiliary information can effectively alleviate these problems. Meanwhile, the employment of attention mechanisms can assist the system in more accurately understand user behavior and preference information, thus providing more precise personalized recommendations. In this paper, the research progress of knowledge graphs and attention mechanisms in recommendation systems is explored through top journals and several Chinese papers.

Accurate grading of rhinitis severity in nasal endoscopy relies heavily on the characterization of key secretion types, notably clear nasal discharge (CND) and purulent nasal secretion (PUS).However, both exhibit ambiguous appearance and high structural variability, posing challenges to automated grading under weak supervision.To address this, we propose Multimodal Learning for Mucus Anomaly Grading (MMAG), which integrates structured prompts with rank-aware vision-language modeling for joint detection and grading.Attribute prompts are constructed from clinical descriptors (e.g., secretion type, severity, location) and aligned with multi-level visual features via a dual-branch encoder.During inference, the model localizes mucus anomalies and maps the input image to severity-specific prompts (e.g., "moderate pus"), projecting them into a rankaware feature space for progressive similarity scoring.Extensive evaluations on CND and PUS datasets show that our method achieves consistent gains over Baseline, improving AUC by 6.31% and 4.79%, and F1 score by 12.85% and 6.03%, respectively.This framework enables interpretable, annotation-efficient, and semantically grounded assessment of rhinitis severity based on mucus anomalies.