Academic community identification in citation networks aims at uncovering internal correlations among scholars. However， heterogeneity of node attributes （content features） and topological structures （association patterns） often cause feature misalignment and information dilution during fusion， and the existing methods often ignore stable association patterns in core subnetworks formed by highly-connected nodes， which limit identification accuracy. To address these issues， a Multi-view Attention Fusion for academic Community Identification （MAFCI） method was proposed. In the method， a dual-view design was adopted： original node attributes and the adjacency matrix were encoded by one view， while core-node information and its topology extracted by k?core were incorporated by the other one view. A sparse graph attention mechanism was applied to extract low-dimensional embeddings from both views， and a dynamic weighting strategy was employed to fuse multi-source information adaptively， thereby balancing local structure and global semantics. In the decoder， the adjacency matrix was reconstructed via inner product and node attributes as well as core information were restored using attention mechanism. At the same time， a self-supervised clustering loss was introduced to guide embedding representations in converging to discriminative cluster centers and enhance community boundary separability. Experimental results on four citation network datasets （Cora， Citeseer， ACM， and DBLP） demonstrate that MAFCI method outperforms nine advanced baseline methods， including DDGAE （Deep Dual Graph Attention auto-Encoder） and BCDAN （Balanced method for Community Detection in Attribute Networks）， on most core metrics. Specifically， on the DBLP dataset， MAFCI method achieves a Normalized Mutual Information （NMI） of 0.513 and an Accuracy （ACC） of 0.809， which are improved by 2.3 and 1.3 percentage points， respectively， compared to the optimal baseline method； on the ACM dataset， MAFCI method achieves an NMI of 0.674 and an F1-score of 0.903， representing improvements of 1.2 and 0.8 percentage points， respectively， compared to the optimal baseline method； on the Citeseer dataset， MAFCI method has the NMI achieved 0.448， which is improved by 0.7 percentage points； on the Cora dataset， MAFCI method has the NMI reached 0.543， also surpassing all comparison methods. It can be seen that MAFCI method enhances the accuracy and robustness of academic community identification and has strong generalization across citation networks of varying scales and structures effectively through dual-view sparse attention and dynamic fusion mechanism.

In the linear injection models of traditional Component Substitution （CS） and Multi-Resolution Analysis （MRA） in remote sensing image pansharpening， the relative spectral response of the sensor used for pansharpening is not considered. At the same time， the insufficient feature extraction of the original images caused by the deep learning-based methods results in the loss of spectral and spatial information in the fusion results. Aiming at the above problems， a pansharpening method combining traditional and deep learning methods was proposed， namely CMRNet. Firstly， CS and MRA were combined with Convolutional Neural Network （CNN） to achieve nonlinearity and improve the performance of pansharpening method. Secondly， the Residual Channel （RC） blocks were designed to realize the fusion and extraction of multi-scale feature information， and the feature maps of different channels were assigned different weights by using Channel Attention （CA） adaptively to learn more effective information. Experiments were conducted on QuickBird and GF1 satellite datasets. Experimental results show that on downscale QuickBird and GF1 datasets， compared with the classic method PanNet， CMRNet has the Peak Signal-to-Noise Ratio （PSNR） increased by 5.48% and 9.62% respectively， and other indexes also improved significantly， which verifies that CMRNet can achieve a better pansharpening effect.