Information popularity prediction is a critical problem in social network analysis. With the increasing prevalence of social platforms, accurate prediction of the diffusion process has become increasingly important. Existing methods mainly rely on graph neural networks to model structural relationships, but they are often insufficient in capturing the complex interplay between temporal evolution and local cascade structures, especially in real-world scenarios involving sparse or rapidly changing cascades. To address this issue, we propose the Cascading Dynamic attention-calibrated Graph Convolutional Network, named CasDacGCN. It enhances prediction performance through spatiotemporal feature fusion and adaptive representation learning. The model integrates snapshot-level local encoding, global temporal modeling, cross-attention mechanisms, and a hypernetwork-based sample-wise calibration strategy, enabling flexible modeling of multi-scale diffusion patterns. Results from experiments demonstrate that the proposed model consistently surpasses existing approaches on two real-world datasets, validating its effectiveness in popularity prediction tasks.

In recent years, the deployment of edge servers has attracted significant research interest, with a focus on maximizing their utilization under resource constraint to improve overall efficiency. However, most existing studies concentrate on initial deployment strategies, paying limited attention to approaches involving incremental expansion. As user demands continue to escalate, many edge systems are facing overload situations that hinder their ability to meet performance requirements. To tackle these challenges, this paper introduces an auxiliary edge-server deployment strategy designed to achieve load balancing across edge systems and alleviate local server overloads. The problem is herein referred to as the Auxiliary Edge Server Deployment (A–ESD) problem, and the aim is to determine the optimal deployment scheme for auxiliary edge servers. A–ESD is modeled as a multi-objective optimization problem subject to global constraints and is demonstrated to be NP-hard. An enhanced genetic algorithm called LBA–GA is proposed to efficiently solve the A–ESD problem. The algorithm is designed to maximize overall load balance while minimizing total system delay. Extensive experiments conducted on real-world datasets demonstrate that LBA–GA outperforms existing methods, delivering superior load balancing, reduced latency, and higher cost-effectiveness.

This paper presents an in-depth study on the prediction of clinical prognosis and feature correlation in patients with hemorrhagic stroke. Utilizing a publicly available dataset on hemorrhagic stroke, a comprehensive model was established to predict the clinical prognosis of patients with hemorrhagic stroke by analyzing clinical data and imaging information, and the importance of associated characteristics was analyzed. Experimental results demonstrate that the random forest algorithm performs well in predicting multi-class problems, accurately forecasting patient prognosis, and determining the importance of each feature in relation to the target variable (90-day mRs scores). Age, blood pressure, time interval between onset of illness and first imaging, and imaging-related features were identified as important influencing factors. These findings provide valuable insights for clinical diagnosis and treatment.

With the development of advanced generative technologies such as generative adversarial networks and diffusion models, AI-generated images have become extremely realistic. As a result, there is an urgent need for efficient detection tools to combat misinformation and protect societal trust and personal privacy. Current detection methods perform well when identifying content from known generative models, but their performance drops when facing unknown or emerging technologies. Moreover, most detection methods focus on detecting local artifacts, overlooking the extraction and effective fusion of global relationships and multi-scale features, which makes it difficult to capture the complex patterns in AI-generated images. To address these challenges, this paper proposes a multi-scale self-adaptive feature fusion network (MSAFNet). Specifically, the proposed MSAFNet utilizes a global noise feature extraction module to capture global noise patterns in images, compensating for the shortcomings of existing methods that focus solely on local noise features. Meanwhile, it adopts a texture feature extraction module to capture subtle texture anomalies and a color feature extraction module to enrich color feature information, enhancing the expression capability of color features and improving the recognition of complex features in AI-generated images. Lastly, we introduce a self-adaptive feature fusion module to capture complementary information and the importance of multi-scale features, ensuring efficient fusion of feature information across different dimensions. Experimental results on the large-scale DFFD and CIFAKE datasets demonstrate that the proposed MSAFNet achieves higher classification accuracy compared to other AI-generated image detection methods.