The fast growth of digital content in streaming systems has made the problem of too much information more serious. It also brings big challenges to traditional recommendation methods that use experience-based similarity or simple neural network models. We put forward an improved graph convolution framework for personalized movie recommendations to solve three key problems: poor expandability, sparse data, and difficulties in modeling high-level interactions. First, we build a bipartite graph of users and items based on a big dataset of movie ratings. Then we use a simple multi-layer graph convolution method to get high-level collaborative information through standardized neighborhood spread. Different from standard LightGCN models that use inner-product calculation for scoring, our method combines an MLP prediction module with Batch Normalization, non-linear activation functions and Dropout regularization. This design lets us model the interactions between users and items more clearly and keeps the system structure efficient at the same time. The test results from big interaction data sets show the model has steady convergence and good generalization ability. It also gets competitive results in top-K recommendation tests, and there is no obvious overfitting during the training process. We find that mixing simple graph spread with non-linear prediction can improve both the ability to show data features and recommendation precision in big and sparse data environments. This research provides a framework that can expand well for recommendation systems with better structure. It also lays a good base for the future combination of graph learning and semantic model building.

Remaining Useful Life (RUL) prediction is crucial for predictive maintenance in complex engineering systems. In recent years, deep learning methods have become the dominant approach for RUL prediction due to their ability to capture complex temporal dependencies. Long Short-Term Memory (LSTM) networks, originally designed for sequence modeling, have been widely applied in time-series prediction tasks. The Transformer architecture, known for its powerful attention mechanism, has achieved remarkable success in various sequential data analysis domains. However, these methods typically assume a single global degradation pattern, which may limit their performance under varying operating conditions. To address this issue, this paper presents a two-fold investigation: first, a comparative performance analysis of three prominent architectures—LSTM, Transformer, and Mixture of Experts (MoE). Second, we focus on the optimization of the MoE framework by proposing a Regime-Aware MoE (RA-MoE). This model integrates regime identification techniques (K-Means, HMM, and VAE) to optimize the gating mechanism. Experimental results show that while LSTM remains the most robust performer among the candidate architectures, the proposed RA-MoE significantly enhances the performance of the standard MoE architecture, demonstrating the effectiveness of regime-aware optimization in complex scenarios.

Risks in supply chains and financial networks propagate through graph structure; early warning and targeted intervention are critical for mitigating cascading failures. Existing methods suffer from the prediction–intervention decoupling: prediction models are trained without awareness of downstream intervention decisions, and intervention policies rely on fixed heuristics (e.g., hand-tuned mixing weights) that do not adapt to data. We propose a decision-aware framework that couples GNN-based risk prediction with a data-adaptive intervention policy—the mixing weight between predicted risk and structural centrality is learned from validation performance rather than being fixed a priori. Epidemic dynamics (SIR, LTM) provide features; a GCN backbone predicts node risk; the intervention policy is selected by maximizing intervention benefitΔon a validation set. Experiments on Email-Enron, Facebook, and Wiki-Vote show that the adaptive policy achieves AUC 0.78 andΔ22.4%, outperforming fixed-heuristic baselines (centrality-only15.8%, prediction-only19.6%) and retaining advantage under edge noise and limited observation. We argue that learning to intervene, which closes the loop between prediction and intervention via validation-driven policy selection, is a principled step toward decision-aware risk management.

Adolescent mental health has emerged as a critical public health challenge, with traditional screening methods often hindered by time lags and limited reach. As social media becomes a primary channel for emotional expression among youth, automated sentiment analysis offers a promising pathway for real-time monitoring. This study constructs an automated recognition system using a large-scale corpus of 52,573 labeled social media entries across seven psychological dimensions, including depression, anxiety, suicidal ideation, and stress. By employing the TF-IDF algorithm for multi-dimensional feature extraction and a Logistic Regression model for multi-class classification, the proposed scheme achieves an overall recognition accuracy of 74.8%. Experimental results reveal significant linguistic patterns across mental states: the "normal" category exhibited the highest discriminability (F1-score = 0.896), while the "stress" category proved the most challenging to identify (F1-score = 0.553) due to its semantic overlap with daily emotional fluctuations. Feature analysis further confirms that specific "psychological fingerprints"—such as the high frequency of first-person pronouns in the depression group and uncertainty-related queries in the anxiety group—can serve as reliable predictors. This research validates the feasibility of large-scale, non-invasive psychological screening and provides a data-driven framework for early campus crisis intervention and precise psychological support.