Mental health concerns have surged, highlighting the need for innovative solutions to enhance accessibility and effectiveness of support. This study investigates user expectations of AI-driven therapists and iteratively develops AI-based psychological assistants to cater to the growing demand for accessible mental health support. The researchers develop the PSYNAV system, an AI-driven web-based intervention, and comprises three distinct iterations, which include (i) a survey and interviews on users' demand for psychological intervention and support; (ii) a low-fi prototype codesigned with psychology students and computer science students; (iii) a high-fi prototype which can be accessed through the webpage to gain feedback and provide further improvement ideas. Each iteration contributes to a comprehensive understanding and refinement of the AI-driven intervention.

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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.

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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.

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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.

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