With the passage of time, the constant changes in relevant factors, and the daily maintenance of tailings ponds, the difficulty of tailings pond safety management is increasing day by day. In order to systematically improve the early warning ability for tailings pond dam break risk, the relationship between and influence of various related dam break risk factors of tailings ponds are utilised and the combination with dual attention is innovatively proposed. The risk prediction model for tailings ponds, EEMD-DA-LSTM, is improved. First, Pearson correlation coefficients are used to analyse the correlation between risk factors of tailings ponds. Then, the EEMD method is used to decompose the nonlinear displacement sequence, and the weights of input features are dynamically adjusted by double attention (DA). Finally, the LSTM network model is constructed to predict the displacement change. Taking valley-type tailings pond WKB-1 and mountainside tailings pond WKB-2 as examples, the dam break risk prediction models for tailings ponds are constructed based on three different models, the prediction results of different models are compared and analysed, and the prediction accuracy of the models is evaluated by three different evaluation criteria. The research results show that the integration of the EEMD-LSTM model with the DA model, that is, the EEMD-DA-LSTM model, has a better prediction effect for the dam break risk of tailings ponds WKB-1 and WKB-2 than other models through experimental verification. Therefore, the EEMD-DA-LSTM model is of great significance for preventing and resolving the safety risks of tailings ponds. It is valuable for practitioners in the mining industry and environmentally sustainable development.

Math Word Problems (MWP) solving involves language comprehension and mathematical reasoning. Most of the existing models are primarily based on deep learning methods. However, deep learning models often exhibit large model scales (e.g., large number of parameters), high feature dimensions, and an imbalance between accuracy and computational efficiency. To address these issues, We propose a novel dual-decoder model for solving Mathematical Word Problems (MWP), which is constructed using a Lie Group intrinsic mean feature matrix and named the Dual-Decoder Neural Symbolic Machine (DDNSM). This model projects data samples onto a Lie Group manifold to effectively reduce the output feature dimensions of the encoder through a feature matrix built around the Lie Group intrinsic mean, thereby reducing the computational load in the decoding stage and improving model inference efficiency. Additionally, we designed a dual-decoder system, comprising a decoder based on global and local attention mechanisms and another structured around a tree built from the Lie Group intrinsic mean. Extensive experiments on multiple challenging public datasets demonstrate that our model achieves competitive results in MWP tasks, achieving competitive results in MWP tasks with improved accuracy and computational efficiency.

Effective Automated Geometric Problem Solving (AGP) requires a deep integration of visual perception and textual comprehension. To address this, we propose a dual-stream fusion model that injects deep semantic understanding from a Pre-trained Language Model (PLM) into the geometric diagram parsing pipeline. Our core innovation is a Semantic-Guided Cross-Attention (SGCA) mechanism, which uses the global semantic intent of the problem text to direct attention toward key visual primitives. This yields context-enriched visual representations that serve as inputs to a Graph Neural Network (GNN), enabling relational reasoning that is not only perception-driven but also context-aware. By explicitly bridging the semantic gap between text and diagrams, our approach delivers more robust and accurate predictions. To the best of our knowledge, this is the first study to introduce a semantic-guided cross-attention mechanism into geometric diagram parsing, establishing a new paradigm that effectively addresses the cross-modal semantic gap and achieves state-of-the-art performance. This is particularly effective for parsing problems involving geometric symmetries, where textual cues often clarify or define symmetrical relationships not obvious from the diagram alone.