Traffic flow prediction is of great importance in traffic planning, road resource management, and congestion mitigation. However, existing prediction have significant limitations in modeling multi-scale spatial-temporal features, particularly in capturing temporal periodicity and spatial dependency in dynamically evolving traffic networks. This paper proposes a novel framework of traffic flow prediction, referred to as Adaptive Graph Fusion Dual-scale Convolutional Network (AGFDCN), which integrates spatial-temporal dynamic graphs with dual-scale convolutional networks. Specifically, we introduce a Dual-Scale Temporal Network, which combines long- and short-term dilated causal convolutions with a temporal decay-aware attention mechanism to efficiently capture traffic patterns across multiple temporal scales. Furthermore, we design a Dynamic Adaptive Graph Module, which models complex spatial dependencies in traffic networks through an adaptive graph fusion mechanism and a dual-path attention-gated module. Finally, the temporal and spatial representations are integrated by employing a gated fusion mechanism, enhancing the overall prediction performance. Experimental results obtained based on three highway datasets (i.e., PEMS04, PEMS07 and PEMS08) verify that the proposed model outperforms several state-of-the-art baselines in various evaluation metrics. Compared to the spatial-temporal graph model AGCRN with best performance in the baseline models, the proposed model exhibits significant improvements across all datasets: it achieves reduces of MAE by 42.07% and RMSE by 35.43% on PEMS04; MAE by 28.35% and RMSE by 29.28% on PEMS07; and MAE by 30.52% and RMSE by 30.73% on PEMS08, respectively, validating its effectiveness in modeling complex spatial-temporal traffic data and its robustness in handling sudden traffic changes.

Multimodal Aspect-Based Sentiment Analysis (MABSA) aims to detect sentiment polarity toward specific aspects by leveraging both textual and visual inputs. However, existing models suffer from weak aspect-image alignment, modality imbalance dominated by textual signals, and limited reasoning for implicit or ambiguous sentiments requiring external knowledge. To address these issues, we propose a unified framework named Gated-Linear Aspect-Aware Multimodal Sentiment Network (GLAMSNet). First of all, an input encoding module is employed to construct modality-specific and aspect-aware representations. Subsequently, we introduce an image–aspect correlation matching module to provide hierarchical supervision for visual-textual alignment. Building upon these components, we further design a Gated-Linear Aspect-Aware Fusion (GLAF) module to enhance aspect-aware representation learning by adaptively filtering irrelevant textual information and refining semantic alignment under aspect guidance. Additionally, an External Language Model Knowledge-Guided mechanism is integrated to incorporate sentiment-aware prior knowledge from GPT-4o, enabling robust semantic reasoning especially under noisy or ambiguous inputs. Experimental studies conducted based on Twitter-15 and Twitter-17 datasets demonstrate that the proposed model outperforms most state-of-the-art methods, achieving 79.36% accuracy and 74.72% F1-score, and 74.31% accuracy and 72.01% F1-score, respectively.