This study proposes an improved attention-guided convolutional bidirectional long short-term memory (IAC-BiLSTM) model, which integrates a modified convolutional block attention module (CBAM) with a convolutional neural network (CNN) and BiLSTM architectures. Meanwhile, this model is applied to the behavior recognition of building areas. For seven categories of “transferable behaviors” carefully selected from the University of Central Florida 101 (UCF101) and Human Motion Database 51(HMDB51) datasets, these behaviors have direct correspondences with typical monitoring targets in buildings. This enables effective mapping between public datasets and specific application scenarios. In summary, this study verifies the superior performance and broad application potential of the IAC-BiLSTM model in building behavior recognition. Hence, the study provides a solid theoretical and practical foundation for optimizing intelligent building management systems.
As the green economy continuously integrates into the digital financial system, the structure of credit risk in consumer finance has changed accordingly. Traditional models gradually exhibit limitations in characterizing complex feature interactions and nonlinear relationships. To tackle this problem, this study establishes an attention-enhanced multi-head self-attention deep factorization machine. The attention mechanism is introduced into low-order feature interactions, and the multi-head self-attention mechanism is fused with deep neural networks in high-order interaction parts to achieve multi-level joint feature modeling. Experiments are carried out using the Lending Club dataset. This study verifies that the proposed method has strong adaptability and stability in consumer finance credit risk identification under the background of digital economy.
This study applies a two-stage data envelopment analysis (DEA)-Tobit framework to evaluate logistics efficiency and its determinants in 12 provinces of Western China from 2014 to 2023. Logistics efficiency is assessed using DEA, which yields measures of comprehensive, pure technical, and scale efficiency. Subsequently, a Tobit regression model examines the causal mechanisms underlying efficiency variations. The results reveal a general upward trend in regional logistics efficiency, alongside persistent inter-provincial disparities. Infrastructure quality, industrial structure upgrading, information technology development, and the scale of specialized logistics personnel exhibit significant positive effects. Based on these findings, policy recommendations are proposed, focusing on transport network optimization, industrial transformation, logistics informatization, and talent cultivation to support high-quality logistics development in the region.
Handwritten mathematical expression recognition aims to recover symbol sequences and hierarchical relations from two-dimensional layouts. Existing methods often rely on linear sequence modeling or local structural composition, making it difficult to jointly capture the main chain and nested dependencies, leading to structural entanglement and unstable parsing. To address this issue, this paper proposes a structural re-indexing and nested decoding network. A structural re-indexing module separates the main chain and hierarchical relations on shared features and uses mutual constraints to model their dependency. An offset field rearranges features into a disentangled representation, reducing relational ambiguity. Based on this, a nested decoder progressively constructs expressions. The main chain provides the global framework, and local structures are expanded at key positions according to relation predictions. Experiments show that the structural re-indexing and nested decoding network achieves higher accuracy, robustness, and structural consistency, especially for complex expressions and long sequences.
This study explored whether digital trace data could effectively predict the process of low-carbon urbanization and promote data-driven decisions for sustainable urban development. The study integrated 12 types of digital traces, including mobile phone signaling, point of interest check-ins, traffic checkpoint records, shared bicycle trajectories, nighttime light remote sensing, and enterprise electricity consumption data. Meanwhile, it established a multidimensional indicator system based on population mobility, energy intensity, and spatial interaction. A hybrid model combining a spatiotemporal graph convolutional network and long short-term memory was proposed. The study demonstrated that digital traces could reveal carbon emission drivers that traditional methods failed to capture. Unstructured data, such as point of interest check-ins in social platforms and food delivery heatmaps, could serve as leading indicators for low-carbon governance. Therefore, this study opened new pathways for global urban low-carbon transitions based on digital footprints.
