Socially regulated learning (SoRL) is an important way to maintain the sustainable development of collaborative learning (CL). Usually, learners can achieve sustainable and high-quality SoRL with the intervention of teachers. To improve the appropriateness of the intervention, teachers need to clarify the profiles of SoRL to which learners belong, as well as the influence of collaborative learning motivation (CLM) and the relevant background variables. This study used three non-duplicate samples to provide evidence for the psychometric properties of the SoRL and CLM scales through item analysis, exploratory factor analysis (sample 1, n = 531), and confirmatory factor analysis (sample 2, n = 1278). The profiles of SoRL among university students were determined through latent profile analysis (sample 3, n = 909). This study identified three profiles of regulation (strong SoRL, progressive SoRL, and weak SoRL). The analysis of multivariate variance and multiple logistic regression methods further explored the differences in the dimensions of SoRL structures across different profiles and the extent to which CLM and background variables predicted profiles. The results showed that collaborative motivation (CM) and learning motivation (LM) were the predictors of learners’ transformation from a low regulation level to a medium regulation level. CM, LM, altruistic motivation, and major background were the predictors of learners’ transition from the medium regulation level to the high regulation level. Accordingly, teachers can provide learners with an appropriate external intervention to promote the improvement of SoRL. This study contributes to improving learners’ SoRL levels and promoting the sustainable development of education. In the future, the changing characteristics of learners’ SoRL profiles over time will be explored, and the application of learning process data will be strengthened.

Social network is the mainstream medium of current information dissemination, and it is particularly important to accurately predict its propagation law. In this paper, we introduce a social network propagation model integrating multiple linear regression and infectious disease model. Firstly, we proposed the features that affect social network communication from three dimensions. Then, we predicted the node influence via multiple linear regression. Lastly, we used the node influence as the state transition of the infectious disease model to predict the trend of information dissemination in social networks. The experimental results on a real social network dataset showed that the prediction results of the model are consistent with the actual information dissemination trends.

Traditional image processing methods or machine learning approaches for addressing crack detection problems typically cater to specific scenarios. As the scene changes, the detection accuracy of such methods is significantly affected, lacking robustness across multiple application scenarios. To adapt to diverse application contexts, improvements have been made upon the original crack detection method, CrackFormer, leading to the proposal of a detection approach based on the Gaussian Scale Mixture (GSM) model, known as GSM-CrackFormer. Firstly, a Gaussian Scale Mixture model is constructed to describe Gaussian distributions related to crack features. Subsequently, a signal transformer is designed, incorporating a gating mechanism to convert crack feature information generated by the distributions into guiding signals for enhancing semantic features. Additionally, a novel up-down sampling strategy is introduced to further balance the relationship between model receptive fields and its capability to capture detailed features. By adjusting the loss function, the imbalance issue between crack and non-crack pixels is effectively mitigated. Finally, we conduct experiments on the CrackSeg9k dataset to evaluate the performance of our proposed method. Our experimental results demonstrate that GSM-CrackFormer outperforms the methods compared in the paper, achieving a global best (ODS) of 0.784, a monograph best (OIS) of 0.785, and a mean intersection ratio (MIoU) of 0.828.

With the rapid advancement of network technology and the Internet of Things (IoT), massive, high-dimensional traffic data pose significant challenges to Network Intrusion Detection Systems (NIDS). Existing deep learning methods face two major limitations: (1) Insufficient feature extraction: single models struggle to concurrently capture local and global features, while serial hybrid architectures are prone to feature information loss. (2) Superficial feature fusion: existing strategies overlook deep interactions between features and lack adaptive filtering mechanisms to eliminate redundant information. To address these issues, a Parallel Local–Global Feature Extraction (PLGFE) module is constructed, incorporating a dual-stream architecture with parallel Temporal Convolutional Network (TCN) and Bidirectional Long Short-Term Memory (BiLSTM) branches. This module utilizes TCN’s dilated convolutions and BiLSTM’s bidirectional memory units to concurrently extract complementary local and global features. The Cross-Attention Gating (CAG) module integrates cross-attention and gating mechanisms for feature fusion: the former enables deep interaction between heterogeneous features, while the latter adaptively selects key features and suppresses redundant interaction features through gated weights. Experiments on the CICIDS2017 and CICIoV2024 datasets demonstrate the proposed method achieves multi-classification accuracies of 99.85% and 99.64%, with F1-Scores of 97.03% and 97.64%, respectively. Comparisons and ablation experiments confirm its competitive detection performance and component effectiveness.