Single-image super-resolution (SISR) typically focuses on restoring various degraded low-resolution (LR) images to a single high-resolution (HR) image. However, during SISR tasks, it is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture features. This challenge can lead to issues such as model collapse, lack of rich details and texture features in the reconstructed HR images, and excessive time consumption for model sampling. To address these problems, this paper proposes a Latent Feature-oriented Diffusion Probability Model (LDDPM). First, we designed a conditional encoder capable of effectively encoding LR images, reducing the solution space for model image reconstruction and thereby improving the quality of the reconstructed images. We then employed a normalized flow and multimodal adversarial training, learning from complex multimodal distributions, to model the denoising distribution. Doing so boosts the generative modeling capabilities within a minimal number of sampling steps. Experimental comparisons of our proposed model with existing SISR methods on mainstream datasets demonstrate that our model reconstructs more realistic HR images and achieves better performance on multiple evaluation metrics, providing a fresh perspective for tackling SISR tasks.

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.

Mangrove forests play a crucial role in coastal ecosystem protection and carbon sequestration processes. However, monitoring remains challenging due to the forests’ complex spatial distribution characteristics. This study addresses three key challenges in mangrove monitoring: limited high-quality datasets, the complex spatial characteristics of mangrove distribution, and technical difficulties in high-resolution image processing. To address these challenges, we present two main contributions. (1) Using multi-source high-resolution satellite imagery from China’s new generation of Earth observation satellites, we constructed the Mangrove Semantic Segmentation Dataset of Beihai, Guangxi (MSSDBG); (2) We propose a novel Multi-scale Fusion Attention Unified Perceptual Network (MFA-UperNet) for precise mangrove segmentation. This network integrates Cascade Pyramid Fusion Modules, a Multi-scale Selective Kernel Attention Module, and an Auxiliary Edge Neck to process the unique characteristics of mangrove remote sensing images, particularly addressing issues of scale variation, complex backgrounds, and boundary accuracy. The experimental results demonstrate that our approach achieved a mean Intersection over Union (mIoU) of 94.54% and a mean Pixel Accuracy (mPA) of 97.14% on the MSSDBG dataset, significantly outperforming existing methods. This study provides valuable tools and methods for monitoring and protecting mangrove ecosystems, contributing to the preservation of these critical coastal environments.

Although some progress has been made in deep learning-based remote sensing image change detection, the complexity of scenes and the diversity of changes in remote sensing images lead to challenges related to background interference. For instance, remote sensing images typically contain numerous background regions, while the actual change regions constitute only a small proportion of the overall image. To address these challenges in remote sensing image change detection, this paper proposes a Dynamic Adaptive Context Attention Network (DACA-Net) based on an exchanging dual encoder–decoder (EDED) architecture. The core innovation of DACA-Net is the development of a novel Dynamic Adaptive Context Attention Module (DACAM), which learns attention weights and automatically adjusts the appropriate scale according to the features present in remote sensing images. By fusing multi-scale contextual features, DACAM effectively captures information regarding changes within these images. In addition, DACA-Net adopts an EDED architectural design, where the conventional convolutional modules in the EDED framework are replaced by DACAM modules. Unlike the original EDED architecture, DACAM modules are embedded after each encoder unit, enabling dynamic recalibration of T1/T2 features and cross-temporal information interaction. This design facilitates the capture of fine-grained change features at multiple scales. This architecture not only facilitates the extraction of discriminative features but also promotes a form of structural symmetry in the processing pipeline, contributing to more balanced and consistent feature representations. To validate the applicability of our proposed method in real-world scenarios, we constructed an Unmanned Aerial Vehicle (UAV) remote sensing dataset named the Guangxi Beihai Coast Nature Reserves (GBCNR). Extensive experiments conducted on three public datasets and our GBCNR dataset demonstrate that the proposed DACA-Net achieves strong performance across various evaluation metrics. For example, it attains an F1 score (F1) of 72.04% and a precision(P) of 66.59% on the GBCNR dataset, representing improvements of 3.94% and 4.72% over state-of-the-art methods such as semantic guidance and spatial localization network (SGSLN) and bi-temporal image Transformer (BIT), respectively. These results verify that the proposed network significantly enhances the ability to detect critical change regions and improves generalization performance.

Small-object semantic segmentation remains challenging in urban driving scenes due to limited pixel occupancy, blurred boundaries, and the constraints imposed by lightweight deployment. To address these issues, this paper presents a lightweight semantic segmentation framework that enhances boundary awareness and contextual representation while maintaining computational efficiency. The proposed method integrates an edge-supervised boundary gating module to emphasize object boundaries, an efficient multi-scale context aggregation strategy to mitigate scale variation, and a lightweight feature enhancement mechanism for effective feature fusion. Edge supervision is introduced as an auxiliary regularization signal and does not require additional manual annotations. Extensive experiments conducted on multiple benchmark datasets, including Cityscapes, CamVid, PASCAL VOC 2012, and IDDA, demonstrate that the proposed framework consistently improves segmentation performance, particularly for small-object categories, while preserving a favorable balance between accuracy and efficiency.