Jun Wang

Forest foods play a vital role in enhancing dietary diversity, human health, and the sustainable use of forest ecosystems. However, structured and machine-readable resources that systematically describe their taxonomic and nutritional attributes remain scarce. To fill this gap, we introduce ForestFoodKG, a comprehensive resource that integrates taxonomic hierarchy and nutritional composition of 1191 forest food items. The resource consists of two components—(i) the ForestFoodKG dataset, containing standardized taxonomic and nutritional records across seven biological levels, and (ii) the ForestFoodKG Knowledge Graph (ForestFoodKG-KG), which semantically links forest food entities using named entity recognition and relation extraction. The constructed graph comprises 4492 entities and 14,130 semantic relations, providing a structured foundation for intelligent querying, nutrition analytics, and ecological informatics. All data were manually verified and made publicly available in CSV format on GitHub. ForestFoodKG serves as the first structured knowledge base for forest foods, promoting data-driven research in nutrition science, sustainable forestry, and knowledge-based decision-making.

Forest change detection plays a vital role in remote sensing research, serving as a cornerstone for ecological protection and sustainable environmental management. While existing research on change detection primarily focuses on urban areas and croplands, forest change detection remains underdeveloped. As the demand for real-time monitoring of forest changes grows in response to challenges like climate change and environmental degradation, advancements in remote sensing technologies make it possible to bridge this research gap. The complexity of forest change detection stems from high seasonal and inter-annual variability, with fluctuations often showing strong similarities across years. In this paper, we introduce FCDNet:A Multi-Scale Attention Networkfor Forest Change DetectionUsing Dual-Temporal Very-High-Resolution Remote Sensing Images. The proposed network is designed to enhance the precision and robustness of change detection through the integration of multiscale feature aggregation and an adaptive channel attention strategy. This design enables FCDNet to sensitively identify fine-grained forest alterations while maintaining strong adaptability to spatial and temporal variability in the input imagery. Comprehensive experiments confirm that FCDNet delivers superior performance compared with existing state-of-the-art approaches, showing notable advantages under challenging scenarios characterized by seasonal transitions and inter-annual fluctuations. FCDNet achieves impressive F1 scores of 78.51%, 90.48%, and 92.57% on the Forest Change Detection (FCD), LEVIR-CD, and WHU-CD datasets, respectively, delivering state-of-the-art results. This approach holds significant potential for advancing forest monitoring, providing a reliable tool for researchers and policymakers to better understand and protect forest ecosystems.

Traditional clustering algorithms often struggle with high-dimensional and non-uniformly distributed data, where low-density boundary samples are easily disturbed by neighboring clusters, leading to unstable and distorted clustering results. To address this issue, we propose a Group-driven Clustering via Gravitational Attraction and Optimization (GCAO) algorithm. GCAO introduces a group-level optimization mechanism that aggregates low-density boundary points into collaboratively moving groups, replacing the traditional point-based contraction process. By combining local density estimation with neighborhood topology, GCAO constructs effective gravitational interactions between groups and their surroundings, enhancing boundary clarity and structural consistency. Using groups as basic motion units, a gravitational contraction strategy ensures globally stable and directionally consistent convergence. Experiments on multiple high-dimensional datasets demonstrate that GCAO outperforms 11 representative clustering methods, achieving average improvements of 37.13%, 52.08%, 44.98%, and 38.81% in NMI, ARI, Homogeneity, and ACC, respectively, while maintaining competitive efficiency and scalability. These results highlight GCAO's superiority in preserving cluster integrity, enhancing boundary separability, and ensuring robust performance on complex data distributions.