Tree species in relatives refer to species belonging to the same genus with high morphological similarity and small botanical differences, making it difficult to perform classification and usually requiring manual identification by experts. To reduce labor costs and achieve accurate species identification, we conducted research on the image classification of tree species in relatives based on deep learning and proposed a dual-branch feature fusion Vision Transformer model. This model is designed with a dual-branch architecture and two effective blocks, a Residual Cross-Attention Transformer Block and a Multi-level Feature Fusion method, to enhance the influence of shallow network features on the final classification and enable the model to capture both overall image information and detailed features. Finally, we conducted ablation studies and comparative experiments to validate the effectiveness of the model, achieving an accuracy of 90% on the tree relatives dataset.

Deriving meaningful mineralization information from raw geospatial datasets is fundamental to the sustainable evaluation and management of mineral resources. As a cornerstone of mineral resource evaluation, identifying geochemical anomalies often faces the significant challenge of sampling bias in practical applications. Strong spatial unevenness often leads to information loss in traditional geostatistical models, where critical anomaly structures may be over-smoothed or obscured. To address this limitation, this study proposes a knowledge-driven adaptive direct sampling (KD-ADS) framework. This approach functions as a geospatial context-aware reconstruction engine. It integrates a multi-factor knowledge-driven weighting system to prioritize regions with high information value and incorporates a dynamic context-aware neighborhood module that adapts to local statistical characteristics. Using 1268 samples from the Jiulian Mountains tungsten metallogenic belt, ablation studies demonstrate the individual contributions of the knowledge-driven weighting and adaptive neighborhood modules to improving reconstruction accuracy and spatial connectivity. Comparative experiments with the traditional direct sampling (DS) algorithm demonstrate that KD-ADS achieves a more accurate reconstruction of geochemical fields and better preserves discrete high-value mineralization anomalies and spatial heterogeneity under sampling-bias conditions. This approach improves the reproducibility of mineralization enrichment patterns and enhances computational efficiency, providing data science-driven support for sustainable mineral exploration and resource allocation.

Recognizing fine-grained giant panda behaviors from long-term infrared camera-trap videos remains challenging due to subtle motion variations, complex natural environments, and the inability of vision-only models to exploit high-level semantic information. To address these challenges, we propose VTS-PANDA, a unified multimodal framework that jointly models temporal visual dynamics and ecology-aware semantic priors. The framework introduces three complementary components: a depthwise temporal convolution module that enhances local inter-frame motion cues at the input stage, an MLLM-guided semantic prior module that generates structured ecological descriptions through constrained prompting and integrates them with visual features via a gated fusion mechanism, and a CLIP-based similarity enhancement module that explicitly aligns visual representations with behavioral text prompts to improve discriminability. Extensive experiments on wild giant panda infrared video datasets demonstrate that the proposed framework effectively captures temporal motion patterns, spatial structures, and high-level ecological semantics, achieving robust and interpretable behavior recognition in challenging real-world environments. This work provides a semantic-guided framework for automated wildlife behavior understanding and offers potential support for intelligent long-term ecological monitoring and precision conservation.