The fusion classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) data has gained widespread attention because of its ability to obtain more comprehensive spatial and spectral information. However, the heterogeneous gap between HSI and LiDAR data also adversely affects the classification performance. Despite the excellent performance of traditional multimodal fusion classification models, language information containing much linguistic priori knowledge to enrich visual representations needs to be addressed. Therefore, we design a Spectral-Spatial-Language fusion network (S ² LFNet), which can fuse visual and language features to broaden the semantic space using linguistic priori knowledge commonly shared between spectral features and spatial features. First, we propose a dual-channel cascaded image fusion encoder (DCIFencoder) for visual feature extraction and progressive feature fusion of different levels for HSI and LiDAR data. Then, three aspects of Text data are designed to extract linguistic priori knowledge using the Text encoder. Finally, contrastive learning is utilized to construct a unified semantic space, and Spectral-Spatial-Language fusion features are obtained for classification tasks. We evaluate the classification performance of the proposed S ² LFNet on three datasets through extensive experiments, and the results show that it outperforms the state-of-the-art fusion classification methods.

To address the issues of low solid–liquid mixing and mass transfer efficiency and difficult real-time regulation in the resource utilization of non-ferrous metal smelting slag, this study constructs a research framework integrating Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) coupling models and machine learning. The framework systematically investigates particle motion characteristics and mass transfer laws in stirred tanks and enables an intelligent prediction of key parameters. Through a CFD-DEM two-way coupling simulation, the study quantifies particle dispersion characteristics using relative standard deviation (RSD) and calculates the mass transfer coefficient (k) based on the Hughmark model, revealing the effects of particle size and impeller speed on mixing and mass transfer efficiency. For parameter prediction, particle motion and mass transfer data are used to train a multi-model prediction library, with model performance evaluated through comparative experiments. The results show that increasing the rotational speed shortens the particle mixing time, reduces RSD values by 25–40%, increases the coupling force, and decreases stability during the circulation phase. Different machine learning (ML) algorithms exhibit varying performances in the time-series prediction of particle motion characteristics and real-time prediction of mass transfer coefficients. Notably, GA-BP achieves a fitting degree R of 0.99 in both predictions, meeting the requirements for the structural optimization and intelligent regulation of stirred tanks. This research provides theoretical support and technical pathways for the structural optimization and intelligent control of stirred tanks, offering engineering application value in fields such as hydrometallurgy and solid waste resource utilization.

Large vision and language models deployed at edge encounter continuously evolving input distributions, including not only new tasks but also highly unbalanced long-tail classes. For example, smart-surveillance cameras frequently capture common objects such as pedestrians and cars, while only occasionally observing tail classes like horseback riders or stroller pushers. However, most existing long-tail mitigation techniques are designed for fixed pretraining data. This often leads to poor accuracy of tail classes on evolving data and incurs high computational costs on edge devices. In this article, we propose EdgeTail, a lightweight long-tail mitigation method for edge-side continual learning. EdgeTail’s key design features are: (i) optimal long-tailed mitigation solution search, which adaptively selects the best long-tail learning method for the current distribution/task; and (ii) graph attention classifier and multi-branch adapter, which improves the quality and stability of tail class representations with small overheads. We implement EdgeTail in PyTorch and extensively evaluate it against state-of-the-art methods. The results show that EdgeTail improves the average accuracy by 36.09% under fixed training windows and by 31.12% under different training window sizes.