With 6G-enabled Intelligent Internet of Vehicles (IIoV) generating massive amounts of sensory data, traditional deep learning models struggle to capture long-range relationships across different sensor types while preserving privacy. This paper proposes DT-Trans, a privacy-preserving federated learning framework that combines Digital Twin technology with Vision Transformers. Our framework first trains a global perception model on synthetic digital twin data, then fine-tunes it efficiently for real-world vehicles. By grouping vehicles with similar driving patterns and allowing them to collaboratively train personalized model components, DT-Trans achieves significant accuracy improvements while maintaining data privacy. The Twin-Enhanced Vision Transformer (TE-ViT) is introduced as the global perception backbone; it is pre-trained on massive synthetic DT data and then fine-tuned via parameter-efficient LoRA adapters to bridge the domain gap between virtual and physical worlds. The Cluster-Enhanced Decoupled PFL (CD-PFL-Trans) algorithm splits each TE-ViT into (i) a shared Transformer encoder (base layer) and (ii) client-specific Transformer decoder heads (personalized layer). Hierarchical clustering on decoder parameters groups clients with similar traffic patterns, enabling group-wise aggregation without exchanging raw sensory data. DT-Trans outperforms CNN-based FedAvg/FedPer by 9.3%-16.2% mAP on V&PKITTI perception tasks and up to 42.8% accuracy improvement on CINIC-10 classification under severe heterogeneity, while reducing on-device FLOPs by 34 % via Transformer sparsity techniques. Our work advances Transformer architectures for scalable, privacy-preserving perception in IIoV.

The proliferation of IoT devices has limited the efficiency of heterogeneous data communication in distributed environments and increased security risks. Balancing scalability, efficiency and data privacy in IoT transaction systems becomes critical, and intent-based networks enable optimal configuration with minimal intervention. To optimize the network management environment, we propose a three-stage execution scheme for blockchain cross-shard transactions, which combined with a timeout rollback mechanism ensures atomicity and reduces latency. In addition, we design a fragment-based consensus protocol utilizing a verifiable random function, which improves the consensus efficiency through the randomness of committee member selection. In order to enhance system security, we introduce a reputation evaluation mechanism and a malicious node detection method based on normalized entropy. The mechanism dynamically adjusts the reputation value of a node according to its performance in the consensus process, so that high-reputation nodes can play a greater role in the consensus and detect malicious nodes in the network accordingly. By embedding this mechanism into a network management framework based on users’ intention, it can accurately realize users’ expectations for network performance optimization, security enhancement and efficient operation. Experiments show that our scheme not only improves communication efficiency, but also enhances the security of sharded transactions, effectively matching users’ high-level intentions for network scalability, efficiency, and data privacy.

Abstract At present, the mainstream mode of machine learning algorithms is the data‐driven method, which mainly relies on the self‐learning ability of deep neural networks and continuously evolving models in data‐driven training. However, the pure data‐driven method has some critical problems, such as high data collection cost, poor interpretability and easy to be be disturbed by noise. Although the knowledge‐driven method has high stability, it lacks self‐learning and evolution ability in the face of comprehensive and complex problems. In recent years, the convergence of data and domain knowledge has combined the advantages of both learning paradigms. One typical way is to embed domain knowledge into the data‐driven model to improve the interpretability of the model, and then use the self‐learning ability of the data‐driven model to explore knowledge, and continuously iterate the domain knowledge to form a closed loop. The data‐knowledge dual‐driven methods have brought transformative innovations in machine learning. This review first introduced the advantages and necessity of the data‐knowledge dual‐driven model in the field of artificial intelligence. Then, the applications of the data‐knowledge dual‐driven model in the smart marine field were introduced. Finally, the challenges and trends of the data‐knowledge dual‐driven artificial intelligence are anticipated.

ABSTRACT Driven by the dual pressures of rapid global population growth and escalating climate change, there is a growing demand for real‐time monitoring of crop nitrogen levels to support precision agriculture. This necessity has catalysed the integration of crop modelling techniques with remote sensing technologies. Addressing challenges such as multi‐source remote sensing data heterogeneity and limited generalisation in nitrogen inversion models for cotton canopies, this paper designs a novel inversion framework based on the assimilation of diverse remote sensing sources and mechanistic crop models. Firstly, this paper employed spectral resampling techniques, fuzzy logic for uncertainty quantification, and Pearson correlation analysis to harmonise differences in spectral characteristics and spatial resolution between Sentinel‐2A and Landsat 8 imagery, ultimately identifying eight nitrogen‐sensitive features. Subsequently, a multi‐scale feature enhancement module was developed to improve representational richness. Additionally, the paper employed a satellite image fusion module, which effectively reduced data heterogeneity errors by 12.7% across sources. Building on this, a hybrid GNN‐EnKF model was proposed. GNN was used to establish spatial neighbourhood dependencies, while EnKF dynamically adjusted the parameters within the WOFOST crop model. This approach successfully fuses data‐driven learning with physically based modelling. Experimental evaluations revealed that the proposed architecture attained a mAP of 95.83%, outperforming baseline models such as ResNet18 (83.92%) and Transformer (92.84%), demonstrating robust adaptability in complex agricultural settings. In conclusion, the framework presented in this paper offers a high‐accuracy nitrogen monitoring solution tailored for precision farming, and provides strong data support for cotton nitrogen deficiency and additional fertilisation.

As a key economic crop, jujube’s external morphology directly affects quality grading and market value. However, traditional inspection methods relying on manual sampling or 2D image analysis suffer from inefficiency and limited feature characterization, particularly in quantifying complex geometric traits such as irregular wrinkles and localized depressions on jujube surfaces. Existing 3D reconstruction techniques have been applied in agricultural product inspection but face challenges in widespread adoption due to high costs and low resolution. This study proposes a single-view high-resolution RGB 3D reconstruction method for jujube based on generative artificial intelligence. Specifically, we designed a two-stage single-view 3D reconstruction framework and modified the cross-attention layers in the U-shaped network architecture of a stable video diffusion mode to meet the requirements of high-resolution and clear texture reconstruction for jujube. Additionally, we improved training and inference efficiency through parallel computing and achieved automation integration with Unmanned Agents. The proposed method successfully reconstructed 3D models from single-view 1,024 1,024 RGB images of jujube. Our model achieves a PSNR of 23.52 and an SSIM of 0.86 on the public dataset.This approach provides a low-cost, high-precision 3D digital solution for non-destructive phenotyping of jujube, offering practical value for advancing intelligent sorting and quality evaluation in agricultural production.

With the deepening integration of the Internet of Things (IoT) and Artificial Intelligence (AI), intelligent warehousing systems are increasingly confronted with the challenges of achieving high-precision navigation and task scheduling in dynamic environments. Although existing research has made notable strides in simultaneous localization and mapping (SLAM) and task scheduling, persistent issues–such as point cloud noise interference in dynamic scenes, inefficiencies in computational resource allocation, and insufficient multi-sensor collaboration–continue to constrain system performance. To address these challenges, this study proposes an AI-driven task scheduling SLAM framework named KORS designed to enhance navigational robustness and scheduling efficiency in dynamic warehousing environments. This study proposes the KCPoint model to achieve precise segmentation and elimination of dynamic point clouds. Building upon the PointNet++ architecture, KCPoint integrates K-Nearest Neighbors Enhanced Farthest Point Sampling (KFPS) and a Convolutional Block Attention Module (CBAM) to enhance feature extraction. In addition, a task scheduling mechanism is introduced to address the dynamic allocation of computational resources within vehicular networks. Relative to FAST-LIO2, the KORS system reduces absolute pose error (APE) by 31.32% and improves computational efficiency by 17.88% on the NCD and NCLT datasets. Furthermore, compared to conventional methods based on particle swarm optimization and genetic algorithms, the task scheduling algorithm achieves comparable decision-making benefits while reducing single-decision latency by over 42

This research proposes a solution integrating AIGC technology with optimized object detection networks to address challenges in brain tumor identification under the context of Medical Industry 5.0. First, to mitigate data scarcity, DCGAN is employed to augment the Br35H dataset by generating high-quality synthetic samples, enhancing model generalization. Second, a hierarchical feature-enhanced EfficientDet (EfficientDet-HFE) model is developed by combining FPN and EfficientDet architectures, fusing high-level semantic information with low-level spatial details to optimize feature transmission pathways. Additionally, the SimAM is introduced, integrated with a global-local feature optimization strategy to construct a recursive attention module. As BiFPN iterates progressively, the capacity for key region feature expression and the efficiency of multi-scale feature extraction are significantly enhanced. In response to the computational resource constraints of medical devices, LAMP techniques are applied to compress the network structure. With the assistance of fine-tuning strategies, the model parameters are reduced to 32.20 MB while preserving detection performance. Experimental results demonstrate that this method achieves 92.25% recall, 93.36% precision, 92.80% F1 Score, and 94.99% mAP on the augmented dataset, validating its efficacy in tumor detection. This research offers a lightweight, IoT-compatible solution for brain tumor detection, promoting the integration of AI-driven diagnostics into Healthcare Industry 5.0 ecosystems.

With the continuous development of artificial intelligence, more and more human–computer interaction (HCI) applications have begun to appear in the field of education. This article investigates the relationship between the HCI and cognitive load and academic emotion by comparing the effect of classroom discussion in the smart classroom with that of traditional classroom discussion and classroom discussion that focuses too much on learning interest. By using classroom test questionnaire and interview questionnaire, the experiment counted the test scores and questionnaire satisfaction of participants in different classroom discussions, and obtained the learning effect and the emotional state and satisfaction degree of learners under different classroom discussions. It is concluded that the artificial intelligence in the HCI of smart classroom should always take the amount of cognitive load of learners as the core, improve the academic emotion of learners as the auxiliary, reduce the cognitive load of learners as much as possible, and improve the academic emotion of learners within a reasonable range, so as to improve the learning effect. It also provides methods and suggestions for how to reduce learners’ cognitive load and how to reasonably improve their academic emotions in smart classroom.

In recent years, the combination of hyperspectral imagery and deep learning has been widely used in agricultural Artificial Intelligence of Things (AIoT), such as agricultural product quality assessment and crop disease detection. However, this often comes at the cost of substantial computational power and energy consumption. In this paper, we focused on data-efficient green computing for low-carbon jujube moisture content detection. First, in order to compress the hyperspectral images capacity, a spectral selection algorithm based on swarm intelligence was proposed to screen necessary and sensitive spectral dimensions. Then, a spectral reconstruction model was established to realize the selected hyperspectral bands reconstruction from RGB image, aiming to reduce the high cost of hyperspectral imaging. Finally, a model based on the fusion of spectral data and reconstructed image was constructed to realize efficient jujube moisture content detection. The experimental results show that the 10 feature bands screened by the proposed selection method can adequately characterize the water information of jujube, and the proposed reconstruction model outperforms other works with the MRAE of 0.1635. The carbon emissions of our proposed reconstruction model are significantly lower than other methods. Further, the spectral-image fusion model achieves satisfactory detection result of jujube moisture content, with the RMSE of 0.0082. In summary, the proposed spectral selection, reconstruction, and detection methods can achieve high precision while reducing carbon emissions, which have important guidance for low-carbon and sustainable agricultural AIoT applications.