Traffic flow prediction has become an important component of intelligent transportation systems. However, high-precision traffic flow prediction (especially long-term prediction) is still very challenging due to the complex spatial-temporal dependence of urban traffic data. In this paper, a novel Multi-scale Temporal and Enhance Spatial Transformer (MTESformer) model is proposed to capture complex dynamic spatial-temporal dependencies. MTESformer provides a reasonable feature embedding of periodic characteristics of traffic; it can recognize different temporal feature patterns and capture long-term dependencies, and efficiently focuses on two different node-space dependencies (long-range and neighboring nodes dependencies). Specifically, we develop a special multi-scale convolution unit that unites temporal self-attention to capture a wider range of dynamic temporal dependencies from a multi-receptive field and identify different temporal feature patterns. Secondly, we design a novel Enhance Spatial Transformer module, which can better focus on the dynamic spatial dependencies among nodes by fusing their neighborhood information. Experimental results on the public transportation network datasets METR-LA, PEMS-BAY, PEMS04, and PEMS08 data show that our proposed method outperforms most of the baseline models and outperforms the state-of-the-art models in long-term prediction. (The MAE of 60min prediction of our model on METR-LA, PEMS-BAY dataset is 3.37, 1.87, and the MAPE is 9.62%, 4.35%, respectively, and all of them outperform the PDFormer on PEMS04 and PEMS08 dataset).

The information sharing among internet-connected vehicles is close, but there may be the issue of vehicles providing false information. This paper proposes a new incentive mechanism scheme for VANET to provide a decentralized and trusted service management. We introduce a trust-based message evaluation system, incorporating reputation assessment and negative feedback functions to evaluate the credibility of messages and assessments. A reward and punishment mechanism based on repeated games is designed to incentivize vehicles to proactively share information, thereby preventing false information and defamation attacks. Additionally, a blockchain-based distributed trust management scheme is designed to ensure the consistency of trust management storage through the consensus mechanism of the blockchain, reducing reliance on centralized institutions. Simulation results demonstrate that the establishment of reputation and incentive mechanisms among vehicles enhances the enthusiasm for information sharing and improves the accuracy of identifying false messages.

With the rapid development of communication technologies, the demand for security and automation of driving has promoted the development of the Internet of Vehicles (IoV). The IoV aims to provide users with a safer, more comfortable, and more efficient driving experience. However, the current IoV also faces a series of potential security risks and privacy breaches, which has further propelled research on trust management for vehicular networks. The introduction of the blockchain has resolved the issue of data security in IoV trust management. However, the blockchain is limited by its own performance and scalability, making it unsuitable for large-scale networks. In order to enhance the transaction-processing efficiency of blockchain-based trust management solutions and address their scalability limitations, this paper presents a graph partition-based blockchain-sharding protocol. Simulation results on real-world datasets demonstrate that the proposed scheme exhibits better scalability compared to existing blockchain-based approaches and can accommodate larger-scale device access.

Semi-supervised learning (SSL) is critical for medical image segmentation but often struggles with network dependency and pseudo-label error accumulation. To address these issues, we propose a fractal-dimension-guided hierarchical contrastive learning dual-student framework(FD-HCL). We extend the Mean Teacher architecture with a dual-student design and introduce an independence-aware exponential moving average (I-EMA) update mechanism to mitigate model coupling. For enhanced feature learning, we devise a hierarchical contrastive learning (HCL) mechanism guided by voxel uncertainty, spanning global, high-confidence, and low-confidence regions. We further improve structural integrity by incorporating a fractal-dimension (FD)-weighted consistency loss and integrating a novel uncertainty-aware bidirectional copy–paste (UB-CP) augmentation. Extensive experiments on the LA and BraTS 2019 datasets demonstrate the state-of-the-art performance of our framework across 10% and 20% labeled data settings. On the LA dataset with 10% labeled data, our method achieved a Dice score that outperformed the best existing approach by 0.68%. Similarly, under the 10% labeling setting on the BraTS 2019 dataset, we surpassed the state-of-the-art Dice score by 0.55%.