With the rapid advancement of the intelligent Internet of Vehicles (IoV), accurate traffic sign classification is essential to ensure driving safety and improve environmental perception. However, conventional image classification models often rely on local features and spatial domain processing, lacking global context modeling and facing computational limitations. To address these challenges, this paper proposes a quantum discrete Fourier transform-based hierarchical self-attention Vision Transformer (QHSA-ViT). Using the parallelism and high-dimensional feature extraction capabilities of quantum computing, the proposed model enhances the quality and efficiency of representation. Specifically, a quantum frequency domain feature representation (QFDFR) module based on a quantum discrete Fourier transform (QDFT) is introduced to capture rich spectral features, while a quantum self-attention fusion (QSAF) module built on a linear combination of unitaries (LCU) and generalized quantum singular value transformation (GQSVT) integrates multilevel attention. The experimental results on five benchmark datasets, including GTSRB, show that QHSA-ViT outperforms baseline models with an average improvement of 9.01% in accuracy and 8.48% in the F1 score. These results validate the effectiveness of the proposed model and highlight its practical applicability and scalability for understanding traffic scenes in intelligent IoV.

The convergence of federated learning (FL) and blockchain in edge-end-cloud systems offers promising opportunities for privacy-preserving collaborative intelligence. However, existing blockchain-enhanced FL (BFL) approaches remain vulnerable to malicious participants and lack robust protection for model updates. To address these issues, we propose SEPP-FLBC, a Secure and Efficient Privacy Protection framework based on Federated Learning and Blockchain Committees. SEPP-FLBC introduces a novel blockchain committee consensus mechanism to validate model updates and defend against unreliable nodes. It further employs a refined multi-party communication paradigm to facilitate indirect and secure data interactions, reducing the risk of information leakage. Additionally, differential privacy noise is applied to model updates to enhance resistance to inference attacks. A formal convergence analysis is conducted to ensure model stability and minimize overhead. Extensive experiments on benchmark datasets demonstrate that SEPP-FLBC achieves superior accuracy while maintaining strong privacy guarantees and communication efficiency, outperforming state-of-the-art BFL methods in both security and performance.

The classical CAP theorem reveals fundamental limitations in distributed system design, namely the impossibility of simultaneously achieving strong Consistency, high Availability, and Partition tolerance. As an important distributed ledger technology, blockchain systems also face these constraints. Recent research has attempted to alleviate this problem through consensus layer or physical layer optimization techniques. However, these methods fail to achieve optimal availability due to the mismatch problem between network layer and physical layer topologies. Therefore, this paper designs a P2P broadcasting protocol that matches physical topology structures at the network layer, namely Matching-Gossip, serving as an intelligent adapter between physical topology and consensus protocols to collectively address the CAP trilemma. Experimental results demonstrate that blockchain systems based on Matching-Gossip achieve trilemma efficiency coefficients exceeding 95%, simultaneously meeting engineering requirements for strong consistency, high availability, and partition tolerance, thereby breaking through traditional CAP limitations on blockchain system design.

Federated learning has emerged as a promising distributed learning paradigm that enables model training across decentralized devices while preserving data privacy. However, two critical challenges hinder its practical deployment: model performance degradation due to client drift in high data heterogeneity scenarios and communication gap due to varying client communication capabilities. In this paper, we provide a comprehensive analysis of these challenges and propose Flexible Sparse Personalized Federated Learning (FSPFL), a novel framework that jointly optimizes model personalization and communication efficiency. FSPFL adaptively allocates local model sparsity while incorporating personalization mechanisms to trade off model performance and communication efficiency. Extensive experiments demonstrate that FSPFL significantly mitigates the communication gap, and outperforms existing methods. Our results show that FSPFL improves communication efficiency by up to$4.1 \times$than the baselines while maintaining similar model accuracy in heterogeneous scenarios where clients have diverse data distributions and communication capabilities.

The development of supply chain systems demands optimization of various technologies in terms of efficiency and resource conservation. As an emerging technology, cross-chain technology in blockchain aims to achieve interoperability and resource sharing between different blockchain networks, enhancing data liquidity and system efficiency. However, relay chains in cross-chain interactions require storing a large number of transaction records, leading to excessive communication and storage loads, which can cause network performance degradation, storage resource exhaustion, and low transaction processing efficiency. To address these issues, this paper proposes a supply chain system-driven efficient quantum blockchain cross-chain transaction scheme (SCS-QBCT). Firstly, SCS-QBCT uses the quantum Fourier transform (QFT) to convert transaction records on relay chains from the time domain to the frequency domain, reducing data redundancy and significantly lowering storage space consumption. Secondly, a multifunctional smart contract, designed to include conventional functions, enables value transfer, transaction withdrawal, transaction query, node identity management, and transaction type identification. Furthermore, inverse quantum Fourier transform (IQFT) is used to restore quantum state transaction records in blocks to classical records, supporting transaction query requests. Finally, the experimental results and theoretical analysis show that SCS-QBCT performs excellently in reducing storage consumption, improving system efficiency, practicality, and security, and meeting the optimization goals of supply chain systems.

With the popularization of digital information, reversible data hiding in ciphertext has become a critical research focus in privacy protection in cloud storage. A reversible data hiding method for encrypted images is proposed: Reversible Data Hiding in Encrypted Images with Adaptive Multi-directional MED and Huffman Code based on Interval-Wise Dynamic Prediction Axes (RDHEI-AHIDA). Firstly, the original image is predicted by the gradient Adaptive Multi-Directional Median Edge Detector (AM-MED) to obtain the critical gradient and the position of the Interval-wise Dynamic Prediction Axes (IDP-Axes). Then, information bits are allocated at intervals on the IDP-Axes. Combining the determined position of the IDP-Axes and the critical gradient, the prediction error values of the original image are calculated and recorded. After the image is encrypted, according to the distribution of prediction error values, an adaptive Huffman code rule is established, and pixel marking, classification and auxiliary information embedding are carried out. Finally, the secret data is embedded by the bit replacement method. Compared with the state-of-the-art RDHEI methods, experimental results show that RDHEI-AHIDA not only provides a higher pure payload while ensuring security but also exhibits certain robustness.

Anticancer peptides (ACPs) are the types of peptides that have been demonstrated to have anticancer activities. Using ACPs to prevent cancer could be a viable alternative to conventional cancer treatments because they are safer and display higher selectivity. Due to ACP identification being highly lab-limited, expensive and lengthy, a computational method is proposed to predict ACPs from sequence information in this study. The process includes the input of the peptide sequences, feature extraction in terms of ordinal encoding with positional information and handcrafted features, and finally feature selection. The whole model comprises of two modules, including deep learning and machine learning algorithms. The deep learning module contained two channels: bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN). Light Gradient Boosting Machine (LightGBM) was used in the machine learning module. Finally, this study voted the three models' classification results for the three paths resulting in the model ensemble layer. This study provides insights into ACP prediction utilizing a novel method and presented a promising performance. It used a benchmark dataset for further exploration and improvement compared with previous studies. Our final model has an accuracy of 0.7895, sensitivity of 0.8153 and specificity of 0.7676, and it was increased by at least 2% compared with the state-of-the-art studies in all metrics. Hence, this paper presents a novel method that can potentially predict ACPs more effectively and efficiently. The work and source codes are made available to the community of researchers and developers at https://github.com/khanhlee/acp-ope/.