Multi-dimensional encrypted data aggregation enables smart meters to transmit multi-source local electricity consumption data by encrypting and packaging data, ensuing users’ privacy and flexible power resource scheduling. This paper presents a lightweight fault tolerant multi-dimensional encrypted data aggregation scheme in fog computing-assisted smart grids, called FT-MEDA. To ensure transmitted electricity consumption data confidentiality, smart meters package multi-dimensional data by utilizing Chinese Remainder Theorem, and encrypt them under the modified symmetric homomorphic encryption. In addition, a key negotiation mechanism is devised to avoid the dependency on third-party trusted parties for important parameters allocation. Even if some smart meters within the negotiation group are compromised, the privacy of normal smart meters retains protected. In FT-MEDA, fog nodes (FN) deployed in smart grids, execute edge computing on integrity verification and generating aggregated encrypted data reports which will be sent to the control center (CC) for data anslysis. Security analysis indicates that FT-MEDA not only satisfies basic security requirements (e.g., confidentiality, integrity), but also resists collusion attacks and key leakage. Performance evaluation demonstrates that FT-MEDA has lightweight performance advantages in practical deployment of smart grids.

Accurate classification of Electrocardiogram (ECG) signals is critical for arrhythmia diagnosis, yet remains challenging due to complex morphological variations and noise. To address this, the paper proposes a novel Associative Learning and Non-causal Caputo-enhanced Manta Ray Foraging Optimization (ANCMRFO) algorithm that significantly improves ECG classification performance. The key innovations include an associative learning strategy that enhances feature representation by modeling interdependencies between ECG waveform segments during optimization, and a non-causal fractional-order derivative mechanism that effectively captures long-range dependencies in ECG temporal patterns. The algorithm's superior convergence properties are rigorously validated on CEC2022 benchmark functions. Applied to optimize Temporal Convolutional Network (TCN) architectures for ECG classification, ANCMRFO achieves 97.3% balanced accuracy on the MIT-BIH arrhythmia dataset for five-class classification. The optimized model demonstrates particular strength in distinguishing subtle arrhythmia, while maintaining robust performance under noisy conditions. These results establish ANCMRFO as an effective solution for intelligent ECG analysis in clinical applications.

Frequently corresponding to syntactic components, the Maximal-length Noun Phrase (MNP) possesses abundant syntactic and semantic information and acts a certain semantic role in sentences. Recognition of MNP plays an important role in Natural Language Processing and lays the foundation for analyzing and understanding sentence structure and semantics. By comparing the essence of different MNPs, this article defines the MNP in the Tibetan language from the perspective of syntax tree. A total of 6,038 sentences are extracted from the syntax tree corpus, the structure type, boundary feature, and frequency of MNPs are analyzed, and the MNPs are recognized by applying the sequence tagging model and the syntactic analysis model. The accuracy, recall, and F1 score of the recognition results of applying sequence tagging model are 87.14%, 84.72%, and 85.92%, respectively. The accuracy, recall, and F1 score of the recognition results of applying syntactic analysis model are 87.66%, 87.63%, and 87.65%, respectively.