Due to the complex structural hierarchy, with deeply nested associative relations between entities such as equipment, specifications, and business processes, intelligent power grid engineering is challenging. Meanwhile, limited by the fragmented data and loss of contextual information, the generated reports are prone to the problems such as content redundancy and omission of critical information, failing to meet the demands of efficient decision-making and accurate management in modern power systems. To address these issues, this paper proposes a knowledge graph (KG)-enhanced framework to automatically generate electric power engineering reports. In the KG construction phase, a feature-fused entity recognition model named BERT-BiLSTM-CRF is adopted to improve the accuracy of entity recognition in scenarios involving power engineering professional terminology, thereby solving the problem of ambiguous entity boundaries in traditional models; then a BERT-attention relation extraction model is proposed to enhance the completeness of extracting complex hierarchical and implicit relations in power grid data. In the report generation phase, an improved Transformer architecture is adopted to accurately transform structured knowledge into natural language reports that comply with engineering specifications, addressing the issue of semantic inconsistency caused by the loss of structural information in existing models. By validating with real-world projects, the results show that the proposed framework significantly outperforms existing baseline models in entity recognition, confirming its superiority and applicability in practical engineering.

Zero-day attacks present a critical cybersecurity challenge for Internet of things (IoT) infrastructures, where the inability of signature-based intrusion detection systems (IDSs) to recognize novel threat behaviors compromises both system reliability and operational continuity. Existing hybrid IDS solutions often struggle to balance accurate classification of known attacks with reliable anomaly detection, particularly under the computational constraints of IoT environments. To address this gap, we introduce ZeroDefense, an adaptive fusion-based IDS designed for simultaneous detection of known intrusions and emerging zero-day threats. The framework employs a four-layer architecture consisting of i) feature standardization and class balancing, ii) anomaly detection using isolation forest, autoencoder, and local outlier factor, iii) fine-grained attack classification via random forest, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and attentive interpretable tabular learning (TabNet), and iv) a confidence-aware fusion engine that adaptively selects the most reliable decision path. Suspicious or previously unseen traffic is isolated early through fused anomaly scoring, while benign and known-malicious flows are processed through supervised classification for precise attack labeling. With an anomaly cascaded decision pipeline, a dynamic confidence-driven fusion mechanism, and a deployment-conscious design, ZeroDefense enables real-time inference on IoT edge gateways. Evaluation on the CICIoT2023 benchmark demonstrates 99.94% overall accuracy and 95.64% macro-average F1-score for known attacks, while 5.76% of traffic is successfully flagged as potential zero-day activity, with inference latency maintained below 100 ms/flow. These results indicate that ZeroDefense offers a scalable, resilient, and practically deployable defense capability for modern IoT infrastructures.

With the growing deployment of unmanned aerial vehicles (UAVs) swarms in national defense, military operations, and emergency response, secure and reliable intra-swarm identity authentication has become critical for ensuring coordinated action and mission reliability. To address the drawbacks of public key infrastructure (PKI) based authentication in UAV swarms, namely, complex certificate management, strong dependence on centralized authorities, and authentication latency, we propose a certificateless identity authentication scheme for UAV swarms built on blockchain sharding. The scheme leverages sharding to execute authentication in parallel across multiple shards, significantly improving efficiency. Each UAV locally generates its public/private key pair and then adopts a registration-based encryption (RBE) mechanism: A registration algorithm binds the device identity to its key on the blockchain, ensuring public verifiability and immutability of identity mapping. On this basis, an authentication algorithm runs in which the initiator produces an authentication signature using a common reference string (CRS), on-chain public-key registration information, and its local private key, and the verifier rapidly validates the authentication message using the on-chain registration data and the identity of the initiator. The experimental results demonstrate that the proposed scheme achieves low-latency and high-throughput identity authentication in large-scale UAV swarm environments, providing a solid technical foundation and broad application prospects for trustworthy UAV swarm identity authentication.

Owing to superior breakdown voltage and excellent robustness, the beta-gallium oxide (β-Ga₂O₃) power device has emerged as a pivotal research frontier in power electronics. Although advanced packaging strategies, including nano-silver paste sintering, alumina direct bond copper (DBC) substrates, and flip-chip structures, have been adopted to mitigate the intrinsic low thermal conductivity of β-Ga₂O₃. However, a further reduction in the thermal resistance while maintaining high reliability remains a challenge. This study introduces a novel packaging methodology that synergistically integrates nano-silver films with aluminum nitride active metal brazing (AMB-AlN) substrates, achieving an ultra-low junction-to-case thermal resistance. By comprehensive reliability assessments on β-Ga₂O₃ Schottky barrier diodes (SBDs) and hetero-junction diodes (HJDs), the results demonstrate that the SBDs and HJDs exhibit surge current densities of 0.876 kA/cm² and 0.778 kA/cm², respectively, which represents a significant advancement in device performance benchmarks. These advancements provide critical insights into packaging design for high-reliability ultrawide bandgap semiconductor systems.

This study presents a comprehensive theoretical investigation of the electronic and structural properties of a series of fractal molecular architectures derived from benzene and progressively extended toward circumcoronene-like graphene analogues. Molecular geometries were constructed using GaussView 6, while all quantum-chemical calculations were carried out within the Gaussian 09 package using density functional theory (DFT) at the B3LYP/6-31G level, ensuring a reliable balance between computational accuracy and efficiency. The gradual expansion of the hexagonal framework resulted in a systematic reduction in the energy gap between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO), indicating enhanced electronic delocalization and improved charge-transport characteristics in higher-order fractal structures. Electron density contour maps revealed an increased π-electron symmetry in the central regions of the larger systems, whereas smaller units exhibited an edge-localized electron density, consistent with the development of extended π–π conjugation. In addition, the density of states (DOS) spectra demonstrated a pronounced broadening of both occupied and virtual states with an increasing structural size, confirming the strong correlation between the fractal growth and electronic transport behavior. Furthermore, the analysis of the HOMO and LUMO distributions showed an orbital broadening and enhanced spatial symmetry in advanced fractal geometries, accounting for the observed reduction in the energy gap. These results indicate that coronene-based fractal structures exhibit significant potential for applications in conductive nanomaterials and molecular electronics, as their electronic and structural properties can be finely tuned through controlled fractal branching, enabling tailored performance in next-generation nanoscale devices.

In this paper, a comprehensive evaluation on the silicon/silicon carbide (Si/SiC) hybrid switch is performed through by experimental tests in terms of both electrical performance and robustness under extreme stresses. Based on the optional turn-on and turn-off delay times under the efficiency control mode obtained from the double-pulse test (DPT), both nondestructive and destructive single-pulse avalanche tests are conducted on the Si/SiC hybrid switch as well as on the two discrete device branches inside the hybrid switch. In addition, the avalanche voltage, critical avalanche energy, and peak avalanche current, which intrinsically characterize the unclamped-inductive-switching (UIS) avalanche characteristics, are carefully examined. In this way, the physical factors dominating the UIS characteristics of the hybrid switch, thus limiting its single-pulse avalanche withstand capability, are specifically and comprehensively identified; the underlying physical mechanisms are analyzed and revealed in depth, and how the gate control sequence affects the UIS characteristics of the hybrid switch is extensively investigated. We additionally carry out short-circuit (SC) tests under the fault-under-load (FUL) condition and perform a parallel in-depth analysis to experimentally determine which branch dominates the SC withstand capability of the hybrid switch. Our experimental study indicates that, for both SC robustness and single-pulse avalanche capability, the limiting factor is a single device branch among the two parallel discrete devices, and the UIS behavior is sensitive to the variation of the gate turn-off delay time T_{off_delay}. The study conducted in this paper not only provides deep academic insights into the electrical performance and reliability of the Si/SiC hybrid switch, but also offers fundamental theoretical principles and technical evidence to support more efficient and long-term reliable applications of the hybrid switch in the industrial fields.

The intelligent transportation systems require secure, low-latency, and reliable communication architectures to enable the real-time vehicular application. This paper proposes an edge-intelligent semantic aggregation (EISA) framework for 6G unmanned aerial vehicle (UAV)-assisted Internet of vehicles (IoV) networks that integrates task-driven semantic communication, deep reinforcement learning (DRL)-based edge intelligence, and blockchain-based semantic validation across 6G terahertz (THz) links. UAVs in the proposed architecture serve as adaptive edge nodes that receive semantically vital information about the vehicle at any given stage, optimize aggregation and transmission parameters dynamically, and guarantee data integrity through a structured, lightweight consortium blockchain that signs semantically detailed representations rather than raw packets. Simulation results from a hybrid NS-3, MATLAB, and Python environment indicate that the proposed framework can achieve up to 45% reduction in end-to-end latency, an approximately 70% increase in throughput, and semantic efficiency with blockchain verification delays of less than 20 ms (more than 98%). These findings support the effectiveness of the proposed co-design for achieving context-aware, energy-efficient, and reliable communication under heavy-traffic conditions. The proposed framework provides a flexible and scalable foundation for next-generation 6G-enabled automotive networks, with subsequent growth toward federated learning-based collaborative intelligence, digital-twin-assisted traffic modeling, and quantum-safe blockchain mechanisms to enhance scalability, intelligence, and long-term security.