With the continuous development of technology, blockchain has been widely used in various fields by virtue of its decentralization, data integrity, traceability, and anonymity. However, blockchain still faces many challenges, such as scalability and security issues. Artificial intelligence, with its powerful data processing capability, pattern recognition ability, and adaptive optimization algorithms, can improve the transaction processing efficiency of blockchain, enhance the security mechanism, and optimize the privacy protection strategy, thus effectively alleviating the limitations of blockchain in terms of scalability and security. Most of the existing related reviews explore the application of AI in blockchain as a whole but lack in-depth classification and discussion on how AI can empower the core aspects of blockchain. This paper explores the application of artificial intelligence technologies in addressing core challenges of blockchain systems, specifically in terms of scalability, security, and privacy protection. Instead of claiming a deep theoretical integration, we focus on how AI methods, such as machine learning and deep learning, have been effectively adopted to optimize blockchain consensus algorithms, improve smart contract vulnerability detection, and enhance privacy-preserving mechanisms like federated learning and differential privacy. Through comprehensive classification and discussion, this paper provides a structured overview of the current research landscape and identifies potential directions for further technical collaboration between AI and blockchain technologies.

With the rapid development of blockchain technology, consensus algorithms have become a significant research focus. Practical Byzantine Fault Tolerance (PBFT), as a widely used consensus mechanism in consortium blockchains, has undergone numerous enhancements in recent years. However, existing review studies primarily emphasize broad comparisons of different consensus algorithms and lack an in-depth exploration of PBFT optimization strategies. The lack of such a review makes it challenging for researchers and practitioners to identify the most effective optimizations for specific application scenarios. In this paper, we review the improvement schemes of PBFT from three key directions: communication complexity optimization, dynamic node management, and incentive mechanism integration. Specifically, we explore hierarchical networking, adaptive node selection, multi-leader view switching, and a hybrid consensus model incorporating staking and penalty mechanisms. Finally, this paper presents a comparative analysis of these optimization strategies, evaluates their applicability across various scenarios, and offers insights into future research directions for consensus algorithm design.

Dielectric elastomer actuators (DEA) have emerged as promising candidates for underwater exploration due to their high energy density and flexibility. However, the accumulation of residual charges compromises actuation efficiency, and conventional single-mode discharge strategies struggle to balance dynamic responsiveness with locomotion stability. Therefore, we present CRAB-EDM, a multi-modal underwater soft robot inspired by crab locomotion and powered by temporally modulated electro-discharge control. By integrating the distributed coordination mechanism of crab-like multi-foot motion with the electromechanical coupling behavior of DEA, we design a six-foot soft robotic architecture coupled with a tri-modal control framework: high-frequency fast discharge enhances dynamic actuation response, medium-frequency delayed discharge improves displacement-frequency synergy, and low-frequency coordinated discharge reinforces gait stability. Through ballast–limb coordination mechanism with a multi-modal control strategy, the nine-gram robot demonstrates versatile behaviors such as turning, jumping, and overing obstacle. Experimental results validate that the synergy between bio-inspired morphology and residual charge modulation significantly improves locomotion efficiency and terrain adaptability, offering a low-noise, highly adaptable solution for constrained underwater exploration.

The effects of grain boundary misorientation angle (θ) on mechanical properties and the mechanism of plastic deformation of the Ni/Ni₃Al interface under tensile loading were investigated using molecular dynamics simulations. The results show that the space lattice arrangement at the interface is dependent on grain boundary misorientations, while the interfacial energy is dependent on the arrangement. The interfacial energy varies in a W pattern as the grain boundary misorientation increases from 0° to 90°. Specifically, the interfacial energy first decreases and then increases in both segments of 0–60° and 60–90°. The yield strength, elastic modulus, and mean flow stress decrease as the interfacial energy increases. The mechanism of plastic deformation varies as the grain boundary misorientation angle (θ) increases from 0° to 90°. When θ = 0°, the microscopic plastic deformation mechanisms of the Ni and Ni₃Al layers are both dominated by stacking faults induced by Shockley dislocations. When θ = 30°, 60°, and 80°, the mechanisms of plastic deformation of the Ni and Ni₃Al layers are the decomposition of stacking faults into twin grain boundaries caused by extended dislocations and the proliferation of stacking faults, respectively. When θ = 90°, the mechanisms of plastic deformation of both the Ni and Ni₃Al layers are dominated by twinning area growth resulting from extended dislocations.

The crack propagation process in single-crystal aluminum plate (SCAP) with central cracks under tensile load was simulated by molecular dynamics method. Further, the effects of model size, crack length, temperature, and strain rate on strength of SCAP and crack growth were comprehensively investigated. The results showed that, with the increase of the model size, crack length, and strain rate, the plastic yield point of SCAP occurred in advance, the limit stress of plastic yield decreased, and the plastic deformability of material increased, but the temperature had less effect and sensitivity on the strength and crack propagation of SCAP. The model size affected the plastic deformation and crack growth of the material. Specifically, at small scale, the plastic deformation and crack propagation in SCAP are mainly affected through dislocation multiplication and slip. However, the plastic deformation and crack propagation are obviously affected by dislocation multiplication and twinning in larger scale.

This paper reports the effects of carbon fiber-reinforced polymer (CFRP) length on the failure process, pattern and crack propagation for a strengthened concrete beam with an initial notch. The experiments measuring load-bearing capacity for concrete beams with various CFRP lengths have been performed, wherein the crack opening displacements (COD) at the initial notch are also measured. The application of CFRP can significantly improve the load-bearing capacity, and the failure modes seem different with various CFRP lengths. The stress profiles in the concrete material around the crack tip, at the end of CFRP and at the interface between the concrete and CFRP are then calculated using the finite element method. The experiment measurements are validated by theoretical derivation and also support the finite element analysis. The results show that CFRP can significantly increase the ultimate load of the beam, while such an increase stops as the length reaches 0.15 m. It is also concluded that the CFRP length can influence the stress distribution at three critical stress regions for strengthened concrete beams. However, the optimum CFRP lengths vary with different critical stress regions. For the region around the crack tip, it is 0.15 m; for the region at the interface it is 0.25 m, and for the region at the end of CFRP, it is 0.30 m. In conclusion, the optimum CFRP length in this work is 0.30 m, at which CFRP strengthening is fully functioning, which thus provides a good reference for the retrofitting of buildings.