There are a large number of bearings in aircraft engines that are subjected to extreme operating conditions, such as high temperature, high speed, and heavy load, and their fatigue, wear, and other failure problems seriously affect the reliability of the engine. The complex and variable bearing operating conditions can lead to differences in the distribution of data between the source and target operating conditions, as well as insufficient labels. To solve the above challenges, a multi-scale attention mechanism-based domain adversarial neural network strategy for bearing fault diagnosis (MADANN) is proposed and verified using Case Western Reserve University bearing data and PT500mini mechanical bearing data in this paper. First, a multi-scale feature extractor with an attention mechanism is proposed to extract more discriminative multi-scale features of the input signal. Subsequently, the maximum mean discrepancy (MMD) is introduced to measure the difference between the distribution of the target domain and the source domain. Finally, the fault diagnosis process of the rolling is realized by minimizing the loss of the feature classifier, the loss of the MMD distance, and maximizing the loss of the domain discriminator. The verification results indicate that the proposed strategy has stronger learning ability and better diagnosis performance than shallow network, deep network, and commonly used domain adaptive models.

Regenerative medicine is increasingly leveraging the synergies between bioinspired conductive biomaterials and 3D/4D bioprinting to replicate the native electroactive and hierarchical microenvironments essential for functional tissue restoration. However, a critical gap remains in the intelligent integration of these technologies to achieve dynamic, responsive tissue regeneration. This review introduces a “bioinspired material–printing–function” triad framework to systematically synthesize recent advances in: (1) tunable conductive materials (polymers, carbon-based systems, metals, MXenes) designed to mimic the electrophysiological properties of native tissues; (2) advanced 3D/4D printing technologies (vat photopolymerization, extrusion, inkjet, and emerging modalities) enabling the fabrication of biomimetic architectures; and (3) functional applications in neural, cardiac, and musculoskeletal tissue engineering. We highlight how bioinspired conductive scaffolds enhance electrophysiological behaviors—emulating natural processes such as promoting axon regeneration cardiomyocyte synchronization, and osteogenic mineralization. Crucially, we identify multi-material 4D bioprinting as a transformative bioinspired approach to overcome conductivity–degradation trade-offs and enable shape-adaptive, smart scaffolds that dynamically respond to physiological cues, mirroring the adaptive nature of living tissues. This work provides the first roadmap toward intelligent electroactive regeneration, shifting the paradigm from static implants to dynamic, biomimetic bioelectronic microenvironments. Future translation will require leveraging AI-driven bioinspired design and organ-on-a-chip validation to address challenges in vascularization, biosafety, and clinical scalability.

Fault diagnosis of rotating machinery is widely recognized as a challenging problem. Recent advances in combining convolutional neural networks (CNNs) and Transformers have expanded the capabilities of intelligent fault diagnosis. However, in real-world industrial settings, three critical challenges substantially affect fault diagnosis performance: resource-constrained deployment environments, variable operating conditions, and cross-domain adaptability requirements. These challenges frequently undermine the efficacy of existing algorithms, particularly in sustaining robust performance while meeting lightweight implementation requirements. To address these challenges, a novel lightweight fault diagnosis framework, termed ARE-MGCAFormer, is introduced in this paper. Firstly, an Adaptive Residual Enhancement (ARE) block is specifically designed to extract multi-local receptive field features from vibration signals. The integrated gating mechanism utilizes learnable parameters to adaptively balance the contributions of deep separable convolutions and inverse residual blocks, dynamically adjusting to varying signal features and noise levels while maintaining a lightweight design. Secondly, a Multi-Group Coordinate Attention (MGCA) mechanism is incorporated to effectively extract critical detailed features across the entire signal range while reducing computational complexity by distributing attention across multiple feature groups. Experimental verification was conducted using the gearbox dataset from Xi’an Jiaotong University (XJTU) and the rolling bearing fault dataset from the University of Ottawa (OU). The results demonstrate that the proposed framework exhibits superior lightweight characteristics and robustness in fault diagnosis tasks compared to recent mainstream frameworks based on CNNs and Transformers.

The Zebra Optimization Algorithm (ZOA) is a newly proposed heuristic algorithm, which simulates the foraging behavior and defense strategy of zebras in nature. Like other optimization algorithms, ZOA also has some limitations. In this paper, an improved Zebra Optimization Algorithm (IZOA) was proposed. An adaptive oscillation weight is used to replace the random operator of ZOA , enabling the algorithm to adaptively adjust the search space and avoid falling into local optima. Additionally, this paper designs an improved golden sine operator added to ZOA. By using the search characteristics of the sine function and the golden ratio in GoldSA, the algorithm's convergence and optimization accuracy are further improved. The last replacement strategy was formulated to mimic the survival status of zebras in nature, improving the algorithm's search capabilities in complex scenarios. Finally, to assess the optimization performance of IZOA, this paper utilized the 30 benchmark functions in CEC-2017 and three common engineering problems for experiments, and the results clearly demonstrated the significant advantages of IZOA.