Journal of Bionic Engineering最新文献

Locomotion performance degradation after carrying payloads is a significant challenge for insect-scale microrobots. Previously, a legged microrobot named BHMbot with a high load-carrying capacity based on front-leg actuation configuration and efficient running gait was proposed. However, insects, mammals and reptiles in nature typically use their powerful rear legs to achieve rapid running gaits for predation or risk evasion. In this work, the load-carrying capacity of the BHMbots with front-leg actuation and rear-leg actuation configurations is comparatively studied. Simulations based on a dynamic model with four degrees of freedom, along with experiments, have been conducted to analyze the locomotion characteristics of the two configurations under different payload masses. Both simulation and experimental results indicate that the load-carrying capacity of the microrobots is closely related to their actuation configurations, which leads to different dynamic responses of the microrobots after carrying varying payload masses. For microrobots with body lengths of 15 mm, the rear-leg actuation configuration exhibits a 31.2% enhancement in running speed compared to the front-leg actuation configuration when unloaded. Conversely, when carrying payloads exceeding 5.7 times the body mass (350 mg), the rear-leg actuation configuration demonstrates an 80.1% reduction in running speed relative to the front-leg actuation configuration under the same payload conditions.

Flexible pressure sensors have excellent prospects in applications of human-machine interfaces, artificial intelligence and human health monitoring due to their bendable and lightweight characteristics compared to rigid pressure sensors. However, arising from the limited compressibility of soft materials and the hardening of microstructures at the device interface, there is always a trade-off between high sensitivity and broad sensing range for most flexible pressure sensors, which results in a gradual saturation response and limits their practical applications. Herein, inspired by the distinct pressure perception function of crocodile receptors, a highly sensitive and wide-range flexible pressure sensor with multiscale microdomes and interlocked architecture is developed via a facile PS-decorated molding method. Combined with interlocked architecture, the multiscale dome-shaped structured interface enhances the compressibility of the material through structural complementarity, increases the contact area between functional materials, which compensates for the stiffness induced by the deformation of dense microscale columns. This effectively mitigates structural hardening across a wide pressure range, leading to the overall high performance of the sensor. As a result, the obtained sensor exhibits a low detection limit of 5 Pa, a high sensitivity of 6.14 kPa^− 1, a wide measurement range up to 231 kPa, short response/recovery time of 56 ms/69 ms, outstanding stability over 10,000 cycles. Considering these excellent properties, the sensor shows promising potential in health monitoring, human-computer interaction, wearable electronics. This study presents a strategy for the fabrication of flexible pressure sensors exhibiting high sensitivity and a wide pressure response range.

Reducing the resistance of vehicles, ships, aircraft and other means of transport during movement can significantly improve the speed, save energy and reduce emissions. After billions of years of continuous evolution, organisms in nature have gradually developed the ability to move at high speed to achieve better survival. These evolved organisms provide a perfect template for the human development of drag reduction materials. Revealing the unique physiological structural characteristics of organisms and their relationship with resistance during movement can provide a feasible approach to solving the problem of reducing friction resistance. Whether flying in the sky, running on the ground, swimming in the water, or even living in the soil, many creatures in various environments have the ability to reduce resistance. Driven by these inspirations, researchers have done a lot of work to explore and imitate these biological epidermis structures to achieve drag reduction. In this paper, the biomimetic drag reduction materials is introduced in detail in the order of drag reduction mechanism, structural characteristics of biological epidermis (including marine animals, flying animals, soil animals and plants), biomimetic preparation methods, performance testing methods and application fields. Finally, the potential of various biomimetic drag reduction materials in engineering application and the problems to be overcome are summarized and prospected. This paper can help readers comprehensively understand the research progress of biomimetic drag reduction materials, and provide reference for further designing the next generation of drag reduction materials.

Antennae are significant chemosensory and mechanosensory organs for insects and need careful maintenance. Bees use a pair of comb-like tools located on the forelimbs to brush and remove contaminants from their antennae. We filmed antenna grooming in three different bee species and observed that all bees raise their heads while grooming their antennae. We conducted a study to examine the effects of the distinctive grooming apparatus, as well as the antenna’s material and structural characteristics, on grooming behavior in both free-head and constrained-head scenarios. Head-raising increases the grooming speed by 300% compared to the situation where the head is constrained. It allows the bees to scrape the antennae 5 times per second. In addition, we proposed a mechanical model based on the morphological data to determine that raising the head increases the contact force by 50%. These findings will facilitate the development of innovative approaches for cleaning extended structures featuring bristly surfaces.

This article reviews recent advancements, innovative strategies, and the key challenges in Drug Delivery Systems (DDS) for bone regeneration, focusing on tissue engineering. It highlights the limitations of current surgical interventions for bone regeneration, particularly autogenic bone grafts, and discusses the exploration of alternative materials and methods, including allogeneic and xenogeneic bone grafts, synthetic materials, and biodegradable polymers. The objective is to provide a comprehensive understanding of how contemporary DDS can be optimized and integrated with tissue engineering approaches for more effective bone regeneration therapies. The review explained the mechanisms through which DDS enhance bone repair processes, identifies critical factors influencing their efficacy and safety, and offers an overview of current trends and future perspectives in the field. It emphasizes the need for advanced strategies in bone regeneration that focus on precise control of DDS to address bone conditions such as osteoporosis, trauma, and genetic predispositions leading to fractures.

In the realm of video understanding, the demand for accurate and contextually rich video captioning has surged with the increasing volume and complexity of multimedia content. This research introduces an innovative solution for video captioning by integrating a Convolutional BiLSTM Convolutional Bidirectional Long Short-Term Memory (BiLSTM) constructed Variational Sequence-to-Sequence (CBVSS) approach. The proposed framework is adept at capturing intricate temporal dependencies within video sequences, enabling a more nuanced and contextually relevant description of dynamic scenes. However, optimizing its parameters for improved performance remains a crucial challenge. In response, in this research Golden Eagle Optimization (GEO) a metaheuristic optimization technique is used to fine-tune the Convolutional BiLSTM variational sequence-to-sequence model parameters. The application of GEO aims to enhancing the CBVSS ability to produce more exact and contextually rich video captions. The proposed attains an overall higher Recall of 59.75% and Precision of 63.78% for both datasets. Additionally, the proposed CBVSS method demonstrated superior performance across both datasets, achieving the highest METEOR (25.67) and CIDER (39.87) scores on the ActivityNet dataset, and further outperforming all compared models on the YouCook2 dataset with METEOR (28.67) and CIDER (43.02), highlighting its effectiveness in generating semantically rich and contextually accurate video captions.

Heterogeneous manufacturing is a topic that continues to receive attention. As an emerging manufacturing technology, additive manufacturing can provide strong technical support for heterogeneous manufacturing. In this study, both homogeneous and heterogeneous composite tubular bionic components were fabricated based on the cold metal transition technology, and the influence of deposition current on the microstructure and mechanical properties of the components was studied. The results show that the interface of the as-deposited heterogeneous composite component is well bonded, and there is an obvious mechanical interlocking structure. The compressive yield strength and elongation of the heterogeneous composite components are higher than those of the homogeneous components, and are positively correlated with the deposition current. Due to the fluctuation of element content, there are a large number of fine grain structures at the interface of the heterogeneous composite components, which increases the mechanical properties.

In bone tissue engineering, scaffold design must achieve specific mechanical compatibility with implantation sites, critically determining implant performance. This study developed four cylindrical Ti6Al4V bone scaffolds via selective laser melting (SLM), incorporating distinct lattice architectures: Face-Centered Cubic (FCC), Body-Centered Cubic (BCC), Glass Sponge (GS), and Auxetic Structures (AS). Integrated experimental characterization and finite element simulations revealed exceptional mechanical superiority of FCC scaffolds, demonstrating 7-fold greater maximum stress compared to BCC, GS, and AS counterparts. Furthermore, FCC scaffolds exhibited optimal performance metrics including plateau stress (1.2–1.4 GPa), densification strain (0.15–0.25), energy absorption (85–100 MJ/m³), and specific energy absorption (45–55 kJ/kg). These findings confirm that the unique energy dissipation mechanisms inherent to FCC lattice geometry significantly enhance energy absorption efficiency. The study provides a theoretical foundation for developing mechanically adaptive bone implants, particularly advancing clinical applications requiring enhanced energy absorption capabilities.

The segmentation of retinal vessels and coronary angiographs is essential for diagnosing conditions such as glaucoma, diabetes, hypertension, and coronary artery disease. However, retinal vessels and coronary angiographs are characterized by low contrast and complex structures, posing challenges for vessel segmentation. Moreover, CNN-based approaches are limited in capturing long-range pixel relationships due to their focus on local feature extraction, while ViT-based approaches struggle to capture fine local details, impacting tasks like vessel segmentation that require precise boundary detection. To address these issues, in this paper, we propose a Global–Local Hybrid Modulation Network (GLHM-Net), a dual-encoder architecture that combines the strengths of CNNs and ViTs for vessel segmentation. First, the Hybrid Non-Local Transformer Block (HNLTB) is proposed to efficiently consolidate long-range spatial dependencies into a compact feature representation, providing a global perspective while significantly reducing computational overhead. Second, the Collaborative Attention Fusion Block (CAFB) is proposed to more effectively integrate local and global vessel features at the same hierarchical level during the encoding phase. Finally, the proposed Feature Cross-Modulation Block (FCMB) better complements the local and global features in the decoding stage, effectively enhancing feature learning and minimizing information loss. The experiments conducted on the DRIVE, CHASEDB1, DCA1, and XCAD datasets, achieving AUC values of 0.9811, 0.9864, 0.9915, and 0.9919, F1 scores of 0.8288, 0.8202, 0.8040, and 0.8150, and IOU values of 0.7076, 0.6952, 0.6723, and 0.6878, respectively, demonstrate the strong performance of our proposed network for vessel segmentation.

Alzheimer’s Disease (AD), a prevalent neurodegenerative disorder characterized by memory loss and cognitive decline, poses significant challenges for individuals and society. Multimodal data fusion has emerged as a promising approach for AD diagnosis, with Graph Convolutional Networks (GCNs) effectively capturing irregular brain information. However, traditional GCN methods face limitations in representing and integrating multimodal data, often resulting in feature mismatch. In this study, we propose a novel Kolmogorov-Arnold Graph Attention Network (KAGAN) model to address this issue through semantic-level alignment. KAGAN incorporates a Multimodal Feature Construction method (MuStaF) to extract structural and functional features from T1- and T2-weighted images, and a Multimodal Graph Adjacency Matrix Construction method (MuGAC) to integrate clinical information, modeling intricate relationships across modalities. Experiments conducted on the ADNI dataset demonstrate the superiority of KAGAN in AD/CN/MCI classification, achieving an accuracy of 98.29 ± 1.21%. This highlights KAGAN’s potential for early AD diagnosis by enabling interactive learning and fusion of multimodal features at the semantic level. The source code of our proposed model and the related datasets are available at https://github.com/sheeprra/KAGAN.