Biomimetics

Acknowledging recent breakthroughs in the context of deep bio-inspired neural networks, several architectural deep network options have been deployed to create intelligent systems. The foundations of convolutional neural networks are influenced by hierarchical processing in the visual cortex. The graph neural networks mimic the communication of biological neurons. Considering these two computation methods, a novel deep ensemble network is used to propose a bio-inspired deep graph network for creating an intelligent supply chain model. An automated smart supply chain helps to create a more agile, resilient and sustainable system. Improving the sustainability of the network plays a key role in the efficiency of the supply chain’s performance. The proposed bio-inspired Chebyshev ensemble graph network (Ch-EGN) is hybrid learning for creating an intelligent supply chain. The functionality of the proposed deep network is assessed on two different databases including SupplyGraph and DataCo for risk administration, enhancing supply chain sustainability, identifying hidden risks and increasing the supply chain’s transparency. An average accuracy of 98.95% is obtained using the proposed network for automatic delivery status prediction. The performance metrics regarding multi-class categorization scenarios of the intelligent supply chain confirm the efficiency of the proposed bio-inspired approach for sustainability and risk management.

This study presents a novel bio-inspired multi-cell concave tube (BMCT) inspired by the biomimicry of horse tail grass plants. Following the simulation validation, a comprehensive investigation into the crashworthiness of this structure under axial impact was conducted. Concurrently, both experimental and theoretical analyses were employed to substantiate the reliability of the simulation data. Comparative results concerning crashworthiness indicate that, relative to other structures, the BMCT maintains a relatively constant initial peak force while simultaneously enhancing energy absorption capacity at equivalent mass. Specifically, when compared to corresponding hierarchical multi-cell tubes with the same number of cells, the BMCT exhibits a 41.04% increase in crush force efficiency (CFE) while preserving a relatively unchanged initial peak crushing force (IPCF). Additionally, variations in hierarchical levels yield a 21.22% increase in CFE at the same mass.

Myriapods achieve remarkable obstacle-crossing capability through inter-segment pitch adjustment and coordinated anterior–posterior propulsion, providing valuable biomimetic inspiration for engineering design. Articulated tracked vehicles, connecting front and rear units via pitch mechanisms, exhibit functional similarity to myriapod body segments. This study develops a comprehensive dynamic model for articulated tracked vehicle pitch motion to reveal its biomimetic connection with myriapod locomotion. A quadratic-function-based non-uniform track–ground contact pressure distribution method with zero-gradient boundary conditions is proposed, effectively eliminating the non-physical negative pressure issue inherent in traditional assumptions. Systematic analyses reveal that the front unit provides primary traction under pitch-up conditions, forming a front-pulling-rear driving mode, while the rear unit dominates under pitch-down and acceleration conditions, forming a rear-pushing-front driving mode. Through pitch attitude adjustment, the maximum surmountable vertical-wall height increased from 263 to 593 mm, representing a 125.45% improvement. This traction distribution pattern closely matches the anterior-guidance and posterior-propulsion mechanism observed in myriapod locomotion. This study quantitatively validates the functional analogy between articulated tracked vehicle pitch dynamics and myriapod inter-segment coordination, providing theoretical foundations for bio-inspired tracked vehicle design.