Biomimetics

This experimental review discusses evolutionarily approved, naturally pre-designed skeletal architectures of marine keratosan sponges in the form of 3D scaffolds, which have garnered increasing interest in the fields of structural and functional biomimetics as well as in tissue engineering. It has been demonstrated that these renewable, ready-to-use natural scaffolds can undergo further modifications through specialized treatments such as metallization and carbonization, enabling the creation of functional biomaterials while maintaining the species-specific hierarchical 3D structure. The study presented remarkable findings, including the demonstration of the unique shape-memory behavior of these scaffolds even after two months of exposure to high mechanical pressure at temperatures exceeding 100 °C. Additionally, the cytocompatibility and biological performance of natural and carbonized (1200 °C) spongin scaffolds, derived from selected bath sponges, were comparatively investigated with respect to growth and proliferation of human MG-63 osteoblastic cells. Understanding whether carbonization universally enhances osteogenic capabilities or selectively amplifies the inherent architectural advantages remains to be critical for the rational design of sponge-derived scaffolds in bone and structural tissue engineering applications.

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.