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.

Radar image extrapolation serves as a fundamental methodology in meteorological forecasting, facilitating precise short-term weather prediction through spatiotemporal sequence analysis. Conventional approaches remain constrained by progressive image degradation and artifacts, substantially limiting operational forecasting reliability. This research introduces E-HEOA—an enhanced deep learning architecture with integrated hyperparameter optimization. Our framework incorporates two fundamental innovations: (a) a hybrid metaheuristic optimizer merging a Gaussian-mutated ESOA and Cauchy-mutated HEOA for autonomous learning rate and dropout optimization and (b) embedded ConvLSTM2D modules for enhanced spatiotemporal feature preservation. Experimental validation on 170,000 radar echo samples demonstrates superior performance, demonstrating considerable enhancement in almost all aspects relative to the baseline model while establishing new state-of-the-art benchmarks in prediction fidelity, convergence efficiency, and structural similarity metrics.

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.

The blood-brain barrier (BBB) is one of the most selective physiological interfaces in the human body. Transendothelial electrical resistance (TEER) has become a widely adopted quantitative metric for assessing its in vitro structural and functional integrity. Although TEER measurements are routinely incorporated into BBB-on-chips, the absence of harmonized electrode architectures, measurement settings, and reporting standards continues to undermine reproducibility and translational reliability among laboratories. This systematic review provides the first comprehensive classification and critical comparison of electrode configurations used for TEER assessment, specifically within BBB-on-chip systems. Eligible studies were analyzed and categorized according to electrode design, fabrication method, integration strategy, and operational constraints. We critically evaluated six principal electrode architectures, highlighting their performance trade-offs in terms of uniformity of current distribution, long-term stability, scalability, and compatibility with dynamic shear conditions. Furthermore, we propose a bioinspired TEER reporting framework that consolidates essential metadata, including electrode specification, temperature control, viscosity effects, and blank resistance correction. Our analysis proposes screen-printed and hybrid silver-indium tin oxide (ITO) electrodes as promising candidates for next-generation BBB platforms. Moreover, our review provides a structured roadmap for standardizing TEER electrode design and reporting practices to facilitate interlaboratory consistency and accelerate the adoption of BBB-on-chip systems as truly biomimetic platforms for predictive neuropharmacological workflows.

With the advancement of task-oriented reinforcement learning (RL), the capability of quadruped robots for motion generation and complex task completion has significantly improved. However, current control strategies require extensive domain expertise and time-consuming design processes to acquire operational skills and achieve multi-task motion control, often failing to effectively manage complex behaviors composed of multiple coordinated actions. To address these limitations, this paper proposes a motion policy generation method for quadruped robots based on multimodal motion primitives and imitation learning. A multimodal motion library was constructed using 3D engine motion design, motion capture data retargeting, and trajectory planning. A temporal domain-based behavior planner was designed to combine these primitives and generate complex behaviors. We developed a RL-based imitation learning training framework to achieve precise trajectory tracking and rapid policy deployment, ensuring the effective application of actions/behaviors on the quadruped platform. Simulation and physical experiments conducted on the Lite3 quadruped robot validated the efficacy of the proposed approach, offering a new paradigm for the deployment and development of motion strategies for quadruped robots.

Bone regeneration in critical-size defects requires biomaterials that provide both structural support and appropriate osteoinductive cues. Natural nacre contains an organic matrix rich in acidic macromolecules with reported osteogenic activity; however, its in vivo regenerative potential remains insufficiently explored. This study evaluated the bone regenerative capacity of nacre-derived materials alone and combined with oxidized porous silicon microparticles (pSi-MP), a bioactive material known to release silicic acid and support mineralized tissue formation. Critical-size defects were created in four caudal vertebrae of Wistar rats and filled with nacre, pSi-MP, a nacre–pSi composite, or left empty. After 60 days, bone formation was assessed using micro-computed tomography and non-decalcified histology. Empty defects failed to regenerate, whereas nacre and pSi-MP individually promoted partial mineralized tissue deposition. The nacre–pSi composite produced the most extensive repair, showing near-complete defect bridging, higher bone mineral density, and seamless integration of particles within newly formed bone. No inflammation or adverse reactions were observed, and osteoid deposition occurred directly on material surfaces. These findings demonstrate that nacre-derived materials exert intrinsic osteogenic effects in vivo and that combining nacre with porous silicon yields a synergistic response that significantly enhances bone regeneration. The composite represents a promising candidate for future bone repair strategies.

Peri-implantitis is an inflammatory disease caused by bacterial colonization that leads to progressive bone loss around dental implants. Implantoplasty is widely used for biofilm removal; however, it alters the titanium surface, generating particle release and impairing surface properties. This study evaluated whether a citric acid-based solution supplemented with collagen and magnesium cations could enhance hard and soft tissue regeneration following implantoplasty. Three surfaces were analyzed: physiological saline (Ctr), 25% citric acid (AC), and citric acid with collagen and magnesium nitrate hexahydrate (AC500/Mg). Surface roughness and wettability were assessed on titanium discs. Cytocompatibility, cell adhesion, and proliferation were evaluated using fibroblasts and osteoblasts up to 21 days, and mineralization was analyzed by alkaline phosphatase. In vivo studies were conducted in New Zealand rabbits with implants placed in the femur and muscle tissue. Surface roughness did not differ among treatments, while wettability significantly increased with citric acid-based solutions. All treatments showed good cytocompatibility. AC500/Mg significantly enhanced cell adhesion, proliferation, and osteoblast mineralization, showing threefold higher activity than controls at 21 days. In vivo, AC500/Mg exhibited greater bone contact (67%) and direct muscle integration, whereas AC and Ctr showed lower bone contact and fibrotic encapsulation. These results indicate that AC500/Mg improves soft and hard tissue responses without altering roughness, suggesting its potential as a regenerative strategy following implantoplasty.

Currently, bio-inspired metaheuristic algorithms play an important role in computer vision for assessing surface cracks. Also, manufacturing industries need non-destructive technologies based on biomimetics theory for characterizing micro-crack contours to determine surface quality. In this way, it is necessary to develop bio-inspired algorithms to construct crack contour models for determining crack regions through an optical microscope system. In this study, a metaheuristic genetic algorithm is implemented to build crack contour models by means of Bezier functions and crack coordinates. The contour modeling is performed by a microscope vision system based on micro-laser line scanning, which provides the crack coordinates through a broken laser line in the crack region. Thus, the metaheuristic algorithm builds the crack contour model by fitting the Bezier functions toward the crack topography. At this stage, an objective function moves the Bezier functions toward the crack topography via control points. The proposed technique provides micro-scale crack contours with a relative error smaller than 2%. Thus, the proposed crack contour modeling enhances the traditional crack contour inspection based on microscope image processing. This contribution is supported by a comparison between the proposed technique and the crack characterization performed via conventional image processing algorithms.

Most pediatric exoskeletons for cerebral palsy (CP) focus on lower-limb assistance and neglect trunk control, limiting rehabilitation outcomes. This study presents a self-aligning trunk–limb exoskeleton that integrates trunk stabilization with active lower-limb support. The design includes a hip–waist rapid adjustment mechanism, a bioinspired gear-rolling knee joint, modular thigh–shank structures, a trunk support module, and a body-weight support device. To enable transparent and coordinated assistance under pathological gait conditions, a continuous gait progress-based multi-joint control framework is developed. Joint motion is described as continuous gait progress over the full gait cycle (0–100%), and joint-specific progress estimates are fused into a unified system-level reference using observability-weighted circular statistics. Inter-joint coordination is achieved through phase-consistency-based temporal modulation implemented, enabling smooth synchronization while preserving joint-level autonomy and motion continuity. Technical evaluation—comprising kinematic misalignment analysis, simulation validation, and gait trials—demonstrated a 66.8% reduction in hip misalignment and an 87.4% reduction in knee misalignment. Gait parameters under exoskeleton-assisted walking closely matched baseline walking, confirming natural kinematic preservation without interference. These results indicate that the proposed trunk–limb exoskeleton improves human–robot synergy, enhances postural stability, and provides a promising solution for pediatric gait rehabilitation in CP.