Biomimetics doi: 10.3390/biomimetics11050338

Authors: Radost Ilieva Ivalina Avramova Ognyan Petrov Diana Rabadjieva

Understanding the differences between synthetic and natural hydroxyapatite under conditions that mimic the oral environment, particularly the demineralization and remineralization processes of dental enamel, is essential for assessing their suitability as enamel models in biomineralization studies. The present study aims to systematically compare the structural, chemical, and morphological properties of well-crystallized synthetic carbonated hydroxyapatite (CHA) and natural non-biogenic hydroxyapatite (HA) before and after exposure to solutions with demineralizing and remineralizing activity. Two highly informative surface characterization techniques—X-ray photoelectron spectroscopy (XPS) and scanning electron microscopy (SEM)—were employed to examine the resulting surface changes. In addition, powder X-ray diffraction and infrared analyses were used to characterize the initial samples. Demineralization was induced using a lactic acid-based solution, while remineralization was performed through a two-step treatment involving polycarboxybetaine followed by artificial saliva. The results show that natural HA contains an additional fluorapatite phase and a wider range of trace elements (Na, F, Si), leading to a more complex structure. During demineralization, synthetic CHA exhibits more pronounced surface changes and faster dissolution, whereas natural HA demonstrates greater chemical stability. The remineralization process leads to the formation of new surface layers on both materials. Synthetic CHA develops a fine-grained, homogeneous layer enriched in carbonate and hydrated species, while natural HA shows localized crystal growth within structural defects. The results demonstrate that natural HA exhibits greater chemical stability during demineralization and a more enamel-like response during remineralization, whereas synthetic CHA undergoes more pronounced surface restructuring and forms a highly hydrated, carbonate-rich surface layer.

Biomimetics doi: 10.3390/biomimetics11050337

Authors: Asif Ullah Zhendong Song Waqar Riaz Yizhi Shao Xiaozhi Qi

A distinct typing interface using surface electromyography (sEMG) can facilitate silent, hands-free typing by interpreting muscle activity in relation to specific keystrokes. Character-level recognition poses greater challenges than coarse gesture recognition because it is sensitive to subtle temporal variations and overlapping muscle dynamics. Temporal features are essential for typing recognition because keypresses may differ in duration, force, and accompanying hand movements across users. This paper proposes TransTCNet, a two-stage deep neural network architecture with a causal convolutional layer for learning local features and a transformer-based component for learning long-range temporal interactions. We evaluated our network on a publicly available 26-class typing sEMG dataset acquired from 19 individuals. The model achieved a validation accuracy of 96.53%, exceeding the baseline models. Our study revealed generalization among participants, and the AUC values were also high (>0.994) across all classes. The model was highly reliable and exhibited high prediction confidence (>0.9), enabling us to achieve a high training accuracy (97.86%) for real-time filtering decisions. TransTCNet could be suitable for wearable and edge devices due to its efficient architecture and low inference cost. The model’s ability to consistently decode fine-grained neuromuscular signals across users makes it well-suited for real-time applications such as adaptive user interfaces, virtual and augmented reality, prosthetic control, and communication systems.

Biomimetics doi: 10.3390/biomimetics11050336

Authors: Zhenjiang Wei Chengchun Zhang Hongzhi Sun Chun Shen Meihong Gao Meihui Zhu

Erosive wear in pipe elbows subjected to liquid–solid two-phase flow is a major cause of material degradation and service failure in industrial piping systems. In this study, erosion characteristics of pipe elbows were investigated through erosion mapping experiments and numerical simulations. The effects of flow velocity and particle diameter on erosion location and intensity were analyzed. Erosion was found to be mainly concentrated on the outer wall of the elbow within the angular range of 10° to 90°, and both erosion intensity and affected area increased with increasing particle diameter and flow velocity. Dean vortices were shown to play an important role in particle transport and erosion distribution, especially for small particles. Inspired by the ribbed morphology of shells, a biomimetic elbow was further designed and evaluated through an orthogonal numerical study considering flow velocity, particle diameter, rib number, and rib diameter. The results indicate that the ribbed structure can effectively improve erosion resistance by altering particle trajectories, reducing particle impact probability, and dissipating kinetic energy through low-velocity rotating flow between adjacent ribs. This finding provides useful inspiration for addressing erosive wear problems in engineering applications.

Biomimetics doi: 10.3390/biomimetics11050335

Authors: Hongmei Bai Taosuo Wu Jianfu Luo Na Ta

This paper proposes a multi-strategy improved pied kingfisher optimizer (MSIPKO), a novel metaheuristic algorithm designed to address constrained optimization problems (COPs). COPs are widely encountered in engineering and industrial applications and are characterized by complex constraints that restrict the feasible solution space and often lead to multiple local optima. To enhance the performance of the original pied kingfisher optimizer (PKO), three strategies are incorporated: (i) a reverse differential crossover mechanism to improve global exploration and maintain population diversity; (ii) an enhanced diving-fishing operator to strengthen local exploitation; and (iii) an improved commensalism phase to enrich search directions and increase robustness. The performance of MSIPKO is evaluated on 12 benchmark functions from the IEEE Congress on Evolutionary Computation 2006 (CEC 2006) test suite and six classical engineering optimization problems. Experimental results demonstrate that MSIPKO outperforms several state-of-the-art algorithms in terms of optimization accuracy, convergence speed, and stability, particularly for high-dimensional, nonlinear, and multi-constrained problems. Moreover, MSIPKO achieves superior or comparable solutions with fewer function evaluations, indicating its high efficiency and adaptability. These results confirm that MSIPKO is a promising tool for solving complex real-world constrained optimization problems. Future work will focus on extending the proposed algorithm to multi-objective and large-scale optimization scenarios.

Biomimetics doi: 10.3390/biomimetics11050334

Authors: Lida Zare Lahijan Saeed Meshgini Reza Afrouzian

Ischemic stroke, a major cause of global disability, is characterized by the blockage of an artery leading to reduced cerebral blood flow and subsequent brain injury. Automatic segmentation of ischemic stroke lesions in Computed Tomography Perfusion (CTP) maps is critical for accurate diagnosis, treatment planning, and outcome assessment. However, the accuracy of traditional methods remains limited, with Dice Similarity Coefficient (DSC) values around 68%. To address this challenge, we propose a deep learning-based model inspired by biological systems and brain mechanisms, which emulates natural information processing to enhance ischemic stroke lesion segmentation. The proposed network architecture consists of five graph convolutional layers that automatically extract and classify features from CTP images. We evaluated the model using the ISLES 2018 database, achieving a DSC of 75.41% and a Jaccard Index of 74.52%, representing significant improvements over previous methods. Notably, the proposed approach performs robustly in noisy environments, maintaining accuracy above 60% even at SNR = −4. These results demonstrate the potential of biomimetic-inspired networks for automatic ischemic stroke segmentation.

Biomimetics doi: 10.3390/biomimetics11050333

Authors: Sheng Zhang Ke Li Zhonghua Luo

Currently, small moving object detection remains a highly challenging problem, primarily attributable to four critical factors: limited pixel coverage, blurred texture features, indistinguishability from small-object-like background features (i.e., false positives), and vulnerability to environmental noise interference. The remarkable sensitivity of the Drosophila visual system to small moving objects, which originates from a specialized type of neuron known as “lobula columnar 11” (LC11), has provided inspiration for addressing this challenge. Current bio-inspired visual models have achieved certain advances. However, detection performance against real-world complex dynamic natural environments still requires further improvement. To address the challenge of limited detection accuracy for small moving objects against real-world complex dynamic natural environments, this paper proposes a Motion Small Object Detection (MSOD) model inspired by the Drosophila Vision Small Object Motion Sensitivity (DVSOMS) mechanism, namely DVSOMS-MSOD. The model consists of four stages: The first stage is preliminary processing of visual stimuli, where visual stimuli are perceived, converted to grayscale, and blurred. The second stage is the motion neural pathway, where visual signals are first decomposed into parallel ON and OFF neural pathway signals; then, the neural feedback mechanism is incorporated between the medulla and lobula complex, and the complete Hassenstein–Reichardt correlator (HRC) is integrated into the lobula complex; finally, the LC11 neuron is utilized to detect small moving objects and extract their location information. The third stage is the contrast neural pathway, where visual signals are first processed by the central and surrounding local neighborhoods, then local contrast information is calculated. The fourth stage is the integration of motion and contrast neural pathways, where the mushroom body generates motion trajectories using the location information of small moving objects, and subsequently generates contrast trajectories using the local contrast information and motion trajectories to more finely detect small moving objects. Under real-world complex dynamic natural environment datasets, compared with conventional machine learning methods for moving object detection, the proposed model achieved improvements of 77.82% and 78.70% in detection performance and output quality, respectively, while reducing running time by 10.60%. Compared with bio-inspired visual models for small moving object detection, the proposed model achieved improvements of 28.24% and 43.15% in detection accuracy and detection performance, respectively, but the running time increased by 43.40%. The proposed model demonstrates certain advantages in detection performance, output quality, and detection accuracy; however, its real-time performance still warrants further optimization.

Biomimetics doi: 10.3390/biomimetics11050330

Authors: Laura Del Gaudio Stefano Lattanzio Roberta Di Pietro Silvia Sancilio

Biomaterials play a central role in tissue engineering and regeneration by providing scaffolds that support cell adhesion, proliferation and differentiation while modulating the surrounding microenvironment. They represent promising alternatives to traditional surgical approaches that may lead to complications or tissue damage, and their performance is influenced by chemical composition, mechanical behavior, architecture and interfacial properties, all of which can be precisely tuned through advanced fabrication and surface modification strategies. This review synthesizes evidence from a comprehensive literature search across major scientific databases, focusing on highly cited studies and available clinical data, and examines natural and synthetic biomaterials, their biological responses, functional characteristics, and surface modification methods. Emphasis is placed on Cold Atmospheric Plasma (CAP), which selectively modifies the outermost nanolayer of materials, enhancing hydrophilicity, functional group density, protein adsorption and overall cell–material interactions, as well as improving drug loading capacity. The review also considers stem cell interactions with biomaterials and emerging applications of artificial intelligence (AI) for predicting performance and guiding material optimization. Overall, the analysis highlights that natural matrices provide intrinsic bioactivity, synthetic polymers offer tunable mechanics and degradation profiles, and composite systems integrate these advantages. Advances in technologies such as electrospinning and 3D/4D printing enable precise control over architecture, supporting cell colonization and vascularization. Collectively, developments in CAP treatments and AI-driven design strategies are strengthening the regenerative potential of biomaterials and advancing their clinical translation.

Biomimetics doi: 10.3390/biomimetics11050332

Authors: Xinzhe Lu Jianshuo An Latai Ga Xiaopeng Bai Daochun Xu Wenbin Li

Tree-climbing robots are primarily utilized for pruning and harvesting in tall trees; however, limited structural degrees of freedom (DoFs) reduce their flexibility in complex environments. To improve the flexibility and environmental adaptability of the robots, this study proposes a novel three-armed claw-type tree-climbing robot inspired by gibbons. A 14 DoFs prototype with a total mass of approximately 2.52 kg was developed, comprising three manipulator arms and independently actuated claws. Kinematic models were separately established for the series-connected arms and the parallel-connected moving platform, with accuracy verified through numerical simulations. Based on these models, a control system was implemented, and a physical prototype was tested in field climbing experiments. Grasping tests on surfaces of varying roughness, including moist tree trunks, artificial wood, and smooth steel plates, demonstrated the adaptability of the claw to diverse materials. The robot successfully climbed trunks inclined at 52–90°, supporting a maximum payload of 1.81 kg; each full gait cycle averaged approximately 4 min. These results indicate that the robot can successfully imitate the movements of gibbons during climbing, thereby verifying the feasibility and practical application value of this bionic design in real-world forestry environments.

Biomimetics doi: 10.3390/biomimetics11050331

Authors: Cebrail Turkeri Serdar Ekinci Davut Izci Dacheng Li Erdal Akin

This study addresses pressure regulation in an induction-motor-driven centrifugal fan and introduces two complementary novelties: a Softsign-PI controller that shapes the tracking error via a Softsign nonlinearity before PI regulation and a hybrid starfish optimization with a differential evolution (hSFOA-DE) scheme for automatically tuning the controller parameters. The approach is evaluated on an experimentally validated nonlinear fan–motor model and benchmarked against modern metaheuristics—starfish optimization algorithm (SFOA), animated oat optimization (AOO), electric eel foraging optimization (EEFO), differential evolution (DE), particle swarm optimization (PSO)—as well as classical tunings—Murrill-based 2-DOF PID, Tyreus–Luyben PID and Ziegler–Nichols PI. Statistical summaries and boxplots indicate superior central tendency with reduced run-to-run variability; fitness–evolution curves show faster convergence; and time-domain performance metrics confirm improved transient and steady-state behaviour. Objective function comparisons further show the lowest values of both the Zwe-Lee Gaing (ZLG) and integral of absolute error (IAE), supporting advantages in robustness and tracking accuracy of the proposed approach. These gains reduce overshoot and cumulative error, which can lessen throttling losses and actuator duty in fan/pump service, suggesting potential energy and maintenance benefits.

Biomimetics doi: 10.3390/biomimetics11050328

Authors: Emir Kutluay Oğuzhan Gültekin Yiğit Yazıcıoğlu

In this study, a novel flying legged robot configuration with enhanced obstacle-crossing capability is introduced. Legged robots, especially RHex robots, already possess high obstacle-crossing capability; however, the obstacle size that can be overcome is directly dependent on the leg length. Although stair climbing–descending, obstacle course and inclined surface algorithms have been studied for the RHex robot, flight capability has not been explored. In this study, this improvement is achieved with minimal impact on the RHex’s design by adding just a thruster as an additional propulsion system to propel the robot into flight. The attitude control is realized using the mass actuation of the robot legs, similar to how animals like lizards and cats utilize their limbs or tails as inertial appendages to stabilize their body pitch during mid-air maneuvers. Instead of direct and complete flight control, the aim was a temporary flight similar to obstacle-clearing flights of chickens. Hence, a nonlinear 2D model is developed to investigate the kinematics and dynamics of the RHex robot. Equations of motion are derived, linearized and used in a state feedback regulator design; the regulator is also expanded for reference tracking.

Biomimetics doi: 10.3390/biomimetics11050329

Authors: Mohammad Aliff Shakir Junfeng Zhu Abdul Khalil H.P.S. Mardiana Idayu Ahmad

Nature-inspired material design has gained increasing attention in the development of sustainable biocomposites for applications requiring the integration of structural performance and functional efficiency. However, many lignocellulosic composites still depend on synthetic binders and fail to achieve a strong effective interaction between constituents, resulting in suboptimal mechanical integrity and thermal behavior while limiting their environmental advantages. This study aims to develop binderless biocomposite panels from Hevea brasiliensis-derived spent mushroom substrate (SMS) and Elaeis guineensis empty fruit bunch (EFB) fibers, emphasizing the synergistic interaction between components for energy-efficient building applications. Chemical characterization revealed complementary roles, with EFB contributing a high cellulose content (57.60%) for reinforcement and SMS providing a higher lignin content (30.51%) for enhanced rigidity and natural binding. Panels were fabricated via hot pressing at a target density of 0.8 g/cm3 without additives. Mechanical properties were evaluated through specific flexural, tensile, internal bond, and impact testing, while thermal conductivity and thickness swelling were used to assess functional performance. The 60% SMS with 40% EFB composition exhibited optimal performance, achieving a specific flexural strength of 20.26 MPa, a flexural modulus of 1943.76 MPa, tensile strength of 6.12 MPa, an internal bond strength of 2.06 MPa, an impact strength of 15.35 kJ/m2, a thickness swelling of 44.80%, and a thermal conductivity of 0.234 W/m.K. These results demonstrate that the combined effect of SMS and EFB in binderless biocomposites derived from secondary products offers a promising biomimetic pathway for designing recyclable, high-performance materials suitable for sustainable and energy-efficient construction systems.

Biomimetics doi: 10.3390/biomimetics11050327

Authors: Simona Liliana Iconaru Steluta Carmen Ciobanu Coralia Bleotu Mikael Motelica-Heino Daniela Predoi

This study reports on the physico-chemical and in vitro biological characterization of chromium-doped hydroxyapatite (10CrHAp, Cr3+, Ca10-xCrx(PO4)6(OH)2, xCr = 0.1) and chromium-doped hydroxyapatite in dextran matrix (10CrHAp-Dx) coatings, prepared for the first time via the spin coating technique. X-ray diffraction analysis and Rietveld refinement were used to characterize the materials. Fourier-transform infrared (FTIR) spectroscopy confirmed the presence of functional groups specific to hydroxyapatite. Scanning electron microscopy (SEM) observations revealed the presence of a conglomerate of nanoparticles distributed unevenly across the coatings surface. Atomic force microscopy (AFM) showed that both coatings presented continuous surfaces with uniform morphology. The in vitro biocompatibility of 10CrHAp and 10CrHAp-Dx coatings was evaluated using human osteoblast-like MG63 cell line and MTT assay. SEM and MM visualization assessed the cell adhesion and proliferation and morphological changes in the adhered cells. The antibacterial properties of the 10CrHAp and 10CrHAp-Dx coatings was assessed in vitro against two of the most common bacterial reference strains, Pseudomonas aeruginosa ATCC 27853 and Staphylococcus aureus ATCC 25923. Overall, the coatings achieved log reductions up to ~9.35, corresponding to a bacterial kill rate (for S. aureus) exceeding 99.99%, with 10CrHAp-Dx showing slightly superior performance. Similar behavior (log reductions of ~8.6 and ~8.9, respectively, indicating a sustained antibacterial effect and >99.99% bacterial elimination) was observed and for Pseudomonas aeruginosa. AFM was used to evaluate the bacterial cells interaction with the coating’s surfaces. The biological assays demonstrated that both coatings possess notable antibacterial activity, underscoring their potential in biomedical applications, particularly in the design of new antimicrobial devices.

Biomimetics doi: 10.3390/biomimetics11050326

Authors: Sihan Yuan Zhipeng Li Junjie Zhang

Low-altitude terrain-following flight is essential for obtaining high-quality data in unmanned aerial vehicle (UAV) aeromagnetic surveys, but achieving efficient and safe path planning within complex terrains remains challenging. To address this issue, a Q-learning-assisted multi-strategy Starfish Optimization Algorithm (QMSFOA) is proposed for offline path planning. The proposed algorithm integrates four improvement strategies: (1) employing a Sobol sequence combined with Refraction Opposition-based Learning for population initialization to enhance population diversity; (2) adopting a hybrid adaptive differential mutation mechanism to improve search efficiency; (3) utilizing Q-learning to intelligently schedule optimization modes, thereby accelerating convergence speed; (4) introducing an adaptive t-distribution elite perturbation strategy to refine convergence accuracy. Experimental results on the CEC-2022 benchmark suite indicate that QMSFOA achieves the best convergence accuracy on nine functions and exhibits a superior performance across most metrics compared with the competing algorithms. Simulation experiments of aeromagnetic surveys in complex 3D terrains demonstrate that paths planned by QMSFOA satisfy kinematic and obstacle avoidance constraints while reducing path costs by approximately 25% compared with the standard Starfish Optimization Algorithm (SFOA). Additionally, the standard deviation is reduced by one to two orders of magnitude compared with the competing algorithms. These results demonstrate that the proposed method provides an efficient, reliable, and intelligent solution for high-precision UAV geophysical exploration in complex environments.

Biomimetics doi: 10.3390/biomimetics11050325

Authors: Kelsi M. Rutledge Sean P. Colin John H. Costello Noa Yoder Simon R. Anuszczyk Kelly R. Sutherland Brad L. Gemmell John O. Dabiri

Ocean monitoring is essential for understanding climate change and marine ecosystem dynamics, yet achieving comprehensive global coverage remains a challenge in oceanography. Current technologies face limitations in cost, power, hardware, and depth capacity that restrict widespread monitoring capabilities. Here we show that biohybrid robotic jellyfish (Aurelia aurita) can serve as autonomous vertical ocean profilers by integrating microcontrollers with positively buoyant sensor payloads, achieving controlled vertical-profiling capabilities. Laboratory experiments demonstrated repeatable up–down trajectories, quantified force balance limits, and identified predictable, size-dependent descent swimming speeds. Field deployments in Massachusetts coastal waters and the open ocean off the Florida Keys demonstrated field operation to ocean depths >25 m with successful in situ temperature and depth measurements. To our knowledge, this represents the first biohybrid jellyfish platform to combine autonomous, pressure-triggered vertical profiling with onboard oceanographic sensing in natural marine environments. This approach leverages the global distribution and remarkable swimming efficiency of living jellyfish while eliminating propulsion power requirements by utilizing the animal’s natural swimming capabilities. While further development is required for long-term ocean deployment, this study lays the groundwork for a new class of biohybrid ocean-sensing platforms with advantages in cost, power, and mission flexibility, providing a pathway toward dense sensor networks and increased ocean monitoring observations.

Biomimetics doi: 10.3390/biomimetics11050324

Authors: Fahad Alghamdi Saad Alqithami

In organizational cyber crises, incident response and official communication form coupled control loops, yet they are usually engineered separately. We present V-CHIMERA (Verified Coupled Human–Information–Machine Incident Response Architecture), a framework for organizational cyber crisis response under misinformation that jointly models cyber state, belief dynamics, trust, and communication governance. The framework combines three elements: an explicit cyber–social coupling architecture, a runtime protocol shield for communication safety, and immune-gated coupling (IGC) that uses danger signaling, tolerance thresholds, and immune memory to regulate when social feedback should affect operational response and how strongly counter-messaging should be targeted. Across three representative scenarios—ransomware rumor, outage rumor, and exfiltration scam—and eight seeds per scenario, all shielded policies achieved zero executed protocol violations. Relative to naive coupled control, IGC reduced cyber-harm area under the curve (AUC) by 57.6% in ransomware rumor and 42.6% in outage rumor while also reducing misbelief. Results were scenario-dependent rather than uniformly dominant: in exfiltration scam, a broadcast-only ablation outperformed targeted messaging, showing that targeting can fail when diffusion rapidly crosses community boundaries. Sensitivity analysis further shows that IGC attenuates the brittleness observed under strong coupling and weak moderation. The results suggest that biomimetic regulation is valuable not because coupling always helps, but because it prevents overreaction, clarifies when targeting should be used, and yields safer organizational defaults for misinformation-aware incident response.

Biomimetics doi: 10.3390/biomimetics11050323

Authors: Alexane Thibodeau Aiden Smith Stéphane Chabaud Geneviève Nadeau Jean Ruel Stéphane Bolduc

Stress urinary incontinence (SUI) affects a significant proportion of women and often requires surgical intervention when conservative treatments fail. While midurethral slings (MUS) are widely used, concerns over complications such as mesh exposure/erosion and chronic pain have driven interest in regenerative medicine alternatives. This review explores emerging strategies, including stem cell therapies, platelet-rich plasma injections, decellularized extracellular matrix scaffolds, injectable hydrogels, and bioengineered slings. These approaches aim to restore continence by promoting tissue regeneration, improving biocompatibility, and reducing adverse reactions. We evaluate their mechanisms, reported outcomes, and current stage of development, supported by in vitro and in vivo model data. Although promising, these technologies face challenges related to cell viability, scaffold integration, and clinical translation. Continued interdisciplinary research is essential to optimize these therapies and bring safer, more effective solutions to patients. Regenerative strategies may ultimately redefine the future of SUI treatment by offering biologically integrated, long-lasting alternatives to synthetic slings. To date, no tissue-engineered or regenerative biomimetic sling has received regulatory approval for routine clinical use in the management of stress urinary incontinence.

Biomimetics doi: 10.3390/biomimetics11050322

Authors: Yuwei Fan Zhilin Cheng Youyou Cheng

Metaheuristic algorithms can fail to balance global exploration and local exploitation, occasionally becoming trapped in suboptimal regions on highly multimodal problems. The Groupers and Moray Eels (GME) algorithm, inspired by the associative hunting strategies of marine predators, provides a cooperative optimization framework. However, the sequential interaction phases of GME can fail to maintain diverse topological coverage across heavily constrained landscapes. To address these limitations, we propose an enhanced variant, GPS-CC-GME. The approach improves the initial agent distribution by deploying a number-theoretic Good Point Set (GPS) generation protocol to establish a uniformly dispersed starting space. In addition, algorithmic stagnation is addressed through a dual-crossover search architecture. A horizontal crossover stage enforces information sharing among randomized agents to sustain global diversity, and a vertical crossover phase isolates specific dimensional vectors within individual agents for localized fine-tuning. We evaluated the proposed model on the CEC2017 benchmark suite, where it secured the highest overall ranking compared to the baseline GME and several standard metaheuristics. GPS-CC-GME was then applied to a high-dimensional optimization scenario for petroleum reservoir production. The algorithm yielded higher Net Present Value (NPV) metrics than the canonical framework. The results indicate that embedding deterministic initialization and bidirectional mutation operators into multipredator models can improve search outcomes in non-linear engineering tasks.

Biomimetics doi: 10.3390/biomimetics11050321

Authors: Zihao Cheng Li Cao Yang Qiu Yinggao Yue

Aiming at the problems of uneven population initialization distribution, easy trapping in local optima, unbalanced exploration and exploitation capabilities, insufficient optimization accuracy and convergence speed of the original Greater Cane Rat Algorithm (GCRA), this paper proposes a Chaos-Integrated Difference-Enhanced Greater Cane Rat Algorithm (CEGCRA). Firstly, the algorithm adopts the piecewise chaotic map to generate the initial population, which effectively improves the uniformity and diversity of the population and reduces the risk of premature convergence. Secondly, an accumulated difference foraging strategy is designed to integrate the position and fitness difference information between individuals and the optimal individual, dynamically adjust the search direction and step size, and realize the adaptive balance between global exploration and local exploitation capabilities. Finally, the dynamic switching mechanism between the exploration and exploitation stages of the algorithm is improved, and the boundary constraint handling strategy is optimized to further enhance the algorithm stability. To verify the performance of the CEGCRA, comparative experiments were carried out on the CEC2014 and CEC2020 benchmark test suites. The results show that compared with the original GCRA, the optimal fitness value of the CEGCRA is reduced by an average of 35.3%, the standard deviation is reduced by an average of 22.7%, and the convergence speed is increased by an average of 28.9%. In two typical engineering constrained optimization problems, namely, welded beam design and cantilever beam design, the cost of the welded beam solved by the CEGCRA is 12.5% lower than that of the original GCRA and 8.7% lower than that of the PSO algorithm; the weight of the cantilever beam is 0.012% lower than that of the original GCRA and 0.008% lower than that of the GA, with a constraint satisfaction rate of 100%. The experimental results fully prove that the CEGCRA is superior to the original GCRA and seven comparison algorithms such as PSO, DE and SSA in terms of optimization accuracy, convergence speed, robustness and constraint handling ability and can effectively solve complex engineering optimization problems with high dimensionality, nonlinearity and multiple constraints.

Biomimetics doi: 10.3390/biomimetics11050320

Authors: Zhong Hu Haiping Hong Tim Lin

Biomimetic materials mimic biological structures and functions. They are crucial for addressing complex challenges in tissue engineering, sustainable architecture, and energy storage. Traditionally, designing these materials requires slow, resource-intensive trial-and-error methods and physics-based simulations. Recently, Artificial Intelligence (AI) and Machine Learning (ML) have transformed this field. They translate biological intelligence into actionable engineering logic and rapidly explore massive design spaces. Despite rapid advancements, the field still faces several critical bottlenecks, including complexity mismatches, data scarcity, and limited interpretability. This review examines AI-driven biomimetic design across five primary “interfaces”: (1) Biological Pattern Recognition, (2) Structural Optimization, (3) Generative Morphogenesis, (4) Adaptive Fabrication, and (5) Data-Driven Discovery Platforms. The review also outlines future perspectives, especially the shift toward autonomous “closed-loop” laboratories. In these labs, AI will manage the entire workflow, i.e., design, synthesis, and testing, without human intervention. Future efforts will likely focus on multi-model data mining to understand complex, life-like properties. Furthermore, research aims to develop Explainable AI (XAI) to ensure deterministic modeling in safety-critical applications. The ultimate goal is a synergistic relationship. AI will design materials, but these materials, using biomimetic metabolic or neural models, will also help construct more efficient AI architectures.

Biomimetics doi: 10.3390/biomimetics11050319

Authors: Jingshu Shi Hongwu Zhu Yifei Yang Bowen Liu Xingjun Wang

Data-driven exoskeletons offer the potential for adaptive augmentation of human mobility. Yet their widespread adoption is hindered by labor-intensive biomechanical data collection and manual tuning. Herein, this study presents a highly efficient synthetic data approach to facilitate data-driven pipelines. We leveraged an Adversarial Motion Priors (AMP) agent to learn stylized walking within a massively parallel, physics-based simulation. The resulting high-fidelity data were collected and validated against OpenSim inverse dynamics pipelines. Further, we trained an end-to-end torque prediction algorithm using the collected data. A novel CNN-Transformer architecture was developed to map contralateral swing-phase data to variable-length push-off torque profiles. This enabled real-time, adaptive torque assistance of exoskeletons for variable-speed walking. A custom ankle exoskeleton was used to demonstrate robust sim-to-real transferability. Our system achieved an average root mean square error of approximately 0.081 ± 0.015 newton-meters per kilogram and an average R2 of 0.836 ± 0.050 across speeds ranging from 0.6 to 1.75 m·s−1. The controller significantly reduced user-positive ankle mechanical work by up to 14 ± 6.30%. Finally, our multi-sensor configuration exhibited inherent fault tolerance, ensuring safe operation even under partial sensor failure. By taking a scalable, data-driven approach, this work offers a practical pathway toward deploying autonomous exoskeletons in versatile, real-world environments.

Biomimetics doi: 10.3390/biomimetics11050318

Authors: Hui Chen Zhenya Wang Shikai Zhang Ligang Yao

Soft robotic hands are well suited for handling fragile and geometrically diverse objects, yet many existing designs still rely on fixed finger layouts, which limits grasping adaptability when object size varies substantially. To address this issue, this study proposes a four-finger pneumatic soft robotic hand with a synchronous variable-stroke base mechanism. The design combines a rigid reconfigurable base with compliant soft fingers, allowing the radial positions of the fingers to be adjusted before grasping. A system-level kinematic model is established to describe the relationship between base stroke, finger bending, and the reachable workspace of the hand. A prototype is fabricated, and comparative grasping experiments are conducted under fixed-stroke and variable-stroke configurations using objects with different grasping cross-sections. The results show that the proposed mechanism achieves stable geometric reconfiguration and improves grasping performance when the initial finger spacing is matched to the object size. In particular, the variable-stroke configuration provides better grasp stability and a wider usable grasping range than the fixed-stroke configuration. These findings indicate that geometric reconfiguration at the hand level is an effective way to enhance the adaptability of multi-finger soft robotic hands.

Biomimetics doi: 10.3390/biomimetics11050317

Authors: Minchae Kang Suyeon Seo Eunsol Park Min-Woo Han

Underwater soft robots offer many potential applications, including exploration, search, and rescue missions. Notably, these recently developed underwater soft robots present a safer and more adaptable alternative to rigid robots currently in use. Their flexible and deformable bodies enable them to easily adapt to challenging underwater environments and interact with diverse aquatic creatures and structures. In this paper, we present a soft buoyancy gripper that can manage buoyancy and adjust its position in the water without relying on external mechanisms. Modulating the volume of internal fluid can function both as a gripper and adjust buoyancy as needed. When buoyancy is reduced and fluid volume is minimized, the gripper can securely grasp objects, while increased fluid volume and buoyancy allow for delicate object placement. During experiments, the gripper successfully grasped and released multiple objects. When an extra channel was added, the crawling motion was achieved. The buoyancy control system demonstrates versatility and adaptability, offering the possibility of safe underwater exploration and research. Its ability to operate without harming marine environments or organisms makes it suitable for underwater research.

Biomimetics doi: 10.3390/biomimetics11050316

Authors: Qingpeng Wen Haomin Zhu Yuepeng Zhang Linzhong Xia Bo Gao Zhuozhen Li

Action chunking strategies in robot imitation learning struggle to dynamically balance between long-range motion efficiency and short-range operational precision due to their fixed planning horizon. This paper presents an Adaptive Action Chunking framework that enables robots to dynamically predict the optimal action chunk length based on real-time visual context. We design an end-to-end dual-branch network comprising a shared visual encoder, a parallel action prediction head, and a chunk-size prediction head. Experiments on two real-world bimanual robot manipulation tasks (transport-and-place and flip-and-handover) demonstrate that the method autonomously derives two distinct intelligent strategy patterns—phase-aware switching and sustained high-frequency adjustment—in response to task uncertainty. It significantly outperforms fixed-chunk baselines in both success rate and efficiency. Ablation studies confirm that the performance gain stems from the adaptive decision-making mechanism itself.

Biomimetics doi: 10.3390/biomimetics11050315

Authors: Junye Yao Jinhua Zhang Zhiyan Zhong Huimin He Hongxin Wang

Insects can achieve rapid and precise collision detection despite having limited neural resources. This efficiency provides a vital reference for the development of artificial collision detection systems. Existing bio-inspired models typically include LGMD-based and correlation-based methods. Methods in the former category suffer from a non-linear dependency of warning time on the object’s contrast against the background due to the strong reliance on inter-frame intensity differences. While the latter effectively describe motion perception by leveraging local motion information derived from a delay-and-correlation mechanism, they lack precise spatial boundaries, failing to isolate the actual moving target across irrelevant background dynamics. In this paper, we propose a bio-inspired visual system with a motion-contour-guided mechanism to suppress false-positive background movement while achieving contrast-independent looming warning generation. Specifically, the proposed visual system is composed of two synergistic pathways. The first pathway is designed to extract motion cues and spatial perception of motion via neuronal ensemble coding, whereas the second pathway is developed to extract the contour of the moving target by employing geometric contour evolution. By integrating this derived contour with localized motion cues, the system analyzes the dynamic evolution of the target’s boundary to identify potential collision threats. Benefiting from this fusion of structure and motion, experimental results demonstrate that the proposed visual system is more robust than conventional bio-inspired models in collision detection across distinct contrast scenarios.

Biomimetics doi: 10.3390/biomimetics11050314

Authors: Taoli Du Ziming Wang Yue Wang Ming Ma Wenhui Li

Multi-modal Magnetic Resonance Imaging (MRI) provides complementary information for clinical diagnosis, yet its large-scale storage, privacy sensitivity, and annotation cost pose significant challenges. Inspired by biological vision systems, which integrate multi-sensory inputs and compress experiences into compact memory representations, we propose a bio-inspired framework termed Contrast-Guided Multi-modal Dataset Distillation (CGMDD). In biological perception, different sensory channels observe the same environment from complementary perspectives, while hierarchical neural processing ensures perceptual consistency across modalities. Meanwhile, memory systems such as the associated medial temporal lobe structures consolidate redundant experiences into efficient representations for long-term storage. Motivated by these principles, CGMDD treats multi-modal MRI as multi-view perceptual signals and introduces a hierarchical cross-modal contrastive learning mechanism that enforces perceptual alignment across modalities, analogous to multi-level processing in the visual cortex. Furthermore, we design a dynamic dataset distillation strategy that mimics memory consolidation by compressing large-scale data into compact, informative synthetic representations through gradient-based optimization. The proposed framework jointly optimizes perceptual alignment and memory compression in an end-to-end manner, achieving a biologically plausible integration of perception and learning. Experimental results on two MRI datasets demonstrate that CGMDD can compress the original dataset to 5% of its size while maintaining competitive performance, even with only 30% of the labels. These findings highlight the effectiveness of bio-inspired mechanisms in building efficient, robust, and privacy-preserving computer vision systems.

Biomimetics doi: 10.3390/biomimetics11050313

Authors: Adalberto Nieto Nacari Marin-Calvo

The increasing deployment of wind energy has brought renewed attention to aeroacoustic noise generated by wind turbine blades, where broadband noise is primarily associated with vortex shedding at the trailing edge (TE) and leading edge (LE) of airfoils. Owls, particularly Tyto alba, exhibit wing morphologies such as serrations, velvet-like surfaces, and fringes that enable silent flight through aerodynamic noise suppression. This study presents a scoping review of the scientific literature on owl-inspired serration strategies applied to aerodynamic airfoils and wind turbine blades. The literature search was conducted across major academic databases, including Scopus, ScienceDirect, SpringerLink, and MDPI, covering publications from 1970 to 2025. A total of 69 experimental and numerical studies focusing on LE and TE serrations was analyzed. The review integrates aeroacoustic analysis with bio-inspired design perspectives. The analyzed studies consistently show that serrated geometries modify vortex dynamics and turbulence structures, leading to measurable acoustic benefits. Experimentally, the largest reductions reported for aerodynamic airfoils reached about 7 dB for both LE and TE serrations, mainly as broadband noise attenuation, in specific frequency ranges. Numerically, the highest reported reduction reached up to 21 dB for a serrated TE configuration, corresponding to spectral SPL reduction mainly below 1.6 kHz. The reviewed studies also indicate that the associated aerodynamic response is strongly configuration-dependent, ranging from limited penalties to measurable changes in lift, drag, power output, or structural loading. Numerical simulations further support experimental findings and highlight the importance of geometric parameters such as serration amplitude, wavelength, and spacing. Overall, bio-inspired serrations represent a promising passive strategy for aeroacoustic noise mitigation in wind turbines, drones, and rotating aerodynamic systems. Future research should focus on the multi-objective optimization of serration geometry, large-scale experimental validation, and the integration of bio-inspired concepts into industrial blade designs.

Biomimetics doi: 10.3390/biomimetics11050312

Authors: Rong Cheng Zhiwei Sun Kun Qi Wangyu Wu Lingling Xu

As multi-view datasets expand across diverse practical fields, feature selection (FS) has become an indispensable preparatory stage for machine learning models. Nevertheless, real-world multi-view data is often unlabeled and distributed among isolated clients, posing significant challenges to traditional centralized methods due to privacy concerns and communication constraints. Furthermore, existing centralized and federated approaches frequently suffer from entrapment in local optima and lack robust convergence guarantees. To address these issues, we propose Fed-MUFSHT, a federated framework for multi-view unsupervised FS (MUFS) that integrates tensor learning with a novel metaheuristic optimizer, Hierarchical-Cognitive Tianji’s Horse Racing Optimization (HC-THRO). Within the federated learning paradigm, Fed-MUFSHT follows a dual-stage local optimization process. Stage 1 applies HC-THRO, which integrates Hierarchical Competitive Learning and Adaptive Cognitive Mapping to simulate multi-level strategic competition and cognitive adaptation among individuals. This design enhances global exploration, adaptive learning, and fine-grained feature selection in high-dimensional spaces. Stage 2 employs a TL module based on canonical polyadic (CP) decomposition to perform missing-view imputation and refine latent representation learning. At the global level, a privacy-preserving aggregation strategy based on Normalized Mutual Information (NMI) and feature weights enables efficient model coordination without exposing raw data. Comparative experiments on several public benchmark datasets reveal that Fed-MUFSHT maintains clear advantages over strong competing methods, showing better optimization results together with more dependable convergence characteristics. The overall evidence suggests that the proposed approach is both robust and effective for distributed optimization tasks involving privacy protection.

Biomimetics doi: 10.3390/biomimetics11050311

Authors: Fangyan Chen Xiangcheng Wu Weimin Qi Zhiming Wang Zhiyu Wang Peng Li

Wireless Sensor Networks (WSNs) enable energy-efficient data collection in dynamic environments but continue to face the dual challenges of severely constrained node energy and the spatiotemporal heterogeneity of data traffic. Inspired by honeybee foraging behavior, this paper proposes a hybrid optimization framework that integrates mixed-integer linear programming (MILP) and Markov decision processes (MDP), utilizing Q-learning for adaptive decision-making. The proposed framework systematically maps the dual-layer decision-making mechanism of honeybee foraging onto a synergistic architecture combining MILP-based global planning and MDP-based local adaptation, offering a novel bio-inspired solution for mobile sink trajectory planning and adaptive routing. Specifically, the upper-level MILP module simulates a colony-level global assessment of distant nectar sources, generating an initial global trajectory by determining the optimal access sequence of cluster heads to minimize the movement cost of the mobile sink. The lower-level Q-learning module simulates the individual-level local adaptation, where bees adjust harvesting behavior in real-time based on nectar quality and distance. This module continuously optimizes routing parameters based on real-time network states, including residual energy, the ratio of surviving nodes, data queue lengths, and cluster head density. The algorithm employs an ϵ-greedy strategy to balance exploration and exploitation, while a periodic decision-update mechanism is introduced to harmonize computational efficiency with learning stability. Furthermore, a multi-objective reward function is designed to jointly optimize energy efficiency, network lifetime, end-to-end latency, and path length. Extensive simulation results demonstrate that the proposed MILP-MDP hybrid framework significantly outperforms several representative baseline algorithms in terms of network lifetime extension and energy balance. These findings validate that the integration of bio-inspired foraging strategies and reinforcement learning provides an efficient and robust solution for trajectory planning and adaptive routing in dynamic WSNs.

Biomimetics doi: 10.3390/biomimetics11050310

Authors: Kaicheng Zhang Kairu Li Peiyao Wang Yixuan Sheng

Electrotactile feedback is a compact approach for providing tactile cues in robotic teleoperation, but personalized calibration remains time-consuming because tactile perception varies across users. To address this problem, this study develops a subject-informed multi-layer finite element model of fingertip electric-field distribution coupled with a neural-response model and proposes a simulation-derived configuration-ranking method termed the Perceived Correctness Score (PCS). A gradient boosting regression model is then used to recommend among 36 candidate electrode diameter–spacing combinations. Validation was conducted using a custom-developed 3×2 multi-channel fingertip electrotactile stimulation system in a shape/area recognition task involving six healthy subjects. The predicted PCS showed a moderate positive correlation with the measured mean recognition accuracy across configurations (Pearson r=0.48, p<0.05). The model achieved Top-1 exact matching for three of six subjects and Top-5 coverage for five of six subjects. Compared with conventional exhaustive psychophysical calibration, the proposed method reduced the average configuration time from 122.7 min to 16.0 min, corresponding to an efficiency improvement of 87.0%. These results show that model-guided ranking can substantially reduce the burden of individualized electrotactile configuration.

Biomimetics doi: 10.3390/biomimetics11050308

Authors: Xuemei Zhu Weijie Guo Yang Shen Jingchun Guo Shirong Li Zhiqiang Chang

The Kangaroo Escape Optimizer (KEO) is a recently proposed biomimetic metaheuristic inspired by the adaptive escape strategies of kangaroos in predator–prey interactions. Although effective, KEO-like algorithms based on many populations may suffer from premature convergence and loss of population diversity when addressing complex, multimodal, and constrained optimization problems. This paper proposes an Enhanced Kangaroo Escape Optimizer (EKEO) that integrates Differential Evolution Mutation (DEM) and Quasi-Oppositional Learning (QOL) to address fundamental limitations in exploration–exploitation balance. From a biomimetic perspective, DEM mimics the refined high-frequency muscular adjustments of a kangaroo during close-range evasion, enabling local refinement around promising solutions, while QOL emulates the animal’s sudden directional changes and scanning behavior to preserve population diversity and escape local optima. Their principled integration yields a robust optimization framework that consistently outperforms state-of-the-art and classical metaheuristics across benchmark functions and real-world engineering problems. The findings suggest a generalizable design principle for biomimetic hybrid metaheuristics, demonstrating that coupling directed exploitation with diversity-preserving exploration leads to reliable high-performance optimization. The performance of EKEO is rigorously evaluated in two phases. First, its optimization accuracy and convergence speed are benchmarked against 11 state-of-the-art and classical metaheuristics on 23 classical benchmark functions and the CEC 2019 test suite. Second, its practical applicability and constraint-handling effectiveness are validated on four real-world engineering design problems: step-cone pulley, gear system, tubular column, and pressure vessel design. The experimental results are supported by comprehensive statistical analyses (including Wilcoxon rank-sum tests) and convergence curves, showing that EKEO consistently outperforms its competitors in solution quality, convergence speed, and robustness. These findings establish EKEO as a competitive, reliable, and versatile biomimetic optimization tool suitable for solving complex continuous and constrained engineering optimization problems.

Biomimetics doi: 10.3390/biomimetics11050309

Authors: Chengtao Du Jinzhong Zhang Jie Fang

The black-winged kite algorithm (BKA) integrates the Cauchy mutation strategy and the leader selection strategy to simulate high-altitude circling exploration, fixed-point diving attack, and group cooperative migration of the black-winged kites to approximate the global optimal solution. The BKA exhibits deficiencies in ponderous convergence efficacy, inefficient calculation precision, and insufficient population diversity. To strengthen the convergence property and computational practicability, an enhanced BKA with multiple strategies (MSBKA) is advocated to accommodate global optimization and constrained engineering applications. The objective is to systematically verify its advancement and competitiveness and accurately actualize the global optimal solution. The ranking-based differential mutation can strengthen population information interaction, accelerate convergence efficiency, restrain premature convergence, diminish homogenization competition, promote exploration and exploitation, intensify elite individual guidance, downscale ineffective iterations, and materialize orderly population renewal. The simplex method can execute the local refinement operations of reflection, expansion, compression and contraction, strengthen local mining efficiency, ameliorate solution accuracy, abate parameter sensitivity, eschew local optimal traps, accelerate accurate convergence, and preserve the optimal individual potential. The elite opposition-based learning strategy can fabricate reverse solutions, expand the monolithic detection space, shorten the convergence process, elevate the quality of initial and iterative solutions, boost population diversity, guide intelligent search direction, and relieve premature convergence. The MSBKA utilizes deficiency orientation, strategy adaptation, and collaborative search to accomplish the realistic demands of high-precision, high-efficiency and strong constraint adaptation, surmount the static trade-off dilemma, endow a strong directional abscond mechanism to replace random perturbation, and actualize the inertia of directional exploration and the blind spots of solution exploitation. Twenty-three benchmark functions and six real-world engineering designs are employed to authenticate theoretical superiority and engineering practicability. The experimental results demonstrate that the MSBKA incorporates strong practicability and reliability to strengthen information interaction, restrain search stagnation, diminish convergence oscillation and fluctuation, facilitate globalized discovery and localized extraction, expedite convergence efficacy, ameliorate solution precision, and consolidate stability and robustness.

Biomimetics doi: 10.3390/biomimetics11050307

Authors: Zhengyang Liu Tianruo Guo Yuyan He Shiwei Zheng Xiaoyu Song Cuixia Dai Jiaxi Li Xinyu Chai Yao Chen Liming Li

Visual prostheses aim to approximate biomimetic visual function by electrically simulating surviving retinal neurons. Improving the spatial resolution of electrically elicited artificial vision remains a critical challenge for retinal prostheses. We investigate how dynamic virtual channel (DVC) parameters shape retinal ganglion cell (RGC) population responses to improve spatial precision and activation efficiency in epiretinal stimulation. We developed a computational modeling framework to quantify DVC performance using a hierarchical optimization strategy. First, static virtual channels (SVCs) were used to map how current ratio (α) and stimulus intensity govern RGC activation, defining an optimal SVC parameter space. Building on this baseline, DVC protocols were refined by evaluating the combined effects of inter-virtual–channel interval (ΔT), α, and intensity. This strategy significantly reduces the complexity of DVC parameter optimization. Under SVC stimulation, increasing intensity improved the linearity of receptive field (RF) centroid displacement with α, while α and intensity jointly set RF centroid location and activated area. Under DVC stimulation, ΔT strongly modulated RGC activation, especially at short intervals. Initializing from SVC-optimized parameters, tuning ΔT and intensity produced more confined activation at lower stimulus intensities than SVC, indicating that DVC can serve as a novel stimulation strategy to enhance spatial precision and activation efficiency in retinal stimulation. This study provides the first systematic analysis of retinal DVC stimulation and a practical optimization framework for next-generation prostheses.

Biomimetics doi: 10.3390/biomimetics11050306

Authors: Shengnan Li Taiju Yin

As engineering systems grow in complexity, reliable metaheuristic optimizers are increasingly essential. While swarm intelligence algorithms are widely applied, recent approaches like the Cuckoo Catfish Optimizer (CCO) can experience premature convergence due to limited local exploitation and simplistic boundary handling. To address these limitations, this paper proposes the Dung Beetle with Reflection CCO (DBRCCO), integrating two principal mechanisms. First, an adaptive local search strategy inspired by dung beetle foraging is incorporated to intensify exploitation within dynamically contracting regions. Second, a momentum-preserving reflecting boundary mechanism replaces traditional clamping, maintaining population diversity near constraint edges. DBRCCO is evaluated against eight contemporary metaheuristic algorithms using the 29 CEC2017 benchmark functions and a reservoir production optimization problem. Statistical analyses indicate that DBRCCO achieves competitive performance, securing a Friedman ranking of 1.5172 (p<0.05). In the reservoir application, DBRCCO improves the mean Net Present Value (NPV) by 12.54% while reducing variance by over 72% relative to the standard CCO. These findings suggest that DBRCCO offers a stable and effective alternative for complex optimization tasks.

Biomimetics doi: 10.3390/biomimetics11050305

Authors: Suppasin Ngamlikitlert Minsung Kim Suwin Sleesongsom

Bird strikes are a key threat to aircraft wing leading edges. This investigation evaluates a honeycomb block reinforcement concept to improve bird strike resistance while maintaining structural efficiency. A validated simulation was developed using an explicit dynamic finite element approach, in which the bird was modeled as a soft body using smoothed particle hydrodynamics, and the wing leading edge was represented with a honeycomb block reinforcement concept. A design of experiments based on McKay Latin hypercube sampling was applied to comprehensively examine the effects of the geometric parameters on the maximum von Mises stress and maximum deformation. Response surface regression models were then constructed to approximate the impact responses and analyze the model correctness. These models were subsequently integrated into a constrained optimization methodology using sequential quadratic programming and population-based integrated learning to minimize deformation while limiting stress below the material yield threshold. The optimized honeycomb and skin configuration demonstrated a noticeable optimization of the maximum deformation within the yield stress limit compared with the baseline design. The results confirm that the proposed honeycomb block reinforcement concept, combined with a regression-based optimization strategy, constitutes a practical, computationally effective approach to improving bird strike resistance and provides a feasible design option for future impact-resistant wing leading-edge designs.

Biomimetics doi: 10.3390/biomimetics11050304

Authors: Ali Yildiz Ali Akdagli Filiz Karaomerlioglu Gökhan Yüksek Davut Izci Vedat Tümen Serdar Ekinci Mohammad Salman Mohammad Al-Rabayah

The demand for hardware-efficient interference suppression algorithms is growing with the increasing density in wireless communication networks. In this paper, a robust position-only null steering method for linear antenna arrays is proposed based on Honey Formation Optimization with Single Component (HFOSC), a metaheuristic algorithm founded on the ripening process of honey in beehives. By optimizing only the element locations, the proposed method avoids the use of phase shifters and attenuators, thus reducing implementation complexity while maintaining flexibility in pattern control. A 30-element linear array with Chebyshev excitation is used to test the technique under representative interference scenarios such as single-null, multiple-null, and wide-sector nulling cases, as well as constrained practical designs. The simulation results demonstrate that the proposed approach can realize strong interference suppression across different cases while maintaining the main-beam shape and acceptable sidelobe performance. In idealized discrete-interference cases, nulls below −90 dB are achieved, while in a practical constrained design with a minimum inter-element spacing of 0.5λ and a position resolution of 0.1λ, a null depth of −72.89 dB is still achieved, confirming the practical applicability of the method. Moreover, comparative results with GA, PSO, and DE over 100 independent runs illustrate that HFOSC achieves the lowest optimization cost and the smallest standard deviation, along with a favorable overall trade-off between beam preservation and null suppression, with statistically significant superiority in optimization performance. The proposed method does not require phase shifters and attenuators, providing a simple, hardware-friendly, and robust solution for adaptive interference cancellation in wireless communication systems.

Biomimetics doi: 10.3390/biomimetics11050303

Authors: Stanislav N. Gorb Longjian Xue Barbara Mazzolai Phillip B. Messersmith

Biomimetics seeks to translate principles from living systems into innovative engineering solutions by drawing on the remarkable efficiency, adaptability, and multifunctionality found in nature [...]

Biomimetics doi: 10.3390/biomimetics11050302

Authors: Lei Guo Nancheng Ma Zhuoxuan Wang Rumeng Liu

Spiking neural networks (SNNs) offer inherent advantages in processing temporal information. However, their network topologies are predominantly algorithm-generated, lacking constraints from biological brain connectivity, which limits their bio-plausibility. In our previous work, we constructed a spiking neural network (SNN) by incorporating the topological structure of functional brain networks derived from fMRI data of healthy subjects and proposed an fMRISNN model. This model was further employed as the reservoir layer of a liquid state machine (LSM) to build a speech recognition framework. In this framework, the Lyon ear model and the BSA were used to encode speech signals into spike sequences; however, this approach suffers from high computational cost and limited adaptability to temporal variations. To address these limitations, we propose an enhanced Mel-frequency cepstral coefficient (MFCC)-driven sparse spike encoding method. For the speech recognition task, we systematically compare the two preprocessing pipelines in terms of spike number, spike sparsity, encoding time, and downstream speech recognition performance. Experimental results show that the proposed method generates substantially fewer spikes, achieves markedly higher sparsity, and requires significantly less encoding time, while maintaining nearly the same recognition accuracy under the same LSM-based framework. These findings indicate that improved speech input representation can enhance the computational efficiency of SNN-based speech recognition without compromising recognition capability. In addition, the fMRISNN model significantly outperforms several baseline models with algorithmically generated topologies. Compared with mainstream models reported in the literature, although the deep convolutional neural network (CNN) still achieves higher absolute recognition accuracy, the fMRISNN exhibits clear advantages in terms of model parameter size and theoretical energy efficiency.

Biomimetics doi: 10.3390/biomimetics11050301

Authors: Ibrahim E. Helal Mahmoud F. Ahmed Ahmed M. Abdellatif Mohamed A. Hashem Hatim A. Al-Abbadi Elsayed Metwally

Decellularized extracellular matrix (dECM) scaffolds derived from xenogeneic tissues represent promising biomaterials for tissue engineering. In this study, dECM scaffolds were developed and characterized from four ovine tissues—skin, tunica vaginalis, fascia lata, and pericardium—using a detergent-based decellularization protocol to evaluate decellularization efficiency and extracellular matrix (ECM) preservation. Decellularization was performed using a sequential detergent-based protocol with sodium dodecyl sulfate and Triton X-100. Decellularization efficacy and matrix preservation were evaluated through gross examination, histological analysis, scanning electron microscopy (SEM), and residual DNA quantification. Gross inspection revealed increased translucency and reduced pigmentation in decellularized tissues compared with native counterparts, indicating effective cellular removal while maintaining overall tissue architecture. Histological assessment confirmed the complete absence of nuclear and cytoplasmic material, alongside preservation of collagen-rich extracellular matrix organization. SEM analysis demonstrated well-maintained ultrastructural features, including aligned collagen fibers and porous ECM architecture, with complete removal of epithelial and stromal cellular elements. Quantitative analysis revealed approximately 94% reduction in residual DNA content across all decellularized tissues compared with native controls. This study demonstrated that the employed detergent-based protocol reliably produces structurally preserved, acellular scaffolds from multiple ovine tissues. The resulting biomaterials exhibit structural characteristics that support their potential use in tissue engineering applications, pending further functional validation.

Biomimetics doi: 10.3390/biomimetics11050300

Authors: Juan Martín-Gómez Jesús Hidalgo-Carrillo M. Carmen Herrera-Beurnio Alejandro Ariza-Pérez Alberto Marinas Francisco J. Urbano

Biotemplated strategies inspired by natural architecture have emerged as an effective strategy to improve the performance of photocatalytic materials. In this work, TiO2-based photocatalysts were synthesized using olive leaves as a biological template to reproduce their hierarchical microstructure and enhance photocatalytic hydrogen production. The artificial olive leaf (AOL) support was obtained through a biotemplated ion-exchange process followed by hydrolysis and calcination. It was then modified by photodeposition of Au or Pt nanoparticles. The materials were characterized by SEM, XRD, N2 adsorption–desorption, UV–Vis spectroscopy, and XPS to evaluate their structural and optical properties. SEM confirmed the successful replication of both the external morphology and internal architecture of the olive leaf, while XRD revealed low crystallinity with anatase as the only TiO2 phase. Optical characterization showed a reduced band gap (~2.97 eV), and extended absorption toward the visible region, with Au nanoparticles exhibiting a plasmonic band at ~550 nm, whereas Pt enhanced light-harvesting efficiency. XPS indicated the presence of oxygen vacancies and Ti3+ species that promote metal–support interactions. Photocatalytic glycerol photoreforming showed a strong enhancement in hydrogen production after noble metal incorporation, reaching up to 14-fold under UV irradiation and 23-fold under simulated solar light for the Pt-modified catalyst, highlighting the synergy between biotemplated structuring and noble metal deposition.

Biomimetics doi: 10.3390/biomimetics11050299

Authors: Neslihan Aydın Ebubekir Beyazoglu Irfan Karagoz

One of the engineering applications inspired by nature is bio-inspired wings. The aerodynamic properties and autorotation characteristics of samara wing models have been studied extensively using both experimental and numerical methods. However, the three-dimensional flow behavior and angle of attack interaction around a natural samara wing are not yet fully understood. This study investigates the flow behavior around a samara wing model, with the aim of underlying physics and qualitatively analyzing the flow field, as well as the aerodynamic forces and stresses. Since the samara wing and the flow around it are three-dimensional, the difficulty of experimental investigation was taken into account, and the numerical analysis was performed using Computational Fluid Dynamics techniques. The results obtained from the numerical solution of the governing equations for three-dimensional turbulent flow were verified with experimental data. The calculations were performed by varying the angle of attack of the model wing between 0 and 50 degrees at 10-degree intervals. Depending on the angle of attack, the velocity field around the wing, surface pressure, and stress distributions, vortex structures formed on the wing and streamlines were analyzed, and the results were presented. This study and its results on this model may lead to the development and optimization of the model and its use in turbines or air vehicles.

Biomimetics doi: 10.3390/biomimetics11050297

Authors: Yu Liu Maosheng Fu Chaochuan Jia Zhengyu Liu Xuemei Zhu Bao Zhou Jingya Zhang Hai Liu

Unmanned aerial vehicle path planning faces multiple challenges in terms of effectiveness and safety. Traditional optimization methods are difficult to use to effectively find the best route. An enhanced artificial lemming optimization algorithm (ALAEN) is proposed here, which introduces stochastic differential mutation and Beta opposition-based learning into the artificial lemming algorithm (ALA). The comparison with other algorithms on the CEC2017 test set shows that it can effectively improve the optimization ability and convergence speed of the artificial lemming algorithm. Among all algorithms, ALA has an overall ranking of 5.45 and ALAEN has a ranking of 1.34. The ability of ALAEN to solve the actual problem of UAV trajectory planning is tested on two different maps, and it is found that it can effectively improve the path planning ability and ensure safety compared with the ALA. In the small map scene, the average cost function of ALA is 92.999, and the average cost function of ALAEN is 91.598, which is a significant improvement. Compared with other algorithms, ALAEN has the shortest trajectory route and trajectory cost function.

Biomimetics doi: 10.3390/biomimetics11050298

Authors: Fengjia Ju Zijian Wang Mingda Ge Hongzhe Jin Jie Zhao

When a rigid object is manipulated by dual arms to form a closed chain, the dual-arm motion must satisfy closed-chain constraints. Although synchronized motion can be achieved by strictly tracking predefined global trajectories, the presence of dynamic obstacles necessitates reactive local planning. However, existing local planning methods designed for single-arm manipulators cannot guarantee synchronization between dual arms. To address this limitation, we propose a dual-arm reactive synchronized motion controller (SMC) by incorporating closed-chain constraints on dual-arm slack velocities based on spherical geometric velocity constraints, and by implementing a flexible master-slave arm switching strategy. As a result, the proposed controller achieves synchronized dual-arm control while preserving excellent motion performance, including manipulability enhancement, obstacle avoidance, and compliance with joint angle and velocity constraints. Simulations and experiments on a humanoid upper-body robot validate the effectiveness of the proposed approach.

Biomimetics doi: 10.3390/biomimetics11050296

Authors: Menghan Zou Yuchuang Tong Tianbo Yang Zhengtao Zhang

Industrial surface inspection is fundamental to advanced manufacturing, yet reliable robotic image acquisition in complex geometries remains challenging due to severe occlusions and the inherent trade-off between resolution and coverage. Inspired by human visual inspection behaviors and perception–action coordination mechanisms, this paper proposes a hierarchical trajectory optimization framework for robotic image acquisition based on measured point clouds. Specifically, a multi-constraint preprocessing model is developed to emulate human-like active perception strategies, enabling occlusion-aware viewpoint generation over complex concave and convex surfaces with adaptive camera orientation. Building upon this, a multi-objective trajectory optimization method is introduced to coordinate global coverage and local motion efficiency, jointly optimizing viewpoint sequencing, path length, and motion smoothness hierarchically. To further enhance flexibility in constrained environments, a Pose Reachability Augmented Generative Adversarial Network (PRAGAN) is proposed to learn feasible and adaptable imaging postures under kinematic constraints. Experimental results on an industrial robotic platform equipped with 2D and 3D vision systems demonstrate 100% coverage of key surface areas, a 47.0% reduction in path length, and a 37.5% decrease in solution time compared with the baseline in the physical experiments, while ensuring collision-free operation. Both simulation and real-world experiments validate that the proposed framework effectively captures human-inspired perception and motion coordination, providing a practical and scalable solution for complex industrial surface inspection.

Biomimetics doi: 10.3390/biomimetics11050295

Authors: Thies H. Büscher Stanislav N. Gorb

In nature, biological systems employ adhesion and friction in various contexts [...]

Biomimetics doi: 10.3390/biomimetics11050294

Authors: Paulo Figueroa-Torrez Orlando Duran Broderick Crawford Felipe Cisternas-Caneo

This study presents a multi-period Generalized Cell Formation Problem with Machine Availability (GCFP-MA) aimed at designing manufacturing cells that explicitly account for equipment reliability, maintainability, and temporal degradation. The proposed model extends classical formulations by introducing (i) availability-based constraints derived from Mean Time Between Failures (MTBF) and Mean Time to Repair (MTTR) and Markov-Chain models, (ii) downtime penalty costs reflecting non-production losses, and (iii) a multi-period horizon that captures system dynamics over time. To solve the resulting NP-hard problem, the Black Widow Optimizer (BWO)—a population-based metaheuristic inspired by cannibalistic reproduction—is implemented and validated against an exhaustive search benchmark. Computational experiments confirm that the BWO attains the global optimum with substantially reduced computational effort, achieving a balanced trade-off between exploration and exploitation. Results highlight that incorporating availability and repair dynamics prevents infeasible or over-optimistic configurations and yields cost-effective, robust cell layouts. The proposed approach provides both theoretical and practical contributions by integrating availability engineering and production system design within a unified optimization framework.

Biomimetics doi: 10.3390/biomimetics11050293

Authors: Junwei Yang Chenglong Sha Xiangjun Wang Hua Yang

Avian wings enable autonomous control over flight trajectory and speed, and their swept-wing geometry inspires the application of sweep modifications to horizontal-axis wind turbine blades, an approach that is critical for improving aerodynamic performance. Hence, wind tunnel experiments were performed to evaluate the output power and wake features of a baseline straight-bladed and a swept-blade wind turbine. The experimental results demonstrate that inflow turbulence intensity (T.I.) affects the peak power coefficient of the swept-bladed turbine, with power coefficient gains being more significant when the tip speed ratio is greater than 3.0 and under yawed conditions. At a yaw angle of 20°, when the T.I. is 0.5%, 10.5%, and 19.0%, respectively, the corresponding increased values are 13.17%, 3.44%, and 4.68%. Cross-stream velocity in the near-wake region of the swept-bladed turbine is markedly higher than that for the baseline condition. The averaged T.I. in the wake velocity region of the swept-blade conditions is greater than that of the baseline condition at most measurement positions. Moreover, power spectral density (PSD) magnitudes behind the blade tip for the swept-blade configuration are higher than those of the baseline, particularly in the medium- and high-frequency domains. This work clarifies the aerodynamic characteristics of swept-blade wind turbines to varying levels of turbulent inflow.

Biomimetics doi: 10.3390/biomimetics11050292

Authors: Jiashuai Du Changlun Chen Yingxin Zhang Fangming Zhang Xuechang Zhang Hubiao Wang

The selective harvesting of leaves from flexible-stem crops remains a major challenge in agricultural mechanization due to stem compliance, heterogeneous petiole strength, and unstable tool–crop interaction. To address these issues, a bio-inspired ring-cutting and compliant clamping harvesting mechanism is proposed for low-damage selective harvesting under complex terrain conditions. Inspired by the adaptive attachment behavior of octopus suckers, a flexible compliant clamping interface combined with a ring-shaped sliding cutting structure was developed to stabilize flexible stems during harvesting. A coupled kinematic–force analytical model was established to characterize the interaction between tool motion, stem feeding, and cutting behavior. In addition, a sliding cutting mechanics model was introduced to analyze the relationship between cutting force and sliding angle. Dynamic multibody simulations were performed using ADAMS to verify the motion feasibility and trajectory stability of the proposed harvesting mechanism. Bench-scale experiments were conducted using mulberry branches as a representative flexible-stem crop, and a response surface methodology based on a Box–Behnken experimental design was applied to optimize key operational parameters. The optimal parameter combination included a chain linear speed of 0.18 m·s−1, a feeding speed of 0.30 m·s−1, and an installation angle of 36°. Under these conditions, the missed harvest rate was reduced to 9.2–9.8%, demonstrating improved harvesting stability compared with conventional rigid cutting mechanisms. The results indicate that integrating compliant stabilization with sliding cutting provides an effective engineering strategy for selective harvesting of flexible-stem crops in complex agricultural environments.

Biomimetics doi: 10.3390/biomimetics11050291

Authors: Ali Abulkasim Mohamed Brian Morrow Stella Mireles Carlos A. Jurado Mark A. Antal Silvia Rojas-Rueda Hamid Nurrohman Franklin Garcia-Godoy

Objective: CAD-CAM technology enables biomimetic dentistry by producing highly accurate, minimally invasive restorations that replicate the biomechanical behavior of intact teeth. This study evaluated the fracture resistance of overlays with margins at different supragingival levels, including a flat occlusal design and compared them with conventional full crowns. All restorations were fabricated from chairside CAD/CAM resin-matrix ceramic for maxillary premolars. Methods and Materials: Sixty-four CAD/CAM resin-matrix ceramic restorations were fabricated and randomly assigned to four groups (n = 16): (1) overlay with a margin 2 mm above the gingiva (Ov2m); (2) overlay with a 4 mm supragingival margin (Ov4m); (3) overlay with a 4 mm margin and flat occlusal surface (OvF4m); and (4) full-coverage crown with a gingival-level margin (FCC). Preparations were standardized by one operator. Restorations were adhesively cemented to resin dies, thermocycled 10,000 times (5–55 °C), and loaded to failure in a universal testing machine (1 mm/min). Data were analyzed using one-way ANOVA and post hoc tests (α = 0.001). Results: Among overlays, Ov2m showed the highest fracture resistance (1605 ± 88 N), followed by Ov4m (1403 ± 63 N). OvF4m recorded the lowest value (1257 ± 73 N). FCC exhibited the greatest overall resistance (1838 ± 106 N), significantly higher than that of any overlay group. Conclusions: Overlays with margins 2 mm above the gingiva had higher fracture resistance than those with more coronal margins or flat occlusal designs. Full-coverage crowns showed the greatest strength, highlighting the impact of margin position and preparation design on restoration performance.

Biomimetics doi: 10.3390/biomimetics11040290

Authors: Han-Yeol Yang Jeongin Seo Woongrak Choi Eunu Kim Sangho Yeo Soeun Park Haeshin Lee

Kappa-carrageenan (κ-CRG) forms thermo-reversible physical hydrogels via a coil–helix transition and helix bundling, but its sulfate-driven electrostatic repulsion limits mechanical robustness and control over aqueous disintegration. Here, we show that plant-derived polyphenols reprogram κ-CRG gel through sulfate-directed binding in a structure-dependent manner. Tannic acid (TA) selectively engages κ-CRG sulfate groups, yielding transparent gels and a >5-fold increase in storage modulus, whereas the same TA triggers turbidity and precipitation in sulfate-free agarose, supporting sulfate-mediated specificity. Using monomeric pyrogallol as a galloyl analogue, we demonstrate that monovalent interactions partially reinforce κ-CRG but lack cooperative stabilization. Intervention timing further separates mechanism. Pyrogallol produces pathway-dependent mechanics and gelation temperature, while TA is stage-insensitive, consistent with multivalent network annealing. In simulated gastric/intestinal fluids, pyrogallol/κ-CRG gels retain morphology longer, whereas TA/κ-CRG ones disintegrate rapidly yet exhibit strong adhesion to rough substrates and human skin. These findings provide a fully food-grade route to tune κ-CRG mechanics, thermal behavior, adhesion and programmed disintegration.

Biomimetics doi: 10.3390/biomimetics11040289

Authors: Alexandros Efstathiadis Ioanna Symeonidou Konstantinos Tsongas Emmanouil K. Tzimtzimis Dimitrios Tzetzis

The goal of the present paper is to apply a novel biomimetic design strategy for the analysis, emulation, and technical evaluation of design solutions inspired by the morphogenetic logic of the walnut shell microstructure. The shell consists of specialized cells, called sclereids, which develop protrusions and mechanically interlock with neighboring cells, providing exceptional toughness through increased surface contact. To extract and transfer this biological principle, a generative algorithm was developed using the evolutionary solver Galapagos within the Grasshopper visual programming environment. The algorithm generates protrusions on the interfaces of structural blocks and optimizes their contact surface area while maintaining constant block volume. Additional design constraints, including symmetry and manufacturability considerations, were introduced to improve structural performance and computational efficiency. A series of physical specimens with variations in key geometric parameters, such as protrusion number and height, were fabricated using fused filament fabrication (FFF) with PLA material and evaluated through in-plane and out-of-plane three-point bending tests. The results show that increasing the number of protrusions significantly enhances mechanical performance, while increasing their height improves stiffness and interlocking up to a certain threshold, beyond which structural performance decreases due to stress concentration effects. This behavior can be attributed to improved load transfer and stress distribution across the enlarged interfacial area, as well as progressive mechanical engagement between complementary protrusions. The computational model is in good agreement with the experimental results, confirming the validity of the proposed approach. The study demonstrates that biomimetic optimization of interfacial geometry can enhance the mechanical behavior of interlocking systems and provides a framework for translating biological morphogenetic principles into engineering design applications.

Biomimetics doi: 10.3390/biomimetics11040288

Authors: Yubo Mao Wei Xu Jijia Sang Haoan Liu

Automatic modulation recognition (AMR) is increasingly relevant to communication-sensing front ends in robotic and human–robot collaborative systems, where reliable spectrum awareness and adaptive wireless reception are desired. However, existing methods often degrade sharply at low signal-to-noise ratios (SNRs), and large language models (LLMs) are not natively compatible with continuous I/Q signals due to the inherent modality gap. We propose BioLAMR, a GPT-2 adaptation framework for AMR inspired by the auditory system’s parallel time–frequency processing and cortical hierarchy. The framework combines bio-inspired dual-domain feature extraction with parameter-efficient LLM adaptation. BioLAMR includes three components. First, a lightweight dual-domain fusion (LDDF) module extracts complementary time- and frequency-domain features and fuses them through channel and spatial attention. Second, a convolutional embedding module converts continuous I/Q signals into GPT-2-compatible sequences without discrete tokenization. Third, a hierarchical fine-tuning strategy updates only 8.9% of parameters to preserve pretrained knowledge while adapting to modulation recognition. Experiments on the RadioML2016.10a and RadioML2016.10b benchmarks show that BioLAMR achieves overall accuracies of 64.99% and 67.43%, outperforming the strongest competing method by 2.60 and 2.47 percentage points, respectively. Under low-SNR conditions, it reaches 36.78% and 38.14%, the best results among the compared methods. Ablation studies verify the contribution of each component. These results demonstrate that combining dual-domain signal modeling with parameter-efficient GPT-2 adaptation is an effective route to robust AMR in challenging wireless environments.

Biomimetics doi: 10.3390/biomimetics11040287

Authors: Junchao Ni Jianhua Miao Yejun Zheng Li Cao Yang Qiu Yinggao Yue

In response to the decline in population diversity, the imbalance between exploration and exploitation, and the low convergence efficiency in the middle and later stages of the Red-billed Blue Magpie Optimizer (RBMO) when addressing complex optimization problems, this study proposes a multi-strategy enhanced variant termed CLD-RBMO. The proposed algorithm improves the original search mechanism from three perspectives: strengthened global exploration, enhanced local refinement, and directed exploitation in the middle and later stages. During the exploration phase, a hierarchical perturbation mechanism based on Logistic chaotic mapping and Lévy flight is introduced to enhance randomness and spatial coverage in the early search process. In the local exploitation phase, a Cauchy–Gauss hybrid mutation operator is employed to improve the algorithm’s capability to escape from local optima. In the middle and later search stages, a stochastic differential mutation strategy is incorporated to provide population-structure-based directional guidance for individuals, thereby accelerating convergence and improving optimization accuracy. Simulation results on the CEC2017 benchmark test functions indicate that CLD-RBMO demonstrates clear superiority over the original algorithm and several representative swarm intelligence optimization algorithms in terms of optimization accuracy, stability, and overall performance ranking. Convergence curve analysis confirms its dynamic performance improvements across different search stages, and the Wilcoxon rank-sum test further statistically validates the significance of the performance enhancement achieved by the proposed improvements compared with the original algorithm. Moreover, evaluations on two representative mechanical engineering optimization case studies further demonstrate the algorithm’s strong stability and engineering generalization capability.

Biomimetics doi: 10.3390/biomimetics11040286

Authors: Yaowei Yu Meilong Le

Collaborative operation of heterogeneous UAV swarms in dense urban environments remains challenging because right-of-way allocation is often rigid, frequent replanning consumes considerable onboard computation, and paths obtained by purely mathematical optimization may not be easy to execute under real dynamic constraints. This paper presents a physics-informed, event-triggered path planning and control framework, termed Physics-Informed SSA-MPC. Its global search layer is built on the Sparrow Search Algorithm (SSA), whose search mechanism originates from sparrow foraging and anti-predatory behaviors. On this basis, the method combines an event-triggered Stackelberg game for airspace coordination, a physically constrained SSA for global path generation, and an event-triggered MPC for local replanning. Battery State of Health (SoH) is incorporated into the adaptive search process, while Lévy-flight updates are limited by the maximum available acceleration to avoid infeasible path mutations. Local replanning is activated only when predicted safety ellipsoids overlap or tracking errors exceed prescribed thresholds, which helps reduce redundant computation. Simulations in a digital twin of Lujiazui, Shanghai, show that the proposed method shortens path length by 3.3% to 14.9%, reduces obstacle-avoidance latency to 45 ms, and achieves a 100% engineering feasibility rate.

Biomimetics doi: 10.3390/biomimetics11040285

Authors: Jaehyun Lee Jongwoo Kim

In-pipe robots must navigate narrow, curved passages where rigid mechanisms often require bulky steering units. Soft crawlers offer better compliance but typically rely on multiple actuators or reconfigurable contacts to achieve multi-directional motion. Drawing inspiration from biological soft crawlers that exploit directional friction and coordinated anchor–slip patterns, this study focuses on locomotion principles observed in caterpillars, water boatmen, and whirligig beetles. Based on these bioinspired concepts, we present a tendon-driven soft in-pipe robot that combines continuum bending–twisting deformation with modular anisotropic friction pads (AFPs), enabling three locomotion modes using only two motors. AFP inclination, curvature, and ridge geometry were optimized through friction tests, constant-curvature modeling, and finite element analysis to enhance directional adhesion on flat and curved surfaces. A deformation-based locomotion framework was developed to couple tendon actuation with friction orientation, achieving longitudinal crawling, transverse translation, in-place rotation, and smooth transitions via programmed twisting. Driving experiments demonstrated repeatable anchor–slip locomotion with average speeds of 28.6 mm/s, 15.7 mm/s, and 11.5°/s for the three modes. Pipe tests in straight, curved, and T-junction sections further validated stable contact and reliable gait transitions. These findings highlight the potential of friction-programmed continuum robots as compact, bioinspired platforms for advanced in-pipe inspection and diagnostic tasks.

Biomimetics doi: 10.3390/biomimetics11040284

Authors: Wen Yuan Zhihong Zhang

Efficient mixing is a persistent bottleneck in agricultural and agrochemical processing, where rapid and uniform mixing must be achieved under laminar flow with low energy input and gentle shear. Inspired by peristaltic transport in biological systems, this study investigates a bio-inspired flexible-wall squeezing mixer and establishes a two-dimensional computational framework to quantify how periodic wall deformation governs scalar homogenization in a flexible conduit. An Arbitrary Lagrangian–Eulerian dynamic mesh approach is implemented to resolve moving boundaries and to prescribe actuation, enabling the systematic evaluation of the separate and coupled effects of peak wall-normal velocity amplitude A and actuation frequency f on mixing performance. Mixing effectiveness is quantified using a variance-based mixing index MI and a sustained-threshold mixing time ts, and response surface methodology is employed to map the A–f design space and interpret the roles of time-dependent shear, interfacial stretching and folding, and vortex intensification. Relative to a non-actuated baseline, a peak wall-normal velocity amplitude of 3 × 10−3 m s−1 at 2 Hz reduces ts by 21.3%. At fixed f = 3 Hz, increasing A from 1 × 10−3 to 4 × 10−3 m s−1 shortens ts by 10.2%, while at fixed A = 3 × 10−3 m s−1, raising f from 1 to 5 Hz further decreases ts by 6.6% with diminishing gains at the lowest frequencies. The response surface identifies an operating optimum at A = 4 × 10−3 m s−1 and f = 5 Hz, achieving a peak MI of 0.9557 and a minimum ts of 7.81 s. A periodically squeezed physical mixing loop was further examined using fluorescence imaging to assess outlet homogeneity trends. The stabilized outlet coefficient of variation (CV) decreased from about 0.65 without squeezing to 0.60 at 1 Hz and 10 mm s−1, 0.58 at 2 Hz and 10 mm s−1, and 0.54 at 2 Hz and 30 mm s−1, indicating that stronger and faster actuation improves outlet uniformity. The numerical and experimental results are therefore interpreted jointly as mechanistic and trend-level evidence, while a rigorous quantitative prediction for the cylindrical compliant device will require future three-dimensional, compliance-resolved simulations and broader experimental benchmarking.

Biomimetics doi: 10.3390/biomimetics11040283

Authors: Yongbin Sun Rongmao Su

Autonomous traversal flight in unknown 3D environments remains challenging due to mapping bottlenecks and computational latency. Inspired by pigeons navigating cluttered forests through instantaneous visual perception rather than constructing global metric maps, this paper presents a pigeon-inspired depth-reasoning-driven decision framework for agile quadrotor traversal in unmapped spaces without explicit map construction. To ensure feasibility, we leverage a robust state estimation backbone enhanced by deep-learning-based feature matching, providing stable pose feedback under aggressive maneuvers. The core contribution is a pigeon-inspired depth-reasoning framework that translates raw sensory depth data into a hybrid optimization framework, integrating both hard safety constraints and soft geometric smoothness constraints, directly emulating the three avian mechanisms: gap selection via instantaneous depth gradients, path selection that minimizes posture changes, and a safety field driven by the looming effect. By bypassing time-consuming mapping and spatial discretization processes, the framework significantly reduces perception-to-control latency. Finally, validated via simulations and real-world experiments on a resource-constrained quadrotor platform, our map-less approach achieves superior decision frequencies and comparable safety margins to those of state-of-the-art map-based planners. This framework offers a practical, high-frequency solution for autonomous flight where computational resources and environmental knowledge are strictly limited.

Biomimetics doi: 10.3390/biomimetics11040282

Authors: Min Shen Ziqing Zhang Guanxing Qin Dahongnian Zhou Lizhen Du Lianqing Yu

In air jet looms, relay nozzles are critical components in governing airflow velocity and air consumption during the weft insertion process. Although computational fluid dynamics (CFD) offers high-fidelity simulation for aerodynamic analysis, its computational burden hinders its practicality in iterative aerodynamic design of relay nozzles. To address the challenge, this study proposes a data-driven framework integrating a Chebyshev polynomial Kolmogorov–Arnold Network (Chebyshev KAN) surrogate model with an Improved Multi-objective Red-billed Blue Magpie Optimizer (IMORBMO). The accuracy of the Chebyshev KAN model was benchmarked against conventional multilayer perceptrons (MLP), convolutional neural networks (CNN), and the standard Kolmogorov–Arnold Network (KAN). Experimental results demonstrate that the Chebyshev KAN model achieves the lowest mean absolute error (MAE) of 0.103 for airflow velocity and 0.115 for air consumption. Building upon the non-dominated sorting and crowding distance strategies, IMORBMO was developed, incorporating an adaptive mutation mechanism by information entropy for improvement of convergence, diversity, and uniformity of the Pareto-optimal solutions. Comprehensive evaluations on the ZDT and WFG benchmark suites confirm that the IMORBMO consistently attains the best and highly competitive performance, yielding the lowest generation distance (GD), inverted generational distance (IGD) values and the highest hypervolume (HV). Applied to the aerodynamic optimization of a relay nozzle, the proposed framework delivers an optimal aerodynamic design that increases airflow velocity by 10.5% while reducing air consumption by 15.4%, as verified by CFD simulation. The steady-state flow field was simulated by solving the Reynolds-Average NavierStokes equations with the k–ω turbulent model, utilizing Fluent 2025.R2. No-slip wall, inlet pressure and outlet pressures are boundary conditions to the relay nozzle surfaces. This work establishes a computationally efficient and accurate optimization paradigm that holds significant promise for aerodynamic design and other complex real-world engineering applications.

Biomimetics doi: 10.3390/biomimetics11040280

Authors: Belen Şirinoğlu Çapan Vasfiye Işık Tugba Elgün Zeynep Hale Keleş Soner Şişmanoğlu

This study investigated the radiopacity, elemental composition, cytotoxicity, and cytokine responses of contemporary calcium silicate-based cements containing different radiopacifiers. Four cement materials (NeoMTA2, NeoPUTTY, TheraCal PT, and One-Fil PT) were evaluated. Radiopacity was measured using digital radiography with a 10-step aluminum wedge and expressed in mm Al in accordance with ISO 6876; among three calibration models compared, the quadratic provided the best fit. Elemental composition was analyzed by SEM/EDX. Cytotoxicity was assessed on human dental pulp cells using the MTT assay, and IL-6 and IL-10 levels were quantified by ELISA. One-Fil PT (6.61 mm Al) and NeoPUTTY (6.09 mm Al) showed the highest radiopacity, whereas TheraCal PT (1.61 mm Al) did not meet ISO standards. SEM/EDX revealed tantalum in NeoMTA2 and NeoPUTTY, and zirconium in One-Fil PT and TheraCal PT. NeoPUTTY and NeoMTA2 demonstrated superior cell viability, while One-Fil PT showed the lowest. TheraCal PT and One-Fil PT increased IL-6 expression, whereas NeoPUTTY and NeoMTA2 promoted higher IL-10 levels. Within the limitations of this 24-h in vitro assessment, NeoMTA2 and NeoPUTTY exhibited more favorable short-term cytocompatibility and inflammatory profiles together with adequate radiopacity. These findings require confirmation through long-term in vivo and clinical studies.

Biomimetics doi: 10.3390/biomimetics11040281

Authors: Evgenia Dimitriou Nathan Wood Hongmin Qin Zhijian Pei

This study determines the feasible regions of nozzle temperature, extrusion pressure, and printing speed in extrusion-based printing using an acellular sodium alginate–carboxymethylcellulose–collagen I bioink. The tested range of nozzle temperature was from 10 to 35 °C in 5 °C increments, the range of printing speed was from 5 to 20 mm/s in 5 mm/s increments, and the range of extrusion pressure was from 10 to 100 kPa in 10 kPa increments. The feasible regions were defined as the combinations of process parameters that produced continuous extruded lines. Results show that continuous extruded lines were achieved at higher extrusion pressures (70–100 kPa) across most tested printing speeds and nozzle temperatures. In contrast, an extrusion pressure of 10 kPa resulted in discontinuous extruded lines under all tested combinations of nozzle temperature and printing speed, and an extrusion pressure of 20 kPa led to discontinuous extruded lines under all tested printing speeds and all tested temperatures except for 35 °C. Intermediate extrusion pressures required lower printing speeds to produce continuous extruded lines. These results highlight the interaction effects of extrusion pressure and printing speed on maintaining continuous extruded lines across the tested nozzle temperatures. These findings provide practical guidance for selecting extrusion pressures and printing speeds across different nozzle temperatures for printing of a sodium alginate–carboxymethylcellulose–collagen I bioink.

Biomimetics doi: 10.3390/biomimetics11040279

Authors: Jiaxiu Liu Zijian Wang Hongfu Tang Hongzhe Jin Jie Zhao

Humanoid upper-limb robots are an important direction in biomimetic robotics, and inverse kinematics is a key technique for achieving human-like coordinated operation. However, existing inverse kinematics methods for bimanual trajectory tracking often suffer from high computational complexity and limited synchronization performance. To address this, this paper proposes an error-adaptive competition-based inverse kinematics (EAC-IK) approach for bimanual trajectory tracking of humanoid upper-limb robots. First, a unified modeling framework for the absolute tracking errors and synchronization errors of the two arms is established, and the end-effector task constraints are reformulated into a low-dimensional representation, thereby reducing the computational complexity of the original high-dimensional task mapping. Second, to enhance the coordination capability of bimanual operations, an error-adaptive competition mechanism is developed to regulate the weighting coefficients of the two arms online according to their error states. In addition, a virtual second-order command shaper is introduced at the joint level to reconstruct joint trajectories and suppress oscillations induced by input noise and the error-adaptive competition mechanism. Simulation and experimental results on a hyper-redundant humanoid upper-limb robot demonstrate that, compared with the zeroing neural-network-based inverse kinematics method, the proposed method achieves lower tracking and synchronization errors, as well as higher computational efficiency. In the circular trajectory-tracking experiment, the left-arm position and orientation tracking errors decrease from 1.60×10−3m and 4.72×10−3rad to 0.70×10−3m and 0.95×10−3rad, respectively, while the synchronization error decreases from 1.96×10−3 to 1.30×10−3. In addition, the average algorithm runtime decreases from 0.82ms to 0.63ms.

Biomimetics doi: 10.3390/biomimetics11040278

Authors: Haoran Huang Zhenming Yang Junkai Liu Jianhua Pang Zongduo Wu Hangyu Wen Shunjun Li

The mechanism by which fish enhance hydrodynamic performance through collective swimming is a research hotspot in the field of underwater bionic robots. This study employs the Immersed Boundary-Lattice Boltzmann Method (IB-LBM) to conduct numerical simulations on a two-dimensional, single-degree-of-freedom (1-DOF) autonomous propulsion bionic fish swarm. It systematically investigates the effects of swarm size and inter-individual spacing on swimming speed and cost of transport (CoT) under two typical configurations: series and parallel arrangements. Findings reveal that hydrodynamic benefits are highly dependent on the spatiotemporal evolution of flow field structures. In the series configuration, an optimal spacing range of 1.5 L to 2.0 L exists within the school, where the “wake capture” effect is pronounced. Trailing fish achieve a maximum speed increase of approximately 41.1% while significantly reducing energy consumption. However, as spacing increases to 2.5 L, the cooperative gain for front and middle-row individuals rapidly diminishes, and the lead fish even experiences significant performance loss. Uniquely, the trailing fish in the four-fish formation exhibits distinct flow field reorganization and performance recovery at the 4.5 L trailing position. In the parallel formation, the “channel effect” and “blocking effect” of the fluid dominate. The study identifies 0.4 L laterally as the critical instability spacing under the investigated kinematic regime, where strong destructive interference causes a sharp deterioration in individual swimming performance. Additionally, the parallel formation exhibits pronounced positional differentiation. Central individuals, constrained by dual lateral flow fields, experience restricted lateral wake expansion and accelerated energy dissipation, resulting in significantly weaker escape capabilities from low-speed conditions compared to marginal individuals. The vortex-dynamic mechanism revealed herein provides theoretical foundations for formation control in multi-fish biomimetic cooperative systems.

Biomimetics doi: 10.3390/biomimetics11040277

Authors: Junhao Nian Zhenyu Huang Yingsong Zhao Kai Liu

Planar honeycomb structures, especially biomimetic hexagonal honeycombs, are widely used in energy-absorbing equipment because of their excellent out-of-plane deformation resistance. However, their significant mechanical anisotropy, manifested by the large discrepancy between out-of-plane and in-plane responses, greatly restricts their broader applications. Inspired by spiral-reinforced thin-walled biological tubular systems, such as animal tracheae and plant vessels, this study proposes a biomimetic reinforcement strategy by embedding spiral structures along the thin walls of planar honeycombs. To validate the feasibility of the proposed design, biomimetic honeycomb specimens were fabricated using 3D-printing technology and tested under compression along different loading directions. Furthermore, a numerical model validated against the experiments was developed to reveal the underlying enhancement mechanism. The results demonstrate that the proposed biomimetic honeycomb preserves the favorable out-of-plane performance of the conventional hexagonal honeycomb, while improving the in-plane energy absorption capacity by up to 70%. The biomimetic spiral reinforcements enable more effective load transfer under multidirectional loading, resulting in a more uniform plastic stress distribution over the entire structure and activating a larger deformation region for energy dissipation. The present work provides a bioinspired strategy for developing lightweight energy-absorbing structures for potential applications in aerospace, rail vehicles, marine engineering, and civil structures.

Biomimetics doi: 10.3390/biomimetics11040276

Authors: Yue Yan Anil Misra Paulette Spencer Viraj Singh Ranganathan Parthasarathy

Mechano-sorptive phenomena (MSP) refer to the coupled mechanical response of polymers under simultaneous mechanical stress and fluid sorption. The most researched MSP are environmental stress cracking (ESC) and mechano-sorptive creep (MSC). ESC initiates at regions of localized stress and solvent sorption, presenting as brittle fracture, while MSC is characterized by large, time-dependent, and partially recoverable creep associated with transient bulk sorption. ESC experiments can however also result in significant plastic deformation, in which case the term environmental stress yielding (ESY) has been used. Similarly, MSC can evolve into tertiary creep followed by rupture, in which case the phenomenon is termed mechano-sorptive creep rupture (MSCR). Both behaviors originate from solvent diffusion into the amorphous phase, leading to disruption of non-covalent interactions between polymer chains. This review bridges seemingly disconnected research to illustrate that ESC and MSC represent extremes on a continuum of MSP, rather than disparate phenomena. We identify the principles of polymer thermodynamics and experimental methods necessary to separate polymer deformation under MSC into reversible stress-induced swelling and irreversible non-equilibrium deformation. Finally, we illustrate how MSP underline the functionality of several biomimetic materials including dentin adhesives, mutable collagenous tissue, spider silk, tendons, and articular cartilage, as well the synthesis of biomimetic materials by solvent vapor annealing assisted by soft shear.

Biomimetics doi: 10.3390/biomimetics11040275

Authors: Mario Alberto Grave-Capistrán Francesco Lamonaca Giuseppe Carbone Christopher René Torres-SanMiguel

Currently, silicone is a common material used in medical research and biomedical applications. This research aims to characterize extra-soft platinum silicone (shore A 00 50) and compare its mechanical behavior with that of the human thoracic aorta. By developing molds to get samples, for tensile testing according to ISO 37 and ASTM D412, and for compression testing according to ISO 7743 and ASTM D575, using a universal testing machine for tensile and compression tests, and applying digital image correlation (DIC) algorithms, the mechanical properties were characterized in a total of 10 tensile samples and 6 compression samples. The results show an ultimate tensile strength up to 1.77 ± 0.12 MPa in the ASTM samples and 2.10 ± 0.14 MPa in the ISO samples; alongside an incremental elastic module of 80.08 ± 7.94 kPa and 117.98 ± 11.39 kPa; finally, an elongation at break of 1114.49 ± 76.77% and 936.08 ± 63.56%, corresponding to the values of a healthy thoracic aorta. The replica of the thoracic aorta in this material was developed by a brush method, with a thickness of 1.82 mm, a length from the aortic arch to the descending aorta of 200.49 mm, and diameters of 20.45 and 16.05 mm for the ascending and descending aorta, respectively.

Biomimetics doi: 10.3390/biomimetics11040274

Authors: Smaro Kyroglou Antigoni G. Margellou Konstantinos S. Triantafyllidis Patroklos Vareltzis

This study addresses the urgent need for sustainable alternatives to single-use plastics by developing biodegradable composites from peach and apple processing waste employing hot compression molding. Utilizing a definitive screening design, the impact of the process variables, including recipe composition, grinding size, pressure, temperature, and holding time, on the physical (including water resistance) and mechanical properties of the composites was systematically evaluated. Physicochemical and thermal analyses of the dried by-products indicated that processing temperatures below 150 °C prevent the degradation of lignocellulosic constituents. The results demonstrated that increasing both the molding pressure and holding time decreased the composite thickness, while enhancing the stiffness and flexural strength, with modulus of elasticity values exceeding 1000 MPa under optimal conditions. Higher molding temperatures reduced water absorption and diffusivity, particularly in lignin-rich composites, by promoting lignin softening and particle consolidation, resulting in denser structures with limited moisture transport. Biodegradability was assessed through soil burial tests over 200 days, revealing a weight loss ranging from 54.2% to 90.7% among samples, with apple-based composites exhibiting greater degradation compared to peach-based ones. Overall, the study highlights the development of a “green composite” formulation inspired by biomimetic principles, exploiting the natural self-bonding capacity of lignocellulosic biomass, where two different-in-composition biowastes are combined to produce a plastic-free composite material with possible applications in the foodservice industry.

Biomimetics doi: 10.3390/biomimetics11040273

Authors: Xihuan Hou Miao Xu Liang Wei Hongfei Li Zan Li Huiming Xing Shuxiang Guo

To facilitate the practical deployment and engineering implementation of multi-robot coordination for biomimetic underwater spherical robots (BUSRs), it is imperative to develop a formation tracking control method with a simple structure, a small number of tunable parameters, convenient parameter tuning and strong anti-disturbance capability. This study proposes a formation controller integrating virtual structure (VS), consensus protocol, and parallel output-velocity-type active disturbance rejection control (POV-ADRC), denoted as VS-C-POV-ADRC. A rotating global (RG) coordinate system is established to decouple robot positions from heading angles, which makes the parameter tuning more convenient. A double-loop control architecture is constructed, where the outer consensus control loop generates the desired velocity for each robot based on virtual-structure reference positions, and the inner POV-ADRC loop achieves high-precision velocity tracking. The proposed controller features a compact structure with only five adjustable parameters per motion direction, realizing easy engineering implementation and adaptation to the limited computing capacity of BUSRs. The simulation and experiment results demonstrate that the proposed algorithm enables robots to maintain a stable formation and achieve trajectory tracking accuracy within one body length, while exhibiting superior disturbance rejection. The proposed method provides a feasible and practical solution for BUSR formation control.

Biomimetics doi: 10.3390/biomimetics11040272

Authors: Faguo Zhou Yi Wu Zhe You Shuyu Yao Kaile Lyu Menglin Chen Jianshen Yang

To address the key challenges in fire evacuation path planning, such as the tendency to converge to local optima, unbalanced computational efficiency, and suboptimal path quality, this study proposes the enhanced Hiking Optimization Algorithm of Differentiated Weighted Dynamic (WDHOA). The WDHOA integrates a three-phase cooperative framework, incorporating dynamic grouping, hybrid search, and angle generation. Comprehensive evaluations on the CEC 2017 and CEC 2022 benchmark suites demonstrate that WDHOA significantly outperforms eight widely used algorithms, such as LSHADE, RIME, SCA in convergence accuracy, stability, and robustness, especially for high-dimensional and multimodal functions. Wilcoxon rank-sum tests and Friedman tests confirm statistical significance across most functions. Ablation experiment further verifies the effectiveness of the three enhanced strategies. When applied to fire evacuation path planning, WDHOA achieves the best solutions while satisfying all nonlinear constraints. These experiments confirm that WDHOA effectively balance optimization accuracy and practical applicability in fire evacuation path planning problems.

Biomimetics doi: 10.3390/biomimetics11040271

Authors: Donghui Chen Yulin Xiao Shiqi Hao Chong Ning Xiaotong Ma Bingyang Wang Xiao Yang

Friction pairs in wet clutches operate under complex conditions, which can cause surface damage and reduce overall clutch reliability. Surface texturing is an established technique for improving the tribological performance of such mechanical interfaces. Inspired by the wet adhesion properties of tree frog foot pads, a bionic regular hexagonal micro-texture was designed on the mating steel plate. A three-dimensional transient computational fluid dynamics (CFD) numerical methodology was developed and rigorously verified via pin-on-disc friction experiments. Subsequently, this verified numerical framework was extrapolated to establish disc-on-disc CFD models. The results demonstrated that the bionic hexagonal micro-texture altered flow field characteristics, increasing the local maximum flow velocity by 7.9% compared to untextured surfaces. Furthermore, the micro-textured grooves expanded the effective area for convective heat transfer and facilitated local fluid exchange, reducing the maximum average bulk temperature by 20.5% and the maximum radial temperature by 20.7%. Adjusting the structural parameters of these micro-textures further regulated the interfacial flow and temperature fields; notably, deeper grooves induced vortices at land region edges, accelerating flow velocity and decreasing the overall radial temperature gradient. This study provides a theoretical reference for enhancing the thermo-hydrodynamic performance of wet clutch friction pairs.

Biomimetics doi: 10.3390/biomimetics11040270

Authors: Yanjiao Wang Jiaqi Wang

As an innovative metaheuristic algorithm, Genghis Khan Shark Optimization (GKSO) faces challenges, including a tendency towards local optima and poor convergence speed and accuracy. To mitigate these limitations, an improved Genghis Khan shark optimizer (IGKSO) is proposed in this paper. A population partitioning method based on cosine similarity and fitness is introduced, where individuals are strategically assigned to different evolutionary phases: Disadvantaged populations are responsible for the foraging stage. By contrast, advantaged populations dominate the moving stage. In the moving stage, the base vector is randomly selected from multiple candidates, which ensures the evolutionary direction of the population while maintaining its diversity. An adaptive step-size mechanism is introduced to avoid boundary overflow problems. A subspace method is employed to prevent diversity loss during foraging. Additionally, in the hunting stage, a novel opposition-based learning strategy is proposed to moderate the tendency of converging to suboptimal solutions. Furthermore, during the self-protection phase, a criterion for assessing the diversity of the whole population is employed to monitor and supplement diversity in real time. The results of the CEC2017 and CEC2019 benchmark test sets reveal that IGKSO exhibits substantial advantages over the GKSO algorithm and eight other high-performance algorithms in terms of convergence speed and accuracy.

Biomimetics doi: 10.3390/biomimetics11040269

Authors: Xiao Song Chao Mei Yinna Wu Dan He Junwei Zhu Qi Chen Jiaxin Guo Zhengwei Zhao Tonghui Xie Wenbin Liu

Lubricants are essential for water-based drilling fluids. Catechol-based lubricants provide improved lubrication performance owing to their strong adhesion ability through the formation of coordination bonds inspired by mussel adhesion. However, the conventional synthetic ester and amide lubricants suffer from loss of adhesive capability due to hydrolysis and autoxidation. Inspired by mussels and melanin biosynthesis, a biomimetic strategy was developed to synthesize a high-adhesion lubricant with good stability via thiol-quinone Michael addition to restore and stabilize the catechol moiety. Bisphenol A was oxidized to the corresponding quinone using 2-iodoxybenzoic acid. Subsequent Michael addition reaction with 1-octadecanethiol produced a thiol-functionalized lubricant containing catechol moieties and long alkyl chains through an S-catecholyl linkage. Biomimetic principles were incorporated into both the molecular structure and the synthetic route, emulating the structural and functional features of mussel adhesion and melanin biosynthesis. Octadecanethiol provided sulfur-containing extreme-pressure functionality and contributed to strong adsorption on metal surfaces. The molecular structure was confirmed by FTIR, 1H NMR, and 13C NMR. The thiol-functionalized lubricant formed strong coordination with Fe3+ and Fe2+ ions across a wide pH range, with an apparent complexation stoichiometry of 1:1 and conditional stability constants of 4.09 and 5.02, respectively. Bis-coordination formed a cross-linking network. It exhibited good resistance toward autoxidation and thermal stability up to 350 °C. In bentonite-based drilling fluids, the extreme pressure lubrication coefficient and adhesion coefficient at a 1% addition were 0.06 and 0.07, respectively. The coefficient of friction and wear scar diameter were 0.09 and 0.63 mm, respectively. The increased contact angle confirmed strong adsorption of the lubricant on metal surfaces. The lubricant combined strong adhesion, high stability, and excellent compatibility with drilling fluids, highlighting its potential as an advanced biomimetic lubricant. This biomimetic thiol-quinone addition strategy provides an effective approach to overcome the instability of conventional catechol-based lubricants.

Biomimetics doi: 10.3390/biomimetics11040268

Authors: Yang Li Xinjie Qian Jiexin Zhang Xiao Yang Chao Deng

Unmanned Aerial Vehicles (UAVs) have become widely used tools for different applications including surveillance, search and rescue, and package delivery. However, autonomous path planning in dynamic environments with moving obstacles, wind disturbances, and energy constraints remains a significant challenge. This paper proposes a novel Multi-Head Attention Deep Q-Network with Prioritized Experience Replay (MA-DQN + PER) that integrates bio-inspired attention mechanisms with deep reinforcement learning for efficient UAV path planning. Our approach features a 46-dimensional state space that captures all environmental information, including static obstacles, wind conditions, and energy status. The proposed Attention-QNetwork architecture uses four specialized attention heads to selectively focus on different aspects of the environment, including obstacle avoidance, target tracking and energy management, and wind compensation. To improve sample efficiency and convergence speed, we incorporate Prioritized Experience Replay (PER) as well as Prioritized Experience Replay (PER) with a sum-tree data structure to improve sample efficiency and convergence speed. A curriculum learning strategy that includes 10 difficulty levels is designed to progressively enhance the agent’s capabilities. Extensive simulations demonstrate that our MA-DQN + PER approach reaches a 96% task success rate (defined as the percentage of episodes where the UAV successfully reaches the target without collision or battery depletion), while the convergence speed was 68% quicker than that of the baseline DQN. Our method demonstrates superior performance in path efficiency (+17%), energy consumption reduction (−26%), and collision avoidance compared to state-of-the-art algorithms.

Biomimetics doi: 10.3390/biomimetics11040266

Authors: Junbo Li Fan Wu Congrong Xiao

This study investigates the effective translation of complex biological principles into viable engineering solutions within the field of biomimetic design. A critical challenge in current research is the “fuzzy front end” bridging initial biological observations and practical engineering applications. This gap primarily stems from the lack of intermediary models capable of abstracting complex biomechanical data into manufacturable mechanical paradigms. To address this, we propose a systematic biomimetic translation framework that redefines traditional crafts as “Empirically Optimized Biological Analogues” (EOBAs), serving as a logical bridge between biological inspiration and engineering realization. This study contributes by integrating the Analytic Hierarchy Process (AHP) with the Fuzzy Comprehensive Evaluation (FCE) method to construct a quantitative assessment system. This system evaluates translation feasibility, engineering innovation potential, semantic interaction characteristics, and prototype manufacturability. Applying this framework to four intangible cultural heritages in Guangdong, combined with comprehensive expert and public evaluations, revealed that the Guangdong Lion Dance exhibits the highest biomimetic translation potential in terms of morphological clarity and dynamic behavioral characteristics. Consequently, we extracted the core principle of “embodied kinematics for communication” and developed a conceptual multi-segment biomimetic robotic prototype designated as “Kine-Lion”. Ultimately, this research provides a structured methodological reference for biomimetic robotic design, demonstrating that culturally abstracted biological behaviors can be systematically decoded into functional robotic structures. These findings indicate broad application prospects in the domains of human–robot interaction and biomimetic engineering.

Biomimetics doi: 10.3390/biomimetics11040267

Authors: Thiago Puccinelli José Rafael Bordin

Molecular engineering has traditionally followed a structure–function paradigm based on well-defined, folded architectures. However, intrinsically disordered proteins and regions (IDPs/IDRs) reveal that nature also exploits disorder as a functional design strategy. Here, we argue that intrinsic disorder can be understood as a biomimetic design principle for molecular and materials engineering. From a soft matter perspective, IDRs function through statistical ensembles, weak multivalent interactions, and collective behavior rather than fixed structure, with sequence features encoding a molecular grammar that governs phase behavior, viscoelasticity, and responsiveness. These principles closely parallel those found in associative polymers and colloidal systems. Recent advances in coarse-grained modeling, machine learning, and inverse design further enable disorder to be treated as a controllable engineering variable. By reframing intrinsic disorder as a programmable and bioinspired design strategy, this Perspective highlights its potential for the development of adaptive and responsive biomimetic materials.

Biomimetics doi: 10.3390/biomimetics11040265

Authors: Veronica Manescu (Paltanea) Aurora Antoniac Julietta V. Rau Olga N. Plakhotnaia Marco Fosca Gheorghe Paltanea Gabriel Cristescu Iulian Antoniac

In the biodegradable metal class, Mg-based alloys are considered the most promising candidates for temporary implant manufacture. However, their high corrosion rate in physiological media is considered a main drawback for clinical translation. Conversion coatings address the limitations of Mg-based alloys and provide a strategy to control corrosion and improve surface biocompatibility. In this review paper, a detailed analysis of various conversion coating techniques, including ceramic conversion coatings based on metals, polymeric conversion coatings, bioactive conversion coatings, and hybrid conversion coatings, is performed. Attention is devoted to the corrosion process and parameters, as well as to the biological response in relation to bioactivity or biocompatibility. The main angiogenic and osteogenic signaling pathways are described based on the analyzed conversion coatings, and the evolution of the cellular response is estimated. Although significant progress has been made in the field, there are still challenges associated with synchronizing Mg alloy degradation with new bone formation and with precisely guiding cell signaling responses to achieve a desired biological response. An overall conclusion of the paper consists of the fact that conversion coatings are an important topic, as they can enhance the surface of Mg-based alloys, making them prone to clinical translation.

Biomimetics doi: 10.3390/biomimetics11040264

Authors: Xiangyu Li Shuaikang Zheng Chunhua Yuan Xianwen Gao

Weak alternating electric fields are widely used in neuromodulation techniques such as transcranial alternating current stimulation (tACS), yet the precise biophysical mechanisms underlying neuronal responses remain incompletely understood. Current computational models often neglect the electrical properties of the extracellular microenvironment, limiting their predictive accuracy. Motivated by experimentally observed frequency-dependent modulation of neuronal activity, we developed a two-compartment model of hippocampal CA3 pyramidal neurons in which extracellular resistance is explicitly parameterized and systematically examined as a key factor influencing neuronal response properties under external electric fields. Within a dual-compartment Hodgkin–Huxley framework, the neuron is divided into a “soma–basal dendrite unit” and an “apical dendrite unit,” accounting for voltage polarization induced by external fields. Using phase-locking ratio curves and three-dimensional parameter response surface, we systematically characterized neuronal sensitivity to field parameters and examined how potassium equilibrium potential (VK) and extracellular resistance (Rout) modulate these responses. Our results demonstrate that increasing Rout enhances neuronal responsiveness to external fields, while VK variations primarily regulate intrinsic excitability. These findings provide mechanistic insights into the frequency-dependent modulation of neuronal responses under weak electric fields, consistent with phenomena observed in biological neural systems, and provide a mechanistic and theoretical framework for understanding the joint effects of electric field amplitude and frequency on neuronal sensitivity to weak electric fields, which may help inform future neuromodulation strategies.

Biomimetics doi: 10.3390/biomimetics11040263

Authors: Jiaxu Han Jingfu Zhao Yue Zhu Zhibin Song

Legged robots are widely used for walking, running, jumping, and landing on the ground. As mission terrains become increasingly complex, legged robots with greater adaptability are required. However, limited research attention has been paid to enhancing their impact resistance and obstacle-surmounting capabilities. Due to the limitations of motor manufacturing and material, it is more difficult to improve the impact resistance of the motor than to design proper leg lengths. Considering rigid multi-link medium- and large-sized legged robots, we optimize leg lengths to minimize the impact torque on leg joints. An optimal leg-length combination that maximizes obstacle-surmounting capability for medium- and large-size multi-link legged robots is conducted. This research provides a concrete design basis for leg-length optimization in medium- and large-sized multi-link legged robots with the aim of improving impact resistance and obstacle surmounting.

Biomimetics doi: 10.3390/biomimetics11040262

Authors: Maria Tsiftsoglou Yannis Marinakis Magdalene Marinaki

This paper introduces a Hybrid Tyrannosaurus Rex Optimization Algorithm (Hybrid TROA) combined with Variable Neighborhood Search (VNS), two variations of the Path Relinking strategy, and a randomized Nawaz–Enscore–Ham (NEH) heuristic to address the Permutation Flowshop Scheduling Problem (PFSP). The TROA is a novel bio-inspired meta-heuristic algorithm modeled on the hunting behavior of the prehistoric Tyrannosaurus Rex. Leveraging the potential of this newly developed and efficient algorithm, we propose a framework in which an initial population of solutions is generated using the randomized NEH heuristic. These solutions are then further optimized through VNS and Path Relinking, yielding highly satisfactory results for the PFSP. First, we consider two optimization criteria separately: the makespan and the total flow time. Next, we conduct a comparative study of the Hybrid TROA against other prominent meta-heuristics, along with a statistical analysis using non-parametric tests, to determine the best-performing method for each objective. According to our findings, the Hybrid TROA proves to be the most suitable method in this study for minimizing both targets. Finally, recognizing that contemporary industry demands both high productivity and energy efficiency, we propose an energy-efficient version of the classic PFSP, simultaneously considering two criteria for optimization: the makespan and total energy consumption. Our study introduces a novel objective function that achieves balanced optimization by integrating both criteria.

Biomimetics doi: 10.3390/biomimetics11040261

Authors: Lijun Zhou Zhaoyue Tan Xia Sheng Xinjian Feng

The rational design and regulation of interfacial microenvironments represents an effective strategy for enhancing reaction performance. Previous studies have demonstrated that constructing air–liquid–solid triphase interfaces can substantially enhance catalytic reactions involving gaseous reactants. However, research on regulating the triphasic interfacial microenvironment remains limited and challenging. Herein, we fabricated a triphase photocatalytic system by depositing hydrophobic materials onto ordered TiO2 porous (OTP), achieving significantly enhanced performance in visible-light-driven dye-sensitized photooxidation. Further, we regulated the triphasic microenvironment by systematically adjusting the chain length of hydrophobic molecules. It was found that the chain length greatly affects the interfacial properties, including O2 concentration, the organic molecule adsorption and the interfacial electron transfer efficiency, thereby influencing photocatalytic reaction kinetics and pathways. We demonstrated a high-performance triphase photocatalytic system using 1H,1H,2H,2H-perfluorooctyl triethoxysilane as the hydrophobic material, which optimized multiple interfacial properties through synergistic effects, leading to optimal photocatalytic performance.

Biomimetics doi: 10.3390/biomimetics11040260

Authors: Fernando Valencia Fernando Nadal María Prado-Novoa

Understanding knee kinematics during gait is essential for the design of prostheses, orthoses, and biomimetic mechanisms. In many biomechanical analyses, tibiofemoral motion is simplified to the sagittal plane, allowing the locus of the instantaneous center of rotation (ICR) to describe joint kinematics derived from the instantaneous axis of rotation (IAR). However, it remains unclear whether ICR trajectories obtained from simplified flexion–extension tasks can represent those observed during gait. This study analyzes the sagittal-plane trajectory of the tibiofemoral ICR during gait swing, standing swing, seated swing, and squat. Motion data from 21 healthy participants were captured using videogrammetry, and the instantaneous axis of rotation (IAR) was computed from homogeneous transformation matrices using the Mozzi–Chasles theorem. Sagittal-plane ICR trajectories were derived and compared within subjects across tasks. Significant differences were found between gait and all other movements in both trajectory shape and spatial position. The shape metric (S), which quantifies differences in trajectory geometry, showed mean values ranging from 0.82 to 1.04 with very large effect sizes (Cohen’s d = 2.90 to 4.47, p < 0.0001). The centroid distance metric (M), which measures the overall spatial displacement between trajectories, indicated positional differences ranging from 8.15 mm to 12.37 mm between trajectories also showing very large effect sizes (Cohen’s = 1.72–3.40, p < 0.0001). Additionally, the mean deviation of the IAR from the sagittal plane ranged from 14° to 18° during gait, whereas smaller deviations were observed in non–weight-bearing swing movements. These results demonstrate that tibiofemoral ICR trajectories are task-dependent and that simplified flexion–extension tasks do not fully reproduce the knee kinematics observed during gait. Consequently, the use of gait-derived ICR trajectories, together with their variability, provides a more suitable basis for the design and optimization of polycentric mechanisms, enabling the development of devices that more closely replicate real biomechanics and are potentially better adapted to the user.

Biomimetics doi: 10.3390/biomimetics11040259

Authors: Shuxin Fan Yonghong Deng Zhibin Li

Accurate inverse kinematics for rehabilitation robotic arms remains challenging because of strong nonlinearity, multiple feasible joint configurations, and strict joint-limit constraints. Inspired by the cooperative construction, adaptive exploration, and collective information-sharing behaviors of beavers, this study develops an improved biomimetic beaver behavior optimizer (IBBO) for optimization-based inverse kinematics solving. In the proposed framework, biologically inspired cooperative search is translated into an engineering-oriented numerical strategy through four complementary mechanisms: a strict elitist replacement with rollback to preserve population fitness consistency, a momentum-inspired information transfer scheme to accumulate effective search directions, a lightweight memetic coordinate-wise local search to strengthen late-stage exploitation, and an adaptive builder–disturbance schedule to progressively shift the search from exploration to refinement. The optimization capability of IBBO is first evaluated on the CEC2017 benchmark suite, where it demonstrates competitive accuracy and robustness. It is then applied to inverse kinematics solving for representative rehabilitation robotic arms by minimizing pose errors under joint constraints. The experimental results show that IBBO can consistently generate feasible joint solutions with improved terminal pose accuracy and stable convergence compared with baseline metaheuristics. Beyond numerical improvement, this study provides a biomimetic optimization framework that transfers beaver-inspired cooperative behaviors into rehabilitation robotics, offering an effective computational approach for constrained inverse kinematics problems.

Biomimetics doi: 10.3390/biomimetics11040258

Authors: Oskar Gülcher Celeste Koster Jolanda Kluin Paul Gründeman

Large animal models are a critical component of the preclinical evaluation of mechanical cardiac implants, enabling assessment of safety and performance under physiological conditions that cannot be adequately reproduced in vitro. Choosing a suitable animal model is important for both scientifically valid and ethically responsible preclinical evaluation. However, interspecies differences between animal models and humans pose significant challenges for relevant translation of preclinical findings to clinical outcomes. This narrative review provides a comprehensive overview of commonly used large animal models (sheep, goats, pigs, and calves) for the preclinical assessment of mechanical cardiac implants, including prosthetic heart valves, ventricular assist devices, and total artificial hearts. We summarize key anatomical and physiological characteristics that influence device implantation, chronic follow-up, and translational value. Emphasis is placed on three critical outcome domains for preclinical evaluation of mechanical cardiac implants: calcification, thrombogenicity, and hemodynamic performance. Species- and age-dependent differences in calcification are reviewed, identifying juvenile sheep as a worst-case model for early manifestation and detection of graft mineralization. Interspecies differences in coagulation biology are examined, showing attenuated platelet responses in sheep and closer similarity between porcine and human platelet behavior, supporting pigs as the preferred thrombogenicity model. Hemodynamic evaluation strategies in acute and chronic large-animal studies are discussed, with particular emphasis on circulatory demands influenced by somatic growth and on device adaptability under varying loading conditions. Overall, this review provides practical, outcome-driven guidance for large animal model selection and experimental design in mechanical cardiac implant research, while identifying key limitations, knowledge gaps, and the need for standardized reporting to improve the translational reliability of preclinical studies. Based on the findings presented in this review, we conclude that there is no single animal model capable of evaluating all relevant aspects of a device. Instead, different animal models provide distinct advantages depending on the outcomes of interest.

Biomimetics doi: 10.3390/biomimetics11040257

Authors: Shunping Zhao Yuki Inoue Zhenyu Chen Yicong Lin Junru Chen E. Tonatiuh Jimenez-Borgonio J. Carlos Sanchez-Garcia Yinlai Jiang Hiroshi Yokoi Xiaobei Jing Xu Yong

To meet daily grasping needs under lightweight, low-complexity wearable constraints, this study proposes an underactuated multi-finger prosthetic hand with transmission–control co-design to achieve predictable multi-joint synergies and stable grasps under limited actuation. The prototype uses six miniature motors to drive 14 joint degrees of freedom (DOFs): four fingers have active metacarpophalangeal actuation with tendon-driven underactuated proximal and distal interphalangeal joints, while the thumb provides two independently controlled DOFs for opposition expansion and posture adjustment. It supports five-finger power grasps, tripod pinches, and lateral pinches. To mitigate tendon slack and stroke inconsistency, active/passive tendon-length constraints are defined, and an equal-stroke configuration is obtained via chord-to-arc mapping. A layered STM32F767-based controller combines a reference rotation range limit (free motion) with encoder speed-decay detection (contact/near-stall) to realize per-finger termination and overdrive protection without force/tactile sensors. Experiments report a total mass of 176.6 g and a peak single-finger driving force of approximately 2.8 N. Following the Feix GRASP taxonomy (33 types), the hand reproduces 24 types (72.7%), covering power, intermediate and precision grasps, both thumb abduction/adduction postures, and palm–pad–side opposition/contact, with stable grasp formation across objects of varying geometries.

Biomimetics doi: 10.3390/biomimetics11040256

Authors: Tasneem Alluhaidan Benjamin Hung Masoumah Qaw Isadora M. Garcia Mary Anne S. Melo

Biomimetics in dental restorative materials has gradually shifted from simply copying the appearance of natural teeth to better understanding how those tissues actually behave. Instead of focusing only on aesthetics, there is now more attention on how enamel and dentin function in real conditions, how they respond to stress, interact with their surroundings, and change over time. Because of this, newer materials are no longer just passive fillers; they are being designed to reflect aspects of natural tooth structure, composition, and behavior within the oral environment. This review brings together key ideas in this area, recent developments, and the challenges that remain. One issue that often comes up is how terms like bioinspired, biomimetic, and bioactive are used. They are sometimes treated as if they mean the same thing, but in practice, they point to different goals or levels of complexity in material design. For instance, some studies focus on creating more organized composite structures or mimicking natural mineralization processes, while others focus on antibacterial surfaces or peptide-based approaches that may support remineralization. There is also growing interest in materials that respond to environmental changes, such as shifts in pH or the early stages of wear. Even with promising laboratory results, these materials are not yet widely used in everyday clinical practice. Several issues continue to slow their adoption, including unclear terminology, limited availability of testing models that reflect real oral conditions, and a lack of long-term clinical data. Part of the challenge lies in the lack of consistent terminology, which can make it harder to compare findings across studies. Manufacturing challenges also remain, particularly when scaling up more complex systems. Moving forward, progress will depend on closer collaboration across disciplines, including materials science, oral biology, microbiology, and digital manufacturing. Such efforts will be important for developing restorative materials that behave more like natural tissues and perform reliably over time inside the mouth.

Biomimetics doi: 10.3390/biomimetics11040255

Authors: Davut Izci Adil Ozcayci Serdar Ekinci Irfan Okten Erdal Akin Gokhan Yuksek Ali Akdagli Ali Yildiz Filiz Karaomerlioglu

Accurate temperature regulation is essential for ensuring product quality, operational safety, and energy efficiency in industrial electric furnace systems. However, the inherent thermal inertia, time-delay effects, and nonlinear dynamics of furnace processes often make precise temperature control a challenging task. Motivated by these challenges, this study proposes an optimization-based control framework aimed at improving the temperature regulation performance of electric furnace systems. The proposed approach integrates a proportional–integral–derivative (PID) controller with the recently developed gazelle optimization algorithm (GOA) for automatic tuning of the controller parameters. First, a mathematical model of the electric furnace is established to describe the dynamic relationship between the control input and the furnace temperature output. Based on this model, a PID controller is implemented to regulate the furnace temperature. The parameters of the PID controller are then optimized using GOA, a nature-inspired metaheuristic algorithm that mimics the adaptive predator–prey survival strategies observed in gazelle herds. In order to achieve a balanced improvement in both steady-state and transient performance, a composite objective function is introduced. The proposed performance index combines the integral of absolute error with additional transient performance indicators related to maximum overshoot and settling time. The effectiveness of the proposed GOA-based tuning framework is evaluated through extensive simulation studies and statistical analyses conducted over multiple independent optimization runs. The results demonstrate stable convergence behavior, with the optimization process achieving a minimum objective value of 2.4251, a maximum value of 2.5347, and an average value of 2.4674 across 25 runs. The optimized control system exhibits improved dynamic characteristics, including a rise time of 1.8509 s, a settling time of 3.6834 s, and a low overshoot of 1.5104%. To further assess its effectiveness, the proposed GOA–PID control strategy is compared with several widely used controller tuning methods reported in the literature, including genetic algorithm, Ziegler–Nichols, Cohen–Coon, Nelder–Mead, and direct synthesis approaches. Comparative results indicate that the proposed method achieves a superior balance between response speed, stability, and temperature tracking accuracy.

Biomimetics doi: 10.3390/biomimetics11040254

Authors: Xiangyu Cheng Min Zhou Liping Zhang Zikai Zhang

Metaheuristic optimization algorithms have attracted considerable research interest for solving complex optimization problems, yet many existing algorithms suffer from premature convergence and an inadequate balance between exploration and exploitation. The Coati Optimization Algorithm (COA) is a recently proposed nature-inspired metaheuristic that models the hunting and escape behaviors of coatis; however, it exhibits limited search diversity and tends to stagnate in local optima on high-dimensional, multimodal landscapes. This paper proposes an Improved Coati Optimization Algorithm (ICOA) that integrates four complementary enhancement strategies: (1) a Dynamic Adaptive Step-Size strategy that combines Lévy flights with Student’s t-distribution perturbations for heavy-tailed exploration; (2) a Population-Adaptive Dynamic Perturbation strategy that incorporates differential evolution operators with fitness-proportional scaling; (3) an Iterative-Cyclic Differential Perturbation strategy that employs sinusoidal scheduling and population-differential guidance; and (4) a Cosine-Adaptive Gaussian Perturbation strategy for refined exploitation with time-decaying intensity. ICOA is evaluated on 29 CEC2017, 10 CEC2020, and 12 CEC2022 benchmark functions across dimensions ranging from 10 to 100, compared against seven state-of-the-art algorithms in each benchmark suite. A statistical analysis using the Friedman test and the Wilcoxon rank-sum test confirms that ICOA achieves overall rank 1 on all three benchmark suites, with Friedman mean ranks of 1.207 (CEC2017, D=100), 1.000 (CEC2020, D=10), and 2.208 (CEC2022, D=10); the CEC2020 result should be interpreted in the context of its low dimensionality. A scalability analysis across four dimensionalities (10D, 30D, 50D, 100D) demonstrates consistent first-place rankings with mean ranks between 1.000 and 1.207. An ablation study and a sensitivity analysis of the strategy activation probability validate the contribution of each individual strategy and the optimality of the 50% activation setting. Furthermore, ICOA achieves the best results on all six constrained engineering design problems tested, with all improvements confirmed as statistically significant (p<0.05).

Biomimetics doi: 10.3390/biomimetics11040253

Authors: Nasser Ayidh Alqahtani

Biological systems regulate motion and suppress unwanted vibrations through learning, adaptation, and predictive control under uncertainty. Inspired by these principles, Bayesian system identification has emerged as a powerful framework for modeling and estimation, particularly in the presence of uncertainty in structural systems. Flexible structures in aerospace and robotics require advanced control to mitigate vibrations under model uncertainty. This paper proposes a data-driven strategy leveraging a Gaussian Process (GP) integrated within a Nonlinear Model Predictive Control (NMPC) framework. The core innovation lies in using a Gaussian Process Nonlinear AutoRegressive model with eXogenous input (GP-NARX) as a probabilistic predictor to capture structural dynamics while quantifying uncertainty. The operational mechanism involves a tight coupling where the GP provides multi-step-ahead forecasts that the NMPC optimizer uses to minimize a cost function subject to constraints. Validated through simulations on Duffing oscillators, linear oscillators, and cantilever beams, the GP-NMPC achieved an 88.2% reduction in displacement amplitude compared to uncontrolled systems. Quantitative analysis shows high predictive accuracy, with a Root Mean Square Error (RMSE) of 0.0031 and a Standardized Mean-Squared Error (SMSE) below 0.05. Furthermore, Mean Standardized Log Loss (MSLL) evaluations confirm the reliability of the predictive uncertainty within the control loop. These results demonstrate strong performance in both regulation and tracking tasks, justifying this Bayesian-predictive coupling as a powerful approach for high-performance structural vibration control and a potential foundation for bio-inspired mechanical design.

Biomimetics doi: 10.3390/biomimetics11040252

Authors: Zhaozhi Wang Dongxu Duan Lei Yang Xu Bai Zhibin Jiao Chenliang Wu Jing Zhao Zhihui Zhang

Inspired by the Bouligand helicoidal architecture of the dactyl club of the peacock mantis shrimp, this study employed direct ink writing (DIW) 3D printing to construct a three-level synergistic toughening system composed of nano-SiO2, microscale flake alumina, and a macroscale helicoidal structure. The effects of nano-SiO2 content, Bouligand helix angle, and flake alumina content on the flexural strength and fracture toughness of the composite ceramics were systematically investigated. The results showed that the optimal nano-SiO2 addition was 7 wt%, yielding a fracture toughness of 1.03 MPa·m1/2, which was 13% higher than that of pure alumina. The introduced intergranular glassy phase transformed the rigid grain-boundary bonding into a moderately strong gradient interface, resulting in higher fracture toughness for all SiO2-containing samples than for pure alumina. The Bouligand structure further increased the fracture toughness to a maximum of 1.45 MPa·m1/2 at a helix angle of 10°, representing a 39% improvement over the 0° sample. When microscale flake alumina was incorporated into the optimal matrix containing 7 wt% SiO2, the best overall mechanical performance was achieved at a flake alumina content of 5 wt%, where the flakes directly dissipated fracture energy through pull-out, fracture, and bridging mechanisms. The synergistic effect of the three structural levels was most pronounced at a helix angle of 20°, at which the sample containing 5 wt% flake alumina achieved a fracture toughness of 2.07 MPa·m1/2 with almost no loss in flexural strength, corresponding to a 113% improvement over the sample without flake alumina. These results demonstrate that three-level synergy can be achieved through nanoscale interfacial optimization, microscale energy dissipation by reinforcing phases, and macroscale crack deflection induced by the helicoidal structure, thereby providing important theoretical and experimental support for the multiscale design of high-performance bioinspired ceramic materials.

Biomimetics doi: 10.3390/biomimetics11040251

Authors: Işıl Kutbay Zeynep Gerdan Murat Çolak Yasemin Tabak Abdullah Tahir Şensoy

The sternum protects the intrathoracic organs and contributes to chest wall mechanics, which makes reconstruction after tumor resection, trauma, or infection a demanding biomechanical problem. This study presents an in silico workflow for preselecting materials for sternal implants before physical prototyping. After a virtual resection, an anatomically conformal implant was designed and candidate biomaterials were screened in CES Selector using density, elastic modulus, fatigue strength, fracture toughness, toxicity, medical grade suitability, and MRI safety. A representative subset of the screened candidates was then compared by finite element modeling in terms of stress transfer and deformation. Seventeen candidates met the screening criteria. Ti-13Nb-13Zr showed an elastic modulus of about 80 GPa, and the titanium-based candidates showed deformation values of about 0.96 to 1.03 mm, whereas GF PEEK reached about 1.74 mm. The stress shielding index also showed that titanium-based materials remained on the implant-dominant side, while polymer-based materials shifted stress transfer toward bone. Taken together, the findings suggest that Ti-13Nb-13Zr offers the best overall balance for load-bearing sternal reconstruction, whereas PEEK-based systems may be more suitable within the present model for hybrid or adjunct designs. The proposed workflow can support early implant planning and guide future experimental and clinical studies.

Biomimetics doi: 10.3390/biomimetics11040250

Authors: Emilia Georgiana Prisăcariu Oana Dumitrescu

Micro air vehicles (MAVs) operating at low Reynolds numbers face aerodynamic and structural challenges that differ significantly from those encountered by conventional aircrafts. Nature provides effective solutions to these constraints, as insects, birds, and bats demonstrate highly efficient flight through integrated interactions between morphology, kinematics, and unsteady aerodynamic mechanisms. This review examines how biological flight principles can inform the design of next-generation MAVs. The study first analyzes biological flight strategies across insects, birds, and bats, with emphasis on scaling laws and physiological adaptations relevant to small-scale flight. It then reviews key unsteady aerodynamic phenomena governing low-Reynolds-number flight, including leading-edge vortex stability, wing–wake interactions, tandem-wing effects, and ground influence, as well as current modeling approaches ranging from quasi-steady methods to high-fidelity Navier–Stokes simulations. Building on these principles, the paper discusses biomimetic design strategies for MAV wings, structural–aerodynamic coupling, and actuation technologies used to replicate flapping flight. Existing MAV demonstrators inspired by biological flyers are analyzed, including concepts relevant to planetary exploration environments. Finally, the review identifies current technological limitations and research gaps in materials, actuation, aerodynamic modeling, and system integration. By synthesizing insights from biology and engineering, this work highlights key directions for the development of efficient, adaptable biomimetic MAV platforms capable of operating in complex environments.

Biomimetics doi: 10.3390/biomimetics11040249

Authors: Fatin A. Hasanain Rotana M. Abulaban Nouf S. Almeganni Hani M. Nassar

Glass ionomer cements (GICs) are considered functionally biomimetic as they participate in ion-exchange processes that partially resemble the behavior of natural enamel and dentin, chemically bond to dental hard tissues, and release fluoride. While GICs are designed to interact with aqueous oral environments, their exposure to dietary beverages may affect their esthetic stability and water-related behavior within the oral environment. For biomimetic restorative materials to perform successfully in the oral environment, they must maintain not only bioactive properties but also esthetic stability and resistance to water-related degradation during exposure to dietary beverages. This study evaluated beverage-induced color changes, water sorption, and water solubility of six GICs following their immersion in coffee, tea, berry juice, cola, and distilled water (n = 5 per material per solution). Color measurements were recorded at baseline and after 2, 4, 6, and 8 weeks using a spectrophotometer, and color change (ΔE) values were calculated using the CIE L*a*b* system. Specimen mass was measured at baseline, after 8 weeks of immersion and then after 4 weeks of desiccation. Data were analyzed using repeated-measures Analysis of Variance (ANOVA) and Fisher’s least significant difference post hoc tests (α = 0.05). The results showed time, material, and solution significantly affected ΔE (p < 0.001). Tea produced the greatest discoloration overall, followed by coffee. ChemFil exhibited the greatest staining susceptibility, while Fuji II showed the lowest staining susceptibility. Water sorption and solubility were material- and solution-dependent. Clinically relevant discoloration of GICs was found when immersed in common beverages over time, with tea showing the strongest staining effect. These findings indicate that although GICs exhibit biomimetic characteristics through their interaction with tooth structures and aqueous environments, their long-term esthetic stability and resistance to environmental challenges should also be considered when selecting restorative materials for clinically visible areas.

Biomimetics doi: 10.3390/biomimetics11040248

Authors: Zhaozhi Wang Weicong Wang Xinhui Duan Xu Bai Zhibin Jiao Chenliang Wu Jing Zhao Zhihui Zhang

Inspired by the erosion-resistant dorsal armor of the desert scorpion, this study developed biomimetic ZTA ceramic composites with enhanced resistance to solid particle erosion. Three biomimetic configurations, namely convex-bump (CH-O), convex-curved-surface (CH-CS), and convex hybrid rigid–flexible (CH-HS) structures, were fabricated by direct ink writing (DIW) 3D printing. Their erosion performance was evaluated by gas–solid two-phase erosion tests at impact angles ranging from 15° to 90°, and the underlying mechanisms were elucidated through erosion morphology analysis, actual impact angle analysis, and stress-wave propagation analysis. The results showed that the erosion rate of all samples first increased and then decreased with increasing impact angle, reaching a maximum at around 60°. Compared with the smooth control sample, CH-O exhibited lower erosion resistance under low-angle erosion conditions but showed clear improvement under high-angle erosion conditions, with the erosion resistance increased by 18.39–32.54%. CH-CS further improved the erosion resistance of CH-O, with enhancements of 14.31–53.92% at low impact angles and 24.57–35.17% at high impact angles. Among all the biomimetic designs, CH-HS exhibited the best overall erosion resistance, showing an additional improvement of 9.22–32.16% over CH-CS across the tested impact angle range. The superior erosion resistance was attributed to the synergistic effects of convex-bump morphology, curved-surface-induced particle deflection, and rigid–flexible coupling. These biomimetic features modified the actual impact angle of the particles, deflected their trajectories, reduced direct particle impact, and generated a shadow effect, while the flexible layer dissipated impact energy through reflection unloading at the rigid–flexible interface. This study provides a novel strategy for the biomimetic design of erosion-resistant ceramic composites and offers new insights into mitigating erosion damage in ceramic-based mechanical components.

Biomimetics doi: 10.3390/biomimetics11040247

Authors: Yangyang Jiang

Multilevel threshold image segmentation is a key task in image processing, yet it faces challenges such as low search efficiency in high-dimensional spaces, difficulty in balancing segmentation accuracy and stability, and insufficient adaptability to complex scenes. Existing solutions mainly include traditional thresholding methods and metaheuristic optimization-based schemes, but they still face limitations in high-dimensional and complex segmentation tasks. The standard Seagull Optimization Algorithm (SOA) suffers from shortcomings including a single exploration mechanism, weak local exploitation capability, and a tendency for population diversity to deteriorate, making it difficult to meet the demands of high-dimensional optimization. To address these issues, this paper proposes a multi-strategy fused improved Seagull Optimization Algorithm (MFISOA), which integrates three strategies: adaptive cooperative foraging, differential evolution-driven exploitation, and centroid opposition-based boundary control. These strategies jointly construct a collaborative optimization framework with dynamic resource allocation, fine local search, and population diversity maintenance, thereby improving global exploration efficiency, local exploitation accuracy, and population stability. To evaluate the optimization performance of MFISOA, numerical simulation experiments were conducted on the CEC2017 and CEC2022 benchmark test suites, and comparisons were made with nine other mainstream advanced algorithms. The results show that MFISOA outperforms the competing algorithms in terms of optimization accuracy, convergence speed, and operational stability. Its superiority is further verified by the Wilcoxon rank-sum test and the Friedman test, with statistical significance (p < 0.05). In the multilevel threshold image segmentation task, using the Otsu criterion as the objective function, MFISOA was tested on nine benchmark images under 4-, 6-, 8-, and 10-threshold segmentation scenarios. The results indicate that MFISOA achieves better performance on metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Feature Similarity Index (FSIM), enabling more accurate characterization of image grayscale distribution features and producing higher-quality segmentation results. This study provides an efficient and reliable approach for numerical optimization and multilevel threshold image segmentation.

Biomimetics doi: 10.3390/biomimetics11040246

Authors: Wencke Krings Tamina Riesel Thomas M. Kaiser Alexander Daasch Ellen Schulz-Kornas Stanislav N. Gorb

Understanding how geometry governs interfacial contact and material removal is central to designing efficient bioinspired surface systems. Gastropod radular teeth form natural arrays of microscale cutting elements optimized for repeated interaction with compliant and semi-rigid substrates, yet experimentally validated shape–performance relationships remain limited. Here, we isolate geometric effects on interfacial mechanics using stereolithography-printed biomimetic tooth arrays inspired by the taenioglossan radula of the hard-substrate grazer Spekia zonata. Two morphologically distinct tooth types (central and marginal) were systematically varied in cusp and stylus geometry (four variants each), while array configuration, material, and boundary conditions were kept constant. Tooth stiffness was quantified in bending tests as load-induced height reduction. Interfacial performance was assessed using a controlled pull-through assay in agarose substrates of two stiffness levels (0.4% and 0.8%), with continuous force recording and measurement of removed mass. Marginal-tooth geometries were stiffer and consistently removed more substrate than central variants. Although work increased substantially in stiffer gels, removal did not scale proportionally and declined for central teeth, revealing a decoupling between mechanical input and yield. Performance correlated with active engagement rather than work alone, indicating geometry-limited contact regimes. These findings establish geometry-controlled stiffness and engagement as key parameters for efficient abrasive interfaces.

Biomimetics doi: 10.3390/biomimetics11040245

Authors: Yan Chen Longda Wang Xiujiang Zhu Gang Liu

Intelligent parking systems have been recognized as a core technological intervention for resolving parking garage shortages and advancing traffic safety. Nevertheless, it remains challenging to generate a smooth, accurate, and optimal parking trajectory when employing conventional intelligent path optimization algorithms. Hence, building upon a newly designed optimization model for intelligent vehicle parking path planning, this study develops an improved immune moth–flame optimization algorithm (IIMFO). Specifically, aiming at the shortest path length and smooth enough trajectory, we leverage a cubic spline interpolation-driven path planning model to resolve the complex automatic parking trajectory optimization problem. To significantly strengthen the optimization effect, we introduce immune concentration selection, nonlinear decaying adaptive inertia weight adjustments, and elite opposition-based learning mechanisms to improve the immune moth–flame algorithm. Based on the evaluation results of the test functions, as well as the simulation and semi-automatic experiments of the real-world scenario of intelligent vehicle parking path optimization, the results indicate that the improved strategy can achieve better parking trajectories.

Biomimetics doi: 10.3390/biomimetics11040244

Authors: Zefang Chang Guangrong Wu Hao Chen He Zhang Hao Luan Zhijian Yang

Computational models of the MLG1 neurons in crab Neohelice granulata have been developed to detect and spatially localize looming stimuli. However, existing models suffer from significant performance degradation in dim scenarios, primarily due to visual signal corruption from stochastic noise such as photon shot noise. To address this challenge, we propose a computational framework that embeds Daubechies wavelet directly into ON/OFF visual pathways. The ON/OFF mechanism separates the input signals in parallel based on luminance changes to capture dynamic differences between target and background. Embedding Daubechies wavelet enables multi-scale frequency decomposition, allowing the model to suppress high-frequency noise while enhancing low-frequency looming trends. This process extracts low-frequency components and high-frequency details, providing the MLG1 neuron with more discriminative feature inputs. Experimental results demonstrate that the model achieves reliable looming spatial localization under extremely low contrast conditions, offering a robust methodology for bionic vision in extreme dim light environments.

Biomimetics doi: 10.3390/biomimetics11040243

Authors: Mona Gafar Shahenda Sarhan Abdullah M. Shaheen Ahmed S. Alwakeel

This paper develops an intelligent Enhanced Starfish Optimization (ESFO) algorithm for optimizing a secure wireless communication infrastructure. The Starfish Optimization (SFO) algorithm is inspired by starfish biology, using the integrated modeling of the arm-based exploration, preying, and regeneration behaviors of starfish. To further enhance the exploitation capability of the standard Starfish Optimization (SFO), the proposed Enhanced Starfish Optimization (ESFO) integrates a fitness-based interacting mechanism within the exploitation phase. This innovative modification improves local search accuracy, preserves population diversity, and mitigates premature convergence without introducing additional control parameters. Moreover, the proposed Enhanced Starfish Optimization (ESFO) is designed for secure wireless transmission, which is considered one of the main topics in next-generation wireless network infrastructure. The investigated network addresses the use of Simultaneously Transmitting and Reflecting RIS (STAR-RIS) in the security of the physical layer. This implemented STAR-RIS has a coupled phase shift to create reflected and transmission links, unlike traditional Reconfigurable Intelligent Surface (RIS). In this regard, we create a safe beamforming architecture that optimizes both Base Station (BS) precoding vectors and STAR-RIS transmission/reflection coefficients. In order to validate the efficiency of the proposed Enhanced Starfish Optimization (ESFO) algorithm, it is compared to several benchmark optimizers such as standard Starfish Optimization (SFO), Dhole Optimizer (DO), Neural Network Algorithm (NNA), Crocodile Ambush Optimization Algorithm (CAOA), and white shark Optimizer (WSO). These comparisons include several scenarios based on the transmitted power threshold which is varied in the range of 20 to 70 dBm with step of 5 dBm. The simulation results show that the proposed Enhanced Star Fish Optimization (ESFO) algorithm consistently outperforms existing benchmark approaches. This study supports future intelligent communication infrastructures in terms of secrecy and achievable rates over a range of transmit power levels. In particular, ESFO improves performance by up to 20–25% while converging 40–50% faster than traditional optimization algorithms, demonstrating its usefulness and resilience in STAR-RIS-assisted secure communication systems. The suggested ESFO-enabled architecture outperforms standard RIS-based systems in terms of secrecy capacity, according to numerical studies, and low-resolution STAR-RIS phase-shifters are sufficient to ensure robust secrecy performance.

Biomimetics doi: 10.3390/biomimetics11040242

Authors: Yılmaz Seryar Arıkuşu

This paper proposes a Black Kite Algorithm (BKA)-based hyperparameter optimization method for Artificial Neural Network (ANN) training, mitigating local minimum issues associated with conventional training techniques. The resulting BKA-ANN model is then employed to estimate PID controller parameters for DC motor speed regulation. A large-scale dataset of 100,000 samples was generated via MATLAB simulation, with reference speed and load torque stochastically varied, and optimal PID parameters determined by minimizing the ITAE criterion for each operating condition. The optimized controller was evaluated under various operating conditions including transient response, frequency domain analysis (phase margin and bandwidth), parametric robustness, and load disturbance suppression, along with control effort and energy consumption assessments. The proposed BKA-ANN approach was benchmarked against nine algorithms: hybrid atom search optimization-simulated annealing (hASO-SA), harris hawks optimization (HHO), Henry gas solubility optimization with opposition-based learning (OBL/HGSO), atom search optimization (ASO), henry gas solubility op-timization (HGSO), stochastic fractal search(SFS), grey wolf optimization (GWO), sine–cosine algorithm (SCA), and Standard ANN. Simulation results indicate that BKA-ANN achieves stable performance across all tested scenarios, with minimal oscillation and competitive settling time compared to the evaluated algorithms.

Biomimetics doi: 10.3390/biomimetics11040241

Authors: Tim Tchervonenko Alexander Sauer Thabata Alcântara Ferreira Ganga Heike Beismann Eduardo Keller Rorato Míriam Raquel Diniz Zanetti Maria Elizete Kunkel

Orthoses are widely used to support or modulate neuromuscular and skeletal function; however, their clinical effectiveness is often limited by discomfort, poor adaptability, and suboptimal human–device interaction. Biomimetics has emerged as a structured design paradigm capable of enhancing orthotic performance by systematically translating biological principles into engineering solutions. This scoping review examined biomimetic strategies in the development of orthoses. A structured search was conducted across PubMed, IEEE Xplore, Web of Science, and Scopus (2000–2025). Of 453 identified records, 14 met the inclusion criteria. Biomimetic orthosis research emerged predominantly after 2012, with increased activity after 2021. Human-based biological models, particularly muscle–tendon systems, predominated. Most studies relied on functional abstraction and were implemented using cable-driven or electromechanical actuation. None of the included studies explicitly referenced established biomimetics standards (e.g., ISO 18458), and descriptions of biological analysis, abstraction, and transfer were frequently incomplete. Experimental validation was generally limited to prototype-level testing, small sample sizes, and short-term evaluations, with no longitudinal or multicenter studies identified. These findings reveal a structural imbalance between conceptual biomimetic inspiration and structured methodological implementation. Based on this analysis, a structured biomimetic workflow is proposed to enhance traceability, reporting clarity, and clinical translation in the development of orthosis.

Biomimetics doi: 10.3390/biomimetics11040240

Authors: Gokhan Kayhan Seyma Hasbolat Unal

Artificial Neural Networks (ANNs) are models that learn patterns in input-output data. Since traditional optimization methods often get trapped in local optima when determining weight and bias values, identifying optimal parameters and enhancing network performance remain significant research areas. Heuristic algorithms are also generally used in solving optimization problems and are used to train ANNs. In the study, the parameter optimization of the ANN model was carried out using the Aquila Optimizer (AO), a recent metaheuristic algorithm, and a hybrid Aquila Optimizer optimized ANN model (AOANN) was proposed. Hybridization of algorithms contributes to the improvement of optimization performance. In this study, the proposed model was assessed on empirical datasets, including Cancer, Iris, Glass, and Wine, and its performance was compared with that of well-established ANN models. The results of the evaluation revealed that the proposed AOANN, a soft computation model, demonstrated stability in solving classification problems.

Biomimetics doi: 10.3390/biomimetics11040239

Authors: Zixiang Zhang Dening Song Jinghua Li

Personalized itinerary planning for large-scale passengers under resource constraints is a critical challenge in enhancing the operational efficiency and service quality of cruise tourism. Traditional clustering methods, which primarily rely on geometric similarity, often fail to address the intricate coupling between passenger preferences and finite venue capacities, lacking predictive capability for the ultimate planning quality. To overcome these limitations, this study proposes a novel bio-inspired data-driven hybrid optimization framework for the cruise itinerary planning task unit partition. The framework innovatively integrates a Genetic Balanced Clustering Algorithm (GBCA) for multi-objective passenger grouping, Kernel Principal Component Analysis (KPCA) for feature extraction from preference data, an improved Adaptive Spiral Flying Sparrow Search Algorithm (ASFSSA) for hyperparameter optimization, and a Kernel Extreme Learning Machine (KELM) for data-driven prediction of itinerary planning quality. This synergy enables the framework to dynamically allocate venue capacities based on group preferences and optimize partitioning towards maximizing overall benefits, ensuring load balance and fairness. Extensive experiments on simulated cruise scenarios demonstrate that the proposed framework significantly outperforms conventional methods, improving segmentation quality by at least 40% while exhibiting superior convergence speed and stability. This work provides a scalable, intelligent solution for complex resource-constrained scheduling problems, showcasing the effective application of bio-inspired data-driven methodologies in engineering optimization.