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Biomimetics, Volume 10, Issue 9 (September 2025) – 59 articles

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18 pages, 15272 KB  
Article
IDP-Head: An Interactive Dual-Perception Architecture for Organoid Detection in Mouse Microscopic Images
by Yuhang Yang, Changyuan Fan, Xi Zhou and Peiyang Wei
Biomimetics 2025, 10(9), 614; https://doi.org/10.3390/biomimetics10090614 - 11 Sep 2025
Abstract
The widespread application of organoids in disease modeling and drug development is significantly constrained by challenges in automated quantitative analysis. In bright-field microscopy images, organoids exhibit complex characteristics, including irregular morphology, blurred boundaries, and substantial scale variations, largely stemming from their dynamic self-organization [...] Read more.
The widespread application of organoids in disease modeling and drug development is significantly constrained by challenges in automated quantitative analysis. In bright-field microscopy images, organoids exhibit complex characteristics, including irregular morphology, blurred boundaries, and substantial scale variations, largely stemming from their dynamic self-organization that mimics in vivo tissue development. Existing convolutional neural network-based methods are limited by fixed receptive fields and insufficient modeling of inter-channel relationships, making them inadequate for detecting such evolving biological structures. To address these challenges, we propose a novel detection head, termed Interactive Dual-Perception Head (IDP-Head), inspired by hierarchical perception mechanisms in the biological visual cortex. Integrated into the RTMDet framework, IDP-Head comprises two bio-inspired components: a Large-Kernel Global Perception Module (LGPM) to capture global morphological dependencies, analogous to the wide receptive fields of cortical neurons, and a Progressive Channel Synergy Module (PCSM) that models inter-channel semantic collaboration, echoing the integrative processing of multi-channel stimuli in neural systems. Additionally, we construct a new organoid detection dataset to mitigate the scarcity of annotated data. Extensive experiments on both our dataset and public benchmarks demonstrate that IDP-Head achieves a 5-percentage-point improvement in mean Average Precision (mAP) over the baseline model, offering a biologically inspired and effective solution for high-fidelity organoid detection. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2025)
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31 pages, 1863 KB  
Article
Human Activity Recognition with Noise-Injected Time-Distributed AlexNet
by Sanjay Dutta, Tossapon Boongoen and Reyer Zwiggelaar
Biomimetics 2025, 10(9), 613; https://doi.org/10.3390/biomimetics10090613 - 11 Sep 2025
Abstract
This study investigates the integration of biologically inspired noise injection with a time-distributed adaptation of the AlexNet architecture to enhance the performance and robustness of human activity recognition (HAR) systems. It is a critical field in computer vision which involves identifying and interpreting [...] Read more.
This study investigates the integration of biologically inspired noise injection with a time-distributed adaptation of the AlexNet architecture to enhance the performance and robustness of human activity recognition (HAR) systems. It is a critical field in computer vision which involves identifying and interpreting human actions from video sequences and has applications in healthcare, security and smart environments. The proposed model is based on an adaptation of AlexNet, originally developed for static image classification and not inherently suited for modelling temporal sequences for video action classification tasks. While our time-distributed AlexNet efficiently captures spatial and temporal features and suitable for video classification. However, its performance can be limited by overfitting and poor generalisation to unseen scenarios, to address these challenges, Gaussian noise was introduced at the input level during training, inspired by neural mechanisms observed in biological sensory processing to handle variability and uncertainty. Experiments were conducted on the EduNet, UCF50 and UCF101 datasets. The EduNet dataset was specifically designed for educational environments and we evaluate the impact of noise injection on model accuracy, stability and overall performance. The proposed bio-inspired noise-injected time-distributed AlexNet achieved an overall accuracy of 91.40% and an F1 score of 92.77%, outperforming other state-of-the-art models. Hyperparameter tuning, particularly optimising the learning rate, further enhanced model stability, reflected in lower standard deviation values across multiple experimental runs. These findings demonstrate that the strategic combination of noise injection with time-distributed architectures improves generalisation and robustness in HAR, paving the way for resource-efficient and real-world-deployable deep learning systems. Full article
(This article belongs to the Section Bioinspired Sensorics, Information Processing and Control)
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51 pages, 10350 KB  
Article
An Improved Greater Cane Rat Algorithm with Adaptive and Global-Guided Mechanisms for Solving Real-World Engineering Problems
by Yepei Chen, Zhangzhi Tian, Kaifan Zhang, Feng Zhao and Aiping Zhao
Biomimetics 2025, 10(9), 612; https://doi.org/10.3390/biomimetics10090612 - 10 Sep 2025
Abstract
This study presents an improved variant of the greater cane rat algorithm (GCRA), called adaptive and global-guided greater cane rat algorithm (AGG-GCRA), which aims to alleviate some key limitations of the original GCRA regarding convergence speed, solution precision, and stability. GCRA simulates the [...] Read more.
This study presents an improved variant of the greater cane rat algorithm (GCRA), called adaptive and global-guided greater cane rat algorithm (AGG-GCRA), which aims to alleviate some key limitations of the original GCRA regarding convergence speed, solution precision, and stability. GCRA simulates the foraging behavior of the greater cane rat during both mating and non-mating seasons, demonstrating intelligent exploration capabilities. However, the original algorithm still faces challenges such as premature convergence and inadequate local exploitation when applied to complex optimization problems. To address these issues, this paper introduces four key improvements to the GCRA: (1) a global optimum guidance term to enhance the convergence directionality; (2) a flexible parameter adjustment system designed to maintain a dynamic balance between exploration and exploitation; (3) a mechanism for retaining top-quality solutions to ensure the preservation of optimal results.; and (4) a local perturbation mechanism to help escape local optima. To comprehensively evaluate the optimization performance of AGG-GCRA, 20 separate experiments were carried out across 26 standard benchmark functions and six real-world engineering optimization problems, with comparisons made against 11 advanced metaheuristic optimization methods. The findings indicate that AGG-GCRA surpasses the competing algorithms in aspects of convergence rate, solution precision, and robustness. In the stability analysis, AGG-GCRA consistently obtained the global optimal solution in multiple runs for five engineering cases, achieving an average rank of first place and a standard deviation close to zero, highlighting its exceptional global search capabilities and excellent repeatability. Statistical tests, including the Friedman ranking and Wilcoxon signed-rank tests, provide additional validation for the effectiveness and importance of the proposed algorithm. In conclusion, AGG-GCRA provides an efficient and stable intelligent optimization tool for solving various optimization problems. Full article
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23 pages, 4045 KB  
Article
Advanced Robust Heading Control for Unmanned Surface Vessels Using Hybrid Metaheuristic-Optimized Variable Universe Fuzzy PID with Enhanced Smith Predictor
by Siyu Zhan, Qiang Liu, Zhao Zhao, Shen’ao Zhang and Yaning Xu
Biomimetics 2025, 10(9), 611; https://doi.org/10.3390/biomimetics10090611 - 10 Sep 2025
Abstract
With the increasing deployment of unmanned surface vessels (USVs) in complex marine operations such as ocean monitoring, search and rescue, and military reconnaissance, precise heading control under environmental disturbances and system delays has become a critical challenge. This paper presents an advanced robust [...] Read more.
With the increasing deployment of unmanned surface vessels (USVs) in complex marine operations such as ocean monitoring, search and rescue, and military reconnaissance, precise heading control under environmental disturbances and system delays has become a critical challenge. This paper presents an advanced robust heading control strategy for USVs operating under these demanding conditions. The proposed approach integrates three key innovations: (1) an enhanced Smith predictor for accurate time-delay compensation, (2) a variable-universe fuzzy PID controller with self-adaptive scaling domains that dynamically adjust to error magnitude and rate of change, and (3) a hybrid metaheuristic optimization algorithm combining beetle antennae search, harmony search, and genetic algorithm (BAS-HSA-GA) for optimal parameter tuning. Through comprehensive simulations using a Nomoto first-order time-delay model under combined white noise and second-order wave disturbances, the system demonstrates superior performance with over 90% reduction in steady-state heading error and ≈30% faster settling time compared to conventional PID and single-optimization fuzzy PID methods. Field trials under sea-state 4 conditions confirm 15–25% lower tracking error in realistic operating scenarios. The controller’s stability is rigorously verified through Lyapunov analysis, while comparative studies show significant improvements in S-shaped path tracking performance, achieving better IAE/ITAE metrics than DRL, ANFC, and ACO approaches. This work provides a comprehensive solution for high-precision, delay-resilient USV heading control in dynamic marine environments. Full article
(This article belongs to the Section Bioinspired Sensorics, Information Processing and Control)
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23 pages, 1998 KB  
Article
Hybrid Cuckoo Search–Bees Algorithm with Memristive Chaotic Initialization for Cryptographically Strong S-Box Generation
by Sinem Akyol
Biomimetics 2025, 10(9), 610; https://doi.org/10.3390/biomimetics10090610 - 10 Sep 2025
Abstract
One of the essential parts of contemporary cryptographic systems is s-boxes (Substitution Boxes), which give encryption algorithms more complexity and resilience due to their nonlinear structure. In this study, we propose CSBA (Cuckoo Search–Bees Algorithm), a hybrid evolutionary method that combines the strengths [...] Read more.
One of the essential parts of contemporary cryptographic systems is s-boxes (Substitution Boxes), which give encryption algorithms more complexity and resilience due to their nonlinear structure. In this study, we propose CSBA (Cuckoo Search–Bees Algorithm), a hybrid evolutionary method that combines the strengths of Cuckoo Search and Bees algorithms, to generate s-box structures with strong cryptographic properties. The initial population is generated with a high-diversity four-dimensional Memristive Lu chaotic map, taking advantage of the random yet deterministic nature of chaotic systems. This proposed method was designed with inspiration from biological systems. It was developed based on the foraging strategies of bees and the reproductive strategies of cuckoos. This nature-inspired structure enables an efficient scanning of the solution space. The resultant s-boxes’ fitness was assessed using the nonlinearity value. These s-boxes were then optimized using the hybrid CSBA algorithm suggested in this paper as well as the Bees algorithm. The performance of the proposed approaches was measured using SAC, nonlinearity, BIC-SAC, BIC-NL, maximum difference distribution, and linear uniformity (LU) metrics. Compared to other studies in the literature that used metaheuristic algorithms to generate s-boxes, the proposed approach demonstrates good performance. In particular, the average value of 109.75 obtained for the nonlinearity metric demonstrates high success. Therefore, this study demonstrates that robust and reliable s-boxes can be generated for symmetric encryption algorithms using the developed metaheuristic algorithms. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation: 3rd Edition)
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17 pages, 2861 KB  
Article
High-Accuracy Lower-Limb Intent Recognition: A KPCA-ISSA-SVM Approach with sEMG-IMU Sensor Fusion
by Kaiyang Yin, Pengchao Hao, Huanli Zhao, Pengyu Lou and Yi Chen
Biomimetics 2025, 10(9), 609; https://doi.org/10.3390/biomimetics10090609 - 10 Sep 2025
Abstract
Accurately decoding human locomotion intention from physiological signals remains a significant hurdle for the seamless control of advanced rehabilitation devices like exoskeletons and intelligent prosthetics. Conventional recognition methods often falter, exhibiting limited accuracy and struggling to capture the complex, nonlinear dynamics inherent in [...] Read more.
Accurately decoding human locomotion intention from physiological signals remains a significant hurdle for the seamless control of advanced rehabilitation devices like exoskeletons and intelligent prosthetics. Conventional recognition methods often falter, exhibiting limited accuracy and struggling to capture the complex, nonlinear dynamics inherent in biological data streams. Addressing these critical limitations, this study introduces a novel framework for lower-limb motion intent recognition, integrating Kernel Principal Component Analysis (KPCA) with a Support Vector Machine (SVM) optimized via an Improved Sparrow Search Algorithm (ISSA). Our approach commences by constructing a comprehensive high-dimensional feature space from synchronized surface electromyography (sEMG) and inertial measurement unit (IMU) data—a potent combination reflecting both muscle activation and limb kinematics. Critically, KPCA is employed for nonlinear dimensionality reduction; leveraging the power of kernel functions, it transcends the linear constraints of traditional PCA to extract low-dimensional principal components that retain significantly more discriminative information. Furthermore, the Sparrow Search Algorithm (SSA) undergoes three strategic enhancements: chaotic opposition-based learning for superior population diversity, adaptive dynamic weighting to adeptly balance exploration and exploitation, and hybrid mutation strategies to effectively mitigate premature convergence. This enhanced ISSA meticulously optimizes the SVM hyperparameters, ensuring robust classification performance. Experimental validation, conducted on a challenging 13-class lower-limb motion dataset, compellingly demonstrates the superiority of the proposed KPCA-ISSA-SVM architecture. It achieves a remarkable recognition accuracy of 95.35% offline and 93.3% online, substantially outperforming conventional PCA-SVM (91.85%) and standalone SVM (89.76%) benchmarks. This work provides a robust and significantly more accurate solution for intention perception in human–machine systems, paving the way for more intuitive and effective rehabilitation technologies by adeptly handling the nonlinear coupling characteristics of sEMG-IMU data and complex motion patterns. Full article
(This article belongs to the Special Issue Data-Centric Engineering for Sustainable Future with AI and Human-in-the-Loop)
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19 pages, 9017 KB  
Article
Collagen Formulation in Xenogeneic Bone Substitutes Influences Cellular Responses in Periodontal Regeneration: An In Vitro Study
by Priscilla Pelaez-Cruz, Pia López Jornet and Eduardo Pons-Fuster
Biomimetics 2025, 10(9), 608; https://doi.org/10.3390/biomimetics10090608 - 10 Sep 2025
Abstract
Background: Bone regeneration is a key therapeutic objective in periodontology, particularly in the treatment of alveolar defects caused by periodontal disease, dentoalveolar trauma, or surgical interventions. Among current regenerative strategies, collagen-enriched biomaterials have demonstrated an active role in modulating cellular behavior during bone [...] Read more.
Background: Bone regeneration is a key therapeutic objective in periodontology, particularly in the treatment of alveolar defects caused by periodontal disease, dentoalveolar trauma, or surgical interventions. Among current regenerative strategies, collagen-enriched biomaterials have demonstrated an active role in modulating cellular behavior during bone repair. However, the specific effects of different collagen formulations on human dental pulp stem cells (hDPSCs) have not yet been fully characterized. Objective: To evaluate the impact of xenogeneic bone grafts with and without collagen—OsteoBiol® Gen-Os® (GO), OsteoBiol® GTO® (GTO), and Geistlich Bio-Oss® (BO)—on cell viability, adhesion, migration, osteogenic differentiation, and mineralization potential of hDPSCs, and to explore the molecular mechanisms underlying their effects. Methods: In vitro assays were conducted to assess viability (MTT and fluorescence staining), adhesion (SEM), migration (wound healing assay), and mineralization (Alizarin Red S staining). Gene expression analyses (RT-qPCR) were performed for adhesion/migration markers (FN, SDF-1, COL1A1), angiogenic/proliferation markers (VEGF, FGF2), and osteogenic differentiation markers (RUNX2, ALP, COL1A1). Results: GO showed a higher early expression of genes associated with adhesion, migration, angiogenesis (FN, SDF-1, VEGF and FGF2: p < 0.05; COL1A1: p < 0.01), and osteogenic differentiation (7 days: COL1A1 and ALP (p < 0.001)); (14 days: RUNX2, ALP: p < 0.001; COL1A1: p < 0.05), indicating a sequential activation of molecular pathways and mineralization capacity comparable to the control group. GTO demonstrated the best biocompatibility, with significantly higher cell viability (p < 0.05), strong adhesion, and markedly increased mineralization at 21 days (p < 0.001), despite moderate early gene expression. BO showed reduced cell viability at 10 mg/mL (p < 0.05) and 20 mg/mL (p < 0.001), with mineralization levels similar to the control group. Conclusion: Collagen-based xenografts demonstrate favorable interactions with hDPSCs, enhancing viability and promoting osteogenic differentiation. Our findings suggest that beyond the presence of collagen, the specific formulation of these biomaterials may modulate their biological performance, highlighting the importance of material design in optimizing regenerative outcomes. Clinical Significance: The formulation of collagen in xenogeneic bone substitutes may be a determining factor in enhancing periodontal regenerative outcomes by modulating the early cellular response and osteogenic activity in stem cell-based tissue engineering. Full article
(This article belongs to the Section Biomimetics of Materials and Structures)
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18 pages, 4937 KB  
Article
Cam-Based Simple Design of Constant-Force Suspension Backpack to Isolate Dynamic Load
by Haotian Ju, Zihang Guan, Junchen Liu, Yao Huang, Kerui Sun, Lele Li, Weimao Wang, Tianjiao Zheng, Quan Xiong, Jie Zhao and Yanhe Zhu
Biomimetics 2025, 10(9), 607; https://doi.org/10.3390/biomimetics10090607 - 10 Sep 2025
Abstract
Prolonged load carriage with ordinary backpacks (OBs) can cause muscle fatigue and skeletal injuries. Research indicates that suspended backpacks can effectively reduce energy expenditure; however, existing elastic rope-based suspension backpacks struggle to adapt to different speeds, while active suspension backpacks gain significant additional [...] Read more.
Prolonged load carriage with ordinary backpacks (OBs) can cause muscle fatigue and skeletal injuries. Research indicates that suspended backpacks can effectively reduce energy expenditure; however, existing elastic rope-based suspension backpacks struggle to adapt to different speeds, while active suspension backpacks gain significant additional weight due to the incorporated motors and batteries. This paper presents a novel cam-based constant-force suspension backpack (CCSB). The CCSB employs a cam–spring mechanism with near-zero suspension stiffness to minimize the inertial forces generated by load oscillations. A test platform was constructed to evaluate the constant-force performance of the mechanism, showing a maximum error of less than 1.96%. Load-carrying experiments were conducted at different walking speeds. Laboratory test results show that, compared with OBs, the CCSB reduces peak accelerative vertical force by an average of 84.47% and reduces human metabolic costs by 10.58%. Outdoor tests show that the CCSB can reduce transportation consumption by 8.26%. The CCSB’s compact structure makes it more suitable for commercialization and demonstrates significant potential for practical applications. Full article
(This article belongs to the Special Issue Bionic Technology—Robotic Exoskeletons and Prostheses: 3rd Edition)
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17 pages, 4687 KB  
Article
Non-Bactericidal Antifouling Coating Inspired by the “Swinging Effect” of Coral Tentacles in Waves
by Yue Yin, Jianfu Wang and Xu Zheng
Biomimetics 2025, 10(9), 606; https://doi.org/10.3390/biomimetics10090606 - 10 Sep 2025
Abstract
Inspired by the free swing of coral tentacles driven by water currents to actively repel microbial attachment, we have identified a unique physical anti-fouling strategy: coral “swinging effect” anti-fouling. Taking the fleshy soft coral (Sarcophyton trocheliophorum) as an example, its surface [...] Read more.
Inspired by the free swing of coral tentacles driven by water currents to actively repel microbial attachment, we have identified a unique physical anti-fouling strategy: coral “swinging effect” anti-fouling. Taking the fleshy soft coral (Sarcophyton trocheliophorum) as an example, its surface is covered with numerous soft tentacles. These coral tentacles utilize the force of water current fluctuations to freely sway, resembling a “feather duster” waving to repel microorganisms attempting to settle and establish themselves. Based on this characteristic, this study delves into the living habits of corals, observing the expansion and contraction cycles of their tentacles. Simultaneously, simulations of the anti-fouling performance of coral tentacles were conducted. It demonstrates that the “swinging effect” of the tentacles can effectively prevent the attachment of fouling organisms. Furthermore, this study uses S. trocheliophorum as a biomimetic prototype to design and prepare an artificial coral-mimic substrate (ACMS). It employs the common marine Gram-negative bacterium Paracoccus pantotrophus as a microbial sample to test anti-fouling performance in both pure static water environments and low-flow water environments. The results showed that the 13 mm-long ACMS could bend and overlap the surface of the rear tentacles to the greatest extent under the unidirectional scouring action of low-speed water flow (3.5 m/s), forming an anti-fouling protective layer. Additionally, the “swinging effect” phenomenon generated by the tentacles under water flow scouring demonstrated excellent anti-fouling effects. This study not only provides further evidence for research on coral antifouling performance but also offers new concepts and ideas for antifouling strategies in low-flow water environments, such as stationary ships in ports and underwater infrastructure facilities at docks. Full article
(This article belongs to the Section Biomimetics of Materials and Structures)
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31 pages, 3177 KB  
Article
A Concept for Bio-Agentic Visual Communication: Bridging Swarm Intelligence with Biological Analogues
by Bryan Starbuck, Hanlong Li, Bryan Cochran, Marc Weissburg and Bert Bras
Biomimetics 2025, 10(9), 605; https://doi.org/10.3390/biomimetics10090605 - 9 Sep 2025
Abstract
Biological swarms communicate through decentralized, adaptive behaviors shaped by local interactions, selective attention, and symbolic signaling. These principles of animal communication enable robust coordination without centralized control or persistent connectivity. This work presents a proof of concept that identifies, evaluates, and translates biological [...] Read more.
Biological swarms communicate through decentralized, adaptive behaviors shaped by local interactions, selective attention, and symbolic signaling. These principles of animal communication enable robust coordination without centralized control or persistent connectivity. This work presents a proof of concept that identifies, evaluates, and translates biological communication strategies into a generative visual language for unmanned aerial vehicle (UAV) swarm agents operating in radio-frequency (RF)-denied environments. Drawing from natural exemplars such as bee waggle dancing, white-tailed deer flagging, and peacock feather displays, we construct a configuration space that encodes visual messages through trajectories and LED patterns. A large language model (LLM), preconditioned using retrieval-augmented generation (RAG), serves as a generative translation layer that interprets perception data and produces symbolic UAV responses. Five test cases evaluate the system’s ability to preserve and adapt signal meaning through within-modality fidelity (maintaining symbolic structure in the same modality) and cross-modal translation (transferring meaning across motion and light). Covariance and eigenvalue-decomposition analysis demonstrate that this bio-agentic approach supports clear, expressive, and decentralized communication, with motion-based signaling achieving near-perfect clarity and expressiveness (0.992, 1.000), while LED-only and multi-signal cases showed partial success, maintaining high expressiveness (~1.000) but with much lower clarity (≤0.298). Full article
(This article belongs to the Special Issue Recent Advances in Bioinspired Robot and Intelligent Systems)
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16 pages, 1842 KB  
Article
Effect of Substrate Compliance on the Jumping Mechanism of the Tree Frog (Polypedates dennys)
by Rui Zhou, Baowen Zhang, Zhouyi Wang and Zhendong Dai
Biomimetics 2025, 10(9), 604; https://doi.org/10.3390/biomimetics10090604 - 9 Sep 2025
Abstract
Animal locomotion in complex environments depends on the ability to adaptively regulate movement in response to substrate mechanics. Tree frogs (Polypedates dennysi), which combine jumping and adhesive capabilities, inhabit arboreal habitats with a wide range of compliant substrates. While previous studies [...] Read more.
Animal locomotion in complex environments depends on the ability to adaptively regulate movement in response to substrate mechanics. Tree frogs (Polypedates dennysi), which combine jumping and adhesive capabilities, inhabit arboreal habitats with a wide range of compliant substrates. While previous studies have offered preliminary insights into their locomotion, the biomechanical mechanisms underlying their adaptability remain poorly characterized. In this study, we developed a stiffness-adjustable takeoff substrate supported by four springs, and combined it with a 3D motion capture system to analyze the jumping dynamics and kinematics of frogs across a broader range of compliant substrates. We found that energy recovery from the substrate was influenced by compliance. On the stiffest substrate, up to 50% of the stored energy was recovered during takeoff, whereas highly compliant substrates caused nonlinear damping, energy dissipation, and even takeoff failure. During takeoff, frogs generated peak normal forces up to 6 times their body weight and fore–aft forces up to 4.5 times their body weight. However, force generation showed limited adaptability to substrate mechanics, while takeoff velocity exhibited stronger adaptability to changes in compliance. These findings reveal a trade-off between substrate mechanics and jump performance. This work provides biomechanical insight into substrate preference and informs the design of bioinspired systems capable of efficient locomotion on compliant substrates. Full article
(This article belongs to the Special Issue Adhesion and Friction in Biological and Bioinspired Systems)
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20 pages, 3941 KB  
Article
Self-Supervised Voice Denoising Network for Multi-Scenario Human–Robot Interaction
by Mu Li, Wenjin Xu, Chao Zeng and Ning Wang
Biomimetics 2025, 10(9), 603; https://doi.org/10.3390/biomimetics10090603 - 9 Sep 2025
Abstract
Human–robot interaction (HRI) via voice command has significantly advanced in recent years, with large Vision–Language–Action (VLA) models demonstrating particular promise in human–robot voice interaction. However, these systems still struggle with environmental noise contamination during voice interaction and lack a specialized denoising network for [...] Read more.
Human–robot interaction (HRI) via voice command has significantly advanced in recent years, with large Vision–Language–Action (VLA) models demonstrating particular promise in human–robot voice interaction. However, these systems still struggle with environmental noise contamination during voice interaction and lack a specialized denoising network for multi-speaker command isolation in an overlapping speech scenario. To overcome these challenges, we introduce a method to enhance voice command-based HRI in noisy environments, leveraging synthetic data and a self-supervised denoising network to enhance its real-world applicability. Our approach focuses on improving self-supervised network performance in denoising mixed-noise audio through training data scaling. Extensive experiments show our method outperforms existing approaches in simulation and achieves 7.5% higher accuracy than the state-of-the-art method in noisy real-world environments, enhancing voice-guided robot control. Full article
(This article belongs to the Special Issue Intelligent Human–Robot Interaction: 4th Edition)
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11 pages, 429 KB  
Review
Bioinspired Approaches and Their Philosophical–Ethical Dimensions: A Narrative Review
by Louisa Estadieu, Julius Fenn, Michael Gorki, Philipp Höfele and Oliver Müller
Biomimetics 2025, 10(9), 602; https://doi.org/10.3390/biomimetics10090602 - 9 Sep 2025
Abstract
The environmental crisis demands transformative solutions on both technological and societal levels. Bioinspired approaches, which draw from the principles of natural systems, have emerged as a promising interdisciplinary framework to address these challenges. These approaches not only drive technological innovation but also provoke [...] Read more.
The environmental crisis demands transformative solutions on both technological and societal levels. Bioinspired approaches, which draw from the principles of natural systems, have emerged as a promising interdisciplinary framework to address these challenges. These approaches not only drive technological innovation but also provoke critical philosophical and ethical discourse, particularly in the field of biomimicry. Philosophical and ethical questions include: How can we ethically justify drawing inspiration from nature without exploiting it? How might a shift toward a bioinspired perspective alter our relationship with nature? How could a reorientation toward nature influence ethical frameworks and guide human behavior toward the environment? This narrative review systematically examines key philosophical and ethical perspectives within biomimicry, while focusing on potentials as well as limitations of these approaches to the environmental crisis. In doing so, it explores key perspectives such as “biomimetic ethics”, the “ontology of nature”, “bioinclusivity”, and the “naturalistic fallacy”. Full article
(This article belongs to the Special Issue Biomimetics—A Chance for Sustainable Developments: 2nd Edition)
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16 pages, 4426 KB  
Article
Scalable Fabrication of Biomimetic Antibacterial Nanospikes on PMMA Films Using Atmospheric-Pressure Low-Temperature Plasma
by Masashi Yamamoto, Kentaro Tada, Ayumu Takada and Atsushi Sekiguchi
Biomimetics 2025, 10(9), 601; https://doi.org/10.3390/biomimetics10090601 - 8 Sep 2025
Abstract
Antibacterial surfaces inspired by biological micro- and nanostructures, such as those found on the wings of cicadas and dragonflies, have attracted interest due to their ability to inhibit bacterial adhesion and damage microbial membranes without relying on chemical agents. However, conventional fabrication techniques [...] Read more.
Antibacterial surfaces inspired by biological micro- and nanostructures, such as those found on the wings of cicadas and dragonflies, have attracted interest due to their ability to inhibit bacterial adhesion and damage microbial membranes without relying on chemical agents. However, conventional fabrication techniques like photolithography or nanoimprinting are limited by substrate shape, size, and high operational costs. In this study, we developed a scalable method using atmospheric-pressure low-temperature plasma (APLTP) to fabricate sharp-edged nanospikes on solvent-cast polymethyl methacrylate (PMMA) films. The nanospikes were formed through plasma-induced modification of pores in the film, followed by annealing to control surface wettability while maintaining structural sharpness. Atomic force microscopy confirmed the formation of micro/nanostructures, and contact angle measurements revealed reversible hydrophilicity. Antibacterial performance was evaluated against Escherichia coli using ISO 22196 standards. While the film with only plasma treatment reduced bacterial colonies by 30%, the film annealed after plasma treatment achieved an antibacterial activity value greater than 5, with bacterial counts below the detection limit (<10 CFU). These findings demonstrate that APLTP offers a practical route for large-area fabrication of biomimetic antibacterial coatings on flexible polymer substrates, holding promise for future applications in healthcare, packaging, and public hygiene. Full article
(This article belongs to the Section Biomimetic Surfaces and Interfaces)
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17 pages, 3935 KB  
Article
Markerless Force Estimation via SuperPoint-SIFT Fusion and Finite Element Analysis: A Sensorless Solution for Deformable Object Manipulation
by Qingqing Xu, Ruoyang Lai and Junqing Yin
Biomimetics 2025, 10(9), 600; https://doi.org/10.3390/biomimetics10090600 - 8 Sep 2025
Abstract
Contact-force perception is a critical component of safe robotic grasping. With the rapid advances in embodied intelligence technology, humanoid robots have enhanced their multimodal perception capabilities. Conventional force sensors face limitations, such as complex spatial arrangements, installation challenges at multiple nodes, and potential [...] Read more.
Contact-force perception is a critical component of safe robotic grasping. With the rapid advances in embodied intelligence technology, humanoid robots have enhanced their multimodal perception capabilities. Conventional force sensors face limitations, such as complex spatial arrangements, installation challenges at multiple nodes, and potential interference with robotic flexibility. Consequently, these conventional sensors are unsuitable for biomimetic robot requirements in object perception, natural interaction, and agile movement. Therefore, this study proposes a sensorless external force detection method that integrates SuperPoint-Scale Invariant Feature Transform (SIFT) feature extraction with finite element analysis to address force perception challenges. A visual analysis method based on the SuperPoint-SIFT feature fusion algorithm was implemented to reconstruct a three-dimensional displacement field of the target object. Subsequently, the displacement field was mapped to the contact force distribution using finite element modeling. Experimental results demonstrate a mean force estimation error of 7.60% (isotropic) and 8.15% (anisotropic), with RMSE < 8%, validated by flexible pressure sensors. To enhance the model’s reliability, a dual-channel video comparison framework was developed. By analyzing the consistency of the deformation patterns and mechanical responses between the actual compression and finite element simulation video keyframes, the proposed approach provides a novel solution for real-time force perception in robotic interactions. The proposed solution is suitable for applications such as precision assembly and medical robotics, where sensorless force feedback is crucial. Full article
(This article belongs to the Special Issue Bio-Inspired Intelligent Robot)
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11 pages, 2878 KB  
Article
Bioinspired Polyvinyl Alcohol-Based Foam Fabricated via Supercritical Carbon Dioxide Foaming for Atmospheric Water Harvesting
by Yingying Chen, Changjun Guo, Hao Wang, Jiabao Lu, Heng Xie and Ting Wu
Biomimetics 2025, 10(9), 599; https://doi.org/10.3390/biomimetics10090599 - 8 Sep 2025
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Abstract
The intensifying freshwater crisis underscores the critical need for all-weather, low-energy atmospheric water harvesting technologies. Inspired by the scale-like protrusions and interconnected channels of Tillandsia leaves that enable efficient water capture and release, a polyvinyl alcohol-based foam featuring a three-dimensional porous structure is [...] Read more.
The intensifying freshwater crisis underscores the critical need for all-weather, low-energy atmospheric water harvesting technologies. Inspired by the scale-like protrusions and interconnected channels of Tillandsia leaves that enable efficient water capture and release, a polyvinyl alcohol-based foam featuring a three-dimensional porous structure is fabricated using the supercritical carbon dioxide foaming technology. Compared to the traditional freeze-drying method, this approach significantly reduces preparation energy consumption and shortens the production cycle. Lithium chloride integration endows the foam with exceptional moisture absorption capacity, reaching 300% of its weight. Leveraging graphene’s outstanding photothermal conversion properties, the foam achieves a photothermal dehydration rate of 80.7% within 80 min under 1 Sun irradiation, demonstrating a rapid water release capacity. Furthermore, the polyvinyl alcohol-based foam exhibits no performance degradation after 60 cycles, indicating remarkable stability. This technology provides a scalable, low-cost, and all-climate-applicable solution for water-scarce regions. Full article
(This article belongs to the Special Issue Design and Fabrication of Biomimetic Smart Materials)
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12 pages, 5729 KB  
Communication
Biomimetic Dual-Sensing Bone Scaffolds: Characterization and In Vitro Evaluation Under Dynamic Culturing Conditions
by Damion T. Dixon, Erika N. Landree and Cheryl T. Gomillion
Biomimetics 2025, 10(9), 598; https://doi.org/10.3390/biomimetics10090598 - 8 Sep 2025
Viewed by 127
Abstract
The regeneration of large segmental bone defects remains a significant challenge. While electrical stimulation has demonstrated the potential to accelerate bone healing, clinical translation has been hindered by the lack of safe, localized delivery methods. In this study, we present a novel strategy [...] Read more.
The regeneration of large segmental bone defects remains a significant challenge. While electrical stimulation has demonstrated the potential to accelerate bone healing, clinical translation has been hindered by the lack of safe, localized delivery methods. In this study, we present a novel strategy combining piezoelectric and electrically conductive polymers with allograft demineralized bones to create stimuli-responsive, biologically relevant scaffolds via pneumatic 3D printing. These scaffolds exhibit enhanced piezoelectric potential and tunable electrical properties, enabling both electrical and mechanical stimulation of cells (without external stimulators). Under dynamic culturing conditions (i.e., ultrasound stimulation), human bone marrow-derived mesenchymal stromal cells cultured on these scaffolds displayed significantly elevated osteogenic protein expression (i.e., alkaline phosphatase and osteocalcin) and mineralization (confirmed via xylenol orange mineral staining) after two weeks. This work introduces a bioinspired, printable ink in conjunction with a simple fabrication approach for creating dual-responsive scaffolds with high potential for functional bone tissue regeneration. Full article
(This article belongs to the Special Issue Biomimetic Materials for Bone Tissue Engineering)
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11 pages, 2963 KB  
Communication
Optimization Design of Haptic Units for Perception Feedback Interfaces Based on Vibrotactile Amplitude Modulation
by Weichao Guo, Jingchen Huang, Lechuan Zhou, Yun Fang, Li Jiang and Xinjun Sheng
Biomimetics 2025, 10(9), 597; https://doi.org/10.3390/biomimetics10090597 - 7 Sep 2025
Viewed by 186
Abstract
Tactile sensation is a crucial sensory pathway for humans to acquire information from the environment, and vibration feedback is one form of tactile feedback, offering advantages such as low cost, ease of integration, and high comfort. Avoiding mechanical crosstalk without changing the spacing [...] Read more.
Tactile sensation is a crucial sensory pathway for humans to acquire information from the environment, and vibration feedback is one form of tactile feedback, offering advantages such as low cost, ease of integration, and high comfort. Avoiding mechanical crosstalk without changing the spacing between vibration units is a significant challenge in the design of haptic interfaces. This work focuses on the joint optimization design of vibration source characteristics and packaging materials of vibration units. From a theoretical modeling perspective, we explore the correlation between material properties and the amplitude of vibrations generated on the skin surface. A three-layer vibration unit optimization design scheme using a pogo pin structure is thus proposed. Parameters are optimized through finite element analysis, and experimental results prove that the three-layer vibration unit with pogo pins has amplitude modulation capabilities, laying the foundation for the design of array-based vibration tactile feedback interfaces and human-inspired grasp control. Full article
(This article belongs to the Special Issue Human-Inspired Grasp Control in Robotics 2025)
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41 pages, 28333 KB  
Article
ACPOA: An Adaptive Cooperative Pelican Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation
by YuLong Zhang, Jianfeng Wang, Xiaoyan Zhang and Bin Wang
Biomimetics 2025, 10(9), 596; https://doi.org/10.3390/biomimetics10090596 - 6 Sep 2025
Viewed by 345
Abstract
Multi-threshold image segmentation plays an irreplaceable role in extracting discriminative structural information from complex images. It is one of the core technologies for achieving accurate target detection and regional analysis, and its segmentation accuracy directly affects the analysis quality and decision reliability in [...] Read more.
Multi-threshold image segmentation plays an irreplaceable role in extracting discriminative structural information from complex images. It is one of the core technologies for achieving accurate target detection and regional analysis, and its segmentation accuracy directly affects the analysis quality and decision reliability in key fields such as medical imaging, remote sensing interpretation, and industrial inspection. However, most existing image segmentation algorithms suffer from slow convergence speeds and low solution accuracy. Therefore, this paper proposes an Adaptive Cooperative Pelican Optimization Algorithm (ACPOA), an improved version of the Pelican Optimization Algorithm (POA), and applies it to global optimization and multilevel threshold image segmentation tasks. ACPOA integrates three innovative strategies: the elite pool mutation strategy guides the population toward high-quality regions by constructing an elite pool composed of the three individuals with the best fitness, effectively preventing the premature loss of population diversity; the adaptive cooperative mechanism enhances search efficiency in high-dimensional spaces by dynamically allocating subgroups and dimensions and performing specialized updates to achieve division of labor and global information sharing; and the hybrid boundary handling technique adopts a probabilistic hybrid approach to deal with boundary violations, balancing exploitation, exploration, and diversity while retaining more useful search information. Comparative experiments with eight advanced algorithms on the CEC2017 and CEC2022 benchmark test suites validate the superior optimization performance of ACPOA. Moreover, when applied to multilevel threshold image segmentation tasks, ACPOA demonstrates better accuracy, stability, and efficiency in solving practical problems, providing an effective solution for complex optimization challenges. Full article
(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications: 2nd Edition)
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29 pages, 1504 KB  
Review
Bioprinted Scaffolds for Biomimetic Applications: A State-of-the-Art Technology
by Ille C. Gebeshuber, Sayak Khawas, Rishi Sharma and Neelima Sharma
Biomimetics 2025, 10(9), 595; https://doi.org/10.3390/biomimetics10090595 - 5 Sep 2025
Viewed by 354
Abstract
This review emphasizes the latest developments in bioprinted scaffolds in tissue engineering, with a focus on their biomimetic applications. The accelerated pace of development of 3D bioprinting technologies has transformed the ability to fabricate scaffolds with the potential to replicate the structure and [...] Read more.
This review emphasizes the latest developments in bioprinted scaffolds in tissue engineering, with a focus on their biomimetic applications. The accelerated pace of development of 3D bioprinting technologies has transformed the ability to fabricate scaffolds with the potential to replicate the structure and function of native tissues. Bioprinting methods such as inkjet, extrusion-based, laser-assisted, and digital light processing (DLP) approaches have the potential to fabricate complex, multi-material structures with high precision in geometry, material composition, and cellular microenvironments. Incorporating biomimetic design principles to replicate the mechanical and biological behaviors of native tissues has been of major research interest. Scaffold geometries that support cell adhesion, growth, and differentiation essential for tissue regeneration are mainly of particular interest. The review also deals with the development of bioink, with an emphasis on the utilization of natural, synthetic, and composite materials for enhanced scaffold stability, printability, and biocompatibility. Rheological characteristics, cell viability, and the utilization of stimuli-responsive bioinks are also discussed in detail. Their utilization in bone, cartilage, skin, neural, and cardiovascular tissue engineering demonstrates the versatility of bioprinted scaffolds. Despite the significant advancements, there are still challenges that include achieving efficient vascularization, long-term integration with host tissues, and scalability. The review concludes by underlining future trends such as 4D bioprinting, artificial intelligence-augmented scaffold design, and the regulatory and ethical implications involved in clinical translation. By considering these challenges in detail, this review provides insight into the future of bioprinted scaffolds in regenerative medicine. Full article
(This article belongs to the Special Issue Biomimetic Nanotechnology Vol. 4: Advances in Biomimetic Nanotechnology)
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22 pages, 657 KB  
Systematic Review
A Systematic Review of Metal Composite Bone Grafts in Preclinical Spinal Fusion Models
by Christian Rajkovic, Mahnoor Shafi, Naboneeta Sarkar, Vaughn Hernandez, Liwen Yang and Timothy F. Witham
Biomimetics 2025, 10(9), 594; https://doi.org/10.3390/biomimetics10090594 - 5 Sep 2025
Viewed by 234
Abstract
Successful arthrodesis is a crucial factor in spinal fusion surgery, maximizing the likelihood of improved quality of life. The incorporation of metals into bone grafts has been demonstrated to enhance fusion rates through various osteoinductive and osteoconductive pathways. A systematic review was conducted [...] Read more.
Successful arthrodesis is a crucial factor in spinal fusion surgery, maximizing the likelihood of improved quality of life. The incorporation of metals into bone grafts has been demonstrated to enhance fusion rates through various osteoinductive and osteoconductive pathways. A systematic review was conducted to investigate the utility of metal composite bone grafts in promoting arthrodesis in spinal fusion preclinical studies. PubMed/MEDLINE was queried to identify studies investigating metal composite bone grafts in animal models of spinal fusion. Non-spinal fusion animal models were excluded. Risk of bias was assessed using the SYRCLE risk of bias tool. After screening a total of 1554 articles, 17 articles were included in our review. Metal composite bone grafts with bioactive agents had significantly greater fusion rates than metal composite only bone grafts (p < 0.001) and similar fusion rates compared to non-metal comparator bone grafts (p = 0.172). Bone grafts containing strontium and magnesium had the greatest fusion rates compared to other metals and had significantly greater fusion rates than those of silicon-containing bone grafts (p = 0.02 and p = 0.04, respectively). Bone quality and bone volume percentages of fusion masses formed by metal composite bone grafts were enhanced via the addition of bioactive agents such as stem cells, rhBMP-2, autograft, and poly (lactic-co-glycolic acid). The adverse event rate was 3.0% in all animal surgeries. Metal composite bone grafts show promise as osteoinductive agents to promote arthrodesis in spinal fusion, and their osteoinductive capability is enhanced with the synergistic addition of osteogenic factors such as stem cells and autograft. Full article
(This article belongs to the Section Biomimetics of Materials and Structures)
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16 pages, 2105 KB  
Article
Research on Target Localization Method for Underwater Robot Based on the Bionic Lateral Line System of Fish
by Xinghua Lin, Enyu Yang, Guozhen Zan, Hang Xu, Hao Wang and Peilong Sun
Biomimetics 2025, 10(9), 593; https://doi.org/10.3390/biomimetics10090593 - 5 Sep 2025
Viewed by 209
Abstract
This paper is based on the fish lateral line sensing mechanism and aims to determine the coupling relationship between the flow field sensing signal and target source position information. Firstly, according to the flow field distribution characteristics of the target source, the equivalent [...] Read more.
This paper is based on the fish lateral line sensing mechanism and aims to determine the coupling relationship between the flow field sensing signal and target source position information. Firstly, according to the flow field distribution characteristics of the target source, the equivalent multipole model of the flow field disturbance during the underwater motion of the SUBOFF model is constructed, and then the target localization function based on the least squares method is established according to the theory of potential flow, and the residual function of the target localization is solved optimally using the quasi-Newton method (QN) to obtain the estimated position of the target source. On this basis, a curved bionic lateral line sensing array is constructed on the surface of a robotic fish, and the estimated location of the target source is obtained. The curvilinear bionic lateral line sensing array is constructed on the surface of the robotic fish, and the effectiveness and robustness of the above localization methods are analysed to validate whether the fish lateral line uses the pressure change to sense the underwater target. Full article
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33 pages, 7900 KB  
Article
Multi-Strategy Improved Red-Billed Blue Magpie Optimization Algorithm and Its Applications
by Yancang Li, Jiaqi Zhi, Xinle Wang and Binli Shi
Biomimetics 2025, 10(9), 592; https://doi.org/10.3390/biomimetics10090592 - 5 Sep 2025
Viewed by 237
Abstract
To address the issues of low convergence accuracy, poor population diversity, and susceptibility to local optima in the Red-billed Blue Magpie Optimization Algorithm (RBMO), this study proposes a multi-strategy improved Red-billed Blue Magpie Optimization Algorithm (SWRBMO). First, an adaptive T-distribution-based sinh–cosh search strategy [...] Read more.
To address the issues of low convergence accuracy, poor population diversity, and susceptibility to local optima in the Red-billed Blue Magpie Optimization Algorithm (RBMO), this study proposes a multi-strategy improved Red-billed Blue Magpie Optimization Algorithm (SWRBMO). First, an adaptive T-distribution-based sinh–cosh search strategy is used to enhance global exploration and speed up convergence. Second, a neighborhood-guided reinforcement strategy helps the algorithm avoid local optima. Third, a crossover strategy is also introduced to improve convergence accuracy. SWRBMO is evaluated on 15 benchmark functions selected from the CEC2005 test suite, with ablation studies on 12 of them, and further validated on the CEC2019 and CEC2021 test suites. Across all test sets, its convergence behavior and statistical significance are analyzed using the Wilcoxon rank-sum test. Comparative experiments on CEC2019 and CEC2021 demonstrate that SWRBMO achieves faster convergence and higher accuracy than RBMO and other competitive algorithms. Finally, four engineering design problems further confirm its practicality, where SWRBMO outperforms other methods by up to 99%, 38.4%, 2.4%, and nearly 100% in the respective cases, highlighting its strong potential for real-world engineering applications. Full article
(This article belongs to the Section Biological Optimisation and Management)
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26 pages, 1127 KB  
Article
LSTM-Enhanced TD3 and Behavior Cloning for UAV Trajectory Tracking Control
by Yuanhang Qi, Jintao Hu, Fujie Wang and Gewen Huang
Biomimetics 2025, 10(9), 591; https://doi.org/10.3390/biomimetics10090591 - 4 Sep 2025
Viewed by 280
Abstract
Unmanned aerial vehicles (UAVs) often face significant challenges in trajectory tracking within complex dynamic environments, where uncertainties, external disturbances, and nonlinear dynamics hinder accurate and stable control. To address this issue, a bio-inspired deep reinforcement learning (DRL) algorithm is proposed, integrating behavior cloning [...] Read more.
Unmanned aerial vehicles (UAVs) often face significant challenges in trajectory tracking within complex dynamic environments, where uncertainties, external disturbances, and nonlinear dynamics hinder accurate and stable control. To address this issue, a bio-inspired deep reinforcement learning (DRL) algorithm is proposed, integrating behavior cloning (BC) and long short-term memory (LSTM) networks. This method can achieve autonomous learning of high-precision control policy without establishing an accurate system dynamics model. Motivated by the memory and prediction functions of biological neural systems, an LSTM module is embedded into the policy network of the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. This structure captures temporal state patterns more effectively, enhancing adaptability to trajectory variations and resilience to delays or disturbances. Compared to memoryless networks, the LSTM-based design better replicates biological time-series processing, improving tracking stability and accuracy. In addition, behavior cloning is employed to pre-train the DRL policy using expert demonstrations, mimicking the way animals learn from observation. This biomimetic plausible initialization accelerates convergence by reducing inefficient early-stage exploration. By combining offline imitation with online learning, the TD3-LSTM-BC framework balances expert guidance and adaptive optimization, analogous to innate and experience-based learning in nature. Simulation experimental results confirm the superior robustness and tracking accuracy of the proposed method, demonstrating its potential as a control solution for autonomous UAVs. Full article
(This article belongs to the Special Issue Bio-Inspired Robotics and Applications 2025)
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24 pages, 2467 KB  
Review
Suture Materials: Conventional and Stimulatory-Responsive Absorbable Polymers with Biomimetic Function
by Francesco Nappi
Biomimetics 2025, 10(9), 590; https://doi.org/10.3390/biomimetics10090590 - 4 Sep 2025
Viewed by 294
Abstract
Suture materials are of pivotal importance in the process of wound healing, as they provide support to growing tissue. The application of suture materials is an intricate process that extends beyond mere closure of skin wounds. Rather, it encompasses a wide range of [...] Read more.
Suture materials are of pivotal importance in the process of wound healing, as they provide support to growing tissue. The application of suture materials is an intricate process that extends beyond mere closure of skin wounds. Rather, it encompasses a wide range of surgical procedures. It is evident that suture materials possess a high degree of versatility, as evidenced by their application in a broad range of surgical disciplines, including, but not limited to, plastic surgery, neurosurgery, vascular surgery and ocular surgery. Additionally, their application extends to wound treatment and the repair of the musculo-skeletal system and the urogenital tract. This review underscores the pivotal role of sutures in contemporary medicine and surgery. The selection of suture material must be made with the utmost attention to the physical and biological characteristics of the material concerned. The process is characterised by a multifaceted evaluation encompassing the following: first, the assessment of the wound in question; secondly, the healing rate of different tissue types; and thirdly, a thorough appraisal of the patient’s overall physical condition. Advances in suture material technology have given rise to a wider range of sutures, thereby enhancing the existing array of options. Simultaneously, suture needles have undergone a progressive process of technological refinement, resulting in a more comprehensive range of alternatives with a heightened level of precision for specific applications in tissue engineering. Recent experimental investigations have employed an animal model, underpinned by biomechanical analysis. It is evident from the findings of these studies that absorbable sutures fulfil a scaffolding function. The hypothesis concerning the biomimetic function of the materials under investigation was corroborated by the results of biomechanical behaviour and histological examination. This review explores the functionality of both absorbable sutures and novel polymers, investigating their potential application as scaffolding materials within clinical contexts. Full article
(This article belongs to the Special Issue Biological and Bioinspired Materials and Structures: 2nd Edition)
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52 pages, 8807 KB  
Article
DRIME: A Distributed Data-Guided RIME Algorithm for Numerical Optimization Problems
by Jinghao Yang, Yuanyuan Shao, Bin Fu and Lei Kou
Biomimetics 2025, 10(9), 589; https://doi.org/10.3390/biomimetics10090589 - 3 Sep 2025
Viewed by 208
Abstract
To address the shortcomings of the RIME algorithm’s weak global exploration ability, insufficient information exchange among populations, and limited population diversity, this work proposes a distributed data-guided RIME algorithm called DRIME. First, this paper proposes a data-distribution-driven guided learning strategy that enhances information [...] Read more.
To address the shortcomings of the RIME algorithm’s weak global exploration ability, insufficient information exchange among populations, and limited population diversity, this work proposes a distributed data-guided RIME algorithm called DRIME. First, this paper proposes a data-distribution-driven guided learning strategy that enhances information exchange among populations and dynamically guides populations to exploit or explore. Then, a soft-rime search phase based on weighted averaging is proposed, which balances the development and exploration of RIME by alternating with the original strategy. Finally, a candidate pool is utilized to replace the optimal reference point of the hard-rime puncture mechanism to enrich the diversity of the population and reduce the risk of falling into local optima. To evaluate the performance of the DRIME algorithm, parameter sensitivity analysis, policy effectiveness analysis, and two comparative analyses are performed on the CEC-2017 test set and the CEC-2022 test set. The parameter sensitivity analysis identifies the optimal parameter settings for the DRIME algorithm. The strategy effectiveness analysis confirms the effectiveness of the improved strategies. In comparison with ACGRIME, TERIME, IRIME, DNMRIME, GLSRIME, and HERIME on the CEC-2017 test set, the DRIME algorithm achieves Friedman rankings of 1.517, 1.069, 1.138, and 1.069 in different dimensions. In comparison with EOSMA, GLS-MPA, ISGTOA, EMTLBO, LSHADE-SPACMA, and APSM-jSO on the CEC-2022 test set, the DRIME algorithm achieves Friedman rankings of 2.167 and 1.917 in 10 and 30 dimensions, respectively. In addition, the DRIME algorithm achieved an average ranking of 1.23 in engineering constraint optimization problems, far surpassing other comparison algorithms. In conclusion, the numerical optimization experiments successfully illustrate that the DRIME algorithm has excellent search capability and can provide satisfactory solutions to a wide range of optimization problems. Full article
(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications: 2nd Edition)
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15 pages, 3348 KB  
Article
Optimizing Maritime Search and Rescue Planning via Genetic Algorithms: Incorporating Civilian Vessel Collaboration
by Seung-Yeol Hong and Yong-Hyuk Kim
Biomimetics 2025, 10(9), 588; https://doi.org/10.3390/biomimetics10090588 - 3 Sep 2025
Viewed by 269
Abstract
This study proposes a biomimetic optimization approach for maritime Search and Rescue (SAR) planning using a Genetic Algorithm (GA). The goal is to maximize the number of detected drifting targets by optimally deploying both official and civilian Search and Rescue Units (SRUs). The [...] Read more.
This study proposes a biomimetic optimization approach for maritime Search and Rescue (SAR) planning using a Genetic Algorithm (GA). The goal is to maximize the number of detected drifting targets by optimally deploying both official and civilian Search and Rescue Units (SRUs). The proposed method incorporates a POD-adjusted fitness function with collision-avoidance constraints and is enhanced by a greedy initialization strategy. To validate its effectiveness, we compare the GA against a baseline method (EAGD) that combines a (1 + 1)-Evolutionary Algorithm with greedy deployment, across 24 experiments involving 2 realistic maritime scenarios and 12 coverage conditions. Results show that GA consistently achieves higher average fitness and stability, particularly under stress-test settings involving only civilian vessels. The findings underscore the potential of biomimetic algorithms for real-time, flexible, and scalable SAR planning, while highlighting the value of civilian participation in emergency maritime operations. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2025)
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23 pages, 3668 KB  
Article
Graph-Driven Micro-Expression Rendering with Emotionally Diverse Expressions for Lifelike Digital Humans
by Lei Fang, Fan Yang, Yichen Lin, Jing Zhang and Mincheol Whang
Biomimetics 2025, 10(9), 587; https://doi.org/10.3390/biomimetics10090587 - 3 Sep 2025
Viewed by 354
Abstract
Micro-expressions, characterized by brief and subtle facial muscle movements, are essential for conveying nuanced emotions in digital humans, yet existing rendering techniques often produce rigid or emotionally monotonous animations due to the inadequate modeling of temporal dynamics and action unit interdependencies. This paper [...] Read more.
Micro-expressions, characterized by brief and subtle facial muscle movements, are essential for conveying nuanced emotions in digital humans, yet existing rendering techniques often produce rigid or emotionally monotonous animations due to the inadequate modeling of temporal dynamics and action unit interdependencies. This paper proposes a graph-driven framework for micro-expression rendering that generates emotionally diverse and lifelike expressions. We employ a 3D-ResNet-18 backbone network to perform joint spatio-temporal feature extraction from facial video sequences, enhancing sensitivity to transient motion cues. Action units (AUs) are modeled as nodes in a symmetric graph, with edge weights derived from empirical co-occurrence probabilities and processed via a graph convolutional network to capture structural dependencies and symmetric interactions. This symmetry is justified by the inherent bilateral nature of human facial anatomy, where AU relationships are based on co-occurrence and facial anatomy analysis (as per the FACS), which are typically undirected and symmetric. Human faces are symmetric, and such relationships align with the design of classic spectral GCNs for undirected graphs, assuming that adjacency matrices are symmetric to model non-directional co-occurrences effectively. Predicted AU activations and timestamps are interpolated into continuous motion curves using B-spline functions and mapped to skeletal controls within a real-time animation pipeline (Unreal Engine). Experiments on the CASME II dataset demonstrate superior performance, achieving an F1-score of 77.93% and an accuracy of 84.80% (k-fold cross-validation, k = 5), outperforming baselines in temporal segmentation. Subjective evaluations confirm that the rendered digital human exhibits improvements in perceptual clarity, naturalness, and realism. This approach bridges micro-expression recognition and high-fidelity facial animation, enabling more expressive virtual interactions through curve extraction from AU values and timestamps. Full article
(This article belongs to the Section Bioinspired Sensorics, Information Processing and Control)
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27 pages, 3660 KB  
Article
Deep Learning-Based Evaluation of Postural Control Impairments Caused by Stroke Under Altered Sensory Conditions
by Armin Najipour, Siamak Khorramymehr, Mehdi Razeghi and Kamran Hassani
Biomimetics 2025, 10(9), 586; https://doi.org/10.3390/biomimetics10090586 - 3 Sep 2025
Viewed by 287
Abstract
Accurate and timely detection of postural control impairments in stroke patients is crucial for effective rehabilitation and fall prevention. Traditional clinical assessments often rely on qualitative observation and handcrafted features, which may fail to capture the nonlinear and uncertain nature of postural deficits. [...] Read more.
Accurate and timely detection of postural control impairments in stroke patients is crucial for effective rehabilitation and fall prevention. Traditional clinical assessments often rely on qualitative observation and handcrafted features, which may fail to capture the nonlinear and uncertain nature of postural deficits. This study addresses these limitations by introducing a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) with Type-2 fuzzy logic activation to robustly classify sensory dysfunction under altered balance conditions. Using an EquiTest-derived dataset of 8316 labeled samples from 700 participants across six standardized sensory manipulation scenarios, the proposed method achieved 97% accuracy, 96% precision, 97% sensitivity, and 96% specificity, outperforming conventional CNN and other baseline classifiers. The approach demonstrated resilience to measurement noise down to 1 dB SNR, confirming its robustness in realistic clinical environments. These results suggest that the proposed system can serve as a practical, non-invasive tool for clinical diagnosis and personalized rehabilitation planning, supporting data-driven decision-making in stroke care. Full article
(This article belongs to the Special Issue Innovative Biomimetics: Integrating Machine Learning, Neuropsychology, and Cognitive Neuroscience in Applied Psychological Research)
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15 pages, 5376 KB  
Article
Photothermal Porous Material with Gradient Hydrophobicity for Fast and Highly Selective Oil/Water Separation and Crude Oil Recovery
by Tianwen Wang, Song Song, Shiwen Bao, Yanfeng Gong, Yujue Wang, Chuncai Wang, Wenshao Ma, Nuo Liu, Kunyan Sui, Jun Gao and Xueli Liu
Biomimetics 2025, 10(9), 585; https://doi.org/10.3390/biomimetics10090585 - 3 Sep 2025
Viewed by 358
Abstract
Oil spills and oily wastewater discharges have posed severe threats to the ecosystem and human health, yet efficient cleanup and recovery remain huge challenges. The absorption of crude oil is especially difficult due to its high viscosity. In this study, we propose a [...] Read more.
Oil spills and oily wastewater discharges have posed severe threats to the ecosystem and human health, yet efficient cleanup and recovery remain huge challenges. The absorption of crude oil is especially difficult due to its high viscosity. In this study, we propose a strategy for the fast and highly selective absorption of crude oil as well as other oils and organic solvents with variable viscosity by combining the desert beetle’s back-inspired gradient hydrophobicity with the photothermal effect to enhance the absorption rate. The oil-absorbent material was prepared through the alkylsilane-based gradient chemical modification of MXene-polyurethane sponges. The hydrophobic gradient across the composite sponge offers an extra driving force for the selective oil wetting in the sponge. Owing to the synergistic effect between gradient wettability and photothermal heating, a faster absorption rate, in addition to the high separation rate, was achieved for a variety of oils, including thick crude oil, thin crude oil, and light diesel oil, compared to that without gradient wettability. The as-prepared material is robust with good repeatability for the oil absorption. The surface silane modification was also demonstrated to help prevent the oxidation of MXene, facilitating the long-term stability of the material. This study will enlighten the development of fast and highly selective liquid absorbents. Full article
(This article belongs to the Special Issue Bio-Inspired Nanochannels)
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