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Biomimetics

Biomimetics is an international, peer-reviewed, open access journal on biomimicry and bionics, published monthly online by MDPI. The International Society of Bionic Engineering (ISBE) is affiliated with Biomimetics.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, PMC, Ei Compendex, CAPlus / SciFinder, and other databases.
Journal Rank: JCR - Q1 (Engineering, Multidisciplinary) / CiteScore - Q2 (Biomedical Engineering)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.1 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the first half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 3.9 (2024); 5-Year Impact Factor: 4.0 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2313-7673

Latest Articles

26 pages, 14849 KiB

Open AccessArticle

EAB-BES: A Global Optimization Approach for Efficient UAV Path Planning in High-Density Urban Environments

by Yunhui Zhang, Wenhong Xiao and Shihong Yin

Biomimetics 2025, 10(8), 499; https://doi.org/10.3390/biomimetics10080499 (registering DOI) - 31 Jul 2025

This paper presents a multi-strategy enhanced bald eagle search algorithm (EAB-BES) for 3D UAV path planning in urban environments. EAB-BES addresses key limitations of the traditional bald eagle search (BES) algorithm, including slow convergence, susceptibility to local optima, and poor adaptability in complex [...] Read more.

This paper presents a multi-strategy enhanced bald eagle search algorithm (EAB-BES) for 3D UAV path planning in urban environments. EAB-BES addresses key limitations of the traditional bald eagle search (BES) algorithm, including slow convergence, susceptibility to local optima, and poor adaptability in complex urban scenarios. The algorithm enhances solution space exploration through elite opposition-based learning, balances global search and local exploitation via an adaptive weight mechanism, and refines local search directions using block-based elite-guided differential mutation. These innovations significantly improve BES’s convergence speed, path accuracy, and adaptability to urban constraints. To validate its effectiveness, six high-density urban environments with varied obstacles were used for comparative experiments against nine advanced algorithms. The results demonstrate that EAB-BES achieves the fastest convergence speed and lowest stable fitness values and generates the shortest, smoothest collision-free 3D paths. Statistical tests and box plot analysis further confirm its superior performance in multiple performance metrics. EAB-BES has greater competitiveness compared with the comparative algorithms and can provide an efficient, reliable and robust solution for UAV autonomous navigation in complex urban environments. Full article

(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation: 3rd Edition)

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19 pages, 8681 KiB

Open AccessArticle

Design and Implementation of a Biomimetic Underwater Robot Propulsion System Inspired by Bullfrog Hind Leg Movements

by Yichen Chu, Yahui Wang, Yanhui Fu, Mingxu Ma, Yunan Zhong and Tianbiao Yu

Biomimetics 2025, 10(8), 498; https://doi.org/10.3390/biomimetics10080498 - 30 Jul 2025

Underwater propulsion systems are the fundamental functional modules of underwater robotics and are crucial in intricate underwater operational scenarios. This paper proposes a biomimetic underwater robot propulsion scheme that is motivated by the hindlimb movements of the bullfrog. A multi-linkage mechanism was developed [...] Read more.

Underwater propulsion systems are the fundamental functional modules of underwater robotics and are crucial in intricate underwater operational scenarios. This paper proposes a biomimetic underwater robot propulsion scheme that is motivated by the hindlimb movements of the bullfrog. A multi-linkage mechanism was developed to replicate the “kicking-and-retracting” motion of the bullfrog by employing motion capture systems to acquire biological data on their hindlimb movements. The FDM 3D printing and PC board engraving techniques were employed to construct the experimental prototype. The prototype’s biomimetic and motion characteristics were validated through motion capture experiments and comparisons with a real bullfrog. The biomimetic bullfrog hindlimb propulsion system was tested with six-degree-of-freedom force experiments to evaluate its propulsion capabilities. The system achieved an average thrust of 2.65 N. The effectiveness of motor drive parameter optimization was validated by voltage comparison experiments, which demonstrated a nonlinear increase in thrust as voltage increased. This design approach, which transforms biological kinematic characteristics into mechanical drive parameters, exhibits excellent feasibility and efficacy, offering a novel solution and quantitative reference for underwater robot design. Full article

(This article belongs to the Special Issue Bio-Inspired Underwater Propulsion: Actuation, Sensing, Processing and Control)

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23 pages, 5330 KiB

Open AccessArticle

Explainable Reinforcement Learning for the Initial Design Optimization of Compressors Inspired by the Black-Winged Kite

by Mingming Zhang, Zhuang Miao, Xi Nan, Ning Ma and Ruoyang Liu

Biomimetics 2025, 10(8), 497; https://doi.org/10.3390/biomimetics10080497 - 29 Jul 2025

Although artificial intelligence methods such as reinforcement learning (RL) show potential in optimizing the design of compressors, there are still two major challenges remaining: limited design variables and insufficient model explainability. For the initial design of compressors, this paper proposes a technical approach [...] Read more.

Although artificial intelligence methods such as reinforcement learning (RL) show potential in optimizing the design of compressors, there are still two major challenges remaining: limited design variables and insufficient model explainability. For the initial design of compressors, this paper proposes a technical approach that incorporates deep reinforcement learning and decision tree distillation to enhance both the optimization capability and explainability. First, a pre-selection platform for the initial design scheme of the compressors is constructed based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The optimization space is significantly enlarged by expanding the co-design of 25 key variables (e.g., the inlet airflow angle, the reaction, the load coefficient, etc.). Then, the initial design of six-stage axial compressors is successfully completed, with the axial efficiency increasing to 84.65% at the design speed and the surge margin extending to 10.75%. The design scheme is closer to the actual needs of engineering. Secondly, Shapley Additive Explanations (SHAP) analysis is utilized to reveal the influence of the mechanism of the key design parameters on the performance of the compressors in order to enhance the model explainability. Finally, the decision tree inspired by the black-winged kite (BKA) algorithm takes the interpretable design rules and transforms the data-driven intelligent optimization into explicit engineering experience. Through experimental validation, this method significantly improves the transparency of the design process while maintaining the high performance of the DDPG algorithm. The extracted design rules not only have clear physical meanings but also can effectively guide the initial design of the compressors, providing a new idea with both optimization capability and explainability for its intelligent design. Full article

(This article belongs to the Special Issue Advances in Biological and Bio-Inspired Algorithms)

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39 pages, 10816 KiB

Open AccessArticle

A Novel Adaptive Superb Fairy-Wren (Malurus cyaneus) Optimization Algorithm for Solving Numerical Optimization Problems

by Tianzuo Yuan, Huanzun Zhang, Jie Jin, Zhebo Chen and Shanshan Cai

Biomimetics 2025, 10(8), 496; https://doi.org/10.3390/biomimetics10080496 - 27 Jul 2025

Superb Fairy-wren Optimization Algorithm (SFOA) is an animal-based meta-heuristic algorithm derived from Fairy-wren’s behavior of growing, feeding, and avoiding natural enemies. The SFOA has some shortcomings when facing complex environments. Its switching mechanism is not enough to adapt to complex optimization problems, and [...] Read more.

Superb Fairy-wren Optimization Algorithm (SFOA) is an animal-based meta-heuristic algorithm derived from Fairy-wren’s behavior of growing, feeding, and avoiding natural enemies. The SFOA has some shortcomings when facing complex environments. Its switching mechanism is not enough to adapt to complex optimization problems, and it faces a weakening of population diversity in the late stage of optimization, leading to a higher possibility of falling into local optima. In addition, its global search ability needs to be improved. To address the above deficiencies, this paper proposes an Adaptive Superb Fairy-wren Optimization Algorithm (ASFOA). To assess the ability of the proposed ASFOA, three groups of experiments are conducted in this paper. Firstly, the effectiveness of the proposed improved strategies is checked on the CEC2018 test set. Second, the ASFOA is compared with eight classical/highly cited/newly proposed metaheuristics on the CEC2018 test set, in which the ASFOA performed the best overall, with average rankings of 1.621, 1.138, 1.483, and 1.966 in the four-dimensional cases, respectively. Then the convergence and robustness of ASFOA is verified on the CEC2022 test set. The experimental results indicate that the proposed ASFOA is a competitive metaheuristic algorithm variant with excellent performance in terms of convergence and distribution of solutions. In addition, we further validate the ability of ASFOA to solve real optimization problems. The average ranking of the proposed ASFOA on 10 engineering constrained optimization problems is 1.500. In summary, ASFOA is a promising variant of metaheuristic algorithms. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 3rd Edition)

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24 pages, 10460 KiB

Open AccessArticle

WGGLFA: Wavelet-Guided Global–Local Feature Aggregation Network for Facial Expression Recognition

by Kaile Dong, Xi Li, Cong Zhang, Zhenhua Xiao and Runpu Nie

Biomimetics 2025, 10(8), 495; https://doi.org/10.3390/biomimetics10080495 - 27 Jul 2025

Facial expression plays an important role in human–computer interaction and affective computing. However, existing expression recognition methods cannot effectively capture multi-scale structural details contained in facial expressions, leading to a decline in recognition accuracy. Inspired by the multi-scale processing mechanism of the biological [...] Read more.

Facial expression plays an important role in human–computer interaction and affective computing. However, existing expression recognition methods cannot effectively capture multi-scale structural details contained in facial expressions, leading to a decline in recognition accuracy. Inspired by the multi-scale processing mechanism of the biological visual system, this paper proposes a wavelet-guided global–local feature aggregation network (WGGLFA) for facial expression recognition (FER). Our WGGLFA network consists of three main modules: the scale-aware expansion (SAE) module, which combines dilated convolution and wavelet transform to capture multi-scale contextual features; the structured local feature aggregation (SLFA) module based on facial keypoints to extract structured local features; and the expression-guided region refinement (ExGR) module, which enhances features from high-response expression areas to improve the collaborative modeling between local details and key expression regions. All three modules utilize the spatial frequency locality of the wavelet transform to achieve high-/low-frequency feature separation, thereby enhancing fine-grained expression representation under frequency domain guidance. Experimental results show that our WGGLFA achieves accuracies of 90.32%, 91.24%, and 71.90% on the RAF-DB, FERPlus, and FED-RO datasets, respectively, demonstrating that our WGGLFA is effective and has more capability of robustness and generalization than state-of-the-art (SOTA) expression recognition methods. Full article

(This article belongs to the Special Issue New Biomimetic Advances in Signal and Image Processing for Biomedical Applications 2025)

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22 pages, 2974 KiB

Open AccessArticle

An Enhanced Grasshopper Optimization Algorithm with Outpost and Multi-Population Mechanisms for Dolomite Lithology Prediction

by Xinya Yu and Parhat Zunu

Biomimetics 2025, 10(8), 494; https://doi.org/10.3390/biomimetics10080494 - 25 Jul 2025

The Grasshopper Optimization Algorithm (GOA) has attracted significant attention due to its simplicity and effective search capabilities. However, its performance deteriorates when dealing with high-dimensional or complex optimization tasks. To address these limitations, this study proposes an improved variant of GOA, named Outpost [...] Read more.

The Grasshopper Optimization Algorithm (GOA) has attracted significant attention due to its simplicity and effective search capabilities. However, its performance deteriorates when dealing with high-dimensional or complex optimization tasks. To address these limitations, this study proposes an improved variant of GOA, named Outpost Multi-population GOA (OMGOA). OMGOA integrates two novel mechanisms: the Outpost mechanism, which enhances local exploitation by guiding agents towards high-potential regions, and the multi-population enhanced mechanism, which promotes global exploration and maintains population diversity through parallel evolution and controlled information exchange. Comprehensive experiments were conducted to evaluate the effectiveness of OMGOA. Ablation studies were performed to assess the individual contributions of each mechanism, while multi-dimensional testing was used to verify robustness and scalability. Comparative experiments show that OMGOA has better optimization performance compared to other similar algorithms. In addition, OMGOA was successfully applied to a real-world engineering problem—lithology prediction from petrophysical logs—where it achieved competitive classification performance. Full article

(This article belongs to the Special Issue Advances in Biological and Bio-Inspired Algorithms)

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25 pages, 51196 KiB

Open AccessArticle

Research on Robot Obstacle Avoidance and Generalization Methods Based on Fusion Policy Transfer Learning

by Suyu Wang, Zhenlei Xu, Peihong Qiao, Quan Yue, Ya Ke and Feng Gao

Biomimetics 2025, 10(8), 493; https://doi.org/10.3390/biomimetics10080493 - 25 Jul 2025

In nature, organisms often rely on the integration of local sensory information and prior experience to flexibly adapt to complex and dynamic environments, enabling efficient path selection. This bio-inspired mechanism of perception and behavioral adjustment provides important insights for path planning in mobile [...] Read more.

In nature, organisms often rely on the integration of local sensory information and prior experience to flexibly adapt to complex and dynamic environments, enabling efficient path selection. This bio-inspired mechanism of perception and behavioral adjustment provides important insights for path planning in mobile robots operating under uncertainty. In recent years, the introduction of deep reinforcement learning (DRL) has empowered mobile robots to autonomously learn navigation strategies through interaction with the environment, allowing them to identify obstacle distributions and perform path planning even in unknown scenarios. To further enhance the adaptability and path planning performance of robots in complex environments, this paper develops a deep reinforcement learning framework based on the Soft Actor–Critic (SAC) algorithm. First, to address the limited adaptability of existing transfer learning methods, we propose an action-level fusion mechanism that dynamically integrates prior and current policies during inference, enabling more flexible knowledge transfer. Second, a bio-inspired radar perception optimization method is introduced, which mimics the biological mechanism of focusing on key regions while ignoring redundant information, thereby enhancing the expressiveness of sensory inputs. Finally, a reward function based on ineffective behavior recognition is designed to reduce unnecessary exploration during training. The proposed method is validated in both the Gazebo simulation environment and real-world scenarios. Experimental results demonstrate that the approach achieves faster convergence and superior obstacle avoidance performance in path planning tasks, exhibiting strong transferability and generalization across various obstacle configurations. Full article

(This article belongs to the Section Biological Optimisation and Management)

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20 pages, 3825 KiB

Open AccessArticle

Diffangle-Grasp: Dexterous Grasp Synthesis via Fine-Grained Contact Generation and Natural Pose Optimization

by Meng Ning, Chong Deng, Ziheng Zhan, Qianwei Yin and Xue Xia

Biomimetics 2025, 10(8), 492; https://doi.org/10.3390/biomimetics10080492 - 25 Jul 2025

Grasping objects with a high degree of anthropomorphism is a critical component in the field of highly anthropomorphic robotic grasping. However, the accuracy of contact maps and the irrationality of the grasping gesture become challenges for grasp generation. In this paper, we propose [...] Read more.

Grasping objects with a high degree of anthropomorphism is a critical component in the field of highly anthropomorphic robotic grasping. However, the accuracy of contact maps and the irrationality of the grasping gesture become challenges for grasp generation. In this paper, we propose a reasonably improved generation scheme, called Diffangle-Grasp, consisting of two parts: contact map generation based on a conditional variational autoencoder (CVAE), sharing the potential space with the diffusion model, and optimized grasping generation, conforming to the physical laws and the natural pose. The experimental findings demonstrate that the proposed method effectively reduces the loss in contact map reconstruction by 9.59% in comparison with the base model. Additionally, it enhances the naturalness by 2.15%, elevates the success rate of grasping by 3.27%, reduces the penetration volume by 11.06%, and maintains the grasping simulation displacement. The comprehensive comparison and qualitative analysis with mainstream schemes also corroborate the rationality of the improvement. In this paper, we provide a comprehensive account of our contributions to enhancing the accuracy of contact maps and the naturalness of grasping gestures. We also offer a detailed technical feasibility analysis for robotic human grasping. Full article

(This article belongs to the Section Locomotion and Bioinspired Robotics)

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15 pages, 2230 KiB

Open AccessArticle

Exploring the Rheological Properties of 3D Bioprinted Alginate-Based Hydrogels for Tissue Engineering

by R. Palacín-García, L. Goñi and T. Gómez-del Río

Biomimetics 2025, 10(8), 491; https://doi.org/10.3390/biomimetics10080491 - 24 Jul 2025

The development of alginate/polyacrylamide hydrogels for various biomedical applications has attracted significant interest, particularly due to their potential use in wound healing and tissue engineering. This study explores the fabrication of these hydrogels via 3D bioprinting with ultraviolet light curing, focusing on how [...] Read more.

The development of alginate/polyacrylamide hydrogels for various biomedical applications has attracted significant interest, particularly due to their potential use in wound healing and tissue engineering. This study explores the fabrication of these hydrogels via 3D bioprinting with ultraviolet light curing, focusing on how the alginate concentration and curing speed impact their mechanical properties. Rheological testing was employed to examine the viscoelastic behavior of alginate/polyacrylamide hydrogels manufactured using a 3D bioprinting technique. The relaxation behavior and dynamic response of these hydrogels were analyzed under torsional stress, with relaxation curves fitted using a two-term Prony series. Fourier Transform Infrared (FTIR) spectroscopy was also employed to assess biocompatibility and the conversion of acrylamide. This study successfully demonstrated the printability of alginate/polyacrylamide hydrogels with varying alginate contents. The rheological results indicated that 3D bioprinted hydrogels exhibited significantly high stiffness, viscoelasticity, and long relaxation times. The curing speed had a minimal impact on these properties. Additionally, the FTIR analysis confirmed the complete conversion of polyacrylamide, ensuring no harmful effects in biological applications. The study concludes that 3D bioprinting significantly enhances the mechanical properties of alginate/polyacrylamide hydrogels, with the alginate concentration playing a key role in the shear modulus. These hydrogels show promising potential for biocompatible applications such as wound healing dressings. Full article

(This article belongs to the Special Issue Biological and Bioinspired Materials and Structures: 2nd Edition)

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25 pages, 8652 KiB

Open AccessArticle

Performance Improvement of Seismic Response Prediction Using the LSTM-PINN Hybrid Method

by Seunggoo Kim, Donwoo Lee and Seungjae Lee

Biomimetics 2025, 10(8), 490; https://doi.org/10.3390/biomimetics10080490 - 24 Jul 2025

Accurate and rapid prediction of structural responses to seismic loading is critical for ensuring structural safety. Recently, there has been active research focusing on the application of deep learning techniques, including Physics-Informed Neural Networks (PINNs) and Long Short-Term Memory (LSTM) networks, to predict [...] Read more.

Accurate and rapid prediction of structural responses to seismic loading is critical for ensuring structural safety. Recently, there has been active research focusing on the application of deep learning techniques, including Physics-Informed Neural Networks (PINNs) and Long Short-Term Memory (LSTM) networks, to predict the dynamic behavior of structures. While these methods have shown promise, each comes with distinct limitations. PINNs offer physical consistency but struggle with capturing long-term temporal dependencies in nonlinear systems, while LSTMs excel in learning sequential data but lack physical interpretability. To address these complementary limitations, this study proposes a hybrid LSTM-PINN model, combining the temporal learning ability of LSTMs with the physics-based constraints of PINNs. This hybrid approach allows the model to capture both nonlinear, time-dependent behaviors and maintain physical consistency. The proposed model is evaluated on both single-degree-of-freedom (SDOF) and multi-degree-of-freedom (MDOF) structural systems subjected to the El-Centro ground motion. For validation, the 1940 El-Centro NS earthquake record was used, and the ground acceleration data were normalized and discretized for numerical simulation. The proposed LSTM-PINN is trained under the same conditions as the conventional PINN models (e.g., same optimizer, learning rate, and loss structure), but with fewer training epochs, to evaluate learning efficiency. Prediction accuracy is quantitatively assessed using mean error and mean squared error (MSE) for displacement, velocity, and acceleration, and results are compared with PINN-only models (PINN-1, PINN-2). The results show that LSTM-PINN consistently achieves the most stable and precise predictions across the entire time domain. Notably, it outperforms the baseline PINNs even with fewer training epochs. Specifically, it achieved up to 50% lower MSE with only 10,000 epochs, compared to the PINN’s 50,000 epochs, demonstrating improved generalization through temporal sequence learning. This study empirically validates the potential of physics-guided time-series AI models for dynamic structural response prediction. The proposed approach is expected to contribute to future applications such as real-time response estimation, structural health monitoring, and seismic performance evaluation. Full article

(This article belongs to the Special Issue Biomimetics and Bioinspired Artificial Intelligence Applications: 2nd Edition)

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19 pages, 41284 KiB

Open AccessArticle

Coordinated Dual-Fin Actuation of Bionic Ocean Sunfish Robot for Multi-Modal Locomotion

by Lidong Huang, Zhong Huang, Quanchao Liu, Zhihao Song, Yayi Shen and Mengxing Huang

Biomimetics 2025, 10(8), 489; https://doi.org/10.3390/biomimetics10080489 - 24 Jul 2025

This paper presents a bionic dual-fin underwater robot, inspired by the ocean sunfish, that achieves multiple swimming motions using only two vertically arranged fins. This work demonstrates that a mechanically simple platform can execute complex 2-D and 3-D motions through advanced control strategies, [...] Read more.

This paper presents a bionic dual-fin underwater robot, inspired by the ocean sunfish, that achieves multiple swimming motions using only two vertically arranged fins. This work demonstrates that a mechanically simple platform can execute complex 2-D and 3-D motions through advanced control strategies, eliminating the need for auxiliary actuators. We control the two fins independently so that they can perform cooperative actions in the water, enabling the robot to perform various motions, including high-speed cruising, agile turning, controlled descents, proactive ascents, and continuous spiraling. The swimming performance of the dual-fin robot in executing multi-modal locomotion is experimentally analyzed through visual measurement methods and onboard sensors. Experimental results demonstrate that a minimalist dual-fin propulsion system of the designed ocean sunfish robot can provide speed (maximum cruising speed of 1.16 BL/s), stability (yaw amplitude less than 4.2°), and full three-dimensional maneuverability (minimum turning radius of 0.89 BL). This design, characterized by its simple structure, multiple motion capabilities, and excellent motion performance, offers a promising pathway for developing robust and versatile robots for diverse underwater applications. Full article

(This article belongs to the Special Issue Bionic Robotic Fish: 2nd Edition)

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28 pages, 1547 KiB

Open AccessReview

Brain–Computer Interfaces in Parkinson’s Disease Rehabilitation

by Emmanuel Ortega-Robles, Ruben I. Carino-Escobar, Jessica Cantillo-Negrete and Oscar Arias-Carrión

Biomimetics 2025, 10(8), 488; https://doi.org/10.3390/biomimetics10080488 - 23 Jul 2025

Parkinson’s disease (PD) is a progressive neurological disorder with motor and non-motor symptoms that are inadequately addressed by current pharmacological and surgical therapies. Brain–computer interfaces (BCIs), particularly those based on electroencephalography (eBCIs), provide a promising, non-invasive approach to personalized neurorehabilitation. This narrative review [...] Read more.

Parkinson’s disease (PD) is a progressive neurological disorder with motor and non-motor symptoms that are inadequately addressed by current pharmacological and surgical therapies. Brain–computer interfaces (BCIs), particularly those based on electroencephalography (eBCIs), provide a promising, non-invasive approach to personalized neurorehabilitation. This narrative review explores the clinical potential of BCIs in PD, discussing signal acquisition, processing, and control paradigms. eBCIs are well-suited for PD due to their portability, safety, and real-time feedback capabilities. Emerging neurophysiological biomarkers—such as beta-band synchrony, phase–amplitude coupling, and altered alpha-band activity—may support adaptive therapies, including adaptive deep brain stimulation (aDBS), as well as motor and cognitive interventions. BCIs may also aid in diagnosis and personalized treatment by detecting these cortical and subcortical patterns associated with motor and cognitive dysfunction in PD. A structured search identified 11 studies involving 64 patients with PD who used BCIs for aDBS, neurofeedback, and cognitive rehabilitation, showing improvements in motor function, cognition, and engagement. Clinical translation requires attention to electrode design and user-centered interfaces. Ethical issues, including data privacy and equitable access, remain critical challenges. As wearable technologies and artificial intelligence evolve, BCIs could shift PD care from intermittent interventions to continuous, brain-responsive therapy, potentially improving patients’ quality of life and autonomy. This review highlights BCIs as a transformative tool in PD management, although more robust clinical evidence is needed. Full article

(This article belongs to the Special Issue Advances in Brain–Computer Interfaces (BCI): Challenges and Opportunities)

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18 pages, 8296 KiB

Open AccessArticle

Survival Is Skin Deep: Toughness of the Outer Cactus Stem with Insights for Technical Envelopes

by Patricia Soffiatti, Natália O. Bonfante, Maria Clara L. Jaculiski and Nick P. Rowe

Biomimetics 2025, 10(8), 487; https://doi.org/10.3390/biomimetics10080487 - 23 Jul 2025

Cacti are of interest for new bio-inspired technologies because of their remarkable adaptations to extreme environments. Recently, they have inspired functional designs from nano fibres to optimised buildings and architectures. We investigate the diversity of cactus skin properties in terms of toughness and [...] Read more.

Cacti are of interest for new bio-inspired technologies because of their remarkable adaptations to extreme environments. Recently, they have inspired functional designs from nano fibres to optimised buildings and architectures. We investigate the diversity of cactus skin properties in terms of toughness and resistance to cutting damage. Cacti are well known for their extreme adaptations to harsh environments, with soft, fleshy stems that expand and contract with water uptake and storage. This functioning is made possible by an extendable outer envelope (skin) and a fluted 3-dimensional structure of the stem. We explore the mechanical toughness and underlying structural organisation of the cactus skin in four species of cactus showing different growth forms. The toughness properties of the cactus skin is only one part of a multi-functional structure for surviving in extreme environments. The study suggests that survival involves a relatively “light” investment of tough materials in the outer envelope instead of a rigid “defensive” layer. This is capable of elastic deformation and enables water storage in challenging, arid environments. The main purpose of this article is to demonstrate the diversity of skin toughness and underlying structures in the biological world as providing potential new designs for technical envelopes. Full article

(This article belongs to the Special Issue Editorial Board Members' Collection Series: Biomimetics of Materials, Functions, Structures and Processes 2025)

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15 pages, 2557 KiB

Open AccessArticle

Multiline Laser Interferometry for Non-Contact Dynamic Morphing of Hierarchical Surfaces

by Biagio Audia, Caterina Maria Tone, Pasquale Pagliusi, Alfredo Mazzulla, George Papavieros, Vassilios Constantoudis and Gabriella Cipparrone

Biomimetics 2025, 10(8), 486; https://doi.org/10.3390/biomimetics10080486 - 23 Jul 2025

Hierarchical surface structuring is a critical aspect of advanced materials design, impacting fields ranging from optics to biomimetics. Among several laser-based methods for complex structuring of photo-responsive surfaces, the broadband vectorial interferometry proposed here offers unique performances. Such a method leverages a polychromatic [...] Read more.

Hierarchical surface structuring is a critical aspect of advanced materials design, impacting fields ranging from optics to biomimetics. Among several laser-based methods for complex structuring of photo-responsive surfaces, the broadband vectorial interferometry proposed here offers unique performances. Such a method leverages a polychromatic laser source, an unconventional choice for holographic encoding, to achieve deterministic multiscale surface structuring through interference light patterning. Azopolymer films are used as photosensitive substrates. By exploring the interaction between optomechanical stress modulations at different spatial periodicities induced within the polymer bulk, we demonstrate the emergence of hierarchical Fourier surfaces composed of multiple deterministic levels. These structures range from sub-micrometer to tens of micrometers scale, exhibiting a high degree of control over their morphology. The experimental findings reveal that the optical encoding scheme significantly influences the resulting topographies. The polarization light patterns lead to more regular and symmetric hierarchical structures compared to those obtained with intensity patterns, underscoring the role of vectorial light properties in controlling surface morphologies. The proposed method is fully scalable, compatible with more complex recording schemes (including multi-beam interference), and it is applicable to a wide range of advanced technological fields. These include optics and photonics (diffractive elements, polarimetric devices), biomimetic surfaces, topographical design, information encoding, and anti-counterfeiting, offering a rapid, reliable, and versatile strategy for high-precision surface structuring at a submicrometric scale. Full article

(This article belongs to the Special Issue Laser Photonics and Micro/Nano Fabrication: Towards Innovations in Biomimetic Surface Engineering)

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18 pages, 1592 KiB

Open AccessArticle

A List-Based Parallel Bacterial Foraging Algorithm for the Multiple Sequence Alignment Problem

by Ernesto Rios-Willars, María Magdalena Delabra-Salinas and Alfredo Reyes-Acosta

Biomimetics 2025, 10(8), 485; https://doi.org/10.3390/biomimetics10080485 - 23 Jul 2025

A parallel bacterial foraging algorithm was developed for the multiple sequence alignment problem. Four sets of homologous genetic and protein sequences related to Alzheimer’s disease among various species were collected from the NCBI database for convergence analysis and performance comparison. The main question [...] Read more.

A parallel bacterial foraging algorithm was developed for the multiple sequence alignment problem. Four sets of homologous genetic and protein sequences related to Alzheimer’s disease among various species were collected from the NCBI database for convergence analysis and performance comparison. The main question was the following: is the bacterial foraging algorithm suitable for the multiple sequence alignment problem? Three versions of the algorithm were contrasted by performing a t-test and Mann–Whitney test based on the results of a 30-run scheme, focusing on fitness, execution time, and the number of function evaluations as performance metrics. Additionally, we conducted a performance comparison of the developed algorithm with the well-known Genetic Algorithm. The results demonstrated the consistent efficiency of the bacterial foraging algorithm, while the version of the algorithm based on gap deletion presented an increased number of function evaluations and excessive execution time. Overall, the first version of the developed algorithm was found to outperform the second version, based on its efficiency. Finally, we found that the third bacterial foraging algorithm version outperformed the Genetic Algorithm in the third phase of the experiment. The sequence sets, the algorithm’s Python 3.12 code and pseudocode, the data collected from the executions, and a GIF animation of the convergence on various different sets are available for download. Full article

(This article belongs to the Special Issue Exploration of Bio-Inspired Computing)

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19 pages, 2472 KiB

Open AccessArticle

Immunomodulation Through Fibroblast-Derived Extracellular Vesicles (EVs) Within 3D Polycaprolactone–Collagen Matrix

by Afsara Tasnim, Diego Jacho, Agustin Rabino, Jose Benalcazar, Rafael Garcia-Mata, Yakov Lapitsky and Eda Yildirim-Ayan

Biomimetics 2025, 10(8), 484; https://doi.org/10.3390/biomimetics10080484 - 22 Jul 2025

Extracellular vesicles (EVs) have emerged as promising acellular tools for modulating immune responses for tissue engineering applications. This study explores the potential of human fibroblast-derived EVs delivered within a three-dimensional (3D) injectable scaffold composed of polycaprolactone (PCL) nanofibers and collagen (PNCOL) to reprogram [...] Read more.

Extracellular vesicles (EVs) have emerged as promising acellular tools for modulating immune responses for tissue engineering applications. This study explores the potential of human fibroblast-derived EVs delivered within a three-dimensional (3D) injectable scaffold composed of polycaprolactone (PCL) nanofibers and collagen (PNCOL) to reprogram macrophage behavior and support scaffold integrity under inflammatory conditions. EVs were successfully isolated from human fibroblasts using ultracentrifugation and characterized for purity, size distribution and surface markers (CD63 and CD9). Macrophage-laden PNCOL scaffolds were prepared under three conditions: macrophage-only (MP), fibroblast co-encapsulated (F-MP), and EV-encapsulated (EV-MP) groups. Structural integrity was assessed via scanning electron microscopy and Masson’s trichrome staining, while immunomodulatory effects were evaluated through metabolic assays, gene expression profiling, and immunohistochemistry for macrophage polarization markers (CD80, CD206). When co-encapsulated with pro-inflammatory (M1) macrophages in PNCOL scaffolds, fibroblast-derived EVs preserved scaffold structure and significantly enhanced macrophage metabolic activity compared to the control (MP) and other experimental group (F-MP). The gene expression and immunohistochemistry data demonstrated substantial upregulation of anti-inflammatory markers (TGF-β, CD163, and CCL18) and surface protein CD206, indicating a phenotypic shift toward M2-like macrophages for EV-encapsulated scaffolds relative to the other groups. The findings of this study demonstrate that fibroblast-derived EVs integrated into injectable PCL–collagen scaffolds offer a viable, cell-free approach to modulate inflammation, preserve scaffold structure, and support regenerative healing. This strategy holds significant promise for advancing immuno-instructive platforms in regenerative medicine, particularly in settings where conventional cell therapies face limitations in survival, cost, or safety. Full article

(This article belongs to the Special Issue Biomimetic Application on Applied Bioengineering)

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46 pages, 2471 KiB

Open AccessSystematic Review

Technical Functions of Digital Wearable Products (DWPs) in the Consumer Acceptance Model: A Systematic Review and Bibliometric Analysis with a Biomimetic Perspective

by Liu Yuxin, Sarah Abdulkareem Salih and Nazlina Shaari

Biomimetics 2025, 10(8), 483; https://doi.org/10.3390/biomimetics10080483 - 22 Jul 2025

Design and use of wearable technology have grown exponentially, particularly in consumer products and service sectors, e.g., healthcare. However, there is a lack of a comprehensive understanding of wearable technology in consumer acceptance. This systematic review utilized a PRISMA on peer-reviewed articles published [...] Read more.

Design and use of wearable technology have grown exponentially, particularly in consumer products and service sectors, e.g., healthcare. However, there is a lack of a comprehensive understanding of wearable technology in consumer acceptance. This systematic review utilized a PRISMA on peer-reviewed articles published between 2014 and 2024 and collected on WoS, Scopus, and ScienceDirect. A total of 38 full-text articles were systematically reviewed and analyzed using bibliometric, thematic, and descriptive analysis to understand the technical functions of digital wearable products (DWPs) in consumer acceptance. The findings revealed four key functions: (i) wearable technology, (ii) appearance and design, (iii) biomimetic innovation, and (iv) security and privacy, found in eight types of DWPs, among them smartwatches, medical robotics, fitness devices, and wearable fashions, significantly predicted the customers’ acceptance moderated by the behavioral factors. The review also identified five key outcomes: health and fitness, enjoyment, social value, biomimicry, and market growth. The review proposed a comprehensive acceptance model that combines biomimetic principles and AI-driven features into the technical functions of the technical function model (TAM) while addressing security and privacy concerns. This approach contributes to the extended definition of TAM in wearable technology, offering new pathways for biomimetic research in smart devices and robotics. Full article

(This article belongs to the Special Issue Bionic Wearable Robotics and Intelligent Assistive Technologies)

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47 pages, 10439 KiB

Open AccessArticle

Adaptive Nonlinear Bernstein-Guided Parrot Optimizer for Mural Image Segmentation

by Jianfeng Wang, Jiawei Fan, Xiaoyan Zhang and Bao Qian

Biomimetics 2025, 10(8), 482; https://doi.org/10.3390/biomimetics10080482 - 22 Jul 2025

During the long-term preservation of murals, the degradation of mural image information poses significant challenges to the restoration and conservation of world cultural heritage. Currently, mural conservation scholars focus on image segmentation techniques for mural restoration and protection. However, existing image segmentation methods [...] Read more.

During the long-term preservation of murals, the degradation of mural image information poses significant challenges to the restoration and conservation of world cultural heritage. Currently, mural conservation scholars focus on image segmentation techniques for mural restoration and protection. However, existing image segmentation methods suffer from suboptimal segmentation quality. To improve mural image segmentation, this study proposes an efficient mural image segmentation method termed Adaptive Nonlinear Bernstein-guided Parrot Optimizer (ANBPO) by integrating an adaptive learning strategy, a nonlinear factor, and a third-order Bernstein-guided strategy into the Parrot Optimizer (PO). In ANBPO, First, to address PO’s limited global exploration capability, the adaptive learning strategy is introduced. By considering individual information disparities and learning behaviors, this strategy effectively enhances the algorithm’s global exploration, enabling a thorough search of the solution space. Second, to mitigate the imbalance between PO’s global exploration and local exploitation phases, the nonlinear factor is proposed. Leveraging its adaptability and nonlinear curve characteristics, this factor improves the algorithm’s ability to escape local optimal segmentation thresholds. Finally, to overcome PO’s inadequate local exploitation capability, the third-order Bernstein-guided strategy is introduced. By incorporating the weighted properties of third-order Bernstein polynomials, this strategy comprehensively evaluates individuals with diverse characteristics, thereby enhancing the precision of mural image segmentation. ANBPO was applied to segment twelve mural images. The results demonstrate that, compared to competing algorithms, ANBPO achieves a 91.6% win rate in fitness function values while outperforming them by 67.6%, 69.4%, and 69.7% in PSNR, SSIM, and FSIM metrics, respectively. These results confirm that the ANBPO algorithm can effectively segment mural images while preserving the original feature information. Thus, it can be regarded as an efficient mural image segmentation algorithm. Full article

(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2025)

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27 pages, 5788 KiB

Open AccessArticle

A Novel Artificial Eagle-Inspired Optimization Algorithm for Trade Hub Location and Allocation Method

by Shuhan Hu, Gang Hu, Bo Du and Abdelazim G. Hussien

Biomimetics 2025, 10(8), 481; https://doi.org/10.3390/biomimetics10080481 - 22 Jul 2025

Aiming for convenience and the low cost of goods transfer between towns, this paper proposes a trade hub location and allocation method based on a novel artificial eagle-inspired optimization algorithm. Firstly, the trade hub location and allocation model is established, taking the total [...] Read more.

Aiming for convenience and the low cost of goods transfer between towns, this paper proposes a trade hub location and allocation method based on a novel artificial eagle-inspired optimization algorithm. Firstly, the trade hub location and allocation model is established, taking the total cost consisting of construction and transportation costs as the objective function. Then, to solve the nonlinear model, a novel artificial eagle optimization algorithm (AEOA) is proposed by simulating the collective migration behaviors of artificial eagles when facing a severe living environment. Three main strategies are designed to help the algorithm effectively explore the decision space: the situational awareness and analysis stage, the free exploration stage, and the flight formation integration stage. In the first stage, artificial eagles are endowed with intelligent thinking, thus generating new positions closer to the optimum by perceiving the current situation and updating their positions. In the free exploration stage, artificial eagles update their positions by drawing on the current optimal position, ensuring more suitable habitats can be found. Meanwhile, inspired by the consciousness of teamwork, a formation flying method based on distance information is introduced in the last stage to improve stability and success rate. Test results from the CEC2022 suite indicate that the AEOA can obtain better solutions for 11 functions out of all 12 functions compared with 8 other popular algorithms. Faster convergence speed and stronger stability of the AEOA are also proved by quantitative analysis. Finally, the trade hub location and allocation method is proposed by combining the optimization model and the AEOA. By solving two typical simulated cases, this method can select suitable hubs with lower construction costs and achieve reasonable allocation between hubs and the rest of the towns to reduce transportation costs. Thus, it is used to solve the trade hub location and allocation problem of Henan province in China to help the government make sound decisions. Full article

(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2025)

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18 pages, 2502 KiB

Open AccessArticle

Learning Local Texture and Global Frequency Clues for Face Forgery Detection

by Xin Jin, Yuru Kou, Yuhao Xie, Yuying Zhao, Miss Laiha Mat Kiah, Qian Jiang and Wei Zhou

Biomimetics 2025, 10(8), 480; https://doi.org/10.3390/biomimetics10080480 - 22 Jul 2025

In recent years, the rapid advancement of deep learning techniques has significantly propelled the development of face forgery methods, drawing considerable attention to face forgery detection. However, existing detection methods still struggle with generalization across different datasets and forgery techniques. In this work, [...] Read more.

In recent years, the rapid advancement of deep learning techniques has significantly propelled the development of face forgery methods, drawing considerable attention to face forgery detection. However, existing detection methods still struggle with generalization across different datasets and forgery techniques. In this work, we address this challenge by leveraging both local texture cues and global frequency domain information in a complementary manner to enhance the robustness of face forgery detection. Specifically, we introduce a local texture mining and enhancement module. The input image is segmented into patches and a subset is strategically masked, then texture enhanced. This joint masking and enhancement strategy forces the model to focus on generalizable localized texture traces, mitigates overfitting to specific identity features and enabling the model to capture more meaningful subtle traces of forgery. Additionally, we extract multi-scale frequency domain features from the face image using wavelet transform, thereby preserving various frequency domain characteristics of the image. And we propose an innovative frequency-domain processing strategy to adjust the contributions of different frequency-domain components through frequency-domain selection and dynamic weighting. This Facilitates the model’s ability to uncover frequency-domain inconsistencies across various global frequency layers. Furthermore, we propose an integrated framework that combines these two feature modalities, enhanced with spatial attention and channel attention mechanisms, to foster a synergistic effect. Extensive experiments conducted on several benchmark datasets demonstrate that the proposed technique demonstrates superior performance and generalization capabilities compared to existing methods. Full article

(This article belongs to the Special Issue Exploration of Bioinspired Computer Vision and Pattern Recognition)

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