Biomimetics

22 pages, 657 KB

Open AccessSystematic 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

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

Open AccessArticle

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

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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Graphical abstract

33 pages, 7900 KB

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessReview

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

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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54 pages, 3025 KB

Open AccessArticle

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

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)

15 pages, 3348 KB

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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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35 pages, 7098 KB

Open AccessReview

Recent Advances in Optoelectronic Synaptic Devices for Neuromorphic Computing

by Heeseong Jang, Seohyeon Ju, Seeun Lee, Jaewoo Choi, Ungbin Byun, Kyeongjun Min, Maria Rasheed and Sungjun Kim

Biomimetics 2025, 10(9), 584; https://doi.org/10.3390/biomimetics10090584 - 3 Sep 2025

Abstract

We explore recent advancements in optoelectronic synaptic devices across four key aspects: mechanisms, materials, synaptic properties, and applications. First, we discuss fundamental working principles, including oxygen vacancy ionization, defect trapping, and heterojunction-based charge modulation, which contribute to synaptic plasticity. Next, we examine the [...] Read more.

We explore recent advancements in optoelectronic synaptic devices across four key aspects: mechanisms, materials, synaptic properties, and applications. First, we discuss fundamental working principles, including oxygen vacancy ionization, defect trapping, and heterojunction-based charge modulation, which contribute to synaptic plasticity. Next, we examine the role of 0D, 1D, and 2D materials in optimizing device performance, focusing on their unique electronic, optical, and mechanical properties. We then analyze synaptic properties such as excitatory post-synaptic current (EPSC), visual adaptation, transition from short-term to long-term plasticity (STP to LTP), nociceptor-inspired responses, and associative learning mechanisms. Finally, we highlight real-world applications, including artificial vision systems, reservoir computing for temporal data processing, adaptive neuromorphic computing for exoplanet detection, and colored image recognition. By consolidating recent developments, this paper provides insights into the potential of optoelectronic synaptic devices for next-generation computing architectures, bridging the gap between optics and neuromorphic engineering. Full article

(This article belongs to the Special Issue Bio-Inspired Machine Learning and Evolutionary Computing)

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19 pages, 9850 KB

Open AccessArticle

Micro-CT and Histomorphometric Analysis of Degradability and New Bone Formation of Anodized Mg-Ca System

by Jihyun Kim, Yoona Jung, Yong-Seok Lee, Seong-Won Choi, Geelsu Hwang and Kwidug Yun

Biomimetics 2025, 10(9), 583; https://doi.org/10.3390/biomimetics10090583 - 3 Sep 2025

Abstract

The surface treatments and various magnesium alloys are applied to improve the fast degradation rate and resulting negative effects of magnesium alloys. This study aimed to assess the effect of anodic oxidation treatment of magnesium–calcium (Mg-Ca) systems by creating artificial bone defects in [...] Read more.

The surface treatments and various magnesium alloys are applied to improve the fast degradation rate and resulting negative effects of magnesium alloys. This study aimed to assess the effect of anodic oxidation treatment of magnesium–calcium (Mg-Ca) systems by creating artificial bone defects in the tibia of rats. The cylinder magnesium implants were fabricated using a Mg-xCa (x = 0, 1, 5 wt.%) binary alloy. Degradability and new bone formation were observed at two and six weeks using micro-CT. Histomorphometric parameters were evaluated with Goldner’s trichrome staining. The degradation rate decreased depending on the amount of calcium added. The parameters related to bone formation revealed an increasing pattern depending on the addition of calcium, anodic oxidation, and time. The amount of absorbed magnesium to assess degradability of magnesium implants by the histomorphometric analysis revealed a high value in the untreated group at two and six weeks. Bone healing parameters increased depending on the amount of calcium added, anodic oxidation treatment, and region of interest (ROI—0.5 mm, 1.00 mm, 1.5 mm, and 2.0 mm). Biodegradable magnesium systems have the potential to replace bone screws and plates. Combination with calcium combined with anodization surface treatment can improve initial corrosion resistance and promote bone formation. Full article

(This article belongs to the Section Biomimetics of Materials and Structures)

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Graphical abstract

20 pages, 6162 KB

Open AccessArticle

Design and Optimization of Hierarchical Porous Metamaterial Lattices Inspired by the Pistol Shrimp’s Claw: Coupling for Superior Crashworthiness

by Jiahong Wen, Na Wu, Pei Tian, Xinlin Li, Shucai Xu and Jiafeng Song

Biomimetics 2025, 10(9), 582; https://doi.org/10.3390/biomimetics10090582 - 2 Sep 2025

Abstract

This study, inspired by the impact resistance of the pistol shrimp’s predatory claw, investigates the design and optimization of bionic energy absorption structures. Four types of bionic hierarchical porous metamaterial lattice structures with a negative Poisson’s ratio were developed based on the microstructure [...] Read more.

This study, inspired by the impact resistance of the pistol shrimp’s predatory claw, investigates the design and optimization of bionic energy absorption structures. Four types of bionic hierarchical porous metamaterial lattice structures with a negative Poisson’s ratio were developed based on the microstructure of the pistol shrimp’s fixed claw. These structures were validated through finite element models and quasi-static compression tests. Results showed that each structure exhibited distinct advantages and shortcomings in specific evaluation indices. To address these limitations, four new bionic structures were designed by coupling the characteristics of the original structures. The coupled structures demonstrated a superior balance across various performance indicators, with the EOS (Eight pillars Orthogonal with Side connectors on square frame) structure showing the most promising results. To further enhance the EOS structure, a parametric study was conducted on the distance d from the edge line to the curve vertex and the length-to-width ratio y of the negative Poisson’s ratio structure beam. A fifth-order polynomial surrogate model was constructed to predict the Specific Energy Absorption (SEA), Crush Force Efficiency (CFE), and Undulation of Load-Carrying fluctuation (ULC) of the EOS structure. A multi-objective genetic algorithm was employed to optimize these three key performance indicators, achieving improvements of 1.98% in SEA, 2.42% in CFE, and 2.05% in ULC. This study provides a theoretical basis for the development of high-performance biomimetic energy absorption structures and demonstrates the effectiveness of coupling design with optimization algorithms to enhance structural performance. Full article

(This article belongs to the Section Biomimetics of Materials and Structures)

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30 pages, 4526 KB

Open AccessArticle

Multi-Strategy Honey Badger Algorithm for Global Optimization

by Delong Guo and Huajuan Huang

Biomimetics 2025, 10(9), 581; https://doi.org/10.3390/biomimetics10090581 - 2 Sep 2025

Abstract

The Honey Badger Algorithm (HBA) is a recently proposed metaheuristic optimization algorithm inspired by the foraging behavior of honey badgers. The search mechanism of this algorithm is divided into two phases: a mining phase and a honey-seeking phase, effectively emulating the processes of [...] Read more.

The Honey Badger Algorithm (HBA) is a recently proposed metaheuristic optimization algorithm inspired by the foraging behavior of honey badgers. The search mechanism of this algorithm is divided into two phases: a mining phase and a honey-seeking phase, effectively emulating the processes of exploration and exploitation within the search space. Despite its innovative approach, the Honey Badger Algorithm (HBA) faces challenges such as slow convergence rates, an imbalanced trade-off between exploration and exploitation, and a tendency to become trapped in local optima. To address these issues, we propose an enhanced version of the Honey Badger Algorithm (HBA), namely the Multi-Strategy Honey Badger Algorithm (MSHBA), which incorporates a Cubic Chaotic Mapping mechanism for population initialization. This integration aims to enhance the uniformity and diversity of the initial population distribution. In the mining and honey-seeking stages, the position of the honey badger is updated based on the best fitness value within the population. This strategy may lead to premature convergence due to population aggregation around the fittest individual. To counteract this tendency and enhance the algorithm’s global optimization capability, we introduce a random search strategy. Furthermore, an elite tangential search and a differential mutation strategy are employed after three iterations without detecting a new best value in the population, thereby enhancing the algorithm’s efficacy. A comprehensive performance evaluation, conducted across a suite of established benchmark functions, reveals that the MSHBA excels in 26 out of 29 IEEE CEC 2017 benchmarks. Subsequent statistical analysis corroborates the superior performance of the MSHBA. Moreover, the MSHBA has been successfully applied to four engineering design problems, highlighting its capability for addressing constrained engineering design challenges and outperforming other optimization algorithms in this domain. Full article

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

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28 pages, 5018 KB

Open AccessArticle

Mechanical Characterization of 3D-Printed Scaffolds: A Multi-Objective Optimization Approach Using Virtual Testing and Homogenization

by Pablo I. León, Uwe Muhlich, Pedro C. Aravena and Gabriela Martínez

Biomimetics 2025, 10(9), 580; https://doi.org/10.3390/biomimetics10090580 - 2 Sep 2025

Abstract

A method to characterize the mechanical properties of cellular materials manufactured using 3D printing, specifically employing the fused deposition modeling (FDM) technique, is developed. Numerical simulations, virtual testing, and optimization based on genetic algorithms are combined in this approach to determine the anisotropic [...] Read more.

A method to characterize the mechanical properties of cellular materials manufactured using 3D printing, specifically employing the fused deposition modeling (FDM) technique, is developed. Numerical simulations, virtual testing, and optimization based on genetic algorithms are combined in this approach to determine the anisotropic properties of the material, which are essential for biomedical applications such as tissue engineering. Homogenization using representative unit cells enabled the calculation of orthotropic properties, including elastic moduli (

E_{1}

,

E_{2}

,

E_{3}

), Poisson’s ratios (

ν_{12}

,

ν_{13}

and

ν_{23}

), and shear moduli (

G_{12}

,

G_{13}

,

G_{23}

). These results validated the virtual tests using an L-shaped beam model, based on a known state of displacements and stresses. In the virtual test of the FDM model, a significant correlation with experimental results was observed, confirming the material’s anisotropy and its influence on deformations and stresses. Meanwhile, the effective medium test demonstrated over 95% agreement between simulated and experimental values, validating the accuracy of the proposed constitutive model. The optimization process, based on multi-objective genetic algorithms, allowed the determination of the material’s mechanical properties through controlled iterations, achieving a strong correlation with the results obtained from the homogenization model. These findings present a new approach for characterizing and optimizing 3D-printed materials using FDM techniques, providing an efficient and reliable method for applications in tissue engineering. Full article

(This article belongs to the Section Biomimetics of Materials and Structures)

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22 pages, 6827 KB

Open AccessArticle

Metaheuristics-Assisted Placement of Omnidirectional Image Sensors for Visually Obstructed Environments

by Fernando Fausto, Gemma Corona, Adrian Gonzalez and Marco Pérez-Cisneros

Biomimetics 2025, 10(9), 579; https://doi.org/10.3390/biomimetics10090579 - 2 Sep 2025

Abstract

Optimal camera placement (OCP) is a crucial task for ensuring adequate surveillance of both indoor and outdoor environments. While several solutions to this problem have been documented in the literature, there are still research gaps related to the maximization of surveillance coverage, particularly [...] Read more.

Optimal camera placement (OCP) is a crucial task for ensuring adequate surveillance of both indoor and outdoor environments. While several solutions to this problem have been documented in the literature, there are still research gaps related to the maximization of surveillance coverage, particularly in terms of optimal placement of omnidirectional camera (OC) sensors in indoor and partially occluded environments via metaheuristic optimization algorithms (MOAs). In this paper, we present a study centered on several popular MOAs and their application to OCP for OC sensors in indoor environments. For our experiments we considered two experimental layouts consisting of both a deployment area, and visual obstructions, as well as two different omnidirectional camera models. The tested MOAs include popular algorithms such as PSO, GWO, SSO, GSA, SMS, SA, DE, GA, and CMA-ES. Experimental results suggest that the success in MOA-based OCP is strongly tied with the specific search strategy applied by the metaheuristic method, thus making certain approaches preferred over others for this kind of problem. Full article

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

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7 pages, 986 KB

Open AccessCommunication

A Call for Bio-Inspired Technologies: Promises and Challenges for Ecosystem Service Replacement

by Kristina Wanieck, M. Alex Smith, Elizabeth Porter, Jindong Zhang, Dave Dowhaniuk, Andria Jones, Dan Gillis, Mark Lipton, Marsha Hinds Myrie, Dawn Bazely, Marjan Eggermont, Mindi Summers, Christina Smylitopoulos, Claudia I. Rivera Cárdenas, Emily Wolf, Peggy Karpouzou, Nikoleta Zampaki, Heather Clitheroe, Adam Davies, Anibal H. Castillo, Michael Helms, Karina Benessaiah and Shoshanah Jacobs Show full author list Hide full author list

Biomimetics 2025, 10(9), 578; https://doi.org/10.3390/biomimetics10090578 - 2 Sep 2025

Abstract

Ecosystem services are crucial for animals, plants, the planet, and human well-being. Decreasing biodiversity and environmental destruction of ecosystems will have severe consequences. Designing technologies that could support, enhance, or even replace ecosystem services is a complex task that the Manufactured Ecosystems Project [...] Read more.

Ecosystem services are crucial for animals, plants, the planet, and human well-being. Decreasing biodiversity and environmental destruction of ecosystems will have severe consequences. Designing technologies that could support, enhance, or even replace ecosystem services is a complex task that the Manufactured Ecosystems Project team considers to be only achievable with transdisciplinarity, as it unlocks new directions for designing research and development systems. One of these directions in the project is bio-inspiration, learning from natural systems as the foundation for manufacturing ecosystem services. Using soil formation as a case study, text-mining of existing scientific literature reveals a critical gap: fewer than 1% of studies in biomimetics address soil formation technological replacement, despite the rapid global decline in natural soil formation processes. The team sketches scenarios of ecosystem collapse, identifying how bio-inspired solutions for equitable and sustainable innovation can contribute to climate adaptation. The short communication opens the discussion for collaboration and aims to initiate future research. Full article

(This article belongs to the Special Issue Bio-Inspired Technologies for Ecosystem Service Support, Enhancement or Replacement)

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21 pages, 2676 KB

Open AccessArticle

DT-HRL: Mastering Long-Sequence Manipulation with Reimagined Hierarchical Reinforcement Learning

by Junyang Zhang, Yilin Zhang, Honglin Sun, Yifei Zhang and Kenji Hashimoto

Biomimetics 2025, 10(9), 577; https://doi.org/10.3390/biomimetics10090577 - 1 Sep 2025

Abstract

Robotic manipulators in warehousing and logistics often face complex tasks that involve multiple steps, frequent task switching, and long-term dependencies. Inspired by the hierarchical structure of human motor control, this paper proposes a Hierarchical Reinforcement Learning (HRL) framework utilizing a multi-task goal-conditioned Decision [...] Read more.

Robotic manipulators in warehousing and logistics often face complex tasks that involve multiple steps, frequent task switching, and long-term dependencies. Inspired by the hierarchical structure of human motor control, this paper proposes a Hierarchical Reinforcement Learning (HRL) framework utilizing a multi-task goal-conditioned Decision Transformer (MTGC-DT). The high-level policy treats the Markov decision process as a sequence modeling task, allowing the agent to manage temporal dependencies. The low-level policy is made up of parameterized action primitives that handle physical execution. This design improves long-term reasoning and generalization. This method is evaluated on two common logistics manipulation tasks: sequential stacking and spatial sorting with sparse reward and low-quality dataset. The main contributions include introducing a HRL framework that integrates Decision Transformer (DT) with task and goal embeddings, along with a path-efficiency loss (PEL) correction and designing a parameterized, learnable primitive skill library for low-level control to enhance generalization and reusability. Experimental results demonstrate that the proposed Decision Transformer-based Hierarchical Reinforcement Learning (DT-HRL) achieves over a 10% higher success rate and over 8% average reward compared with the baseline, and a normalized score increase of over 2% in the ablation experiments. Full article

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

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38 pages, 2697 KB

Open AccessArticle

Liver Tumor Segmentation Based on Multi-Scale Deformable Feature Fusion and Global Context Awareness

by Chenghao Zhang, Lingfei Wang, Chunyu Zhang, Yu Zhang, Jin Li and Peng Wang

Biomimetics 2025, 10(9), 576; https://doi.org/10.3390/biomimetics10090576 - 1 Sep 2025

Abstract

The highly heterogeneous and irregular morphology of liver tumors presents considerable challenges for automated segmentation. To better capture complex tumor structures, this study proposes a liver tumor segmentation framework based on multi-scale deformable feature fusion and global context modeling. The method incorporates three [...] Read more.

The highly heterogeneous and irregular morphology of liver tumors presents considerable challenges for automated segmentation. To better capture complex tumor structures, this study proposes a liver tumor segmentation framework based on multi-scale deformable feature fusion and global context modeling. The method incorporates three key innovations: (1) a Deformable Large Kernel Attention (D-LKA) mechanism in the encoder to enhance adaptability to irregular tumor features, combining a large receptive field with deformable sensitivity to precisely extract tumor boundaries; (2) a Context Extraction (CE) module in the bottleneck layer to strengthen global semantic modeling and compensate for limited capacity in capturing contextual dependencies; and (3) a Dual Cross Attention (DCA) mechanism to replace traditional skip connections, enabling deep cross-scale and cross-semantic feature fusion, thereby improving feature consistency and expressiveness during decoding. The proposed framework was trained and validated on a combined LiTS and MSD Task08 dataset and further evaluated on the independent 3D-IRCADb01 dataset. Experimental results show that it surpasses several state-of-the-art segmentation models in Intersection over Union (IoU) and other metrics, achieving superior segmentation accuracy and generalization performance. Feature visualizations at both encoding and decoding stages provide intuitive insights into the model’s internal processing of tumor recognition and boundary delineation, enhancing interpretability and clinical reliability. Overall, this approach presents a novel and practical solution for robust liver tumor segmentation, demonstrating strong potential for clinical application and real-world deployment. 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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