Biomimetics

35 pages, 12238 KB

Open AccessArticle

Topology and Size Optimization of Trusses by Bone Remodeling: Primary Force-Based Approach

by Burak Kaymak

Biomimetics 2026, 11(3), 223; https://doi.org/10.3390/biomimetics11030223 - 21 Mar 2026

Viewed by 390

This study presents an optimization tool inspired by bone remodeling principles to address the high computational costs of truss topology optimization. Additionally, a new structural analysis method based on primary forces is proposed to overcome the kinematic stability problem. The strategy developed to [...] Read more.

This study presents an optimization tool inspired by bone remodeling principles to address the high computational costs of truss topology optimization. Additionally, a new structural analysis method based on primary forces is proposed to overcome the kinematic stability problem. The strategy developed to obtain the optimal topology optimizes the initial dense ground structure in two stages. In Phase I, unnecessary members in the system are filtered to determine the “primary candidate members”; in Phase II, the final topology is reached through this refined subset. The algorithm performs an effective search in the design space by simulating biological processes that link the rate of mass change in the bone matrix to mechanical stimuli. Numerical results demonstrate high accuracy, as shown by the analytical solution of the 2D Michell truss, with a difference of 1.02%. The results show high consistency with reference studies, providing, in some cases, alternative topologies with the same weight and stiffness as given in the benchmarks. The proposed method achieves significant improvements in computational efficiency, reducing processing times for larger systems by 10 to over 250 times compared to literature benchmarks. Full article

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

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20 pages, 1299 KB

Open AccessArticle

Challenging the Biomimetic Promise 2.0: Negative Spillover of Bio-Inspired Versus Sustainability Framing on Public Perceptions of Bio-Inspired Technologies

by Julius Fenn, Michael Gorki, Stephanie Bugler, Roland Thomaschke, Christian Böffel and Andrea Kiesel

Biomimetics 2026, 11(3), 222; https://doi.org/10.3390/biomimetics11030222 - 19 Mar 2026

Viewed by 435

Abstract

This study investigates how bio-inspired versus sustainability-focused framing influences lay evaluations of a specific bio-inspired building-technology scenario, testing the empirical validity of the so-called “biomimetic promise”. Employing a between-subjects experimental design (

N = 582

), we examined assessments of a weather-responsive self-shading [...] Read more.

This study investigates how bio-inspired versus sustainability-focused framing influences lay evaluations of a specific bio-inspired building-technology scenario, testing the empirical validity of the so-called “biomimetic promise”. Employing a between-subjects experimental design (

N = 582

), we examined assessments of a weather-responsive self-shading façade across bio-inspired, sustainable, and neutral framing conditions. We developed and validated the 12-item Perceived Bio-Inspiration Scale (PBS)—a novel standardized psychometric instrument designed to quantify lay recognition of biomimetic features across visual, intentional, and naturalistic dimensions. While results showed robust direct framing effects, we identified a significant negative spillover: emphasizing biological inspiration significantly reduced the technology’s perceived sustainability, while sustainability framing diminished its perceived bio-inspiration. These findings demonstrate, in this façade context, that laypersons evaluate bio-inspiration and sustainability as cognitively distinct and potentially competing constructs, indicating that “natural-is-better” bias is not universal across all technology domains. Consequently, merely invoking biological origins is insufficient to enhance a technology’s ecological appeal. To foster public trust, science communication should shift from abstract biological metaphors toward a performance-driven communication strategy that prioritizes the disclosure of verifiable life-cycle assessment and specific operational advantages over symbolic nature-based analogies. Full article

(This article belongs to the Section Development of Biomimetic Methodology)

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39 pages, 27667 KB

Open AccessArticle

A Dynamic Multi-Niche Biogeography-Based Optimization Algorithm and Its Application to Robot Path Planning

by Xiaojie Tang, Pengju Qu, Zhengyang He, Chengfen Jia and Qian Zhang

Biomimetics 2026, 11(3), 221; https://doi.org/10.3390/biomimetics11030221 - 19 Mar 2026

Viewed by 436

Abstract

Biogeography-based optimization (BBO) is a population-based metaheuristic algorithm inspired by species migration among habitats. However, the original BBO often suffers from premature convergence and insufficient population diversity when solving complex optimization problems. To address these limitations, this paper proposes a novel dynamic multi-niche [...] Read more.

Biogeography-based optimization (BBO) is a population-based metaheuristic algorithm inspired by species migration among habitats. However, the original BBO often suffers from premature convergence and insufficient population diversity when solving complex optimization problems. To address these limitations, this paper proposes a novel dynamic multi-niche biogeography-based optimization (DMBBO) algorithm. DMBBO incorporates three effective strategies: a dynamic multi-niche population structure to maintain diversity and enhance parallel search capability, a dual-source migration mechanism to improve information exchange efficiency, and a niche-level hybrid elite preservation strategy to stabilize convergence behavior and improve solution quality. Extensive experiments were conducted on the CEC2022, CEC2020, and CEC2019 benchmark test suites under different dimensional settings. The experimental results demonstrated that DMBBO consistently outperformed 23 state-of-the-art algorithms in terms of optimization accuracy, convergence speed, and robustness, with statistically significant improvements validated by Friedman ranking and Wilcoxon rank-sum tests. An ablation study and convergence behavior analysis further confirmed the effectiveness of the proposed strategies. Additionally, DMBBO was applied to robotic path planning problems in grid-based environments involving six different scenarios with varying map sizes and obstacle densities. The results showed that DMBBO is capable of generating shorter and more stable paths in both simple and complex environments, highlighting its strong applicability to constrained optimization problems. Full article

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

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24 pages, 8770 KB

Open AccessArticle

Memetic/Metaphorical Digital Twins: Extending Knowledge Co-Creation Across Economics, Architecture, and Beyond

by Ulrich Schmitt

Biomimetics 2026, 11(3), 220; https://doi.org/10.3390/biomimetics11030220 - 18 Mar 2026

Viewed by 508

Abstract

This article introduces Memetic/Metaphorical Digital Twins (MDTs) as a novel extension of Digital Twin typologies by twinning conceptual schemes, complementing Industrial, Human, and Cognitive Digital Twins. MDTs embed cultural, organizational, and semiotic knowledge into digital frameworks, enabling the recombination and evolution of knowledge [...] Read more.

This article introduces Memetic/Metaphorical Digital Twins (MDTs) as a novel extension of Digital Twin typologies by twinning conceptual schemes, complementing Industrial, Human, and Cognitive Digital Twins. MDTs embed cultural, organizational, and semiotic knowledge into digital frameworks, enabling the recombination and evolution of knowledge structures across disciplines. Drawing on Schlaile’s economic perspectives and Mavromatidis’s architectural lens of entropy and constructal thermodynamics, this study demonstrates how MDTs can address systemic challenges in communication, knowledge transfer, and design. A Digital Community Platform, under development for supporting decentralized Personal Knowledge Management Systems (PKMS), provides the operational foundation, integrating iterative KM cycles to support knowledge co-creation. Its logic and logistics substitute the traditional document paradigm with a memetic approach by utilizing memes as replicable, adaptive knowledge units, thereby mimicking biological evolution and ecosystem resilience in digital platform environments. It aims to offer distributed, decentralized, bottom-up, affordable, knowledge-worker-centric applications prioritizing personalization, mobility, generativity, and entropy reduction; its mission is to serve a knowledge-co-creating community characterized by highly diverse individual Abilities, Contexts, Means, and Ends (ACME) facing increasingly volatile, uncertain, complex, and ambiguous futures (VUCA). A Boundary Object Taxonomy to Omnify Memetic Storytelling (BOTTOMS) is proposed to further structure atomic units of meaning—such as memes, mythemes, narratemes, and reputemes—into a unified framework for authorship and dissemination. The article situates MDTs within a design science research paradigm, outlines current implementation progress, and identifies future developments, including AI-supported curation, personalized metrics, and expanded boundary objects. Together, these contributions position MDTs as a universal framework for adaptive, transdisciplinary knowledge co-creation. Full article

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

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32 pages, 4217 KB

Open AccessReview

Variable Stiffness Structures in Biomimetic Robotic Fish: A Review of Mechanisms, Applications, and Challenges

by Hua Shao, Cong Lin, Zhoukun Yang, Luanjiao Deng, Jinfeng Yang, Xianhong He and Fengran Xie

Biomimetics 2026, 11(3), 219; https://doi.org/10.3390/biomimetics11030219 - 18 Mar 2026

Viewed by 566

Abstract

Biological fish possess the intrinsic ability to dynamically modulate body stiffness to adapt to varying fluid environments, thereby optimizing propulsive efficiency, swimming speed, and maneuverability. In contrast, this capability remains a significant challenge for most existing robotic fish, which typically rely on fixed-stiffness [...] Read more.

Biological fish possess the intrinsic ability to dynamically modulate body stiffness to adapt to varying fluid environments, thereby optimizing propulsive efficiency, swimming speed, and maneuverability. In contrast, this capability remains a significant challenge for most existing robotic fish, which typically rely on fixed-stiffness configurations. This article presents a comprehensive review of variable stiffness structures and their applications in biomimetic robotic fish. The associated technologies are systematically classified into four categories: smart material-driven, bio-inspired, fluid-driven, and hybrid-driven mechanisms. A comparative analysis of state-of-the-art prototypes is conducted, evaluating critical performance metrics including physical dimensions, maximum swimming speed, minimum turning radius, maximum turning rate, and Strouhal number. Furthermore, the specific advantages and technical limitations of each variable stiffness category are critically assessed. Finally, existing challenges in current research are identified, and prospective directions are proposed. The review demonstrates that variable stiffness technology offers significant potential to advance the hydrodynamic performance of robotic fish and facilitate their deployment in practical engineering applications. Full article

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

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16 pages, 1322 KB

Open AccessArticle

Chaos-Embedded Multi-Objective Intelligent Optimization-Based Explainable Classification Model for Determining Cherry Fruit Fly Infestation Levels Using Pomological Data

by Suna Yildirim, Inanc Ozgen, Bilal Alatas and Hakan Yildirim

Biomimetics 2026, 11(3), 218; https://doi.org/10.3390/biomimetics11030218 - 18 Mar 2026

Viewed by 444

Abstract

The European cherry fruit fly (Rhagoletis cerasi L.) poses a significant pest threat to cherry production due to its rapid reproduction and host specificity, causing substantial economic damage. This study presents a novel, explainable, and biologically inspired data-driven classification model based on [...] Read more.

The European cherry fruit fly (Rhagoletis cerasi L.) poses a significant pest threat to cherry production due to its rapid reproduction and host specificity, causing substantial economic damage. This study presents a novel, explainable, and biologically inspired data-driven classification model based on fruit characteristics to support targeted and sustainable pest control strategies. In research conducted at four different locations in Elazığ province, three population classes were determined based on the number of adult individuals caught in traps, and 10 different fruit characteristics were measured in fruit samples belonging to each class. The data used in this study are original data obtained by the authors. To examine the relationship between pomological characteristics of cherry fruit and cherry fruit fly density, the Chaotic Rule-based–Strength Pareto Evolutionary Algorithm2 (CRb-SPEA2) method, developed as a multi-objective and chaos-integrated evolutionary rule mining framework, was adapted. The developed algorithm aimed for high performance, interpretability, and transparency. Accuracy, Precision, and Recall metrics, which are conflicting objectives, were optimized with Pareto-optimal solutions, yielding selectable results for domain experts. To increase population diversity and reduce the risk of early convergence and getting stuck in a local optimum, the Tent chaotic mapping mechanism was also integrated into the system. Furthermore, the model was trained without the need for predefined automatic discretization of the continuous value ranges of the attributes. The proposed model achieved superior results across all classes, with the highest accuracy rate of 82.6% recorded in the High class, demonstrating excellent sensitivity and recall values. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms)

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23 pages, 10022 KB

Open AccessArticle

Biomimetic Dual-Strategy Adaptive Differential Evolution for Joint Kinematic-Residual Calibration with a Neuro-Physical Hybrid Jacobian

by Xibin Ma, Yugang Zhao and Zhibin Li

Biomimetics 2026, 11(3), 217; https://doi.org/10.3390/biomimetics11030217 - 18 Mar 2026

Viewed by 367

Abstract

Improving absolute accuracy in industrial manipulators remains difficult because rigid-body kinematic calibration cannot fully represent configuration-dependent non-geometric effects. Drawing inspiration from biological brain–body co-adaptation, this study presents an Evolutionary Neuro-Physical Hybrid (Evo-NPH) framework in which rigid geometric parameters and neural compensator weights are [...] Read more.

Improving absolute accuracy in industrial manipulators remains difficult because rigid-body kinematic calibration cannot fully represent configuration-dependent non-geometric effects. Drawing inspiration from biological brain–body co-adaptation, this study presents an Evolutionary Neuro-Physical Hybrid (Evo-NPH) framework in which rigid geometric parameters and neural compensator weights are treated as a single co-evolving decision vector. In the offline phase, a Dual-Strategy Adaptive Differential Evolution (DS-ADE) optimizer performs global joint identification using complementary exploration–exploitation behaviors and success-history inheritance, analogous to morphology-control co-evolution in biological systems. In the online phase, a Neuro-Physical Hybrid Jacobian (NPHJ) solver augments the analytical Jacobian with gradients from a Graph Kolmogorov–Arnold Network (GKAN), enabling sensorimotor-like real-time compensation on the learned physical manifold. Experiments on an ABB IRB 120 manipulator with 600 configurations (500 training, 100 testing) report a testing distance-residual RMSE of 0.62 mm, STD of 0.59 mm, and MAX of 0.83 mm. Relative to the uncalibrated baseline, RMSE is reduced by 86.75%; compared with the strongest published baseline, RMSE improves by 23.46%. Ablation results show that joint DS-ADE optimization outperforms a sequential pipeline by 32.6%, and the graph-structured KAN outperforms a parameter-matched MLP by 26.2%. Wilcoxon signed-rank tests (

p < 0.001

) confirm statistical significance. Full article

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

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39 pages, 4467 KB

Open AccessReview

Deep-Sea Biomimetic Manta Ray Robots: A Comprehensive Review Based on Operational Depth Spectrum, Structures, Energy Optimization, and Control Systems

by Lugang Ye, Hongyuan Liu, Qiulin Ding, Zhongming Hu, Weikun Li, Weicheng Cui and Dixia Fan

Biomimetics 2026, 11(3), 216; https://doi.org/10.3390/biomimetics11030216 - 18 Mar 2026

Viewed by 889

Abstract

As deep-sea exploration transitions from large-scale search to precision pinpoint operations, the inherent limitations of traditional “rigid-body and propeller” vehicles—specifically in low-speed maneuverability, environmental compliance, and acoustic stealth—are becoming increasingly apparent. Leveraging its unique integrated “gliding-flapping” locomotion and exceptional maneuverability, the manta ray [...] Read more.

As deep-sea exploration transitions from large-scale search to precision pinpoint operations, the inherent limitations of traditional “rigid-body and propeller” vehicles—specifically in low-speed maneuverability, environmental compliance, and acoustic stealth—are becoming increasingly apparent. Leveraging its unique integrated “gliding-flapping” locomotion and exceptional maneuverability, the manta ray serves as an ideal biological prototype for next-generation deep-sea operational platforms. From a systems engineering perspective, this paper provides a comprehensive review of the current research status and technical evolution of biomimetic manta ray submersibles. First, a technical pedigree centered on “operational depth” is established, illustrating how design paradigms transition from “mechanism replication” in shallow waters to “pressure adaptation” at full-ocean depths. Second, the mechanical challenges in structural design are explored, demonstrating that a “rigid-flexible” gradient distribution strategy is critical to resolving the conflict between pressure resistance and propulsive compliance. Regarding energy and propulsion, the synergistic effects of hybrid gliding-flapping drives and integrated structural batteries in enhancing long-range endurance and energy efficiency are analyzed. Finally, the evolution of motion control architectures—transitioning from spinal-cord-inspired Central Pattern Generator (CPG) rhythmic control to Deep Reinforcement Learning (DRL) featuring embodied intelligence—is outlined. Full article

(This article belongs to the Special Issue Bionics in Engineering Practice: Innovations and Applications)

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21 pages, 9175 KB

Open AccessArticle

Multi-Objective Grey Wolf Optimizer-Tuned LQR Attitude Control of a Three-DOF Hover System

by Abdullah Çakan

Biomimetics 2026, 11(3), 215; https://doi.org/10.3390/biomimetics11030215 - 17 Mar 2026

Viewed by 496

Abstract

Attitude control of unmanned aerial vehicles is a problem that needs to be solved in a reliable manner. The research presented in this paper examines a systematic approach to the design of an LQR state feedback controller for the three-DOF hover system. The [...] Read more.

Attitude control of unmanned aerial vehicles is a problem that needs to be solved in a reliable manner. The research presented in this paper examines a systematic approach to the design of an LQR state feedback controller for the three-DOF hover system. The state space model is used to derive the feedback gain K, with the diagonal elements of the weighting matrices Q and R used as design variables. A multi-objective grey wolf optimizer is used to obtain Q–R matrices based on closed-loop simulations under representative roll, pitch and yaw reference commands. There are four separate multi-objective optimization runs, each using one of four standard error indices which are the integral of absolute error (IAE), the integral of time-weighted absolute error (ITAE), the integral of squared error (ISE) and the integral of time-weighted squared error (ITSE). Each index is used to track roll, pitch and yaw errors at the same time and the resulting non-dominated solution sets are post-processed using TOPSIS to select a compromise knee-point design. The simulation results show that the adjusted LQR parameters lead to feasible tracking performance. The proposed framework provides a systematic and replicable method for LQR weight selection in hover-type attitude control problems under the considered simulation settings. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms)

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17 pages, 1167 KB

Open AccessArticle

HOIMamba: Bidirectional State-Space Modeling for Monocular 3D Human–Object Interaction Reconstruction

by Jinsong Zhang and Yuqin Lin

Biomimetics 2026, 11(3), 214; https://doi.org/10.3390/biomimetics11030214 - 17 Mar 2026

Viewed by 474

Abstract

Monocular 3D human–object interaction (HOI) reconstruction requires jointly recovering articulated human geometry, object pose, and physically plausible contact from a single RGB image. While recent token-based methods commonly employ dense self-attention to capture global dependencies, isotropic all-to-all mixing tends to entangle spatial-geometric cues [...] Read more.

Monocular 3D human–object interaction (HOI) reconstruction requires jointly recovering articulated human geometry, object pose, and physically plausible contact from a single RGB image. While recent token-based methods commonly employ dense self-attention to capture global dependencies, isotropic all-to-all mixing tends to entangle spatial-geometric cues (e.g., contact locality) with channel-wise semantic cues (e.g., action/affordance), and provides limited control for representing directional and asymmetric physical influence between humans and objects. This paper presents HOIMamba, a state-space sequence modeling framework that reformulates HOI reconstruction as bidirectional, multi-scale interaction state inference. Instead of relying on symmetric correlation aggregation, HOIMamba uses structured state evolution to propagate interaction evidence. We introduce a multi-scale state-space module (MSSM) to capture interaction dependencies spanning local contact details and global body–object coordination. Building on MSSM, we propose a spatial-channel grouped SSM (SCSSM) block that factorizes interaction modeling into a spatial pathway for geometric/contact dependencies and a channel pathway for semantic/functional correlations, followed by gated fusion. HOIMamba further performs explicit bidirectional propagation between human and object states to better reflect asymmetric reciprocity in physical interactions. We evaluate HOIMamba on two public benchmarks, BEHAVE and InterCap, using Chamfer distance for human/object meshes and contact precision/recall induced by reconstructed geometry. HOIMamba achieves consistent improvements over representative prior methods. On the BEHAVE dataset, it reduces human Chamfer distance by 8.6% and improves contact recall by 13.5% compared to the strongest Transformer-based baseline, with similar gains observed on the InterCap dataset. Ablation studies on BEHAVE verify the contributions of state-space modeling, multi-scale inference, spatial-channel factorization, and bidirectional interaction reasoning. Full article

(This article belongs to the Special Issue Human–Robot Interaction and Collaboration: Advances in Sensing, Control, and Learning)

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20 pages, 1971 KB

Open AccessArticle

Human–Robot Interaction Strategy of Service Robot with Insufficient Capability in Self-Service Shop

by Wa Gao, Tao He, Yang Ji, Yue Kan and Fusheng Zha

Biomimetics 2026, 11(3), 213; https://doi.org/10.3390/biomimetics11030213 - 16 Mar 2026

Viewed by 558

Abstract

This paper explores the interaction strategies of service robots in self-service shops from a user experience perspective in the case of robots with insufficient capabilities. A Yanshee robot and a self-developed localization-rotation system are employed as the experimental platform. A sales return in [...] Read more.

This paper explores the interaction strategies of service robots in self-service shops from a user experience perspective in the case of robots with insufficient capabilities. A Yanshee robot and a self-developed localization-rotation system are employed as the experimental platform. A sales return in a self-service shop is employed as the experimental scenario. Two types of robot’s insufficient capabilities, three strategies of robots’ apology and a social interaction cue imitated from a human salesperson are considered in the design of interaction strategy between human and robot in this scenario. The results show that robots’ social insufficiency leads to more negative influence on customer experiences of fluency, comprehensibility, impression, intelligence, willingness for future interaction than robots’ performance insufficiency. An empathetic apology when the robot has insufficient performance is an effective interaction strategy. The interaction cue that the robot turns to face customers is not beneficial to customer experiences but does influence the internal relationship between customer experiences during HRI and after HRI. In the case of robots with social insufficiency in a self-service shop, impression, intelligence and interaction capability have positive impacts on the willingness for future interaction, while they are also positively affected by fluency or comprehensibility. In the case of robots with performance insufficiency, impression has a positive impact on willingness, while it is not directly related to fluency. The findings are valuable for informing the interaction design of service robots deployed in shopping, especially in real environments where performance and cost must be balanced. Full article

(This article belongs to the Section Biomimetic Design, Constructions and Devices)

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14 pages, 50163 KB

Open AccessArticle

Stroke Asymmetry in Bird Wing Dynamics During Flight from Video Data

by Valentina Leontiuk, Innokentiy Kastalskiy, Waleed Khalid and Victor B. Kazantsev

Biomimetics 2026, 11(3), 212; https://doi.org/10.3390/biomimetics11030212 - 16 Mar 2026

Viewed by 835

Abstract

The aerodynamics of avian flight provides critical inspiration for the design of bioinspired aerial vehicles, yet the quantitative characterization of free-flight wing kinematics remains challenging. This study employs a neural-network-based motion tracking approach (DeepLabCut) to analyze wingbeat kinematics in free-flying birds from video [...] Read more.

The aerodynamics of avian flight provides critical inspiration for the design of bioinspired aerial vehicles, yet the quantitative characterization of free-flight wing kinematics remains challenging. This study employs a neural-network-based motion tracking approach (DeepLabCut) to analyze wingbeat kinematics in free-flying birds from video data. We automatically digitize key wing points and reconstruct three-dimensional trajectories to quantify asymmetric flapping patterns. Our analysis reveals that while wing oscillations approximate sinusoidal motion, they exhibit statistically significant velocity differences between upstroke and downstroke phases, confirming the stroke asymmetry of avian flapping. Furthermore, using video of a flying frigatebird (Fregata ariel), we quantify the changes in the effective wing area throughout the wingbeat cycle, showing a ~19% variation that significantly impacts lift generation efficiency. These findings provide quantitative benchmarks for avian-inspired wing design and offer insights for optimizing flapping kinematics in bioinspired aerial systems, particularly for enhancing takeoff and landing capabilities in micro air vehicles. Full article

(This article belongs to the Section Development of Biomimetic Methodology)

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14 pages, 1932 KB

Open AccessArticle

Bio-Inspired Energy-Efficient Nanofabricated Electrical Contacts

by Ella M. Gale, Ilyas A. H. Farhat, Suha S. Azhar, Hanno Hildmann, Aaron Stein and A. F. Isakovic

Biomimetics 2026, 11(3), 211; https://doi.org/10.3390/biomimetics11030211 - 16 Mar 2026

Viewed by 440

Abstract

Nanoscale electrical contacts, especially those between materials of dissimilar electronic properties, often represent one of the main causes of drops in energy transfer efficiency. They are also among the sources of above-threshold noise, and their performance often decreases over the lifetime of the [...] Read more.

Nanoscale electrical contacts, especially those between materials of dissimilar electronic properties, often represent one of the main causes of drops in energy transfer efficiency. They are also among the sources of above-threshold noise, and their performance often decreases over the lifetime of the nanodevices. Scale-down limitations from mesoscopic to nanoscale devices, and likewise, of nanoscale to quantum-scale devices are also impeded by contacts’ quality. Making more reliable, energy-efficient electrical contacts is among the goals of the nanoelectronics research within the framework of energy-efficient electronic systems. This report focuses on the design, nanofabrication, and testing of novel shapes of electrical contacts. Lithography and nanofabrication were utilized to mimic the approximate shape of insect setae for mesoscale contacts design. The contacts are tested for elementary charge transport via I–V curves and for the broadband, 1/f noise. Tests show that contacts design leads to a measurable decrease in the energy necessary to operate a contact as a switch by at least 12–20%, depending on temperature, while broadband noise shows measurably lower power spectra, for bio-inspired contacts. The proposed method is open to modifications and improvements as required by various on-chip applications. Full article

(This article belongs to the Section Energy Biomimetics)

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33 pages, 5767 KB

Open AccessArticle

Hyper-Thyro Vision: An Integrated Framework for Hyperthyroidism Diagnostic Facial Image Analysis Based on Deep Learning

by Poonyisa Thepmangkorn and Suchada Sitjongsataporn

Biomimetics 2026, 11(3), 210; https://doi.org/10.3390/biomimetics11030210 - 15 Mar 2026

Viewed by 515

Abstract

This paper presents an integrated multi-modal framework for detecting hyperthyroidism-associated abnormalities, namely exophthalmos and thyroid-related neck swelling, through the joint analysis of frontal facial and neck images using a deep learning-based approach. The objective of this research is to develop an integrated AI [...] Read more.

This paper presents an integrated multi-modal framework for detecting hyperthyroidism-associated abnormalities, namely exophthalmos and thyroid-related neck swelling, through the joint analysis of frontal facial and neck images using a deep learning-based approach. The objective of this research is to develop an integrated AI framework that improves hyperthyroid-related abnormality detection by simultaneously analyzing facial images of both the eye and neck based on pattern clinical knowledge. The multi-modal framework mimics a biological visual mechanism by using a dual-pathway architecture that concurrently processes foveal-like details of the eyes and neck. It integrates these high-resolution visual embeddings with quantitative morphological measurements to simulate a clinician’s ability to fuse observation with physical assessment. The proposed system employs a multi-faceted decision-making process derived from three distinct data components: two from frontal face analysis and one from neck region analysis. Specifically, eye regions extracted from facial images are preprocessed using the YOLOv11s model. The proposed system leverages a dual-pathway processing architecture to extract comprehensive diagnostic features. For the eye dataset, the framework utilizes a face mesh-based eye landmark (FMEL) to extract both eye regions and perform eyes unfold processing. These regions are subsequently analyzed by the proposed sclera map unwrapping engine (SMUE) to derive quantitative sclera metrics from both the left and right eyes. To optimize classification, a dual-branch architecture is employed by integrating CNN visual embeddings with SMUE-derived statistical features through a feature fusion layer. Simultaneously, the neck processing path executes the neck region of interest (ROI) prediction {upper, lower} to segment critical regions for goiter assessment via the proposed neck

μ - σ

ensemble thresholding (NSET) algorithm. The experimental results demonstrate that the proposed algorithm for eye analysis achieved a mean average precision (mAP50) of 96.4%, with a specific mAP50 of 98.6% for the hyperthyroid class. Regarding quantitative scleral measurement, the SMUE process revealed distinct morphological differences, with the experimental data group exhibiting consistently higher pixel distances across the reference points compared with the normal group. Furthermore, the proposed NSET algorithm yielded the highest performance for swollen neck classification with an mAP50 of 92.0%, significantly outperforming the baseline deep learning models while maintaining lower computational complexity. Full article

(This article belongs to the Special Issue Bio-Inspired Intelligence: Bridging Neural Networks, Artificial Intelligence (AI), and Biomimetics for Next-Generation Innovation)

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17 pages, 18685 KB

Open AccessArticle

Fabrication and Drag Reduction Performance of Bionic Surfaces Featuring Staggered Shield Scale Structures

by Xin Gu, Pan Cao, Xiuqin Bai and Yifeng Fu

Biomimetics 2026, 11(3), 209; https://doi.org/10.3390/biomimetics11030209 - 14 Mar 2026

Viewed by 452

Abstract

To investigate the drag reduction mechanism of shark skin placoid scales and develop high-efficiency drag-reducing surfaces, this study designed and fabricated a biomimetic shark skin surface featuring staggered microscale groove structures. The fabrication process involved laser etching on silicon wafers to create a [...] Read more.

To investigate the drag reduction mechanism of shark skin placoid scales and develop high-efficiency drag-reducing surfaces, this study designed and fabricated a biomimetic shark skin surface featuring staggered microscale groove structures. The fabrication process involved laser etching on silicon wafers to create a placoid microstructure template, followed by polydimethylsiloxane (PDMS) replication to obtain biomimetic shark skin samples. Sedimentation experiments demonstrated that the biomimetic surface significantly reduced settling time compared to a smooth surface, achieving a drag reduction rate of 5.65%. Further computational fluid dynamics (CFD) simulations were conducted to analyze the near-wall flow characteristics around the biomimetic surface. The results revealed that the drag reduction mechanism primarily stems from the effective regulation of near-wall laminar flow by the micro-groove structures: a low-velocity fluid layer formed within the grooves reduces the near-wall velocity gradient, thereby decreasing frictional drag, while stable recirculation zones develop within the grooves, contributing to momentum redistribution and reduced energy dissipation. Additionally, the staggered arrangement of the grooves promotes a smoother pressure distribution along the flow direction, mitigating pressure drag by reducing the pressure differential between windward and leeward surfaces. The experimental and simulation results showed excellent agreement (simulated drag reduction rate: 5.08%), collectively verifying the feasibility and effectiveness of the proposed biomimetic placoid structure in achieving fluid drag reduction. Full article

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32 pages, 7928 KB

Open AccessArticle

eXCube2: Explainable Brain-Inspired Spiking Neural Network Framework for Emotion Recognition from Audio, Visual and Multimodal Audio–Visual Data

by N. K. Kasabov, A. Yang, Z. Wang, I. Abouhassan, A. Kassabova and T. Lappas

Biomimetics 2026, 11(3), 208; https://doi.org/10.3390/biomimetics11030208 - 14 Mar 2026

Viewed by 411

Abstract

This paper introduces a biomimetic framework and novel brain-inspired AI (BIAI) models based on spiking neural networks (SNNs) for emotional state recognition from audio (speech), visual (face), and integrated multimodal audio–visual data. The developed framework, named eXCube2, uses a three-dimensional SNN architecture NeuCube [...] Read more.

This paper introduces a biomimetic framework and novel brain-inspired AI (BIAI) models based on spiking neural networks (SNNs) for emotional state recognition from audio (speech), visual (face), and integrated multimodal audio–visual data. The developed framework, named eXCube2, uses a three-dimensional SNN architecture NeuCube that is spatially structured according to a human brain template. The BIAI models developed in eXCube2 are trainable on spatio- and spectro-temporal data using brain-inspired learning rules. Such models are explainable in terms of revealing patterns in data and are adaptable to new data. The eXCube2 models are implemented as software systems and tested on speech and video data of subjects expressing emotional states. The use of a brain template for the SNN structure enables brain-inspired tonotopic and stereo mapping of audio inputs, topographic mapping of visual data, and the combined use of both modalities. This novel approach brings AI-based emotional state recognition closer to human perception, provides a better explainability and adaptability than existing AI systems. It also results in a higher or competitive accuracy, even though this was not the main goal here. This is demonstrated through experiments on benchmark datasets, achieving classification accuracy above 80% on single-modality data and 88.9% when multimodal audio–visual data are used, and a “don’t know” output is introduced. The paper further discusses possible applications of the proposed eXCube2 framework to other audio, visual, and audio–visual data for solving challenging problems, such as recognizing emotional states of people from different origins; brain state diagnosis (e.g., Parkinson’s disease, Alzheimer’s disease, ADHD, dementia); measuring response to treatment over time; evaluating satisfaction responses from online clients; cognitive robotics; human–robot interaction; chatbots; and interactive computer games. The SNN-based implementation of BIAI also enables the use of neuromorphic chips and platforms, leading to reduced power consumption, smaller device size, higher performance accuracy, and improved adaptability and explainability. This research shows a step toward building brain-inspired AI systems. Full article

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16 pages, 8378 KB

Open AccessArticle

Optimization of Ornithopter Energy Efficiency Through Spring-Induced Harmonic Motion

by Jimin Kim and Ji-Chul Ryu

Biomimetics 2026, 11(3), 207; https://doi.org/10.3390/biomimetics11030207 - 13 Mar 2026

Viewed by 439

Abstract

Ornithopters generate lift and thrust through periodic flapping-wing motion. While control-based optimization has been widely studied to improve the flight efficiency of ornithopters, passive mechanical tuning remains underexplored. This study investigates whether integrating a lightweight torsional spring can passively tune a flapping-wing system [...] Read more.

Ornithopters generate lift and thrust through periodic flapping-wing motion. While control-based optimization has been widely studied to improve the flight efficiency of ornithopters, passive mechanical tuning remains underexplored. This study investigates whether integrating a lightweight torsional spring can passively tune a flapping-wing system toward resonance to reduce input power and enhance aerodynamic performance. We evaluated springs of different stiffness on a 3D-printed, motor-driven flapping rig, recording input voltage and current as well as flapping frequency and thrust. Wing kinematics were analyzed using high-speed video, and free-oscillation tests identified a resonant period of ~0.14 s (~7.1 Hz). Experimental results show that an optimally tuned spring-assisted system achieves up to a threefold improvement in thrust efficiency and up to a twofold improvement in kinematic efficiency, compared to the no-spring baseline. Indoor flight tests using a commercial ornithopter (MetaFly) confirmed the improvement, showing a 12.8% increase in average endurance. The spring-assisted configuration also produced smoother stroke reversals, consistent with reduced energy losses. These results demonstrate that a low-complexity, lightweight torsional spring tuned near resonance can provide an effective passive means to enhance both energy efficiency and aerodynamic output in flapping-wing UAVs, serving as a practical, low-cost complement to control-based optimization methods. Full article

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21 pages, 2800 KB

Open AccessArticle

A Trustable Spine Abnormalities Classification System Using ResNet50 and VGG16 Supported by Explainable Artificial Intelligence

by Muhammad Shahrul Zaim Ahmad, Nor Azlina Ab. Aziz, Heng Siong Lim, Anith Khairunnisa Ghazali, Mubashir Ahmad, Farshid Amirabdollahian, Afif Abdul Latiff and Kamarulzaman Ab. Aziz

Biomimetics 2026, 11(3), 206; https://doi.org/10.3390/biomimetics11030206 - 12 Mar 2026

Viewed by 413

Abstract

Deep learning has been applied in various fields and has been proven to provide good results for classification tasks. However, there is limited understanding of a deep learning model’s decisions, so deep learning is commonly described as a black box. Applying deep learning [...] Read more.

Deep learning has been applied in various fields and has been proven to provide good results for classification tasks. However, there is limited understanding of a deep learning model’s decisions, so deep learning is commonly described as a black box. Applying deep learning for critical applications such as medical diagnostic process introduces trust issues. For the deep learning model to be trusted by the medical practitioners, the methods employed by the deep learning model must be seen to be aligned with the diagnostic process employed by the medical practitioners. Explainable methods such as Grad-CAM can be applied to improve the explainability of the deep learning models by providing an visual interpretation of the deep learning classification result decision process. In this study, two deep learning models, VGG16 and ResNet50 are trained using three training methods, one with randomly initialized weights, and two transfer learning methods, which are feature extraction and fine-tuning, to classify the spinal abnormalities based on X-ray images. The classification metrics results are compared and further analyses using Grad-CAM heatmaps are included. The models also evaluated using a stratified five-fold cross-validation, results revealed some disparity between the model’s accuracy and clinical relevance. The randomly initialized VGG16 obtained a classification accuracy of 93.79% but does not focus on clinically relevant regions. On the other hand, not only do the fine-tuned ResNet50 and VGG16 obtain high accuracies of 98.22% and 99.12%, but the heatmaps show that the models focus on more relevant regions. A comparison of the two models shows that the heatmaps produced by the fine-tuned ResNet50 are in more agreement with the clinical view than the fine-tuned VGG16. This study provides a useful reference for interpreting a deep learning-based classification result using explainable method particularly in spine abnormalities analysis with Grad-CAM. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Biomedical Engineering: 2nd Edition)

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28 pages, 2065 KB

Open AccessArticle

Intelligent Control of Magnetic Ball Suspension Systems via a Novel Hyperbolic Tangent PID Controller Tuned by the Artificial Lemming Algorithm

by Serdar Ekinci, Davut Izci, Vedat Tümen, Mostafa Jabari, Emre Çelik and Ali Elrashidi

Biomimetics 2026, 11(3), 205; https://doi.org/10.3390/biomimetics11030205 - 11 Mar 2026

Viewed by 471

Abstract

Magnetic ball suspension (MBS) systems are widely used as benchmark platforms in control engineering due to their nonlinear dynamics and inherent open-loop instability, which pose substantial challenges for conventional linear control strategies. The objective of this study is to investigate a hyperbolic tangent–based [...] Read more.

Magnetic ball suspension (MBS) systems are widely used as benchmark platforms in control engineering due to their nonlinear dynamics and inherent open-loop instability, which pose substantial challenges for conventional linear control strategies. The objective of this study is to investigate a hyperbolic tangent–based proportional–integral–derivative (tanh-PID) control structure for MBS systems and to assess the suitability of the artificial lemming algorithm (ALA) for tuning its parameters within a simulation-based benchmark framework. The proposed approach embeds smooth nonlinear signal shaping through the hyperbolic tangent function directly into the classical PID structure, while controller parameters are obtained via metaheuristic optimization using ALA. A performance index balancing overshoot suppression and tracking error minimization is adopted, and the controller is evaluated on a linearized MBS model to ensure comparability with existing studies. Simulation results demonstrate that the optimized tanh-PID controller achieves improved transient and steady-state performance, including a rise time of 0.0144 s, settling time of 0.0275 s, overshoot of 2.98%, and a steady-state error of 2.69 × 10⁻⁵, when compared with classical PID, fractional-order PID (FOPID), and real PID with second-order derivative (RPIDD²) controllers under identical conditions. The results indicate that bounded nonlinear preprocessing combined with metaheuristic-based parameter tuning can provide an effective and practical control alternative for unstable nonlinear systems such as magnetic ball suspension systems. Full article

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

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26 pages, 3911 KB

Open AccessArticle

Parametric Optimization of VLM Panel Discretization Using Bio-Inspired Crayfish and Aquila Algorithms Coupled with Hybrid RSM-Based Ensemble Machine Learning Surrogate Models: A Case Study

by Yüksel Eraslan and Esmanur Şengün

Biomimetics 2026, 11(3), 204; https://doi.org/10.3390/biomimetics11030204 - 11 Mar 2026

Viewed by 439

Abstract

Fast and reliable aerodynamic predictions are crucial in the early phases of aircraft design, where a quick assessment of various configurations is required. In this context, the Vortex Lattice Method (VLM) is widely adopted due to its computational efficiency; however, its predictive accuracy [...] Read more.

Fast and reliable aerodynamic predictions are crucial in the early phases of aircraft design, where a quick assessment of various configurations is required. In this context, the Vortex Lattice Method (VLM) is widely adopted due to its computational efficiency; however, its predictive accuracy is highly sensitive to panel discretization strategies, which are often determined heuristically. This study proposes a bio-inspired optimization framework for VLM panel discretization and evaluates it through a systematic case study on a representative wing geometry. A grid-convergence analysis was initially carried out to ensure solution independence across various spanwise-to-chordwise panel ratios. Subsequently, a novel Hybrid Response Surface Methodology (HRSM), integrating Box–Behnken and Central Composite experimental designs, was employed to enable a more comprehensive exploration of the factor space while quantifying the effects of clustering parameters at the leading-edge, trailing-edge, root, and tip regions of the wing. The HRSM dataset was further utilized to train Ensemble Machine-Learning surrogate models, which were coupled with bio-inspired Crayfish and Aquila optimization algorithms, alongside a classical Genetic Algorithm (GA) as a performance benchmark, to identify the optimal discretization strategy and to enable a comparative assessment of their convergence behavior and robustness against the numerical noise of the ensemble-based landscape. Compared to base (i.e., uniform) panel distribution, the optimally clustered discretization enhanced overall aerodynamic prediction accuracy by approximately 33%, particularly at low angles of attack, while maintaining robust performance at higher angles. Both algorithms converged to similar minima; however, the Aquila algorithm achieved higher solution consistency, whereas the Crayfish algorithm exhibited greater dispersion despite faster convergence, revealing a multimodal optimization landscape. The variance decomposition revealed that trailing-edge clustering dominated aerodynamic accuracy at low angles of attack, contributing up to 90% of the total variance, whereas tip clustering became increasingly influential at higher angles, exceeding 30%, highlighting the need for adaptive discretization strategies to ensure reliable VLM-based aerodynamic analyses. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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44 pages, 45025 KB

Open AccessArticle

Influence of Graphite, Boron, Zirconium, and Hydroxyapatite Reinforcements on the Mechanostructure of Polyaryletheretherketone–Matrix Hybrid Composites

by Bunyamin Aksakal, Cevher Kursat Macit, Yusuf Er and Merve Ayik

Biomimetics 2026, 11(3), 203; https://doi.org/10.3390/biomimetics11030203 - 10 Mar 2026

Viewed by 395

Abstract

Polyether ether ketone (PEEK) is a high-performance thermoplastic with potential applications in aerospace, automotive, and biomedical components, owing to its exceptional specific strength, thermal stability, and biocompatibility. However, its moderate hardness and limited wear resistance in dry sliding severely constrain its use in [...] Read more.

Polyether ether ketone (PEEK) is a high-performance thermoplastic with potential applications in aerospace, automotive, and biomedical components, owing to its exceptional specific strength, thermal stability, and biocompatibility. However, its moderate hardness and limited wear resistance in dry sliding severely constrain its use in highly loaded tribological contacts. In this study, PEEK-based reinforced hybrid composites were produced utilizing a powder metallurgy technique, with reinforcement fractions of 10 wt.% graphite (Gr), boron (B), hydroxyapatite (HAp), and zirconium (Zr). The processing sequence included homogeneous wet-mixing, uniaxial cold compaction at pressures of 10–30 MPa, and sintering at 250–300 °C. The composition and microstructures were characterized by X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX). Mechanical and tribological performances were assessed by Vickers microhardness, uniaxial compression and dry sliding wear tests. The best-performing Gr-B hybrid composite increased hardness by 240% and compressive strength by 175% compared with unreinforced PEEK. Tribologically, boron-containing PEEK demonstrated up to a 34.7% reduction in the coefficient of friction and approximately a 90% drop in wear-induced mass loss compared with unreinforced PEEK. The resulting Gr-B-reinforced PEEK hybrids are excellent choices for demanding load-bearing and tribological components like aerospace bushings, automotive sliding elements, spinal cages, and orthopedic fixation devices in biomedical applications because of their balanced combination of high hardness, superior wear resistance, and high compressive strength. Full article

(This article belongs to the Special Issue Advances in Biomaterials, Biocomposites and Biopolymers 2026)

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15 pages, 2753 KB

Open AccessArticle

X-Ray Attenuation Properties of Additive Manufacturing and 3D Printing Materials for Mimicking Tissues in Radiographic Phantoms Measured by CT from 70 to 140 kV: 2025 Update

by Thomas Hofmann, Martin Buschmann and Peter Homolka

Biomimetics 2026, 11(3), 202; https://doi.org/10.3390/biomimetics11030202 - 10 Mar 2026

Cited by 1 | Viewed by 637

Abstract

Background: Phantoms are essential in medical imaging, providing reproducible and quantitative means for system and protocol evaluation, image quality assessment, and dosimetry without patient exposure. Additive manufacturing enables rapid, accurate fabrication of phantoms ranging from simple geometries to complex anthropomorphic models. Ongoing developments [...] Read more.

Background: Phantoms are essential in medical imaging, providing reproducible and quantitative means for system and protocol evaluation, image quality assessment, and dosimetry without patient exposure. Additive manufacturing enables rapid, accurate fabrication of phantoms ranging from simple geometries to complex anthropomorphic models. Ongoing developments in 3D printing technologies and polymer formulations have enhanced mechanical properties and printability, but also affect X-ray attenuation behaviour, necessitating an update with current materials to facilitate the choice of appropriate materials mimicking body tissues in radiographic phantoms. Methods: Attenuation properties of 27 photopolymer resins and 22 thermoplastic filaments (based on PLA, ABS, HIPS, PETG/PCTG, and PVB) were quantified using a clinical CT scanner at 70–140 kV to establish reference data for material selection. Results: At 120 kV, resins exhibited attenuation values between 124 and 384 Hounsfield Units (HU), and filaments ranged from −69 to 308 HU (PLA-based filaments: 160 to 241 HU, ABS: −32 to 43 HU, PETG/PCTG: 151 to 308 HU, and HIPS: −69 to −22 HU). Energy dependence of HU values is presented from 70 to 140 kV tube potential. Compared to the 2021 study, a wider selection of X-ray opacities is available. Regarding SLA/DLP printing, resins with higher attenuation were identified, and flexible resins that had provided a choice of low attenuation printing materials in the range of 60 to 90 HU at 120 kV tended to replicate attenuation properties closer to rigid photopolymers; i.e., HU values were slightly higher. In FDM filaments, a wide variation in different PLA-, ABS-, and HIPS-based filaments is found. In copolymers from the PET/PCTG/PETG family, very inhomogeneous X-ray attenuations are still found, as anticipated. Conclusions: The range of X-ray attenuation observed demonstrates that commercially available 3D printing materials can replicate clinically relevant tissues and tissue-equivalent contrasts. Furthermore, the available range of attenuations has increased, as has the finer gradation of these materials. These findings support the design of patient- and task-specific imaging phantoms for optimization of acquisition protocols, image quality evaluation, and radiation dose studies, as well as facilitate the selection of appropriate phantom materials. Full article

(This article belongs to the Special Issue Biomimetic 3D Printing Materials)

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27 pages, 6415 KB

Open AccessArticle

Emergence of Longitudinal Queues in Group Navigation: An Interpretable Approach via Projective Simulation

by Decheng Kong, Kai Xue, Ping Wang and Zeyu Xu

Biomimetics 2026, 11(3), 201; https://doi.org/10.3390/biomimetics11030201 - 10 Mar 2026

Viewed by 416

Abstract

The formation of longitudinal queues is critical for biological and artificial swarm systems to achieve efficient long-distance navigation. However, the “black-box” nature of conventional deep reinforcement learning models often obscures the microscopic rules driving the emergence of such ordered behaviors. To address this [...] Read more.

The formation of longitudinal queues is critical for biological and artificial swarm systems to achieve efficient long-distance navigation. However, the “black-box” nature of conventional deep reinforcement learning models often obscures the microscopic rules driving the emergence of such ordered behaviors. To address this challenge, this paper proposes an interpretable computational model of collective behavior based on Projective Simulation and Episodic Compositional Memory, which enables individuals to learn decision-making strategies within a transparent state–action network. Simulation results demonstrate that the swarm can self-organize into stable and highly elongated longitudinal queues. Crucially, through visualization of microscopic strategies, we reveal a deterministic target-priority mechanism: when local neighbor alignment conflicts with global target orientation, individuals learn to strictly prioritize the target direction, serving as the key driving force for queue formation. Further parametric analysis indicates that the action space granularity exerts a nonlinear impact on stability, identifying moderate control precision as the optimal choice. This study not only provides a transparent computational explanation for the self-organization mechanism of queues in collective motion but also offers a theoretical foundation for designing interpretable swarm navigation systems. Full article

(This article belongs to the Section Bioinspired Sensorics, Information Processing and Control)

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41 pages, 8475 KB

Open AccessArticle

Clustering Performance Analysis Using Chaotic and Lévy Flight-Enhanced Black-Winged Kite Algorithms

by Taybe Alabed and Sema Servi

Biomimetics 2026, 11(3), 200; https://doi.org/10.3390/biomimetics11030200 - 9 Mar 2026

Viewed by 530

Abstract

Clustering is a fundamental unsupervised learning technique used to uncover hidden patterns in unlabeled data. Although metaheuristic algorithms have demonstrated effectiveness in clustering, many suffer from premature convergence and limited population diversity. This study employs the Black-Winged Kite Algorithm (BKA) and its enhanced [...] Read more.

Clustering is a fundamental unsupervised learning technique used to uncover hidden patterns in unlabeled data. Although metaheuristic algorithms have demonstrated effectiveness in clustering, many suffer from premature convergence and limited population diversity. This study employs the Black-Winged Kite Algorithm (BKA) and its enhanced variants, Chaotic BKA (CBKA), Lévy Flight-based BKA (LBKA), and Chaotic Levy BKA (CLBKA), to address these limitations in centroid-based clustering formulated as a Sum of Squared Errors (SSE) minimization problem. Chaotic logistic mapping improves search diversity and adaptability, while Levy flight introduces long-range exploration. In addition, Cauchy based perturbations are incorporated to enhance convergence stability. The algorithms are evaluated on sixteen UCI benchmark datasets, with 30 independent runs conducted under different population and iteration settings. Experimental results show that CLBKA consistently achieves superior clustering performance in terms of accuracy and stability. Statistical validation using the Friedman and Wilcoxon tests confirms significant performance differences, with CLBKA obtaining the lowest mean rank across configurations. The findings indicate that integrating chaotic dynamics and Levy flight mechanisms enhances clustering robustness and optimization efficiency. Full article

(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)

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19 pages, 10107 KB

Open AccessArticle

Bio-Inspired Blade Cascades: Numerical Predictions Versus Experimental Measurements

by Andrei-George Totu, Daniel-Eugeniu Crunțeanu and Dragoș Isvoranu

Biomimetics 2026, 11(3), 199; https://doi.org/10.3390/biomimetics11030199 - 9 Mar 2026

Viewed by 306

Abstract

This work presents a numerical–experimental validation of aeroacoustic predictions for bio-inspired leading edge serrated blade cascades. Transient simulations were carried out on a four-blade cascade using several turbulence modeling strategies commonly applied in broadband noise analysis—Spalart–Allmaras (SA), k−ω SST, k−ε, Scale-Adaptive Simulation (SAS), [...] Read more.

This work presents a numerical–experimental validation of aeroacoustic predictions for bio-inspired leading edge serrated blade cascades. Transient simulations were carried out on a four-blade cascade using several turbulence modeling strategies commonly applied in broadband noise analysis—Spalart–Allmaras (SA), k−ω SST, k−ε, Scale-Adaptive Simulation (SAS), and Large Eddy Simulation (LES)—for assessing their capability to reproduce measured spectra. Multiple timestep resolutions were tested to ensure temporal accuracy. The comparison indicates that below 900 Hz, interaction noise is difficult to evaluate for such applications, whereas in the range from 0.9 to 5 kHz the turbulent jet–blade interaction is clearly captured. In the low-frequency regime (<1 kHz), the SA, SAS, and k−ω SST models exhibit similar behavior, while at higher frequencies SAS provides the closest agreement with experimental results, albeit with a slight tendency to overestimate at the upper end of the spectrum. LES demonstrates a satisfactory performance in reproducing the baseline response. The validation of numerical simulations with experimental results has been achieved, and a complex analysis using pressure measurements on the blade surface for a four-blade cascade configuration shows that turbulent formations lose their coherence quite significantly across several frequency bands. Overall, the results confirm that numerical simulations can reproduce the dominant experimental trends, while emphasizing the model-dependent trade-offs in predicting the acoustic benefits of bio-inspired leading edge serrations. Full article

(This article belongs to the Special Issue Bio-Inspired Propulsion and Fluid Mechanics)

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21 pages, 3553 KB

Open AccessArticle

Synergistic Effects of Biomimetic Structures on Heat Transfer Enhancement and Flow Resistance Reduction

by Kaichen Wang, Yan Shi, Junjie Chen and Yuchi Dai

Biomimetics 2026, 11(3), 198; https://doi.org/10.3390/biomimetics11030198 - 9 Mar 2026

Viewed by 444

Abstract

This study numerically investigated the thermal performance of a rectangular channel incorporating scale-inspired biomimetic protrusion structures with micro-grooves on their surfaces. A three-dimensional numerical model was established and validated against experimental data under identical geometric parameters and boundary conditions, demonstrating good agreement in [...] Read more.

This study numerically investigated the thermal performance of a rectangular channel incorporating scale-inspired biomimetic protrusion structures with micro-grooves on their surfaces. A three-dimensional numerical model was established and validated against experimental data under identical geometric parameters and boundary conditions, demonstrating good agreement in terms of outlet temperature and pressure drop over a wide range of Reynolds numbers. The effects of groove depth on friction factor, Colburn factor, and overall performance evaluation criterion (PEC) were systematically analyzed to elucidate the underlying flow and heat transfer mechanisms. The results indicated that the introduction of biomimetic grooves significantly modified the flow structure and thermal boundary layer development, thereby enhancing fluid mixing and heat transfer. However, excessive groove depth intensified flow separation and pressure loss, leading to performance deterioration. An optimal groove depth of 0.6 mm (approximately 40% of the fin height) was identified, which achieved the best balance between heat transfer enhancement and flow resistance. The findings provide theoretical guidance for the biomimetic surface design of high-efficiency heat exchangers. Full article

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

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36 pages, 1683 KB

Open AccessArticle

A Novel Binary Dream Optimization Algorithm with Data-Driven Repair for the Set Covering Problem

by Broderick Crawford, Hugo Caballero, Gino Astorga, Felipe Cisternas-Caneo, Marcelo Becerra-Rozas, Alan Baeza, Gabriel Bernales, Pablo Puga, Giovanni Giachetti and Ricardo Soto

Biomimetics 2026, 11(3), 197; https://doi.org/10.3390/biomimetics11030197 - 9 Mar 2026

Viewed by 434

Abstract

The Set Covering Problem is a fundamental NP-hard problem in combinatorial optimization and plays a central role in a wide range of industrial decision-making processes, including logistics planning, scheduling, facility location, network design, and resource allocation. In many real-world contexts, problems of this [...] Read more.

The Set Covering Problem is a fundamental NP-hard problem in combinatorial optimization and plays a central role in a wide range of industrial decision-making processes, including logistics planning, scheduling, facility location, network design, and resource allocation. In many real-world contexts, problems of this type are large in scale and highly constrained, which makes exact solution methods computationally impractical and encourages the use of metaheuristic approaches capable of producing high-quality solutions within limited time budgets. In this work, we propose a discrete adaptation of the Dream Optimization Algorithm, focusing on the challenges that emerge when algorithms originally designed for continuous search spaces are applied to binary and strongly constrained models. The continuous search process is mapped onto the binary decision space through a fixed discretization scheme. As a consequence of this transformation, some constraints may not be met, underscoring the importance of effective feasibility restoration mechanisms. Because the discretization stage may produce infeasible solutions and frequently induces plateaus that hinder further improvement, an explicit repair phase becomes necessary to restore feasibility and promote effective search progression. To strengthen this process, the study introduces an adaptive control mechanism based on bandit driven operator selection, which dynamically chooses among different repair procedures during the search. Experimental results on benchmark instances show that the proposed approach consistently achieves high quality solutions with low relative deviation from known optima and stable behavior across independent runs. Full article

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

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23 pages, 972 KB

Open AccessReview

Three-Dimensional Printing of the Epineurium for Peripheral Nerve Repair: A Comprehensive Review of Novel Scaffolds for Nerve Conduits

by Alynah J. Adams, Iulianna C. Taritsa, Kaavian Shariati, Aaron I. Dadzie, Jose A. Foppiani, Maria Jose Escobar-Domingo, Daniela Lee, Angelica Hernandez-Alvarez, Kirsten Schuster, Helen Xun and Samuel J. Lin

Biomimetics 2026, 11(3), 196; https://doi.org/10.3390/biomimetics11030196 - 8 Mar 2026

Viewed by 568

Abstract

Background: Nerve conduits are used to bridge peripheral nerve defects caused by trauma, iatrogenic injury, or oncologic disruption. Three-dimensional (3D) biomimetic scaffolds for peripheral nerve regeneration have advanced significantly in recent years, driven by improvements in printing technology and neuronal seeding techniques. We [...] Read more.

Background: Nerve conduits are used to bridge peripheral nerve defects caused by trauma, iatrogenic injury, or oncologic disruption. Three-dimensional (3D) biomimetic scaffolds for peripheral nerve regeneration have advanced significantly in recent years, driven by improvements in printing technology and neuronal seeding techniques. We report on published designer conduits that can recreate the epineurium, a critical yet challenging-to-manufacture feature of nerve tissue. Methods: A medical librarian conducted a literature search for our systematic review on EMBASE, Web of Science, and PUBMED, following PRISMA guidelines, for articles from January 2010 to January 2026 for the systematic review. Descriptive statistical analysis was performed using Microsoft 365 Suite software. The literature review was conducted using keywords and search terms describing the history and development of 3DP nerve guidance conduits published prior to January 2026. Results: Our search yielded 273 titles, of which 8 were included after full-text review; these studies used 3D printing to generate nerve conduits for preclinical models. Manual data extraction identified studies reporting successful epineurial recreation. The included scaffold materials were polycaprolactone, poly(l-lactide-co-ε-caprolactone), poly(lactic-co-glycolic acid), acrylate resin, and gelatin methacryloyl. In animal model studies, various terms were used to describe the epineurium outer sheath. Despite this variability in nomenclature, many of these reports indicated successful sciatic functional index (SFI) recovery, favorable g-ratios, good durability, high cell viability, and significant neurite elongation at the time of sacrifice. Conclusions: 3DP nerve conduits targeting the epineurium are promising approaches for treating peripheral nerve defects. The constructs promote oriented growth and myelination. Future research on incorporating the epineurium into nerve scaffolds may consider encapsulating NGF to promote more efficient nerve regeneration, standardizing the definition of epineurial recreation, designing mechanical and permeability reporting benchmarks, and evaluating cell strategies using comparable functional and histologic endpoints. Full article

(This article belongs to the Special Issue Advancing Tissue Engineering and Regenerative Medicine Using Next-Gen Biomaterials)

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25 pages, 8082 KB

Open AccessArticle

A Novel Improved Whale Optimization Algorithm-Based Multi-Scale Fusion Attention Enhanced SwinIR Model for Super-Resolution and Recognition of Text Images on Electrophoretic Displays

by Xin Xiong, Zikang Feng, Peng Li, Xi Hu, Jiyan Liu and Xueqing Liu

Biomimetics 2026, 11(3), 195; https://doi.org/10.3390/biomimetics11030195 - 6 Mar 2026

Viewed by 429

Abstract

Electrophoretic Displays (EPDs) are widely adopted in e-readers and portable devices due to their ultra-low power consumption and eye-friendly reflective characteristics. However, inherent hardware limitations, such as low resolution, slow response speed, and display degradation, frequently result in blurred strokes and degraded text [...] Read more.

Electrophoretic Displays (EPDs) are widely adopted in e-readers and portable devices due to their ultra-low power consumption and eye-friendly reflective characteristics. However, inherent hardware limitations, such as low resolution, slow response speed, and display degradation, frequently result in blurred strokes and degraded text readability. While traditional driving waveform optimizations can mitigate these issues, they are device-dependent and require extensive manual calibration. To address these challenges, this paper proposes an Improved Whale Optimization Algorithm-based Multi-scale Fusion Attention-enhanced SwinIR (IWOA-MFA-SwinIR) model for super-resolution and recognition of text images on EPDs. Structurally, the model incorporates a multi-scale fused attention (MFA) module that synergistically integrates channel, spatial, and gated attention mechanisms to precisely capture high-frequency text details while suppressing background noise within the SwinIR architecture. Furthermore, to enhance model robustness and eliminate manual tuning, an Improved Whale Optimization Algorithm (IWOA) is employed to adaptively optimize critical hyperparameters, including embedding dimension (d), attention head count (h), learning rate (

l r

), and dimensionality reduction coefficient (r). Experiments conducted on the TextZoom and EPD datasets demonstrate that the proposed model achieves state-of-the-art performance. In the ablation study, it attains a Peak Signal-to-Noise Ratio (PSNR) of 24.406, a Structural Similarity Index (SSIM) of 0.8837, and a Character Recognition Accuracy (CRA) of 89.81%. In the comparative evaluation, the proposed model consistently outperforms the second-best comparison model across three difficulty levels, yielding approximately a 1% improvement in PSNR, a 0.8% improvement in SSIM, and an 8% improvement in CRA. This confirms the proposed model’s superiority over mainstream comparative models in restoring text fidelity and improving recognition rates. Full article

(This article belongs to the Special Issue Bionics in Engineering Practice: Innovations and Applications)

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18 pages, 2646 KB

Open AccessArticle

Leveraging TIS-Enhanced Crayfish Optimization Algorithm for High-Precision Prediction of Long-Term Achievement in Mathematical Elite Talents

by Shenrun Pan and Qinghua Chen

Biomimetics 2026, 11(3), 194; https://doi.org/10.3390/biomimetics11030194 - 6 Mar 2026

Viewed by 374

Abstract

Traditional talent identification systems often rely on static assessments and overlook the dynamic nature of long-term development. To address this limitation, this study proposes a biomimetic predictive framework inspired by crayfish behavioral ecology. The Crayfish Optimization Algorithm (COA), derived from adaptive foraging and [...] Read more.

Traditional talent identification systems often rely on static assessments and overlook the dynamic nature of long-term development. To address this limitation, this study proposes a biomimetic predictive framework inspired by crayfish behavioral ecology. The Crayfish Optimization Algorithm (COA), derived from adaptive foraging and competition mechanisms observed in crayfish, is enhanced through a Thinking Innovation Strategy (TIS) to form TISCOA for hyperparameter optimization of a Gradient Boosting Decision Tree model. Using a five-year longitudinal dataset of 160 elite mathematical students, the framework models Professional Achievement in Mathematics (PAM) from multidimensional baseline indicators. Comparative experiments with multiple metaheuristic optimizers show that the proposed approach achieves stable generalization performance within the examined cohort. Feature attribution analysis indicates that non-cognitive factors, particularly Emotion Regulation, contribute substantially to long-term outcomes, while temporal variables such as the Latency Period further shape developmental trajectories. Residual analysis highlights heterogeneous patterns that may reflect unobserved contextual influences. Overall, the study demonstrates how a biologically inspired optimization mechanism can support interpretable and stability-oriented longitudinal prediction in small-sample educational settings. Full article

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

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Biomimetics, Volume 11, Issue 3 (March 2026) – 66 articles

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