Biomimetics

31 pages, 44903 KB

Open AccessArticle

Transferring Structural Design Principles from Bamboo to Coreless Filament-Wound Lightweight Composite Trusses

by Pascal Mindermann and Martha Elisabeth Grupp

Biomimetics 2025, 10(12), 840; https://doi.org/10.3390/biomimetics10120840 (registering DOI) - 15 Dec 2025

Bamboo has evolved a highly optimized structural system in its culms, which this study transfers into lightweight fiber composite trusses fabricated by coreless filament winding. Focusing on the structural segmentation involving diaphragms of the biological role model, this design principle was integrated into [...] Read more.

Bamboo has evolved a highly optimized structural system in its culms, which this study transfers into lightweight fiber composite trusses fabricated by coreless filament winding. Focusing on the structural segmentation involving diaphragms of the biological role model, this design principle was integrated into the additive manufacturing process using a multi-stage winding, a tiling approach, and a water-soluble winding fixture. Through a FE-assisted analytical abstraction procedure, the transition to a carbon fiber material system was considered by determining a geometrical configuration optimized for structural mass, bending deflection, and radial buckling. Samples were fabricated from CFRP and experimentally tested in four-point bending. In mass-specific terms, integrating diaphragms into wound fiber composite samples improved failure load by 36%, ultimate load by 62%, and energy absorption by a factor of 7, at a reduction of only 14% in stiffness. Benchmarking against steel and PVC demonstrated superior mass-specific performance, although mōsō bamboo still outperformed all technical solutions, except in energy absorption. Full article

(This article belongs to the Special Issue Advances in Bio-Inspired Design and Characterization of 3D-Printed Multimaterial Composites and Heterogeneous Structures)

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55 pages, 28888 KB

Open AccessArticle

MECOA: A Multi-Strategy Enhanced Coati Optimization Algorithm for Global Optimization and Photovoltaic Models Parameter Estimation

by Hang Chen and Maomao Luo

Biomimetics 2025, 10(12), 839; https://doi.org/10.3390/biomimetics10120839 (registering DOI) - 15 Dec 2025

Abstract

To address the limitations of the traditional Coati Optimization Algorithm (COA), such as insufficient global exploration, poor population cooperation, and low convergence efficiency in global optimization and photovoltaic (PV) model parameter identification, this paper proposes a Multi-strategy Enhanced Coati Optimization Algorithm (MECOA). MECOA [...] Read more.

To address the limitations of the traditional Coati Optimization Algorithm (COA), such as insufficient global exploration, poor population cooperation, and low convergence efficiency in global optimization and photovoltaic (PV) model parameter identification, this paper proposes a Multi-strategy Enhanced Coati Optimization Algorithm (MECOA). MECOA improves performance through three core strategies: (1) Elite-guided search, which replaces the single global best solution with an elite pool of three top individuals and incorporates the heavy-tailed property of Lévy flights to balance large-step exploration and small-step exploitation; (2) Horizontal crossover, which simulates biological gene recombination to promote information sharing among individuals and enhance cooperative search efficiency; and (3) Precise elimination, which discards 20% of low-fitness individuals in each generation and generates new individuals around the best solution to improve population quality. Experiments on the CEC2017 (30/50/100-dimensional) and CEC2022 (20-dimensional) benchmark suites demonstrate that MECOA achieves superior performance. On CEC2017, MECOA ranks first with an average rank of 1.87, 2.07, 1.83, outperforming the second-best LSHADE (2.03, 2.43 and 2.63) and the original COA (9.93, 9.93 and 9.96). On CEC2022, MECOA also maintains the leading position with an average rank of 1.58, far surpassing COA (8.92). Statistical analysis using the Wilcoxon rank-sum test (significance level 0.05) confirms the superiority of MECOA. Furthermore, MECOA is applied to parameter identification of single-diode (SDM) and double-diode (DDM) PV models. Experiments based on real measurement data show that the SDM model achieves an RMSE of 9.8610 × 10⁻⁴, which is only 1/20 of that of COA. For the DDM model, the fitted curves almost perfectly overlap with the experimental data, with a total integrated absolute error (IAE) of only 0.021555 A. These results fully validate the effectiveness and reliability of MECOA in solving complex engineering optimization problems, providing a robust and efficient solution for accurate modeling and optimization of PV systems. Full article

(This article belongs to the Special Issue Bioinspired Computational Intelligence and Optimization in Engineering Systems)

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20 pages, 4922 KB

Open AccessArticle

DNS and Experimental Assessment of Shark-Denticle-Inspired Anisotropic Porous Substrates for Drag Reduction

by Benjamin Kellum Cooper, Sasindu Pinto, Henry Hong, Yang Zhang, Louis Cattafesta and Wen Wu

Biomimetics 2025, 10(12), 838; https://doi.org/10.3390/biomimetics10120838 (registering DOI) - 15 Dec 2025

Abstract

Passive flow control methods are widely used to reduce drag in wall-bounded flows. A recent numerical study on separating turbulent flows over a bump covered with shark denticles revealed the formation of a reverse pore flow (RPF) beneath the denticle crowns under an [...] Read more.

Passive flow control methods are widely used to reduce drag in wall-bounded flows. A recent numerical study on separating turbulent flows over a bump covered with shark denticles revealed the formation of a reverse pore flow (RPF) beneath the denticle crowns under an adverse pressure gradient (APG). This RPF generates an upstream thrust, leading to drag reduction. Motivated by these findings, the present study investigates a bio-inspired Anisotropic Permeable Propulsive Substrate (APPS) that incorporates key geometric features of the shark denticles, enabling thrust generation by the RPF. The designed APPS is evaluated through both direct numerical simulations of turbulent channel flows at

R e_{τ}

= 1500 and experiments using 3D-printed structures in a turbulent boundary layer over a flat-plate model subjected to APG and flow separation (at

R e_{θ}

= 800). Both approaches demonstrate that the APPS successfully reproduces the RPF-induced thrust mechanism of shark denticles. The results further reveal the dependence of the pore flow on pressure gradient and substrate geometry. This work highlights two features of a thrust-generating APPS: a top surface that shields the porous media from the overlying flow while enabling vertical mass exchange, and a bottom region with dominant wall-parallel permeability, which guides the pore flow in the streamwise direction to generate the thrust. Full article

(This article belongs to the Special Issue Bioinspired Aerodynamic-Fluidic Design)

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46 pages, 10909 KB

Open AccessArticle

NDFNGO: Enhanced Northern Goshawk Optimization Algorithm for Image Segmentation

by Xiajie Zhao, Zuowen Bao, Yu Shao and Na Liang

Biomimetics 2025, 10(12), 837; https://doi.org/10.3390/biomimetics10120837 (registering DOI) - 15 Dec 2025

Abstract

The gradual deterioration of fresco pictorial information presents a formidable obstacle for conservators dedicated to protecting humanity’s shared cultural legacy. Currently, scholars in the field of mural conservation predominantly focus on image segmentation techniques as a vital tool for facilitating mural restoration and [...] Read more.

The gradual deterioration of fresco pictorial information presents a formidable obstacle for conservators dedicated to protecting humanity’s shared cultural legacy. Currently, scholars in the field of mural conservation predominantly focus on image segmentation techniques as a vital tool for facilitating mural restoration and protection. However, the existing image segmentation methods frequently fall short of delivering optimal segmentation results. To address this issue, this study introduces a novel mural image segmentation approach termed NDFNGO, which integrates a nonlinear differential learning strategy, a decay factor, and a Fractional-order adaptive learning strategy into the Northern Goshawk Optimization (NGO) algorithm to enhance segmentation performance. Firstly, the nonlinear differential learning strategy is incorporated to harness the diversity and adaptability of differential tactics, thereby augmenting the algorithm’s global exploration capabilities and effectively improving its ability to pinpoint optimal segmentation threshold regions. Secondly, drawing on the properties of nonlinear functions, a decay factor is proposed to achieve a more harmonious balance between the exploration and exploitation phases. Finally, by integrating historical individual data, the Fractional-order adaptive learning strategy is employed to reinforce the algorithm’s exploitation capabilities, thereby further refining the quality of image segmentation. Subsequently, the proposed method was evaluated through tests on twelve mural image segmentation tasks. The results indicate that the NDFNGO algorithm achieves victory rates of 95.85%, 97.9%, 97.9%, and 95.8% in terms of the fitness function metric, PSNR metric, SSIM metric, and FSIM metric, respectively. These findings demonstrate the algorithm’s high performance in mural image segmentation, as it retains a significant amount of original image information, thereby underscoring the superiority of the technology proposed in this study for addressing this challenge. Full article

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

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18 pages, 1395 KB

Open AccessArticle

Multidimensional Optimal Power Flow with Voltage Profile Enhancement in Electrical Systems via Honey Badger Algorithm

by Sultan Hassan Hakmi, Hashim Alnami, Badr M. Al Faiya and Ghareeb Moustafa

Biomimetics 2025, 10(12), 836; https://doi.org/10.3390/biomimetics10120836 (registering DOI) - 14 Dec 2025

Abstract

This study introduces an innovative Honey Badger Optimization (HBO) designed to address the Optimal Power Flow (OPF) challenge in electrical power systems. HBO is a unique population-based searching method inspired by the resourceful foraging behavior of honey badgers when hunting for food. In [...] Read more.

This study introduces an innovative Honey Badger Optimization (HBO) designed to address the Optimal Power Flow (OPF) challenge in electrical power systems. HBO is a unique population-based searching method inspired by the resourceful foraging behavior of honey badgers when hunting for food. In this algorithm, the dynamic search process of honey badgers, characterized by digging and honey-seeking tactics, is divided into two distinct stages, exploration and exploitation. The OPF problem is formulated with objectives including fuel cost minimization and voltage deviation reduction, alongside operational constraints such as generator limits, transformer settings, and line power flows. HBO is applied to the IEEE 30-bus test system, outperforming existing methods such as Particle Swarm Optimization (PSO) and Gray Wolf Optimization (GWO) in both fuel cost reduction and voltage profile enhancement. Results indicate significant improvements in system performance, achieving 38.5% and 22.78% better voltage deviations compared to GWO and PSO, respectively. This demonstrates HBO’s efficacy as a robust optimization tool for modern power systems. In addition to the single-objective studies, a multi-objective OPF formulation was investigated to produce the complete Pareto front between fuel cost and voltage deviation objectives. The proposed HBO successfully generated a well-distributed set of trade-off solutions, revealing a clear conflict between economic efficiency and voltage quality. The Pareto analysis demonstrated HBO’s strong capability to balance these competing objectives, identify knee-point operating conditions, and provide flexible decision-making options for system operators. Full article

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

37 pages, 7634 KB

Open AccessArticle

Predicting and Synchronising Co-Speech Gestures for Enhancing Human–Robot Interactions Using Deep Learning Models

by Enrique Fernández-Rodicio, Christian Dondrup, Javier Sevilla-Salcedo, Álvaro Castro-González and Miguel A. Salichs

Biomimetics 2025, 10(12), 835; https://doi.org/10.3390/biomimetics10120835 (registering DOI) - 13 Dec 2025

Abstract

In recent years, robots have started to be used in tasks involving human interaction. For this to be possible, humans must perceive robots as suitable interaction partners. This can be achieved by giving the robots an animate appearance. One of the methods that [...] Read more.

In recent years, robots have started to be used in tasks involving human interaction. For this to be possible, humans must perceive robots as suitable interaction partners. This can be achieved by giving the robots an animate appearance. One of the methods that can be utilised to endow a robot with a lively appearance is giving it the ability to perform expressions on its own, that is, combining multimodal actions to convey information. However, this can become a challenge if the robot has to use gestures and speech simultaneously, as the non-verbal actions need to support the message communicated by the verbal component. In this manuscript, we present a system that, based on a robot’s utterances, predicts the corresponding gesture and synchronises it with the speech. A deep learning-based prediction model labels the robot’s speech with the types of expressions that should accompany it. Then, a rule-based synchronisation module connects different gestures to the correct parts of the speech. For this, we have tested two different approaches: (i) using a combination of recurrent neural networks and conditional random fields; and (ii) using transformer models. The results show that the proposed system can properly select co-speech gestures under the time constraints imposed by real-world interactions. Full article

(This article belongs to the Special Issue Intelligent Human–Robot Interaction: 4th Edition)

17 pages, 7561 KB

Open AccessArticle

Fine-Grained Image Recognition with Bio-Inspired Gradient-Aware Attention

by Bing Ma, Junyi Li, Zhengbei Jin, Wei Zhang, Xiaohui Song and Beibei Jin

Biomimetics 2025, 10(12), 834; https://doi.org/10.3390/biomimetics10120834 - 12 Dec 2025

Abstract

Fine-grained image recognition is one of the key tasks in the field of computer vision. However, due to subtle inter-class differences and significant intra-class differences, it still faces severe challenges. Conventional approaches often struggle with background interference and feature degradation. To address these [...] Read more.

Fine-grained image recognition is one of the key tasks in the field of computer vision. However, due to subtle inter-class differences and significant intra-class differences, it still faces severe challenges. Conventional approaches often struggle with background interference and feature degradation. To address these issues, we draw inspiration from the human visual system, which adeptly focuses on discriminative regions, to propose a bio-inspired gradient-aware attention mechanism. Our method explicitly models gradient information to guide the attention, mimicking biological edge sensitivity, thereby enhancing the discrimination between global structures and local details. Experiments on the CUB-200-2011, iNaturalist2018, nabbirds and Stanford Cars datasets demonstrated the superiority of our method, achieving Top-1 accuracy rates of 92.9%, 90.5%, 93.1% and 95.1%, respectively. Full article

(This article belongs to the Special Issue Biologically Inspired Vision and Image Processing 2025)

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14 pages, 829 KB

Open AccessArticle

Use of Amalgam and Composite Restorations Among 12-Year-Old Children in Israel: A Retrospective Study

by Rimah Nassar, Tali Chackartchi, Haim Doron, Jonathan Mann, Mordechai Findler and Guy Tobias

Biomimetics 2025, 10(12), 833; https://doi.org/10.3390/biomimetics10120833 - 12 Dec 2025

Abstract

Background: This study examined the trends in restorative dental practice among 12-year-old children treated at a nationwide public health maintenance organization in Israel between 2016 and 2022, focusing on the use of amalgam versus composite resin restorations in permanent premolars and molars. Methods: [...] Read more.

Background: This study examined the trends in restorative dental practice among 12-year-old children treated at a nationwide public health maintenance organization in Israel between 2016 and 2022, focusing on the use of amalgam versus composite resin restorations in permanent premolars and molars. Methods: Data were extracted from electronic health records of the second-largest public health organization in Israel, identifying children who underwent restorative treatments during the study period. Restoration rates were compared overall and stratified by gender, socioeconomic status, and number of surfaces restored. Statistical analysis was conducted using SPSS version 27, employing Levene’s test for equality of variances and Welch’s one-way ANOVA. Results: The results showed a statistically significant decline in amalgam use (p < 0.05) alongside a marked increase in composite resin restorations (p < 0.05), consistent across genders and socioeconomic groups. Notably, composite resins were increasingly selected for complex, multi-surface restorations (p < 0.05). Conclusions: These findings highlight a substantial shift in paediatric restorative practice in Israel, reflecting growing preference for composite resins likely influenced by patient demands and national dental reforms that eliminated financial barriers. The observed trend underscores the importance of continued monitoring of material selection to guide evidence-based practice in pediatric dentistry. Full article

(This article belongs to the Special Issue Biomimetic Strategies to Enhance Bone Tissue Healing, Remodeling and Regeneration: 2nd Edition)

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16 pages, 2128 KB

Open AccessArticle

Robust Motor Imagery–Brain–Computer Interface Classification in Signal Degradation: A Multi-Window Ensemble Approach

by Dong-Geun Lee and Seung-Bo Lee

Biomimetics 2025, 10(12), 832; https://doi.org/10.3390/biomimetics10120832 - 12 Dec 2025

Abstract

Electroencephalography (EEG)-based brain–computer interface (BCI) mimics the brain’s intrinsic information-processing mechanisms by translating neural oscillations into actionable commands. In motor imagery (MI) BCI, imagined movements evoke characteristic patterns over the sensorimotor cortex, forming a biomimetic channel through which internal motor intentions are decoded. [...] Read more.

Electroencephalography (EEG)-based brain–computer interface (BCI) mimics the brain’s intrinsic information-processing mechanisms by translating neural oscillations into actionable commands. In motor imagery (MI) BCI, imagined movements evoke characteristic patterns over the sensorimotor cortex, forming a biomimetic channel through which internal motor intentions are decoded. However, this biomimetic interaction is highly vulnerable to signal degradation, particularly in mobile or low-resource environments where low sampling frequencies obscure these MI-related oscillations. To address this limitation, we propose a robust MI classification framework that integrates spatial, spectral, and temporal dynamics through a filter bank common spatial pattern with time segmentation (FBCSP-TS). This framework classifies motor imagery tasks into four classes (left hand, right hand, foot, and tongue), segments EEG signals into overlapping time domains, and extracts frequency-specific spatial features across multiple subbands. Segment-level predictions are combined via soft voting, reflecting the brain’s distributed integration of information and enhancing resilience to transient noise and localized artifacts. Experiments performed on BCI Competition IV datasets 2a (250 Hz) and 1 (100 Hz) demonstrate that FBCSP-TS outperforms CSP and FBCSP. A paired t-test confirms that accuracy at 110 Hz is not significantly different from that at 250 Hz (p < 0.05), supporting the robustness of the proposed framework. Optimal temporal parameters (window length = 3.5 s, moving length = 0.5 s) further stabilize transient-signal capture and improve SNR. External validation yielded a mean accuracy of 0.809 ± 0.092 and Cohen’s kappa of 0.619 ± 0.184, confirming strong generalizability. By preserving MI-relevant neural patterns under degraded conditions, this framework advances practical, biomimetic BCI suitable for wearable and real-world deployment. Full article

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

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24 pages, 4981 KB

Open AccessArticle

Propulsive Force Characterization of a Bio-Robotic Sea Lion Foreflipper: A Kinematic Basis for Agile Propulsion

by Anthony Drago, Nicholas Marcouiller, Shraman Kadapa, Frank E. Fish and James L. Tangorra

Biomimetics 2025, 10(12), 831; https://doi.org/10.3390/biomimetics10120831 - 12 Dec 2025

Abstract

Unmanned underwater vehicles (UUVs) capable of agile, high-speed maneuvering in complex environments require propulsion systems that can dynamically modulate three-dimensional forces. The California sea lion (Zalophus californianus) provides an exceptional biological model, using its foreflippers to achieve rapid turns and powerful [...] Read more.

Unmanned underwater vehicles (UUVs) capable of agile, high-speed maneuvering in complex environments require propulsion systems that can dynamically modulate three-dimensional forces. The California sea lion (Zalophus californianus) provides an exceptional biological model, using its foreflippers to achieve rapid turns and powerful propulsion. However, the specific kinematic mechanisms that govern instantaneous force generation from its powerful foreflippers remain poorly quantified. This study experimentally characterizes the time-varying thrust and lift produced by a bio-robotic sea lion foreflipper to determine how flipper twist, sweep, and phase overlap modulate propulsive forces. A three-degree-of-freedom bio-robotic flipper with a simplified, low-aspect-ratio planform and single compliant hinge was tested in a circulating flow tank, executing parameterized power and paddle strokes in both isolated and combined-phase trials. The time-resolved force data reveal that the propulsive stroke functions as a tunable hybrid system. The power phase acts as a force-vectoring mechanism, where the flipper’s twist angle reorients the resultant vector: thrust is maximized in a broad, robust range peaking near 45°, while lift increases monotonically to 90°. The paddle phase operates as a flow-insensitive, geometrically driven thruster, where twist angle (0° optimal) regulates thrust by altering the presented surface area. In the full stroke, a temporal-phase overlap governs thrust augmentation, while the power-phase twist provides robust steering control. Within the tested inertial flow regime (Re ≈ 10⁴–10⁵), this control map is highly consistent with propulsion dominated by geometric momentum redirection and impulse timing, rather than circulation-based lift. These findings establish a practical, experimentally derived control map linking kinematic inputs to propulsive force vectors, providing a foundation for the design and control of agile, bio-inspired underwater vehicles. Full article

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

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18 pages, 4265 KB

Open AccessArticle

Animal Species Classification from Vocalizations Using Cochlear-Inspired Audio Features and Machine Learning

by Karim Youssef, Julien Moussa H. Barakat, Ghina El Mir, Sherif Said, Samer Al Kork and Alaa Eleyan

Biomimetics 2025, 10(12), 830; https://doi.org/10.3390/biomimetics10120830 - 11 Dec 2025

Abstract

Biomimetic approaches have gained increasing attention in the development of efficient computational models for sound scene analysis. In this paper, we present a sound-based animal species classification method inspired by the auditory processing mechanisms of the human cochlea. The approach employs gammatone filtering [...] Read more.

Biomimetic approaches have gained increasing attention in the development of efficient computational models for sound scene analysis. In this paper, we present a sound-based animal species classification method inspired by the auditory processing mechanisms of the human cochlea. The approach employs gammatone filtering to extract features that capture the distinctive characteristics of animal vocalizations. While gammatone filterbanks themselves are well established in auditory signal processing, their systematic application and evaluation for animal vocalization classification represent the main contribution of this work. Four gammatone-based feature representations are explored and used to train and test an artificial neural network for species classification. The method is evaluated on a dataset comprising vocalizations from 13 animal species with 50 vocalizations per specie and 2.76 seconds per vocalization in average. The evaluations are conducted to study the system parameters in different conditions and system architectures. Although the dataset is limited in scale compared to larger public databases, the results highlight the potential of combining biomimetic cochlear filtering with machine learning to perform reliable and robust species classification through sound. Full article

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

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18 pages, 8716 KB

Open AccessArticle

Nanomechanical and Optical Properties of Anti-Counterfeiting Nanostructures Obtained by Hydrogel Photoresist in Laser Processing

by Wei Wu, Qingxue Deng, Yuhang Shi and Jiyu Sun

Biomimetics 2025, 10(12), 829; https://doi.org/10.3390/biomimetics10120829 - 11 Dec 2025

Abstract

The microstructures of living creatures are widely used in bionics, and some can generate structural colors on biological surfaces and enable the process of dynamic camouflage. This study presents the hydrogel photoresist synthesized by polymerizing HEMA and MMA in THF solvent with initiator [...] Read more.

The microstructures of living creatures are widely used in bionics, and some can generate structural colors on biological surfaces and enable the process of dynamic camouflage. This study presents the hydrogel photoresist synthesized by polymerizing HEMA and MMA in THF solvent with initiator AIBN. Then, nanostructured gratings were fabricated on the hydrogel photoresists via double-beam interference lithography, and were characterized by scanning electron microscopy, angle-resolved spectroscopy system, and nanoindentation for pattern characterization, and nanomechanical and optical performance, respectively. Under multi-angle incident light, the optical computation of gratings with different depths indicates that a shallow implicit grating does not affect its dynamic color-changing performance. It is established that the laser power of 500 mW, a first exposure time of 5 s, and a second exposure time of 3 s are feasible for achieving efficient anti-counterfeiting nanostructures. The L_500-5-3 has greater Er and H than that of L_500-5 with the second processing, but smaller than ineffective patterns. And the depth of anti-counterfeiting gratings that is less than 0.8 μm is conducive to obtaining anti-counterfeiting gratings with different size parameters. The acquired anti-counterfeiting nanostructures exhibit excellent stability, reliability, and angle-dependent color changes under room light, which provides promising applications for security materials in daily life, sensors, optics, and electronics. Full article

(This article belongs to the Special Issue Bionic Engineering Materials and Structural Design)

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16 pages, 1116 KB

Open AccessArticle

Performance of Hammerstein Spline Adaptive Filtering Based on Fair Cost Function for Denoising Electrocardiogram Signals

by Suchada Sitjongsataporn and Theerayod Wiangtong

Biomimetics 2025, 10(12), 828; https://doi.org/10.3390/biomimetics10120828 - 10 Dec 2025

Abstract

This paper proposes a simplified adaptive filtering approach using a Hammerstein function and the spline interpolation based on a Fair cost function for denoising electrocardiogram (ECG) signals. The use of linear filters in real-world applications has many limitations. Adaptive nonlinear filtering is a [...] Read more.

This paper proposes a simplified adaptive filtering approach using a Hammerstein function and the spline interpolation based on a Fair cost function for denoising electrocardiogram (ECG) signals. The use of linear filters in real-world applications has many limitations. Adaptive nonlinear filtering is a key development in tackling the challenge of discovering the specific characteristics of biomimetic systems for each person in order to eliminate unwanted signals. A biomimetic system refers to a system that mimics certain biological processes or characteristics of the human body, in this case, the individual features of a person’s cardiac signals (ECG). Here, the adaptive nonlinear filter is designed to cope with ECG variations and remove unwanted noise more effectively. The objective of this paper is to explore an individual biomedical filter based on adaptive nonlinear filtering for denoising the corrupted ECG signal. The Hammerstein spline adaptive filter (HSAF) architecture consists of two structural blocks: a nonlinear block connected to a linear one. In order to make a smooth convergence, the Fair cost function is introduced for convergence enhancement. The affine projection algorithm (APA) based on the Fair cost function is used to denoise the contaminated ECG signals, and also provides fast convergence. The MIT-BIH 12-lead database is used as the source of ECG biomedical signals contaminated by random noises modelled by Cauchy distribution. Experimental results show that the estimation error of the proposed HSAF–APA–Fair algorithm, based on the Fair cost function, can be reduced when compared with the conventional least mean square-based algorithm for denoising ECG signals. 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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20 pages, 3862 KB

Open AccessArticle

Hybrid ANFIS–MPA and FFNN–MPA Models for Bitcoin Price Forecasting

by Ceren Baştemur Kaya, Ebubekir Kaya and Eyüp Sıramkaya

Biomimetics 2025, 10(12), 827; https://doi.org/10.3390/biomimetics10120827 - 10 Dec 2025

Abstract

This study introduces two hybrid forecasting models that integrate the Marine Predators Algorithm (MPA) with Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Feed-Forward Neural Networks (FFNN) for short-term Bitcoin price prediction. Daily Bitcoin data from 2022 were converted into supervised time-series structures with multiple [...] Read more.

This study introduces two hybrid forecasting models that integrate the Marine Predators Algorithm (MPA) with Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Feed-Forward Neural Networks (FFNN) for short-term Bitcoin price prediction. Daily Bitcoin data from 2022 were converted into supervised time-series structures with multiple input configurations. The proposed hybrid models were evaluated against six well-known metaheuristic algorithms commonly used for training intelligent forecasting systems. The results show that MPA consistently yields lower prediction errors, faster convergence, and more stable optimization behavior compared with alternative algorithms. Both ANFIS-MPA and FFNN-MPA maintained their advantage across all tested structures, demonstrating reliable performance under varying model complexities. All experiments were repeated multiple times, and the hybrid approaches exhibited low variance, indicating robust and reproducible behavior. Overall, the findings highlight the effectiveness of MPA as an optimizer for improving the predictive performance of neuro-fuzzy and neural network models in financial time-series forecasting. Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications: 2nd Edition)

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14 pages, 2239 KB

Open AccessArticle

Energy-Efficient Path Planning for Snake Robots Using a Deep Reinforcement Learning-Enhanced A* Algorithm

by Yang Gu, Zelin Wang and Zhong Huang

Biomimetics 2025, 10(12), 826; https://doi.org/10.3390/biomimetics10120826 - 10 Dec 2025

Abstract

Snake-like robots, characterized by their high flexibility and multi-joint structure, exhibit exceptional adaptability to complex terrains such as snowfields, jungles, deserts, and underwater environments. Their ability to navigate narrow spaces and circumvent obstacles makes them ideal for operations in confined or rugged environments. [...] Read more.

Snake-like robots, characterized by their high flexibility and multi-joint structure, exhibit exceptional adaptability to complex terrains such as snowfields, jungles, deserts, and underwater environments. Their ability to navigate narrow spaces and circumvent obstacles makes them ideal for operations in confined or rugged environments. However, efficient motion in such conditions requires not only mechanical flexibility but also effective path planning to ensure safety, energy efficiency, and overall task performance. Most existing path planning algorithms for snake-like robots focus primarily on finding the shortest path between the start and target positions while neglecting the optimization of energy consumption during real operations. To address this limitation, this study proposes an energy-efficient path planning method based on an improved A* algorithm enhanced with deep reinforcement learning: Dueling Double-Deep Q-Network (D3QN). An Energy Consumption Estimation Model (ECEM) is first developed to evaluate the energetic cost of snake robot motion in three-dimensional space. This model is then integrated into a new heuristic function to guide the A* search toward energy-optimal trajectories. Simulation experiments were conducted in a 3D environment to assess the performance of the proposed approach. The results demonstrate that the improved A* algorithm effectively reduces the energy consumption of the snake robot compared with conventional algorithms. Specifically, the proposed method achieves an energy consumption of 68.79 J, which is 3.39%, 27.26%, and 5.91% lower than that of the traditional A* algorithm (71.20 J), the bidirectional A* algorithm (94.61 J), and the weighted improved A* algorithm (73.11 J), respectively. These findings confirm that integrating deep reinforcement learning with an adaptive heuristic function significantly enhances both the energy efficiency and practical applicability of snake robot path planning in complex 3D environments. Full article

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19 pages, 8700 KB

Open AccessArticle

Human-Inspired Force-Motion Imitation Learning with Dynamic Response for Adaptive Robotic Manipulation

by Yuchuang Tong, Haotian Liu, Tianbo Yang and Zhengtao Zhang

Biomimetics 2025, 10(12), 825; https://doi.org/10.3390/biomimetics10120825 (registering DOI) - 9 Dec 2025

Abstract

Recent advances in bioinspired robotics highlight the growing demand for dexterous, adaptive control strategies that allow robots to interact naturally, safely, and efficiently with dynamic, contact-rich environments. Yet, achieving robust adaptability and reflex-like responsiveness to unpredictable disturbances remains a fundamental challenge. This paper [...] Read more.

Recent advances in bioinspired robotics highlight the growing demand for dexterous, adaptive control strategies that allow robots to interact naturally, safely, and efficiently with dynamic, contact-rich environments. Yet, achieving robust adaptability and reflex-like responsiveness to unpredictable disturbances remains a fundamental challenge. This paper presents a bioinspired imitation learning framework that models human adaptive dynamics to jointly acquire and generalize motion and force skills, enabling compliant and resilient robot behavior. The proposed framework integrates hybrid force–motion learning with dynamic response mechanisms, achieving broad skill generalization without reliance on external sensing modalities. A momentum-based force observer is combined with dynamic movement primitives (DMPs) to enable accurate force estimation and smooth motion coordination, while a broad learning system (BLS) refines the DMP forcing function through style modulation, feature augmentation, and adaptive weight tuning. In addition, an adaptive radial basis function neural network (RBFNN) controller dynamically adjusts control parameters to ensure precise, low-latency skill reproduction, and safe physical interaction. Simulations and real-world experiments confirm that the proposed framework achieves human-like adaptability, robustness, and scalability, attaining a competitive learning time of 5.56 s and a rapid generation time of 0.036 s, thereby demonstrating its efficiency and practicality for real-time applications and offering a lightweight yet powerful solution for bioinspired intelligent control in complex and unstructured environments. Full article

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24 pages, 6350 KB

Open AccessArticle

Composites Derived from Aluminium-Modified Biphasic Calcium-Phosphate for Bone Regeneration

by Raluca Lucacel-Ciceo, Roxana Dudric, Razvan Hirian, Iulia Lupan, Oana Koblicska, Roxana Strimbu, Radu George Hategan, Dorina Simedru and Zorita Diaconeasa

Biomimetics 2025, 10(12), 824; https://doi.org/10.3390/biomimetics10120824 - 9 Dec 2025

Abstract

In this research, aluminium-doped biphasic calcium phosphate (Al-BCP) was synthesized by co-precipitation and formulated with hydrolyzed collagen and acetylsalicylic acid (ASA) to yield composites designed as a new class of bone-regenerative biomaterials with enhanced biological performance. Undoped and Al-modified powders (5/10 wt% Al [...] Read more.

In this research, aluminium-doped biphasic calcium phosphate (Al-BCP) was synthesized by co-precipitation and formulated with hydrolyzed collagen and acetylsalicylic acid (ASA) to yield composites designed as a new class of bone-regenerative biomaterials with enhanced biological performance. Undoped and Al-modified powders (5/10 wt% Al precursor) were prepared at 40 °C (pH ~ 11) and calcined at 700 °C, and composites were produced at a 1:1:0.1 mass ratio (ceramic–collagen–ASA). Structure and chemistry were assessed by X-ray diffraction (XRD), Fourier-transform infrared (FTIR) and Raman spectroscopies, and X-ray photoelectron spectroscopy (XPS). Morphology and elemental distribution were examined by scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM/EDX). Biological performance was preliminarily evaluated using HaCaT (immortalized human keratinocytes) viability and antibacterial assays against Staphylococcus aureus and Escherichia coli. XRD confirmed a biphasic hydroxyapatite/β-tricalcium phosphate system and showed that Al incorporation shifted the phase balance toward hydroxyapatite (HAp fraction 54.8% in BCP vs. ~68.6–68.7% in Al-doped samples). FTIR/Raman preserved BCP vibrational signatures and revealed collagen/ASA bands in the composites. XPS/EDX verified the expected composition, including surface N 1s from organics and Al at ~2–5 at% for doped samples, with surface Ca/P ≈ 1.15–1.16. SEM revealed multigranular microstructures with homogeneous Al distribution. All composites were non-cytotoxic (≥70% viability); M_Al10_Col_ASA exceeded 90% viability at 12.5% dilution. Preliminary antibacterial assays against Gram-positive and Gram-negative strains showed modest, time-dependent reductions in CFU relative to controls. These results corroborate the compositional/structural profile and preliminary biological performance of Al-BCP–collagen–ASA composites as multifunctional bone tissue engineering materials that foster a bone-friendly microenvironment, warranting further evaluation for bone regeneration. Full article

(This article belongs to the Special Issue Advances in Bioceramics for Bone Regeneration: 2nd Edition)

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32 pages, 4759 KB

Open AccessArticle

Development of a Bayesian Network and Information Gain-Based Axis Dynamic Mechanism for Ankle Joint Rehabilitation

by Huiguo Ma, Yuqi Bao, Jingfu Lan, Xuewen Zhu, Pinwei Wan, Raquel Cedazo León, Shuo Jiang, Fangfang Chen, Jun Kang, Qihan Guo, Peng Zhang and He Li

Biomimetics 2025, 10(12), 823; https://doi.org/10.3390/biomimetics10120823 - 9 Dec 2025

Abstract

In response to the personalized and precise rehabilitation needs for motor injuries and stroke associated with population aging, this study proposes a design method for an intelligent rehabilitation trainer that integrates Bayesian information gain (BIG) and axis matching techniques. Grounded in the biomechanical [...] Read more.

In response to the personalized and precise rehabilitation needs for motor injuries and stroke associated with population aging, this study proposes a design method for an intelligent rehabilitation trainer that integrates Bayesian information gain (BIG) and axis matching techniques. Grounded in the biomechanical characteristics of the human ankle joint, the design fully draws upon biomimetic principles, constructing a 3-PUU-R hybrid serial–parallel bionic mechanism. By mimicking the dynamic variation of the ankle’s instantaneous motion axis and its balance between stiffness and compliance, a three-dimensional digital model was developed, and multi-posture human factor simulations were conducted, thereby achieving a rehabilitation process more consistent with natural human movement patterns. Natural randomized disability grade experimental data were collected for 100 people to verify the validity of the design results. On this basis, a Bayesian information gain framework was established by quantifying the reduction of uncertainty in rehabilitation outcomes through characteristic parameters, enabling the dynamic optimization of training strategies for personalized and precise ankle rehabilitation. The rehabilitation process was modeled as a problem of uncertainty quantification and information gain optimization. Prior distributions were constructed using surface EMG (electromyography) signals and motion trajectory errors, and mutual information was used to drive the dynamic adjustment of training strategies, ultimately forming a closed-loop control architecture of “demand perception–strategy optimization–execution adaptation.” This innovative integration of probabilistic modeling and cross-joint bionic design overcomes the limitations of single-joint rehabilitation and provides a new paradigm for the development of intelligent rehabilitation devices. The deep integration mechanism-based dynamic axis matching and Bayesian information gain holds significant theoretical value and engineering application prospects for enhancing the effectiveness of neural plasticity training. Full article

(This article belongs to the Special Issue Advanced Service Robots: Exoskeleton Robots 2025)

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17 pages, 1330 KB

Open AccessSystematic Review

Chitosan-Based Nanoparticles and Biomaterials for Pulp Capping and Regeneration: A Systematic Review with Quantitative and Evidence-Mapping Synthesis

by Saleh Ali Alqahtani, Mohammad Alamri, Ghadeer Alwadai, Naif N. Abogazalah, Vinod Babu Mathew and Betsy Joseph

Biomimetics 2025, 10(12), 822; https://doi.org/10.3390/biomimetics10120822 - 9 Dec 2025

Abstract

Preserving dental pulp vitality is a key goal in minimally invasive dentistry. Conventional materials such as calcium hydroxide and mineral trioxide aggregate (MTA) are effective but limited in bioactivity and mechanical strength. This systematic review evaluated the biological efficacy of chitosan-based nanoparticles and [...] Read more.

Preserving dental pulp vitality is a key goal in minimally invasive dentistry. Conventional materials such as calcium hydroxide and mineral trioxide aggregate (MTA) are effective but limited in bioactivity and mechanical strength. This systematic review evaluated the biological efficacy of chitosan-based nanoparticles and biomaterials for pulp capping and regeneration. Following PRISMA 2020 guidelines, electronic searches were conducted across five databases up to April 2025. Controlled in vitro and animal studies using chitosan-based nanoparticles, hydrogels, or composite scaffolds were included. Risk of bias was assessed using SYRCLE (animal) and ToxRTool (in vitro), and certainty of evidence was rated via the GRADE-Preclinical framework. Due to methodological heterogeneity, data were synthesized using direction-of-effect coding and visualized through Albatross and heatmap plots. Sixteen studies met the criteria, consistently demonstrating enhanced cell viability, mineralization, and upregulation of odontogenic and angiogenic markers (BMP-2, TGF-β₁, VEGF, DSPP) compared with MTA or calcium hydroxide. Animal models confirmed improved angiogenesis, reparative dentin formation, and pulp vitality preservation. Despite uniformly positive biological outcomes, the overall certainty was rated Low to Very Low owing to small samples and unclear randomization. Chitosan-based biomaterials show promising regenerative potential, warranting well-designed preclinical and clinical studies for translational validation. Full article

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

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