Biomimetics doi: 10.3390/biomimetics11070474

Authors: Abdelraheim Emad Abdelraheim Mohamed Nejlaoui Nasser Ayidh Alqahtani

High-precision robotics is frequently compromised by joint compliance, a factor often over-simplified by traditional rigid-body modeling. This research investigates the structural dynamics of a two-link manipulator, addressing critical discrepancies between experimental data and conventional models. Much like biological musculoskeletal systems, joint flexibility fundamentally influences the dynamic response of articulated structures. While traditional rigid-joint models accurately capture mode shapes, they yield excessive natural frequency prediction errors with peaks reaching 72%. To bridge this gap, a refined Flexible-Joint Finite Element Model (FJFEM) is developed to mimic adaptive joint compliance. This model is integrated with a bio-inspired computational framework (a Double-Stage Genetic Algorithm Framework (DSGAF)) to identify configuration-dependent joint stiffness across the operational workspace, where experimental frequencies f1 and f2 shift nonlinearly from 25.5 Hz to 44 Hz and 92.2 Hz to 51 Hz, respectively. Experimental validation demonstrates that this evolutionary strategy reduces frequency tracking errors to less than 3.5% across all positions, achieving an average identification routine runtime of 1.8 s. By capturing nonlinear compliance behavior, this framework provides a robust foundation for the design, online calibration, and vibration control of advanced flexible robotic systems.

Biomimetics doi: 10.3390/biomimetics11070473

Authors: Paola Taddei Michele Di Foggia Andrea Spinelli Maria Giovanna Gandolfi Carlo Prati Fausto Zamparini

Endodontic irrigant solutions act as crucial pretreatment conditioning agents in dentin biomimetic remineralization, preparing the collagen scaffold for calcium phosphate infiltration and subsequent tooth structure reconstruction. In this study, root dentin discs were exposed for 10 min to five irrigant solutions: sodium hypochlorite (NaClO, 3%), EDTA (17%), citric acid (CA, 10%), chlorhexidine (CHX, 2%), and an innovative experimental formulation containing citric acid (7%) and surfactants. Samples were then aged in Hank’s Balanced Salt Solution (HBSS) at 37 °C for three months to simulate long-term clinical conditions. Physicochemical modifications of the collagen and apatite phases were assessed at each experimental stage using ATR-FTIR spectroscopy, with the ACaP/AAmide I and A870/ACaP absorbance ratios as markers of the degree of mineralization and apatite carbonate content, respectively. Results indicated that CHX- and EDTA-treated dentin exhibited the highest remineralization after ageing, while NaClO impeded remineralization due to collagen degradation. The experimental irrigant produced the most pronounced demineralization, followed by CA; however, it also facilitated significant remineralization, attributed to citrate–collagen binding and surfactant-enhanced apatite nucleation. NaClO selectively degraded collagen and increased apatite crystallinity; CA inhibited apatite nucleation through adsorbed citrate ions, and CHX and EDTA induced minimal alterations. These findings provide molecular-level evidence linking short-term irrigant effects to the long-term potential for dentin biomineralization, with direct implications for irrigant selection in regenerative endodontic protocols. It should be noted that this study was conducted on dentin discs obtained from a single tooth; all findings should therefore be regarded as preliminary and require confirmation in studies with larger, biologically independent sample sizes.

Biomimetics doi: 10.3390/biomimetics11070472

Authors: Mohamed Zaidan Nafaâ Jabeur Ahmed Nait Sidi Moh Tufail Ahmed Ansar-Ul-Haque Yasar

Recently introduced affect-driven UAV controllers model behavioral sensitivity (α) as a static personality-dependent parameter, overlooking the cumulative influence of prolonged operational context. The Pull–Push Engine (PPE) regulates behavioral responses through bounded temporal integration; however, its effective sensitivity remains fixed during execution, limiting adaptive evolution under cumulative operational exposure. To overcome this limitation, this paper introduces the Digital Hormone Layer (DHL), a bounded neuroendocrine-inspired regulatory mechanism that dynamically modulates the PPE’s effective sensitivity αeff. In DHL, three scalar hormones inspired by cortisol-, dopamine-, and oxytocin-regulatory motifs accumulate operational context on a medium timescale. In the evaluated scenarios, the behavioral effect is primarily stress-driven, while reward and operator-engagement channels remain architecturally defined but contribute less prominently. Modulation is constrained within a personality envelope by coefficient construction (Personality Preservation Budget (PPB) ρ = 0.20). On emergency events, the DHL-augmented controller responds up to 1.91× faster under multi-stressor exposure relative to the activation-selectivity (EI-Low, α = 0.495) control (95% bootstrap confidence interval [1.70×, 2.14×]). This indicates the advantage arises from the bounded adaptive DHL trajectory, not from a low steady-state α-value. This interpretation is consistent with the implemented per-step hormone-to-sensitivity coupling, observed as an inverse correlation between accumulated stress and effective sensitivity. Mission-final and total in-flight battery consumption are comparable across the single-agent controllers; no battery-efficiency advantage is claimed. A single-equation multi-agent extension shows that increasing the coupling coefficient reduces inter-agent sensitivity distance from 0.00502 (uncoupled, γ = 0) to 0.00243 at γ = 0.50 (95% CI [0.00225, 0.00261]; a 51.6% reduction; one-way ANOVA F = 82.5, p < 0.0001) while both agents remain within the personality envelope, as evidence of bounded inter-agent coupling; system-level multi-agent properties remain to be evaluated.

Biomimetics doi: 10.3390/biomimetics11070471

Authors: Antonio Libonati Danilo Marroni Giulio Barbalace Giulia Campanella Carla Clemente Francesco Campanella Lucrezia Secreti Vincenzo Campanella

Titanium-based dental implants have evolved significantly, with the development of binary alloys like Ti-15Zr (Roxolid™) representing a pivotal advancement in mechanical performance. Current research focuses on biomimetic surface engineering to further accelerate osseointegration and optimize bone regeneration, particularly in clinically compromised sites. This review constitutes a narrative synthesis of how these strategies replicate the bone extracellular matrix (ECM) through a holistic framework of architectural, mechanical, and biochemical integration. A structured literature search across PubMed, Scopus, and Web of Science (2010–2026) identified relevant studies focusing on the synergy between Ti-15Zr substrates and surface modifications. Evidence confirms that the high fatigue strength of Roxolid™ alloys provides an ideal foundation for advanced, hierarchical surface engineering without compromising structural integrity. This strategy utilizes macro-topography for primary stability, nano-topography for protein adsorption, and bio-functionalization (e.g., RGD peptides and osteogenic ions) to direct mesenchymal stem cell (MSC) differentiation. This synergy accelerates the transition from passive to active osseointegration, effectively bridging the “biological gap” during early healing. Biomimetic engineering transforms implants into instructive biological platforms, improving outcomes for patients with compromised bone quality and facilitating predictable immediate loading protocols.

Biomimetics doi: 10.3390/biomimetics11070470

Authors: Anna Li Yanqiang Yang

Unmanned aerial vehicles (UAVs) have been widely used in surveying and mapping, inspection, emergency rescue, and environmental monitoring. However, effective path planning remains a key challenge in complex three-dimensional terrain, where UAVs must simultaneously cope with terrain undulations, no-fly zones, safety-clearance requirements, and trajectory-smoothness constraints. In addition, conventional intelligent optimization algorithms often suffer from search instability and premature convergence. To address these challenges, this study proposes a reinforcement-learning-driven multi-strategy spherical-vector grey wolf optimizer, termed TLQ-SGWO, where TLQ denotes the combined use of Tent–Logistic hybrid initialization and Q-learning search-strategy scheduling. In the proposed method, candidate trajectories are encoded using spherical-vector increments; Tent–Logistic hybrid initialization is introduced to enhance population diversity; and Q-learning is incorporated to adaptively select search strategies, thereby dynamically balancing exploration and exploitation. A comprehensive cost function integrating path length, threat avoidance, terrain clearance, and trajectory smoothness is further constructed to improve the feasibility and safety of the planned trajectories. Experiments are conducted on the CEC2017 benchmark functions and artificially generated complex mountainous terrain scenarios. On the CEC2017 benchmark suite, TLQ-SGWO achieves the best average rankings in both mean error and standard deviation among the seven compared algorithms, indicating a stronger balance between optimization accuracy and robustness. In artificial mountainous scenarios, TLQ-SGWO obtains the lowest mean path cost in three of the four scenarios and remains statistically comparable to the strongest hybrid baseline in the remaining scenario, while maintaining stable feasible 3D trajectories under increasing no-fly-zone complexity.

Biomimetics doi: 10.3390/biomimetics11070469

Authors: Zhengyu Liu Jiahao Chen

Achieving rapid and accurate obstacle avoidance in complex and dynamic environments remains a significant challenge for robots. To enhance the adaptability and flexibility of humanoid arms for obstacle avoidance, a brain-inspired multi-pathway motion decision-making method is proposed to modulate rational planning and habitual actions of humanoid arms. Firstly, a novel framework integrating both a slow and a fast pathway is designed for motion decision-making tasks. Imitating the rational planning function of the prefrontal cortex, the slow pathway employs an improved planning approach based on Real-Time Rapidly exploring Random Tree Star (RT-RRT*) to execute deliberate decisions, along with an improvement in planning via the Smart technique and the high-efficiency neighbor searching method. Meanwhile, mimicking the habitual responses governed by the striatum, the fast pathway utilizes an action model trained by Soft Actor-Critic to make quick and habitual motions. The model in the fast pathway is also used to guide the sampling strategy in the slow pathway. Moreover, to facilitate the integration and smooth transition between the two pathways, an emotional neural network is designed as the modulation module with inspiration from the structure and function of the amygdala. Based on body and obstacle information, the network generates emotional signals to modulate the involvement degree of the two pathways before each decision-making process. Experimental results demonstrate that the proposed multi-pathway framework achieves a higher obstacle-avoidance success rate than existing methods while generating motion characteristics that are consistent with certain aspects of human obstacle-avoidance behavior.

Biomimetics doi: 10.3390/biomimetics11070468

Authors: Zhenkun Lu Jianyong Zhu Dingfeng Lu Hongze Lv Haolin Gan Zicong An

The Ivy Optimization Algorithm (IVY) is a novel swarm intelligence optimization algorithm that simulates the phototropic growth mechanism of plants. To comprehensively improve the overall optimization performance, this paper proposes an enhanced Ivy Optimization Algorithm (LSIVY) integrating improved Logistics chaotic mapping, sine–cosine operator, and adaptive t-distribution mutation strategy. Firstly, an improved cascaded Logistics chaotic mapping is used for population initialization. The double arcsine transformation improves the ergodicity and uniformity of chaotic sequences, so that initial solutions are distributed more evenly in the search space, population diversity is enhanced, and premature convergence is suppressed. Secondly, the sine–cosine operator is embedded into the position update mechanisms of IVY growth, climbing, and propagation evolution. Nonlinearly decreasing control parameters realize adaptive switching between global exploration and local exploitation and accelerate convergence. Thirdly, an adaptive t-distribution mutation strategy is designed to dynamically adjust mutation intensity according to the iteration cycle and implement directional perturbation at the optimal solution position. It combines the large-scale exploration advantage of the Cauchy distribution and the local fine search merit of the Gaussian distribution, which significantly improves the ability to escape from local optima. Comparative experiments with eight mainstream metaheuristics (DE, WOA, GWO, HHO, DBO, MBWO, AOO, native IVY) are conducted with 30 independent runs on 30-dimensional CEC 2014 (30 test functions) and CEC 2020 (10 composite functions). Quantitatively, LSIVY achieves 20~30 orders of magnitude higher optimization accuracy than standard IVY on unimodal functions, and its average standard deviation across all benchmarks drops by 4–6 orders of magnitude. LSIVY ranks first on all CEC 2020 composite functions, reducing over 30% of iterations compared with native IVY. Three classical constrained mechanical design problems (three-bar truss, cantilever beam, pressure vessel) are adopted for engineering verification. In the pressure vessel case, the average manufacturing cost of LSIVY is reduced by 9.2% against standard IVY, and the standard deviation of three engineering cases decreases by 2–3 orders on average, demonstrating remarkable robustness. The proposed algorithm not only improves the theoretical system of plant-inspired swarm intelligence algorithms but also has great application prospects in mechanical structure lightweight design, industrial equipment cost optimization, and other practical engineering fields.

Biomimetics doi: 10.3390/biomimetics11070467

Authors: Jakeh E. Orr Yan Gai

Background: Head-related transfer functions (HRTFs) contain time and level cues and may be utilized in automatic algorithms to identify locations of sound, a desirable feature for next-generation hearing devices. Due to substantial variability in individual head sizes and ear acoustics, individualized HRTFs are expected to provide the best localization results. However, acquiring individualized HRTFs for each user is time-consuming. Methods: This study constructed three binaural and/or monaural algorithms suitable for hearing devices. A linear classifier was trained on HRTF databases from a subset of subjects and used to predict sound locations for other individuals to evaluate cross-subject variability. Results: Using the CIPIC Database, a “two-step” method achieved a horizontal localization error of 1.0° and a vertical error of 30.4° sequentially. With the 3D3A Database, the horizontal and vertical errors were 5.6° and 36.5°, respectively. Both datasets yielded improved accuracy when frontal and rear hemifields were simulated separately, with trends remaining consistent across databases. When subjects were grouped by gender, classifiers trained on women’s HRTFs performed well in predicting men’s localization, whereas classifiers trained on men’s HRTFs resulted in significantly larger errors. Conclusions: These findings offer insights into the localization cues embedded in HRTFs and demonstrate the influences of inter-subject variability for spatial hearing devices.

Biomimetics doi: 10.3390/biomimetics11070466

Authors: Ana Botrić Goran Gregov

This study presents the innovative design and development of biomimetic origami-based soft robotic grippers capable of two-stage grasping. Inspired by the biological structure of the sea urchin mouth, which combines external rigid teeth with an internal soft membrane, the proposed grippers employ origami architectures to achieve coordinated two-stage grasping. Novel waterbomb and Miura-ori origami architectures were introduced, enabling the formation of external and internal teeth. The developed grippers integrate an elastomeric membrane with an internal origami structure that enables contraction-driven folding under negative-pressure actuation. Multiple gripper configurations with varying dimensions are fabricated using paper and polymer-laminated paper skeletons. An energy-based modeling framework is introduced to describe the pressure–force relationship while accounting for the effects of structural deformation. Experimental evaluations conducted at different negative-pressure values quantified grasping performance and holding force. Imprint-based analysis confirmed the two-stage grasping mechanism, while grasping capability investigations demonstrated compliant interaction with delicate objects. Holding forces were measured using cylindrical metal and spherical wooden test objects of varying sizes and orientations. The waterbomb-based gripper achieved the most consistent performance, particularly for cylindrical objects, reaching a maximum holding force of 70 N, whereas the Miura-ori provided improved adaptability and higher holding forces for spherical objects, reaching 74.8 N, and maximum force-to-weight ratios of 327.2 and 346.6 were achieved for the waterbomb- and Miura-ori-based grippers, respectively.

Biomimetics doi: 10.3390/biomimetics11070465

Authors: Juncheng Zhu Haotian Yang Zhile Yang Yuanjun Guo

Dexterous grasping with multi-finger robotic hands is essential for general-purpose robotic manipulation, but remains challenging due to high-dimensional hand configurations, multimodal grasp distributions, and contact-rich execution dynamics. Existing methods often decouple grasp target generation from execution policy learning, which limits the consistency between generated grasp goals and downstream control. To address this problem, we propose DexGraspDiffuser, a target-coupled grasp and action diffusion framework for dexterous grasping. The first stage, GraspDiffusion, generates diverse and physically plausible target grasps from object point clouds using a compact representation of hand root translation, continuous rotation, and finger joint configuration. The second stage, a Goal-Conditioned Diffusion Policy, predicts temporally coherent action sequences conditioned on the selected target grasp and current observation. During inference, receding-horizon execution enables action-prefix execution and online replanning for improved robustness. Experiments demonstrate that DexGraspDiffuser achieves success rates of 0.76, 0.72, and 0.68 on training objects, unseen objects from seen categories, and objects from unseen categories, respectively. These results correspond to a three-split average success rate of 0.72 and a train-to-unseen generalization gap of 0.08. Compared with the reproduced UniDexGrasp-T baseline under the same object split and evaluation protocol, DexGraspDiffuser improves the three-split average success rate by 3.3 percentage points and reduces the average mean position error by 0.53 cm. This indicates that target-coupled grasp and action diffusion contribute to improved grasp quality, execution accuracy, and closed-loop stability.

Biomimetics doi: 10.3390/biomimetics11070464

Authors: Han Yang Xingwang Huang

The rapid development of intelligent systems has introduced increasingly sophisticated optimization problems across diverse domains. While contemporary metaheuristic algorithms, including the recent Artificial Lemming Algorithm (ALA), have shown considerable promise, they frequently encounter difficulties such as premature convergence, inadequate local refinement, and diminished performance in high-dimensional multimodal environments. To overcome these issues, this study presents HALA, a new hybrid dual-subpopulation optimizer that effectively integrates an enhanced ALA with the SHADE algorithm. HALA employs two interacting subpopulations: one leverages an improved ALA with hybrid t-distribution and Levy flight perturbations to promote persistent long-range exploration and diversity preservation; the other applies SHADE’s success-history adaptation and external archive for accurate local exploitation. Periodic bidirectional elite migration facilitates knowledge transfer between the subpopulations, reducing early stagnation in the enhanced ALA and strengthening SHADE’s global search capability. HALA is thoroughly benchmarked against 17 advanced metaheuristics, including ALA, LSHADE, LSHADE-SPACMA, AOOA, BAEO, BPBO, CCO, CEO, CQALA, DFL, DMOA, DHOA, FGO, KLA, PGA, SO, and SOO, using the IEEE CEC2017 suite in 10, 30, 50, and 100 dimensions and the IEEE CEC2022 suite in 10 dimensions. Comprehensive analyses involving qualitative visualization, convergence curves, boxplots, and statistical tests indicate that HALA achieves competitive or superior solution quality, comparable or faster convergence, and robust stability on a substantial proportion of the test instances. In particular, HALA obtains the most favorable Friedman average ranking values among the compared algorithms, which are 2.55, 2.38, 2.34, and 2.55 for the 10-, 30-, 50-, and 100-dimensional CEC2017 functions, respectively, and 2.58 for the 12 10-dimensional CEC2022 functions. Moreover, HALA is successfully applied to five well-known constrained engineering design problems—pressure vessel, rolling element bearing, tension/compression spring, cantilever beam, and gear train—where it reliably achieves optimal or near-optimal results that match or surpass the compared methods. These findings underscore HALA’s competitive strength and broad potential for practical engineering optimization.

Biomimetics doi: 10.3390/biomimetics11070463

Authors: Zhenkun Lu Mingbin Tang Meng Li Xiangyun Meng Hanjin Shi Rui Xu Zihao Cheng

The Horned Lizard Optimization Algorithm (HLOA) is a newly proposed swarm intelligence optimizer mimicking the defensive and survival behaviors of horned lizards. The original HLOA suffers evident drawbacks when tackling high-dimensional, multimodal and heavily constrained complicated optimization problems. Rapid decline in population diversity in late iterations and insufficient local optimum escape strategies further trigger premature convergence and unsatisfactory optimization precision. To systematically address the above deficiencies and boost the global optimization and engineering applicability of HLOA, this paper proposes a hybrid-strategy improved Horned Lizard Optimization Algorithm (HSHLOA). First, an improved uniform Logistic chaotic mapping replaces conventional random initialization. It enhances the ergodicity and uniformity of initial populations across search spaces and upgrades the quality of initial solutions and population diversity. Second, an adaptive optimal guidance strategy is constructed via nonlinear dynamic adjustment factors. It prioritizes global exploration in early iterations and strengthens local exploitation in later iterations to accelerate convergence and raise optimization accuracy. Third, a lens imaging learning strategy is embedded. It generates adaptive opposite solutions following dynamic convex lens optical imaging rules, strengthens the capability to escape local optima and mitigates premature convergence. To verify the optimization performance of the proposed algorithm, comparative experiments are conducted on the CEC 2017 benchmark test suite under 30-dimensional and 100-dimensional high-dimensional settings. Seven mainstream swarm intelligence algorithms are selected for benchmark comparison. Quantitative analyses cover convergence rate, optimization precision, numerical stability and local optimum escaping ability. Experimental results reveal that HSHLOA outperforms all peer competitors on unimodal, multimodal, hybrid and composite functions with remarkable superiority. The proposed HSHLOA is further applied to three typical constrained engineering optimization cases, including reinforced concrete beam design, three-bar truss design and pressure vessel design. Application results prove that HSHLOA satisfies all engineering constraints steadily and obtains superior structural schemes with higher efficiency. The reliability and superiority of HSHLOA for practical engineering problems are therefore verified.

Biomimetics doi: 10.3390/biomimetics11070461

Authors: Anantharaj Alaganathan Udhayakumar Gunasekaran Gurulakshmi Mariappan Rajkamal Natarajan Ramkumar Vanaraj Gokulakumar Balakrishnan

Biogenic and bioactive nanomaterials synthesized through green approaches have gained considerable attention for therapeutic applications. In this study, zinc oxide nanoparticles (ZnO NPs) were successfully synthesized using an aqueous leaf extract of Leucas aspera as a natural reducing and capping agent to produce phytochemical-functionalized ZnO nanoparticles (LA-ZnO). The synthesized nanoparticles were characterized using UV-Vis, FTIR, XRD, SEM, EDX, and DLS analyses. UV-Vis spectra showed absorption peaks at 391 nm for ZnO and 313 nm for LA-ZnO, while XRD confirmed a hexagonal wurtzite structure with average crystallite sizes of 50.19 and 33.72 nm, respectively. SEM analysis revealed rod-like ZnO and flower-like LA-ZnO morphologies, and EDX confirmed Zn and O with trace carbon from phytochemical surface functionalization. DLS measurements indicated effective colloidal stabilization of LA-ZnO nanoparticles. The bioactive LA-ZnO exhibited concentration-dependent antibacterial activity against Gram-positive and Gram-negative bacteria, showing maximum inhibition against Staphylococcus aureus (18 mm at 2000 µg). In vitro MTT assays demonstrated cytotoxicity toward A549 and MCF-7 cancer cells with IC50 values of 22.55 and 81.15 µg/mL, respectively. These findings highlight Leucas aspera-mediated ZnO nanoparticles as promising biogenic nanomaterials for antimicrobial and therapeutic biomedical applications.

Biomimetics doi: 10.3390/biomimetics11070462

Authors: Chunhua Yuan Deyang Wang Xiangyu Li Xianwen Gao

Excitatory–inhibitory (E-I) local circuits play a central role in synaptic plasticity and neural coding, yet their multilayer learning dynamics remain poorly understood. We constructed a multilayer feedforward spiking neural network with intra-layer E-I connectivity, using Izhikevich neurons to model regular spiking (RS) and fast spiking (FS) cells, and examined cooperative learning under excitatory and inhibitory spike-timing-dependent plasticity (eSTDP and iSTDP). FS-mediated lateral inhibition alleviates the long-term depression bias arising from RS firing rate adaptation via winner-take-all competition, promoting heterogeneous E→E weight differentiation while preserving mean synaptic strength. A 12×12 parameter grid scan shows that iSTDP expands the stable learning region in the E-I parameter space and reveals a sustained cooperative co-evolution of eSTDP and iSTDP during training. For sparse coding, RS adaptation is the primary driver of Lifetime Sparseness, with FS inhibition acting as a cooperative enhancer; the network exhibits low sparseness at the input layer, a rapid increase at the second layer, and a stable plateau in deeper layers. For temporal pattern learning, the selectivity index d′ improved substantially after training, reaching approximately 1.90 times that of the FS-absent condition; both interval sensitivity and pattern generalization tests confirmed that this advantage is robust across biologically plausible inter-group delays and preserved under small temporal jitter. Mutual information analysis reveals a consistent tendency for intra-layer FS circuits to maintain higher stimulus-related information across deep layers, consistent with FS-mediated suppression of non-specific responses. These findings provide computational evidence, within the scope of the present model, for understanding cortical E-I cooperative plasticity and inform design principles for neuromorphic systems with adaptive inhibitory regulation.

Biomimetics doi: 10.3390/biomimetics11070460

Authors: Fatma Karacaoğlu Zülal Deniz Güner Merter Güçlü Elif Didem Özer Nilsun Bağış Kaan Orhan

Objective: This systematic review aimed to evaluate the effects of biomimetic surface engineering strategies applied to dental implants on osseointegration and peri-implant bone regeneration compared with conventional implant surfaces. Materials and Methods: A comprehensive literature search was conducted in the Web of Science, PubMed, and Scopus databases in accordance with the PRISMA guidelines, covering the period from January 2021 to January 2026. A total of 12 studies, including in vivo animal experiments and in vitro investigations, that met the inclusion criteria were analyzed. Risk of bias assessment was performed using the SYR-CLE tool and the ARRIVE guidelines. Results: Biomimetic strategies, including laser texturing, sulfonation, bioactive coatings, and growth factor/peptide functionalization (e.g., BMP-2, FGF-2, and PRF), significantly increased bone–implant contact (BIC), new bone volume (BV/TV), and biomechanical stability (pullout strength and reverse torque) compared to conventional surfaces. These surfaces enhance fixation under conditions of low bone density, such as osteoporosis, and improve infection resistance through antibacterial activity. In addition, these modifications enhance cellular adhesion, osteogenic differentiation, angiogenesis, and immune modulation. Conclusions: Current experimental evidence suggests that biomimetic implant surface engineering transforms dental implants from passive biomaterials into multifunctional bioactive interfaces capable of simultaneously regulating osteogenesis, immune response, angiogenesis, and antibacterial activity. Although promising outcomes have been demonstrated in preclinical studies, standardized long-term human clinical studies are still required to validate translational potential and long-term clinical efficacy.

Biomimetics doi: 10.3390/biomimetics11070459

Authors: Binbin Tu Jiawei Bao Haoyuan Zhou Yan Huo Xiaowei Han Nanmu Hui

UAV path planning in complex static 3D environments involves multiple conflicting objectives and intricate constraints. However, when applied to highly constrained path planning tasks, MOWOA often suffers from a low proportion of feasible solutions, convergence instability, single-leader search bias, and an uneven distribution of Pareto solutions. To address these issues, this study formulates the UAV path planning problem as a multi-objective optimization problem that simultaneously considers path length, threat cost, smoothness cost, and altitude cost, and proposes an improved multi-leader multi-objective whale optimization algorithm (IML-MOWOA). The proposed IML-MOWOA progressively improves three key stages of the optimization process: initial population construction, search guidance, and external archive maintenance. Specifically, an adaptive opposition-based learning initialization strategy is first introduced to improve the feasibility and spatial coverage of initial paths. Based on the resulting non-dominated solution set, a grid-based external archive update strategy is then used to regulate solution density and provide representative candidate leaders from sparse Pareto regions. Subsequently, a multi-leader dynamic weighted search mechanism with Softmax-based cosine annealing integrates these leaders into the WOA update process, thereby enhancing multi-directional path exploration and alleviating premature convergence. Comparative experiments conducted in three static 3D environments of varying complexity demonstrate that the proposed method achieves more robust convergence, better Pareto-front distribution, and more balanced task-level path quality than the benchmark algorithms. In the most challenging scenario, IML-MOWOA achieves the highest number of feasible paths, reduces the mean IGD by 25.04%, and decreases the mean path length, threat cost, smoothness cost, and altitude cost by 1.65%, 28.45%, 53.23%, and 29.88%, respectively, compared with the best-performing competing algorithm for each metric. These results indicate that the proposed algorithm is effective and robust for constrained multi-objective UAV path planning in complex static 3D environments.

Biomimetics doi: 10.3390/biomimetics11070458

Authors: Yapeng Xu Kaishun Hao Caidong Wang Li Xiao Wenming Wang

Inspired by the energy-efficient resonance strategy of the human elbow joint during periodic arm swing, this paper investigates the energy-saving motion and performance of a robotic variable stiffness joint. A modular stiffness adjustment mechanism with continuously adjustable stiffness based on Archimedean spiral grooves is proposed. A co-simulation model using MATLAB (R2022b)/ADAMS (2020) is established, and dynamic equations are derived to reveal the correlation between resonance/anti-resonance frequencies and joint rotational stiffness. Mimicking the biological principle of stiffness-frequency matching, an energy-saving controller leveraging the resonance effect is designed, which includes a motor energy consumption model to quantify losses and an optimization strategy to match the joint rotational stiffness with the load anti-resonance frequency. Simulation results demonstrate that in variable stiffness mode, aligning the system anti-resonance frequency with the task trajectory frequency significantly reduces joint energy consumption, validating the bioinspired approach. In contrast, the high-stiffness (rigid) mode leads to a surge in system energy consumption.

Biomimetics doi: 10.3390/biomimetics11070457

Authors: Ömer Alçin Muhammed Özküçük Muhsin Gençoğlu

Extreme and sudden temperature fluctuations observed as a result of global climate change increase the environmental pressure on energy transmission infrastructure. These meteorological changes significantly increase the risk of failure for porcelain insulators, which exhibit low thermal resistance and are susceptible to sudden arcing and surface deformations. In this study, a hybrid CNN–BiLSTM–SE architecture augmented with the Squeeze-and-Excitation attention mechanism is proposed using surface leakage current signals to diagnose healthy, cracked, and broken structural conditions in three-unit porcelain insulators. The SE block in the architecture dynamically rescales feature maps from CNN layers on a channel-by-channel basis. Thus, it highlights the signal characteristic that is dominant for fault diagnosis just before the BiLSTM units learn temporal dependencies. Leakage current data were obtained under an experimental setup at 60 kV for 15 different conditions covering all possible combinations of healthy, cracked, and broken insulator units. The raw signals were preprocessed with the Savitzky–Golay filter to suppress noise while preserving the diagnostic waveform morphology. 24 features covering time-domain statistics, frequency-domain spectral characteristics, and wavelet-domain energy components were extracted and used as model inputs. The CNN–BiLSTM–SE architecture achieved a classification accuracy of 93.83%, surpassing the standalone CNN (88.89%), BiLSTM (87.65%), and CNN–BiLSTM (91.36%) models, as well as classical machine-learning baselines (SVM: 87.65%, Random Forest: 90.12%, Boosted Trees: 87.65%).

Biomimetics doi: 10.3390/biomimetics11070456

Authors: Yuheng Jiang Yanchen Du Shengli Luo Xiaolong Shu Qingzhuo Yuan Hongliu Yu

To address the lack of standardized safety assessment tools for upper-limb rehabilitation robots, this study developed an integrated testing platform and a quantitative safety assessment framework, demonstrated with FlexoArm1 as a proof-of-concept. A 6-degree-of-freedom bionic arm equipped with multiple sensors was constructed, and a fuzzy PID control algorithm was employed to improve motion trajectory tracking accuracy. A fuzzy multi-criteria safety assessment model was established by combining the Analytic Hierarchy Process (AHP) with the entropy weight method. Experiments were conducted on the rehabilitation robot FlexoArm1. The platform reliably replaced human subjects in range-of-motion testing, interactive torque measurement (peak torque approximately 6.2 N·m in fully active mode), and spasticity simulation, with angular data showing close agreement with Inertial Measurement Unit (IMU) measurements. The assessment model assigned a comprehensive safety score of 70.23 to the tested device, successfully identifying weaknesses in fault detection capability and structural safety design. The proposed bionic-arm-based testing platform and the accompanying safety assessment methodology provide practical tools and a quantitative basis for standardizing safety evaluation and guiding design optimization of upper-limb rehabilitation robots.

Biomimetics doi: 10.3390/biomimetics11070455

Authors: Youngseok Lee

Phosphene-based visual neuroprostheses require encoding schemes that preserve task-relevant information while remaining feasible under safety-constrained stimulation. This study proposes a stress-based evaluation framework that reframes phosphene encoder assessment as a tri-objective operating-envelope problem rather than a single-metric comparison. Four representative encoders—rate, sparse, temporal, and optim—were evaluated under structured perturbations using two simulated prosthetic-vision(SPV) benchmarks: EMNIST Letters for symbolic recognition and a COCO-derived balanced subset for a reduced four-class COCO-derived image-level classification task. The final experimental configuration used 20,000/3000/3000 train/validation/test samples for EMNIST and 4000/1000/1000 for the COCO-derived benchmark. The framework combined structured stress sweeps, residual proxy safety-burden analysis, topology-based representation metrics, and stress-integrated utility summaries within a simulation-based evaluation setting. The results showed that clean-conditioning accuracy alone was insufficient to predict encoder behavior under stress. EMNIST exhibited broad operator-dependent degradation, whereas the COCO-derived benchmark showed a lower but more compressed performance regime. Residual proxy safety burden was only loosely aligned with performance, with moderate dissociation between performance and residual proxy safety burden in EMNIST and weaker alignment between these two axes in the COCO-derived benchmark. In the point-estimate utility summaries, the sparse encoder tended to yield comparatively favorable tri-objective utility values within the present single-run simulation-based framework, simplified SPV percept-synthesis operator, and fixed benchmark-specific decoder setting, primarily because it maintained an almost negligible residual proxy safety burden while preserving competitive performance and topology-based representation metrics. Topology-based analysis further indicated that topology-based representation metrics largely tracked task degradation in EMNIST, whereas topology-based representation metrics showed larger relative variation than decoder accuracy within the evaluated simulation setting under degraded COCO-derived conditions. Taken together, these findings provide an exploratory, benchmark-specific assessment suggesting that phosphene encoder evaluation may benefit from a multi-axis operating-envelope-oriented analysis that jointly considers stressed functional performance, residual proxy safety burden, and topology-based representation metrics within the present simplified SPV and fixed-decoder evaluation setting. These results should therefore be interpreted as simulation-level, configuration-dependent observations under a simplified SPV percept-synthesis operator, with safety-related quantities treated as residual proxy safety-burden summaries rather than as direct physiological, electrochemical, clinical, or implant-specific safety measurements.

Biomimetics doi: 10.3390/biomimetics11070454

Authors: Ruben Pereira João Silveira Susana Dias Sofia Monteiro António Mata Duarte Marques

Background: Tooth bleaching is a conservative aesthetic treatment that may influence tooth translucency, an optical property relevant to biomimetic dentistry. The purpose of this study is to evaluate changes in tooth translucency after three bleaching protocols, while preliminarily testing an objective, image-based, and spatially resolved method. Materials and Methods: A retrospective analysis used data from a previously published randomised clinical trial comparing three bleaching systems: in-office 6% hydrogen peroxide paint-on varnish; at-home 6% hydrogen peroxide prefilled tray; and at-home 16% carbamide peroxide custom tray. Spectrophotometric images of maxillary central incisors and canines were retrieved at different stages, and their translucency maps were processed with ImageJ to quantify the percentage of translucent area, histogram-derived grey intensity and RGB-blue channel metrics. Statistical tests were performed appropriately (α = 0.05). Results: All bleaching protocols produced significant post-treatment increases in translucency-related parameters (p < 0.05), although the in-office protocol showed smaller changes than the at-home techniques (p < 0.05). At six months, the translucent area remained significantly higher in most conditions, whereas histogram-derived metrics showed no significant changes. Correlations between translucency-related parameters and colour/whiteness differences were mostly negligible to weak. Conclusions: The preliminary image-based assessment detected significant and technique-dependent changes in translucency-related parameters following bleaching, with a weak linear association between these changes and colour outcomes.

Biomimetics doi: 10.3390/biomimetics11070453

Authors: Nikoleta Stoyanova Nasko Nachev Ani Georgieva Reneta Toshkova Mariya Spasova

In this study, novel electrospun polymer mats based on biocompatible poly(lactic acid) (PLA) and hydrophilic poly(ethylene glycol) (PEG) were successfully fabricated for the co-delivery of two natural polyphenols, quercetin (QUE) and rosmarinic acid (RA). Scanning electron microscopy (SEM) revealed the formation of defect-free, continuous nanofibers with high interconnected porosity. By mimicking the structural features of the native extracellular matrix, these nanofibrous platforms facilitate pronounced combined antioxidant and anticancer action. X-ray diffraction (XRD) analysis confirmed that the rapid solvent evaporation during electrospinning induced a physical state transformation, converting both QUE and RA from their native crystalline structures into an amorphous dispersion within the polymer fibrous materials, thereby optimizing their potential bioavailability. The obtained hybrid fibrous materials possessed good mechanical properties. Moreover, the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assay demonstrated that the incorporation of PEG enhanced matrix hydrophilicity, allowing the four-component PLA/PEG/QUE/RA mats to achieve the highest antioxidant efficiency (98.1%), suggesting an enhanced, complementary radical-neutralization pathway. Furthermore, in vitro biological assessments against human cervical carcinoma cell line (HeLa) and normal murine embryo fibroblasts BALB/3T3 demonstrated prominent anticancer activity, while noncancerous cells were significantly less affected. The dual-loaded PLA/PEG/QUE/RA fibrous mats induced significant cell shrinkage, chromatin condensation, and apoptotic cell death in HeLa cells, while normal BALB/3T3 fibroblasts retained cell membrane integrity and displayed higher resistance. Modeled after the native extracellular matrix, these bioinspired materials demonstrate significant antioxidant and anticancer activity, highlighting their potential for applications in localized cancer therapy, wound management, and tissue engineering.

Biomimetics doi: 10.3390/biomimetics11070452

Authors: Mingming Zhang Yuying Zhao Huan Li Xi Nan Ning Ma Ruoyang Liu Quan Wen

This is of significant engineering importance for enhancing the operation stability and reliability of aeroengines. To ensure the precise identification of aerodynamic instability, it proposes a deep learning model for multi-source fusion based on cross-attention and bidirectional Long Short-Term Memory (CA_BiLSTM) network. From a high-speed multistage compressor, multi-dimensional feature extraction is performed in the time domain, frequency domain, and entropy value range. Based on dispersion entropy, feature cross-identification is constructed with a multi-level early warning method. In response to the nonlinear aerodynamic parameters, Variational Mode Decomposition (VMD) and Dung Beetle Optimizer (DBO) for global optimization are integrated to construct a VMD_DBO_LSTM-coupled prediction model for aerodynamic stability. To address the limitation of single-point detection, this paper proposes a dual-channel fusion model based on cross-attention mechanism. Through shared convolution and dynamic weighting mechanism, the CA_BiLSTM model can precisely characterize the nonlinear features of the complex flow. It can fully integrate the complementary information of inlet and outlet signals, achieving the collaborative signal characterization. Its anti-interference capability is significantly superior to that of the original single-point signal. Combined with the dispersion entropy threshold, it can detect instability 1580 r in advance, effectively overcoming the problems of information deficiency and incomplete representation caused by traditional single-point monitoring.

Biomimetics doi: 10.3390/biomimetics11070451

Authors: Murat Bengisu Burcu Akdağ Fatma Şahmurat Zehranur Tekin Kamile Nazan Turhan

Citrus peel is examined here as a naturally evolved protective surface, with the goal of developing a transferable quantitative framework for extracting engineering-relevant descriptors from biological protective surfaces and using them as design templates for biomimetic counterparts. A single-specimen-per-species design is adopted to map intra-fruit geometric variation across regions and magnifications; absolute descriptor values are therefore reported as ordinal indicators of inter-species ranking rather than as population means. Five citrus species (lemon, orange, mandarin, grapefruit, and bitter orange) were characterised by mechanical testing (cutting, puncture, and compression; five replicates per fruit), gravimetric peel density and thickness, and scanning electron microscopy (SEM) at 100×–10,000×. The 135-image SEM dataset was processed with an automatic-calibration pipeline performing per-image scale-bar detection, multilevel-Otsu segmentation of albedo air space, cell-bounded surface segment (CBSS) and oil-gland segmentation on flavedo, and grey-level co-occurrence matrix (GLCM) texture analysis with a directional anisotropy index AF. Calibration was consistent across all images (FoV × magnification =403,273±410 μm·×, ±0.10%). Principal component analysis separated flavedo and albedo at every magnification (PC1 + PC2 = 84–92%). Within this dataset, grapefruit showed the densest CBSS cover (1072 mm−2) together with the highest oil-gland density (2.77 mm−2); bitter orange showed the largest CBSS area (23.7 μm2) and the thickest peel (13.1 mm); mandarin showed the most directionally oriented flavedo film (AF=0.0885); and lemon showed the most open albedo (φ2D=36.2%). Oil-gland equivalent diameter was essentially invariant (∼45 μm) across the five fruits, while gland density varied 4.4-fold. The structural metrics define a layered descriptor space—a dense isotropic surface relief versus a thick cellular bulk—that supplies two distinct bioinspired-design priors: dense surface films as a structural prior for selective-permeability membranes and layered cellular cores as a prior for impact-absorbing panels. A modified-atmosphere packaging (MAP)-compatible biomimetic film is identified as one downstream design hypothesis requiring direct gas-permeability verification on synthetic membranes.

Biomimetics doi: 10.3390/biomimetics11070450

Authors: Shan Yang Hongyuan Liu Decai Tang

Flexible robotic fish are emerging as a promising class of deep-sea exploration platforms because they combine compliant bodies, low-disturbance fish-like propulsion, and the potential for distributed sensing and autonomy. Unlike conventional biomimetic robotic fish developed mainly for shallow or moderate-depth environments, deep-sea flexible robotic fish must simultaneously address high hydrostatic pressure, low temperature, darkness, limited communication, constrained power supply, and complex near-bottom terrain. This review synthesizes research at the intersection of deep-sea soft robotics, bio-inspired robotic fish, smart-material actuation, pressure-adaptive packaging, multimodal sensing, and autonomous control. The literature is organized around a system-level design chain: biological mechanisms that inspire pressure adaptation and perception, body architectures that distribute pressure and protect electronics, soft actuators that generate fish-like propulsion, and control strategies that enable near-bottom and long-duration tasks. The review highlights that the central challenge is not any single actuator or material, but the co-design of pressure-adaptive bodies, hybrid soft actuation, reliable interfaces, multimodal perception, energy management, and autonomy. To strengthen engineering translation, this revised review further adds design-principle abstraction, actuator-selection guidance, prototype-level comparison, failure-mode analysis, and a computational design workflow. Future research should prioritize long-term reliability tests, standardized deep-sea evaluation protocols, physics-informed modeling, and integrated prototype demonstrations under realistic mission conditions.

Biomimetics doi: 10.3390/biomimetics11070449

Authors: Hong-Yu Lin Che-Ping Hung Kuo-Yang Tu Fang-Tsen Kuo

In recent decades, humanoids have become more popular in various applications. However, their applications in human life are more than those in industry. In this paper, a humanoid is used to capture the sets of point clouds of an object for three-dimensional reconstruction. The structured light camera is widely used across diverse 3D scanning applications due to its high resolution, rapid acquisition capability, and adaptability to various material surfaces. Therefore, the humanoid developed by our team holds a structured light camera which captures the point clouds of an object put on a platform for the reconstruction of its 3D digital model. The platform is rotated so that the structured light camera can capture the image of all view angles on the object. Meanwhile, the structured light camera captures point clouds, and the camera of the humanoid recognizes the QR code on the platform so that the sets of point clouds can be distinguished by view angles on the object. Then, the automated registration process of the point cloud sets for a 3D model based on the point-to-plane iterative closest point (ICP) algorithm is proposed. The process incorporates preprocessing techniques, such as downsampling and normal vector estimated from plane, and utilizes the ICP algorithm for registration, ultimately achieving markerless and precision automatic merging of multi-view point cloud data. Experimental results demonstrate that the proposed method with the humanoid can effectively improve the completeness and accuracy of 3D reconstruction models, significantly reduce manual intervention, and enhance the system’s versatility and practical feasibility. Key parameters adjusted for more efficient computation of the ICP algorithm are revealed. In addition, the experimental results of the proposed ICP compared with G-ICP are also included.

Biomimetics doi: 10.3390/biomimetics11070448

Authors: Ján Duplák Dušan Knežo

This article presents a systematic analysis of the application of advanced mathematical and computational approaches in dental bioengineering, with a focus on biomaterials processing and machining-related technologies. The aim is to critically synthesize current knowledge on the use of numerical simulations, statistical modeling, and algorithm-based methods in the analysis and optimization of technological processes in dentistry. The review was conducted following the PRISMA framework to ensure a transparent and reproducible selection of relevant studies addressing the intersection of dental applications, manufacturing processes, and computational modeling. The results reveal that the current research does not constitute a unified modeling framework, but rather a heterogeneous set of approaches targeting specific aspects of biomaterial processing. The analyzed studies demonstrate the application of finite element analysis, empirical statistical models, and geometry-based computational methods, particularly in processes such as drilling and grinding of ceramic dental materials. These approaches enable detailed analysis of mechanical and thermal loading conditions, as well as partial optimization of process parameters. However, their applicability is often limited by their empirical nature, lack of integration, and insufficient linkage to real-time process control. The synthesis highlights a significant research gap in the development of integrated and multiphysics modeling frameworks capable of combining mechanical, thermal, and geometrical aspects of machining processes. Future research should focus on the implementation of digital twins, adaptive process control, and personalized modeling strategies to enhance the accuracy, efficiency, and predictability of dental biomaterial processing.

Biomimetics doi: 10.3390/biomimetics11070447

Authors: Pedro C. Ormonde Xiaowei He Kenneth Breuer

A cyber–physical underwater vehicle is equipped with bio-inspired flapping fins positioned on the sides of the vehicle’s main body. The proposed control surfaces are inspired by fish pectoral fins, generating forces and moments that can potentially be harnessed for maneuvering, hovering and station keeping. The streamwise and cross-stream forces produced by the fins are characterized for a range of reduced frequencies and Strouhal numbers. The streamwise forces are shown to be predominantly a function of the fin’s projected frontal area, while the lateral forces also depend on the Strouhal number. When operated simultaneously, different flapping synchronizations can be employed for specific goals; a symmetric motion suppresses the lateral forces, while an anti-symmetric motion decreases the peaks of the streamwise force produced. The cyber–physical vehicle demonstrates how the pair of fins can successfully maneuver the vehicle in the lateral direction.

Biomimetics doi: 10.3390/biomimetics11070446

Authors: Ozren Polašek

Biomimetics often treats convergent evolution as the strongest sign that a biological solution is general. That inference is safest when the constraint does not counter-adapt. Hosts do. In coevolved host-interface systems, recurrence alone cannot tell us whether a solution is translatable. Biomimetic transferability depends first on two axes: conservation of the host target and versatility of the attacking lineage. A conserved target behaves, for translational purposes, like a biochemical constraint, a broad-host parasite has already tested its mechanism across biological variation. The window narrows when the target is taxonomically local, or when the mechanism has become a private molecular conversation inside a narrow dyad. Haematophagous feeders, intracellular protozoans, specialist helminths, and polydnavirus-bearing parasitoid wasps therefore do not offer the same kind of biomimetic object. Some yield molecules, some vulnerability maps, some contextual principles, and some only architecture or analogy. The point is not to mine coevolved systems less, but to stop mistaking coevolutionary success for biomimetic portability.

Biomimetics doi: 10.3390/biomimetics11070445

Authors: Ayşenur Kandemir Turgut Kalay

This study presents and pilot-tests the Biomimetic Design Education Framework, a structured pedagogical model developed to systematize the translation of biological knowledge into furniture design within studio-based educational contexts. Positioned as a pilot implementation, the study introduces the Biomimetic Transfer Matrix as an accompanying analytical tool for assessing the depth of biological knowledge integration in student design work. It is based on 18 student projects developed during a furniture design course, assessed through qualitative content analysis. The projects were evaluated according to four types of biomimetic transfer: formal, structural, mechanical, and functional/behavioral. Results reveal that structural transfer was the most prevalent category (38.9%), followed by functional/behavioral transfer (33.3%), formal transfer (16.7%), and mechanical transfer (11.1%). This distribution indicates that structured pedagogical guidance can successfully direct students beyond surface-level morphological imitation toward deeper principle-based biological abstraction, while also identifying mechanical and system-based transfer as areas requiring targeted curricular development. On this basis, the study presents the Biomimetic Design Education Framework and introduces the Biomimetic Transfer Matrix as an analytical tool for examining different levels of biomimetic knowledge transfer in design. Results underline the importance of structured approaches to support deeper levels of biological abstraction in design education. The findings contribute to SDG 4 (Quality Education) by advancing evidence-based approaches to biomimetic design instruction.

Biomimetics doi: 10.3390/biomimetics11070444

Authors: You Zhang Yang Pan Lingyu Wang Haoran Cui Surong Jiang Liping Ding Shengli Chen Yangshuo Yue Bai Chen

Structural static testing is paramount for validating the structural integrity of critical aerospace components. However, conventional test rigs are often constrained to fixed loading axes and frequently induce parasitic torques. Accurate reproduction of aero-engine pylon flight loads therefore requires a mechanism that combines omnidirectional vector loading, high stiffness, and efficient force transmission. Achieving these coupled requirements is primarily a geometric synthesis problem, yet the associated workspace, stiffness, and load–capacity indices are nonlinear, mutually coupled, and expensive to evaluate over dense pose samples. To address this optimization bottleneck, this work develops a task-specific 6-UPS loading mechanism and a bio-inspired sensitivity-weighted NSGA-II algorithm for its geometric synthesis. Inspired by gene/locus-specific heterogeneity in biological evolution, the algorithm assigns variable-wise search intensities according to design-variable sensitivities, which are estimated using Multivariate Adaptive Regression Splines (MARS). In this way, influential design genes receive stronger local exploitation, whereas less sensitive ones retain broader exploration. Numerical simulations demonstrate that the proposed approach reduces computation time from about 30 h to 3 h relative to direct optimization with the baseline NSGA-II, while simultaneously improving workspace, stiffness, and load-carrying capacity. A hybrid physical prototype was further tested under 240 loaded pose conditions; the system maintained force magnitude errors below 0.64% (63.42 N) and directional deviations below 1.15°. These results support the efficacy of the proposed bio-inspired optimization-based design methodology for high-fidelity static testing of aero-engine pylons under the adopted hybrid setup.

Biomimetics doi: 10.3390/biomimetics11070443

Authors: Seyedrahman Djafaripetroudy Mabel Lagla-Molina Alex Guambo-Galarza Norma Erazo Magdy Echeverría Angel Ordóñez

Microbial fuel cells (MFCs) are emerging as biomimetic bioelectrochemical systems that emulate naturally occurring microbial electron-transfer pathways for stimulus bioenergy generation and wastewater remediation. In this study, food–vegetable leachate (FVL) and sugarcane bagasse-derived biol were evaluated in combination with carbon fiber (CF) and biochar-modified carbon fiber (BCF) electrodes used as membrane components in MFCs. Four configurations, in duplicate, were constructed by coupling two substrates (biol or FVL) with two membrane types (CF and BCF). All systems exhibited progressive anodic acidification and up to a 55% increase in electrical conductivity. The highest voltage output was achieved in MFC-BL-2 (404.59 mV), followed by MFC-FL-1, driven by synergistic interactions between the substrate and biochar-enhanced conductive networks. MFC-FL-1 also demonstrated superior contaminant removal performance, achieving 60% COD reduction, 36% BOD reduction, and 50% NH4+–N removal. SEM–EDS analysis confirmed that biochar-modified electrodes developed a porous structure and substantially enhanced microbial adhesion. FVL-fed systems formed dispersed electroactive biofilms that facilitated electron transfer, whereas biol-fed systems developed compact biofilms that constrained electron flux. By integrating waste-derived lignocellulosic materials with electroactive microbial consortia, this work advances a biomimetic circular bioengineering platform for sustainable bioelectrochemical recovery and wastewater remediation.

Biomimetics doi: 10.3390/biomimetics11070442

Authors: Joel Quarnstrom Abram Smith Owen Barragan Adrian Toquothty Yujiang Xiang

Shoulder injuries due to labor-related lifting tasks are widespread in manufacturing and logistics companies. Prolonged shifts and repetitive motions lead to muscle fatigue, significantly elevating the risk of both acute accidents and chronic musculoskeletal disorders. Many passive exoskeletons which use springs to provide lifting assistance have been commercialized, and many active exoskeletons have been researched. The drawback to passive exoskeletons is the larger the lifting force that they produce, the larger the force required to lower the arms. This contributes to tiring the user. Conversely, active exoskeletons require substantial energy to provide meaningful torque. Furthermore, they pose a safety risk; a sudden power failure could result in an instantaneous loss of support, potentially causing the user to drop a heavy load and sustain injury. This research project proposes a hybrid exoskeleton with a parallel elastic actuator that uses a motorized helical actuator which can be tuned to improve lifting performance. This paper evaluates the kinematics and statics of the proposed exoskeleton, details the design and implementation of the electrical control system, shows mechanism optimization of the mechanical advantage profile, and validates the concept through the construction and experimental testing of a functional prototype.

Biomimetics doi: 10.3390/biomimetics11060441

Authors: Xin Xiong Xiangyu Li Shawn You Bing Zhu Ping Ding Huanhuan Gao Zongqi Hou

Meeting the rigorous performance standards of modern electrified transit necessitates the deployment of high-performance outer rotor PMSMs with elevated power-to-volume ratios. However, their unique internal heat source topology inherently restricts heat dissipation. This limitation risks permanent magnet demagnetization and winding insulation failure. To address these thermal bottlenecks, this paper proposes internal bio-inspired cooling channels. These channels feature micro-scale V-shaped ribs. This design targets a 60 kW outer rotor PMSM. The motor uses a fractional-slot concentrated winding. The analytical procedure commences with the formulation of a transient 2D numerical model utilizing the Time-Stepping Finite Element approach (TS-FEM). It is coupled with the Bertotti model to compute electromagnetic losses. This approach accurately determines losses under high-frequency rated conditions. Results reveal that stator iron loss constitutes the dominant heat source. It accounts for 76.4 percent of the total electromagnetic loss. Furthermore, these losses show severe spatial concentration at the stator teeth. Subsequently, a three-dimensional fluid-solid coupled CFD model is developed. This model evaluates the proposed internal cooling channels. The design integrates bio-inspired vein networks and V-shaped ribs. These internal ribs disrupt the near-wall thermal boundary layer. This disruption enhances the local convective heat transfer. Comparative multiphysics analyses indicate improved hydraulic and thermal performance of the bio-inspired design under the same numerical boundary conditions. The bio-inspired channel achieves a more uniform static pressure distribution and reduces severe fluid stagnation zones. In the numerical model, the maximum stator and permanent magnet temperatures are reduced to 48 °C and 42 °C, respectively. This work provides a numerical design reference for thermal management in high-performance electric aviation.

Biomimetics doi: 10.3390/biomimetics11060440

Authors: Mehdi Khaleghi Farshad Pashootanizadeh Nastaran Khaleghi Sobhan Sheykhivand Sebelan Danishvar VahidReza Ghezavati

Systematic logistics plays a key role in fostering profitable development in supply chains. An intelligent logistics model can help create a more agile, sustainable, and resilient supply chain. In recent years, several brain-inspired deep learning architectures, such as long short-term memory networks, graph neural networks, and convolutional neural networks, have been introduced for intelligent decision-making tasks. From a biomimetic perspective, these models are inspired by biological information-processing mechanisms. Convolutional neural networks reflect hierarchical procedures similar to those in the visual cortex, graph neural networks mimic communication among biological neurons, and LSTM networks are motivated by short-term and long-term memory mechanisms in the brain. Inspired by these biomimetic computational principles, this study proposes a novel hybrid deep learning strategy composed of LSTM, convolutional layers and GraphSAGE geometric layers for smart supply chain logistics management. This strategy enables leveraging information pertaining to LSTM-based long-term dependencies, convolutional local patterns and graph-related hidden connections of the supply chain dataset for intelligent decision-making. The GraphSAGE framework helps with scalable graph learning, which enhances predictive accuracy in the case of unseen data. The optimizer in the proposed methodology performs sequential optimization using the biomimetic particle swarm optimizer and the Adam approach (PSO-Adam), considering the hybrid cost function. The prediction of logistics parameters is investigated using five datasets, including DataCo, Shipping, Smart Logistics, Hospital Supply Chain, and Pharmaceutical Supply Chain. The average accuracies of 97.8%, 100%, 96.6%, 98.7% and 99.4% are obtained for practical multi-category logistics parameter forecasts. The evaluation metrics for ten logistics predictions confirm the effectiveness of the proposed intelligent logistics model and highlight the potential of biomimetic geometric networks for complex supply chain decision-making. The model is a cost-efficient approach with consideration of the prediction capabilities, helping to reduce the occurrence of logistics risks, increase the productivity of the supply chain and affect the supply chain visibility, customer satisfaction, and industry reputation.

Biomimetics doi: 10.3390/biomimetics11060439

Authors: Ceren Baştemur Kaya

Due to the rapidly increasing number of studies conducted using SFO in recent years, a comprehensive and systematic review of the existing literature has become necessary. SFO is a bio-inspired metaheuristic optimization algorithm developed based on the sun-tracking behavior of sunflower plants. Owing to its simple mathematical structure and flexible search capability, SFO has been increasingly applied to various engineering and AI problems. This review study presents a systematic and comprehensive analysis of SFO-based studies published in the literature. The literature search was performed using the Scopus database, and a total of 192 studies were included in the final evaluation process. The reviewed studies were classified into eight major application domains, including engineering design, energy systems, machine learning, image processing, communication systems, robotics, forecasting, and multi-objective optimization. In addition, the distributions of standard, hybrid, and modified SFO approaches were comparatively analyzed. The temporal evolution of SFO studies, hybridization tendencies, application diversity, strengths, limitations, and future research directions were also systematically evaluated. The findings indicate that hybrid and modified SFO structures have become increasingly dominant in recent years, particularly in AI and data-driven optimization applications. Overall, this review provides a broad understanding of the current state and future research potential of SFO-based optimization studies.

Biomimetics doi: 10.3390/biomimetics11060438

Authors: Yang Li Yao Song

Trust in embodied intelligence is dynamic, contextual, and interaction-dependent, but many existing computational approaches still model trust using static similarity structures. This study proposes and evaluates a compositional trust modeling approach based on quantum natural language processing (QNLP). Using open-ended survey responses about human trust in embodied agents, we compared classical NLP clustering and QNLP-based clustering in terms of dimension coverage, semantic coherence, contextual sensitivity, and robustness. The QNLP pipeline captured richer latent structure, producing ten clusters and identifying eight trust dimensions, including two emergent dimensions: calibrated trust and predictive reliability. Compared with classical approaches, QNLP clusters showed improved semantic separation and stronger context retention under preprocessing variation. These findings support a temporally structured view of trust in embodied AI and demonstrate that compositional quantum-inspired representations can reveal nuanced trust dynamics that are difficult to detect with conventional methods. This study contributes both a methodological framework for trust-sensitive text modeling and a theoretical account linking trust formation to retrospective calibration and prospective expectation in human–agent interaction.

Biomimetics doi: 10.3390/biomimetics11060437

Authors: Jens Grotrian

Empirical logic (EL) is a bio-inspired soft computing approach to rule-based decision-making that emphasizes intuitive, experience-based reasoning. While its theoretical foundations have been established in previous work, its practical applicability and accessibility have so far received less attention. This paper addresses this gap by providing two representative application examples from distinct domains: control engineering and cluster analysis. The first example demonstrates the use of EL for the speed control of a DC drive, highlighting its ability to achieve competitive dynamic performance with a small number of intuitive rules. The second example introduces a novel approach to cluster analysis, where cluster structures emerge from the collective interaction of EL rules rather than from the optimization of a predefined objective function. In addition, the paper emphasizes the availability of publicly accessible software realizations of EL, including a Maple-based prototype and a Python framework, which enable direct experimentation and practical use. By combining illustrative applications with executable tools, the paper aims to facilitate the transition from conceptual understanding to practical deployment and to support further exploration of EL in applied soft computing contexts.

Biomimetics doi: 10.3390/biomimetics11060436

Authors: Jingya Zhang Yu Liu Chaochuan Jia Maosheng Fu Yaqi Yang Jiahui Liu Yujie Cheng

To address the inherent limitations of the Dhole Optimization Algorithm (DOA)—limited exploration range, insufficient population diversity, and slow convergence—this paper proposes a Modified Dhole Optimization Algorithm (MDOA) integrating a Beta distribution-based opposition learning strategy, a DE/rand-to-best/1 differential mutation mechanism, and nonlinear parameter control. MDOA is evaluated on 41 CEC2017 and CEC2022 benchmark functions, outperforming 11 state-of-the-art algorithms in convergence speed, accuracy, and robustness. It is then applied to five engineering optimization problems: compression spring design, speed reducer weight minimization, rolling bearing optimization, tubular column design, and moisture content prediction of Dendrobium huoshanense using near-infrared spectroscopy with a BP neural network. The MDOA-BP model reduces MAE, RMSE, MSE, and MAPE by 27.5%, 27.8%, 47.6%, and 31.0%, respectively, while increasing R2 from 0.8339 to 0.9130, achieving the best results among all comparison models. These results demonstrate that MDOA is a highly effective and robust optimizer for complex constrained engineering and high-dimensional optimization tasks.

Biomimetics doi: 10.3390/biomimetics11060435

Authors: Tianqi Chen Zhiyu Qiu Yuki Todo Zheng Tang

In this study, we propose a biologically inspired Self-Organizing Map-based Artificial Visual System (SOM-AVS) for unsupervised orientation detection in static images. By combining a biologically motivated front-end visual processing module with an unsupervised SOM layer, the proposed system captures key characteristics of early-stage visual processing, including localized orientation-sensitive responses and structured feature organization. The model enables the structure of distinct orientation-related representations without requiring labeled data, forming organized response patterns across the neural map. Experimental results demonstrate robustness under various conditions, including noise corruption, restricted perceptual experience, and limited training samples. Furthermore, the model shows adaptive behavior when exposed to new stimuli after initial training, indicating its potential to reflect experience-dependent adjustments in representation. These findings suggest that SOM-AVS provides a useful framework for exploring self-organization mechanisms in artificial visual systems and for developing biologically inspired perception models.

Biomimetics doi: 10.3390/biomimetics11060434

Authors: Lei Jiang Kaixin Lan Xianwei Liu Chaojie Fu Yongbin Jin Hongtao Wang

The design of dexterous robotic hands has long been constrained by empirical paradigms that struggle to balance anthropomorphic fidelity with dynamic performance. This study aims to establish a systematic methodology that bridges this gap through data-driven inverse design. We construct a quantitative association map between design variables and performance metrics using a comprehensive dataset of existing dexterous hands, then apply this map to translate explicit high-frequency dynamic targets into an optimized hardware configuration. The analysis reveals that the dominant principles for high-speed performance—tendon-driven transmission, proximal actuation, and lightweight rigid structures—closely mirror the biomechanical architecture of the human hand. Guided by this convergence, we develop the Beyond Hand, a 20-degree-of-freedom (DoF) anthropomorphic hand that preserves human-scale dimensions. Standardized frequency-response tests across all 15 joints show magnitude attenuation below 3 dB at 14 Hz and cutoff frequencies clustered around 10 Hz. In rhythm-game and Tetris-style manipulation tasks, the hand maintains over 90% accuracy at actuation frequencies up to 12 Hz. These results demonstrate that a performance-driven pathway can systematically elevate the dynamic capabilities of humanoid dexterous hands, offering a scalable framework for biomimetic robotic design.

Biomimetics doi: 10.3390/biomimetics11060433

Authors: Barış Erkut Türk Bersu Bedirhandede Dilan Gizem Doğan Beyza Güney

Background/Objectives: Patient-specific subperiosteal implants are increasingly used to treat severely atrophic ridges due to advances in digital planning and additive manufacturing. This study aimed to evaluate the effects of material type and implant configuration on stress distribution in subperiosteal implant systems and to compare their overall biomechanical performance using a multi-criteria decision framework. Methods: A three-dimensional model of a severely atrophic maxilla was reconstructed to simulate four clinical scenarios combining two configurations (one-piece and two-piece) and two materials (titanium and 60% carbon fiber-reinforced polyetheretherketone). Finite element analysis was conducted to assess stress distribution within the implant body, fixation screws, prosthetic framework, and surrounding bone under vertical and oblique loading conditions. Maximum and minimum principal stresses were evaluated in bone, whereas von Mises stresses were calculated for implant components. The resulting biomechanical indicators were subsequently integrated using an entropy weight–TOPSIS multi-criteria decision analysis. Results: Principal stresses in the surrounding bone showed minimal variation between titanium and 60% carbon fiber-reinforced polyetheretherketone across all configurations. Implant configuration had a more pronounced effect on implant body stress. Under oblique loading, the two-piece configuration demonstrated substantially higher implant stresses than the one-piece design, whereas under vertical loading, lower implant stresses were observed in the two-piece configuration. The multi-criteria analysis ranked the one-piece titanium model highest under oblique loading and the two-piece titanium model highest under vertical loading. Conclusions: Implant configuration and loading direction influenced biomechanical behavior more than material selection in patient-specific subperiosteal implants.

Biomimetics doi: 10.3390/biomimetics11060432

Authors: Xuguang Yang Zhihui Wang Xiaohua Gu Yan Liu

To enhance the thermal management of lithium-ion batteries in new-energy vehicles, various bio-inspired liquid-cooled plate channel designs were investigated to improve hotspot dissipation within the laminar flow regime. A series of three-dimensional numerical simulations were conducted to compare leaf vein-, tree branch-, honeycomb-, and spider web-inspired channels, followed by further optimization to improve thermohydraulic performance. The selected optimized bio-inspired channels were subsequently evaluated against conventional structures. Simulation results indicate that the honeycomb-inspired liquid-cooled plate channel achieved the best performance, followed by the tree branch- and spider web-inspired channels, which exhibited comparable thermohydraulic performance. The leaf vein-inspired channel demonstrated the lowest performance. The key design element for enhanced heat dissipation is the inclusion of longitudinal branch channels, which minimize flow zones with near-zero velocity and effectively mitigate local hotspots. Furthermore, the combination of longitudinal and inclined branch channels can redirect flow direction and enhance fluid mixing. Compared with the conventional channel widely adopted in existing studies, within the Reynolds number range of 260 to 920, the optimized honeycomb-inspired liquid-cooled plate channel achieves a 44.0–49.3% increase in Nusselt number and an 81% enhancement in comprehensive performance metric. Concurrently, thermal resistance is diminished by 2.6–9.2%, and pumping power is reduced by 50.0–56.8%.

Biomimetics doi: 10.3390/biomimetics11060431

Authors: Tianye Luo Jing Hu Bangcheng Zhang Xinming Zhang Shaoming Luo

Dynamic hospital corridor environments are characterized by complex corridor environments, diverse target-scale variations, frequent occlusions, and dense small-object distribution, posing significant challenges to the accuracy and efficiency of the existing methods on resource-constrained platforms. To effectively address these challenges, a high-precision framework CSAWOA (Cross Search Adaptive Whale Optimization Algorithm)-YOLOv8 (You Only Look Once version 8) model for complex medical environments was introduced in this work. By jointly modelling high-level semantic information and low-level cues such as texture and colour, the proposed model achieved a more discriminative and informative feature representation. The T-CBS (Transformer-Convolutional Bottleneck Structure) module, capable of extracting shallow-level features and integrating global contextual information to address target occlusion issues, was also proposed. Furthermore, the integration of the BiFormer module yielded an enhanced feature discriminability, improving small-target recognition while reducing sensitivity to background noise. The classification function was modified, effectively solving the problem of class imbalance in complex corridor environments. The combination of these two concepts achieved an effective balance of diversity in detection and convergence speed, leading to improved optimization performance and greater resistance to local-optimum stagnation. Meanwhile, an improved version of the WOA was developed, termed CSAWOA, enabling automatic hyperparameter optimization for the improved YOLOv8 model. From the experimental results, improvements of 4.9%, 6.1%, and 8.3% in mAP, precision, and recall, respectively, compared to YOLOv8 were demonstrated, while also exhibiting better generalization. Overall, the proposed method provides a reliable and efficient approach for object detection in complex hospital corridors, offering a valuable foundation for future research and real-world healthcare applications.

Biomimetics doi: 10.3390/biomimetics11060430

Authors: Rong Lv Guofa Lei Hanchao Liu Yuhan Sun Wenhua Wang Xuebin Du

Enhancing oil and gas production performance is essential for maintaining the economic sustainability of petroleum enterprises and meeting the increasing global energy requirements. In this context, subsurface production optimization constitutes a fundamental component of strategic reservoir management, directly affecting critical decisions such as well location design and the regulation of operational parameters. Nevertheless, conventional reservoir optimization approaches are frequently constrained by high computational costs and limited optimization effectiveness. To overcome these limitations, evolutionary algorithms have gained considerable attention for addressing complex optimization tasks, owing to their gradient-free nature and strong capability for parallel exploration. This paper proposes a chaotic exploitation orthogonal learning fruit fly optimization algorithm (COFOA) tailored for global optimization and oil and gas production optimization. Specifically, we integrate a chaotic exploitation mechanism and an orthogonal learning strategy to improve the balance between exploration and exploitation. Following the population update in FOA, the chaotic exploitation mechanism is first applied to help the population escape local optima and enhance search efficiency. Subsequently, the orthogonal learning strategy is employed to strengthen the algorithm’s exploitation capability. To evaluate the performance of the improved FOA, extensive experiments were conducted on benchmark functions from IEEE CEC 2017 and IEEE CEC 2022, including ablation studies, scalability tests and comparisons with state-of-the-art algorithms. The results demonstrate that the proposed FOA significantly outperforms competing algorithms in optimizing reservoir production. COFOA demonstrates consistent performance superiority over all compared algorithms in terms of mean NPV. Specifically, it achieves improvements of approximately 2.35% to 16.23% compared with existing methods. Notably, COFOA outperforms strong competitors such as mSCA and BLPSO by 2.35% and 3.81%, respectively, while achieving more significant gains over algorithms such as SCADE (15.31%) and CCMSCSA (16.23%). Even when compared with relatively competitive methods like HGWO and CCMWOA, COFOA still maintains performance improvements of 4.79% and 6.12%, respectively. These results clearly demonstrate the superior optimization capability of COFOA in terms of maximizing NPV under complex reservoir conditions.

Biomimetics doi: 10.3390/biomimetics11060429

Authors: Youjin Seol Myoung Joon Jeon Sayan Deb Dutta Youjin Jeong Ki-Taek Lim

The extracellular matrix (ECM) provides a dynamic microenvironment that regulates cell proliferation, migration, and tissue remodeling during wound healing. However, replicating the structural and functional complexity and ECM heterogeneity of native skin ECM remains challenging with conventional single-material hydrogels. Recent advances in multimaterial 3D bioprinting have enabled the spatial integration of diverse biomaterials within a single construct. Lignocellulose has attracted increasing attention as a promising biomaterial for recreating key structural features of the native ECM because of its fibrous architecture, mechanical strength, and biocompatibility. This review offers a comprehensive and integrated perspective on the use of lignocellulose-based multimaterial printing to recreate ECM-mimicking architectures, an underexplored area at the intersection of biomaterials and biofabrication. The roles of cellulose, hemicellulose, and lignin in printability, scaffold stability, porosity, bioactivity, and wound-healing performance are discussed. Representative studies have demonstrated that lignocellulose-based multimaterial bioinks provide porous architectures that support cell adhesion, proliferation, and tissue regeneration. These benefits are accompanied by improved mechanical performance, as cellulose nanofibers exhibit elastic moduli exceeding 100 GPa, and lignin-containing hydrogels have achieved compressive moduli of up to 135 kPa. Such mechanical advantages make lignocellulosic materials particularly attractive for fabricating ECM-mimicking scaffolds that require long-term structural integrity. Finally, key design considerations and current limitations associated with lignocellulose-based multimaterial bioprinting are critically discussed. A framework for the rational design of lignocellulose-based multimaterial bioinks is presented, together with future directions toward gradient and adaptive scaffolds, smart wound dressings, and advanced wound-healing applications.

Biomimetics doi: 10.3390/biomimetics11060428

Authors: Chen Huang Beining Yang Yan Huo

Traditional grey wolf optimizer (GWO) frequently suffers from insufficient search diversity, unstable stage transition, and premature convergence when addressing complex optimization tasks. To overcome these limitations, this paper proposes an improved grey wolf optimizer with a Q-learning-guided fitness–distance-weighted selector. For the proposed QLFDGWO framework, first, chaotic mapping is introduced to generate a more diverse initial population. A cosine nonlinear convergence factor is employed to improve adjustment capability during the search process. Additionally, a Q-learning-based strategy selection mechanism is constructed to enable adaptive switching between exploration and exploitation. To further improve the leadership structure of GWO, a Q-learning-guided fitness–distance-weighted selection mechanism is designed, in which the beta and delta wolves are selected by jointly considering fitness quality and spatial distance from the alpha wolf. A dynamic threshold-weighted update strategy is designed to enhance the convergence accuracy and stability of the population. Finally, the proposed algorithm is benchmarked against five representative optimization algorithms using the CEC2017 benchmark function set. Experimental results indicate that QLFDGWO achieves satisfactory performance in terms of optimization accuracy, convergence speed, and robustness. In addition, QLFDGWO is applied to three-dimensional (3D) unmanned aerial vehicle (UAV) path planning under a range of complex scenarios. Simulation results demonstrate that the proposed method can generate feasible, safe flight paths that satisfy terrain and obstacle constraints.

Biomimetics doi: 10.3390/biomimetics11060427

Authors: Tarek H. Mokhtar Somaih M. Bakr Qusai R. Khashman

Biological thin-sheet systems, including leaves, insect wings, and flowering organs, achieve adaptive deformation through distributed compliance, segmentation, curvature, and controlled opening. Kirigami offers a bio-inspired route for translating such deformation logics into programmable thin-sheet surfaces; however, the geometric parameters that most strongly influence elastic displacement remain insufficiently quantified, especially across different loading regimes. This study investigates Bio-Inspired Regime-Dependent Parameter Selection in Parametric Kirigami through twenty-five laser-cut specimens spanning five boundary shapes and three thermoplastic substrates. Specimens were tested under two contrasting regimes: quasi-static tensile loading and gravity-drape loading. Elastic displacement was measured under eight-point boundary fixation and analyzed using regime-separated Pearson correlations, Bonferroni-corrected significance testing (α/18 = 0.0028), and shape-controlled partial correlations. Under tensile loading, the Number of Offsets (r = 0.807), Segments per Offset (r = −0.603), and outer-boundary void perimeter (r = 0.621) showed the strongest Bonferroni-robust associations with displacement. Under gravity-drape loading, effects were weaker and more curvature-sensitive, indicating that parameter relevance is not universal but regime-dependent. Within the tested parametric design space, the study provides an experimentally grounded basis for selecting Kirigami geometric parameters in thin-sheet structures whose adaptive deformation logic is analogous to compliant systems found in nature.

Biomimetics doi: 10.3390/biomimetics11060426

Authors: Jaemoo Hong Keon Myung Lee

As recent Multi-Layer Perceptron (MLP) mixer models have achieved state-of-the-art performance in time series forecasting, modeling each MLP-mixer as a separate expert within a mixture is expected to extend the representational capacity of the model, allowing each expert to be activated in response to time-varying inputs. However, extending MLP-mixers into a Mixture-of-Experts (MoE) architecture introduces a significant increase in the number of trainable parameters, rendering the model more challenging to train. To mitigate this problem, we propose a method that composes a fully trainable global expert and multiple non-trainable local experts. Specifically, our approach clones the weights of the global expert into the local experts and then modifies their weight distributions using moment learning, a recently proposed unconventional method for training neural networks. Concretely, each local expert is produced by applying moment-based transformations to a shared copy of the global expert’s weights, so that expert specialization is obtained without independently training the additional experts. Experimental results using a lightweight Time Series Mixer (TSMixer) architecture demonstrate that our method achieves performance competitive with fully trainable MoE counterparts, without introducing a significant increase in trainable parameters. Across multiple benchmark settings, the proposed model attains forecasting accuracy on par with, and in several cases favorable to, a fully trainable multi-expert baseline while adding only a small fraction of the extra trainable parameters that such a baseline requires, and this efficiency is further corroborated by measurements of memory footprint as well as an effect-size-based assessment of the observed differences.

Biomimetics doi: 10.3390/biomimetics11060425

Authors: Angeliki Tsantiri Nikolaos I. Mourkiotis Hector Katifelis Xanthippi Dereka Maria Gazouli Nefeli Lagopati

Periodontal disease remains one of the leading causes of tooth loss worldwide, highlighting the need for effective regeneration of alveolar bone, periodontal ligament, and cementum. The structural complexity and unique biological behavior of these tissues have historically posed significant challenges for clinical regeneration strategies. The primary therapeutic approach used is guided bone regeneration; however, it has certain limitations, such as morbidity, low structural integrity and dimensional stability. Recent advances in 3-dimensional (3D) bioprinting have made it possible to fabricate customized scaffolds with precise architecture and spatial organization that closely mimic normal periodontal structures. The incorporation of multifunctional nanocomposite biomaterials and nanoparticles further enhances the performance of the scaffolds by increasing mechanical strength, bioactivity and controlling degradation rates. These advanced scaffolds function as dynamic microenvironments that support cell adhesion, proliferation and differentiation, ultimately promoting tissue regeneration. Furthermore, their multifunctional properties allow for the controlled release of growth factors, anti-inflammatory and antimicrobial agents, as well as the incorporation of stem cells and bioactive molecules that facilitate angiogenesis. This review investigates and critically evaluates modern approaches for the regeneration of periodontal tissues through scaffolds, biomaterials and 3D bioprinting technologies, as well as to assess their effectiveness compared to established clinical practices.

Biomimetics doi: 10.3390/biomimetics11060424

Authors: Chao Wang Longtian Zhang Hao Liu Kaicheng Yu Jing Wu Mingkang Zhu

To address the limited understanding of the aerodynamic characteristics of bird-inspired flapping-wing aircraft across different flight phases and the unclear flow field interaction mechanisms between the wings and tail, this study performs three-dimensional numerical simulations based on a self-developed prototype using ANSYS Fluent and the overset mesh method. The aerodynamic effects of key tail parameters under different flight conditions are quantitatively evaluated, and the mechanisms of bidirectional wing–tail aerodynamic coupling are investigated. The results show that tail twist has a negligible influence on instantaneous lift and thrust during level flight, with a maximum variation of only 0.2 N, but significantly affects the overall aerodynamic moments of the aircraft. When the tail twist angle increases from 15° to 20°, the pitching moment increases by 6%. In contrast, during climbing flight, the tail pitch angle has a pronounced effect on lift and thrust, and its aerodynamic influence depends strongly on the aircraft angle of attack. At an aircraft angle of attack of 15°, the difference between the maximum and minimum cycle-averaged pitching moments reaches 0.2 N·m. Further analysis of vorticity fields and pressure distributions confirms the existence of distinct wing–tail aerodynamic coupling. The tail not only directly modifies the aerodynamic forces and moments acting on the aircraft but also alters the wing-generated flow structures, while the wing wake simultaneously influences the aerodynamic effectiveness of the tail. This bidirectional wing–tail aerodynamic coupling plays a critical role in shaping the aerodynamic response of the aircraft under different flight conditions. These findings clarify the aerodynamic roles of key tail parameters and reveal the underlying flow field interaction mechanisms across different flight phases, providing a theoretical basis for motion-parameter optimization and precise attitude control of bird-inspired flapping-wing aircraft.

Biomimetics doi: 10.3390/biomimetics11060423

Authors: Merter Güçlü Duru Aras Tosun Nilsun Bağış Mohammadreza Dastouri Alp Can Rabia Karaaslan

Osseointegration is defined as the structural and functional integration between alveolar bone and a dental implant. Photo-functionalization (PF) refers to ultraviolet (UV)-induced surface modifications of titanium implants, including changes in physicochemical properties and biological responsiveness. The aim of this study was to evaluate the effects of PF on early osteoblast responses related to osseointegration on titanium dental implants in vitro. We hypothesized that PF applied to titanium implants enhances early osteoblast responses related to osseointegration. Sixteen titanium dental implants were divided into two equal groups of eight: untreated (PF−) and PF-treated (PF+). PF+ implants were exposed to UV light at 172 nm for 10 s. An additional cell-only control group was incubated without an implant. All groups were cultured in vitro with SAOS-2 human osteoblast-like cells. Cell proliferation and viability were assessed using standard in vitro assays, and DNA damage was evaluated using the Terminal deoxynucleotidyl transferase [TdT] dUTP Nick End Labeling (TUNEL) assay. Early cellular responses related to osseointegration were assessed by evaluating adhesion-related vinculin levels and alkaline phosphatase (ALP) activity. After 24 h of incubation, cell proliferation was comparable between the groups, whereas after 48 h, cell number was significantly lower in the PF− group compared with the PF+ group and the control group (p = 0.039). Osteoblast viability was significantly lower in the PF− group than in the control group (53% vs. 94%, p = 0.002), while the PF+ group showed a numerically higher viability value than the PF− group (70% vs. 53%). TUNEL assay showed no statistically significant difference in DNA damage among the groups, although the PF− group showed a slightly higher TUNEL-positive cell ratio (p = 0.563). Vinculin levels were significantly higher in the PF+ group at both 24 and 48 h compared with the PF− group and control group (p < 0.0001). ALP activity increased significantly over time in cells incubated with PF-treated implants (p < 0.0001). Within the limitations of this exploratory pilot in vitro study, UV photo-functionalization was associated with more favorable early osteoblast-like cell responses on titanium dental implants, particularly in terms of proliferation, adhesion-related vinculin levels, and ALP response. PF did not increase TUNEL-positive cell ratios compared with untreated implants under the present experimental conditions. These findings should be interpreted as preliminary biological evidence and require confirmation through larger experimental designs, detailed physicochemical surface characterization, and in vivo validation.

Biomimetics doi: 10.3390/biomimetics11060422

Authors: Arar Al Tawil Amnah Alshahrani Bilal Ibrahim Alqudah Hana Fathi

The Cuckoo Catfish Optimizer (CCO) is a recent swarm method with four built-in movement strategies. Its weakness is not the moves themselves but the way it chooses among them: a fixed chain of random-versus-threshold (rand>C) tests that ignores how each agent is actually doing and keeps no memory of which move has been paying off. On harder, higher-dimensional problems, this rigidity drains diversity and the search stalls. We propose CCO-RL, which hands the choice of move to a small tabular Q-learning controller. For every agent at every iteration, the controller reads a 48-state summary of the agent’s crowding, its recent stagnation and how far the run has progressed, then picks one of the four moves. A bounded reward and a decaying ε-greedy rule let it learn a policy online with no extra function evaluations. We test CCO-RL against the original CCO and eight popular metaheuristics on CEC2017 (D=30,50) and CEC2022 (D=20): 70 instances, 30 runs each. CCO-RL earns the best overall Friedman rank (1.69) and significantly beats every external competitor according to the Nemenyi test. It also finds the best mean design in three engineering problems.

Biomimetics doi: 10.3390/biomimetics11060421

Authors: Celia Hughes Robert D. Johnston Dylan Armfield Desmond McCarthy Ewa Klusak Emily Growney Evelyn Campbell Caitríona Lally

Aortic stenosis is predominantly treated through transcatheter bioprosthetic heart valve implantation. However, the materials used in these devices are prone to premature failure. Polymer heart valves provide an alternative to current commercial devices, offering materials with greater durability and customisation through fibre reinforcement. Given the wide range of available materials and structures, there is a need for a systematic and efficient approach to designing and optimising novel bioinspired polymeric leaflets. This work presents a framework that employs computational modelling and Design of Experiments (DOE) tools to optimise bioinspired, 3D-printed, fibre-reinforced polymer leaflets made using melt electrowriting (MEW). Here, finite element (FE) models are created to represent MEW fibre-reinforced polymer leaflets for application in a transcatheter aortic heart valve. The behaviour of this valve under physiological loading conditions is modelled to predict valve performance and leaflet material response. These models were first used to investigate the impact of fibre orientation on valve performance and leaflet response, thereby demonstrating the benefits of a bioinspired fibre reinforcement structure. Using a DOE approach, the structural combination of MEW fibre reinforcement and an elastomeric matrix was optimised to improve valve performance and reduce leaflet stress and strain. Overall, the framework offers an efficient and versatile methodology for optimising fibre-reinforced polymer leaflets using an in silico approach, thereby reducing the need for physical prototyping and testing of these next-generation devices during early product development.

Biomimetics doi: 10.3390/biomimetics11060420

Authors: İlknur Akay Dede Ayşenur Yazım Cemile Kedici Alp

The stamp technique is a biomimetic approach that enables accurate reproduction of preoperative occlusal morphology in direct composite restorations; however, the rheological properties of restorative materials may influence both morphological adaptation and wear behavior. This in vitro study aimed to evaluate the morphological accuracy of different composite resins applied using the stamp technique and to quantify volumetric changes after toothbrushing simulation using three-dimensional analysis. Sixty standardized mandibular first molar model teeth were assigned to four groups (n = 15): Filtek Z250, Filtek One Bulk Fill, SonicFill 3, and G-ænial Universal Injectable. Digital scans were obtained at baseline, after restoration, and after brushing, and analyzed using OraCheck software to calculate volumetric gain (T0–T1) and volumetric loss (T1–T2). Significant differences were observed among groups for both outcomes (p < 0.001). G-ænial Universal Injectable showed the highest morphological accuracy but also the greatest wear, whereas SonicFill demonstrated lower morphological accuracy with superior wear resistance. No significant correlation was found across all groups; however, within each group, restorations with lower morphological accuracy tended to exhibit greater wear. These findings indicate that morphological accuracy and wear resistance are material-dependent and suggest that achieving a balance between accurate reproduction and long-term preservation of occlusal morphology remains a challenge.

Biomimetics doi: 10.3390/biomimetics11060419

Authors: Ali Asghari Mohammadhossein Mohammadi

Metaheuristic algorithms are widely used to find optimal or near-optimal solutions for complex problems by taking inspiration from natural behaviors and processes. Although many different methods have been developed, a common problem in many of them is maintaining a good balance between exploration and exploitation and avoiding local optima. To deal with this issue, this paper proposes a new method called the Blackcap Optimization Algorithm (BCOA), which is inspired by the navigation and migration behavior of Blackcap birds. Instead of using complicated distance calculations, the proposed method is based on angular movement vectors. The movement of each search agent is controlled by an angle-based mathematical model that combines the global best angle, a successful neighboring angle, and an adaptive exponential disturbance factor. In addition, the algorithm uses a quasi-genetic path transition mechanism to combine successful parent paths together, along with a territorial competition stage. This structure helps reduce computational cost and improves the balance between exploration and exploitation. The performance of the proposed algorithm is tested on 32 benchmark functions and seven engineering and network optimization problems. The simulation results show that BCOA has a good ability to avoid local optima and can achieve acceptable convergence speed and cost reduction compared to several existing methods.

Biomimetics doi: 10.3390/biomimetics11060418

Authors: Hakan Işıker Ali Akdağlı Volkan Yamaçlı Zeki Yetgin İbrahim Çağrı Barutçu Kadir Abacı Furkan Gözükara

The power flow problem is one of the most challenging tasks in power systems, affecting both generation cost and energy quality. Optimal power flow (OPF) further complicates this task by requiring the optimal adjustment of system variables and parameters. This paper adapts the Modified Effective Butterfly Optimizer (MEBO) to solve multi-objective optimal power flow (MOOPF) problems with the contribution of optimized weighting using multiple Pareto archives. MEBO is an advanced optimization algorithm that utilizes population reduction and parameter learning to guide subsequent searches for unconstrained problems. The proposed technique has been tested on IEEE 30 and 57 bus test systems, and the results have been compared with existing methods reported in the literature. In the paper, four single-objective functions, namely generator cost, active power loss, fuel emission, and voltage deviation, are used to construct four multi-objective (MO) problems: cost–loss, cost–voltage, cost-emission, and emission–loss. For the cost-emission case, the proposed MEBO achieved compromised solutions of 791.1951 $/h fuel cost with 0.10873 ton/h emission and 801.8172 $/h fuel cost with 0.10044 ton/h emission under different Pareto-based optimization metrics. In the emission–loss case, the algorithm obtained 0.20539 ton/h emission with 3.1403 MW/h power loss, demonstrating the effectiveness of the proposed approach in balancing conflicting objectives. The Pareto curves of MEBO in achieving MO problems are presented, along with the suggested compromised solutions acquired from the literature. In the literature, this is the first application of MEBO for solving MOOPF problems. The results demonstrate that MEBO performs better than most other alternatives; this shows potential for further improvements with respect to the MOOPF problem.

Biomimetics doi: 10.3390/biomimetics11060417

Authors: Rosa Devesa-Rey Elena Arce Silvia Losada-Pérez Miguel Ángel Álvarez-Feijoo Raquel Leirós-Rodríguez

The rapid development of additive manufacturing has enabled the production of personalized biomedical devices, including custom orthoses that must retain their structural integrity under demanding physiological conditions. This study evaluates the performance of 3D-printed polymers—blue and white polylactic acid (PLA), Standard Blue Resin, and an ecological soy-based resin—after exposure to simplified, controlled saline environments related to sweat contact and hygiene-associated conditions. Moisture absorption and Shore A hardness were analyzed as response variables to assess material stability under different experimental conditions. A surface methodology based on a Box–Behnken design was used to quantify the effects of specimen thickness (x1), NaCl concentration (x2), and immersion time (x3) on the selected dependent variables. The results indicate that Standard Blue Resin showed the greatest surface hardness stability, whereas the bio-based materials (PLA and ecological resin) were more susceptible to moisture absorption, particularly in thinner polymeric sheets. The fitted quadratic models provide a predictive framework for optimizing material selection and geometric design in biomimetic wearable devices, supporting the development of orthoses with improved durability, hygiene, and long-term functional performance.

Biomimetics doi: 10.3390/biomimetics11060416

Authors: Fan Li Siyu Liu Shuaiwei Xu Huimin Duan Yanchao Wang Jingan Li

Extracellular vesicles (EVs), naturally secreted by cells as nanoscale lipid bilayer structures, have become a research hotspot in biomedicine owing to their excellent biocompatibility, low immunogenicity, and inherent ability to cross biological barriers. This review systematically summarizes recent advances in EVs as natural nanomaterials. The biogenesis mechanisms of EVs are outlined, followed by a comparative analysis of the advantages and limitations of mainstream isolation and purification methods, including ultracentrifugation, size-exclusion chromatography, and microfluidic technologies. The core guiding role of the MISEV 2023 guidelines in standardizing EV characterization is highlighted. Engineering strategies to enhance EV therapeutic efficacy—including parental cell modification, post-isolation physicochemical tailoring, and hybrid vesicle construction—are then reviewed, followed by a comparative analysis of mainstream isolation technologies, emphasizing the trade-offs between purity and yield. Distinct from conventional descriptive reviews, this article establishes a strong biomimetic framework to scrutinize engineering strategies, including parental cell genetic modification, post-isolation physicochemical tailoring, and the fabrication of hybrid bio-synthetic vesicles. The design principles governing targeted delivery, drug-loading physics, and in vivo pharmacokinetic stability are critically evaluated through the lens of biomimetic nanotechnology. Furthermore, we identify critical research gaps and technical bottlenecks impeding clinical translation, offering a forward-looking perspective on the evolution of EVs from natural messengers into standardized precision medicine platforms.

Biomimetics doi: 10.3390/biomimetics11060415

Authors: Davide Vurro Salvatore Del Basso Simone Luigi Marasso Alberto Ballesio Giuseppe Tarabella Pasquale D’Angelo Victor Erokhin

Organic memristive devices are promising components for neuromorphic systems. Although based on solution-processable materials, their fabrication often involves complex, resource-intensive processes. Here, we report the fabrication of organic memristive devices using aerosol jet printing to deposit both the active channel based on proprietary polyaniline-based bioink and PEDOT:PSS electrodes. Polymers printing has been carried out both on rigid and flexible substrates, the latter with the aim of demonstrating a flexible device not subjected to films delamination upon bending. By optimizing printing parameters, we achieved devices exhibiting high ON/OFF current ratios exceeding 100 and rapid switching dynamics, with performance comparable on glass and Kapton supports. Morphological and electrical characterizations revealed that channel thickness and uniformity critically influence resistive switching behavior. These findings demonstrate that aerosol jet printing enables scalable, low-material-consumption production of flexible organic memristive devices suitable for neuromorphic applications, potentially facilitating their integration into complex, energy-efficient bio-inspired circuits.

Biomimetics doi: 10.3390/biomimetics11060414

Authors: Elena Arce Silvia Losada-Pérez Rosa Devesa-Rey Miguel Ángel Álvarez-Feijoo Pablo Agregán Raquel Leirós-Rodríguez

Plantar orthoses play an important role in podiatric care, as they help to redistribute plantar loads, improve foot function, and support the treatment of various conditions, including diabetic foot disease. In this context, additive manufacturing has substantially expanded the capacity to produce customized orthoses through digital geometry acquisition, computational design, and controlled fabrication. From a biomimetic and bionic perspective, 3D-printed plantar orthoses can be understood as engineered interfaces that reproduce, support, or modulate key biomechanical functions of the human foot, including load redistribution, shock attenuation, adaptive stiffness, and gait stabilization. Additive manufacturing enables these biological and biomechanical principles to be translated into patient-specific devices through controlled geometry, graded structures, and material selection. Moreover, from a sustainability perspective, recycled polylactic acid (rPLA) has emerged as a material of potential interest for this type of application, not only because of its compatibility with 3D-printing processes but also because it offers the possibility of reusing polymer waste and reducing the consumption of virgin raw materials in devices whose service life may be limited. This review examines the conditional viability of recycled PLA for 3D-printed plantar orthoses by integrating direct clinical evidence on orthotic function with indirect technical evidence from material-level and process-level studies. The reviewed literature indicates that recycled PLA may offer environmental and economic benefits; however, repeated thermomechanical reprocessing may alter viscosity, dimensional consistency, crystallinity, interlayer adhesion, and mechanical reliability. Recent orthosis-focused studies show that extrusion-based technologies can be applied to customized insoles, lattice or internally reinforced structures, multimaterial systems, and emerging smart concepts; however, most of these developments still rely on virgin or ad hoc-designed materials rather than recycled feedstocks. Overall, the available evidence suggests that recycled PLA should not yet be regarded as a direct substitute for virgin PLA in plantar orthoses. At present, the evidence supporting the use of recycled PLA in plantar orthoses is predominantly indirect and technical rather than directly clinical. Its use appears technically promising, but its viability remains conditional and depends on feedstock traceability, control of the manufacturing process, the suitability of material properties for device function, and validation of the orthosis under clinical conditions.

Biomimetics doi: 10.3390/biomimetics11060413

Authors: Yubao Xu Yuebo Wu Jinzhong Zhang

The greater cane rat algorithm (GCRA) draws inspiration from the seasonal behavioral patterns of the greater cane rats: extensive roaming during the non-breeding period for global exploration, and aggregative foraging during the reproductive period for local exploitation. The GCRA leverages independent movement and population aggregation to iteratively update positions in pursuit of the optimal solution, which exhibits inherent structural deficiencies: precipitous population diversity collapse, lethargic convergence dynamics, suboptimal computational precision, high susceptibility to local optima, and severe dimensional scalability. This paper proposes a modified complex-valued encoding GCRA (CGCRA) that exploits the mathematical structure of complex numbers to construct a two-dimensional search domain on the complex plane and facilitate collaborative optimization. The CGCRA maps the decision variables onto the complex domain, the real part executes the native foraging mechanism for local fine-grained exploitation, and the imaginary part exploits phase rotation to generate global exploratory perturbations. The CGCRA leverages a dual-encoding redundancy mechanism with inherent error tolerance to attenuate result volatility, augment information capacity and population heterogeneity, elevate search adaptability and disturbance rejection, accelerate parallel computation and exploration efficiency, and facilitate spatial transformation and multi-dimensional data manipulation. Twenty-three benchmark functions and twelve real-world engineering designs are employed to assess the CGCRA’s stability and practical feasibility rigorously. The CGCRA delivers comprehensive spatial mapping and adaptive coordination to facilitate population collaboration and bolster resilience, expedite exhaustive research, and advance optimization efficiency. The experimental results demonstrate that the CGCRA emphasizes instructive superiority and practical utility to regulate exploration and exploitation, reduce result dispersion, mitigate search stagnation, accelerate convergence efficiency, elevate solution precision, and fortify stability and robustness.

Biomimetics doi: 10.3390/biomimetics11060412

Authors: Kun Bi Zhuoyi Chen Tianran He

The conventional robotic position control is gradually being replaced by force control, which is commonly used in humanoid robot applications that require force interaction with the environment, force transmission, or contact. A high-back-drive-efficiency actuator with a high-torque-density permanent magnet motor connecting the low-ratio planetary reducer is widely applied in interactive robotic systems without a torque/force sensor. This paper proposes a high-torque-density permanent magnet motor with an external rotor structure, which can realize torque enhancement by the increased air-gap diameter and better space utilization by the internal planetary reducer, i.e., the reducer inside the stator. First, the motor topologies with different slot/pole number combinations are introduced. Then, the optimization of a slot/pole number combination is elaborated for the maximum torque and torque mass density. In addition, the influence of the slot/pole number combination on the torque characteristic and overload capability is investigated by the finite element (FE) method. The experimental results of the prototype motor are provided to verify the analysis.

Biomimetics doi: 10.3390/biomimetics11060411

Authors: Niancheng Guo Yujing Zhang Hao Cheng Wei Zhao Yang Gao Wei Li Yanle Li

From the perspective of human vibration perception, reducing vibration stimuli transmitted to occupants is essential for improving ride comfort and reducing fatigue. Intelligent dampers, as key actuators in semi-active suspension systems, provide adjustable damping capabilities for vibration control. This article combines them with biomimetic control principles to study the vibration control of semi-active suspension. The effects of damper forward and inverse models, damping force ranges, and time delays on suspension performance were analyzed. The results show that a function prediction-based damper model, a damping force range below 0.2 times and above 1.4 times the passive curve, and a 10 ms delay could balance vibration reduction and economy. Particle swarm optimization is used to optimize LQR control parameters for different road grades and typical speeds. Inspired by the adaptive behavior of chameleons, graded weights are assigned according to road characteristics, with greater emphasis on comfort on Grade A and B roads and driving stability on Grade C and D roads. The results show that proper matching of damper models and parameter constraints can fully exploit the adjustable damping capability of smart dampers. These findings provide a theoretical basis for designing and optimizing semi-active suspension control strategies.

Biomimetics doi: 10.3390/biomimetics11060410

Authors: Kangrong Liu Ji Wang Wei Yang Shiwei Wang Jianxiang Wang Jinhai Zhang Zhaorui Zhang Xinlei An Jizhao Liu

With the development of Internet of Things (IoT) technology, fire detection systems based on multi-sensor fusion have become critical infrastructure to ensure public safety. Due to environmental noise and sensor heterogeneity, these systems often suffer from high rates of false alarms and missed detections. Although existing machine learning approaches have partially improved classification accuracy, their overall performance remains limited. Inspired by the cognitive mechanisms of the human brain, we developed an improved mind-linked continuous-coupled neural network (ML-CCNN) based on the existing continuous-coupled neural network (CCNN). We propose a parameter adaptation mechanism that modulates neural activations through a global threshold. We utilized the synthetic minority oversampling technique (SMOTE) to mitigate data imbalance and transformed sample feature vectors into matrices for training. Our model achieved an accuracy of 99.96% on our own dataset and 99.97% on the public Smoke Detection Dataset (SDD), which highlights ML-CCNN’s potential for fire detection.

Biomimetics doi: 10.3390/biomimetics11060409

Authors: Ganbayar Batchuluun Sung Jae Lee Su Jin Im Kang Ryoung Park

The deep classifiers used for chest X-ray diagnosis can be sensitive to the visual frame around the evidence, producing correct labels for the wrong reasons by relying on the background, borders, text marks, or acquisition artifacts. This framing sensitivity reduces their trustworthiness, and they can fail under a distribution shift. Inspired by biological vision, especially figure–ground segregation and selective attention, we propose a bio-inspired adversarial attention alignment training process that encourages evidence-centered decisions without changing the classifier structure. A classifier is first trained with image-level labels. Class activation mapping (CAM) is then used to produce a differentiable heatmap that indicates where the model attends. We treat this heatmap as a generated localization map and train a discriminator to distinguish the generated heatmaps from ground truth masks. The classifier is updated using a joint objective that preserves the classification performance while pushing its CAM heatmap toward a mask-like structure, reducing the reliance on background cues. We also introduce evaluation measures for the test phase, including augmentation inconsistency (prediction flip rate under angle-based augmentations) and framing sensitivity (CAM energy outside the mask). The experiments show improved lung-focused attention and robustness, while requiring masks only during training and no additional inputs at inference.

Biomimetics doi: 10.3390/biomimetics11060408

Authors: Yapeng Shi Zhen Chen Ivan Mokiets Songhao Piao Teng Zhang Lianzhao Zhang

Resolving the kinematic redundancy of 7-DoF humanoid arms to generate natural, human-like motions remains a fundamental challenge in biomimetic robotics. This paper presents a hybrid inverse kinematics (IK) framework that learns a pose-dependent redundancy parameter and integrates it into a differential IK solver. Specifically, we employ the stereographic Shoulder–Elbow–Wrist (SEW) angle as a well-conditioned geometric parameterization. This formulation transforms the algorithmic singularity into a unidirectional half-line, which can be oriented outside the typical reachable workspace. To specify the optimal configuration within the self-motion manifold, a motion dataset was collected by teleoperating a humanoid arm via an anthropomorphic wearable exoskeleton. This approach translates operator-specific postural preferences into the robot’s joint space. A lightweight neural network was then trained to learn the mapping from end-effector poses to these operator-specific SEW angles. By incorporating the predicted SEW angle as a dynamic secondary objective in the null space of the primary tracking task, the proposed framework enables natural redundancy resolution while preserving end-effector tracking accuracy. Both simulations and real-robot experiments were conducted to validate the approach. Results show that, compared to the average performance of static fixed-parameter strategies, the proposed method improves the Joint Configuration Quality Index (CQI) by 22.5% and reduces energy costs by 11.3%. Moreover, the sub-millisecond inference latency (0.44 ms) facilitates seamless integration into real-time control pipelines.

Biomimetics doi: 10.3390/biomimetics11060407

Authors: Zhongzhen Yan Yi Yu Yuan Cao Jie Gao

To address the challenges of high-dimensional nonlinearity, multimodal landscapes, and stringent constraints prevalent in modern engineering design, traditional meta-heuristic algorithms often suffer from a loss of population diversity and premature convergence. Inspired by the social collaborative predation and collective information interaction behaviors of P. prominens (jumping spiders), this study proposes a novel bio-inspired meta-heuristic optimization algorithm, termed the Experience Exchange Strategy-Enhanced Philoponella Prominens Optimization (EESPPO). The proposed EESPPO integrates an Experience Exchange Strategy framework to reshape the search dynamics of the population through three progressive evolutionary stages: (1) In the Experience Scarcity (ESC) stage, the algorithm focuses on the construction and dynamic maintenance of an experience library to ensure the effective preservation of high-quality historical information; (2) In the Experience Crossover (ECR) stage, a random guidance vector generation mechanism is introduced to significantly enhance population behavioral diversity and the capability to escape local optima; (3) In the Experience Sharing (ESH) stage, an adaptive fusion update strategy is employed to achieve efficient information interaction and co-evolution among individuals. These three stages operate synergistically within the optimization cycle to establish a dynamic balance between global exploration and local exploitation, effectively overcoming the inherent defects of premature convergence in traditional meta-heuristics. Extensive empirical analysis based on the CEC2017 benchmark functions confirms that EESPPO comprehensively outperforms 12 existing advanced algorithms (including PPO, HSO, SGA, PSO, FLO, DE, HO, WOA, KEO, GWO, FDB-AGSK, and IVYPSO) in terms of convergence accuracy and robustness. Furthermore, the application of EESPPO to four challenging engineering design problems confirms its superiority. The experimental results validate the high precision and feasibility of EESPPO in solving complex constrained engineering problems.

Biomimetics doi: 10.3390/biomimetics11060406

Authors: Zhiwei Ye Yawen Yan Yujun Ma Fan Ma Ting Cai

Feature selection is essential for improving classification performance in high-dimensional biomedical data, yet conventional metaheuristic algorithms often suffer from premature convergence and loss of population diversity. To address these issues, this paper proposes a Feature Grouping and Improved Hybrid Breeding Optimization framework (FGIHBO). First, the original feature space is hierarchically partitioned using the Maximum Relevance Minimum Redundancy criterion and Symmetric Uncertainty analysis to alleviate the curse of dimensionality. Then, a Multi-Strategy Synergistic Improved Hybrid Breeding Optimization (MSIHBO) algorithm is developed by incorporating Grey Wolf Optimizer (GWO) guidance and a Shannon entropy-adaptive simulated annealing mechanism to balance exploration and exploitation. Experimental results on the CEC2022 benchmark suite demonstrate that MSIHBO provides robust optimization performance across diverse problem categories. Furthermore, evaluations on eleven high-dimensional biomedical datasets show that FGIHBO achieves average classification accuracies ranging from 92.77% to 97.66%. Compared with representative algorithms, including Multi-strategy Improved Grey Wolf Optimizer (MIGWO), Hybrid Whale Optimization Algorithm based on Gathering strategy (HWOAG), Dynamic Crow Search Algorithm (DCSA), GWO, Hybrid Breeding Optimization (HBO), Hybrid Breeding Optimization based on Lévy flight and Elite Opposition-Based Learning strategy (LEHBO), and MSIHBO, the proposed framework improves average classification accuracy by 1.47–27.46%, with the largest gain observed on dataset D10 relative to HWOAG. These results confirm the effectiveness, robustness, and scalability of the proposed framework for high-dimensional biomedical feature selection.

Biomimetics doi: 10.3390/biomimetics11060404

Authors: Chunli Xiang Jing Zhou Zhiwei Ye Zenggang Xiong An Song Dingfeng Song Jie Sun

High-dimensional small-sample data are widely encountered in medical diagnosis, bioinformatics, and industrial inspection, where traditional feature selection methods often suffer from premature convergence and local optima. To address these issues, this paper proposes a Hybrid Breeding-based Co-evolutionary Ant Colony Optimization method (HBACO) for feature selection. Inspired by the principle of hybrid breeding, in which individuals with distinct traits produce superior offspring through cross recombination, inheritance of desirable genes and continuous evolution, the proposed algorithm establishes a three-population collaborative framework. It consists of an ACO-based search population, an HRO-based evolutionary population and a cooperative feedback population that evolve iteratively together. Furthermore, we devise a heuristic strategy integrating correlation and genetic characteristics to help mine high-value feature subsets. Meanwhile, a collaborative pheromone updating mechanism is adopted to realize efficient knowledge sharing among populations. Experiments conducted on 13 high-dimensional datasets, including Colon and Lung, demonstrate that HBACO achieves superior classification accuracy, feature reduction performance, and convergence behavior compared with 10 representative algorithms. Specifically, HBACO improves the average classification accuracy by 3.9% and achieves an average feature dimensionality reduction rate of 91.4%. Statistical tests further confirm the significance of the proposed method. The results indicate that HBACO provides an effective and robust solution for high-dimensional feature selection problems.

Biomimetics doi: 10.3390/biomimetics11060405

Authors: Peter A. Gloor

We report a cross-station replication of endogenous circadian rhythms in plant bioelectric voltage, recorded continuously for 42 days at three independent sensor stations within a public science exhibition (Phänomena, Dietikon, Switzerland; March–April 2026). Three primrose (Primula vulgaris) stations were equipped with custom Biolingo bioelectric sensors (ESP32 + AD8232) and recorded autonomously through approximately 21,000 visitor interactions. We extracted DC-invariant spectral features from 5–10 s voltage windows (n = 78,431 quality-filtered files) and fitted two-stage cosinor models with bootstrap 95% confidence intervals. All three stations show a robust 24 h rhythm in the 1–5 Hz band power (bp1–5), with peak-to-trough amplitudes between 0.35× and 1.19× of mesor (R2med 0.72–0.87). Acrophase varies across stations from 05:00 to 11:00 local time. Critically, the rhythm survives an overnight-only restriction (18:00–09:00, no visitors) at all three stations, ruling out visitor presence as the rhythm driver. The most visitor-intensive station (faces of museum visitors triggering an emotion-recognition installation) additionally shows a sharp daytime amplitude collapse coincident with the exhibition opening at 09:00, during the hours of sustained visitor presence. This temporal coincidence is consistent with—though not by itself proof of—the cardiovascular-mechanosensory coupling characterized at single-subject resolution in a companion study. We argue that bp1–5—the spectral band most directly related to plant action-potential activity—carries an endogenous circadian signal in Primula vulgaris and that this station-level signal co-varies with sustained nearby human presence in a manner consistent with frequency-selective mechanosensory coupling, although the observational design cannot establish this mechanism. From a biomimetic perspective, this suggests that the plant’s evolved bioelectric sensing apparatus might be leveraged as a live ambient biosensor for nearby human activity, complementing the more common biomimetic approach of replicating plant sensing in synthetic devices.

Biomimetics doi: 10.3390/biomimetics11060403

Authors: Joachim Enax Pascal Fandrich Erik Schulze zur Wiesche Bennett T. Amaechi

Hypophosphites (also known as phosphinates) are the salts of hypophosphorous acid (also known as phosphinic acid), H3PO2. Various hypophosphite salts are known such as calcium hypophosphite (Ca(H2PO2)2) and sodium hypophosphite (Na(H2PO2)). Hypophosphites were proposed as a potential treatment for tuberculosis as early as the 1850s; however, they were found to be ineffective against this disease. Following this, there was a period of around 100 years during which no new studies on hypophosphites were published. Subsequent in vitro studies have shown that hypophosphites can be used as potential antibacterial food preservatives. Currently, calcium hypophosphite is used in commercial food supplements for children, as this compound is a suitable source of calcium ions. It also has other advantageous properties, including exceptionally high water solubility (154 g/L at 25 °C), a neutral taste, a high mass fraction of calcium per molecule (23.6%), and an excellent safety profile. Recent studies have shown its potential as an active ingredient in the field of oral care. Since biological mechanisms such as tooth and bone formation and natural remineralization due to saliva rely on calcium ions, calcium hypophosphite can be regarded as a biomimetic agent. Upon contact with phosphate from saliva, calcium hypophosphite forms hydroxyapatite; this imitation of physiological mineralization and crystallization processes in the human body further underlines its biomimetic character. This review summarizes and discusses the available literature on hypophosphites in human health and related fields.

Biomimetics doi: 10.3390/biomimetics11060402

Authors: Anis Semlali Sana Tamzini Liudmila Cazacova

Sustainability challenges in the built environment demand a shift in architectural education from form-based approaches toward adaptive, systems-oriented, and performance-driven thinking. This paper examines an integrated pedagogical model combining biomimicry, parametric thinking, and modular design to enhance sustainable design learning in architectural studios. Using a qualitative case study approach, this research investigates Architectural Design Studio 4 at the American University of Ras Al Khaimah (AURAK), where third-year students followed a three-stage discovery-based process. Students first analyzed biological systems to identify transferable principles, then translated these principles into parametric modules using computational tools such as Dynamo and Revit, and finally applied the systems to high-rise architectural design. The findings indicate that integrating biomimicry with parametric workflows encouraged optimization, adaptability, and reusable design strategies rather than fixed outcomes. Modular design approaches helped students manage architectural complexity, while computational tools supported performance-based exploration and informed decision-making. The absence of a predetermined final design fostered critical thinking, creativity, and problem-solving skills. This study contributes empirical evidence to architectural education research by demonstrating that process-based, discovery-oriented studios can strengthen students’ understanding of sustainability, systems logic, and adaptability, preparing future architects for contemporary environmental and technological challenges.

Biomimetics doi: 10.3390/biomimetics11060401

Authors: Yafeng Zhao Xiang Li Shuaipeng Hao Min Yu Yanli Gao Shiwei Fan

In edge deployment scenarios, low-light image enhancement faces a trade-off between model complexity and perceptual quality, limiting lightweight models under resource constraints. To address this problem, this paper proposes a perceptual quality optimization model inspired by biological visual mechanisms. Specifically, a GT-Mean loss is introduced to simulate the luminance adaptation property of the mammalian retina, effectively mitigating optimization bias caused by exposure inconsistency in imaging sensors, while the LPIPS loss, aligned with the perceptual preferences of the human visual system (HVS), is incorporated to enhance subjective visual quality. From a structural perspective, inspired by the multi-scale perception of insect compound eyes, biologically selective attention, and color constancy mechanisms, the proposed model integrates an efficient texture-aware attention module, an enhanced multi-scale feature fusion strategy, and a chrominance denoising module. Experimental results demonstrate that, while maintaining an extremely low parameter count of only 0.52 M, the proposed model consistently outperforms existing lightweight methods on the LOL series datasets in terms of PSNR, SSIM, and LPIPS. This work provides an efficient perceptual quality optimization solution for bioinspired visual sensing under resource-constrained conditions.

Biomimetics doi: 10.3390/biomimetics11060400

Authors: Kemal Keskin

The Dynamic Economic Dispatch (DED) problem underpins the cost-efficient and reliable operation of modern power systems, yet valve-point loading, ramp-rate coupling, and the growing share of intermittent wind, photovoltaic, and pumped-storage hydro (PSH) resources render it highly non-convex. Metaheuristic methods typically require large computational budgets and hand-crafted constraint-handling rules, whereas deep reinforcement learning agents rarely guarantee the feasibility of the schedules they produce. To address both limitations, this paper proposes a Two-Stage PPO–RLMPA framework that couples data-driven policy learning with a biomimetic metaheuristic search inspired by marine predator–prey dynamics. In the first stage, a Proximal Policy Optimization (PPO) agent is trained on a Markov Decision Process reformulation of DED in which a deterministic Safety Layer projects every raw action onto the feasible set defined by capacity, ramp-rate, and power-balance constraints, so the policy only observes physically viable transitions. In the second stage, the PPO dispatch is refined by the RLMPA module, a Marine Predators Algorithm (MPA) whose exploration–exploitation balance, Lévy-flight foraging, and Fish Aggregating Devices (FADs) attraction mechanisms emulate strategies documented in marine ecosystems; its step-size factor and FADs probability are further adapted online by a Deep Q-Network. This biomimetics-informed refinement translates predator–prey foraging intelligence into economically efficient thermal dispatch under valve-point non-convexity. Across 30 independent runs on ten- and twenty-unit benchmark systems with wind, PV, and PSH integration, the framework attains best costs of USD 368,763 and USD 737,348 on Test Systems 1 and 2, corresponding to reductions of approximately 1.1% and 4.4% over the CFCEP baseline, with zero post-repair constraint violations in every run.

Biomimetics doi: 10.3390/biomimetics11060399

Authors: Gabriel X. Colborn Justin Pilgrim Ka Ho Pragya Natarajan Arnia Goode Jeffrey K. Catterlin Michael Krause Terak Hornik Emil P. Kartalov

Artificial muscles are an emerging class of actuators designed to mimic the compliant, efficient, and versatile behavior of biological muscles for fields including the following: soft robotics, prosthetics, wearable enhancements, haptic interfaces, and biomedical devices. These systems encompass various actuation mechanisms, including pneumatic, hydraulic, thermal, ionic, electrochemical, and electrostatic. Each with distinct tradeoffs in voltage, strain, output force, bandwidth, efficiency, and manufacturability. Among them, electrostatic actuators have attracted increased attention due to their fast response times, high energy densities, strong compatibility with soft materials, and scalability from microscale devices to large-area and stacked actuators. However, challenges such as dielectric breakdown, material fatigue, and fabrication complexity continue to limit widespread deployment. This review presents a structured classification of various artificial muscle technologies and an in-depth examination of electrostatic actuators including dielectric elastomers, electrostrictive and ferroelectric polymers, liquid crystal elastomers, electrostatic film motors, stacked architectures, and microscale/milliscale devices. In this review the operating principles, materials, architectures, performance characteristics, and failure modes of electrostatic actuators will be discussed. Additionally, a comparison will highlight tradeoffs across actuator families based on metrics such as voltage, force, strain, bandwidth, and manufacturability. Lastly, we outline future research directions in materials, physics-informed modeling, system integration, and scalable fabrication necessary to advance electrostatic artificial muscles toward practical, real-world deployment.

Biomimetics doi: 10.3390/biomimetics11060398

Authors: Emilia Georgiana Prisăcariu Oana Dumitrescu Mihail Sima Vlad Aparece-Scutariu Sergiu Strătilă Raluca Andreea Roșu Cleopatra Cuciumita Iulian Vlăducă Silvia Bica

Flapping-wing flight offers a promising solution for aerial mobility in low-density environments such as the Martian atmosphere, where conventional rotorcraft faces significant performance constraints. However, the coupled aerodynamic and structural mechanisms governing lift generation at low Reynolds numbers remain insufficiently understood. This study investigates the aeroelastic and unsteady aerodynamic behaviour of a bio-inspired flapping wing using an integrated experimental–numerical framework. High-speed imaging is employed to extract representative wing kinematics, including flapping frequency, stroke amplitude, and rotational motion. A geometrically scaled wing model is developed based on Reynolds number similitude and analysed using finite element methods to characterise its dynamic response. Aeroelastic behaviour is evaluated through modal transient simulations, while aerodynamic performance is assessed using both vortex-lattice modelling and computational fluid dynamics. The results show strong coupling between bending and torsional modes, with the structural response highly dependent on excitation frequency relative to the natural modes. Near-resonant conditions lead to amplified deformation and distinct phase relationships, while aerodynamic simulations reveal vortex-dominated lift generation. These findings provide a physics-based framework for the design and analysis of flapping-wing systems operating in low-Reynolds-number and low-density flight regimes.

Biomimetics doi: 10.3390/biomimetics11060397

Authors: Jinzhong Zhang Hongkai Li Tan Zhang Zhen He

The greater cane rat algorithm (GCRA) represents an emerging swarm intelligence paradigm derived from the instinctual survival patterns exhibited by greater cane rats (GCRs), which simulates the typical male-dominated survival patterns of the GCR species, including rainy-season mating and reproduction behaviors, dry-season behavioral differentiation of solitary males and clustered females, and their nonlinear adaptive foraging characteristics. Nevertheless, the original GCRA suffers from inherent defects in complex and high-dimensional optimization scenarios, encompassing premature convergence phenomena, inadequate local exploitation proficiency, constrained convergence precision, and a proneness to stagnation at local optima, which severely restrict its practical engineering application. To address the aforementioned limitations, this work introduces an enhanced hybrid variant of the greater cane rat algorithm, amalgamated with Teaching-and-Learning-Based Optimization (TLBO) and designated as the TLGCRA, incorporating three pivotal targeted innovations. Specifically, the TLGCRA innovatively introduces the two-stage teacher–student interactive learning mechanism of TLBO on the basis of retaining the core evolutionary and behavioral characteristics of the original GCRA, which effectively compensates for the insufficient local disturbance capability of the original algorithm and enriches population diversity to avoid local optimum stagnation. Furthermore, an adaptive parameter tuning strategy is innovatively designed and embedded in the iterative optimization process, which dynamically balances the global exploration and local exploitation capabilities of the algorithm, fundamentally improving the low learning efficiency and weak mining performance of the GCRA. A suite of computational simulations is conducted across 23 canonical benchmark functions and six representative constrained engineering design optimization scenarios. The introduced TLGCRA is benchmarked against the canonical GCRA, LPSO, and ten cutting-edge metaheuristic approaches. Empirical outcomes substantiate that the TLGCRA attains marked performance advantages in terms of convergence velocity, solution precision, and algorithmic resilience. In particular, the optimized design effectively improves the optimal solution precision of the algorithm in complex multimodal function optimization, and the standard deviation of multiple independent runs in six engineering application cases is close to zero, verifying its excellent stability. Statistical verification employing the Friedman test and Wilcoxon signed-rank test additionally corroborates that the TLGCRA exhibits statistically robust and dependable optimization efficacy. In summary, the proposed innovative fusion strategies endow the TLGCRA with stronger environmental adaptability and comprehensive optimization performance, enabling it to realize faster convergence speed and higher computational accuracy, as well as outstanding stability and robustness, thus furnishing a viable resolution framework for intricate constrained engineering optimization challenges.

Biomimetics doi: 10.3390/biomimetics11060396

Authors: Vivek Kumar Dhimole Niraj Kumar Seul-Yi Lee Soo-Jin Park

Skeletal muscle mechanics arise from hierarchical fiber–matrix interactions spanning multiple length scales. This study presents a computationally efficient, composite-inspired multiscale finite element framework that links muscle microstructure to whole-muscle behavior through explicit periodic numerical homogenization. Muscle fibers and endomysium are resolved at the microscale, their homogenized response is propagated to fascicles embedded in the perimysium at the mesoscale, and the resulting properties are incorporated into a three-dimensional macroscale muscle model including the epimysium. Unlike phenomenological continuum models or computationally intensive chemo-electro-mechanical approaches, the proposed framework enables scalable three-dimensional simulations while preserving microstructural load-transfer mechanisms. The predicted stress–strain relationships in uniaxial tensile loading were in agreement with experimental values, with differences of about 1–3%. Passive elasticity of muscle is simulated in the present research in order to provide the computation model as a benchmark in further development into the active contraction and the viscoelastic behavior. Additionally, it provides a modeling basis for patient-specific studies under varied pathological conditions.

Biomimetics doi: 10.3390/biomimetics11060395

Authors: Liliana-Laura Bădiță-Voicu Roxana-Mariana Nechita Adrian-Cătălin Voicu Marius-Ionel Anton Dana-Corina Deselnicu Corina-Ionela Dumitrescu Cristian Radu Badea

Wearable pressure-sensing orthoses are increasingly used in neurorehabilitation to support gait recovery, monitor plantar pressure distribution, and improve patient mobility during repetitive therapy sessions. The performance of these devices depends strongly on the materials used in the skin-contact layer, since material properties influence comfort, flexibility, durability, and force transmission during daily use. This study proposes a hybrid multi-criteria decision-making framework based on the Analytic Hierarchy Process (AHP) and the VIKOR method for material selection in sensor-integrated plantar orthoses. Five candidate materials, ethylene vinyl acetate (EVA), polyethylene (PE), polyurethane (PU), cobalt–chromium–molybdenum alloy (CoCrMo), and polypropylene (PP), were evaluated using five criteria: comfort and skin compatibility, elasticity, fatigue resistance, density, and energy dissipation. AHP was applied to determine the relative importance of the evaluation criteria using expert judgment, while VIKOR was used to rank the material alternatives and identify the compromise solution. The results showed that polyurethane achieved the best overall performance due to its balanced behavior in comfort, elasticity, and fatigue resistance, which are essential properties for long-term wearable neurorehabilitation devices. A sensitivity analysis confirmed that moderate variations in expert weighting did not modify the final ranking. Compared with conventional selection approaches based mainly on isolated material properties, the proposed framework offers a clear and reproducible method for integrating mechanical and user-related requirements into the material selection process for wearable orthoses.

Biomimetics doi: 10.3390/biomimetics11060394

Authors: Alejandro A. Salinas-Aguilar Sebastian Arriaga-Marin Carlos A. Perez-Ramirez Ignacio Cervantes-Gutierrez Irving A. Cruz-Albarran Andres Emilio Hurtado-Perez Manuel Toledano-Ayala

Effective diabetes management heavily relies on appropriate insulin administration, which strongly depends on the correct administration strategy. In this sense, insulin administration plays a fundamental role, as its use depends on the patient’s clinical condition and diabetes type. Traditional syringe-based methods require proper training to ensure that insulin is successfully delivered into the subcutaneous tissue, where it can be absorbed and metabolized; however, it is desirable to develop an insulin applicator that does not require training for its appropriate use. Aiming to provide support solutions that help patients to develop a correct administration technique, a biodesign-based methodology, coupled with biomimetic concepts, is employed to design a device that assists the user in creating a stable skin fold and guiding needle orientation during injection without requiring exhaustive training for device usage. A three-step approach is employed for the design, where computational fluid dynamics (CFD) and finite element analysis (FEA) methods are employed to ensure that the device produces a laminar insulin flow and the device strength is tested. It should be pointed out both methods are required since complications produced by sudden flows must be avoided, with CFD allowing assessment of the device mechanical properties in terms of the device strength. Initial functional evaluation indicates that the proposed approach does not require extensive training or complex operational procedures, facilitating its integration into everyday use. The device design is validated from the results obtained for the CFD analysis, as no turbulent flow is produced, whereas the FEA indicates that the geometrical form can handle the stresses produced by the folding generation without generating excessive deformations. Moreover, an infrared thermography analysis is also carried out to find out if the folding force generation is located in the zone of interest, the results of which indicate that the device operates in the desired physical zone.

Biomimetics doi: 10.3390/biomimetics11060393

Authors: Zhaoran Yin Chao Zhou Xiaofei Wang Xiaocun Liao Jian Wang

Optimizing flapping-foil kinematics for underwater propulsion is challenging due to strong temporal coupling and nonlinear fluid–structure interactions. Most existing approaches rely on parameterized motion profiles, which restrict the accessible design space. A non-parametric kinematic optimization framework based on the discrete adjoint method is developed, enabling direct optimization of time-resolved motions without predefined functional forms. A Morison-based low-order hydrodynamic model, calibrated against Computational Fluid Dynamics (CFD), is employed for efficient evaluation within a validated regime. Results show that optimized motions substantially enhance propulsion performance over conventional sinusoidal motions, yielding non-sinusoidal, high-efficiency kinematics. In thrust-maximization cases, the optimized kinematics achieve a 50.29% increase in mean thrust by redistributing heave and pitch amplitudes and timing. Under balanced thrust–power conditions, the optimized motions consistently outperform sinusoidal counterparts. In power-minimization cases, a “generator-like” regime emerges, indicating a reversal of net energy transfer enabled by the non-parametric formulation. These results demonstrate that non-parametric optimization provides enhanced design flexibility and improved propulsion performance, offering a practical framework for biomimetic underwater propulsion design.

Biomimetics doi: 10.3390/biomimetics11060392

Authors: Haitao Xie Zhuo Luo Zhiwei Ye Wen Zhou Xianjing Zhou Donglei Xu Mingming Zhao

Efficient task scheduling in heterogeneous IoT–Fog environments is challenging due to limited fog resources, diverse task demands, and conflicting QoS objectives. This paper proposes ASQS, a Quality-Source-driven Multi-Anchor Synchronized Search algorithm for IoT–Fog task scheduling. ASQS is biomimetically motivated by collective search behaviors in natural systems, where distributed exploration, collective memory, and probabilistic cooperation support an exploration–exploitation balance. Specifically, ASQS constructs quality layers from candidate schedules, extracts representative quality-source anchors, and reuses them through an ACO-inspired probabilistic synchronization mechanism, thereby improving the utilization of high-quality historical search information. FNO-based search and Lévy-flight perturbation are further incorporated to enhance directional guidance and long-range exploration. Experiments on 33 benchmark functions, ablation studies, sensitivity analysis, standard fog scheduling scenarios, and large-scale task-intensive scenarios were conducted to evaluate ASQS. The results show that ASQS achieves competitive optimization accuracy, stable convergence, and superior comprehensive scheduling performance in terms of fitness, makespan, latency, load balance, and constraint handling. In particular, the large-scale experiment with 100 fog nodes and up to 8000 IoT tasks verifies the scalability of ASQS under heavy workload pressure. Statistical tests further confirm the reliability of the observed improvements. These results demonstrate that ASQS is an effective, scalable, and biomimetically motivated optimizer for IoT–Fog task scheduling.

Biomimetics doi: 10.3390/biomimetics11060391

Authors: Bolin Liu Linyang Chai Chao Tian Hengbo Chen Huan Shen Qian Qi Jilei Fan Chufei Tang Aihong Ji

Conventional thrust vector control nozzles are severely constrained by a single-pivot deflection paradigm, which induces asymmetric shock reflections and adverse boundary layer separation at large angles. Multi-segmented serial configurations offer a promising alternative to overcome these limitations by distributing the total deflection across multiple joint interfaces, thereby achieving large terminal angles and smooth flow-path curvatures. To realize such a configuration, this study draws inspiration from the abdominal bending mechanism of the damselfly Ischnura elegans during mating wheel formation. Real-time video recording and morphological characterizations identified abdominal segments VI and VII as critical for high-amplitude bending under load. Finite element analysis under muscular actuation elucidated the biomechanical synergy, which was rigorously verified through mesh convergence and material property sensitivity checks. Inspired by this biological system, a multi-segmented nozzle configuration incorporating discrete elastic elements and a centralized cable-driven layout was designed and evaluated using multibody dynamics and computational fluid dynamics. The nozzle achieved a continuous 61.20° deflection within 8 s under subsonic exhaust conditions, successfully stabilizing periodic supersonic shock structures and completely suppressing adverse boundary layer separation. These findings turn biological bending into a thrust vectoring method, giving insights for next-generation agile aerospace propulsion systems.

Biomimetics doi: 10.3390/biomimetics11060389

Authors: Mingbin Tang Yejun Zheng Lianbao Li Li Cao Zihao Cheng

Complex engineering optimization problems often exhibit high-dimensional, multi-constraint, and nonlinear characteristics. Traditional deterministic optimization methods rely on gradient information and have limited optimization ranges, making it difficult to meet the requirements of efficient and accurate solutions. Intelligent optimization algorithms have become the core means of solving such problems. Aiming at the limitations of the standard artificial lemming algorithm (ALA), such as insufficient population diversity, premature convergence, weak local exploitation ability, and slow convergence speed, which make it difficult to meet the requirements of solving complex engineering optimization problems, this paper proposes a reverse mutation for optimization learning artificial lemming algorithm (RMALA). Based on the ALA algorithm, the algorithm integrates three strategies: Cauchy mutation, the improved salp swarm algorithm (ISSA), and reverse mutation for optimization learning. The Cauchy mutation is used to maintain population diversity and avoid premature convergence of the algorithm. The improved salp swarm algorithm enhances the local exploitation ability of the algorithm and improves the optimization accuracy. Reverse mutation for optimization learning guides the population toward the global optimal solution region and accelerates the convergence speed. The significant experimental results show that in the CEC2017 and CEC2022 standard test sets, as well as the three classic engineering constrained optimization problems of welded beams, cantilever beams, and pressure vessels, RMALA’s optimization accuracy is improved by more than 30% compared to the original ALA, and its convergence speed is improved by more than 25%. Its stability and robustness are better than those of five new swarm intelligence algorithms proposed in recent years. It can efficiently solve complex high-dimensional, nonlinear constrained optimization problems and has high significant engineering application value and academic innovation.

Biomimetics doi: 10.3390/biomimetics11060390

Authors: Tuğrul Yağbasan Ömür Akyazı Hayati Türe Bekir Dizdaroğlu

Biomimetic optimizers are increasingly used to solve complex engineering problems, yet their performance depends strongly on how effectively they preserve diversity while maintaining selection pressure toward promising regions. In this study, the Starfish Optimization Algorithm (SFOA) is enhanced through fitness–distance-aware selection control, leading to two improved variants: Fitness–Distance Balance Starfish Optimization Algorithm (FDBSFOA) and Dynamic Fitness–Distance Balance Starfish Optimization Algorithm (dFDBSFOA). The proposed framework guides candidate selection using both solution quality and spatial diversity relative to the current best solution, while the dynamic variant further adapts this balance over the course of the search to improve exploration in early iterations and exploitation near convergence. The proposed methods are evaluated on the IEEE CEC2017, CEC2020, and CEC2022 benchmark suites under a unified maximum function evaluation budget, MaxFEs = 10,000 × D, with 21 independent runs, and are further validated on constrained engineering design problems. Performance is assessed using convergence behavior, robustness indicators, computational overhead, and nonparametric statistical tests. The results show that the proposed variants improve the robustness and search efficiency of baseline SFOA, with dFDBSFOA providing the most consistent overall performance while introducing a controlled and interpretable computational overhead. These findings suggest that diversity-aware selection can serve as an effective design principle for strengthening biomimetic optimization frameworks. The current study focuses mainly on continuous, single-objective, and stationary benchmark problems, while the engineering-design validation also includes constrained and discrete/integer-coded cases. Extending the proposed strategy to dynamic, noisy, large-scale mixed-integer, or multi-objective settings remains future work.

Biomimetics doi: 10.3390/biomimetics11060388

Authors: Liliana Bobirică Cristina Modrogan Constantin Bobirică Oanamari Daniela Orbuleţ

This study investigates the bio-inspired photocatalytic degradation of humic acids using TiO2-functionalized clinoptilolite (C–TiO2) and Ag-doped TiO2 (C–TiO2/Ag) under UV irradiation. TiO2 acts as an artificial analogue of naturally occurring photoactive mineral phases, while clinoptilolite provides a biomimetic scaffold mimicking mineral–organic interfaces. Ag doping enhances charge separation and promotes reactive oxygen species formation, accelerating degradation. The effects of pH and catalyst composition were evaluated over a range of conditions, including the native pH of the humic solution. Degradation was monitored via changes in UV254 absorbance, VIS436 absorbance, and COD values, revealing a multistage pathway: rapid decolorization of chromophoric groups, slower breakdown of aromatic structures, and final mineralization. Acidic conditions further enhanced performance through increased adsorption and ROS (reactive oxygen species) generation, while measurable activity persisted at near-natural pH values. Kinetic analysis indicated pseudo-first-order behavior, with the highest apparent rate constants obtained for VIS436 removal under C–TiO2/Ag at pH 3 (k = 0.0166 min−1), followed by COD1 (k = 0.0190 min−1), confirming faster oxidation of labile fractions and slower mineralization of recalcitrant intermediates. Therefore, the results demonstrate that semiconductor–mineral hybrid systems can serve as biomimetic platforms that reproduce and accelerate natural self-purification processes, providing mechanistic insights into nature-inspired pathways for water treatment.

Biomimetics doi: 10.3390/biomimetics11060387

Authors: Paulo Figueroa-Torrez Broderick Crawford Orlando Durán Martín Jurado-Camacho Dayana Roxana Andrade Roque Adrian Vargas-Gutierrez Felipe Cisternas-Caneo

The Cell Formation Problem plays a fundamental role in cellular manufacturing due to its impact on efficiency, flexibility, and reliability. Its complexity increases under real-world conditions involving alternative process routes and machine reliability constraints, leading to the Generalized Cell Formation Problem with machine reliability. Researchers have classified the Cell Formation Problem as an NP-Hard problem. To address this computational complexity, this study presents a comparative and hybrid evaluation of the Black Widow Optimizer and the Golden Eagle Optimizer for the Generalized Cell Formation Problem with machine reliability, examining whether mechanisms derived from the Black Widow Optimizer can enhance the search behavior of the Golden Eagle Optimizer. The Black Widow Optimizer provides strong intensification through procreation, cannibalism, and mutation mechanisms, whereas the Golden Eagle Optimizer provides a balanced search process through its cruise and attack strategies. Experimental results show that the Black Widow Optimizer achieved better individual performance than the Golden Eagle Optimizer, with average RPD values of 0.855% and 1.068%, respectively. However, the hybrid strategy based on incorporating the mutation mechanism into the Golden Eagle Optimizer produced the best result, reaching an RPD of 0.592%. The study also employed the Wilcoxon–Mann–Whitney statistical test to validate the performance differences among algorithms, and the respective Big-O computational complexity was calculated. These findings highlight the potential of hybrid metaheuristics for designing robust and efficient manufacturing systems.

Biomimetics doi: 10.3390/biomimetics11060386

Authors: Anghel Constantin Cristian Radu Badea Roxana-Mariana Nechita Corina-Ionela Dumitrescu Bogdan Marian Verdete Florentina Badea Sorin Ionuț Badea

The implementation of wearable systems in postural rehabilitation offers new perspectives for continuous monitoring, yet their success depends on the interaction between technical parameters and clinical requirements. This study analyzes clinical performance factors to identify strategic levers determining recovery effectiveness. It examines indicators such as postural deviation detection, sensitivity to minor motion changes, suitability for home monitoring, continuous monitoring capability, clinical relevance of extracted parameters, and the capability to assess patient progress over time. Using the DEMATEL methodology, the study highlights influences among these factors in a clinical context. This structural analysis separates primary drivers from rehabilitation outcomes. To refine the analysis, the MICMAC method classifies factors by driving and dependence power, distinguishing determinant, relay, dependent, and autonomous variables. The approach provides an objective basis for managers and designers to prioritize resources toward functionalities with the greatest systemic impact on patient progress. The combined DEMATEL–MICMAC framework enhances decision-making by linking causal relationships with clear hierarchical categorization. The findings may support the integration of wearable technologies into rehabilitation practice by identifying the clinical performance factors with the strongest influence within the system.

Biomimetics doi: 10.3390/biomimetics11060385

Authors: Xuanqi Yuan Jinlu Qin Xiaohan Zhong

To address the limitations of the original Secretary Bird Optimization Algorithm (SBOA), such as insufficient population diversity, weak local exploitation ability, and a tendency to become trapped in local optima when solving complex optimization problems, this paper proposes a Multi-Strategy Improved Secretary Bird Optimization Algorithm (MISBOA). First, a chaotic elite initialization strategy is introduced to improve the quality and diversity of the initial population. Second, an adaptive spiral Lévy flight strategy is designed to enhance the balance between global exploration and local exploitation during the iterative process. Third, a dynamic neighborhood-guided mutation strategy is incorporated to maintain population diversity and improve convergence accuracy in the later search stage. To validate the effectiveness of the proposed algorithm, MISBOA is comprehensively evaluated on the IEEE CEC2014, CEC2017, and CEC2020 benchmark suites. Experimental results demonstrate that MISBOA achieves superior convergence speed, optimization accuracy, and robustness compared with several representative metaheuristic algorithms. Furthermore, MISBOA is applied to Otsu-based multilevel threshold image segmentation. The segmentation performance is assessed using PSNR, FSIM, SSIM, and visual quality comparisons. The results indicate that MISBOA can generate more accurate and stable segmentation outcomes, demonstrating its strong potential for solving complex numerical optimization and artistic image segmentation problems.

Biomimetics doi: 10.3390/biomimetics11060384

Authors: Xin-Yu Tian Zhen-Guo Zhao Peng Chen Yan-Peng Ni

Waterborne polyurethane (WPU) is widely used in flexible films and textile finishing, but its intrinsic flammability, severe melt dripping, and sensitivity to polar additives restrict its fire-safe applications. Herein, a phosphorous acid-protonated chitosan (PCS) was designed as an emulsion-adaptable bio-based modifier and incorporated into cationic WPU via a facile aqueous blending route, yielding transparent multifunctional composite films and flame-retardant textile coatings. Unlike conventional flame-retardant WPU systems that rely on reactive monomers or suffer from poor emulsion compatibility, this work proposes an emulsion-compatible strategy based on PCS, enabling the simultaneous integration of dispersion stability, flame retardancy, and antibacterial functionality within a single system. PCS could be stably accommodated in the WPU latex without visible precipitation or demulsification after centrifugation, and the resulting films preserved a continuous matrix structure with uniformly distributed PCS-rich nanodomains. Rheological analyses revealed that the polar groups of PCS established strong intermolecular associations with urethane segments, strengthening the physical network. The char residue at 700 °C increased from 0.7 wt% for neat WPU to 32.7 wt% for WPU/PCS-5. Meanwhile, WPU/PCS-5 achieved a limiting oxygen index of 35.4% and a UL-94 V-0 rating, while its peak heat release rate and total heat release were reduced by 73.4% and 41.8%, respectively. The composite films also showed nearly complete antibacterial efficiency against Escherichia coli and Staphylococcus aureus. As a textile coating, WPU/PCS-5 enabled immediate self-extinguishing of cotton fabric, increased the limiting oxygen index from 18.5% to 27.2%, and reduced the damaged length from 30.0 to 11.0 cm. This work demonstrates that an emulsion-compatible strategy based on PCS can effectively integrate dispersion stability, fire safety, multifunctionality, and coating applicability into WPU materials.

Biomimetics doi: 10.3390/biomimetics11060383

Authors: Maxim Sakharov

Memetic algorithms achieve strong optimization performance by combining population-based global search with local refinement operators, yet their effectiveness critically depends on the design and management of memes. Local search strategies are typically handcrafted, problem-specific, and fixed prior to execution. This paper proposes a fourth-generation memetic framework in which Large Language Models (LLMs) are embedded directly into the optimization loop as adaptive generators of local search operators. At each triggering point, the LLM receives a structured state vector encoding the current search dynamics and generates a candidate meme in the form of an executable Python function. Generated operators are subject to a two-stage validation procedure combining semantic similarity assessment and implementation-level comparison, ensuring that only sufficiently novel and syntactically correct operators are admitted to a dynamically growing meme library. A cooldown-regulated triggering mechanism balances periodic and stagnation-based generation, while a probability-weighted selection policy prioritizes newly generated memes without discarding previously validated ones. The proposed framework is evaluated on the CEC 2017 benchmark suite for continuous black-box optimization and compared against classical memetic algorithms. Experimental results demonstrate that the LLM-driven approach consistently outperforms non-LLM memetic baselines, confirming the viability of generative language models as adaptive heuristic components within population-based optimization.

Biomimetics doi: 10.3390/biomimetics11060382

Authors: Ayşenur Yazım Cemile Kedici Alp Ceyda Sarı

The biomimetic restoration of occlusal morphology aims to replicate the natural form and function of dental tissues while preserving structural integrity. The stamp technique enables accurate reproduction of occlusal anatomy in posterior restorations; however, the extent to which different composite systems maintain marginal integrity and reduce microleakage when this technique is applied remains unclear. This in vitro study evaluated the microleakage of bulk-fill resin composites with different viscosities applied using the stamp technique. A total of 120 extracted human molars (n = 10 per group) were prepared with standardized Class I and Class II cavities and restored using SonicFill™, VisCalor, or Filtek™ Bulk-Fill composites, applied either in a single or two increments. After thermocycling, specimens were dye-penetrated, sectioned, and analyzed using ImageJ. Data were analyzed by two-way ANOVA and Tukey’s HSD test (p < 0.05). In Class I cavities, occlusal microleakage differed significantly among materials (p = 0.001), with SonicFill™ showing the lowest values. In Class II cavities, gingival microleakage was significantly affected by both material and placement technique (p = 0.001). Incremental placement significantly reduced gingival microleakage for SonicFill™ and Filtek™ Bulk-Fill. Material viscosity and placement strategy influence marginal adaptation under stamp technique conditions. Low-viscosity bulk-fill composites demonstrated improved sealing, while incremental placement enhanced marginal integrity, particularly in Class II cavities.

Biomimetics doi: 10.3390/biomimetics11060379

Authors: Nadir Subaşi

Precise classification of dry bean varieties holds critical importance for agricultural sustainability, food security, and the preservation of seed quality standards. Traditional classification methods rely on human intervention and exhibit significant error rates; this necessitates the use of high-performance machine learning models and effective optimization strategies. This study aims to propose an innovative framework that optimizes the hyperparameters of the Extreme Gradient Boosting model for classifying seven different bean varieties on the Dry Bean Dataset using meta-heuristic algorithms. Within this study, critical parameters of the XGBoost model, such as learning rate, tree depth, and subsampling rates, have been systematically tuned using Slime Mould, Modified SMA (mSMA), mSMA_plus, Particle Swarm Optimization, and Grey Wolf Optimizer algorithms. The effectiveness of the proposed methods has been comparatively evaluated against commonly used GridSearch and RandomSearch techniques in the literature. The experimental results, assessed using accuracy, F1-score, precision, and recall metrics, reveal that the proposed mSMA_plus algorithm achieves a peak classification accuracy of 99.39% and an F1-score of 0.9939. This marks a clear architectural advancement over baseline frameworks, raising the classification accuracy baseline by approximately 1.15% compared to traditional GridSearch approaches within a total execution timeline of 507.55 s.

Biomimetics doi: 10.3390/biomimetics11060380

Authors: Tongli He Xiwen Yang Wanzhen Huang Fan Zhang Guodong Li Ze Niu Jianhong Gan Zhibin Li Xun Deng Tinghui Chen Peiyang Wei Shuai Li Xiaoli Peng

Agricultural cold chain logistics is characterized by inherent challenges—product perishability, high carbon emissions, and stringent time windows—which are further exacerbated by dynamic disruptions. Existing methods suffer from slow adaptability, unstable multi-objective convergence, and severe cold-start issues. This work falls within the broad scope of biomimetics—the science of emulating nature’s time-tested strategies to solve complex engineering problems—and bio-inspired data-driven methods and their applications in engineering control, optimization, and artificial intelligence. The proposed H-MODRL framework embodies core biomimetic principles: the Genetic Algorithm (GA) mimics Darwinian natural selection and genetic inheritance, the Sparrow Search Algorithm (SSA) abstracts the cooperative foraging and anti-predation behaviors of sparrow populations in nature, and the Arrhenius-based freshness-decay model captures the biochemical kinetics governing perishable biological products. By synergistically integrating these biological evolution principles, swarm intelligence, and deep learning, the framework tackles real-world logistics complexity in a manner directly inspired by living systems. This study presents a well-organized hybrid optimization framework (H-MODRL) that couples a three-stage hybrid evolutionary mechanism, synergistically integrating heuristic warm-start, evolutionary policy guidance, and deep reinforcement learning decision-making. First, an improved genetic algorithm combined with the earliest deadline first strategy constructs a feasible initial population satisfying hard time-window constraints. Second, a large neighborhood search-enhanced chaotic sparrow search algorithm builds a high-quality elite guidance set for policy learning. Third, a physics-based multi-objective proximal policy optimization model embedded with Arrhenius equation-derived freshness-decay kinetics performs online decision-making. Experiments demonstrate that pre-computed all-pairs shortest paths and an O(1) hash-based dynamic-disruption indexing mechanism support fast online replanning. On heterogeneous simulated terrains based on real Chinese geospatial data, H-MODRL outperforms state-of-the-art algorithms across four objectives—logistics cost, carbon emissions, terminal freshness, and delivery time—while exhibiting compact, low-variance performance distributions, thereby validating its engineering robustness and practical value in complex agricultural cold chain environments.

Biomimetics doi: 10.3390/biomimetics11060381

Authors: Junhong Luo Mengnan Zhu Yongbo Xiao Yuanhao Long Xiaoting Zhang Hui Cao Javid Atai Jing Xiao Xuesong Chen

Conventional P300-based brain–computer interfaces (BCIs) commonly rely on two-dimensional (2D) visual flashing, which may induce visual fatigue and limit immersion, thereby restricting long-term usability and system performance. To address these limitations, this study proposes an immersive P300-BCI framework integrating a three-dimensional morphological stimulation paradigm, termed 3D-Morph, with self-adaptive Bayesian linear discriminant analysis (SA-BLDA). Instead of using color or luminance flickering, the proposed paradigm employs dynamic 2D-to-3D morphological transformations of virtual objects in a virtual reality environment to enhance target-related event-related potentials while preserving visual immersion. SA-BLDA further adjusts the number of stimulation rounds according to classification confidence to balance accuracy and interaction efficiency. Experiments with 24 participants showed that the proposed system outperformed the conventional 2D paradigm. In offline analysis, the proposed method achieved an average classification accuracy of 94.17% and an information transfer rate (ITR) of 25.50 bits/min, significantly outperforming the 2D paradigm (87.29% accuracy, 22.75 bits/min ITR, both p<0.001, Cohen’s d≥1.22). In online experiments, the 3D-Morph paradigm achieved an average accuracy of 91.46% and an ITR of 37.23 bits/min, compared with 83.96% and 28.74 bits/min for the conventional 2D paradigm (both p<0.01, Cohen’s d≥1.14). The average response time was reduced by 0.46 s (p<0.01, Cohen’s d=0.78), and the processing time per stimulation round (PT) of SA-BLDA was significantly reduced from 48.54±10.47 ms in the 2D paradigm to 26.40±9.41 ms in the 3D-Morph paradigm (p<0.01, Cohen’s d=2.34), corresponding to a 45.61% reduction in computational time per round. NASA-TLX evaluations indicated a significantly lower subjective workload across all dimensions (all p<0.05, Cohen’s d≥0.76). These results demonstrate that combining 3D-Morph stimulation with SA-BLDA can significantly improve classification performance, interaction efficiency, and user experience, providing a feasible framework for immersive and practical P300-BCI applications.

Biomimetics doi: 10.3390/biomimetics11060378

Authors: Shuxin Wang Yonglong Deng Nuomei Lan Li Cao Zihao Cheng Mengji Xiong

Coverage optimization of wireless sensor networks (WSNs) faces challenges such as uneven node distribution and vulnerability to coverage blind spots. This paper introduces and improves the Northern Goshawk Optimization (NGO) algorithm: the Logistic chaotic map is adopted to initialize the population for enhanced ergodicity, a nonlinear dynamic weight is introduced to balance global exploration and local exploitation, and a Gaussian–Lévy hybrid mutation mechanism is integrated to strengthen the ability to escape from local optima. Experiments on standard test functions show that the improved algorithm (INGO) can stably approach the theoretical optimal values for both unimodal and multimodal functions. The convergence speed and solution accuracy are significantly superior to those of the original NGO, with a smaller standard deviation and stronger robustness. INGO is applied to the coverage optimization of 2D and 3D WSNs, with coverage rate as the fitness function, and the optimal node deployment coordinates are output through iterative optimization. Simulation results show that INGO achieves a best coverage rate of 98.32% in the 2D scenario, which is 7.72 percentage points higher than the 90.6% of NGO. In the 3D scenario, the best coverage rate reaches 72.32%, 6.78 percentage points higher than the 65.54% of NGO. Meanwhile, INGO yields more uniform node deployment and effectively reduces coverage blind spots. Its convergence curve is smooth and oscillation-free in the late iteration stage, and the stability is significantly better than that of NGO. With proper settings of population size and iteration times, INGO can achieve better coverage performance, providing a reliable technical solution for the efficient deployment of wireless sensor networks in complex environments.

Biomimetics doi: 10.3390/biomimetics11060377

Authors: Jiahui Yuan Rui Zhang Yazhou Zhao Weidong Zhou Lan Tian Guoyang Liu

Motor imagery brain–computer interfaces (MI-BCIs) have important applications in neurorehabilitation, assistive communication, and non-muscular human–machine interaction. From a bionic neural-interfacing perspective, MI-BCI decoding provides a computational bridge between biological motor intention and external machine control. However, reliable motor imagery electroencephalography (MI-EEG) classification remains challenging due to the highly non-stationary features of MI-EEG and limited interpretability. In this work, we propose PG-MCTFormer, a prior-guided multi-scale convolutional Transformer for MI-EEG classification that integrates rhythm-aware temporal filtering, dual-scale spatial modeling, and contextual decoding within a unified architecture. We evaluated the model on the publicly available BCI Competition IV 2a dataset, achieving 85.08% average accuracy and a Cohen’s kappa of 0.80, with significant performance improvement over the traditional methods. Comprehensive multi-view interpretability analyses in the frequency, temporal, and spatial domains further show that the learned filters remain aligned with canonical MI-related bands, discriminative evidence concentrates in the middle-to-late imagery interval, and the spatial prior is refined into subject-adaptive sensorimotor topographic patterns. These results indicate that explicit neurophysiological priors can improve both the robustness and the interpretability of MI-EEG decoders for biomimetic neural-interface applications.

Biomimetics doi: 10.3390/biomimetics11060376

Authors: Xingze Chen Xin Wang Huansheng Wu Cong Wang Yonghua Wang Linpeng Liu Ji’an Duan

Flexible pressure sensors with a porous architecture are highly desirable for wearable health monitoring and intelligent human–machine interaction, owing to their excellent comfort and conformability to human motion. However, conventional porous sensors often suffer from poor signal accuracy and unstable output, which limit their capability for precision sensing. To address these challenges, we designed and fabricated a flexible pressure sensor with exceptional linearity by mimicking the unique surface structure of Iron Cross Begonia (Begonia masoniana) leaves. The sensor is constructed using a readily available melamine foam as the backbone: a porous sensing scaffold is first obtained via a simple dip-coating process, and a film featuring bioinspired protrusions is fabricated by repeated replica molding. Lamination of these two components yields a stacked sensor device. Characterization demonstrates that the sensor achieves a broad pressure detection range of up to 350 kPa, with a minimum resolvable pressure of 250 Pa, and exhibits an excellent linearity of 0.999 over its entire working range (0–350 kPa). Moreover, the sensor shows stable responses under varying loading frequencies, is capable of detecting low-frequency signals, and retains its performance without notable degradation even after 5000 repeated loading-unloading cycles. In practical applications, the sensor accurately monitors flexion and extension movements of the wrist, finger, neck, and knee, capturing human motion signals with high fidelity. Furthermore, it enables information encoding and transmission through finger gestures. The proposed bioinspired structural design strategy effectively enhances the overall performance of porous pressure sensors, offering a new paradigm for the development of flexible sensing devices with promising applications in wearable health monitoring, human motion detection, and human–machine interaction.

Biomimetics doi: 10.3390/biomimetics11060375

Authors: Ján Duplák Samuel Mikuláško

This study focuses on streamlining the manufacturing process for milling dental prosthetic components from biopolymer materials in order to achieve the best possible surface roughness. Various combinations of cutting parameters were systematically tested in experiments, and their impact on the final surface roughness of the material was analyzed. The study provides a comprehensive view of how variations in cutting speed, feed per tooth and cutting depth affect the final surface quality. The results show that the appropriate configuration of cutting parameters can significantly improve surface roughness, reducing the need for additional finishing and increasing production efficiency. The findings provide valuable information for the manufacture of polymer-based dental prosthetic components, support process optimization, and contribute to the development of accurate and reproducible computer-aided design and computer-aided manufacturing (CAD/CAM) manufacturing procedures. A full factorial design of experiments (DoE) approach was applied to evaluate the influence of cutting speed, feed per tooth, and cutting depth on the resulting surface roughness. The results confirmed that feed per tooth represented the most influential machining parameter affecting the resulting Ra values. The optimized cutting conditions resulted in the lowest surface roughness and improved process stability compared to manufacturer-recommended machining conditions.