Biomimetics

22 pages, 5545 KB

Open AccessArticle

A Bio-Inspired Weather-System Sensing Framework for Physically Constrained Precipitation Nowcasting Correction

by Youming Qu, Xian Feng, Linyan Luo, Xun Deng, Runqing Kang, Guanru Lv, Jiachi Shi, Wei Peng, Jianhong Gan, Kun Cai, Peiyang Wei and Zhibin Li

Biomimetics 2026, 11(8), 526; https://doi.org/10.3390/biomimetics11080526 - 24 Jul 2026

Abstract

Accurate correction of gridded numerical weather prediction precipitation forecasts remains challenging because many end-to-end deep learning correction models treat meteorological variables as undifferentiated data channels and therefore provide limited physical interpretability. Inspired by general principles of biological environmental sensing, selective information processing, and [...] Read more.

Accurate correction of gridded numerical weather prediction precipitation forecasts remains challenging because many end-to-end deep learning correction models treat meteorological variables as undifferentiated data channels and therefore provide limited physical interpretability. Inspired by general principles of biological environmental sensing, selective information processing, and regulatory constraint learning, this study proposes PCPNet, a bio-inspired and physically constrained precipitation correction framework. The framework does not imitate a specific biological organ or species; instead, it abstracts three information-processing principles into a meteorological correction task. First, key weather-system cues, including low-level shear lines, trough-ridge effects, upper-level jet-stream forcing, vorticity-divergence-related vertical motion, and water-vapor flux convergence, are quantified as structured diagnostic fields. This transforms the subjective synoptic diagnosis of forecasters into automated grid-based sensing features. Second, these diagnostic cues are fused with numerical weather prediction variables and terrain descriptors in an encoder–attention–decoder network, allowing the model to emphasize dynamically important precipitation-triggering regions. Third, water-vapor conservation and terrain-forcing relationships are embedded as differentiable constraint losses, providing training-time constraint-based regulation that guides the corrected precipitation field toward physically consistent solutions. The method is evaluated from 2021 to 2023 in Hunan Province, China, using hourly numerical weather prediction model outputs as input features, China Meteorological Administration Land Data Assimilation System gridded analysis data as the training target, and independent meteorological station observations for strict cross-validation. PCPNet reduces the mean absolute error by 22.1% compared with the uncorrected China Meteorological Administration Land Data Assimilation System gridded precipitation products and outperforms Linear Regression, Bagging, Boosting, Multi-Layer Perceptron, TabNet, and Tree-based Progressive Regression Models by 12.9%, 13.5%, 16.9%, 10.8%, 14.9%, and 15.9%, respectively. The single-day event analysis provides an initial demonstration of heavy precipitation recovery capability, while comprehensive validation across long-term continuous weather events is planned for future operational deployment to further verify model stability. These results indicate that bio-inspired sensing and regulatory constraint learning can improve both the accuracy and interpretability of precipitation nowcasting correction. Full article

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

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20 pages, 2717 KB

Open AccessArticle

Biomimetic Strategies for Subsurface Enamel Remineralization: Depth-Dependent Effects of Biomimetic Agents Compared with Fluoride

by İlknur Akay Dede and Suat Özcan

Biomimetics 2026, 11(8), 525; https://doi.org/10.3390/biomimetics11080525 - 24 Jul 2026

Abstract

This study compared the remineralization potential of three commercially available fluoride-free biomimetic formulations containing nanohydroxyapatite (nHAp), casein phosphopeptide–amorphous calcium phosphate (CPP-ACP), and bioactive glass (BAG) with a conventional 1450 ppm fluoride-containing dentifrice using cross-sectional microhardness and SEM analyses. Seventy extracted human mandibular molars [...] Read more.

This study compared the remineralization potential of three commercially available fluoride-free biomimetic formulations containing nanohydroxyapatite (nHAp), casein phosphopeptide–amorphous calcium phosphate (CPP-ACP), and bioactive glass (BAG) with a conventional 1450 ppm fluoride-containing dentifrice using cross-sectional microhardness and SEM analyses. Seventy extracted human mandibular molars were randomly allocated to five groups (n = 14). Artificial carious lesions were created by 72 h of demineralization followed by a 14-day pH-cycling regimen with twice-daily treatment applications. Cross-sectional microhardness was measured at depths of 30, 50, 100, and 150 µm, and SEM was used to evaluate morphological changes. Data were analyzed using one-way ANOVA with appropriate post hoc tests (Tukey’s HSD or Tamhane’s T2, depending on variance homogeneity) and repeated-measures ANOVA to assess depth-dependent differences among groups (α = 0.05). Demineralization significantly reduced microhardness at all depths (p < 0.05). At 30 µm, the nHAp, CPP-ACP, and fluoride formulations showed similar remineralization, whereas the BAG-containing formulation exhibited lower superficial recovery. At 100–150 µm, the nHAp formulation demonstrated the greatest recovery, followed by the CPP-ACP and BAG formulations, while the fluoride dentifrice showed progressively lower recovery. A significant depth-by-group interaction confirmed distinct depth-dependent remineralization profiles. Within the limitations of this in vitro study, all tested formulations promoted enamel remineralization but exhibited different depth-dependent recovery patterns. Full article

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10 pages, 247 KB

Open AccessArticle

Effect of Bond Strength of Chitosan Containing Cavity Disinfectant on Deep or Superficial Dentin

by Derya Gursel Surmelioglu, Zeyneb Merve Ozdemir, Songul Ozdogen, Derya Dogan Evlice and Sevim Atilan Yavuz

Biomimetics 2026, 11(8), 524; https://doi.org/10.3390/biomimetics11080524 - 24 Jul 2026

Abstract

Chlorhexidine (CHX) has been widely reported as an inhibitor of matrix metalloproteinases (MMPs), contributing to the preservation of dentin bond integrity, while chitosan (CS) has been proposed as a natural biomodifier with potential collagen-stabilizing and cross-linking effects on the dentin matrix. These properties [...] Read more.

Chlorhexidine (CHX) has been widely reported as an inhibitor of matrix metalloproteinases (MMPs), contributing to the preservation of dentin bond integrity, while chitosan (CS) has been proposed as a natural biomodifier with potential collagen-stabilizing and cross-linking effects on the dentin matrix. These properties make both agents relevant candidates for improving the durability of adhesive interfaces. This in vitro study evaluated the effect of CS- and CHX-containing cavity disinfectants on the microtensile bond strength (µTBS) of a two-step self-etch adhesive applied to superficial and deep dentin. Forty-eight sound human molars were randomly assigned to superficial and deep dentin groups, which were further subdivided according to surface treatment: control (no treatment), 2% CHX, and 2.5% CS. A two-step self-etch adhesive system (Clearfil SE Bond) and resin composite were applied according to the manufacturers’ instructions. After storage and thermocycling, beam-shaped specimens were prepared and subjected to µTBS testing. Data were analyzed using appropriate statistical methods, and significance was set at p < 0.05. CS pretreatment resulted in higher µTBS values compared with the control group in both superficial and deep dentin, with statistically significant differences observed (p < 0.05), while CHX-treated groups showed intermediate values, with no consistent statistically significant difference compared with CS. In general, superficial dentin exhibited higher bond strength values than deep dentin (p < 0.05). Within the limitations of this in vitro study, chitosan-containing cavity disinfectant was associated with significantly higher µTBS values compared with the untreated control in both superficial and deep dentin when used with a two-step self-etch adhesive. However, these findings should be interpreted with caution and should not be generalized beyond the specific materials and experimental conditions evaluated. Full article

(This article belongs to the Special Issue Dentistry and Craniofacial District: The Role of Biomimetics 2026)

43 pages, 8856 KB

Open AccessReview

A Comprehensive Review of Bio-Inspired Surface and Morphological Structures for Enhancing Aerodynamic and Hydrodynamic Performances: Trends, Mechanisms, and Future Horizons

by Masuruddin Shaik and Wei-Xi Huang

Biomimetics 2026, 11(8), 523; https://doi.org/10.3390/biomimetics11080523 - 23 Jul 2026

Abstract

Bio-inspired wing designs have emerged as a revolutionary approach to enhancing aerodynamic and hydrodynamic performance across a wide range of engineering applications. This review presents a comprehensive exploration of the biological principles, physical mechanisms, and performance outcomes associated with aerodynamic characteristics of aerial [...] Read more.

Bio-inspired wing designs have emerged as a revolutionary approach to enhancing aerodynamic and hydrodynamic performance across a wide range of engineering applications. This review presents a comprehensive exploration of the biological principles, physical mechanisms, and performance outcomes associated with aerodynamic characteristics of aerial vehicles through fine-scale biomimetic features. This review categorizes these bio-inspired surface and morphological models into static, superhydrophobic, and dynamic smart structures. Furthermore, it examines the underlying fluid mechanics through both experimental and computational studies, detailing key mechanisms including vortex manipulation, transition delay, reconfiguration, wake stabilization and flow deceleration. While demonstrating strong potential, the field faces challenges in scalability, structural integration, environmental durability, and performance under variable flow conditions. Future directions emphasize multifunctionality and standardized validation protocols. By unifying insights from nature with advances in biomimetics and fluid dynamics, this review outlines a roadmap for the next generation of intelligent, high-performance aerodynamic and hydrodynamic systems. Full article

(This article belongs to the Section Biomimetic Surfaces and Interfaces)

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Graphical abstract

20 pages, 13349 KB

Open AccessArticle

Mechanics-AI: A Bio-Inspired Physics Intelligence Pipeline for Cross-Domain Engineering Prediction and Sustainable Design

by Yuyang Wei, Weijie Fei, Jiarong Wang and Luzheng Bi

Biomimetics 2026, 11(8), 522; https://doi.org/10.3390/biomimetics11080522 - 23 Jul 2026

Abstract

Mechanistic simulation and machine learning are powerful but complementary tools: physics-based simulation is interpretable yet computationally expensive and blind to real-world context, whereas machine learning is fast but data-hungry and opaque. Biological systems resolve this tension elegantly, coupling physically grounded mechanoreceptor sensing with [...] Read more.

Mechanistic simulation and machine learning are powerful but complementary tools: physics-based simulation is interpretable yet computationally expensive and blind to real-world context, whereas machine learning is fast but data-hungry and opaque. Biological systems resolve this tension elegantly, coupling physically grounded mechanoreceptor sensing with higher-level neural interpretation that places those signals in context. Inspired by this layered architecture, we present Mechanics-AI, an open-source framework that mirrors the same sensing-then-interpretation logic computationally. A first learning layer (ML1) emulates expensive finite-element, computational fluid dynamics and multiphysics simulations to produce interpretable physical metrics such as stress, strain, shear, and thermal and moisture fields, while a second layer (ML2) fuses these metrics with heterogeneous real-world metadata to predict categorical outcomes and design recommendations. Eight algorithms are benchmarked automatically, the most accurate is selected for each task, and Shapley additive explanations expose the dominant physical drivers to preserve interpretability. The framework is demonstrated across three independent domains using a single unchanged pipeline: forensic traumatic brain injury prediction, optimisation of a bio-inspired humanoid bioreactor for tissue engineering, and a zero-emission building (ZEBAI) framework that couples thermo-hygro-mechanical simulation with Sobol-sampled surrogate modelling to design sustainable, low-carbon envelopes from recycled aggregate concrete by balancing structural safety, energy and embodied carbon. Despite entirely different physics, data and objectives, the same architecture generalises across all three, showing that bio-inspired, layered coupling of mechanistic simulation and contextual learning offers a reusable, interpretable route to cross-domain engineering prediction and sustainable design. Full article

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

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30 pages, 3511 KB

Open AccessArticle

Bio-Inspired Anisotropic On-Manifold Guidance for Bearing-Only UAV Target Localization Under No-Fly Zone Constraints

by Zeyuan Li, Linzhe Chen, Haoqiang Liu and Kelin Lu

Biomimetics 2026, 11(8), 521; https://doi.org/10.3390/biomimetics11080521 - 23 Jul 2026

Abstract

In constrained environments with no-fly zones (NFZs), bearing-only UAV target localization requires improving UAV-target relative geometry while avoiding NFZs. Inspired by the avoidance behavior observed in flying insects driven by perceptual stimuli, this paper proposes a bio-inspired anisotropic on-manifold guidance method for bearing-only [...] Read more.

In constrained environments with no-fly zones (NFZs), bearing-only UAV target localization requires improving UAV-target relative geometry while avoiding NFZs. Inspired by the avoidance behavior observed in flying insects driven by perceptual stimuli, this paper proposes a bio-inspired anisotropic on-manifold guidance method for bearing-only UAV target localization under NFZ constraints. First, to quantify the measurement information with respect to the UAV-target relative geometry under unknown sensor bias, a projected uncertainty field is proposed. Its spatial gradient drives a perception-guided control law, and its components are further used to characterize local information sensitivity as a perceptual stimulus. Second, this sensitivity is incorporated into NFZ margin adaptation to construct an anisotropic dual-layer manifold field, enabling the UAV to adapt the avoidance margin according to the local information distribution. An on-manifold modulation mechanism is then applied to generate feasible motion under NFZ constraints. Simulations in NFZ-free, nonconvex NFZ, and dense NFZ scenarios show that the proposed method generates UAV trajectories that avoid NFZs while maintaining accurate and convergent target localization. Full article

(This article belongs to the Special Issue Bio-Inspired Modes of Flight)

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19 pages, 18860 KB

Open AccessArticle

Assessment of the Adhesion to Decellularized Cardiac Patches of Stem Cells Through Single-Cell Force Spectroscopy (SCFS)

by Rafael Daza, Marcos Vázquez, Raquel Tabraue-Rubio, Luis Colchero, Manuel Elices, Ricardo Sanz-Ruiz, Gustavo V. Guinea, Fivos Panetsos, José Pérez-Rigueiro and María Eugenia Fernández-Santos

Biomimetics 2026, 11(8), 520; https://doi.org/10.3390/biomimetics11080520 - 23 Jul 2026

Abstract

Cardiovascular diseases (CVDs) remain a leading source of morbidity and mortality worldwide. Although heart transplantation is an established treatment for selected patients with end-stage heart failure, the difficulty in obtaining enough donors and the problems associated with compatibility force the search for new [...] Read more.

Cardiovascular diseases (CVDs) remain a leading source of morbidity and mortality worldwide. Although heart transplantation is an established treatment for selected patients with end-stage heart failure, the difficulty in obtaining enough donors and the problems associated with compatibility force the search for new strategies to mitigate these effects. Cardiac patches, a combination of therapeutic elements, such as cells and drugs deposited on a biomimetic scaffold, are particularly promising for repairing the long-term cardiac damage from a perspective based on tissue engineering. However, building these patches entails the compliance with extensive and exhaustive conditions and regulations. Key issues identified in the building process of cardiac patches are the cell–scaffold interaction, including the intensity and duration of this interaction. The present study is intended to develop a robust procedure that allows quantifying the adhesion between decellularized matrices and cells through single-cell force spectroscopy (SCFS) measurements. Determining this adhesion force is essential to ensure cell retention and therapeutic action in the damaged area and, therefore, to be able to design an advanced therapy medicinal product. The whole procedure is validated by measuring the adhesion of allogeneic adipose-derived mesenchymal stromal cells to a decellularized porcine myocardium patch. Full article

(This article belongs to the Special Issue Bioinspired Materials, Surfaces, and Structures: 10th Anniversary Special Issue)

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Graphical abstract

11 pages, 7634 KB

Open AccessArticle

CMOS-Compatible AlScN Memristor on Silicon Exhibiting Short-Term Memory for Reservoir Computing

by Woohyun Park, Hyojeong Chae, Maria Rasheed and Sungjun Kim

Biomimetics 2026, 11(8), 519; https://doi.org/10.3390/biomimetics11080519 - 23 Jul 2026

Abstract

We report a CMOS-compatible ferroelectric memristor based on a TiN/AlScN/n⁺ Si metal ferroelectric semiconductor (MFS) structure, fabricated entirely via low-temperature sputtering processes. The ultrathin AlScN film exhibits robust ferroelectricity with a high remanent polarization (2P_r ≈ 80.91 μC/cm²) and [...] Read more.

We report a CMOS-compatible ferroelectric memristor based on a TiN/AlScN/n⁺ Si metal ferroelectric semiconductor (MFS) structure, fabricated entirely via low-temperature sputtering processes. The ultrathin AlScN film exhibits robust ferroelectricity with a high remanent polarization (2P_r ≈ 80.91 μC/cm²) and excellent endurance over 10⁵ cycles, while maintaining uniform switching across cells. Notably, the use of a heavily doped silicon bottom electrode enables full compatibility with conventional back-end-of-line (BEOL) CMOS processes and facilitates integration with silicon-based circuits. Beyond stable memory performance, the device demonstrates volatile short-term memory (STM) behavior originating from depolarization field-induced polarization relaxation, which is essential for neuromorphic dynamics. Leveraging this STM feature, the device was implemented as a physical reservoir in a reservoir computing (RC) framework, achieving 97.64% classification accuracy on the MNIST dataset using temporally coded inputs. These results highlight the potential of AlScN-based ferroelectric memristors as dynamic CMOS-compatible building blocks for in-memory and neuromorphic computing. Full article

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15 pages, 4035 KB

Open AccessArticle

Polymer Composition Modulates Dental Stem Cell Response and Mineralization in Electrospun Scaffolds for Hard Tissue Regeneration

by Caroline Anselmi, Sepideh Aminmansour, Igor Paulino Mendes Soares, Alexandre Henrique dos Reis-Prado, Sahar Aminmansour, Owen Liepman, Renan Dal-Fabbro, Josimeri Hebling and Marco C. Bottino

Biomimetics 2026, 11(8), 518; https://doi.org/10.3390/biomimetics11080518 - 23 Jul 2026

Abstract

Material selection is crucial to hard tissue regeneration, and matching scaffold properties to those of the target tissue can improve clinical outcomes. This study compared the physicochemical, mechanical, and biological performance of fibrous scaffolds fabricated from polycaprolactone (PCL), polydioxanone (PDO), and gelatin methacryloyl [...] Read more.

Material selection is crucial to hard tissue regeneration, and matching scaffold properties to those of the target tissue can improve clinical outcomes. This study compared the physicochemical, mechanical, and biological performance of fibrous scaffolds fabricated from polycaprolactone (PCL), polydioxanone (PDO), and gelatin methacryloyl (GelMA) for hard tissue regeneration. Polymeric fibers were produced by electrospinning, and their morphological, physical, and mechanical properties were characterized by scanning electron microscopy (SEM, n = 2), swelling and degradation analyses (n = 8), water contact angle measurements (n = 16), and tensile testing (n = 8). In addition, periodontal ligament stem cells (PDLSCs), alveolar bone marrow stem cells (aBMSCs), and dental pulp stem cells (DPSCs) were seeded onto the scaffolds to evaluate cell spreading (n = 4), viability (n = 8), and mineralized matrix formation (n = 6). Data were analyzed using one- or two-way ANOVA followed by appropriate post hoc tests (α = 5%). All polymers formed homogeneous fibrous scaffolds, with diameters within the nanoscale range. PDO and GelMA showed higher swelling than PCL, while PCL retained approximately 95% of its initial mass after three months. PCL and PDO showed higher elongation at break, tensile strength, and Young’s modulus than GelMA. Both PDO and GelMA displayed contact angles below 90°, with GelMA showing the lowest values. In vitro, all polymers were cytocompatible: PDO and GelMA enhanced DPSC viability at 7 days, whereas GelMA produced the highest viability for PDLSCs and aBMSCs at that time point. GelMA also promoted the highest mineralized matrix formation for DPSCs and PDLSCs, with no significant differences among polymers for aBMSCs. Overall, GelMA scaffolds promoted greater cell viability and mineralized matrix formation, while PCL and PDO provided superior mechanical properties, highlighting the importance of balancing biological and mechanical requirements when designing scaffolds for hard tissue regeneration. Full article

(This article belongs to the Special Issue Next-Generation Biomaterials and Bio-Inspired Strategies for Oral and Maxillofacial Regeneration)

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12 pages, 6934 KB

Open AccessArticle

Comparative Quasi-Static Compressive Loading Performance of Two Ultrathin Ceramic Occlusal Veneers for Minimally Invasive Restorations

by Francisco Garcia-Torres, Juan Pablo Flores-Ortega, Gabriela A. Gamundi-Cantu, Silvia Rojas-Rueda, Jose L. Ayala-Herrera, Mark Adam Antal, Carlos A. Jurado and Hamid Nurrohman

Biomimetics 2026, 11(7), 517; https://doi.org/10.3390/biomimetics11070517 - 22 Jul 2026

Abstract

Background: In the field of minimally invasive restorative dentistry, ultrathin (<0.5 mm thickness) ceramic occlusal veneers are increasing being used as alternatives to full-coverage crowns, particularly for mild occlusal wear and not deep caries. However, only limited research attention has been given to [...] Read more.

Background: In the field of minimally invasive restorative dentistry, ultrathin (<0.5 mm thickness) ceramic occlusal veneers are increasing being used as alternatives to full-coverage crowns, particularly for mild occlusal wear and not deep caries. However, only limited research attention has been given to how marked reduction in the thickness of a veneer restoration affects its mechanical performance. The purpose of the present in vitro study was to compare the performance of restorations that comprised a 0.3 mm thick zirconia veneer to the case when a lithium disilicate veneer was used, under quasi-static compressive loading. Methods: Forty extracted human molars, without caries, cracks or fractures and with intact coronal structure, were randomly assigned to two groups: lithium disilicate occlusal veneers (n = 20) and zirconia occlusal veneers (n = 20). The teeth were embedded in acrylic resin up to the cementoenamel junction. Digital scans were used to record the original anatomy and guide restoration design. Standardized occlusal preparations were performed using a 0.3 mm reduction protocol and verified with silicone guides to support a biomimetic, tooth-preserving approach. After preparation, the teeth were rescanned, and restorations were designed and fabricated using CAD/CAM technology. Lithium disilicate restorations were milled from Ivoclar Porcelain System [IPS], esthetic maximized [e.max] computer-aided design [CAD] blocks, whereas zirconia restorations were milled from Prettau 3 zirconia discs. Restorations were adhesively cemented with dual-cure resin cement following material-specific surface treatment protocols. Fracture resistance was tested using a universal testing machine under compressive loading until failure. Results: The fracture loads with lithium disilicate and zirconia occlusal veneers were 481.45 ± 68.23 N and 720.93 ± 95.44 N, respectively. Fracture was catastrophic in lithium disilicate occlusal veneers whereas it was not so when zirconia veneer was used. Conclusion: Quasi-static compressive fracture load when a lithium disilicate veneer was used was significantly lower than when a zirconia veneer was used. Full article

(This article belongs to the Special Issue Biomimetic Bonded Restorations for Dental Applications: 2nd Edition)

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22 pages, 5262 KB

Open AccessArticle

Evolutionary, Neural, or LLM-Driven Heuristic Generation? A Unified Ant Colony Optimization Benchmark for Nature-Inspired Routing Heuristics on the TSP and CVRP

by Haoyuan Wu and You Wu

Biomimetics 2026, 11(7), 516; https://doi.org/10.3390/biomimetics11070516 - 22 Jul 2026

Abstract

Biomimetic optimization transfers biological information-processing mechanisms into computational systems. Ant colony optimization (ACO) is a canonical example: artificial ants functionally abstract pheromone-mediated stigmergy, decentralized exploration, trail decay through algorithmic evaporation, and adaptive path reinforcement. Building on this functional biological analogue, we present a [...] Read more.

Biomimetic optimization transfers biological information-processing mechanisms into computational systems. Ant colony optimization (ACO) is a canonical example: artificial ants functionally abstract pheromone-mediated stigmergy, decentralized exploration, trail decay through algorithmic evaporation, and adaptive path reinforcement. Building on this functional biological analogue, we present a controlled cross-paradigm evaluation of routing-heuristic generation. A standardized interface embeds human-designed rules, the genetic programming hyper-heuristic GHPP, a resource-constrained DeepACO-MLP proxy, and an offline ReEvo-style proxy into the same ACO solver. The methods are evaluated on held-out TSP and CVRP instances in terms of solution quality, reported generation or training cost, interpretability, and cross-scale behavior under a matched distribution. GHPP yields the shortest routes at all tested scales; the ReEvo-offline proxy and strong human-designed rules generally form a second tier, whereas the resource-constrained neural proxy degrades markedly as problem size increases. These results do not establish an intrinsic ranking of full-capability paradigms. Instead, they show that method selection depends on the operating constraint and on evidence provenance: longer locally measured offline search favors GHPP, while auditable explicit rules characterize the human and ReEvo-offline proxies. By holding the ant-inspired execution mechanism fixed and varying the source of heuristic information, the benchmark clarifies how evolutionary, neural, and LLM-style design strategies interact with a common biomimetic substrate. Full article

(This article belongs to the Special Issue New Frontiers in Evolutionary Algorithms: Learning from Nature’s Optimization Strategies)

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29 pages, 14739 KB

Open AccessArticle

Biomimicry at the Landscape Scale: Agent-Based Model Simulating Beaver-Inspired Construction

by Federico Oliva, Jordan Kennedy, Justin Werfel, Karen Lee Bar-Sinai and Amir Degani

Biomimetics 2026, 11(7), 515; https://doi.org/10.3390/biomimetics11070515 - 22 Jul 2026

Abstract

Natural landscape morphology emerges from continuous, reciprocal interactions between biological agents and their physical environment. Despite its broad application across diverse scientific fields, agent-based modeling remains underexplored in the context of non-human geomorphological change. This paper presents a bio-inspired multi-agent framework to investigate [...] Read more.

Natural landscape morphology emerges from continuous, reciprocal interactions between biological agents and their physical environment. Despite its broad application across diverse scientific fields, agent-based modeling remains underexplored in the context of non-human geomorphological change. This paper presents a bio-inspired multi-agent framework to investigate how individual animal behaviors, specifically those of the North American beaver, shape adaptive landscapes. To capture dynamic task specialization, we introduce an architecture that abstracts alternating behavioral preferences into two operational states: Explorers (focused on resource identification) and Builders (focused on localized engineering). Deployed in a dynamic environment characterized by seasonal vegetation mean-reversion and a dynamic hydrological proxy, our targeted parameter sweeps and Monte Carlo replications demonstrate that decentralized stigmergic heuristics drive emergent spatial patterns. Quantitative metric analysis across varying colony sizes shows that while smaller swarms maintain a stable ecological equilibrium, larger populations trigger an apparent non-linear expansion of the hydrological network via active bank erosion. By establishing this foundational framework, this work provides an open-source tool to further explore non-human agency and regenerative strategies in landscape architecture and environmental design. Full article

(This article belongs to the Special Issue Biomimicry, Eco-Mimicry and Eco-Design in Architecture: Transformation towards Sustainable Regeneration)

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34 pages, 2868 KB

Open AccessArticle

Bio-Inspired Gaze and Neural Command Fusion for Assistive Smartphone Interaction

by Marius-Valentin Drăgoi, Ionuț Nisipeanu, Iuliana Marin, Cozmin Adrian Cristoiu and Cătălin-George Alexe

Biomimetics 2026, 11(7), 514; https://doi.org/10.3390/biomimetics11070514 - 22 Jul 2026

Abstract

This paper presents an assistive smartphone interaction system that combines mobile gaze tracking with EEG-based BCI commands. The Android application estimates the user’s gaze with the front camera, MediaPipe facial landmarks, a TinyTrackerS TFLite model, temporal smoothing, and a calibrated mapping from model [...] Read more.

This paper presents an assistive smartphone interaction system that combines mobile gaze tracking with EEG-based BCI commands. The Android application estimates the user’s gaze with the front camera, MediaPipe facial landmarks, a TinyTrackerS TFLite model, temporal smoothing, and a calibrated mapping from model output to screen coordinates. The gaze point is used to locate the intended screen area, while the BCI layer uses Emotiv Cortex commands for click, scroll, and back actions. A FastAPI and MongoDB backend manages profiles, calibration data, validation reports, runtime data, and WebSocket control events. Android Accessibility is used to execute the selected actions, with raw tap fallback when needed. The system was tested with 36 student volunteers during a short Patient Assist task. In the evaluation, 33 out of 36 gaze mappings were promoted to the active profile. The average static mean error was 390.02 px, and the average static p95 error was 789.09 px. BCI command success was 89.58% for click, 72.22% for scroll, and 77.78% for back. The Android layer acknowledged 228 out of 234 accepted control events. The average usability score was 4.21 out of 5. Full article

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

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26 pages, 1658 KB

Open AccessArticle

Wolf-Pack-Inspired Distributed Encirclement for UAV–USV Systems via Visual Connectivity Preservation

by Jinheng Xiao, Shihan Kong, Jinan Sun, Yingnan Li, Fang Wu and Junzhi Yu

Biomimetics 2026, 11(7), 513; https://doi.org/10.3390/biomimetics11070513 - 21 Jul 2026

Abstract

This paper presents a wolf-pack-inspired distributed encirclement framework for heterogeneous unmanned aerial vehicle–unmanned surface vehicle (UAV–USV) teams operating with limited communication and intermittent visual sensing. The proposed growth-based visual connectivity encirclement (GB-VCE) method separates high-level role evolution from low-level motion execution. Each agent [...] Read more.

This paper presents a wolf-pack-inspired distributed encirclement framework for heterogeneous unmanned aerial vehicle–unmanned surface vehicle (UAV–USV) teams operating with limited communication and intermittent visual sensing. The proposed growth-based visual connectivity encirclement (GB-VCE) method separates high-level role evolution from low-level motion execution. Each agent accumulates local evidence from target observation, visual connectivity contribution, motion stability, and platform characteristics, enabling smooth transitions among leader, relay, searcher, and encircler roles. Visual links and relay relations are treated as coordination resources rather than auxiliary sensing constraints, allowing target-related information to propagate through the team without centralized fusion or persistent all-to-all communication. Simulations with paired random seeds show that GB-VCE improves encirclement accuracy, target-information coverage, convergence consistency, and heterogeneous role complementarity compared with fixed-role, static-leader, UAV-only, and USV-only baselines. The results indicate that biomimetic role growth and visual connectivity preservation provide an interpretable and robust coordination principle for air–sea cooperative encirclement. Full article

(This article belongs to the Special Issue Advances in Biomimetics: 10th Anniversary)

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40 pages, 449 KB

Open AccessSystematic Review

Machine Learning for Graduation Prediction in Higher Education: A Systematic Review with a Bio-Inspired Optimization Perspective

by Andrés Yáñez, Broderick Crawford, Eric Monfroy, Álex Paz, José Barrera-García, Felipe Cisternas-Caneo, Álvaro Peña Fritz and Ricardo Soto

Biomimetics 2026, 11(7), 512; https://doi.org/10.3390/biomimetics11070512 - 21 Jul 2026

Abstract

Timely graduation, time-to-degree, and degree completion are key indicators of student progression and institutional effectiveness in higher education. This study presents a PRISMA-based systematic literature review of machine learning approaches for graduation-related prediction, with attention to predictive targets, pipeline components, scalability, and bio-inspired [...] Read more.

Timely graduation, time-to-degree, and degree completion are key indicators of student progression and institutional effectiveness in higher education. This study presents a PRISMA-based systematic literature review of machine learning approaches for graduation-related prediction, with attention to predictive targets, pipeline components, scalability, and bio-inspired optimization. Searches in Web of Science Core Collection and Scopus identified 278 records, of which 25 studies published between 2021 and 2025 met the eligibility criteria. The findings show that most studies formulated graduation prediction as a supervised classification task, relied heavily on academic performance variables, and frequently used tree-based or ensemble models. Feature selection, explainability, and hyperparameter optimization were commonly reported, but bio-inspired optimization was actively implemented in only two studies through Particle Swarm Optimization, Genetic Algorithms, or Ant Colony Optimization. The evidence base also remains limited in scalability, as most studies used single-institution datasets and provided little external validation. These findings identify an opportunity for Bio-Inspired Educational Analytics through scalable feature selection, efficient hyperparameter optimization, model simplification, and multi-objective trade-off analysis. Future research should evaluate whether lightweight, hybrid, and multi-objective metaheuristics can support accurate, interpretable, fair, and transferable graduation prediction systems. Full article

(This article belongs to the Special Issue Bio-Inspired Algorithms and Systems: From Nature’s Concepts to Scalable Solutions)

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20 pages, 9354 KB

Open AccessArticle

Fabrication of Bioinspired Hydrogels Using Carboxyphenylboronic Acid-Grafted Polyethylenimine and Polyvinyl Alcohol for Potential Wound Dressing Applications

by Lei Nie, Zihan Sun, Shichang Cheng and Ling Wang

Biomimetics 2026, 11(7), 511; https://doi.org/10.3390/biomimetics11070511 - 21 Jul 2026

Abstract

Tissue adhesives are gaining increasing attention as efficient alternatives to conventional wound closure methods, yet their clinical translation is often hindered by insufficient wet adhesion and inadequate biocompatibility. Drawing inspiration from nature’s robust wet-adhesion strategies, particularly dynamic covalent interactions and reversible crosslinking, we [...] Read more.

Tissue adhesives are gaining increasing attention as efficient alternatives to conventional wound closure methods, yet their clinical translation is often hindered by insufficient wet adhesion and inadequate biocompatibility. Drawing inspiration from nature’s robust wet-adhesion strategies, particularly dynamic covalent interactions and reversible crosslinking, we report a family of bioinspired composite hydrogels fabricated from 4-carboxyphenylboronic acid-grafted polyethylenimine (4-CPBA-PEI) and polyvinyl alcohol (PVA) that serve as versatile bioadhesives. The polyethylenimine with different molecular weights (18,000, 70,000, and 100,000 Da) was used to prepare the 4-CPBA-PEI derivatives via EDC/NHS-mediated amidation. The resulting hydrogels exhibited three-dimensional interconnected porous networks with tunable pore dimensions and equilibrium swelling ratios (ranging from 400% to 700%), closely correlated with the PEI molecular weight. Rheological measurements confirmed typical viscoelasticity, shear-thinning behavior, and outstanding self-healing performance, which are mainly attributed to hydrogen bonds and dynamic borate ester bonds in the network. The hydrogels firmly adhered to the surfaces of diverse matrices, such as glass, rubber, metal, plastic, wood, human skin, and wet mouse organs. Additionally, the obtained hydrogels exhibited high 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS) radical-scavenging activity (>85%), excellent hemocompatibility (hemolysis rate < 0.5%), and potent intracellular reactive oxygen species (ROS) scavenging activity. Cytocompatibility studies using NIH 3T3 fibroblasts demonstrated low cytotoxicity and favorable cytocompatibility. This biomimetic design yields multifunctional hydrogels that integrate tunable physical properties, wet-surface attachment, self-healing, antioxidant activity, and good biocompatibility, suggesting their potential as wound dressing candidates. Full article

(This article belongs to the Special Issue Bioinspired Composite Materials: Structure Leading to Functional and Mechanical Performance)

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21 pages, 12707 KB

Open AccessArticle

Bio-Inspired Riblet Structures on Hyperelastic FKM Sheets: A Simulation-Guided Process-Window Screening for Roll-to-Roll Hot Embossing

by Jiangpeng Liu, Jie Xu, Chaogang Ding, Debin Shan and Bin Guo

Biomimetics 2026, 11(7), 510; https://doi.org/10.3390/biomimetics11070510 - 21 Jul 2026

Abstract

V-shaped riblets are widely studied shark-skin-inspired microstructures, but their continuous high-fidelity replication on soft hyperelastic substrates remains challenging because large substrate deformation complicates complete profile filling. This study establishes an Abaqus-based finite-element process-window and morphology-screening method for roll-to-roll (R2R) hot embossing of 100 [...] Read more.

V-shaped riblets are widely studied shark-skin-inspired microstructures, but their continuous high-fidelity replication on soft hyperelastic substrates remains challenging because large substrate deformation complicates complete profile filling. This study establishes an Abaqus-based finite-element process-window and morphology-screening method for roll-to-roll (R2R) hot embossing of 100

μ

m-scale V-shaped riblets on a fluoroelastomer (FKM) sheet as a model hyperelastic substrate. A Yeoh hyperelastic law calibrated from room-temperature uniaxial tension was implemented in a three-dimensional large-deformation contact simulation. Embossing temperature T and imposed nip-compression depth D were examined as screening variables, with formed riblet height, filling ratio, and auxiliary field indicators used to evaluate the forming response. The simulated filling ratio increased from about 69% to 76% as T increased from 120 to 180 °C and from about 39% to 76% as D increased from 60 to 120

μ

m, indicating that nip-compression depth exerted the stronger geometric control over profile filling. R2R hot embossing experiments and laser-confocal profilometry evaluated the retained riblet morphology. For the 160 °C D-series, the measured retained morphology followed the simulated filling trend, with a filling-ratio RMSE of 3.09 percentage points and top-width RMSE of 2.01

μ

m. The integrated numerical–experimental framework provides an experimentally supported manufacturing basis for process-window selection and retained-morphology control in the R2R hot embossing of riblet-textured hyperelastic sheets. Full article

(This article belongs to the Special Issue Biomimetic Approaches and Materials in Engineering)

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58 pages, 16354 KB

Open AccessArticle

A Learning-Guided Meta-Heuristic Approach for Task Offloading in Four-Tier IoT Networks: A Hybrid UCB-ACO Algorithm

by Lütfiye Özlem Akkan

Biomimetics 2026, 11(7), 509; https://doi.org/10.3390/biomimetics11070509 - 20 Jul 2026

Abstract

The rapid development and expansion of the Internet of Things (IoT) ecosystem require managing increasing computational demands with the aid of advanced hierarchical architectures. The integration of a complete four-tier hierarchy—including Mobile, Edge, Fog, and Cloud layers—and the priority requirements are being overlooked [...] Read more.

The rapid development and expansion of the Internet of Things (IoT) ecosystem require managing increasing computational demands with the aid of advanced hierarchical architectures. The integration of a complete four-tier hierarchy—including Mobile, Edge, Fog, and Cloud layers—and the priority requirements are being overlooked in the literature despite the effort of existing studies. The goal of this study is to fill these gaps by proposing a novel, context-aware task-offloading framework designed for multi-dimensional ecosystems involving multi-server and multi-application environments. A targeted biomimetic approach is utilized at the core of this research. The decentralized foraging behavior of biological swarms is translated into a concrete engineering solution. This solution is designed specifically for computational offloading and resource management. To achieve this, a “Learning-guided Meta-heuristic” hybrid model is developed. Within this framework, bio-inspired Ant Colony Optimization (ACO) is directly integrated with an Upper Confidence Bound (UCB)-inspired exploration mechanism. Natural, pheromone-based imitation is solely relied upon by traditional biomimetic algorithms. In contrast, higher-order cognitive learning is fully incorporated by this hybrid synergy. Consequently, underlying system dynamics are adaptively learned. Local minima traps are also successfully avoided. This avoidance is achieved by dynamically selecting the optimal layer for each individual task. Both energy consumption and latency are optimized simultaneously. Meanwhile, strict operational feasibility is ensured through a dynamic penalty-based mechanism. Battery and deadline constraints are explicitly handled by this mechanism. Extensive simulations demonstrate the superiority of the proposed UCB-ACO model over state-of-the-art meta-heuristics, including Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), ACO, Artificial Bee Colony Optimization (ABO), and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The findings reveal that the proposed framework outperforms the methods compared by achieving 22.5% lower latency and 23% lower energy consumption. This study effectively maps the current literature and then introduces a pioneering solution for next-generation resource management in distributed computing. Full article

(This article belongs to the Special Issue Evolutionary and Nature-Inspired AI: Bridging the Gap Between Engineering and Computing: 2nd Edition)

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21 pages, 5616 KB

Open AccessArticle

Dual-Stream SPP-CNN for High-Precision sEMG Gesture Recognition in Human–Machine Interfaces

by Zebin Li, Gang Zhang, Lifu Gao, Wenming Wang, Wei Lu, Guocai Liu and Jinzhong Zhang

Biomimetics 2026, 11(7), 508; https://doi.org/10.3390/biomimetics11070508 - 19 Jul 2026

Abstract

Surface electromyography (sEMG) signals directly reflect movement intention and are therefore promising for natural human–machine interaction. However, their inherent non-stationarity and high inter-subject variability remain major obstacles to robust feature extraction and model generalization. To address these challenges, this study proposes a dual-stream [...] Read more.

Surface electromyography (sEMG) signals directly reflect movement intention and are therefore promising for natural human–machine interaction. However, their inherent non-stationarity and high inter-subject variability remain major obstacles to robust feature extraction and model generalization. To address these challenges, this study proposes a dual-stream spatial pyramid pooling convolutional neural network (DSSCNN). In this framework, one-dimensional sEMG segments are transformed into two complementary image representations, continuous wavelet transform (CWT) spectrograms and Gramian angular difference field (GADF) images, forming a dual-channel input that jointly preserves time–frequency dynamics and temporal correlation structures. A dual-stream convolutional architecture then extracts discriminative features from each modality, after which a spatial pyramid pooling (SPP) layer aggregates multi-scale representations, enhancing the network’s capacity to capture robust spatiotemporal patterns. Extensive experiments demonstrate that DSSCNN achieves an average gesture recognition accuracy of 97.88% with low inter-subject variance under intra-subject random split, and 96.59% under leave-one-subject-out (LOSO) protocol. The practical viability of the proposed approach is further validated through real-time control of an unmanned ground vehicle (UGV). These results not only indicate that the dual-stream framework combined with SPP layer provides an effective strategy for high-precision sEMG-based gesture recognition but also provides a promising technical pathway toward next-generation natural human–machine interaction. Full article

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

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