Biomimetics

27 pages, 17384 KB

Open AccessArticle

Numerical Study into the Spanwise Effects for the Three-Dimensional Unsteady Flow over a Bio-Inspired Corrugated Infinite Wing at Low Reynolds Number

by Almajd Alhinai and Torsten Schenkel

Biomimetics 2026, 11(2), 90; https://doi.org/10.3390/biomimetics11020090 - 27 Jan 2026

Abstract

Corrugated insect wings inspire biomimetic aerodynamic design, yet their behaviour at low and transitional Reynolds numbers remains not fully understood. This study presents a three-dimensional computational analysis of flow over an infinite corrugated wing across Reynolds numbers from 10 to 10,000 and angles [...] Read more.

Corrugated insect wings inspire biomimetic aerodynamic design, yet their behaviour at low and transitional Reynolds numbers remains not fully understood. This study presents a three-dimensional computational analysis of flow over an infinite corrugated wing across Reynolds numbers from 10 to 10,000 and angles of attack from −5 to 20°, with emphasis on spanwise effects. An expanded verification and validation procedure ensured numerical reliability. At the lowest Reynolds numbers, the flow is steady and largely two-dimensional, with localised recirculation zones. As Reynolds numbers or angles of attack increase, the flow transitions to periodic vortex shedding, and three-dimensional structures appear. At a Reynolds number of ten thousand, periodic shedding occurs at zero degrees incidence, indicating a shift toward turbulent or bluff body-like behaviour. The examined corrugated profile does not exhibit a lift-to-drag benefit over smooth aerofoils in steady gliding, although root section corrugation helps delay separation in transitional regimes. This behaviour reflects mechanisms used by dragonflies to maintain stable gliding despite textured wings. By extending flow regime classification, the study identifies conditions where two-dimensional assumptions fail and highlights the influence of spanwise flow structures. These findings deepen understanding of insect wing aerodynamics and support biomimetic design of future wings. Full article

(This article belongs to the Special Issue Bioinspired Flapping Wing Aerodynamics: Progress and Challenges: 2nd Edition)

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14 pages, 4164 KB

Open AccessArticle

Influence of Prosthetic Material Properties and Implant Number on Stress Distribution in Implant–Bone Systems Under Bruxism Loading: A Finite Element Study

by Derya Aslan, İsmail Hakkı Korkmaz, Nuran Yanıkoğlu and Abdullah Tahir Şensoy

Biomimetics 2026, 11(2), 89; https://doi.org/10.3390/biomimetics11020089 - 27 Jan 2026

Abstract

This finite element study compared the effects of prosthetic superstructure material and supporting implant number on stresses in implants, multiunit abutments, and restorations, and on peri-implant bone strains under bruxism-like loading. Two posterior mandibular models representing missing left FDI 34–36 were generated: a [...] Read more.

This finite element study compared the effects of prosthetic superstructure material and supporting implant number on stresses in implants, multiunit abutments, and restorations, and on peri-implant bone strains under bruxism-like loading. Two posterior mandibular models representing missing left FDI 34–36 were generated: a 2-implant configuration (implants at 34 and 36) and a 3-implant configuration (implants at 34, 35, and 36), each restored with a three-unit implant-supported fixed bridge. For each configuration, three superstructure materials were simulated: cobalt–chromium (Co–Cr), polyetheretherketone (PEEK), and monolithic zirconia (MZ). Static parafunctional loads were applied as a 500 N oblique load (30° to the implant long axis; 125 N to each buccal cusp) and a 1000 N vertical load applied to the central fossae. Cortical bone generally exhibited higher strain than trabecular bone, and the maximum cortical principal strain under vertical loading averaged approximately 5800 μɛ. The highest implant von Mises stress occurred in the first molar implant of the 2-implant MZ model under oblique loading, while the maximum under vertical loading was 236 MPa (also 2-implant MZ). Prosthetic peak stresses reached 184 MPa under vertical loading (3-implant PEEK composite–veneered model) and 233 MPa under oblique loading (2-implant MZ), with a minimum of 51 MPa in the 3-implant PEEK framework under vertical loading. Overall, increasing implant number reduced the stress/strain values, and MZ showed comparatively higher stress and strain levels. Full article

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

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22 pages, 8373 KB

Open AccessArticle

Real-Time Automated Ergonomic Monitoring: A Bio-Inspired System Using 3D Computer Vision

by Gabriel Andrés Zamorano Núñez, Nicolás Norambuena, Isabel Cuevas Quezada, José Luis Valín Rivera, Javier Narea Olmos and Cristóbal Galleguillos Ketterer

Biomimetics 2026, 11(2), 88; https://doi.org/10.3390/biomimetics11020088 - 26 Jan 2026

Abstract

Work-related musculoskeletal disorders (MSDs) remain a global occupational health priority, with recognized limitations in current point-in-time assessment methodologies. This research extends prior computer vision ergonomic assessment approaches by implementing biological proprioceptive feedback principles into a continuous, real-time monitoring system. Unlike traditional periodic ergonomic [...] Read more.

Work-related musculoskeletal disorders (MSDs) remain a global occupational health priority, with recognized limitations in current point-in-time assessment methodologies. This research extends prior computer vision ergonomic assessment approaches by implementing biological proprioceptive feedback principles into a continuous, real-time monitoring system. Unlike traditional periodic ergonomic evaluation methods such as “Rapid Upper Limb Assessment” (RULA), our bio-inspired system translates natural proprioceptive mechanisms—which enable continuous postural monitoring through spinal feedback loops operating at 50–150 ms latencies—into automated assessment technology. The system integrates (1) markerless 3D pose estimation via MediaPipe Holistic (33 anatomical landmarks at 30 FPS), (2) depth validation via Orbbec Femto Mega RGB-D camera (640 × 576 resolution, Time-of-Flight sensor), and (3) proprioceptive-inspired alert architecture. Experimental validation with 40 adult participants (age 18–25, n = 26 female, n = 14 male) performing standardized load-lifting tasks (6 kg) demonstrated that 62.5% exhibited critical postural risk (RULA ≥ 5) during dynamic movement versus 7.5% at static rest, with McNemar test

p < 0.001

(Cohen’s

h = 1.22

, 95% CI: 0.91–0.97). The system achieved 95% Pearson correlation between risk elevation and alert activation, with response latency of

42.1 \pm 8.3

ms. This work demonstrates technical feasibility for continuous occupational monitoring. However, long-term prospective studies are required to establish whether continuous real-time feedback reduces workplace injury incidence. The biomimetic design framework provides a systematic foundation for translating biological feedback principles into occupational health technology. Full article

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

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15 pages, 10591 KB

Open AccessArticle

Hydraulic Asymmetries for Biological and Bioinspired Valves in Tubular Channels: A Numerical Analysis

by Francesco Varnier, Reza Norouzikudiani, Giovanni Corsi, Daniele Agostinelli, Ido Levin and Antonio DeSimone

Biomimetics 2026, 11(2), 87; https://doi.org/10.3390/biomimetics11020087 - 26 Jan 2026

Abstract

Biological, biomimetic, and engineering systems make extensive use of hydraulic asymmetries to control flow inside tubular structures. Examples span physiological valves, the guided transport observed in shark intestines, and passive devices such as Tesla valves. Here we investigate the mechanisms that generate these [...] Read more.

Biological, biomimetic, and engineering systems make extensive use of hydraulic asymmetries to control flow inside tubular structures. Examples span physiological valves, the guided transport observed in shark intestines, and passive devices such as Tesla valves. Here we investigate the mechanisms that generate these asymmetries using the notion of diodicity, defined as the ratio between pressure drops required to drive the same flow in opposite directions. We first focus on 2D geometries, which allow us to identify and study the main contributions to hydraulic asymmetry: channel geometry and internal obstacles embedded within a channel with rigid walls. By considering both rigid and deformable obstacles, we model channels that always remain open in both directions and channels that can be completely blocked by valve-like structures. We then extend the analysis to 3D geometries, again considering rigid and elastic cases. As a general trend, we find that geometry alone establishes a baseline diodicity, while higher dimensionality and structural reconfiguration consistently amplify the effect. Full article

(This article belongs to the Special Issue Advances in Biomimetics: Patents from Nature)

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30 pages, 3807 KB

Open AccessReview

Flapping Foil-Based Propulsion and Power Generation: A Comprehensive Review

by Prabal Kandel, Jiadong Wang and Jian Deng

Biomimetics 2026, 11(2), 86; https://doi.org/10.3390/biomimetics11020086 - 25 Jan 2026

Abstract

This review synthesizes the state of the art in flapping foil technology and bridges the distinct engineering domains of bio-inspired propulsion and power generation via flow energy harvesting. This review is motivated by the observation that propulsion and power-generation studies are frequently presented [...] Read more.

This review synthesizes the state of the art in flapping foil technology and bridges the distinct engineering domains of bio-inspired propulsion and power generation via flow energy harvesting. This review is motivated by the observation that propulsion and power-generation studies are frequently presented separately, even though they share common unsteady vortex dynamics. Accordingly, we adopt a unified unsteady-aerodynamic perspective to relate propulsion and energy-extraction regimes within a common framework and to clarify their operational duality. Within this unified framework, the feathering parameter provides a theoretical delimiter between momentum transfer and kinetic energy extraction. A critical analysis of experimental foundations demonstrates that while passive structural flexibility enhances propulsive thrust via favorable wake interactions, synchronization mismatches between deformation and peak hydrodynamic loading constrain its benefits in power generation. This review extends the analysis to complex and non-homogeneous environments and identifies that density stratification fundamentally alters the hydrodynamic performance. Specifically, resonant interactions with the natural Brunt–Väisälä frequency of the fluid shift the optimal kinematic regimes. The present study also surveys computational methodologies and highlights a paradigm shift from traditional parametric sweeps to high-fidelity three-dimensional (3D) Large-Eddy Simulations (LESs) and Deep Reinforcement Learning (DRL) to resolve finite-span vortex interconnectivities. Finally, this review outlines the critical pathways for future research. To bridge the gap between computational idealization and physical reality, the findings suggest that future systems prioritize tunable stiffness mechanisms, multi-phase environmental modeling, and artificial intelligence (AI)-driven digital twin frameworks for real-time adaptation. Full article

(This article belongs to the Special Issue Bio-Inspired Flapping Wing Aerodynamics for Propulsion and Power Generation: 2nd Edition)

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24 pages, 4004 KB

Open AccessArticle

Spherical Bezier Curve-Based 3D UAV Smooth Path Planning Utilizing an Efficient Improved Exponential-Trigonometric Optimization

by Yitao Cao, Kang Chen and Gang Hu

Biomimetics 2026, 11(2), 85; https://doi.org/10.3390/biomimetics11020085 - 23 Jan 2026

Abstract

Path planning, as a key technology in unmanned aerial vehicle (UAV) systems, affects the overall efficiency of task completion and is often limited by energy consumption, obstacles, and maneuverability in complex application environments. Traditional algorithms have insufficient performance in nonlinear, multimodal, and multiconstraints [...] Read more.

Path planning, as a key technology in unmanned aerial vehicle (UAV) systems, affects the overall efficiency of task completion and is often limited by energy consumption, obstacles, and maneuverability in complex application environments. Traditional algorithms have insufficient performance in nonlinear, multimodal, and multiconstraints problems. Based on this, this paper proposes an improved exponential-trigonometric optimization (ETO) to solve a 3D smooth path planning model based on a spherical Bezier curve. Firstly, a fixed arc length resampling strategy is proposed to address the issue of the insufficient adaptability of existing path smoothing methods to dynamic threats. Generate a uniformly distributed set of reference points along the Bezier curve and combine it with spherical projection to improve the safety and efficiency of the flight path. On this basis, establish a total cost function that includes four types of costs. Secondly, a new ETO variant called IETO is proposed by introducing the alpha evolution strategy, noise and physical attack strategy, and opposition-based cross teaching strategy into ETO. Then, the effectiveness of IETO for addressing various optimization problems is showcased through population diversity analysis, ablation analysis, and benchmark experiments. Finally, the results of the simulation experiment indicate that IETO stably provides shorter and smoother safe paths for UAVs in three elevation maps with different terrain features. Full article

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16 pages, 1334 KB

Open AccessArticle

Design and Performance Study of a Gradient Honeycomb Vibration-Damping Structure for the Knee Joint

by Shucheng Lou and Li Feng

Biomimetics 2026, 11(1), 84; https://doi.org/10.3390/biomimetics11010084 - 22 Jan 2026

Abstract

Excessive vibration during human knee joint movement poses challenges to biomechanical performance and comfort, which this study aims to mitigate through the design of a bio-inspired honeycomb-based vibration-damping structure, for the purpose of optimizing dynamic vibration absorption efficiency. Three honeycomb geometries—regular triangle, square, [...] Read more.

Excessive vibration during human knee joint movement poses challenges to biomechanical performance and comfort, which this study aims to mitigate through the design of a bio-inspired honeycomb-based vibration-damping structure, for the purpose of optimizing dynamic vibration absorption efficiency. Three honeycomb geometries—regular triangle, square, and regular hexagon—were evaluated via dynamic mechanical simulation, identifying the regular hexagon as the most effective base configuration. Using the control variable method within reasonable parameter ranges, finite element analysis was employed to systematically examine the influence of wall thickness, side length, and gradient of the regular hexagonal honeycomb on its damping performance. The findings demonstrate that vibration damping is maximized under a configuration with a wall thickness of 1.8 mm, a side length of 6 mm, and a gradient of 110%. Full article

(This article belongs to the Special Issue Biomechanics, Wearable Technology, and Data-Driven Decision Making for the Future Sports)

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34 pages, 11900 KB

Open AccessArticle

Influence of Bloat Control on Relocation Rules Automatically Designed via Genetic Programming

by Tena Škalec and Marko Đurasević

Biomimetics 2026, 11(1), 83; https://doi.org/10.3390/biomimetics11010083 - 21 Jan 2026

Abstract

The container relocation problem (CRP) is a critical optimisation problem in maritime port operations, in which efficient container handling is essential for maximising terminal throughput. Relocation rules (RRs) are a widely adopted solution approach for the CRP, particularly in online and dynamic environments, [...] Read more.

The container relocation problem (CRP) is a critical optimisation problem in maritime port operations, in which efficient container handling is essential for maximising terminal throughput. Relocation rules (RRs) are a widely adopted solution approach for the CRP, particularly in online and dynamic environments, as they enable fast, rule-based decision-making. However, the manual design of effective relocation rules is both time-consuming and highly dependent on problem-specific characteristics. To overcome this limitation, genetic programming (GP), a bio-inspired optimisation technique grounded in the principles of natural evolution, has been employed to automatically generate RRs. By emulating evolutionary processes such as selection, recombination, and mutation, GP can explore large heuristic search spaces and often produces rules that outperform manually designed alternatives. Despite these advantages and their inherently white-box nature, GP-generated relocation rules frequently exhibit excessive complexity, which hinders their interpretability and limits insight into the underlying decision logic. Motivated by the biomimetic observation that evolutionary systems tend to favour compact and efficient structures, this study investigates two mechanisms for controlling rule complexity, parsimony pressure, and solution pruning, and it analyses their effects on both the quality and size of relocation rules evolved by GP. The results demonstrate that substantial reductions in rule size can be achieved with only minor degradation in performance, measured as the number of relocated containers, highlighting a favourable trade-off between heuristic simplicity and solution quality. This enables the derivation of simpler and more interpretable heuristics while maintaining competitive performance, which is particularly valuable in operational settings where human planners must understand, trust, and potentially adjust automated decision rules. Full article

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

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19 pages, 1944 KB

Open AccessArticle

Research on Adaptive Cooperative Positioning Algorithm for Underwater Robots Based on Dolphin Group Cooperative Mechanism

by Shiwei Fan, Jiachong Chang, Zicheng Wang, Mingfeng Ding, Hongchao Sun and Yubo Zhao

Biomimetics 2026, 11(1), 82; https://doi.org/10.3390/biomimetics11010082 - 20 Jan 2026

Abstract

Inspired by the remarkable collaborative echolocation mechanisms of dolphin pods, the paper addresses the challenge of achieving high-precision cooperative positioning for clusters of unmanned underwater vehicles (UUVs) in complex marine environments. Cooperative positioning systems for UUVs typically rely on acoustic ranging information to [...] Read more.

Inspired by the remarkable collaborative echolocation mechanisms of dolphin pods, the paper addresses the challenge of achieving high-precision cooperative positioning for clusters of unmanned underwater vehicles (UUVs) in complex marine environments. Cooperative positioning systems for UUVs typically rely on acoustic ranging information to correct positional errors. However, the propagation characteristics of underwater acoustic signals are susceptible to environmental disturbances, often resulting in non-Gaussian, heavy-tailed distributions of ranging noise. Additionally, the strong nonlinearity of the system and the limited observability of measurement information further constrain positioning accuracy. To tackle these issues, this paper innovatively proposes a Factor Graph-based Adaptive Cooperative Positioning Algorithm (FGAWSP) suitable for heavy-tailed noise environments. The method begins by constructing a factor graph model for UUV cooperative positioning to intuitively represent the probabilistic dependencies between system states and observed variables. Subsequently, a novel factor graph estimation mechanism integrating adaptive weights with the product algorithm is designed. By conducting online assessment of residual information, this mechanism dynamically adjusts the fusion weights of different measurements, thereby achieving robust handling of anomalous range values. Experimental results demonstrate that the proposed method reduces positioning errors by 22.31% compared to the traditional algorithm, validating the effectiveness of our approach. Full article

(This article belongs to the Special Issue Bioinspired Robot Sensing and Navigation)

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32 pages, 18470 KB

Open AccessArticle

Enhancing Neuromorphic Robustness via Recurrence Resonance: The Role of Shared Weak Attractors in Quantum Logic Networks

by Yu Huang and Yukio-Pegio Gunji

Biomimetics 2026, 11(1), 81; https://doi.org/10.3390/biomimetics11010081 - 19 Jan 2026

Abstract

Recurrence resonance, a phenomenon that enhances system computational capability by exploiting noise to amplify hidden attractors, holds significant potential for applications such as edge computing and neuromorphic computing. Although previous studies have extensively explored its characteristics, the underlying mechanism regarding its generation remains [...] Read more.

Recurrence resonance, a phenomenon that enhances system computational capability by exploiting noise to amplify hidden attractors, holds significant potential for applications such as edge computing and neuromorphic computing. Although previous studies have extensively explored its characteristics, the underlying mechanism regarding its generation remains unclear. Here, we employed a Stochastic Recurrent Neural Network to simulate neural networks under various coupling conditions. By introducing appropriate inhibitory connections and examining the state transition matrices, we analyzed the characteristics and correlations of attractor landscapes in both global and local systems to elucidate the generative mechanism behind the “Edge of Chaos” dynamics observed under the quantum logic connectivity structure during recurrence resonance. The results show that the strategic introduction of inhibitory connections enriches the system’s attractor landscape without compromising the intensity of recurrence resonance. Furthermore, we find that when neurons are coupled via quantum logic and noise intensity meets specific conditions, the strong attractors of the global system decompose into those of distinct local subsystems, accompanied by the sharing of structurally similar weak attractors. These findings suggest that under quantum logic connectivity, the interaction between the strong attractors of different subsystems is mediated by a background of shared weak attractors, thereby enhancing both the system’s robustness against noise and the diversity of its state evolution. Full article

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23 pages, 3108 KB

Open AccessArticle

Hydrodynamic Study of Flow-Channel and Wall-Effect Characteristics in an Oscillating Hydrofoil Biomimetic Pumping Device

by Ertian Hua, Yang Lin, Sihan Li and Xiaopeng Wu

Biomimetics 2026, 11(1), 80; https://doi.org/10.3390/biomimetics11010080 - 19 Jan 2026

Abstract

To clarify how flow-channel configuration and wall spacing govern the hydrodynamic performance of an oscillating-hydrofoil biomimetic pumping device, this study conducted a systematic numerical investigation under confined-flow conditions. Using a finite-volume solver with an overset-grid technique, we compared pumping performance across three channel [...] Read more.

To clarify how flow-channel configuration and wall spacing govern the hydrodynamic performance of an oscillating-hydrofoil biomimetic pumping device, this study conducted a systematic numerical investigation under confined-flow conditions. Using a finite-volume solver with an overset-grid technique, we compared pumping performance across three channel configurations and a range of channel–wall distances. The results showed that bidirectional-channel confinement suppresses wake deflection and irregular vorticity evolution, enabling symmetric and periodic vortex organization and thereby improving pumping efficiency by approximately 33.6% relative to the single-channel case and by 62.7% relative to the no-channel condition. Wall spacing exhibited a distinctly non-monotonic influence on performance, revealing two high-performance regimes: under extreme confinement (gap ratio

h / c =

1.4), the device attains peak pumping and thrust efficiencies of 19.9% and 30.7%, respectively, associated with a strongly guided jet-like transport mode; and under moderate spacing (

h / c =

2.2–2.6), both efficiencies remain high due to an improved balance between directional momentum transport and reduced vortex-evolution losses. These findings identify key confinement-driven mechanisms and provide practical guidance for optimizing flow-channel design in ultralow-head oscillating-hydrofoil pumping applications. Full article

(This article belongs to the Special Issue Bio-Inspired Flapping Wing Aerodynamics for Propulsion and Power Generation: 2nd Edition)

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29 pages, 6120 KB

Open AccessArticle

Bionic Technology in Prosthetics: Multi-Objective Optimization of a Bioinspired Shoulder-Elbow Prosthesis with Embedded Actuation

by Jingxu Jiang, Gengbiao Chen, Xin Wang and Hongwei Yan

Biomimetics 2026, 11(1), 79; https://doi.org/10.3390/biomimetics11010079 - 19 Jan 2026

Abstract

The development of upper-limb prostheses is often hindered by limited dexterity, a restricted workspace, and bulky designs, primarily due to performance limitations in proximal joints like the shoulder and elbow, which contribute to high user abandonment rates. To overcome these challenges, this paper [...] Read more.

The development of upper-limb prostheses is often hindered by limited dexterity, a restricted workspace, and bulky designs, primarily due to performance limitations in proximal joints like the shoulder and elbow, which contribute to high user abandonment rates. To overcome these challenges, this paper presents a novel, bioinspired, and integrated prosthetic system as an advancement in bionic technology. The design incorporates a shoulder joint based on an asymmetric 3-RRR spherical parallel mechanism (SPM) with actuators embedded within the moving platform, and an elbow joint actuated by low-voltage Shape Memory Alloy (SMA) springs. The inverse kinematics of the shoulder mechanism was established, revealing the existence of up to eight configurations. We employed Multi-Objective Particle Swarm Optimization (MOPSO) to simultaneously maximize workspace coverage, enhance dexterity, and minimize joint torque. The optimized design achieves remarkable performance: (1) 85% coverage of the natural shoulder’s workspace; (2) a maximum von Mises stress of merely 3.4 MPa under a 40 N load, ensuring structural integrity; and (3) a sub-0.2 s response time for the SMA-driven elbow under low-voltage conditions (6 V) at a motion velocity of 6°/s. Both motion simulation and prototype testing validated smooth and anthropomorphic motion trajectories. This work provides a comprehensive framework for developing lightweight, high-performance prosthetic limbs, establishing a solid foundation for next-generation wearable robotics and bionic devices. Future research will focus on the integration of neural interfaces for intuitive control. Full article

(This article belongs to the Special Issue Editorial Board Members' Collection Series: Biomimetic Design, Constructions and Devices in Times of Change 2025)

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25 pages, 5632 KB

Open AccessArticle

Chaos-Enhanced, Optimization-Based Interpretable Classification Model and Performance Evaluation in Food Drying

by Cagri Kaymak, Bilal Alatas, Suna Yildirim, Ebru Akpinar, Gizem Gul Katircioglu, Murat Catalkaya, Orhan E. Akay and Mehmet Das

Biomimetics 2026, 11(1), 78; https://doi.org/10.3390/biomimetics11010078 - 18 Jan 2026

Abstract

Food drying is a widely used preservation technique; however, achieving high energy efficiency while maintaining product quality remains a significant challenge. This study aims to analyze comprehensive experimental data obtained during the hot-air drying process of the Paşa pear (regional pear) and the [...] Read more.

Food drying is a widely used preservation technique; however, achieving high energy efficiency while maintaining product quality remains a significant challenge. This study aims to analyze comprehensive experimental data obtained during the hot-air drying process of the Paşa pear (regional pear) and the system’s autonomous control structure using an explainable artificial intelligence (XAI)-based method. The intelligent drying system, operating for approximately 17.5 h under two temperatures (50 °C and 65 °C) and two air speeds (0.63 m/s and 1.03 m/s), continuously adjusted the temperature and air speed using a PLC-based control mechanism; it ensured stable control throughout the process by monitoring parameters such as product weight, moisture, inlet–outlet temperatures, and air speed in real time. Experimental results showed that drying performance varied significantly with operating conditions, with product mass decreasing from 450 g to 103 g. The innovative aspect of the study is that it obtained quantitative, interpretable rules without discretization by applying the oscillatory chaotic sunflower optimization algorithm (OCSFO) to multidimensional control and process data for the first time. Thanks to its chaotic search mechanism, OCSFO accurately analyzed complex drying dynamics and created rules that achieved over 90% success for high, medium, and low performance classes. The obtained explainable rules clearly demonstrate that drying temperature and air velocity are the dominant determining parameters for drying efficiency, while energy consumption and cabin temperature distribution play a supporting role in distinguishing between efficiency classes. These rules clearly demonstrate how changes in controlled temperature and air velocity, combined with product weight and heat transfer, affect drying performance. Thus, the study offers a robust framework that identifies critical factors affecting drying performance through a transparent artificial intelligence approach that leverages both the autonomous control system and XAI-based rule mining. Full article

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18 pages, 2899 KB

Open AccessArticle

Numerical Investigation on Drag Reduction Mechanisms of Biomimetic Microstructure Surfaces

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

Biomimetics 2026, 11(1), 77; https://doi.org/10.3390/biomimetics11010077 - 18 Jan 2026

Abstract

Biomimetic microstructured surfaces offer a promising passive strategy for drag reduction in marine and aerospace applications. This study employs computational fluid dynamics (CFD) simulations to systematically investigate the drag reduction performance and mechanisms of groove-type microstructures, addressing both geometry selection and dimensional optimization. [...] Read more.

Biomimetic microstructured surfaces offer a promising passive strategy for drag reduction in marine and aerospace applications. This study employs computational fluid dynamics (CFD) simulations to systematically investigate the drag reduction performance and mechanisms of groove-type microstructures, addressing both geometry selection and dimensional optimization. Three representative geometries (V-groove, blade-groove, and arc-groove) were compared under identical flow conditions (inflow velocity 5 m/s, Re = 7.5 × 10⁵) using the shear-stress-transport (SST k-ω) turbulence model, and the third-generation Ω criterion was employed for threshold-independent vortex identification. The results establish a clear performance hierarchy: blade-groove achieves the highest drag reduction rate of 18.2%, followed by the V-groove (16.5%) and arc-groove (14.7%). The analysis reveals that stable near-wall microvortices form dynamic vortex isolation layers that separate the high-speed flow from the groove valleys, with blade grooves generating the strongest and most fully developed vortex structures. A parametric study of blade-groove aspect ratios (h⁺/s⁺ = 0.35–1.0) further demonstrates that maintaining h⁺/s⁺ ≥ 0.75 preserves effective vortex-isolation layers, whereas reducing h⁺/s⁺ below 0.6 causes vortex collapse and performance degradation. These findings establish a comprehensive design framework combining geometry selection (blade-groove > V-groove > arc-groove) with dimensional optimization criteria, providing quantitative guidance for practical biomimetic drag-reducing surfaces. Full article

(This article belongs to the Special Issue Adhesion and Friction in Biological and Bioinspired Systems)

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46 pages, 20947 KB

Open AccessReview

Bioinspired Heat Exchangers: A Multi-Scale Review of Thermo-Hydraulic Performance Enhancement

by Hyunsik Yang, Jinhyun Pi, Soyoon Park and Wongyu Bae

Biomimetics 2026, 11(1), 76; https://doi.org/10.3390/biomimetics11010076 - 16 Jan 2026

Abstract

Heat exchangers are central to energy and process industries, yet performance is bounded by the trade-off between higher heat transfer and greater pressure drop. This review targets indirect-type heat exchangers and organizes bioinspired strategies through a multi-scale lens of surface, texture, and network [...] Read more.

Heat exchangers are central to energy and process industries, yet performance is bounded by the trade-off between higher heat transfer and greater pressure drop. This review targets indirect-type heat exchangers and organizes bioinspired strategies through a multi-scale lens of surface, texture, and network scales. It provides a structured comparison of their thermo-hydraulic behaviors and evaluation methods. At the surface scale, control of wettability and liquid-infused interfaces suppresses icing and fouling and stabilizes condensation. At the texture scale, microstructures inspired by shark skin and fish scales regulate near-wall vortices to balance drag reduction with heat-transfer enhancement. At the network scale, branched and bicontinuous pathways inspired by leaf veins, lung architectures, and triply periodic minimal surfaces promote uniform distribution and mixing, improving overall performance. The survey highlights practical needs for manufacturing readiness, durability, scale-up, and validation across operating ranges. By emphasizing analysis across scales rather than reliance on a single metric, the review distills design principles and selection guidelines for next-generation bioinspired heat exchangers. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Biomimetic Surfaces and Interfaces 2025)

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20 pages, 857 KB

Open AccessArticle

Hybrid Spike-Encoded Spiking Neural Networks for Real-Time EEG Seizure Detection: A Comparative Benchmark

by Ali Mehrabi, Neethu Sreenivasan, Upul Gunawardana and Gaetano Gargiulo

Biomimetics 2026, 11(1), 75; https://doi.org/10.3390/biomimetics11010075 - 16 Jan 2026

Abstract

Reliable and low-latency seizure detection from electroencephalography (EEG) is critical for continuous clinical monitoring and emerging wearable health technologies. Spiking neural networks (SNNs) provide an event-driven computational paradigm that is well suited to real-time signal processing, yet achieving competitive seizure detection performance with [...] Read more.

Reliable and low-latency seizure detection from electroencephalography (EEG) is critical for continuous clinical monitoring and emerging wearable health technologies. Spiking neural networks (SNNs) provide an event-driven computational paradigm that is well suited to real-time signal processing, yet achieving competitive seizure detection performance with constrained model complexity remains challenging. This work introduces a hybrid spike encoding scheme that combines Delta–Sigma (change-based) and stochastic rate representations, together with two spiking architectures designed for real-time EEG analysis: a compact feed-forward HybridSNN and a convolution-enhanced ConvSNN incorporating depthwise-separable convolutions and temporal self-attention. The architectures are intentionally designed to operate on short EEG segments and to balance detection performance with computational practicality for continuous inference. Experiments on the CHB–MIT dataset show that the HybridSNN attains 91.8% accuracy with an F1-score of 0.834 for seizure detection, while the ConvSNN further improves detection performance to 94.7% accuracy and an F1-score of 0.893. Event-level evaluation on continuous EEG recordings yields false-alarm rates of 0.82 and 0.62 per day for the HybridSNN and ConvSNN, respectively. Both models exhibit inference latencies of approximately 1.2 ms per 0.5 s window on standard CPU hardware, supporting continuous real-time operation. These results demonstrate that hybrid spike encoding enables spiking architectures with controlled complexity to achieve seizure detection performance comparable to larger deep learning models reported in the literature, while maintaining low latency and suitability for real-time clinical and wearable EEG monitoring. Full article

(This article belongs to the Special Issue Bioinspired Engineered Systems)

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21 pages, 2947 KB

Open AccessArticle

HFSOF: A Hierarchical Feature Selection and Optimization Framework for Ultrasound-Based Diagnosis of Endometrial Lesions

by Yongjun Liu, Zihao Zhang, Tongyu Chai and Haitong Zhao

Biomimetics 2026, 11(1), 74; https://doi.org/10.3390/biomimetics11010074 - 15 Jan 2026

Abstract

Endometrial lesions are common in gynecology, exhibiting considerable clinical heterogeneity across different subtypes. Although ultrasound imaging is the preferred diagnostic modality due to its noninvasive, accessible, and cost-effective nature, its diagnostic performance remains highly operator-dependent, leading to subjectivity and inconsistent results. To address [...] Read more.

Endometrial lesions are common in gynecology, exhibiting considerable clinical heterogeneity across different subtypes. Although ultrasound imaging is the preferred diagnostic modality due to its noninvasive, accessible, and cost-effective nature, its diagnostic performance remains highly operator-dependent, leading to subjectivity and inconsistent results. To address these limitations, this study proposes a hierarchical feature selection and optimization framework for endometrial lesions, aiming to enhance the objectivity and robustness of ultrasound-based diagnosis. Firstly, Kernel Principal Component Analysis (KPCA) is employed for nonlinear dimensionality reduction, retaining the top 1000 principal components. Secondly, an ensemble of three filter-based methods—information gain, chi-square test, and symmetrical uncertainty—is integrated to rank and fuse features, followed by thresholding with Maximum Scatter Difference Linear Discriminant Analysis (MSDLDA) for preliminary feature selection. Finally, the Whale Migration Algorithm (WMA) is applied to population-based feature optimization and classifier training under the constraints of a Support Vector Machine (SVM) and a macro-averaged F1 score. Experimental results demonstrate that the proposed closed-loop pipeline of “kernel reduction—filter fusion—threshold pruning—intelligent optimization—robust classification” effectively balances nonlinear structure preservation, feature redundancy control, and model generalization, providing an interpretable, reproducible, and efficient solution for intelligent diagnosis in small- to medium-scale medical imaging datasets. Full article

(This article belongs to the Special Issue Bio-Inspired AI: When Generative AI and Biomimicry Overlap)

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37 pages, 21684 KB

Open AccessArticle

Multi-Strategy Improved Pelican Optimization Algorithm for Engineering Optimization Problems and 3D UAV Path Planning

by Ming Zhang, Maomao Luo and Huiming Kang

Biomimetics 2026, 11(1), 73; https://doi.org/10.3390/biomimetics11010073 - 15 Jan 2026

Abstract

To address the path-planning challenge for unmanned aerial vehicles (UAVs) in complex environments, this study presents an improved pelican optimization algorithm enhanced with multiple strategies (MIPOA). The proposed method introduces four main improvements: (1) using chaotic mapping to spread the initial search points [...] Read more.

To address the path-planning challenge for unmanned aerial vehicles (UAVs) in complex environments, this study presents an improved pelican optimization algorithm enhanced with multiple strategies (MIPOA). The proposed method introduces four main improvements: (1) using chaotic mapping to spread the initial search points more evenly, thereby increasing population variety; (2) incorporating a random Lévy-flight strategy to improve the exploration of the search space; (3) integrating a differential evolution approach based on Cauchy mutation to strengthen individual diversity and overall optimization ability; and (4) adopting an adaptive disturbance factor to speed up convergence and fine-tune solutions. To evaluate MIPOA, comparative tests were carried out against classical and modern intelligent algorithms using the CEC2017 and CEC2022 benchmark sets, along with a custom UAV environmental model. Results show that MIPOA converges faster and achieves more accurate solutions than the original pelican optimization algorithm (POA). On CEC2017 in 30-, 50-, and 100-dimensional cases, MIPOA attained the best average ranks of 1.57, 2.37, and 2.90, respectively, and achieved the top results on 26, 21, and 19 test functions, outperforming both POA and other advanced algorithms. For CEC2022 (20 dimensions), MIPOA obtained the highest Friedman average rank of 1.42, demonstrating its effectiveness in complex UAV path-planning tasks. The method enables the generation of faster, shorter, safer, and collision-free flight paths for UAVs, underscoring the robustness and wide applicability of MIPOA in real-world UAV path-planning scenarios. Full article

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

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31 pages, 9004 KB

Open AccessArticle

Multi-Strategy Fusion Improved Walrus Optimization Algorithm for Coverage Optimization in Wireless Sensor Networks

by Ling Li, Youyi Ding, Xiancun Zhou, Xuemei Zhu, Zongling Wu, Wei Peng, Jingya Zhang and Chaochuan Jia

Biomimetics 2026, 11(1), 72; https://doi.org/10.3390/biomimetics11010072 - 15 Jan 2026

Abstract

The Walrus Optimization (WO) algorithm, a metaheuristic inspired by walrus behavior, is known for its competitive convergence speed and effectiveness in solving high-dimensional and practical engineering optimization problems. However, it suffers from a tendency to converge to local optima and exhibits instability during [...] Read more.

The Walrus Optimization (WO) algorithm, a metaheuristic inspired by walrus behavior, is known for its competitive convergence speed and effectiveness in solving high-dimensional and practical engineering optimization problems. However, it suffers from a tendency to converge to local optima and exhibits instability during the iterative process. To overcome these limitations, this study proposes an improved WO (IMWO) algorithm based on the integration of Differential Evolution/best/1 (DE/best/1) mutation, Logistics–Sine–Cosine (LSC) Mapping, and the Beta Opposition-Based Learning (Beta-OBL) strategy. These strategies work synergistically to enhance the algorithm’s global exploration capability, improve its search stability, and accelerate convergence with higher precision. The performance of the IMWO algorithm was comprehensively evaluated using the CEC2017 and CEC2022 benchmark test suites, where it was compared against the original WO algorithm and six other state-of-the-art metaheuristics. Experimental data revealed that the IMWO algorithm achieved average fitness rankings of 1.66 and 1.33 in the two test suites, ranking first among all compared algorithms. The WSN coverage optimization problem aims to maximize the monitored area while reducing perception blind spots under limited node resources and energy constraints, which is a typical complex optimization problem with multiple constraints. In a practical application addressing the coverage optimization problem in Wireless Sensor Networks (WSNs), the IMWO algorithm attained average coverage rates of 95.86% and 96.48% in two sets of coverage experiments, outperforming both the original WO and other compared algorithms. These results confirm the practical utility and robustness of the IMWO algorithm in solving complex real-world engineering problems. Full article

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

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