Biomimetics

21 pages, 4476 KB

Open AccessArticle

Multiphysics Investigation on Thermal Characteristics of Internal Bio-Inspired V-Ribbed Cooling Channels for Outer Rotor PMSM

by Xin Xiong, Xiangyu Li, Shawn You, Bing Zhu, Ping Ding, Huanhuan Gao and Zongqi Hou

Biomimetics 2026, 11(6), 441; https://doi.org/10.3390/biomimetics11060441 - 22 Jun 2026

Viewed by 674

Abstract

Meeting the rigorous performance standards of modern electrified transit necessitates the deployment of high-performance outer rotor PMSMs with elevated power-to-volume ratios. However, their unique internal heat source topology inherently restricts heat dissipation. This limitation risks permanent magnet demagnetization and winding insulation failure. To [...] Read more.

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

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

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48 pages, 9238 KB

Open AccessArticle

Smart Logistics Model for Supply Chain Management via Brain-Inspired Geometric Deep Networks

by Mehdi Khaleghi, Farshad Pashootanizadeh, Nastaran Khaleghi, Sobhan Sheykhivand, Sebelan Danishvar and VahidReza Ghezavati

Biomimetics 2026, 11(6), 440; https://doi.org/10.3390/biomimetics11060440 - 22 Jun 2026

Viewed by 1228

Abstract

Systematic logistics plays a key role in fostering profitable development in supply chains. An intelligent logistics model can help create a more agile, sustainable, and resilient supply chain. In recent years, several brain-inspired deep learning architectures, such as long short-term memory networks, graph [...] Read more.

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

(This article belongs to the Special Issue Bio-Inspired Intelligence: Bridging Neural Networks, Artificial Intelligence (AI), and Biomimetics for Next-Generation Innovation: 2nd Edition)

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53 pages, 2087 KB

Open AccessReview

A Systematic Taxonomy of the Sunflower Optimization Algorithm: Variants, Hybridization Strategies, Applications, and Research Directions

by Ceren Baştemur Kaya

Biomimetics 2026, 11(6), 439; https://doi.org/10.3390/biomimetics11060439 - 20 Jun 2026

Viewed by 447

Abstract

Due to the rapidly increasing number of studies conducted using SFO in recent years, a comprehensive and systematic review of the existing literature has become necessary. SFO is a bio-inspired metaheuristic optimization algorithm developed based on the sun-tracking behavior of sunflower plants. Owing [...] Read more.

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

(This article belongs to the Special Issue Exploration of Bio-Inspired Computing: 3rd Edition)

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21 pages, 491 KB

Open AccessArticle

Toward a Compositional Theory of Trust in Embodied Intelligence: A QNLP Framework for Modeling Context, Interaction, and Trustworthiness

by Yang Li and Yao Song

Biomimetics 2026, 11(6), 438; https://doi.org/10.3390/biomimetics11060438 - 19 Jun 2026

Viewed by 476

Abstract

Trust in embodied intelligence is dynamic, contextual, and interaction-dependent, but many existing computational approaches still model trust using static similarity structures. This study proposes and evaluates a compositional trust modeling approach based on quantum natural language processing (QNLP). Using open-ended survey responses about [...] Read more.

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

(This article belongs to the Special Issue Advanced Human–Robot Interaction Challenges and Opportunities)

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24 pages, 4952 KB

Open AccessArticle

Empirical Logic for Bio-Inspired Soft Computing: Illustrative Applications in Control Engineering and Cluster Analysis

by Jens Grotrian

Biomimetics 2026, 11(6), 437; https://doi.org/10.3390/biomimetics11060437 - 18 Jun 2026

Viewed by 667

Abstract

Empirical logic (EL) is a bio-inspired soft computing approach to rule-based decision-making that emphasizes intuitive, experience-based reasoning. While its theoretical foundations have been established in previous work, its practical applicability and accessibility have so far received less attention. This paper addresses this gap [...] Read more.

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

(This article belongs to the Special Issue Bio-Inspired and Biomimetic Intelligence in Robotics: 3rd Edition)

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42 pages, 15142 KB

Open AccessArticle

A Modified Multi-Strategy Dhole Optimization Algorithm and Its Engineering Applications

by Jingya Zhang, Yu Liu, Chaochuan Jia, Maosheng Fu, Yaqi Yang, Jiahui Liu and Yujie Cheng

Biomimetics 2026, 11(6), 436; https://doi.org/10.3390/biomimetics11060436 - 18 Jun 2026

Viewed by 464

Abstract

To address the inherent limitations of the Dhole Optimization Algorithm (DOA)—limited exploration range, insufficient population diversity, and slow convergence—this paper proposes a Modified Dhole Optimization Algorithm (MDOA) integrating a Beta distribution-based opposition learning strategy, a DE/rand-to-best/1 differential mutation mechanism, and nonlinear parameter control. [...] Read more.

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

R^{2}

from 0.8339 to 0.9130, achieving the best results among all comparison models. These results demonstrate that MDOA is a highly effective and robust optimizer for complex constrained engineering and high-dimensional optimization tasks. Full article

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

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23 pages, 5556 KB

Open AccessArticle

A Biomimetic Visual Sensing Framework: Unsupervised Orientation Topographic Mapping via Self-Organizing Neural Networks

by Tianqi Chen, Zhiyu Qiu, Yuki Todo and Zheng Tang

Biomimetics 2026, 11(6), 435; https://doi.org/10.3390/biomimetics11060435 - 18 Jun 2026

Viewed by 578

Abstract

In this study, we propose a biologically inspired Self-Organizing Map-based Artificial Visual System (SOM-AVS) for unsupervised orientation detection in static images. By combining a biologically motivated front-end visual processing module with an unsupervised SOM layer, the proposed system captures key characteristics of early-stage [...] Read more.

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

(This article belongs to the Special Issue Bionic Vision Applications and Validation)

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20 pages, 9335 KB

Open AccessArticle

Data-Driven Inverse Design Enables a Dexterous Hand with Human-Comparable Dynamic Performance in Structured Tasks

by Lei Jiang, Kaixin Lan, Xianwei Liu, Chaojie Fu, Yongbin Jin and Hongtao Wang

Biomimetics 2026, 11(6), 434; https://doi.org/10.3390/biomimetics11060434 - 18 Jun 2026

Viewed by 828

Abstract

The design of dexterous robotic hands has long been constrained by empirical paradigms that struggle to balance anthropomorphic fidelity with dynamic performance. This study aims to establish a systematic methodology that bridges this gap through data-driven inverse design. We construct a quantitative association [...] Read more.

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

(This article belongs to the Special Issue Bio-Inspired Robots: Design and Application)

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18 pages, 3052 KB

Open AccessArticle

Rehabilitation of the Severely Atrophic Maxilla with Subperiosteal Implants: A Biomechanical and Decision Analysis of Material and Configuration Choices

by Barış Erkut Türk, Bersu Bedirhandede, Dilan Gizem Doğan and Beyza Güney

Biomimetics 2026, 11(6), 433; https://doi.org/10.3390/biomimetics11060433 - 18 Jun 2026

Viewed by 728

Abstract

Background/Objectives: Patient-specific subperiosteal implants are increasingly used to treat severely atrophic ridges due to advances in digital planning and additive manufacturing. This study aimed to evaluate the effects of material type and implant configuration on stress distribution in subperiosteal implant systems and [...] Read more.

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

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

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21 pages, 7349 KB

Open AccessArticle

Bio-Inspired Liquid-Cooled Plates for Enhanced Local Hotspot Dissipation in Lithium-Ion Battery Thermal Management

by Xuguang Yang, Zhihui Wang, Xiaohua Gu and Yan Liu

Biomimetics 2026, 11(6), 432; https://doi.org/10.3390/biomimetics11060432 - 18 Jun 2026

Viewed by 722

Abstract

To enhance the thermal management of lithium-ion batteries in new-energy vehicles, various bio-inspired liquid-cooled plate channel designs were investigated to improve hotspot dissipation within the laminar flow regime. A series of three-dimensional numerical simulations were conducted to compare leaf vein-, tree branch-, honeycomb-, [...] Read more.

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

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

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40 pages, 24197 KB

Open AccessArticle

Research on Object Detection in Cluttered Hospital Corridor Scenes with CSAWOA-YOLOv8

by Tianye Luo, Jing Hu, Bangcheng Zhang, Xinming Zhang and Shaoming Luo

Biomimetics 2026, 11(6), 431; https://doi.org/10.3390/biomimetics11060431 - 17 Jun 2026

Viewed by 513

Abstract

Dynamic hospital corridor environments are characterized by complex corridor environments, diverse target-scale variations, frequent occlusions, and dense small-object distribution, posing significant challenges to the accuracy and efficiency of the existing methods on resource-constrained platforms. To effectively address these challenges, a high-precision framework CSAWOA [...] Read more.

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

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

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26 pages, 1280 KB

Open AccessArticle

Drosophila Optimization Algorithm Based on Chaotic Development Mechanism and Orthogonal Learning Strategy for Reservoir Optimization

by Rong Lv, Guofa Lei, Hanchao Liu, Yuhan Sun, Wenhua Wang and Xuebin Du

Biomimetics 2026, 11(6), 430; https://doi.org/10.3390/biomimetics11060430 - 17 Jun 2026

Viewed by 584

Abstract

Enhancing oil and gas production performance is essential for maintaining the economic sustainability of petroleum enterprises and meeting the increasing global energy requirements. In this context, subsurface production optimization constitutes a fundamental component of strategic reservoir management, directly affecting critical decisions such as [...] Read more.

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

(This article belongs to the Special Issue Bio-Inspired Computation and Its Applications)

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29 pages, 18668 KB

Open AccessReview

Bioinspired 3D Printing of Lignocellulose-Based Multimaterial Composites for Extracellular Matrix-Mimicking Architectures

by Youjin Seol, Myoung Joon Jeon, Sayan Deb Dutta, Youjin Jeong and Ki-Taek Lim

Biomimetics 2026, 11(6), 429; https://doi.org/10.3390/biomimetics11060429 - 16 Jun 2026

Viewed by 927

Abstract

The extracellular matrix (ECM) provides a dynamic microenvironment that regulates cell proliferation, migration, and tissue remodeling during wound healing. However, replicating the structural and functional complexity and ECM heterogeneity of native skin ECM remains challenging with conventional single-material hydrogels. Recent advances in multimaterial [...] Read more.

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

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

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29 pages, 6991 KB

Open AccessArticle

QLFDGWO: Q-Learning-Guided Weighted Fitness–Distance Grey Wolf Optimizer for UAV Path Planning

by Chen Huang, Beining Yang and Yan Huo

Biomimetics 2026, 11(6), 428; https://doi.org/10.3390/biomimetics11060428 - 15 Jun 2026

Cited by 1 | Viewed by 494

Abstract

Traditional grey wolf optimizer (GWO) frequently suffers from insufficient search diversity, unstable stage transition, and premature convergence when addressing complex optimization tasks. To overcome these limitations, this paper proposes an improved grey wolf optimizer with a Q-learning-guided fitness–distance-weighted selector. For the proposed QLFDGWO [...] Read more.

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

(This article belongs to the Special Issue Advances in Biological and Bio-Inspired Algorithms: 2nd Edition)

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40 pages, 3456 KB

Open AccessFeature PaperArticle

Regime-Dependent Elastic Displacement in Bio-Inspired Parametric Kirigami Structures: An Experimental Study of Geometric Parameter Effects

by Tarek H. Mokhtar, Somaih M. Bakr and Qusai R. Khashman

Biomimetics 2026, 11(6), 427; https://doi.org/10.3390/biomimetics11060427 - 15 Jun 2026

Viewed by 501

Abstract

Biological thin-sheet systems, including leaves, insect wings, and flowering organs, achieve adaptive deformation through distributed compliance, segmentation, curvature, and controlled opening. Kirigami offers a bio-inspired route for translating such deformation logics into programmable thin-sheet surfaces; however, the geometric parameters that most strongly influence [...] Read more.

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

(This article belongs to the Special Issue Advancements in Nature-Inspired Engineering: Integrating Biomimicry into Modern Design Practices)

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19 pages, 699 KB

Open AccessArticle

Mixture of TSMixer Experts for Time Series Forecasting

by Jaemoo Hong and Keon Myung Lee

Biomimetics 2026, 11(6), 426; https://doi.org/10.3390/biomimetics11060426 - 15 Jun 2026

Viewed by 602

Abstract

As recent Multi-Layer Perceptron (MLP) mixer models have achieved state-of-the-art performance in time series forecasting, modeling each MLP-mixer as a separate expert within a mixture is expected to extend the representational capacity of the model, allowing each expert to be activated in response [...] Read more.

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

(This article belongs to the Special Issue Advanced Intelligent Systems and Biomimetics)

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28 pages, 4689 KB

Open AccessReview

3D-Bioprinted Multifunctional Nanocomposite Scaffolds for Alveolar Bone–Periodontal Ligament–Root Cementum Regeneration: A Narrative Review

by Angeliki Tsantiri, Nikolaos I. Mourkiotis, Hector Katifelis, Xanthippi Dereka, Maria Gazouli and Nefeli Lagopati

Biomimetics 2026, 11(6), 425; https://doi.org/10.3390/biomimetics11060425 - 15 Jun 2026

Cited by 1 | Viewed by 934

Abstract

Periodontal disease remains one of the leading causes of tooth loss worldwide, highlighting the need for effective regeneration of alveolar bone, periodontal ligament, and cementum. The structural complexity and unique biological behavior of these tissues have historically posed significant challenges for clinical regeneration [...] Read more.

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

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

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23 pages, 41342 KB

Open AccessArticle

Effects of Wing–Tail Coupling on Aerodynamic Performance of Flapping-Wing Aircraft

by Chao Wang, Longtian Zhang, Hao Liu, Kaicheng Yu, Jing Wu and Mingkang Zhu

Biomimetics 2026, 11(6), 424; https://doi.org/10.3390/biomimetics11060424 - 15 Jun 2026

Viewed by 735

Abstract

To address the limited understanding of the aerodynamic characteristics of bird-inspired flapping-wing aircraft across different flight phases and the unclear flow field interaction mechanisms between the wings and tail, this study performs three-dimensional numerical simulations based on a self-developed prototype using ANSYS Fluent [...] Read more.

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

(This article belongs to the Special Issue Advances in Bionic Aircraft, Underwater Vehicles, and Unmanned Aerial–Underwater Vehicles)

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16 pages, 10998 KB

Open AccessArticle

Effects of UV Photo-Functionalization of Titanium Dental Implants on Osteoblast Responses In Vitro

by Merter Güçlü, Duru Aras Tosun, Nilsun Bağış, Mohammadreza Dastouri, Alp Can and Rabia Karaaslan

Biomimetics 2026, 11(6), 423; https://doi.org/10.3390/biomimetics11060423 - 14 Jun 2026

Viewed by 547

Abstract

Osseointegration is defined as the structural and functional integration between alveolar bone and a dental implant. Photo-functionalization (PF) refers to ultraviolet (UV)-induced surface modifications of titanium implants, including changes in physicochemical properties and biological responsiveness. The aim of this study was to evaluate [...] Read more.

Osseointegration is defined as the structural and functional integration between alveolar bone and a dental implant. Photo-functionalization (PF) refers to ultraviolet (UV)-induced surface modifications of titanium implants, including changes in physicochemical properties and biological responsiveness. The aim of this study was to evaluate the effects of PF on early osteoblast responses related to osseointegration on titanium dental implants in vitro. We hypothesized that PF applied to titanium implants enhances early osteoblast responses related to osseointegration. Sixteen titanium dental implants were divided into two equal groups of eight: untreated (PF−) and PF-treated (PF+). PF+ implants were exposed to UV light at 172 nm for 10 s. An additional cell-only control group was incubated without an implant. All groups were cultured in vitro with SAOS-2 human osteoblast-like cells. Cell proliferation and viability were assessed using standard in vitro assays, and DNA damage was evaluated using the Terminal deoxynucleotidyl transferase [TdT] dUTP Nick End Labeling (TUNEL) assay. Early cellular responses related to osseointegration were assessed by evaluating adhesion-related vinculin levels and alkaline phosphatase (ALP) activity. After 24 h of incubation, cell proliferation was comparable between the groups, whereas after 48 h, cell number was significantly lower in the PF− group compared with the PF+ group and the control group (p = 0.039). Osteoblast viability was significantly lower in the PF− group than in the control group (53% vs. 94%, p = 0.002), while the PF+ group showed a numerically higher viability value than the PF− group (70% vs. 53%). TUNEL assay showed no statistically significant difference in DNA damage among the groups, although the PF− group showed a slightly higher TUNEL-positive cell ratio (p = 0.563). Vinculin levels were significantly higher in the PF+ group at both 24 and 48 h compared with the PF− group and control group (p < 0.0001). ALP activity increased significantly over time in cells incubated with PF-treated implants (p < 0.0001). Within the limitations of this exploratory pilot in vitro study, UV photo-functionalization was associated with more favorable early osteoblast-like cell responses on titanium dental implants, particularly in terms of proliferation, adhesion-related vinculin levels, and ALP response. PF did not increase TUNEL-positive cell ratios compared with untreated implants under the present experimental conditions. These findings should be interpreted as preliminary biological evidence and require confirmation through larger experimental designs, detailed physicochemical surface characterization, and in vivo validation. 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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38 pages, 13992 KB

Open AccessArticle

A Q-Learning-Enhanced Cuckoo Catfish Optimizer (CCO-RL): A Comparative Study of Nine Metaheuristics Applied to CEC2017, CEC2022 and Engineering Design Problems

by Arar Al Tawil, Amnah Alshahrani, Bilal Ibrahim Alqudah and Hana Fathi

Biomimetics 2026, 11(6), 422; https://doi.org/10.3390/biomimetics11060422 - 14 Jun 2026

Viewed by 367

Abstract

The Cuckoo Catfish Optimizer (CCO) is a recent swarm method with four built-in movement strategies. Its weakness is not the moves themselves but the way it chooses among them: a fixed chain of random-versus-threshold (

rand > C

) tests that ignores how [...] Read more.

The Cuckoo Catfish Optimizer (CCO) is a recent swarm method with four built-in movement strategies. Its weakness is not the moves themselves but the way it chooses among them: a fixed chain of random-versus-threshold (

rand > C

) tests that ignores how each agent is actually doing and keeps no memory of which move has been paying off. On harder, higher-dimensional problems, this rigidity drains diversity and the search stalls. We propose CCO-RL, which hands the choice of move to a small tabular Q-learning controller. For every agent at every iteration, the controller reads a 48-state summary of the agent’s crowding, its recent stagnation and how far the run has progressed, then picks one of the four moves. A bounded reward and a decaying

ε

-greedy rule let it learn a policy online with no extra function evaluations. We test CCO-RL against the original CCO and eight popular metaheuristics on CEC2017 (

D = 30, 50

) and CEC2022 (

D = 20

): 70 instances, 30 runs each. CCO-RL earns the best overall Friedman rank (

1.69

) and significantly beats every external competitor according to the Nemenyi test. It also finds the best mean design in three engineering problems. Full article

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21 pages, 4864 KB

Open AccessArticle

Optimisation of Bioinspired Fibre Architectures for 3D-Printed Polymer Heart Valves via Melt Electrowriting (MEW) Using FE Modelling and Design of Experiments (FE-DOE)

by Celia Hughes, Robert D. Johnston, Dylan Armfield, Desmond McCarthy, Ewa Klusak, Emily Growney, Evelyn Campbell and Caitríona Lally

Biomimetics 2026, 11(6), 421; https://doi.org/10.3390/biomimetics11060421 - 13 Jun 2026

Viewed by 860

Abstract

Aortic stenosis is predominantly treated through transcatheter bioprosthetic heart valve implantation. However, the materials used in these devices are prone to premature failure. Polymer heart valves provide an alternative to current commercial devices, offering materials with greater durability and customisation through fibre reinforcement. [...] Read more.

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

(This article belongs to the Special Issue Bioinspired Valve Engineering and Cardiovascular Modeling)

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18 pages, 1688 KB

Open AccessArticle

Three-Dimensional Analysis of Morphological Adaptation and Wear in Restorations Performed Using the Stamp Technique with Different Viscosity Composite Resins

by İlknur Akay Dede, Ayşenur Yazım and Cemile Kedici Alp

Biomimetics 2026, 11(6), 420; https://doi.org/10.3390/biomimetics11060420 - 13 Jun 2026

Viewed by 615

Abstract

The stamp technique is a biomimetic approach that enables accurate reproduction of preoperative occlusal morphology in direct composite restorations; however, the rheological properties of restorative materials may influence both morphological adaptation and wear behavior. This in vitro study aimed to evaluate the morphological [...] Read more.

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

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

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52 pages, 10220 KB

Open AccessArticle

Blackcap Optimization Algorithm (BCOA): A Novel Metaheuristic Algorithm for Global and Engineering Optimization Problems

by Ali Asghari and Mohammadhossein Mohammadi

Biomimetics 2026, 11(6), 419; https://doi.org/10.3390/biomimetics11060419 - 13 Jun 2026

Cited by 1 | Viewed by 707

Abstract

Metaheuristic algorithms are widely used to find optimal or near-optimal solutions for complex problems by taking inspiration from natural behaviors and processes. Although many different methods have been developed, a common problem in many of them is maintaining a good balance between exploration [...] Read more.

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

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

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36 pages, 5958 KB

Open AccessArticle

Implementation of Modified Effective Butterfly Optimizer in Solving Multi-Objective Pareto Optimal Power Flow Problem with Renewable Uncertainties

by Hakan Işıker, Ali Akdağlı, Volkan Yamaçlı, Zeki Yetgin, İbrahim Çağrı Barutçu, Kadir Abacı and Furkan Gözükara

Biomimetics 2026, 11(6), 418; https://doi.org/10.3390/biomimetics11060418 - 13 Jun 2026

Viewed by 470

Abstract

The power flow problem is one of the most challenging tasks in power systems, affecting both generation cost and energy quality. Optimal power flow (OPF) further complicates this task by requiring the optimal adjustment of system variables and parameters. This paper adapts the [...] Read more.

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

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

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18 pages, 13980 KB

Open AccessArticle

Bio-Inspired 3D-Printed Polymeric Sheets for Orthoses: Predictive Modeling of Mechanical Integrity and Moisture Absorption

by Rosa Devesa-Rey, Elena Arce, Silvia Losada-Pérez, Miguel Ángel Álvarez-Feijoo and Raquel Leirós-Rodríguez

Biomimetics 2026, 11(6), 417; https://doi.org/10.3390/biomimetics11060417 - 13 Jun 2026

Viewed by 561

Abstract

The rapid development of additive manufacturing has enabled the production of personalized biomedical devices, including custom orthoses that must retain their structural integrity under demanding physiological conditions. This study evaluates the performance of 3D-printed polymers—blue and white polylactic acid (PLA), Standard Blue Resin, [...] Read more.

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

(This article belongs to the Special Issue Next-Generation 3D Bioprinting and Additive Manufacturing: From Digital Design to Functional Biomimetic Systems)

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29 pages, 2186 KB

Open AccessReview

Recent Advances on Extracellular Vesicles: A Natural Nanomaterial for Biomedical Application

by Fan Li, Siyu Liu, Shuaiwei Xu, Huimin Duan, Yanchao Wang and Jingan Li

Biomimetics 2026, 11(6), 416; https://doi.org/10.3390/biomimetics11060416 - 11 Jun 2026

Cited by 1 | Viewed by 1232

Abstract

Extracellular vesicles (EVs), naturally secreted by cells as nanoscale lipid bilayer structures, have become a research hotspot in biomedicine owing to their excellent biocompatibility, low immunogenicity, and inherent ability to cross biological barriers. This review systematically summarizes recent advances in EVs as natural [...] Read more.

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

(This article belongs to the Special Issue Advances in Biogenic and Biomimetic Materials: From Bionanomedicine to Environmental Applications and Beyond: 2nd Edition)

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15 pages, 2059 KB

Open AccessArticle

Printed Organic Memristive Device on Rigid and Flexible Supports for Neuromorphic Applications

by Davide Vurro, Salvatore Del Basso, Simone Luigi Marasso, Alberto Ballesio, Giuseppe Tarabella, Pasquale D’Angelo and Victor Erokhin

Biomimetics 2026, 11(6), 415; https://doi.org/10.3390/biomimetics11060415 - 11 Jun 2026

Viewed by 559

Abstract

Organic memristive devices are promising components for neuromorphic systems. Although based on solution-processable materials, their fabrication often involves complex, resource-intensive processes. Here, we report the fabrication of organic memristive devices using aerosol jet printing to deposit both the active channel based on proprietary [...] Read more.

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

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24 pages, 980 KB

Open AccessReview

3D-Printed Plantar Orthoses and the Conditional Viability of Recycled PLA

by Elena Arce, Silvia Losada-Pérez, Rosa Devesa-Rey, Miguel Ángel Álvarez-Feijoo, Pablo Agregán and Raquel Leirós-Rodríguez

Biomimetics 2026, 11(6), 414; https://doi.org/10.3390/biomimetics11060414 - 11 Jun 2026

Viewed by 689

Abstract

Plantar orthoses play an important role in podiatric care, as they help to redistribute plantar loads, improve foot function, and support the treatment of various conditions, including diabetic foot disease. In this context, additive manufacturing has substantially expanded the capacity to produce customized [...] Read more.

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

(This article belongs to the Special Issue Next-Generation 3D Bioprinting and Additive Manufacturing: From Digital Design to Functional Biomimetic Systems)

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49 pages, 4724 KB

Open AccessArticle

A Modified Complex-Valued Encoding Greater Cane Rat Algorithm for Global Optimization and Constrained Engineering Applications

by Yubao Xu, Yuebo Wu and Jinzhong Zhang

Biomimetics 2026, 11(6), 413; https://doi.org/10.3390/biomimetics11060413 - 11 Jun 2026

Viewed by 419

Abstract

The greater cane rat algorithm (GCRA) draws inspiration from the seasonal behavioral patterns of the greater cane rats: extensive roaming during the non-breeding period for global exploration, and aggregative foraging during the reproductive period for local exploitation. The GCRA leverages independent movement and [...] Read more.

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

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

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18 pages, 6874 KB

Open AccessArticle

Comparative Analysis of High-Torque-Density Permanent Magnet Motors Having Similar Slot and Pole Numbers for Humanoid Robot Applications

by Kun Bi, Zhuoyi Chen and Tianran He

Biomimetics 2026, 11(6), 412; https://doi.org/10.3390/biomimetics11060412 - 11 Jun 2026

Viewed by 901

Abstract

The conventional robotic position control is gradually being replaced by force control, which is commonly used in humanoid robot applications that require force interaction with the environment, force transmission, or contact. A high-back-drive-efficiency actuator with a high-torque-density permanent magnet motor connecting the low-ratio [...] Read more.

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

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

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