Biomimetics

25 pages, 5177 KB

Open AccessArticle

Prediction of Water Chemical Oxygen Demand with Multi-Scale One-Dimensional Convolutional Neural Network Fusion and Ultraviolet–Visible Spectroscopy

by Jingwei Li, Yijing Lu, Yipei Ding, Chenxuan Zhou, Jia Liu, Zhiyu Shao and Yibei Nian

Biomimetics 2025, 10(3), 191; https://doi.org/10.3390/biomimetics10030191 - 20 Mar 2025

Cited by 2 | Viewed by 1208

Abstract

Chemical oxygen demand (COD) is a critical parameter employed to assess the level of organic pollution in water. Accurate COD detection is essential for effective environmental monitoring and water quality assessment. Ultraviolet–visible (UV-Vis) spectroscopy has become a widely applied method for COD detection [...] Read more.

Chemical oxygen demand (COD) is a critical parameter employed to assess the level of organic pollution in water. Accurate COD detection is essential for effective environmental monitoring and water quality assessment. Ultraviolet–visible (UV-Vis) spectroscopy has become a widely applied method for COD detection due to its convenience and the absence of the need for chemical reagents. This non-destructive and reagent-free approach offers a rapid and reliable means of analyzing water. Recently, deep learning has emerged as a powerful tool for automating the process of spectral feature extraction and improving COD prediction accuracy. In this paper, we propose a novel multi-scale one-dimensional convolutional neural network (MS-1D-CNN) fusion model designed specifically for spectral feature extraction and COD prediction. The architecture of the proposed model involves inputting raw UV-Vis spectra into three parallel sub-1D-CNNs, which independently process the data. The outputs from the final convolution and pooling layers of each sub-CNN are then fused into a single layer, capturing a rich set of spectral features. This fused output is subsequently passed through a Flatten layer followed by fully connected layers to predict the COD value. Experimental results demonstrate the effectiveness of the proposed method, as it was compared with three traditional methods and three deep learning methods on the same dataset. The MS-1D-CNN model showed a significant improvement in the accuracy of COD prediction, highlighting its potential for more reliable and efficient water quality monitoring. Full article

(This article belongs to the Special Issue Exploration of Bioinspired Computer Vision and Pattern Recognition)

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21 pages, 85270 KB

Open AccessArticle

Multi-Humanoid Robot Arm Motion Imitation and Collaboration Based on Improved Retargeting

by Xisheng Jiang, Baolei Wu, Simin Li, Yongtong Zhu, Guoxiang Liang, Ye Yuan, Qingdu Li and Jianwei Zhang

Biomimetics 2025, 10(3), 190; https://doi.org/10.3390/biomimetics10030190 - 19 Mar 2025

Cited by 1 | Viewed by 2480

Abstract

Human–robot interaction (HRI) is a key technology in the field of humanoid robotics, and motion imitation is one of the most direct ways to achieve efficient HRI. However, due to significant differences in structure, range of motion, and joint torques between the human [...] Read more.

Human–robot interaction (HRI) is a key technology in the field of humanoid robotics, and motion imitation is one of the most direct ways to achieve efficient HRI. However, due to significant differences in structure, range of motion, and joint torques between the human body and robots, motion imitation remains a challenging task. Traditional retargeting algorithms, while effective in mapping human motion to robots, typically either ensure similarity in arm configuration (joint space-based) or focus solely on tracking the end-effector position (Cartesian space-based). This creates a conflict between the liveliness and accuracy of robot motion. To address this issue, this paper proposes an improved retargeting algorithm that ensures both the similarity of the robot’s arm configuration to that of the human body and accurate end-effector position tracking. Additionally, a multi-person pose estimation algorithm is introduced, enabling real-time capture of multiple imitators’ movements using a single RGB-D camera. The captured motion data are used as input to the improved retargeting algorithm, enabling multi-robot collaboration tasks. Experimental results demonstrate that the proposed algorithm effectively ensures consistency in arm configuration and precise end-effector position tracking. Furthermore, the collaborative experiments validate the generalizability of the improved retargeting algorithm and the superior real-time performance of the multi-person pose estimation algorithm. Full article

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22 pages, 9133 KB

Open AccessArticle

A Robust Disturbance Rejection Whole-Body Control Framework for Bipedal Robots Using a Momentum-Based Observer

by Shuai Heng, Xizhe Zang, Yan Liu, Chao Song, Boyang Chen, Yue Zhang, Yanhe Zhu and Jie Zhao

Biomimetics 2025, 10(3), 189; https://doi.org/10.3390/biomimetics10030189 - 19 Mar 2025

Viewed by 1251

Abstract

This paper presents a complete planner and controller scheme for bipedal robots, designed to enhance robustness against external disturbances. The high-level planner utilizes model predictive control (MPC) to optimize both the foothold location and step duration based on the divergent component of motion [...] Read more.

This paper presents a complete planner and controller scheme for bipedal robots, designed to enhance robustness against external disturbances. The high-level planner utilizes model predictive control (MPC) to optimize both the foothold location and step duration based on the divergent component of motion (DCM) to increase the robustness of generated gaits. For low-level control, we employ a momentum-based observer capable of estimating external forces acting on both stance and swing legs. The full-body dynamics, incorporating estimated disturbances, are integrated into a weighted whole-body control (WBC) to obtain more accurate ground reaction forces needed by the momentum-based observer. This approach eliminates the dependency on foot-mounted sensors for ground reaction force measurement, distinguishing our method from other disturbance estimation methods that rely on direct sensor measurements. Additionally, the controller incorporates trajectory compensation mechanisms to mitigate the effects of external disturbances. The effectiveness of the proposed framework is validated through comprehensive simulations and experimental evaluations conducted on BRUCE, a miniature bipedal robot developed by Westwood Robotics (Los Angeles, CA, USA). These tests include walking under swing leg disturbances, traversing uneven terrain, and simultaneously resisting upper-body pushes. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Biomimetics)

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19 pages, 4442 KB

Open AccessReview

Bonding Protocols for Lithium Disilicate Veneers: A Narrative Review and Case Study

by Silvia Rojas-Rueda, Jose Villalobos-Tinoco, Clint Conner, Staley Colvert, Hamid Nurrohman and Carlos A. Jurado

Biomimetics 2025, 10(3), 188; https://doi.org/10.3390/biomimetics10030188 - 19 Mar 2025

Cited by 3 | Viewed by 5057

Abstract

Background: The bonding protocol for lithium disilicate veneers in the esthetic zone plays a crucial role in modern dental restoration techniques, focusing on the replication of natural tooth properties and esthetics. This process involves several meticulous steps on both ceramic and tooth surfaces [...] Read more.

Background: The bonding protocol for lithium disilicate veneers in the esthetic zone plays a crucial role in modern dental restoration techniques, focusing on the replication of natural tooth properties and esthetics. This process involves several meticulous steps on both ceramic and tooth surfaces to optimize material performance and bond strength. Methods: The objective of this article is to provide an updated review of the literature on the clinical steps for bonding lithium disilicate veneers in the anterior dentition and to document a clinical case where these advanced restorative techniques were applied to treat a female patient seeking to improve her smile. A preliminary review was conducted on the existing literature regarding the clinical protocols for bonding lithium disilicate veneers in the esthetic zone. The main advantage of careful bonding procedures is that they maximize the full potential of the materials’ properties. Results: A review of the literature reveals some minor differences in cleaning the veneers prior to cementation and in the number of steps involved when combining certain materials in a single application process. However, well-executed bonding procedures, following the manufacturer’s recommendations, can maximize the adhesion between the ceramic and the tooth, allowing the restorations to meet the patient’s esthetic demands. Conclusions: Effective bonding of lithium disilicate veneers in the esthetic zone requires multiple treatments on both the ceramic and tooth surfaces. When procedures are followed carefully, long-term esthetic and functional outcomes can be achieved. It is essential that clinicians are familiar with these steps. Proper patient selection, thoughtful treatment planning, and methodical execution of the case can lead to highly esthetic results that satisfy the patient’s demands and ensure long-term success. Full article

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

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21 pages, 4671 KB

Open AccessArticle

Single-Trial Electroencephalography Discrimination of Real, Regulated, Isometric Wrist Extension and Wrist Flexion

by Abdul-Khaaliq Mohamed and Vered Aharonson

Biomimetics 2025, 10(3), 187; https://doi.org/10.3390/biomimetics10030187 - 18 Mar 2025

Viewed by 818

Abstract

Improved interpretation of electroencephalography (EEG) associated with the neural control of essential hand movements, including wrist extension (WE) and wrist flexion (WF), could improve the performance of brain–computer interfaces (BCIs). These BCIs could control a prosthetic or orthotic hand to enable motor-impaired individuals [...] Read more.

Improved interpretation of electroencephalography (EEG) associated with the neural control of essential hand movements, including wrist extension (WE) and wrist flexion (WF), could improve the performance of brain–computer interfaces (BCIs). These BCIs could control a prosthetic or orthotic hand to enable motor-impaired individuals to regain the performance of activities of daily living. This study investigated the interpretation of neural signal patterns associated with kinematic differences between real, regulated, isometric WE and WF movements from recorded EEG data. We used 128-channel EEG data recorded from 14 participants performing repetitions of the wrist movements, where the force, speed, and range of motion were regulated. The data were filtered into four frequency bands: delta and theta, mu and beta, low gamma, and high gamma. Within each frequency band, independent component analysis was used to isolate signals originating from seven cortical regions of interest. Features were extracted from these signals using a time–frequency algorithm and classified using Mahalanobis distance clustering. We successfully classified bilateral and unilateral WE and WF movements, with respective accuracies of 90.68% and 69.80%. The results also demonstrated that all frequency bands and regions of interest contained motor-related discriminatory information. Bilateral discrimination relied more on the mu and beta bands, while unilateral discrimination favoured the gamma bands. These results suggest that EEG-based BCIs could benefit from the extraction of features from multiple frequencies and cortical regions. Full article

(This article belongs to the Special Issue Advances in Brain–Computer Interfaces)

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19 pages, 28961 KB

Open AccessArticle

Human-like Dexterous Grasping Through Reinforcement Learning and Multimodal Perception

by Wen Qi, Haoyu Fan, Cankun Zheng, Hang Su and Samer Alfayad

Biomimetics 2025, 10(3), 186; https://doi.org/10.3390/biomimetics10030186 - 18 Mar 2025

Cited by 3 | Viewed by 2542

Abstract

Dexterous robotic grasping with multifingered hands remains a critical challenge in non-visual environments, where diverse object geometries and material properties demand adaptive force modulation and tactile-aware manipulation. To address this, we propose the Reinforcement Learning-Based Multimodal Perception (RLMP) framework, which integrates human-like grasping [...] Read more.

Dexterous robotic grasping with multifingered hands remains a critical challenge in non-visual environments, where diverse object geometries and material properties demand adaptive force modulation and tactile-aware manipulation. To address this, we propose the Reinforcement Learning-Based Multimodal Perception (RLMP) framework, which integrates human-like grasping intuition through operator-worn gloves with tactile-guided reinforcement learning. The framework’s key innovation lies in its Tactile-Driven DCNN architecture—a lightweight convolutional network achieving 98.5% object recognition accuracy using spatiotemporal pressure patterns—coupled with an RL policy refinement mechanism that dynamically correlates finger kinematics with real-time tactile feedback. Experimental results demonstrate reliable grasping performance across deformable and rigid objects while maintaining force precision critical for fragile targets. By bridging human teleoperation with autonomous tactile adaptation, RLMP eliminates dependency on visual input and predefined object models, establishing a new paradigm for robotic dexterity in occlusion-rich scenarios. Full article

(This article belongs to the Special Issue Biomimetic Innovations for Human–Machine Interaction)

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22 pages, 1180 KB

Open AccessArticle

FedDyH: A Multi-Policy with GA Optimization Framework for Dynamic Heterogeneous Federated Learning

by Xuhua Zhao, Yongming Zheng, Jiaxiang Wan, Yehong Li, Donglin Zhu, Zhenyu Xu and Huijuan Lu

Biomimetics 2025, 10(3), 185; https://doi.org/10.3390/biomimetics10030185 - 17 Mar 2025

Viewed by 828

Abstract

Federated learning (FL) is a distributed learning technique that ensures data privacy and has shown significant potential in cross-institutional image analysis. However, existing methods struggle with the inherent dynamic heterogeneity of real-world data, such as changes in cellular differentiation during disease progression or [...] Read more.

Federated learning (FL) is a distributed learning technique that ensures data privacy and has shown significant potential in cross-institutional image analysis. However, existing methods struggle with the inherent dynamic heterogeneity of real-world data, such as changes in cellular differentiation during disease progression or feature distribution shifts due to different imaging devices. This dynamic heterogeneity can cause catastrophic forgetting, leading to reduced performance in medical predictions across stages. Unlike previous federated learning studies that paid insufficient attention to dynamic heterogeneity, this paper proposes the FedDyH framework to address this challenge. Inspired by the adaptive regulation mechanisms of biological systems, this framework incorporates several core modules to tackle the issues arising from dynamic heterogeneity. First, the framework simulates intercellular information transfer through cross-client knowledge distillation, preserving local features while mitigating knowledge forgetting. Additionally, a dynamic regularization term is designed in which the strength can be adaptively adjusted based on real-world conditions. This mechanism resembles the role of regulatory T cells in the immune system, balancing global model convergence with local specificity adjustments to enhance the robustness of the global model while preventing interference from diverse client features. Finally, the framework introduces a genetic algorithm (GA) to simulate biological evolution, leveraging mechanisms such as gene selection, crossover, and mutation to optimize hyperparameter configurations. This enables the model to adaptively find the optimal hyperparameters in an ever-changing environment, thereby improving both adaptability and performance. Prior to this work, few studies have explored the use of optimization algorithms for hyperparameter tuning in federated learning. Experimental results demonstrate that the FedDyH framework improves accuracy compared to the SOTA baseline FedDecorr by 2.59%, 0.55%, and 5.79% on the MNIST, Fashion-MNIST, and CIFAR-10 benchmark datasets, respectively. This framework effectively addresses data heterogeneity issues in dynamic heterogeneous environments, providing an innovative solution for achieving more stable and accurate distributed federated learning. Full article

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

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41 pages, 5894 KB

Open AccessReview

Biomimetic Polyurethanes in Tissue Engineering

by Edyta Hebda and Krzysztof Pielichowski

Biomimetics 2025, 10(3), 184; https://doi.org/10.3390/biomimetics10030184 - 17 Mar 2025

Cited by 3 | Viewed by 2496

Abstract

Inspiration from nature is a promising tool for the design of new polymeric biomaterials, especially for frontier technological areas such as tissue engineering. In tissue engineering, polyurethane-based implants have gained considerable attention, as they are materials that can be designed to meet the [...] Read more.

Inspiration from nature is a promising tool for the design of new polymeric biomaterials, especially for frontier technological areas such as tissue engineering. In tissue engineering, polyurethane-based implants have gained considerable attention, as they are materials that can be designed to meet the requirements imposed by their final applications. The choice of their building blocks (which are used in the synthesis as macrodiols, diisocyanates, and chain extenders) can be implemented to obtain biomimetic structures that can mimic native tissue in terms of mechanical, morphological, and surface properties. In recent years, due to their excellent chemical stability, biocompatibility, and low cytotoxicity, polyurethanes have been widely used in biomedical applications. Biomimetic materials, with their inherent nature of mimicking natural materials, are possible thanks to recent advances in manufacturing technology. The aim of this review is to provide a critical overview of relevant promising studies on polyurethane scaffolds, including those based on non-isocyanate polyurethanes, for the regeneration of selected soft (cardiac muscle, blood vessels, skeletal muscle) and hard (bone tissue) tissues. Full article

(This article belongs to the Special Issue Biomimetic Scaffolds for Hard Tissue Surgery: 2nd Edition)

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17 pages, 1434 KB

Open AccessArticle

Decoding Brain Signals in a Neuromorphic Framework for a Personalized Adaptive Control of Human Prosthetics

by Georgi Rusev, Svetlozar Yordanov, Simona Nedelcheva, Alexander Banderov, Fabien Sauter-Starace, Petia Koprinkova-Hristova and Nikola Kasabov

Biomimetics 2025, 10(3), 183; https://doi.org/10.3390/biomimetics10030183 - 14 Mar 2025

Cited by 3 | Viewed by 1232

Abstract

Current technological solutions for Brain-machine Interfaces (BMI) achieve reasonable accuracy, but most systems are large in size, power consuming and not auto-adaptive. This work addresses the question whether current neuromorphic technologies could resolve these problems? The paper proposes a novel neuromorphic framework of [...] Read more.

Current technological solutions for Brain-machine Interfaces (BMI) achieve reasonable accuracy, but most systems are large in size, power consuming and not auto-adaptive. This work addresses the question whether current neuromorphic technologies could resolve these problems? The paper proposes a novel neuromorphic framework of a BMI system for prosthetics control via decoding Electro Cortico-Graphic (ECoG) brain signals. It includes a three-dimensional spike timing neural network (3D-SNN) for brain signals features extraction and an on-line trainable recurrent reservoir structure (Echo state network (ESN)) for Motor Control Decoding (MCD). A software system, written in Python using NEST Simulator SNN library is described. It is able to adapt continuously in real time in supervised or unsupervised mode. The proposed approach was tested on several experimental data sets acquired from a tetraplegic person. First simulation results are encouraging, showing also the need for a further improvement via multiple hyper-parameters tuning. Its future implementation on a neuromorphic hardware platform that is smaller in size and significantly less power consuming is discussed too. Full article

(This article belongs to the Special Issue Advances in Brain–Computer Interfaces)

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23 pages, 55937 KB

Open AccessArticle

The Design, Modeling, and Experiment of a Novel Diving-Beetle-Inspired Paddling Propulsion Robot

by Jiang Ding, Jingyu Li, Tianbo Lan, Kai He and Qiyang Zuo

Biomimetics 2025, 10(3), 182; https://doi.org/10.3390/biomimetics10030182 - 14 Mar 2025

Viewed by 962

Abstract

Bionic paddling robots, as a novel type of underwater robot, demonstrate significant potential in the fields of underwater exploration and development. However, current research on bionic paddling robots primarily focuses on the motion mechanisms of large organisms such as frogs, while the exploration [...] Read more.

Bionic paddling robots, as a novel type of underwater robot, demonstrate significant potential in the fields of underwater exploration and development. However, current research on bionic paddling robots primarily focuses on the motion mechanisms of large organisms such as frogs, while the exploration of small and highly agile bionic propulsion robots remains relatively limited. Additionally, existing biomimetic designs often face challenges such as structural complexity and cumbersome control systems, which hinder their practical applications. To address these challenges, this study proposes a novel diving-beetle-inspired paddling robot, drawing inspiration from the low-resistance physiological structure and efficient paddling locomotion of diving beetles. Specifically, a passive bionic swimming foot and a periodic paddling propulsion mechanism were designed based on the leg movement patterns of diving beetles, achieving highly efficient propulsion performance. In the design process, a combination of incomplete gears and torsion springs was employed, significantly reducing the driving frequency of servos and simplifying control complexity. Through dynamic simulations and experimental validation, the robot demonstrated a maximum forward speed of 0.82 BL/s and a turning speed of 18°/s. The results indicate that this design not only significantly improves propulsion efficiency and swimming agility but also provides new design insights and technical references for the development of small bionic underwater robots. Full article

(This article belongs to the Special Issue Biorobotics: Challenges and Opportunities)

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29 pages, 7270 KB

Open AccessReview

Nature-Inspired Solutions for Sustainable Mining: Applications of NIAs, Swarm Robotics, and Other Biomimicry-Based Technologies

by Joven Tan, Noune Melkoumian, David Harvey and Rini Akmeliawati

Biomimetics 2025, 10(3), 181; https://doi.org/10.3390/biomimetics10030181 - 14 Mar 2025

Cited by 2 | Viewed by 2046

Abstract

Environmental challenges, high safety risks and operational inefficiencies are some of the issues facing the mining sector. The paper offers an integrated viewpoint to address these issues by combining swarm robotics, nature-inspired algorithms (NIAs) and other biomimicry-based technologies into a single framework. It [...] Read more.

Environmental challenges, high safety risks and operational inefficiencies are some of the issues facing the mining sector. The paper offers an integrated viewpoint to address these issues by combining swarm robotics, nature-inspired algorithms (NIAs) and other biomimicry-based technologies into a single framework. It presents a systematic classification of each methodology, emphasizing their key advantages and disadvantages as well as considering real-life mining application scenarios, including hazard detection, autonomous transportation and energy-efficient drilling. Case studies are citied to demonstrate how these methodologies work together, and an extensive comparison table considering their applications at mines, such as Boliden, Diavik Diamond Mine, Olympic Dam and others, presents a summary of their scalability and practicality. This paper highlights future directions such as multi-robot coordination and hybrid NIAs, to improve operational resilience and sustainability. It also provides a broad overview of biomimicry and critically examines unresolved issues like real-time adaptation, parameter tuning and mechanical wear. The paper aims to offer a comprehensive insight into using bio-inspired models to enhance mining efficiency, safety and environmental management, while proposing a road map for resolving the issues that continue to be a hurdle for wide adaptation of these technologies in the mining industry. Full article

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

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31 pages, 5646 KB

Open AccessArticle

Hybrid Swarm Intelligence and Human-Inspired Optimization for Urban Drone Path Planning

by Yidao Ji, Qiqi Liu, Cheng Zhou, Zhiji Han and Wei Wu

Biomimetics 2025, 10(3), 180; https://doi.org/10.3390/biomimetics10030180 - 14 Mar 2025

Cited by 2 | Viewed by 1321

Abstract

Urban drone applications require efficient path planning to ensure safe and optimal navigation through complex environments. Drawing inspiration from the collective intelligence of animal groups and electoral processes in human societies, this study integrates hierarchical structures and group interaction behaviors into the standard [...] Read more.

Urban drone applications require efficient path planning to ensure safe and optimal navigation through complex environments. Drawing inspiration from the collective intelligence of animal groups and electoral processes in human societies, this study integrates hierarchical structures and group interaction behaviors into the standard Particle Swarm Optimization algorithm. Specifically, competitive and supportive behaviors are mathematically modeled to enhance particle learning strategies and improve global search capabilities in the mid-optimization phase. To mitigate the risk of convergence to local optima in later stages, a mutation mechanism is introduced to enhance population diversity and overall accuracy. To address the challenges of urban drone path planning, this paper proposes an innovative method that combines a path segmentation and prioritized update algorithm with a cubic B-spline curve algorithm. This method enhances both path optimality and smoothness, ensuring safe and efficient navigation in complex urban settings. Comparative simulations demonstrate the effectiveness of the proposed approach, yielding smoother trajectories and improved real-time performance. Additionally, the method significantly reduces energy consumption and operation time. Overall, this research advances drone path planning technology and broadens its applicability in diverse urban environments. Full article

(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2025)

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28 pages, 14926 KB

Open AccessArticle

Research on Ship Replenishment Path Planning Based on the Modified Whale Optimization Algorithm

by Qinghua Chen, Gang Yao, Lin Yang, Tangying Liu, Jin Sun and Shuxiang Cai

Biomimetics 2025, 10(3), 179; https://doi.org/10.3390/biomimetics10030179 - 13 Mar 2025

Cited by 5 | Viewed by 967

Abstract

Ship replenishment path planning has always been a critical concern for researchers in the field of security. This study proposes a modified whale optimization algorithm (MWOA) to address single-task ship replenishment path planning problems. To ensure high-quality initial solutions and maintain population diversity, [...] Read more.

Ship replenishment path planning has always been a critical concern for researchers in the field of security. This study proposes a modified whale optimization algorithm (MWOA) to address single-task ship replenishment path planning problems. To ensure high-quality initial solutions and maintain population diversity, a hybrid approach combining the nearest neighbor search with random search is employed for initial population generation. Additionally, crossover operations and destroy and repair operators are integrated to update the whale’s position, significantly enhancing the algorithm’s search efficiency and optimization performance. Furthermore, variable neighborhood search is utilized for local optimization to refine the solutions. The proposed MWOA has been tested against several algorithms, including the original whale optimization algorithm, genetic algorithm, ant colony optimization, hybrid particle swarm optimization, and simulated annealing, using traveling salesman problems as benchmarks. Results demonstrate that MWOA outperforms these algorithms in both solution quality and stability. Moreover, when applied to ship replenishment path planning problems of varying scales, MWOA consistently achieves superior performance compared to the other algorithms. The proposed algorithm demonstrates high adaptability in addressing diverse ship replenishment path planning problems, delivering efficient, high-quality, and reliable solutions. Full article

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

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15 pages, 1431 KB

Open AccessArticle

MSBiLSTM-Attention: EEG Emotion Recognition Model Based on Spatiotemporal Feature Fusion

by Yahong Ma, Zhentao Huang, Yuyao Yang, Zuowen Chen, Qi Dong, Shanwen Zhang and Yuan Li

Biomimetics 2025, 10(3), 178; https://doi.org/10.3390/biomimetics10030178 - 13 Mar 2025

Viewed by 1681

Abstract

Emotional states play a crucial role in shaping decision-making and social interactions, with sentiment analysis becoming an essential technology in human–computer emotional engagement, garnering increasing interest in artificial intelligence research. In EEG-based emotion analysis, the main challenges are feature extraction and classifier design, [...] Read more.

Emotional states play a crucial role in shaping decision-making and social interactions, with sentiment analysis becoming an essential technology in human–computer emotional engagement, garnering increasing interest in artificial intelligence research. In EEG-based emotion analysis, the main challenges are feature extraction and classifier design, making the extraction of spatiotemporal information from EEG signals vital for effective emotion classification. Current methods largely depend on machine learning with manual feature extraction, while deep learning offers the advantage of automatic feature extraction and classification. Nonetheless, many deep learning approaches still necessitate manual preprocessing, which hampers accuracy and convenience. This paper introduces a novel deep learning technique that integrates multi-scale convolution and bidirectional long short-term memory networks with an attention mechanism for automatic EEG feature extraction and classification. By using raw EEG data, the method applies multi-scale convolutional neural networks and bidirectional long short-term memory networks to extract and merge features, selects key features via an attention mechanism, and classifies emotional EEG signals through a fully connected layer. The proposed model was evaluated on the SEED dataset for emotion classification. Experimental results demonstrate that this method effectively classifies EEG-based emotions, achieving classification accuracies of 99.44% for the three-class task and 99.85% for the four-class task in single validation, with average 10-fold-cross-validation accuracies of 99.49% and 99.70%, respectively. These findings suggest that the MSBiLSTM-Attention model is a powerful approach for emotion recognition. Full article

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

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23 pages, 4259 KB

Open AccessArticle

Stress Analysis and Stiffness Degradation of Open Cracks Composite Laminates Subjected to External Loads

by Zhicheng Huang, Shengyun Su, Xingguo Wang and Fulei Chu

Biomimetics 2025, 10(3), 177; https://doi.org/10.3390/biomimetics10030177 - 12 Mar 2025

Viewed by 710

Abstract

Composite laminated structures have extensive applications in the field of bionic engineering. Proficient comprehension of the mechanical properties of these structures is instrumental in the advancement of bionic composite materials. The objective of this study is to investigate the stress distribution and degradation [...] Read more.

Composite laminated structures have extensive applications in the field of bionic engineering. Proficient comprehension of the mechanical properties of these structures is instrumental in the advancement of bionic composite materials. The objective of this study is to investigate the stress distribution and degradation of stiffness in composite laminates exhibiting open smooth surface cracks under varying external loads and structural parameters. Utilizing the general series function of the laminate’s axial stress, the general expression for the stress components of the damaged laminate is derived by integrating the equilibrium differential equation, boundary conditions, and stress continuity conditions. The influence of fiber orientation and material properties on the stress distribution within each layer of symmetric composite laminates was examined. Thereafter, the reduction in cross-layer shear modulus was assessed by employing the principle of complementary energy minimization. The impact of structural parameters on shear modulus reduction was explored. The findings indicate that structural and material parameters of symmetric laminates featuring transverse matrix cracks exert a notable influence on the stress distribution and degradation of stiffness within each layer, imparting practical significance to the research outcomes in engineering applications. Full article

(This article belongs to the Special Issue Bioinspired Engineering and the Design of Biomimetic Structures: 2nd Edition)

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37 pages, 7718 KB

Open AccessArticle

EDECO: An Enhanced Educational Competition Optimizer for Numerical Optimization Problems

by Wenkai Tang, Shangqing Shi, Zengtong Lu, Mengying Lin and Hao Cheng

Biomimetics 2025, 10(3), 176; https://doi.org/10.3390/biomimetics10030176 - 12 Mar 2025

Cited by 4 | Viewed by 1144

Abstract

The Educational Competition Optimizer (ECO) is a newly proposed human-based metaheuristic algorithm. It derives from the phenomenon of educational competition in society with good performance. However, the basic ECO is constrained by its limited exploitation and exploration abilities when tackling complex optimization problems [...] Read more.

The Educational Competition Optimizer (ECO) is a newly proposed human-based metaheuristic algorithm. It derives from the phenomenon of educational competition in society with good performance. However, the basic ECO is constrained by its limited exploitation and exploration abilities when tackling complex optimization problems and exhibits the drawbacks of premature convergence and diminished population diversity. To this end, this paper proposes an enhanced educational competition optimizer, named EDECO, by incorporating estimation of distribution algorithm and replacing some of the best individual(s) using a dynamic fitness distance balancing strategy. On the one hand, the estimation of distribution algorithm enhances the global exploration ability and improves the population quality by establishing a probabilistic model based on the dominant individuals provided by EDECO, which solves the problem that the algorithm is unable to search the neighborhood of the optimal solution. On the other hand, the dynamic fitness distance balancing strategy increases the convergence speed of the algorithm and balances the exploitation and exploration through an adaptive mechanism. Finally, this paper conducts experiments on the proposed EDECO algorithm with 29 CEC 2017 benchmark functions and compares EDECO with four basic algorithms as well as four advanced improved algorithms. The results show that EDECO indeed achieves significant improvements compared to the basic ECO and other compared algorithms, and performs noticeably better than its competitors. Next, this study applies EDECO to 10 engineering constrained optimization problems, and the experimental results show the significant superiority of EDECO in solving real engineering optimization problems. These findings further support the effectiveness and usefulness of our proposed algorithm in solving complex engineering optimization challenges. Full article

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

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19 pages, 471 KB

Open AccessReview

Comparing Biomechanical Properties of Bioabsorbable Suture Anchors: A Comprehensive Review

by Dorien I. Schonebaum, Noelle Garbaccio, Maria J. Escobar-Domingo, Sasha Wood, Jade. E. Smith, Lacey Foster, Morvarid Mehdizadeh, Justin J. Cordero, Jose A. Foppiani, Umar Choudry, David L. Kaplan and Samuel J. Lin

Biomimetics 2025, 10(3), 175; https://doi.org/10.3390/biomimetics10030175 - 12 Mar 2025

Cited by 3 | Viewed by 4870

Abstract

Suture anchors (SAs) are medical devices used to connect soft tissue to bone. Traditionally these were made of metal; however, in the past few decades, bio-absorbable suture anchors have been created to overcome revision surgeries and other complications caused by metallic SAs. This [...] Read more.

Suture anchors (SAs) are medical devices used to connect soft tissue to bone. Traditionally these were made of metal; however, in the past few decades, bio-absorbable suture anchors have been created to overcome revision surgeries and other complications caused by metallic SAs. This systematic review aims to analyze the biomechanical properties of these SAs to gain a better understanding of their safety and utilization. A comprehensive systematic review that adhered to the PRISMA guidelines was conducted. Primary outcomes were that the pull-out strength of SAs, the rate of degradation secondarily, and the biocompatibility of all SAs were analyzed. After screening 347 articles, 16 were included in this review. These studies revealed that the pull-out strength of bio-absorbable SAs was not inferior to that of their non-absorbable comparatives. The studies also revealed that the rate of degradation varies widely from 7 to 90 months. It also showed that not all absorbable SAs were fully absorbed within the expected timeframe. This systematic review demonstrates that existing suture anchor materials exhibit comparable pull-out strengths, material-specific degradation rates, and variable biocompatibility. All-suture anchors had promising results in biocompatibility, but evidence fails to identify a single most favorable material. Higher-powered studies that incorporate tissue-specific characteristics, such as rotator cuff tear size, are warranted. To meet demonstrated shortcomings in strength and biocompatibility, we propose silk fibroin as a novel material for suture anchor design for its customizable properties and superior strength. Full article

(This article belongs to the Special Issue Dynamical Response of Biological System and Biomaterial 2024)

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21 pages, 7482 KB

Open AccessArticle

Numerical Analysis of the Aerodynamic Interactions in Tandem Flying Snake Airfoils

by Yuchen Gong, Jiacheng Guo, Alexander He, Ye Sun and Haibo Dong

Biomimetics 2025, 10(3), 174; https://doi.org/10.3390/biomimetics10030174 - 12 Mar 2025

Cited by 1 | Viewed by 1172

Abstract

During gliding, flying snakes flatten their ribs to create an airfoil-like cross-section and adopt S-shape postures, allowing upstream body segments to generate wake structures that affect the aerodynamic performance of downstream segments. This study investigates these interactions using numerical simulations of two-dimensional snake [...] Read more.

During gliding, flying snakes flatten their ribs to create an airfoil-like cross-section and adopt S-shape postures, allowing upstream body segments to generate wake structures that affect the aerodynamic performance of downstream segments. This study investigates these interactions using numerical simulations of two-dimensional snake cross-sectional airfoils. By employing an immersed-boundary-method-based incompressible flow solver with tree topological local mesh refinement, various foil positions and movements were analyzed. The results show that aligning the downstream foil with the upstream foil reduces lift production by 86.5% and drag by 96.3%, leading to a 3.77-fold increase in the lift-to-drag ratio compared to a single airfoil. This improvement is attributed to the vortex–wedge interaction between the upstream vortex and the following foil’s leading edge (wedge), which enhances the gliding efficiency of the posterior body. Furthermore, integrating specific pitching motions with coordinated vortex shedding could further optimize its lift production. These findings provide valuable insights into the aerodynamics of tandem flying snake airfoils, offering guidance for configuring optimal body postures for improving gliding efficiency. Full article

(This article belongs to the Special Issue Bio-Inspired Propulsion and Fluid Mechanics)

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19 pages, 2497 KB

Open AccessArticle

Dynamic Motion-Based Optimization of Support and Transmission Mechanisms for Legged Robots

by Kun Zhang, Zhaoyang Cai and Lei Zhang

Biomimetics 2025, 10(3), 173; https://doi.org/10.3390/biomimetics10030173 - 11 Mar 2025

Viewed by 1083

Abstract

In order to improve the dynamic performance of legged robots, this paper proposes a method for optimizing the parameters of the leg mechanism based on dynamic motion. The proposed method consists of two key parts as follows: support mechanism optimization and transmission mechanism [...] Read more.

In order to improve the dynamic performance of legged robots, this paper proposes a method for optimizing the parameters of the leg mechanism based on dynamic motion. The proposed method consists of two key parts as follows: support mechanism optimization and transmission mechanism optimization. For the support mechanism, a mechanism analysis index based on robot motion energy is introduced to evaluate the robot dynamic motion performance. Under the structure stiffness constraint, this index can quantitatively analyze the influence of the range of motion and structure mass on the robot motion performance, thereby guiding the design of parameters such as the range of motion, structure thickness, and U-flange position of the mechanism. For the transmission mechanism, this paper optimizes the linkage length and knee joint angle for transmission ratio. Considering the variable transmission ratio and robot motion characteristics, the parameters are optimized to reduce the torque and speed requirements of the leg joint. This method determines the optimal mechanism parameters for dynamic performance based on the specified motion energy requirements, and it also optimizes the linkage length. The results show that the peak torque of the knee joint motor is reduced by 18.5%, and the peak speed is reduced by 24.8%. Full article

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14 pages, 252 KB

Open AccessEditor’s ChoiceReview

Applications of Platelet-Rich Fibrin (PRF) Membranes Alone or in Combination with Biomimetic Materials in Oral Regeneration: A Narrative Review

by Javier Valenzuela-Mencia and Francisco Javier Manzano-Moreno

Biomimetics 2025, 10(3), 172; https://doi.org/10.3390/biomimetics10030172 - 11 Mar 2025

Cited by 2 | Viewed by 2606

Abstract

Platelet-rich fibrin (PRF) membranes are a biomaterial derived from the patient’s own blood, used in different medical and dental areas for their ability to promote healing, tissue regeneration, and reduce inflammation. They are obtained by centrifuging the blood, which separates the components and [...] Read more.

Platelet-rich fibrin (PRF) membranes are a biomaterial derived from the patient’s own blood, used in different medical and dental areas for their ability to promote healing, tissue regeneration, and reduce inflammation. They are obtained by centrifuging the blood, which separates the components and concentrates the platelets and growth factors in a fibrin matrix. This material is then moulded into a membrane that can be applied directly to tissues. The use of these PRF membranes is often associated with the use of different biomimetic materials such as deproteinized bovine bone mineral (DBBM), β-tricalcium phosphate (β-TCP), enamel matrix derivative (EMD), and hydroxyapatite (HA). Different indications of PRF membranes have been proposed, like alveolar ridge preservation, alveolar ridge augmentation, guided tissue regeneration (GTR), and sinus floor augmentation. The aim of this narrative review is to check the state-of-the-art and to analyze the existing gaps in the use of PRF membranes in combination with biomimetic materials in alveolar ridge preservation, alveolar ridge augmentation, guided tissue regeneration (GTR), and sinus floor augmentation. Full article

(This article belongs to the Special Issue Bioinspired Materials for Tissue Engineering)

22 pages, 6955 KB

Open AccessArticle

A Novel Multi-Dynamic Coupled Neural Mass Model of SSVEP

by Hongqi Li, Yujuan Wang and Peirong Fu

Biomimetics 2025, 10(3), 171; https://doi.org/10.3390/biomimetics10030171 - 11 Mar 2025

Viewed by 1125

Abstract

Steady-state visual evoked potential (SSVEP)-based brain—computer interfaces (BCIs) leverage high-speed neural synchronization to visual flicker stimuli for efficient device control. While SSVEP-BCIs minimize user training requirements, their dependence on physical EEG recordings introduces challenges, such as inter-subject variability, signal instability, and experimental complexity. [...] Read more.

Steady-state visual evoked potential (SSVEP)-based brain—computer interfaces (BCIs) leverage high-speed neural synchronization to visual flicker stimuli for efficient device control. While SSVEP-BCIs minimize user training requirements, their dependence on physical EEG recordings introduces challenges, such as inter-subject variability, signal instability, and experimental complexity. To overcome these limitations, this study proposes a novel neural mass model for SSVEP simulation by integrating frequency response characteristics with dual-region coupling mechanisms. Specific parallel linear transformation functions were designed based on SSVEP frequency responses, and weight coefficient matrices were determined according to the frequency band energy distribution under different visual stimulation frequencies in the pre-recorded SSVEP signals. A coupled neural mass model was constructed by establishing connections between occipital and parietal regions, with parameters optimized through particle swarm optimization to accommodate individual differences and neuronal density variations. Experimental results demonstrate that the model achieved a high-precision simulation of real SSVEP signals across multiple stimulation frequencies (10 Hz, 11 Hz, and 12 Hz), with maximum errors decreasing from 2.2861 to 0.8430 as frequency increased. The effectiveness of the model was further validated through the real-time control of an Arduino car, where simulated SSVEP signals were successfully classified by the advanced FPF-net model and mapped to control commands. This research not only advances our understanding of SSVEP neural mechanisms but also releases the user from the brain-controlled coupling system, thus providing a practical framework for developing more efficient and reliable BCI-based systems. Full article

(This article belongs to the Special Issue Computational Biology Simulation, Agent-Based Modelling and AI)

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17 pages, 3450 KB

Open AccessArticle

Design and Optimization of an Anthropomorphic Robot Finger

by Ming Cheng, Li Jiang and Ziqi Liu

Biomimetics 2025, 10(3), 170; https://doi.org/10.3390/biomimetics10030170 - 11 Mar 2025

Viewed by 1689

Abstract

The coupled-adaptive underactuated finger offers two motion modes: pre-grasping and self-adaptive grasping. It can execute anthropomorphic pre-grasp motions before the proximal phalanx contacts an object and transitions to adaptive enveloping once contact occurs. The key to designing a coupled-adaptive finger lies in its [...] Read more.

The coupled-adaptive underactuated finger offers two motion modes: pre-grasping and self-adaptive grasping. It can execute anthropomorphic pre-grasp motions before the proximal phalanx contacts an object and transitions to adaptive enveloping once contact occurs. The key to designing a coupled-adaptive finger lies in its configuration and parameter, which are crucial for achieving a more human-like design for the prosthetic hand. Thus, this paper proposes a configuration topology and parameter optimization design method for a three-joint coupled-adaptive underactuated finger. The finger mechanism utilizes a combination of prismatic pairs and a compression spring to facilitate the transition between coupled motion and adaptive motion. This enables the underactuated finger to perform coupled movements in free space and adaptive grasping motions once it makes contact with an object. Furthermore, this paper introduces a finger linkage parameter optimization method that takes the joint motion angles and overall dimensions as constraints, aiming to linearize the joint coupling motion ratios as the primary optimization objective. The design method proposed in this paper not only presents a novel linkage mechanism but also outlines and compares its isomorphic types. Furthermore, the optimization results provide an accurate maximum motion value for the finger. Full article

(This article belongs to the Special Issue Human-Inspired Grasp Control in Robotics)

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15 pages, 5159 KB

Open AccessArticle

Cytocompatibility Study of Stainless Steel 316l Against Differentiated SH-SY5Y Cells

by Eleni Zingkou, Asimina Kolianou, Georgios Angelis, Michail Lykouras, Malvina Orkoula, Georgios Pampalakis and Georgia Sotiropoulou

Biomimetics 2025, 10(3), 169; https://doi.org/10.3390/biomimetics10030169 - 11 Mar 2025

Viewed by 1568

Abstract

Stainless steel (SS) 316l constitutes a popular biomaterial with various applications as implants in cardiovascular and orthopedic surgery, as well as in dentistry. Nevertheless, its cytocompatibility against neuronal cells has not been investigated, a feature that is important for the construction of implants [...] Read more.

Stainless steel (SS) 316l constitutes a popular biomaterial with various applications as implants in cardiovascular and orthopedic surgery, as well as in dentistry. Nevertheless, its cytocompatibility against neuronal cells has not been investigated, a feature that is important for the construction of implants that require contact with neurons, e.g., neuronal electrodes. In addition, most cytocompatibility studies have focused on decorated or surface-modified SS 316l. On the other hand, SH-SY5Y cells are an established cellular model for cytocompatibility studies of potential biomaterials given their ability to differentiate into neuron-like cells. Here, we used retinoic-acid-differentiated SH-SY5Y cells and SH-SY5Y controls to investigate the cytocompatibility and biomimetics of uncoated SS 316l. The assessment of cytocompatibility was based on the determination of differentiation markers by immunofluorescence, RT-qPCR, and the neurite growth of these cells attached on SS 316l and standard tissue culture polystyrene (TCP) surfaces. Even though the neurite length was shorter in differentiated SH-SY5Y cells grown on SS 316l, no other significant changes were found. In conclusion, our results suggest that the uncoated SS 316l mimics a natural bio-surface and allows the adhesion, growth, and differentiation of SH-SY5Y cells. Therefore, this alloy can be directly applied in the emerging field of biomimetics, especially for the development of implants or devices that contact neurons. Full article

(This article belongs to the Special Issue Biomimetic External Prostheses Linked to Implantable Electronics: From Manufacturing and Processing to Overall Characterization, Biological Assessment, and Ethical Challenges)

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35 pages, 13822 KB

Open AccessArticle

UAV Path Planning: A Dual-Population Cooperative Honey Badger Algorithm for Staged Fusion of Multiple Differential Evolutionary Strategies

by Xiaojie Tang, Chengfen Jia and Zhengyang He

Biomimetics 2025, 10(3), 168; https://doi.org/10.3390/biomimetics10030168 - 10 Mar 2025

Cited by 1 | Viewed by 991

Abstract

To address the challenges of low optimization efficiency and premature convergence in existing algorithms for unmanned aerial vehicle (UAV) 3D path planning under complex operational constraints, this study proposes an enhanced honey badger algorithm (LRMHBA). First, a three-dimensional terrain model incorporating threat sources [...] Read more.

To address the challenges of low optimization efficiency and premature convergence in existing algorithms for unmanned aerial vehicle (UAV) 3D path planning under complex operational constraints, this study proposes an enhanced honey badger algorithm (LRMHBA). First, a three-dimensional terrain model incorporating threat sources and UAV constraints is constructed to reflect the actual operational environment. Second, LRMHBA improves global search efficiency by optimizing the initial population distribution through the integration of Latin hypercube sampling and an elite population strategy. Subsequently, a stochastic perturbation mechanism is introduced to facilitate the escape from local optima. Furthermore, to adapt to the evolving exploration requirements during the optimization process, LRMHBA employs a differential mutation strategy tailored to populations with different fitness values, utilizing elite individuals from the initialization stage to guide the mutation process. This design forms a two-population cooperative mechanism that enhances the balance between exploration and exploitation, thereby improving convergence accuracy. Experimental evaluations on the CEC2017 benchmark suite demonstrate the superiority of LRMHBA over 11 comparison algorithms. In the UAV 3D path planning task, LRMHBA consistently generated the shortest average path across three obstacle simulation scenarios of varying complexity, achieving the highest rank in the Friedman test. Full article

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19 pages, 6983 KB

Open AccessArticle

Cochleogram-Based Speech Emotion Recognition with the Cascade of Asymmetric Resonators with Fast-Acting Compression Using Time-Distributed Convolutional Long Short-Term Memory and Support Vector Machines

by Cevahir Parlak

Biomimetics 2025, 10(3), 167; https://doi.org/10.3390/biomimetics10030167 - 10 Mar 2025

Viewed by 956

Abstract

Feature extraction is a crucial stage in speech emotion recognition applications, and filter banks with their related statistical functions are widely used for this purpose. Although Mel filters and MFCCs achieve outstanding results, they do not perfectly model the structure of the human [...] Read more.

Feature extraction is a crucial stage in speech emotion recognition applications, and filter banks with their related statistical functions are widely used for this purpose. Although Mel filters and MFCCs achieve outstanding results, they do not perfectly model the structure of the human ear, as they use a simplified mechanism to simulate the functioning of human cochlear structures. The Mel filters system is not a perfect representation of human hearing, but merely an engineering shortcut to suppress the pitch and low-frequency components, which have little use in traditional speech recognition applications. However, speech emotion recognition classification is heavily related to pitch and low-frequency component features. The newly tailored CARFAC 24 model is a sophisticated system for analyzing human speech and is designed to best simulate the functionalities of the human cochlea. In this study, we use the CARFAC 24 system for speech emotion recognition and compare it with state-of-the-art systems using speaker-independent studies conducted with Time-Distributed Convolutional LSTM networks and Support Vector Machines, with the use of the ASED and the NEMO emotional speech dataset. The results demonstrate that CARFAC 24 is a valuable alternative to Mel and MFCC features in speech emotion recognition applications. Full article

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25 pages, 478 KB

Open AccessReview

Electromyography Signals in Embedded Systems: A Review of Processing and Classification Techniques

by José Félix Castruita-López, Marcos Aviles, Diana C. Toledo-Pérez, Idalberto Macías-Socarrás and Juvenal Rodríguez-Reséndiz

Biomimetics 2025, 10(3), 166; https://doi.org/10.3390/biomimetics10030166 - 10 Mar 2025

Cited by 1 | Viewed by 2588

Abstract

This article provides an overview of the implementation of electromyography (EMG) signal classification algorithms in various embedded system architectures. They address the specifications used for implementation in different devices, such as the number of movements and the type of classification method. Architectures analyzed [...] Read more.

This article provides an overview of the implementation of electromyography (EMG) signal classification algorithms in various embedded system architectures. They address the specifications used for implementation in different devices, such as the number of movements and the type of classification method. Architectures analyzed include microcontrollers, DSP, FPGA, SoC, and neuromorphic computers/chips in terms of precision, processing time, energy consumption, and cost. This analysis highlights the capabilities of each technology for real-time wearable applications such as smart prosthetics and gesture control devices, as well as the importance of local inference in artificial intelligence models to minimize execution times and resource consumption. The results show that the choice of device depends on the required system specifications, the robustness of the model, the number of movements to be classified, and the limits of knowledge concerning design and budget. This work provides a reference for selecting technologies for developing embedded biomedical solutions based on EMG. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Biomedical Engineering)

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16 pages, 19423 KB

Open AccessArticle

Effectiveness of Titanium Occlusive Barriers in Guided Bone Regeneration: A Prospective Analysis of Vertical and Horizontal Bone Augmentation

by Luis Leiva-Gea, Paulino Sánchez-Palomino, Alfonso Lendínez-Jurado, María Daniela Corte-Torres, Isabel Leiva-Gea and Antonio Leiva-Gea

Biomimetics 2025, 10(3), 165; https://doi.org/10.3390/biomimetics10030165 - 7 Mar 2025

Cited by 1 | Viewed by 1599

Abstract

Background: Guided bone regeneration (GBR) is a widely used technique in oral and maxillofacial surgery to restore lost bone. The aim of this study is to evaluate the effectiveness of titanium occlusive barriers in GBR for increasing bone volume in both vertical and [...] Read more.

Background: Guided bone regeneration (GBR) is a widely used technique in oral and maxillofacial surgery to restore lost bone. The aim of this study is to evaluate the effectiveness of titanium occlusive barriers in GBR for increasing bone volume in both vertical and horizontal dimensions. Methods: A prospective analysis was conducted on 11 patients (15 cases) undergoing bone augmentation with titanium barriers combined with bone graft biomaterials for dental implant placement. Bone gain was assessed using pre- and postoperative low-dose cone beam computed tomography (CBCT) measurements in vertical and horizontal planes. Histological analyses evaluated the quality and vascularization of the regenerated bone. Results: Significant bone volume increases were observed, with a mean vertical gain of 7.60 mm (SD 0.23) and a horizontal gain of 5.44 mm (SD 0.39). Histological examination confirmed well-vascularized regenerated bone with minimal residual graft material, effective integration, and the formation of keratinized gingiva. Conclusions: Titanium occlusive barriers in GBR provide a reliable and minimally invasive method for substantial bone regeneration, showing advantages such as ease of handling and reduced invasiveness. Additional studies are recommended to validate these findings and evaluate long-term outcomes. Full article

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11 pages, 3890 KB

Open AccessArticle

Elastomer with Microchannel Nanofiber Array Inspired by Rabbit Cornea Achieves Rapid Liquid Spreading and Reduction of Frictional Vibration Noise

by Bowen Zhang, Lei Jiang and Ruochen Fang

Biomimetics 2025, 10(3), 164; https://doi.org/10.3390/biomimetics10030164 - 7 Mar 2025

Viewed by 895

Abstract

Reducing friction-induced squeal noise is a common issue in daily life and industrial production, particularly for elastomers. However, adjusting structure and wettability in wet environments to solve the friction-induced squeal noise remains a challenge. Here, inspired by rabbit corneas, a microchannel nanofiber array [...] Read more.

Reducing friction-induced squeal noise is a common issue in daily life and industrial production, particularly for elastomers. However, adjusting structure and wettability in wet environments to solve the friction-induced squeal noise remains a challenge. Here, inspired by rabbit corneas, a microchannel nanofiber array composite structure superhydrophilic elastomer material was prepared to achieve rapid liquid spreading and optimize liquid distribution. Researchers have found that when the depth of the groove microchannel is 400 μm and the length of the nanofiber is 5000 nm, water rapidly spreads on the surface in only 430 ms. This reduces self-excited vibration caused by friction, thereby reducing squealing noise by 20 decibels (dB). This article proposes a novel and direct biomimetic squealing noise reduction strategy, which has great potential in solving friction vibration noise problems in industry and daily life, such as automotive wiper blades, engines, oil lubricated bearings, etc. Full article

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

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14 pages, 18226 KB

Open AccessArticle

Smart Bio-Nanocoatings with Simple Post-Synthesis Reversible Adjustment

by Mikhail Kryuchkov, Zhehui Wang, Jana Valnohova, Vladimir Savitsky, Mirza Karamehmedović, Marc Jobin and Vladimir L. Katanaev

Biomimetics 2025, 10(3), 163; https://doi.org/10.3390/biomimetics10030163 - 7 Mar 2025

Viewed by 1169

Abstract

Nanopatterning of signal-transmitting proteins is essential for cell physiology and drug delivery but faces challenges such as high cost, limited pattern variability, and non-biofriendly materials. Arthropods, particularly beetles (Coleoptera), offer a natural model for biomimetic nanopatterning due to their diverse corneal nanostructures. Using [...] Read more.

Nanopatterning of signal-transmitting proteins is essential for cell physiology and drug delivery but faces challenges such as high cost, limited pattern variability, and non-biofriendly materials. Arthropods, particularly beetles (Coleoptera), offer a natural model for biomimetic nanopatterning due to their diverse corneal nanostructures. Using atomic force microscopy (AFM), we analyzed Coleoptera corneal nanocoatings and identified dimpled nanostructures that can transform into maze-like/nipple-like protrusions. Further analysis suggested that these modifications result from a temporary, self-assembled process influenced by surface adhesion. We identified cuticular protein 7 (CP7) as a key component of dimpled nanocoatings. Biophysical analysis revealed CP7’s unique self-assembly properties, allowing us to replicate its nanopatterning ability in vitro. Our findings demonstrate CP7’s potential for bioinspired nanocoatings and provide insights into the evolutionary mechanisms of nanostructure formation. This research paves the way for cost-effective, biomimetic nanopatterning strategies with applications in nanotechnology and biomedicine. Full article

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

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19 pages, 2616 KB

Open AccessArticle

Specific Neural Coding of Complex Neural Network Based on Time Coding Under Various Exterior Stimuli

by Lei Guo, Zhixian Wang, Yihua Song and Huan Liu

Biomimetics 2025, 10(3), 162; https://doi.org/10.3390/biomimetics10030162 - 6 Mar 2025

Viewed by 981

Abstract

Specific neural coding (SNC) forms the basis of information processing in bio-brain, which generates distinct patterns of neural coding in response to corresponding exterior forms of stimulus. The performance of SNC is extremely dependent on brain-inspired models. However, the bio-rationality of a brain-inspired [...] Read more.

Specific neural coding (SNC) forms the basis of information processing in bio-brain, which generates distinct patterns of neural coding in response to corresponding exterior forms of stimulus. The performance of SNC is extremely dependent on brain-inspired models. However, the bio-rationality of a brain-inspired model remains inadequate. The purpose of this paper is to investigate a more bio-rational brain-inspired model and the SNC of this brain-inspired model. In this study, we construct a complex spiking neural network (CSNN) in which its topology has the small-word property and the scale-free property. Then, we investigated the SNC of CSNN under various strengths of various stimuli and discussed its mechanism. Our results indicate that (1) CSNN has similar neural time coding under same kind of stimulus; (2) CSNN has significant SNC based on time coding under various exterior stimuli; (3) our discussion implies that the inherent factor of SNC is synaptic plasticity. Full article

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