Biomimetics最新文献

{"title":"Prediction of Water Chemical Oxygen Demand with Multi-Scale One-Dimensional Convolutional Neural Network Fusion and Ultraviolet-Visible Spectroscopy.","authors":"Jingwei Li, Yijing Lu, Yipei Ding, Chenxuan Zhou, Jia Liu, Zhiyu Shao, Yibei Nian","doi":"10.3390/biomimetics10030191","DOIUrl":"10.3390/biomimetics10030191","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"10 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11940155/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143708373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bonding Protocols for Lithium Disilicate Veneers: A Narrative Review and Case Study.","authors":"Silvia Rojas-Rueda, Jose Villalobos-Tinoco, Clint Conner, Staley Colvert, Hamid Nurrohman, Carlos A Jurado","doi":"10.3390/biomimetics10030188","DOIUrl":"10.3390/biomimetics10030188","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"10 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11940404/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143708289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Robust Disturbance Rejection Whole-Body Control Framework for Bipedal Robots Using a Momentum-Based Observer.","authors":"Shuai Heng, Xizhe Zang, Yan Liu, Chao Song, Boyang Chen, Yue Zhang, Yanhe Zhu, Jie Zhao","doi":"10.3390/biomimetics10030189","DOIUrl":"10.3390/biomimetics10030189","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"10 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11940329/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143708099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Humanoid Robot Arm Motion Imitation and Collaboration Based on Improved Retargeting.","authors":"Xisheng Jiang, Baolei Wu, Simin Li, Yongtong Zhu, Guoxiang Liang, Ye Yuan, Qingdu Li, Jianwei Zhang","doi":"10.3390/biomimetics10030190","DOIUrl":"10.3390/biomimetics10030190","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"10 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11939925/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143708344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-Trial Electroencephalography Discrimination of Real, Regulated, Isometric Wrist Extension and Wrist Flexion.","authors":"Abdul-Khaaliq Mohamed, Vered Aharonson","doi":"10.3390/biomimetics10030187","DOIUrl":"10.3390/biomimetics10030187","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"10 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11939923/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143707964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human-like Dexterous Grasping Through Reinforcement Learning and Multimodal Perception.","authors":"Wen Qi, Haoyu Fan, Cankun Zheng, Hang Su, Samer Alfayad","doi":"10.3390/biomimetics10030186","DOIUrl":"10.3390/biomimetics10030186","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"10 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11940771/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143708295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biomimetic Polyurethanes in Tissue Engineering.","authors":"Edyta Hebda, Krzysztof Pielichowski","doi":"10.3390/biomimetics10030184","DOIUrl":"10.3390/biomimetics10030184","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"10 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11940237/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143708123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FedDyH: A Multi-Policy with GA Optimization Framework for Dynamic Heterogeneous Federated Learning.","authors":"Xuhua Zhao, Yongming Zheng, Jiaxiang Wan, Yehong Li, Donglin Zhu, Zhenyu Xu, Huijuan Lu","doi":"10.3390/biomimetics10030185","DOIUrl":"10.3390/biomimetics10030185","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"10 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11940811/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143708292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Design, Modeling, and Experiment of a Novel Diving-Beetle-Inspired Paddling Propulsion Robot.","authors":"Jiang Ding, Jingyu Li, Tianbo Lan, Kai He, Qiyang Zuo","doi":"10.3390/biomimetics10030182","DOIUrl":"10.3390/biomimetics10030182","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"10 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11940506/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143708128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding Brain Signals in a Neuromorphic Framework for a Personalized Adaptive Control of Human Prosthetics.","authors":"Georgi Rusev, Svetlozar Yordanov, Simona Nedelcheva, Alexander Banderov, Fabien Sauter-Starace, Petia Koprinkova-Hristova, Nikola Kasabov","doi":"10.3390/biomimetics10030183","DOIUrl":"10.3390/biomimetics10030183","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"10 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11940436/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143708311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}