Biomimetics最新文献

Biomimetic approaches have gained increasing attention in the development of efficient computational models for sound scene analysis. In this paper, we present a sound-based animal species classification method inspired by the auditory processing mechanisms of the human cochlea. The approach employs gammatone filtering to extract features that capture the distinctive characteristics of animal vocalizations. While gammatone filterbanks themselves are well established in auditory signal processing, their systematic application and evaluation for animal vocalization classification represent the main contribution of this work. Four gammatone-based feature representations are explored and used to train and test an artificial neural network for species classification. The method is evaluated on a dataset comprising vocalizations from 13 animal species with 50 vocalizations per specie and 2.76 seconds per vocalization in average. The evaluations are conducted to study the system parameters in different conditions and system architectures. Although the dataset is limited in scale compared to larger public databases, the results highlight the potential of combining biomimetic cochlear filtering with machine learning to perform reliable and robust species classification through sound.

The microstructures of living creatures are widely used in bionics, and some can generate structural colors on biological surfaces and enable the process of dynamic camouflage. This study presents the hydrogel photoresist synthesized by polymerizing HEMA and MMA in THF solvent with initiator AIBN. Then, nanostructured gratings were fabricated on the hydrogel photoresists via double-beam interference lithography, and were characterized by scanning electron microscopy, angle-resolved spectroscopy system, and nanoindentation for pattern characterization, and nanomechanical and optical performance, respectively. Under multi-angle incident light, the optical computation of gratings with different depths indicates that a shallow implicit grating does not affect its dynamic color-changing performance. It is established that the laser power of 500 mW, a first exposure time of 5 s, and a second exposure time of 3 s are feasible for achieving efficient anti-counterfeiting nanostructures. The L_500-5-3 has greater Er and H than that of L_500-5 with the second processing, but smaller than ineffective patterns. And the depth of anti-counterfeiting gratings that is less than 0.8 μm is conducive to obtaining anti-counterfeiting gratings with different size parameters. The acquired anti-counterfeiting nanostructures exhibit excellent stability, reliability, and angle-dependent color changes under room light, which provides promising applications for security materials in daily life, sensors, optics, and electronics.

This paper proposes a simplified adaptive filtering approach using a Hammerstein function and the spline interpolation based on a Fair cost function for denoising electrocardiogram (ECG) signals. The use of linear filters in real-world applications has many limitations. Adaptive nonlinear filtering is a key development in tackling the challenge of discovering the specific characteristics of biomimetic systems for each person in order to eliminate unwanted signals. A biomimetic system refers to a system that mimics certain biological processes or characteristics of the human body, in this case, the individual features of a person's cardiac signals (ECG). Here, the adaptive nonlinear filter is designed to cope with ECG variations and remove unwanted noise more effectively. The objective of this paper is to explore an individual biomedical filter based on adaptive nonlinear filtering for denoising the corrupted ECG signal. The Hammerstein spline adaptive filter (HSAF) architecture consists of two structural blocks: a nonlinear block connected to a linear one. In order to make a smooth convergence, the Fair cost function is introduced for convergence enhancement. The affine projection algorithm (APA) based on the Fair cost function is used to denoise the contaminated ECG signals, and also provides fast convergence. The MIT-BIH 12-lead database is used as the source of ECG biomedical signals contaminated by random noises modelled by Cauchy distribution. Experimental results show that the estimation error of the proposed HSAF-APA-Fair algorithm, based on the Fair cost function, can be reduced when compared with the conventional least mean square-based algorithm for denoising ECG signals.

Snake-like robots, characterized by their high flexibility and multi-joint structure, exhibit exceptional adaptability to complex terrains such as snowfields, jungles, deserts, and underwater environments. Their ability to navigate narrow spaces and circumvent obstacles makes them ideal for operations in confined or rugged environments. However, efficient motion in such conditions requires not only mechanical flexibility but also effective path planning to ensure safety, energy efficiency, and overall task performance. Most existing path planning algorithms for snake-like robots focus primarily on finding the shortest path between the start and target positions while neglecting the optimization of energy consumption during real operations. To address this limitation, this study proposes an energy-efficient path planning method based on an improved A* algorithm enhanced with deep reinforcement learning: Dueling Double-Deep Q-Network (D3QN). An Energy Consumption Estimation Model (ECEM) is first developed to evaluate the energetic cost of snake robot motion in three-dimensional space. This model is then integrated into a new heuristic function to guide the A* search toward energy-optimal trajectories. Simulation experiments were conducted in a 3D environment to assess the performance of the proposed approach. The results demonstrate that the improved A* algorithm effectively reduces the energy consumption of the snake robot compared with conventional algorithms. Specifically, the proposed method achieves an energy consumption of 68.79 J, which is 3.39%, 27.26%, and 5.91% lower than that of the traditional A* algorithm (71.20 J), the bidirectional A* algorithm (94.61 J), and the weighted improved A* algorithm (73.11 J), respectively. These findings confirm that integrating deep reinforcement learning with an adaptive heuristic function significantly enhances both the energy efficiency and practical applicability of snake robot path planning in complex 3D environments.

This study introduces two hybrid forecasting models that integrate the Marine Predators Algorithm (MPA) with Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Feed-Forward Neural Networks (FFNN) for short-term Bitcoin price prediction. Daily Bitcoin data from 2022 were converted into supervised time-series structures with multiple input configurations. The proposed hybrid models were evaluated against six well-known metaheuristic algorithms commonly used for training intelligent forecasting systems. The results show that MPA consistently yields lower prediction errors, faster convergence, and more stable optimization behavior compared with alternative algorithms. Both ANFIS-MPA and FFNN-MPA maintained their advantage across all tested structures, demonstrating reliable performance under varying model complexities. All experiments were repeated multiple times, and the hybrid approaches exhibited low variance, indicating robust and reproducible behavior. Overall, the findings highlight the effectiveness of MPA as an optimizer for improving the predictive performance of neuro-fuzzy and neural network models in financial time-series forecasting.

Recent advances in bioinspired robotics highlight the growing demand for dexterous, adaptive control strategies that allow robots to interact naturally, safely, and efficiently with dynamic, contact-rich environments. Yet, achieving robust adaptability and reflex-like responsiveness to unpredictable disturbances remains a fundamental challenge. This paper presents a bioinspired imitation learning framework that models human adaptive dynamics to jointly acquire and generalize motion and force skills, enabling compliant and resilient robot behavior. The proposed framework integrates hybrid force-motion learning with dynamic response mechanisms, achieving broad skill generalization without reliance on external sensing modalities. A momentum-based force observer is combined with dynamic movement primitives (DMPs) to enable accurate force estimation and smooth motion coordination, while a broad learning system (BLS) refines the DMP forcing function through style modulation, feature augmentation, and adaptive weight tuning. In addition, an adaptive radial basis function neural network (RBFNN) controller dynamically adjusts control parameters to ensure precise, low-latency skill reproduction, and safe physical interaction. Simulations and real-world experiments confirm that the proposed framework achieves human-like adaptability, robustness, and scalability, attaining a competitive learning time of 5.56 s and a rapid generation time of 0.036 s, thereby demonstrating its efficiency and practicality for real-time applications and offering a lightweight yet powerful solution for bioinspired intelligent control in complex and unstructured environments.

In this research, aluminium-doped biphasic calcium phosphate (Al-BCP) was synthesized by co-precipitation and formulated with hydrolyzed collagen and acetylsalicylic acid (ASA) to yield composites designed as a new class of bone-regenerative biomaterials with enhanced biological performance. Undoped and Al-modified powders (5/10 wt% Al precursor) were prepared at 40 °C (pH ~ 11) and calcined at 700 °C, and composites were produced at a 1:1:0.1 mass ratio (ceramic-collagen-ASA). Structure and chemistry were assessed by X-ray diffraction (XRD), Fourier-transform infrared (FTIR) and Raman spectroscopies, and X-ray photoelectron spectroscopy (XPS). Morphology and elemental distribution were examined by scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM/EDX). Biological performance was preliminarily evaluated using HaCaT (immortalized human keratinocytes) viability and antibacterial assays against Staphylococcus aureus and Escherichia coli. XRD confirmed a biphasic hydroxyapatite/β-tricalcium phosphate system and showed that Al incorporation shifted the phase balance toward hydroxyapatite (HAp fraction 54.8% in BCP vs. ~68.6-68.7% in Al-doped samples). FTIR/Raman preserved BCP vibrational signatures and revealed collagen/ASA bands in the composites. XPS/EDX verified the expected composition, including surface N 1s from organics and Al at ~2-5 at% for doped samples, with surface Ca/P ≈ 1.15-1.16. SEM revealed multigranular microstructures with homogeneous Al distribution. All composites were non-cytotoxic (≥70% viability); M_Al10_Col_ASA exceeded 90% viability at 12.5% dilution. Preliminary antibacterial assays against Gram-positive and Gram-negative strains showed modest, time-dependent reductions in CFU relative to controls. These results corroborate the compositional/structural profile and preliminary biological performance of Al-BCP-collagen-ASA composites as multifunctional bone tissue engineering materials that foster a bone-friendly microenvironment, warranting further evaluation for bone regeneration.

In response to the personalized and precise rehabilitation needs for motor injuries and stroke associated with population aging, this study proposes a design method for an intelligent rehabilitation trainer that integrates Bayesian information gain (BIG) and axis matching techniques. Grounded in the biomechanical characteristics of the human ankle joint, the design fully draws upon biomimetic principles, constructing a 3-PUU-R hybrid serial-parallel bionic mechanism. By mimicking the dynamic variation of the ankle's instantaneous motion axis and its balance between stiffness and compliance, a three-dimensional digital model was developed, and multi-posture human factor simulations were conducted, thereby achieving a rehabilitation process more consistent with natural human movement patterns. Natural randomized disability grade experimental data were collected for 100 people to verify the validity of the design results. On this basis, a Bayesian information gain framework was established by quantifying the reduction of uncertainty in rehabilitation outcomes through characteristic parameters, enabling the dynamic optimization of training strategies for personalized and precise ankle rehabilitation. The rehabilitation process was modeled as a problem of uncertainty quantification and information gain optimization. Prior distributions were constructed using surface EMG (electromyography) signals and motion trajectory errors, and mutual information was used to drive the dynamic adjustment of training strategies, ultimately forming a closed-loop control architecture of "demand perception-strategy optimization-execution adaptation." This innovative integration of probabilistic modeling and cross-joint bionic design overcomes the limitations of single-joint rehabilitation and provides a new paradigm for the development of intelligent rehabilitation devices. The deep integration mechanism-based dynamic axis matching and Bayesian information gain holds significant theoretical value and engineering application prospects for enhancing the effectiveness of neural plasticity training.

Preserving dental pulp vitality is a key goal in minimally invasive dentistry. Conventional materials such as calcium hydroxide and mineral trioxide aggregate (MTA) are effective but limited in bioactivity and mechanical strength. This systematic review evaluated the biological efficacy of chitosan-based nanoparticles and biomaterials for pulp capping and regeneration. Following PRISMA 2020 guidelines, electronic searches were conducted across five databases up to April 2025. Controlled in vitro and animal studies using chitosan-based nanoparticles, hydrogels, or composite scaffolds were included. Risk of bias was assessed using SYRCLE (animal) and ToxRTool (in vitro), and certainty of evidence was rated via the GRADE-Preclinical framework. Due to methodological heterogeneity, data were synthesized using direction-of-effect coding and visualized through Albatross and heatmap plots. Sixteen studies met the criteria, consistently demonstrating enhanced cell viability, mineralization, and upregulation of odontogenic and angiogenic markers (BMP-2, TGF-β₁, VEGF, DSPP) compared with MTA or calcium hydroxide. Animal models confirmed improved angiogenesis, reparative dentin formation, and pulp vitality preservation. Despite uniformly positive biological outcomes, the overall certainty was rated Low to Very Low owing to small samples and unclear randomization. Chitosan-based biomaterials show promising regenerative potential, warranting well-designed preclinical and clinical studies for translational validation.

Estrogen deficiency is a primary cause of osteoporosis, compromising bone mineral density that may impair peri-implant healing. Given the compromised bone environment associated with estrogen deficiency, strategies such as particle reduction via sonochemistry are promising approaches to enhance regenerative outcomes. However, its effects in promoting bone formation remain insufficiently explored. Therefore, this study evaluated the potential of two sonicated biomaterials to improve peri-implant repair in ovariectomized rats. Fifty female rats were allocated into five groups: blood clot (CLOT), Biogran^® (BGN), sonicated Biogran^® (BGS), Bio-Oss^® (BON), and sonicated Bio-Oss^® (BOS). Tibial peri-implant defects were created 30 days after ovariectomy and analyzed 28 days later by removal torque, microcomputed tomography, and confocal microscopy. BGS exhibited the highest removal torque (6.28 Ncm), followed by BON (5.37 Ncm), BOS (3.92 Ncm), BGN (3.15 Ncm), and CLOT (2.58 Ncm). Micro-CT revealed bone volume fraction (BV/TV) values of 8.07% (CLOT), 6.47% (BOS), 6.02% (BGS), 5.55% (BGN), and 2.84% (BON). For the trabecular number (Tb.N), BGS (1.11 mm^-1) showed a significant increase compared with BGN (0.69 mm^-1), p < 0.05. These findings show that sonochemically modified bioactive glass improves mechanical stability and trabecular microarchitecture under estrogen-deficient conditions. However, further studies are needed to standardize sonication parameters for different biomaterials and expand their translational applicability.