Journal of Bionic Engineering最新文献

Unmanned aerial vehicles (UAVs) are widely utilized in area coverage tasks due to their flexibility and efficiency in geographic information acquisition. However, complex boundary conditions in actual water area maps often reduce coverage efficiency. To address this issue, this paper proposes a map preprocessing algorithm that linearizes boundary lines and processes concave areas into concave polygons, followed by gridding the map. Additionally, a collaborative area coverage method for UAV swarms is introduced based on region partitioning, which considers the comprehensive cost of energy consumption and time. An improved Hungarian algorithm is utilized for region partitioning, and a Dubins-A*-based plowing area full coverage path planning method is proposed to achieve path smoothing and collaborative coverage of each partition. Two sets of simulation experiments are conducted. The first experiment verifies the effectiveness of the map preprocessing algorithm, and the second compares the proposed collaborative area coverage algorithm with other methods, demonstrating its performance advantages.

Aberrant activation of Receptor Tyrosine Kinases (RTKs) is a well-established trigger of tumorigenesis, and the overuse of RTK inhibitors often leads to drug resistance and tumor recurrence. While current Drug-Target Interaction (DTI) prediction methods (including those based on heterogeneous information networks) have shown promise, they remain limited in their ability to fully capture the nature of DTIs and often lack interpretability. To overcome these limitations, this study introduces a novel hybrid optimization model termed MDBO-RF, which integrates a Modified Dung Beetle Optimizer (MDBO) with Random Forest (RF). The key innovation lies in the enhancement of the DBO algorithm through a quaternion-based learning mechanism and the Cauchy mutation strategy, specifically designed to overcome the slow convergence and susceptibility to local optima that plague traditional metaheuristic algorithms used for hyperparameter tuning. The model leverages commonly used molecular descriptors to enhance the prediction of Tyrosine Kinase (TK) inhibitory activity and enable efficient compound screening. Our results demonstrate that MDBO-RF achieves a 3.41% increase in prediction accuracy compared to the standard RF model and outperforms several other contemporary machine learning approaches. The model effectively streamlines the RTK inhibitor screening process by improving prediction accuracy in multi-target competitive binding scenarios and reducing false-positive screening due to off-target effects. This work underscores the value of hybrid optimization strategies in bioinformatics and provides a robust, interpretable tool for accelerating drug discovery.

Ion-exchange Polymer-Metal Composites (IPMCs) gain huge attentions due to large deformation, rapid electromechanical response, and high energy conversion efficiency. Deflection of IPMC arises from the volumetric swelling effect induced by the concentration gradient of hydrated cations between the two electrodes, thus the volume of hydrated cation determines the motion magnitude and direction of IPMC. H ion is one of the most commonly used driving cations for IPMC. However, due to its unique characteristics, particularly the inability to accurately quantify its hydration volume, existing literatures primarily focus on the physical driving models for metallic cations, i.e., Na⁺, no driving model for the H ion is reported until now. This paper proposes a novel model of H ion escape from the water’s body-centered cubic lattice to count the hydration volume. Number (n) of water molecules carried by the H ion is solved by combining the Lennard-Jones potential energy function with Maxwell’s velocity distribution. The specific n value is equivalent to 4.04 for the H ion inside Nafion electrolyte under a 3.0 V DC electric field. Substituting it into the classic Friction Model (proposed by Tadokoro et al. at 2000), actuation behaviors of H ion driven IPMC were therefore achieved through Matlab calculations and Abaqus simulations. The calculated results of dynamic displacement and force highly match to the experimental data form the Nafion IPMC actuator driven by same electric field, showing a highly reliability of the established escape model.

This research presents a Human Lower Limb Activity Recognition (HLLAR) system that identifies specific activities and predicts the angles of the knees simultaneously, based on the EMG signals. The HLLAR systems streamlines the research on the lower limb activities. The HILLAR model includes Discrete Hermite Wavelets Transform-based Synchrosqueezing (DHWTS), Deep Two-Layer Multiscale Convolutional Neural Network (DTLMCNN), and Generalized Regression Neural Network (GRNN) as feature extraction, activity recognition, and knee angle prediction respectively. Electromyography signal-based automatic lower limb activity detection is crucial to rehabilitation and human movement analysis. Yet several of these methods face issues in feature extraction in complex data, overlapping signals, extraction of crucial parameters, and adaptation constraints. This research aims classify lower limb activities and predict knee joint angles from electromyography signals using HILLAR model. The model is validated on two datasets, comprising 26 subjects performing three classes of activities: walking, standing, and sitting. The proposed model obtained a classification accuracy of 99.95%, along with significant achievements in precision (99.93%), recall (99.91%), and F1-score (99.93%). The generalized regression neural network predicted angles of the knee joint with a root mean squared error of 1.25%. Robustness is demonstrated through consistent results in five-fold cross-validation and statistical significance testing (p-value = 0.004, McNemar’s test). Additionally, the proposed model showed superior performance over baseline methods by reducing error rates by 18% and decreasing processing time to 0.98 s.

Pain, as a common symptom, seriously affects the patient’s health. The aim of this work was to study the physiological responses of the brain and identify the features of Electroencephalography (EEG) signals related to friction pain. The results showed that the primary brain activation evoked by friction pain was located in the Prefrontal Cortex (PFC). The activation area decreased, and the negative activation intensity in the PFC region increased with increasing intensity of pain. The inhibitory interactions between different brain regions, especially between the PFC and primary somatosensory cortex (SI) regions were enhanced, and excitatory-inhibitory connections between the medial and lateral pain pathways were balanced during pain perception. The percentage power spectral density of the α rhythm (D_α), dominant singularity strength (α_peak) and longest vertical line (V_max) of EEG signals induced by pain significantly decreased, and the percentage power spectral density of the β rhythm (D_β) significantly increased. The combination of multiple features of D_α, D_β, α_peak and V_max could significantly improve the average recognition accuracy of different pain states. This study elucidated the neural processing mechanisms of friction-induced pain, and EEG features associated with friction pain were extracted and recognized. It was helpful to study the brain feedback mechanisms of pain and control signals of Brain-Computer Interface (BCI) system related to pain.

Micro/nanorobots represent a groundbreaking advancement in nanotechnology, with applications spanning medicine, environmental remediation, and industrial processes. A major challenge in their development is achieving efficient and biocompatible propulsion. Enzyme-driven propulsion, particularly using catalase, offers a promising solution due to its ability to decompose hydrogen peroxide (H₂O₂) into water and oxygen, generating thrust for autonomous movement. Compared to metal-based catalysts, catalase-powered systems exhibit superior biocompatibility and lower toxicity, making them ideal for biomedical applications. This review explores the role of catalase in micro/nanorobot propulsion, highlighting self-propulsion mechanisms, different nanorobot types, and their applications in drug delivery, infection treatment, cancer therapy, and biosensing. Additionally, recent advancements in biodegradable enzyme-powered nanorobots and their potential in overcoming biological barriers are discussed. With further research, catalase-driven nanorobots could revolutionize targeted therapy and diagnostic techniques, paving the way for innovative solutions in nanomedicine.

Radiative cooling passively emits heat to outer space without energy input, offering promise for energy-efficient thermal management. It is an important solution to promote the low-carbon environmental protection strategy. With the continuous development of radiative cooling technologies, the material selection, preparation process, structural design, and application fields have also made more diverse progress. Therefore, this review aims to systematically introduce the fundamental concepts and underlying principles of radiative cooling. A summary of the commonly used materials for radiative cooling is provided. In addition, the advanced fabrication processes and structural designs of radiative cooling materials are further explored and discussed. Subsequently, the unique functions of radiative cooling materials are highlighted to enhance their applicability and usefulness across various fields. An overview of combining radiative cooling materials with different fields is also provided. In reality, these applications hold the potential to improve thermal management across a range of fields. Finally, it summarizes the shortcomings and great potential of radiative cooling materials in various fields. It also looks forward to the future, aiming to promote the progress and widespread adoption of radiative cooling technologies.

To solve the problem of abnormal abrasion of Cu-Based Friction Materials (CBFMs), Bionic Non-Smooth Surface (BNS) on friction surface of CBFMs was constructed based on bionic principles, and the optimal bionic prototype was selected by Finite Element Method (FEM). In addition, the bionic parameters were optimized by Response Surface Method (RSM). Samples holding BNS were prepared by Laser Processing, tribological properties were tested by a Friction and Wear Tester and worn surface morphology was characterized by a Scanning Electron Microscope (SEM). The results showed that BNS on friction surface could regulate the stress distribution and alleviate the peak stress. Among all samples, the coupled texture of pit-hexagonal got the minimum peak stress. During braking, bionic texture could also collect wear debris or change the motion forms from sliding to rotation, which can reduce abnormal abrasion. The wear rate was reduced by 19.31%. The results in this paper can provide a new idea for enhancing the tribological properties of CBFMs, and can also lay the foundation for further research of bionic tribology.

Tactile sensing of subcutaneous organ vibrations provides a promising route toward human–machine interfaces and wearable diagnostics, particularly for voice rehabilitation and silent-speech communication. Here, we present a bioinspired piezoelectric vibration sensor that mimics the graded stiffness and stress-based transduction mechanism of otolithic cilia in the human vestibular system. The device consists of a trapezoidal cantilever array with tip inertial masses, fabricated through a hybrid stereolithography 3D printing and laser micromachining process for rapid prototyping without cleanroom facilities. Finite-element modeling and experimental measurements demonstrate a fundamental resonance near 1.2 kHz, a 5% flat-bandwidth of 350 Hz, and an in-band charge sensitivity of 3.17 pC/g. A wearable proof-of-concept test further verifies the sensor’s ability to reproducibly distinguish phoneme-specific vibration patterns in both time and frequency domains. This work establishes a foundation for bioinspired tactile sensing front-ends in wearable voice interfaces and other intelligent diagnostic systems integrated with machine-learning algorithms.

Floating ring bearings are widely used in high-speed turbomachinery such as turbochargers and turbogenerators. Researchers have recently explored various surface texturing strategies on the inner surface of floating rings to enhance bearing performance. In this study, the herring patterns are textured on the inner surface of the floating ring. This pattern is inspired by the secondary flight feathers of the Indian pigeon, which aid the bird in reducing viscous drag during flight. The resulting Herringbone Textured Floating Ring Bearing (HTFRB) is investigated for its potential application in locomotive turbochargers. The HTFRB is numerically modeled using the Reynolds equation to evaluate the bearing’s pressure distribution and static characteristics, including load-carrying capacity, power loss, and side leakage. Dynamic characteristics are determined by solving the zeroth- and first-order perturbed Reynolds equation. A Sobol sensitivity analysis is conducted to quantify the influence of groove parameters — helix angle, groove depth, groove width ratio, and number of grooves — on bearing performance metrics. An artificial intelligence-based optimization framework, integrating artificial neural networks and adaptive neuro-fuzzy inference systems, is developed to maximize load carrying capacity while minimizing power loss, side leakage, and friction coefficient. The optimized texture parameters obtained from this framework are employed to validate the ANN model and evaluate the static and dynamic characteristics of the HTFRB. The dynamic coefficients of the HTFRB are further employed to evaluate the stability and robustness of the turbocharger rotor-HTFRB system. This study underscores the potential of combining bio-inspired texture design with numerical modeling and AI-based optimization to develop high-performance HTFRB.