Biomimetics

This study proposes an index-based quantum neural network (QNN) model, built upon a variational quantum circuit (VQC), as a surrogate framework for the static analysis of truss structures. Unlike coordinate-based models, the proposed QNN uses discrete member and node indices as inputs, and it adopts a separate-domain strategy that partitions the structure for parallel training. This architecture reflects the way nature organizes and optimizes complex systems, thereby enhancing both flexibility and scalability. Independent quantum circuits are assigned to each separate domain, and a mechanics-informed loss function based on the force method is formulated within a Lagrangian dual framework to embed physical constraints directly into the training process. As a result, the model achieves high prediction accuracy and fast convergence, even under complex structural conditions with relatively few parameters. Numerical experiments on 2D and 3D truss structures show that the QNN reduces the number of parameters by up to 64% compared to conventional neural networks, while achieving higher accuracy. Even within the same QNN architecture, the separate-domain approach outperforms the single-domain model with a 6.25% reduction in parameters. The proposed index-based QNN model has demonstrated practical applicability for structural analysis and shows strong potential as a quantum-based numerical analysis tool for future applications in building structure optimization and broader engineering domains. Full article

With environmental challenges intensifying, a fundamental understanding and sustainable management of ongoing transition processes are crucial. Biological evolution provides valuable lessons on how to adapt and thrive under changing conditions. By studying its key principles, we identified analogies between biological evolution and technological transitions in terms of both the Multi-Level Perceptive and the path dependency model. The comparative study also revealed that, despite contrasting time scales, the generation-based and version-based developments are comparable. In addition, interesting similarities were found in the increase and decrease of variety and between fitness and consistency. The lessons learned from biology include “Give it a try”, “Do not close for reconstruction”, and “Keep older versions in the innovation process”. Based on this comparison, we aim to gain insights for a better understanding of how to manage technology transitions and to derive concrete indicators for assessing and monitoring them. In doing so, we can provide action-oriented guidance for developing more sustainable technological solutions for major ongoing transitions, such as the energy transition. Full article

Webbed-foot gliding water entry is a characteristic water-landing strategy employed by swans and other large waterfowls, demonstrating exceptional low-impact loading and remarkable motion stability. These distinctive biomechanical features offer significant potential for informing the design of cross-medium vehicles’ (CMVs’) water-entry systems. To analyze the hydrodynamic mechanisms and flow characteristics during swan webbed-foot gliding entry, the three-dimensional bionic webbed-foot water-entry process was investigated through a computational fluid dynamics (CFD) method coupled with global motion mesh (GMM) technology, with a particular emphasis on elucidating the regulatory effects of entry parameters on dynamic performance. The results demonstrated that the gliding water-entry process can be divided into two distinct phases: stable skipping and surface gliding. During the stable skipping phase, the motion trajectory exhibits quasi-sinusoidal periodic fluctuations, accompanied by multiple water-impact events and significant load variations. In the surface-gliding phase, the kinetic energy of the bionic webbed foot progressively decreases while maintaining relatively stable load characteristics. Increasing the water-entry velocity will enhance impact loads while simultaneously increasing the skipping frequency and distance. Increasing the water-entry angle will primarily intensify the impact load magnitude while slightly reducing the skipping frequency and distance. An optimal pitch angle of 20° provides maximum glide-skip stability for the bio-inspired webbed foot, with angles exceeding 25° or below 15° leading to motion instability. This study on webbed-foot gliding entry behavior provided insights for developing novel bio-inspired entry strategies for cross-medium vehicles, while simultaneously advancing the optimization of impact-mitigation designs in gliding water-entry systems. Full article

In this study, a preparation method of superhydrophobic composite coating based on a titanium alloy (Ti-6Al-4V) substrate is proposed. The micro-scale pit array structure was fabricated via laser etching technology. Utilizing the synergistic effects of epoxy resin (EP), polydimethylsiloxane (PDMS), and fluorinated nanosilica (F-SiO₂), we successfully prepared an EP@PDMS@F-SiO₂ composite coating. The effects of the contents of EP, PDMS, and F-SiO₂ on the surface wettability, mechanical stability, and UV durability were studied by optimizing the coating ratio through orthogonal experiments. The results show that the micro–nano composite structure formed by laser etching can effectively fix the coating particles and provide excellent superhydrophobicity on the surface. The coating retains high hydrophobicity after paper abrasion (1000 cm under a 200 g load), demonstrating the mechanical stability of the armor-like structure, High-content F-SiO₂ coatings exhibit greater UV durability. In addition, the coating surface has low droplet adhesion and self-cleaning capabilities for efficient contaminant removal. The research provides theoretical and technical support for the design and engineering application of a non-fluorinated, environmentally friendly superhydrophobic coating. Full article

Hydraulic legged robots, with advantages such as high load capacity and power density, have become a strategic driving force in advancing intelligent mobile platform technologies. However, their high energy consumption significantly limits long-duration endurance and efficient operational performance. In this paper, inspired by the excellent autonomous energy-efficient consciousness of mammals endowed by natural evolution, a bionic energy-efficient inverse kinematics method based on neural networks (EIKNN) is proposed for the energy-efficient motion planning of hydraulic legged robots with redundant degrees of freedom (RDOFs). Firstly, the dynamic programming (DP) algorithm is used to solve the optimal joint configuration with minimum energy loss as the goal, and the training data set is generated. Subsequently, the inverse kinematic model of the leg with minimum energy loss is learned based on neural network (NN) simulation of the autonomous energy-efficient consciousness endowed to mammals by natural evolution. Finally, extensive comparative experiments validate the effectiveness and superiority of the proposed method. This method not only significantly reduces energy dissipation in hydraulic legged robots but also lays a crucial foundation for advancing hydraulic legged robot technology toward high efficiency, environmental sustainability, and long-term developmental viability. Full article

Variations in facial complexion serve as a telltale sign of underlying health conditions. Precisely categorizing facial complexions poses a significant challenge due to the subtle distinctions in facial features. Three multi-feature facial complexion classification algorithms leveraging convolutional neural networks (CNNs) are proposed. They fuse, splice, or independently train the features extracted from distinct facial regions of interest (ROI), respectively. Innovative frameworks of the three algorithms can more effectively exploit facial features, improving the utilization rate of feature information and classification performance. We trained and validated the three algorithms on the dataset consisting of 721 facial images that we had collected and preprocessed. The comprehensive evaluation reveals that multi-feature fusion and splicing classification algorithms achieve accuracies of 95.98% and 93.76%, respectively. The optimal approach combining multi-feature CNN with machine learning algorithms attains a remarkable accuracy of 97.78%. Additionally, these experiments proved that the multidomain combination was crucial, and the arrangement of ROI features, including the nose, forehead, philtrum, and right and left cheek, was the optimal choice for classification. Furthermore, we employed the EfficientNet model for training on the face image as a whole, which achieves a classification accuracy of 89.37%. The difference in accuracy underscores the superiority and efficacy of multi-feature classification algorithms. The employment of multi-feature fusion algorithms in facial complexion classification holds substantial advantages, ushering in fresh research directions in the field of facial complexion classification and deep learning. Full article

To address the limitations of traditional single-motor bionic ornithopters in terms of environmental adaptability and lift capacity, this study proposes a dual-motor independently driven system utilizing a cross-shaft single-gear crank mechanism to achieve adjustable flap speed and wing frequency, thereby enabling asymmetric flapping for enhanced environmental adaptability. The design integrates a two-stage reduction gear group to optimize torque transmission and an S1223 high-lift airfoil to improve aerodynamic efficiency. Multiphysics simulations combining computational fluid dynamics (CFD) and finite element analysis (FEA) demonstrate that, under flapping frequencies of 1–3.45 Hz and wind speeds of 1.2–3 m/s, the optimized model achieves 50% and 60% improvements in lift and thrust coefficients, respectively, compared to the baseline. Concurrently, peak stress in critical components (e.g., cam disks and wing rods) is reduced by 37% to 41 MPa, with significantly improved stress uniformity. These results validate the dual-motor system’s capability to dynamically adapt to turbulent airflow through the precise control of wing kinematics, offering innovative solutions for applications such as aerial inspection and precision agriculture. Full article

While early studies on macroscopic self-assembly peaked in the late 20th century, recent research continues to explore and expand the field’s potential through innovative materials and external control strategies. To harness this potential, a unit cell was designed and 3D-printed that could form a face-centered cubic lattice and stabilize it through a biomimetic mechanism for mechanical interlocking. The wing coupling structures of the brown marmorated stink bug were examined under a scanning electron microscope to be used as a source of bio-inspiration for the interlocking mechanism. A total of 20 unit cells were studied in five different self-assembly processes and in different compression scenarios. A maximum average of 34% of unit cells remained stable, and 20% were mechanically interlocked after self-assembly tests. The compression tests performed on a single unit cell revealed that the cell can withstand forces up to 1000 N without any plastic deformation. Pyramid configurations from 5-unit cells were manually assembled and assessed in compression tests. They showed an average compression force of 294 N. As the first study focused on self-assembly through mechanical interlocking, further studies that change the unit cell production and self-assembly processes are expected to improve upon these results. Full article

Robot learning from human demonstration pioneers an effective mapping paradigm for endowing robots with human-like operational capabilities. This paper proposes a bio-signal-guided robot adaptive stiffness learning framework grounded in the conclusion that muscle activation of the human arm is positively correlated with the endpoint stiffness. First, we propose a human-teleoperated demonstration platform enabling real-time modulation of robot end-effector stiffness by human tutors during operational tasks. Second, we develop a dual-stage probabilistic modeling architecture employing the Gaussian mixture model and Gaussian mixture regression to model the temporal–motion correlation and the motion–sEMG relationship, successively. Third, a real-world experiment was conducted to validate the effectiveness of the proposed skill transfer framework, demonstrating that the robot achieves online adaptation of Cartesian impedance characteristics in contact-rich tasks. This paper provides a simple and intuitive way to plan the Cartesian impedance parameters, transcending the classical method that requires complex human arm endpoint stiffness identification before human demonstration or compensation for the difference in human–robot operational effects after human demonstration. Full article

Acrylic resin composites with a high filler loading, consisting of a fluoride-containing hydrotalcite incorporated into silica nanoparticles, were prepared. The filler was obtained by a multi-step process. First, ZnAl hydrotalcite in fluoride form (HTlc/F) was functionalised with tetraethoxysilane to form Si-O-M bonds (M = Al or Zn) with the brucitic layers. The ethoxysilane groups exposed on the layers were used as nucleation seeds for silica nanoparticles. The composite, named SiO₂@HTlc/F, was then functionalised with 3-(trimethoxysilyl)-propyl methacrylate groups and used as a filler for acrylic resins. The methacrylate groups on the surface of the inorganic composite participated in the polymerisation process of the resin by minimising the phase separation between inorganic and polymer through the formation of chemical bonds at the polymer–inorganic interface. The filler in the resin increases the degree of polymerisation, bringing it to values very close to 100%. Finally, preliminary studies on the release of fluoride anions showed that they are released slowly over time. Full article

Background: Multicultural education and second-language acquisition engaged neural networks, supporting executive function, memory, and social cognition in adulthood, represent powerful forms of brain-inspired multisensory learning. The neuroeducational framework integrates neuroscience with pedagogical practice to understand how linguistically and culturally rich environments drive neuroplasticity and cognitive adaptation in adult learners. Objective: This systematic review synthesizes findings from 80 studies examining neuroplasticity and cognitive outcomes in adults undergoing multicultural and second-language acquisition, focusing on underlying neural mechanisms and educational effectiveness. Methods: The analysis included randomized controlled trials and longitudinal studies employing diverse neuroimaging techniques (fMRI, MEG, DTI) to assess structural and functional brain network changes. Interventions varied in terms of immersion intensity (ranging from limited classroom contact to complete environmental immersion), multimodal approaches (integrating visual, auditory, and kinesthetic elements), feedback mechanisms (immediate vs. delayed, social vs. automated), and learning contexts (formal instruction, naturalistic acquisition, and technology-enhanced environments). Outcomes encompassed cognitive domains (executive function, working memory, attention) and socio-emotional processes (empathy, cultural adaptation). Results: Strong evidence demonstrates that multicultural and second-language acquisition induce specific neuroplastic adaptations, including enhanced connectivity between language and executive networks, increased cortical thickness in frontal–temporal regions, and white matter reorganization supporting processing efficiency. These neural changes are correlated with significant improvements in working memory, attentional control, and cognitive flexibility. Immersion intensity, multimodal design features, learning context, and individual differences, including age and sociocultural background, moderate the effectiveness of interventions across adult populations. Conclusions: Adult multicultural and second-language acquisition represents a biologically aligned educational approach that leverages natural neuroplastic mechanisms to enhance cognitive resilience. Findings support the design of interventions that engage integrated neural networks through rich, culturally relevant environments, with significant implications for cognitive health across the adult lifespan and for evidence-based educational practice. Full article

Amphibious robots require efficient locomotion strategies to enable smooth transitions between terrestrial and aquatic environments. Drawing inspiration from the undulatory movements of aquatic organisms such as cuttlefish and knifefish, this study introduces a bio-inspired propulsion system that emulates natural wave-based locomotion to improve adaptability and propulsion efficiency. A novel mechanism combining crank–rocker and sliding components is proposed to generate wave-like motions in robotic legs and fins, supporting both land crawling and aquatic paddling. By adopting a rigid–flexible coupling design, the system achieves a balance between structural integrity and motion flexibility. The effectiveness of the mechanism is systematically investigated through kinematic modeling, animation-based simulation, and experimental validation. The developed kinematic model captures the principles of wave propagation via the Crank–Slider–Rocker structure, offering insights into motion efficiency and thrust generation. Animation simulations are employed to visually validate the locomotion patterns and assess coordination across the mechanism. A functional prototype is fabricated and tested in both terrestrial and aquatic settings, demonstrating successful amphibious locomotion. The findings confirm the feasibility of the proposed design and underscore its potential in biomimetic robotics and amphibious exploration. Full article

Achieving the appropriate primary stability for immediate or early loading in areas with low-density bone, such as the posterior maxilla, is challenging. A three-dimensional (3D) stabilization implant design featuring a tapered body with continuous cutting flutes along the length of the external thread form, with a combination of curved and linear geometric surfaces on the thread’s crest, has the capacity to enhance early biomechanical and osseointegration outcomes compared to implants with traditional buttressed thread profiles. Commercially available implants with a buttress thread design (TP), and an experimental implant that incorporated the 3D stabilization trimmed-thread design (TP 3DS) were used in this study. Six osteotomies were surgically created in the ilium of adult sheep (N = 14). Osteotomy sites were randomized to receive either the TP or TP 3DS implant to reduce site bias. Subjects were allowed to heal for either 3 or 12 weeks (N = 7 sheep/time point), after which samples were collected en bloc (including the implants and surrounding bone) and implants were either subjected to bench-top biomechanical testing (e.g., lateral loading), histological/histomorphometric analysis, or nanoindentation testing. Both implant designs yielded high insertion torque (ITV ≥ 30 N⋅cm) and implant stability quotient (ISQ ≥ 70) values, indicative of high primary stability. Qualitative histomorphological analysis revealed that the TP 3DS group exhibited a continuous bone–implant interface along the threaded region, in contrast to the TP group at the early, 3-week, healing time point. Furthermore, TP 3DS’s cutting flutes along the entire length of the implant permitted the distribution of autologous bone chips within the healing chambers. Histological evaluation at 12 weeks revealed an increase in woven bone containing a greater presence of lacunae within the healing chambers in both groups, consistent with an intramembranous-like healing pattern and absence of bone dieback. The TP 3DS macrogeometry yielded a ~66% increase in average lateral load during pushout testing at baseline (T = 0 weeks, p = 0.036) and significantly higher bone-to-implant contact (BIC) values at 3 weeks post-implantation (p = 0.006), relative to the traditional TP implant. In a low-density (Type IV) bone model, the TP 3DS implant demonstrated improved performance compared to the conventional TP, as evidenced by an increase in baseline lateral loading capacity and increased BIC during the early stages of osseointegration. These findings indicate that the modified implant configuration of the TP 3DS facilitates more favorable biomechanical integration and may promote more rapid and stable bone anchorage under compromised bone quality conditions. Therefore, such improvements could have important clinical implications for the success and longevity of dental implants placed in regions with low bone density. Full article

This paper presents a comprehensive review of recent advancements in radionuclide-labeled biomaterials for cancer therapy, with a particular focus on the characteristics, production methods, and labeling techniques of α-particle, β-particle, and Auger electron-based radiotherapy. It explores innovative strategies for targeted delivery systems and highlights the advantages of theranostics and combination therapies. The application of radionuclide-labeled biomaterials in various cancer types, including prostate cancer, breast cancer, neuroendocrine tumors, gliomas, and melanoma, is systematically summarized. Furthermore, the article critically examines current technological bottlenecks and challenges in clinical translation, while proposing future directions such as AI-assisted dose optimization and multimodal combination therapies. This review provides essential theoretical foundations and practical insights to facilitate the clinical translation of radionuclide-labeled biomaterials. Full article

In this study, the power system of a bionic robotic fish has been significantly simplified, resulting in a reduced volume and enhanced flexibility of both the structure and movement. To comprehensively understand the dynamics, a fluid–structure interaction (FSI) analysis was conducted, considering the intricate interplay between the mollusk’s structure and the surrounding fluid. This analysis took into account the dissipation due to fluid viscosity and the influence of the wake performance around the mollusk. The study examined the relationships between the driving frequency of the input signal and various parameters such as fluid pressure, propulsion force, and propulsion displacement of the soft robot fish head. With the robot fish’s head fixed, the amplitude of propulsion motion and propulsion force were measured. The simulation results closely matched the experimental findings, indicating their potential to predict the propulsion characteristics of the soft robot fish in fluid environments and further improve its performance. Full article

Although existing motion transfer methods for bionic robot arms are based on kinematic equivalence or simplified dynamic models, they frequently fail to tackle dynamic compliance and real-time adaptability in complex human-like motions. To address this shortcoming, this study presents a motion transfer method from the human arm to a bionic robot arm based on the hybrid PSO-RF (Particle Swarm Optimization-Random Forest) algorithm to improve joint space mapping accuracy and dynamic compliance. Initially, a high-precision optical motion capture (Mocap) system was utilized to record human arm trajectories, and Kalman filtering and a Rauch–Tung–Striebel (RTS) smoother were applied to reduce noise and phase lag. Subsequently, the joint angles of the human arm were computed through geometric vector analysis. Although geometric vector analysis offers an initial estimation of joint angles, its deterministic framework is subject to error accumulation caused by the occlusion of reflective markers and kinematic singularities. To surmount this limitation, this study designed five action sequences for the establishment of the training database for the PSO-RF model to predict joint angles when performing different actions. Ultimately, an experimental platform was built to validate the motion transfer method, and the experimental verification showed that the system attained high prediction accuracy (R² = 0.932 for the elbow joint angle) and real-time performance with a latency of 0.1097 s. This paper promotes compliant human–robot interaction by dealing with joint-level dynamic transfer challenges, presenting a framework for applications in intelligent manufacturing and rehabilitation robotics. Full article

Integrating bioinspired design and additive manufacturing into engineering education fosters innovation to meet the growing demand for accessible, personalized assistive technologies. This paper presents the outcomes of an international course, “3D Prosthetics and Orthotics”, offered to undergraduate students in the Biomimetic program at Westfälische Hochschule (Germany), in collaboration with the 3D Orthotics and Prosthetics Laboratory at the Federal University of São Paulo—UNIFESP (Brazil). The course combined theoretical and hands-on modules covering digital modeling (CAD), simulation (CAE), and fabrication (CAM), enabling students to develop bioinspired assistive devices through a Project-based learning approach. Working in interdisciplinary teams, students addressed real-world rehabilitation challenges by translating biological mechanisms into engineered solutions using additive manufacturing. Resulting prototypes included a hand prosthesis based on the Fin Ray effect, a modular finger prosthesis inspired by tendon–muscle antagonism, and a cervical orthosis designed based on stingray morphology. Each device was digitally modeled, mechanically analyzed, and physically fabricated using open-source and low-cost methods. This initiative illustrates how biomimetic mechanisms and design can be integrated into education to generate functional outcomes and socially impactful health technologies. Grounded in the Mao3D open-source methodology, this experience demonstrates the value of combining nature-inspired principles, digital fabrication, Design Thinking, and international collaboration to advance inclusive, low-cost innovations in assistive technology. Full article

To improve the traction performance of mobile mechanisms on soft ground, such as paddy fields, tidal flats, and swamps, a mallard (Anas platyrhynchos) foot was adopted as a bionic prototype to explore the influence and contribution of the plantar morphology of the toes and webbing on the anti-subsidence function during its locomotion on wet and soft substrates and to apply this to the bionic design of high-traction wheel grousers. A handheld three-dimensional laser scanner was used to scan the main locomotion postures of a mallard foot during ground contact, and the Geomagic Studio software was utilized to repair the scanned model. As a result, the main three-dimensional geometric models of a mallard foot during the process of touching the ground were obtained. The plantar morphology of a mallard foot was divided into three typical parts: the plantar irregular edge curve, the lateral webbing surface, and the medial webbing surface. The main morphological feature curves/surfaces were extracted through computer-aided design software for the fitting and construction of a mathematical model to obtain the fitting equations of the three typical parts, and the mathematical model construction of the plantar irregular morphology of the mallard foot was completed. In order to verify the sand-fixing and flow-limiting characteristics of this morphological feature, based on the discrete element method (DEM), the numerical simulation of the interaction between the plantar surface of the mallard foot and sand particles was carried out. The simulation results show that during the process of the mallard foot penetration into the loose medium, the lateral and medial webbing surfaces cause the particles under the foot to mainly move downward, effectively preventing the particles from spreading around and significantly enhancing the solidification effect of the particles under the sole. Based on the principle and technology of engineering bionics, the plantar morphology and movement attitude characteristics of the mallard were extracted, and the characteristics of concave middle and edge bulge were applied to the wheel grouser design of paddy field wheels. Two types of bionic wheel grousers with different curved surfaces were designed and compared with the traditional wheel grousers of the paddy field wheel. Through pressure-bearing simulation and experiments, the resistance of different wheel grousers during the process of penetrating into sand particles was compared, and the macro–micro behaviors of particle disturbance during the pressure-bearing process were analyzed. The results show that a bionic wheel grouser with unique curved surfaces can well encapsulate sand particles at the bottom of the wheel grouser, and it also has a greater penetration resistance, which plays a crucial role in improving the traction performance of the paddy field wheel and reducing the disturbance to the surrounding sand particles. This paper realizes the transformation from the biological model to the mathematical model of the plantar morphology of the mallard foot and applies it to the bionic design of the wheel grousers of the paddy field wheels, providing a new solution for improving the traction performance of mobile mechanisms on soft ground. Full article

Snake-like robots face critical challenges in energy-efficient locomotion and smooth gait transitions, limiting their real-world deployment. This study introduces a bio-inspired compliant joint design integrated with a hierarchical neural oscillator network and an energy-optimized control framework. The joint mimics biological skeletal flexibility using specialized wheeled mechanisms and adaptive parallel linkages, while the control network enables adaptive gait generation and seamless transitions through a phase-smoothing algorithm. Critically, this work adopts a synergistic design philosophy where mechanical components and control parameters are co-optimized through shared dynamic modeling. The proposed predictive control strategy optimizes locomotion speed while minimizing energy consumption. Experimental simulations demonstrate that the method achieves an 18% higher average forward speed (0.0563 m/s vs. 0.0478 m/s) with 7% lower energy use (0.1952 J vs. 0.2107 J) compared to conventional approaches. Physical prototype testing confirms these improvements under real-world conditions, showing a 12.9% speed increase (0.0531 m/s vs. 0.0470 m/s) and 7.3% energy reduction (0.2147 J vs. 0.2317 J). By unifying mechanical flexibility and adaptive control parameter tuning, this work bridges dynamic performance and energy efficiency, offering a robust solution for unstructured environments. Full article