Biomimetics最新文献

Jellyfish biohybrid robots have been demonstrated to be successfully programmed to perform vertical sampling profiles of the ocean water column. However, the jellyfish's endogenous swimming behavior can interfere with the controlled swim cycles, decreasing performance. Further, the model animal used to date, Aurelia aurita, is a relatively slow, weakly swimming species. To enhance the performance of the biohybrid vehicles, we tested whether removing the swimming pacemaker of the jellyfish, the rhopalia, eliminated endogenous movements and enhanced responsiveness of the jellyfish to the swim controller. Further, we tested the responsiveness of two fast-swimming jellyfish species, the rhizostome Cassiopea spp. and the cubomedusae Alatina alata. We found in field trials, where the jellyfish swam controlled vertical profiles in the ocean, that removal of rhopalia eliminated all endogenous behaviors and greatly improved the responsiveness of the jellyfish to the swim controller. This was especially true for species with strong endogenous behaviors that prevented the controller from manipulating swim pulses. Further, we found that both Cassiopea spp. and A. alata were highly responsive to the swim controller and that these faster-swimming jellyfish species greatly increased the speed at which the biohybrid vehicle could traverse vertical profiles in the water column. These enhancements greatly increase the reliability and versatility of jellyfish biohybrid robot vehicles.

Biomimicry is an engineering field where inspiration from nature is leveraged to engineer sustainable solutions. Biomimicry is not a subject typically taught in undergraduate curriculum. This study explores the effects of intercultural context on the learning of biomimicry. Visiting students from the United States of America and home students from Singapore gathered for a one-day workshop on biomimicry in Singapore. The workshop consisted of a lecture with in-class activities and laboratory experiments in groups, followed by students' presentations. The students' responses to pre- and post-workshop surveys are analyzed, along with their answers from the in-class activities and their presentations. The results show that the international context of the biomimicry workshop made an overall positive contribution to the motivation, appreciation, and enjoyment of all students. Some differences were observed between the visiting and home students, which likely stemmed from the visiting students being better prepared for the event. However, despite high levels of enjoyment and communication, the learning outcomes lacked technical depth and sustainability focus. This suggests the need for a consistent and higher level of preparation and guidance for all participating students on these topics. This study serves as a preliminary example of a workshop that explores the global theme of biomimicry in an international and intercultural setting. Similar workshops could be conducted with larger and more diverse student populations for more robust results. This work could inspire other educators in engineering to explore ways to prepare students for more holistic education.

Hand gesture classification using surface electromyography (sEMG) is fundamental for prosthetic control and human-machine interaction. However, most existing studies focus on high-density recordings or large gesture sets, leaving limited evidence on performance in low-channel, reduced-gesture configurations. This study addresses this gap by comparing a classical convolutional neural network (CNN), inspired by Atzori's design, with a Convolutional Vision Transformer (CViT) tailored for compact sEMG systems. Two datasets were evaluated: a proprietary Myo-based collection (10 subjects, 8 channels, six gestures) and a subset of NinaPro DB3 (11 transradial amputees, 12 channels, same gestures). Both models were trained using standardized preprocessing, segmentation, and balanced windowing procedures. Results show that the CNN performs robustly on homogeneous signals (Myo: 94.2% accuracy) but exhibits increased variability in amputee recordings (NinaPro: 92.0%). In contrast, the CViT consistently matches or surpasses the CNN, reaching 96.6% accuracy on Myo and 94.2% on NinaPro. Statistical analyses confirm significant differences in the Myo dataset. The objective of this work is to determine whether hybrid CNN-ViT architectures provide superior robustness and generalization under low-channel sEMG conditions. Rather than proposing a new architecture, this study delivers the first systematic benchmark of CNN and CViT models across amputee and non-amputee subjects using short windows, heterogeneous signals, and identical protocols, highlighting their suitability for compact prosthetic-control systems.

Portfolio optimization is fundamental to modern finance, enabling investors to construct allocations that balance risk and return while satisfying practical constraints. When transaction costs and cardinality limits are incorporated, the problem becomes a computationally demanding mixed-integer quadratic program. This work demonstrates how principles from biomimetics-specifically, the computational strategies employed by biological neural systems-can inspire efficient algorithms for complex optimization problems. We demonstrate that this problem can be reformulated as a constrained quadratic program and solved using dynamics inspired by spiking neural networks. Building on recent theoretical work showing that leaky integrate-and-fire dynamics naturally implement projected gradient descent for convex optimization, we develop a solver that alternates between continuous gradient flow and discrete constraint projections. By mimicking the event-driven, energy-efficient computation observed in biological neurons, our approach offers a biomimetic pathway to solving computationally intensive financial optimization problems. We implement the approach in Python and evaluate it on portfolios of 5 to 50 assets using five years of market data, comparing solution quality against mixed-integer solvers (ECOS_BB), convex relaxations (OSQP), and particle swarm optimization. Experimental results demonstrate that the SNN solver achieves the highest expected return (0.261% daily) among all evaluated methods on the 50-asset portfolio, outperforming exact MIQP (0.225%) and PSO (0.092%), with runtimes ranging from 0.5 s for small portfolios to 8.4 s for high-quality schedules on large portfolios. While current Python runtimes are comparable to existing approaches, the key contribution is establishing a path to neuromorphic hardware deployment: specialized SNN processors could execute these dynamics orders of magnitude faster than conventional architectures, enabling real-time portfolio rebalancing at institutional scale.

Studies on autonomous underwater vehicles (AUVs) have gained momentum in recent years, and a special type of AUV, the robotic fish, has become a significant topic, with a superior maneuverability to traditional AUVs. In this paper, a prediction strategy for the hydrodynamic performance of a robotic fish to analyze undulatory swimming behaviors is proposed. The two-dimensional robotic fish model for computational fluid dynamics (CFD) simulations is constructed, and a dynamic network method is applied to orient the generated network based on the wavy motion. For the thrust force of the fin, a body traveling wave is derived. In the simulations, the effects of kinematic parameters such as flapping frequency and speed on swimming efficiency and drag are analyzed, and thrust force production, power expenditure, and overall efficiency of swimming are examined. Later, a deep learning-based prediction model is designed from the obtained parameters, and force predictions are performed. Long short-term memory (LSTM)-, convolutional neural network (CNN)-, and gated recurrent network (GRU)-based time series prediction models are used, and their variations are compared. In these experiments, while the CNN-GRU achieves the higher prediction performance for the root mean square error, with 0.0228, other approaches give a lower performance, between 0.0233 and 0.0359. The proposed method demonstrates a superior performance in CNN and LSTM models and exhibits lower prediction errors.

Diabetic wounds remain a major clinical challenge due to impaired angiogenesis, chronic inflammation, oxidative stress, and persistent infection, all of which delay tissue repair. Conventional dressings provide only passive protection and fail to modulate the wound microenvironment effectively. Chitosan (CS) is a naturally derived polysaccharide inspired by biological structures in crustaceans and fungi. It has emerged as a multifunctional biomimetic polymer with excellent biocompatibility, antimicrobial activity, and hemostatic properties. Recent advances in biomimetic materials science have enabled the development of stimuli-responsive CS hydrogels. These systems can sense physiological cues such as pH, temperature, glucose level, light, and reactive oxygen species (ROS). These smart systems emulate natural wound healing mechanisms and adapt to environmental changes. They release bioactive agents on demand and promote tissue homeostasis through controlled angiogenesis and collagen remodeling. This review discusses the biomimetic design rationale, crosslinking mechanism, and emerging strategies underlying single and dual-responsive hydrogel systems. It further emphasizes how nature-inspired structural and functional designs accelerate diabetic wound repair and outlines the current challenges and future prospects for translating these bioinspired intelligent hydrogels into clinical wound care applications.

The hierarchical structure and multifunctional properties of bio-based cellular materials, particularly cellulose, hemicellulose, and lignin, have attracted increasing attention and interest due to their sustainability and versatility. Recent advances in computational modeling and machine learning strategies have provided transformative insights into the molecular, mechanical, thermal, and electronic behaviors of these biopolymers. This review categorizes the conducted studies based on key material properties and discusses the computational methods utilized, including quantum mechanical approaches, atomistic and coarse-grained molecular dynamics, finite element modeling, and machine learning techniques. For each property, such as structural, mechanical, thermal, and electronic, we have analyzed the progress made in understanding inter- and intra-molecular interactions, deformation mechanisms, phase behavior, and functional performance. For instance, atomistic simulations have shown that cellulose nanocrystals exhibit a highly anisotropic elastic response, with axial elastic moduli ranging from approximately 100 to 200 GPa. Similarly, thermal transport studies have shown that the thermal conductivity along the chain axis (≈5.7 W m^-1 K^-1) is nearly an order of magnitude higher than that in the transverse direction (≈0.7 W m^-1 K^-1). In recent years, this research area has also experienced rapid advancement in data-driven methodologies, with the number of machine learning applications for biopolymer systems increasing more than fourfold over the past five years. By bridging multiscale modeling and data-driven approaches, this review aims to illustrate how these techniques can be integrated into a unified framework to accelerate the design and discovery of high-performance bioinspired materials. Eventually, we have discussed emerging opportunities in multiscale modeling and data-driven discovery to outline future directions for the design and application of high-performance bioinspired materials. This review aims to bridge the gap between molecular-level understanding and macroscopic functionality, thereby supporting the rational design of next-generation sustainable materials.

The development of composite materials with tunable dielectric properties that preserve mechanical performance is essential for next-generation radio engineering devices. In this study, composite filaments based on acrylonitrile-butadiene-styrene (ABS) with 0-40 wt.% TiO₂ solid loading were developed for 3D printing. The dielectric permittivity and mechanical properties of the 3D-printed parts strongly depend on the TiO₂ content. Using these filaments, we fabricated biomimetic lattices based on triply periodic minimal surfaces (TPMSs) using fused filament fabrication (FFF). The intrinsic porosity of the TPMS lattices further enables tuning of dielectric permittivity, facilitating their integration into gradient-index components. This multifunctionality was demonstrated by fabricating a spherical Luneburg lens prototype, which exhibited stable antenna performance in the 8.0-12.5 GHz frequency range. The results confirm that TPMS lattices based on the ABS-TiO₂ composite can simultaneously deliver mechanical robustness and dielectric tunability, opening new pathways toward multifunctional components for advanced radio engineering systems and beyond.

The design of sustainable polymer systems with tunable properties is essential for next-generation functional materials. This study examines the influence of a phenol-free modified rosin resin (Unik Print™ 3340, UP)-a maleic anhydride- and fumaric acid-modified gum rosin-on the structural, thermal, rheological, and mechanical behavior of four poly(lactic acid) (PLA) grades with different molecular weights and crystallinity. Blends containing 3 phr of UP were prepared by melt compounding. Thermogravimetric analysis showed that the incorporation of UP did not alter the thermal degradation of PLA, confirming stability retention. In contrast, differential scanning calorimetry revealed that UP affected thermal transitions, suppressing crystallization and melting in amorphous PLA grades and shifting the crystallization temperature to lower values in semi-crystalline grades. The degree of crystallinity decreased for low-molecular-weight semi-crystalline PLA but slightly increased in higher-molecular-weight samples. Mechanical tests indicated that UP acted as a physical modifier, increasing toughness by over 25% for all PLA grades and up to 60% in the amorphous, low-molecular-weight grade. Rheological measurements revealed moderate viscosity variations, while FESEM analysis confirmed microstructural features consistent with improved ductility. Overall, UP resin enables fine tuning of the structure-property relationships of PLA without compromising stability, offering a sustainable route for developing bio-based polymer systems with enhanced mechanical performance and potential use in future biomimetic material designs.

Dental caries is one of the most widespread chronic infectious diseases for humans. It results in localized destruction of dental hard tissues and has negative impacts on systemic health. Aims: This study aims to design, model, and control a novel 4-DOF dental robotic manipulator, Dentatron, specifically tailored for dental applications. The objectives were to (1) develop a compact robotic arm optimized for dental workspace constraints, (2) implement and compare three controllers-Computed Torque Control (CTC), Fuzzy Logic Control (FLC), and Neural Network Adaptive Control (NNAC), (3) evaluate tracking accuracy, transient response, and robustness in step and trajectory tasks, and (4) assess the potential of adaptive neural controllers for future clinical integration. Materials and Methods: The Dentatron system integrates a custom-designed robotic manipulator with adaptive controllers. The methodology consists of five main stages: robot modeling, control design, neural network adaptation, training, and evaluation. Simulations were performed to evaluate performance across joint tracking and Cartesian trajectory tasks using MATLAB 2022. Human-inspired trajectory design is fundamental to the Dentatron control and simulation framework to emulate the continuous curvature and minimum jerk characteristics of human upper-limb motion. The desired end-effector paths were formulated using fifth-degree polynomial trajectories that produce bell-shaped velocity profiles with gradual acceleration changes. Results: The study revealed that the Neural Network Adaptive Controller (NNAC) achieved the fastest convergence and lowest tracking error (<3 mm RMSE), consistently outperforming Fuzzy Logic Control (FLC) and Computed Torque Control (CTC). NNAC consistently provided precise joint tracking with minimal overshoot, while FLC ensured smoother but slower responses, and CTC exhibited large overshoot and persistent oscillations, requiring precise modeling to remain competitive. Conclusion: NNAC demonstrated the most robust and accurate control performance, highlighting its promise for safe, precise, and clinically adaptable robotic assistance in dentistry. Dentatron represents a step toward the development of compact dental robots capable of enhancing the precision and efficiency of future dental procedures.