Unmanned underwater vehicles (UUVs) capable of agile, high-speed maneuvering in complex environments require propulsion systems that can dynamically modulate three-dimensional forces. The California sea lion (Zalophus californianus) provides an exceptional biological model, using its foreflippers to achieve rapid turns and powerful propulsion. However, the specific kinematic mechanisms that govern instantaneous force generation from its powerful foreflippers remain poorly quantified. This study experimentally characterizes the time-varying thrust and lift produced by a bio-robotic sea lion foreflipper to determine how flipper twist, sweep, and phase overlap modulate propulsive forces. A three-degree-of-freedom bio-robotic flipper with a simplified, low-aspect-ratio planform and single compliant hinge was tested in a circulating flow tank, executing parameterized power and paddle strokes in both isolated and combined-phase trials. The time-resolved force data reveal that the propulsive stroke functions as a tunable hybrid system. The power phase acts as a force-vectoring mechanism, where the flipper’s twist angle reorients the resultant vector: thrust is maximized in a broad, robust range peaking near 45°, while lift increases monotonically to 90°. The paddle phase operates as a flow-insensitive, geometrically driven thruster, where twist angle (0° optimal) regulates thrust by altering the presented surface area. In the full stroke, a temporal-phase overlap governs thrust augmentation, while the power-phase twist provides robust steering control. Within the tested inertial flow regime (Re ≈ 104–105), this control map is highly consistent with propulsion dominated by geometric momentum redirection and impulse timing, rather than circulation-based lift. These findings establish a practical, experimentally derived control map linking kinematic inputs to propulsive force vectors, providing a foundation for the design and control of agile, bio-inspired underwater vehicles.

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.

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.

Microneedle (MN) patches comprise a promising platform for transdermal delivery of macromolecular therapeutics. However, achieving sufficient mechanical strength for skin penetration while maintaining high biocompatibility and efficient antibody loading remains a major challenge. In this study, we designed and developed a core–shell-structured hydrogel MN patch composed of a silk fibroin core and a protein-based shell layer for antibody loading and potential transdermal release. The latter was constructed using a fusion protein consisting of the B and C domains of Staphylococcus aureus protein A (BC) and a tyrosine-rich mussel adhesive protein (MAP), thereby enabling antibody binding via the BC domains. By harnessing biomimetic design strategies, the BC-MAP shell facilitates antibody immobilization via specific affinity interactions, while the silk fibroin core provides substantial mechanical strength: the MN patch demonstrated a penetration force approximately 4.2 times greater than that required to pierce porcine skin. Collectively, our core–shell-structured hydrogel MN patch is a promising platform for transdermal antibody delivery.

The discovery of the fifth gravitational wave, GW170817, and its electromagnetic counterpart, resulting from the merger of neutron stars by the LIGO and Virgo teams, marked a major milestone in astronomy. It was the first time that gravitational waves and light from the same cosmic event were observed simultaneously. The LIGO detectors in the United States recorded the signal for 100 s, longer than in previous detections. The merging of neutron stars emits both gravitational and electromagnetic waves across all frequencies—from radio to gamma rays. However, pinpointing the exact source remains difficult, requiring rapid sky scanning to locate it. To address this challenge, the Gravitational-Wave Optical Transient Observer (GOTO) project was established. It is specifically designed to detect optical light from transient events associated with gravitational waves, enabling faster follow-up observations and a deeper study of these short-lived astronomical phenomena, which appear and disappear quickly in the universe. In astrophysics, it has become more important to find astronomical transient events like supernovae, gamma-ray bursts, and stellar flares because they are linked to extreme cosmic processes. However, finding these short-lived events in huge sky survey datasets, like those from the GOTO project, is very hard for traditional analysis methods. This study suggests a deep learning methodology employing Convolutional Neural Networks (CNNs) to enhance transient classification. CNNs are based on how biological vision systems work and how they are structured. They mimic how animal brains hierarchically process visual information, making it possible to automatically find complex spatial patterns in astronomical images. Transfer learning and fine-tuning on pretrained ImageNet models are utilized to emulate adaptive learning observed in biological organisms, enabling swift adaptation to new tasks with minimal data. Data augmentation methods like rotation, flipping, and noise injection mimic changes in the environment to improve model generalization. Dropout and different batch sizes are used to stop overfitting, which is similar to how biological systems use redundancy and noise tolerance. Ensemble learning strategies, such as Soft Voting and Weighted Voting, draw inspiration from collective intelligence in biological systems, integrating multiple CNN models to enhance decision-making robustness. Our findings indicate that this bio-inspired framework substantially improves the precision and dependability of transient detection, providing a scalable solution for real-time applications in extensive sky surveys such as GOTO.

The Red-Billed Blue Magpie Optimizer (RBMO) is a recently introduced swarm-based meta-heuristic that has shown strong potential in engineering optimization but remains under-explored. To address its inherent limitations, this paper proposes an Enhanced RBMO (ERBMO) that synergistically incorporates two key strategies: a dominant-group-based two-stage covariance-driven strategy that captures evolutionary trends to improve population quality while reinforcing global exploration, and a Powell mechanism (PM) that eliminates dimensional stagnation and markedly strengthens convergence. Extensive experiments on the CEC 2017 benchmark suite demonstrate that ERBMO outperforms ten basic and improved algorithms in global exploration, local convergence accuracy and robustness, attaining Friedman ranks of 1.931, 1.621, 1.345 and 1.276 at 10D, 30D, 50D and 100D, respectively. Furthermore, empirical studies on practical engineering design problems confirm the algorithm’s capability to consistently deliver high-quality solutions, highlighting its broad applicability to real-world constrained optimization tasks. In future work, we will deploy the algorithm for real-world tasks such as UAV path-planning and resource-scheduling problems.

Cactaceae are plants equipped with spines and adapted to extremely arid environments. In particular, Echinocactus grusonii spines are almost cylindrical structures, which may occasionally present an enlargement of their proximal cross sectional area. In this work, the spines of Echinocactus grusonii were explored as a possible source of biomimetic inspiration for the design and the implantation strategies of self-inserting intraneural interfaces. More specifically, the elastic stability of spines was theoretically studied for structures able to puncture the surface of an external object, as well as for structures unable to pierce it. The biomimicry of Echinocactus grusonii spines suggested an improved insertion strategy for self-inserting intraneural interfaces together with structural changes able to increase their elastic stability. The theoretical approach provided in this work was able to predict an increase of the first buckling threshold up to 39% for not puncturing self-inserting neural interfaces, and up to 59% for puncturing ones.

This review presents the novel synthesis of nature-inspired drilling strategies specifically tailored for extraterrestrial environments, where conventional technologies fail under the environmental conditions and power and mass constraints. Biomimetic drilling, inspired by insects, mollusks, reptiles, and other organisms, offers novel solutions for extraterrestrial subsurface exploration. Numerous organisms efficiently penetrate materials with low energy, using little force, and adapt to flexible substrates, which are essential capabilities for use off this planet. Traditional rotary and percussive drills do not function well under microgravity, at the end of the temperature spectrum, or in low energy and mass environments, such as landers which are typically under 300 kg and 200 W of power available. Nature-inspired approaches such as the reciprocating carpenter bee style have been shown to reduce overhead forces by as much as 50%; clam-like fluidization reduces drag by 90%; and sandfish-inspired methods improve mobility in granular media by 40%. These also improve the in situ resource utilization (ISRU) approaches for efficient sampling, water ice extraction, and planetary surface operations. This paper focuses on bio-drilling with other biological models, their engineering analogs, and exploration models for off-Earth use. Based on this synthesis, the paper recommends prioritizing dual-reciprocating and oscillatory mechanisms for near-term missions, while pursuing hybrid, AI-driven, and wear-resistant designs for long-term exploration. These approaches will help to improve penetration efficiency, reduce power demands, and extend the drilling system’s lifespan in challenging extraterrestrial environments.

Biological neural networks undergo dynamic structural and functional changes during development, yet how their controllability evolves across different life stages remains largely unexplored. Here, we investigate the neural network of Caenorhabditis elegans (C. elegans), a fully mapped model organism, to examine changes in network controllability from larval stages to adulthood. Using structural controllability and target control frameworks, we show that while global neural controllability progressively increases with developmental complexity, muscle-target controllability declines after early larval stages, indicating a functional shift in control priorities. Furthermore, a comparative analysis between hermaphroditic and male adults reveals that overall controllability remains similar despite substantial differences in neural architecture, with hermaphrodites exhibiting slightly higher efficiency. These findings highlight fundamental principles of how neural circuits reorganize during maturation and suggest that controllability analysis can provide valuable insights into neural function, sex-specific behaviors, and potential applications in modeling developmental and degenerative disorders.

In bone conduction (BC) hearing, sound is transmitted directly to the cochlea via skull vibrations, bypassing the outer and middle ear. This provides a therapeutic option for patients with conductive or mixed hearing loss and single-sided deafness. Although finite-element models have advanced understanding of the mechanisms underlying BC, progress toward personalized treatment strategies remains limited by a lack of standardized, experimentally validated, subject-specific models. This study proposes a hierarchical validation framework to support the development and validation of individualized computational models of the human head under BC stimulation. The framework spans four anatomical levels: system, subsystems, structures, and tissues. This approach enables systematic acquisition of data from intact cadaver heads down to isolated material domains. To demonstrate the applications of the framework, an experimental study was conducted on a single cadaver head, targeting three levels: the intact head (system), extracted bone pieces (structures), and isolated cortical layers (tissues). Subsystems were not addressed. High-resolution photon-counting computed tomography (CT) and energy-integrating cone-beam CT were used to acquire anatomical data. One-dimensional laser Doppler vibrometry was used to capture vibrational responses of bone pieces and cortical layers under wet and dry conditions. Representative results were analyzed to assess the impact of preparation state on resonance behavior. Comparative analysis showed that photon-counting CT provided superior structural resolution compared with energy-integrating cone-beam CT, particularly at the full-head (system) level. Vibrational measurements at the structure and tissue levels from the same anatomical region revealed broadly consistent resonance vibration patterns, enabling comparison of resonance frequencies. The influence of hydration state and thickness reduction on vibrational behavior was highlighted. The proposed framework provides a scalable methodology for validation of subject-specific BC models with the potential for more accurate BC simulations based on the hypothesis of functional variability rooted in anatomical variability. Obvious use cases would include the development of improved hearing aid designs and personalized treatments. In parallel, a successful correlation of anatomical and functional variability can serve as inspiration for design principles of metamaterials.