Reducing the resistance of vehicles, ships, aircraft and other means of transport during movement can significantly improve the speed, save energy and reduce emissions. After billions of years of continuous evolution, organisms in nature have gradually developed the ability to move at high speed to achieve better survival. These evolved organisms provide a perfect template for the human development of drag reduction materials. Revealing the unique physiological structural characteristics of organisms and their relationship with resistance during movement can provide a feasible approach tosolving the problem of reducing friction resistance. Whether flying in the sky, running on the ground, swimming in the water, or even living in the soil, many creatures in various environments have the ability to reduce resistance. Driven by these inspirations, researchers have done a lot of work to explore and imitate these biological epidermis structures to achieve drag reduction. In this paper, the biomimetic drag reduction materials is introduced in detail in the order of drag reduction mechanism, structural characteristics of biological epidermis (including marine animals, flying animals, soil animals and plants), biomimetic preparation methods, performance testing methods and application fields. Finally, the potential of various biomimetic drag reduction materials in engineering application and the problems to be overcome are summarized and prospected. This paper can help readers comprehensively understand the research progress of biomimetic drag reduction materials, and provide reference for further designing the next generation of drag reduction materials.

Annelid-inspired robots exhibit excellent motion adaptability and structural compliance, enabling them to navigate con-lfined, hazardous, or complex environments such as pipelines, soil, or the gastrointestinal tract. This review summarizes key developments in their bionic part design, actuation methods, material selection, and performance characteristics.　Comparative analyses show that different actuation strategies (e.g., pneumatic, shape memory alloys, and electroactive polymers, ete.) need to be weighed in terms of their advantages, limitations, and applicable environments. Materials likesilicone rubber and SMA are evaluated for their strength, flexibility, and energy performance. Quantitative benchmarks of velocity, load capacity, and energy consumption are presented to highlight design-performance correlations. Prospective research directions include the integration of multifunctional adaptive materials, real-time feedback sensing systems, and scalable architectures for autonomous operation in unstructured environments.

Birds have developed near-perfect structures and functionality over millions of years of natural evolution. To improve the efficiency of fixed-wing vehicles in different environments, researchers have developed deformable wings inspired by the wing structures of birds. Shape Memory Alloy (SMA) is applied as a smart material to the deformable wing. Compared with other drive methods, SMA actuators have the advantages of high drive capacity and a simple structure for driving wing deformation. According to the shape memory effect, SMA actuators are classified as single-range and dual-range actuators. The wing structure designed for each SMA drive is unique. By comparing and analyzing the structures of airfoils, airfoils with similar drive forms and deformation structures are put together for review and discussion. The deformable wings are categorized into out-of-face deformation, in-face deformation, airfoil curvature deformation, and combined deformation with multiple degrees of freedom based on the structure and location of the wing that produces the deformation. An overview of the deformed wing is introduced by telling the bionic theory of seagulls. The principles of deformation of the wing, the mechanics of the SMA actuator mechanism, and the aerodynamic characteristics of the deformable wing are presented. The structure and working principle of SMA actuators for each type of deformable wing are explained in detail. Methods and approaches to study the deformability of deformable wings are analyzed and summarized. This work provides comprehensive insights and perspectives for future studies of SMA-driven deformable airfoils.

This review article presents a comprehensive overview of emerging technologies in bone tissue engineering (BTE). This rapidly advancing field addresses the challenges of bone defects and injuries beyond traditional treatments like autografts and allografts. The study highlights the integration of 3D bioprinting, stem cell therapy, gene therapy, biomaterials, nanotechnology, and computational modeling as transformative approaches in BTE. Developing biomimetic scaffolds, advanced bio-inks, and composite nanomaterials has enhanced seaffold design, improving mechanical properties and biocompatibility. Innovatiohs in gene therapy and bioactive molecule delivery are showcased for their ability to modulate cellular behavior and enhance osteogenesis. Stem cell-based therapies leverage the regenerative potential of mesenchymal stem cells, facilitating tissue integration and functional restoration. Computational tools, including finite element analysis　(FEA) and agent-based modelling, aid in the optimization of scaffold design, predicting mechanical responses and biological behaviors. Despite notable progress, signifieant challenges, such as achieving reliable vascularization, sealable manu-facturing of engineered constructs, and effective clinical translation, remain substantial barriers to widespread adoption.　Future research efforts focused on refining these technologies are vital for translating innovative strategies into elinical practice, paving the way for personalized regenerative solutions in bone repair.

This article reviews recent advancements, innovative strategies, and the key challenges in Drug Delivery Systems (DDS)　for bone regeneration, focusing on tissue engineering. It highlights the limitations of current surgical interventions forbone regeneration, particularly autogenic bone grafts, and discusses the exploration of alternative materials and methods, including allogeneic and xenogeneic bone grafts, synthetic materials, and biodegradable polymers. The objective is to provide a comprehensive understanding of how contemporary DDS can be optimized and integrated with tissue engineering approaches for more effective bone regeneration therapies. The review explained the mechanisms through which DDS enhance bone repair processes, identifies critical factors influencing their efficacy and safety, and offers an overview of current trends and future perspectives in the field. It emphasizes the need for advanced strategies in bone regeneration that focus on precise control of DDS to address bone conditions such as osteoporosis, trauma, and genetic predispositions leading to fractures.

Peri-implant mucositis is the mucosal inflammatory lesion around implants that does not result in the loss of the peri-implant bone that supports them. Furthermore, Peri-implantitis (PI), a medical condition affecting the tissues surrounding dental implants, is characterized by inflammation and a progressive loss of supporting bone. Of the several types of Nanoparticles (NPs), a lot of research has been done on the effects of Metal NPs (MNPs)—such as those made of silver, zinc, and copper—and non-MNPs—such as those made of Graphene Oxide (GO), Carbon-based NPs (CNPs), and Chitosan (CS) NPs—on peri-implant microorganisms. These NPs serve as antibacterial and anti-inflammatory agents and cover dental implants. Furthermore, Peri-implant Disease (PID) and many others in the oral and dental domains may be effectively treated using Green Synthesis (GS) NPs enabled by various biological sources. Compared to chemical and physical processes, GS offers several benefits, including non-toxicity, pollution-free production, environmental friendliness, cost-effectiveness, and sustainability. Hence, the significance of GS NPs, both MNPs and non-MNPs, was first explored in this work. Using eco-friendly methods, we then reviewed the PID-related effects of various MNPs and non-MNPs synthesized. NPs, both MNPs and non-MNPs, have great potential as a future therapy for PI, and the environmentally friendly manufacturing process may play a significant role in this development. Consequently, we have looked into the benefits and drawbacks of this treatment method in terms of clinical practice in our study. Research from reputable sources, such as PubMed and Google Scholar, was used to compile the papers included in the review article. Researchers may make progress in producing MNPs and non-MNPs NPs for treating PI by adopting GS.

Detecting and distinguishing infrared radiation for non-invasive medical diagnostic purposes has been attempted for basic surface temperature assessment since the middle of the 20th century. However, the long wavelength and low energy of infrared radiation impede the detection of signals from deeper tissue layers, significantly limiting its use in diagnostics. To overcome these limitations, a novel approach was developed by combining a semiconductor gallium arsenide chip and prism-based optics that enabled the detection of signals in the infrared and terahertz spectrum. Challenges related to penetration depth and thermal noises were addressed by neural network modelling.

Tensegrity structures, embodying the principles of continuous tensioning and discrete compression, have emerged as fundamental frameworks in locomotive soft robotics for navigating uneven and unpredictable environments, owing to their flexible and resilient traits. By means of a straightforward and cost-effective method to achieve structure-driven, vibration-driven tensegrity shows great potential, particularly in tasks demanding random exploration. However, the design guidance for vibration-driven tensegrity and their performance evaluation in unstructured terrain remain unrevealed due to the complex dynamics of the structure. This paper presents a small six-bar tensegrity robot, driven by wireless vibration motors, designed for deployment in disaster rescue and search scenarios. Finite element simulation is used to investigate how structural characteristics, excitation parameters, and the arrangement of motors affect the kinematic performance of this tensegrity system. A prototype of the six-bar tensegrity robot with three motors located on the lower ends of the three lower struts is designed and manufactured after the numerical simulations. A simple control policy which adjusts the motion of the tensegrity robot by turning on or off the motors on different locations is proposed. The prototype with and without the control policy is tested in man-made environments of various complexity. It shows that the ability and efficiency of the tensegrity robot in exploring unstructured environments is significantly enhanced by the proposed control policy. It is believed that the potential of the vibration-driven tensegrity robot could be further exploited by integrating multi-source sensors and more intelligent control policies.

Autonomous legged robots, capable of navigating uneven terrain, can perform a diverse array of tasks. However, designing locomotion controllers remains challenging. In particular, designing a controller based on durable and reliable proprioceptive sensors, is essential for achieving adaptability. Presently, the controller must either be manually designed for specific robots and tasks, or developed using machine-learning techniques, which require extensive training time and result in complex controllers. Inspired by animal locomotion, we propose a simple yet comprehensive closed-loop modular framework that utilizes minimal proprioceptive feedback (i.e., the Coxa–Femur (CF) joint angle), enabling a quadruped robot to efficiently navigate unpredictable and uneven terrains, including the step and slope. The framework comprises a basic neural control network capable of rapidly learning optimized motor patterns, and a straightforward module for sensory feedback sharing and integration. In a series of experiments, we show that integrating sensory feedback into the base neural control network aids the robot in continually learning robust motor patterns on flat, step, and slope terrain, compared with the open-loop base framework. Sharing sensory feedback information across the four legs enables a quadruped robot to proactively navigate unpredictable steps with minimal interaction. Furthermore, the controller remains functional even in the absence of sensor signals. This control configuration was successfully transferred to a physical robot without any modifications.

Locomotion performance degradation after carrying payloads is a significant challenge for insect-scale microrobots. Previously, a legged microrobot named BHMbot with a high load-carrying capacity based on front-leg actuation configuration and efficient running gait was proposed. However, insects, mammals and reptiles in nature typically use their powerful rear legs to achieve rapid running gaits for predation or risk evasion. In this work, the load-carrying capacity of the BHMbots with front-leg actuation and rear-leg actuation configurations is comparatively studied. Simulations based on a dynamic model with four degrees of freedom, along with experiments, have been conducted to analyze the locomotion characteristics of the two configurations under different payload masses. Both simulation and experimental results indicate that the load-carrying capacity of the microrobots is closely related to their actuation configurations, which leads to different dynamic responses of the microrobots after carrying varying payload masses. For microrobots with body lengths of 15 mm, the rear-leg actuation configuration exhibits a 31.2% enhancement in running speed compared to the front-leg actuation configuration when unloaded. Conversely, when carrying payloads exceeding 5.7 times the body mass (350 mg), the rear-leg actuation configuration demonstrates an 80.1% reduction in running speed relative to the front-leg actuation configuration under the same payload conditions.

This article presents the design of a microfabricated bio-inspired flapping-wing Nnano Aaerial Vvehicle (NAV), driven by an electromagnetic system. Our approach is based on artificial wings composed of rigid bodies connected by compliant links, which optimise aerodynamic forces though replicating the complex wing kinematics of insects. The originality of this article lies in a new design methodology based on a triple equivalence between a 3D model, a multibody model, and a mass/spring model (0D) which reduces the number of parameters in the problem. This approach facilitates NAV optimisation by using only the mass/spring model, thereby simplifying the design process while maintaining high accuracy. Two wing geometries are studied and optimised in this article to produce large-amplitude wing motions (approximately 40^\circ ), and enabling flapping and twisting motion in quadrature. The results are validated thanks to experimental measurements for the large amplitude and through finite element simulations for the combined motion, confirming the effectiveness of this strategy for a NAV weighing less than 40 mg with a wingspan of under 3 cm.

Throughout the previous studies, none of them are involved in analysing the downwash flow effect on the control surface of the Flapping Wing Rotor (FWR). An overset CFD numerical model is built up and validated to study the downwash flow’s effect on the stability of the FWR. After simulation, a cone like self-lock region which acts as the critical condition determining the stability of FWR is found. Only when the flow’s resultant velocity acting on the control surface lies in the stable region, the FWR can keep stable. The size of the cone like self-lock stable region can be enlarged by increasing the maximum feasible deflection angle constrained by mechanical design or enhancing the equivalent downwash flow velocity. Among all the simulated cases, when J?=?2.67 (f=5 Hz, \dot {\psi }=5 r/s), the largest average equivalent downwash flow velocities are found. On the other hand, the recovery torque could be enhanced due to the increase of the arm of the lateral force. According to these simulation results, a 43 g FWR model with two control surfaces and two stabilizers is then designed. A series of flight tests is then conducted to help confirm the conclusion of the mechanism research in this work. Overall, this study points out several strategies to increase the flight stability of the FWR and finally realizes the stable climb flight and mild descent flight of the FWR.

The stiffness information of the grasped object at the initial contact stage can be effectively used to adjust the grasping force of the prosthetic hand, thereby preventing damage to the object. However, the object’s deformation and contact force are often minimal during the initial stage and not easily obtained directly. Additionally, stiffness estimation methods for prosthetic hands often require contact sensors, which can easily lead to poor contact issues. To address the above issues, this paper proposes the model-based stiffness estimation of grasped objects for underactuated prosthetic hands without force sensors. First, the kinematic model is linearized at the contact points to achieve the estimation of the linkage angles in the underactuated prosthetic hand. Secondly, the motor parameters are estimated using the Kalman filter method, and the grasping force is obtained from the dynamic model of the underactuated prosthetic hand. Finally, the contact model of the prosthetic hand grasping an object is established, and an online stiffness estimation method based on the contact model for the grasped object is proposed using the iterative reweighted least squares method. Experimental results show that this method can estimate the stiffness of grasped objects within 250 ms without contact sensors.

This paper presents a novel rehabilitation robot designed to address the challenges of Passive Range of Motion (PROM) exercises for frozen shoulder patients by integrating advanced scapulohumeral rhythm stabilization. Frozen shoulder is characterized by limited glenohumeral motion and disrupted scapulohumeral rhythm, with therapist-assisted interventions being highly effective for restoring normal shoulder function. While existing robotic solutions replicate natural shoulder biomechanics, they lack the ability to stabilize compensatory movements, such as shoulder shrugging, which are critical for effective rehabilitation. Our proposed device features a 6 Degrees of Freedom (DoF) mechanism, including 5 DoF for shoulder motion and an innovative 1 DoF Joint press for scapular stabilization. The robot employs a personalized two-phase operation: recording normal shoulder movement patterns from the unaffected side and applying them to guide the affected side. Experimental results demonstrated the robot’s ability to replicate recorded motion patterns with high precision, with Root Mean Square Error (RMSE) values consistently below 1 degree. In simulated frozen shoulder conditions, the robot effectively suppressed scapular elevation, delaying the onset of compensatory movements and guiding the affected shoulder to move more closely in alignment with normal shoulder motion, particularly during arm elevation movements such as abduction and flexion. These findings confirm the robot’s potential as a rehabilitation tool capable of automating PROM exercises while correcting compensatory movements. The system provides a foundation for advanced, personalized rehabilitation for patients with frozen shoulders.

Antennae are significant chemosensory and mechanosensory organs for insects and need careful maintenance. Bees use a pair of comb-like tools located on the forelimbs to brush and remove contaminants from their antennae. We filmed antenna grooming in three different bee species and observed that all bees raise their heads while grooming their antennae. We conducted a study to examine the effects of the distinctive grooming apparatus, as well as the antenna’s material and structural characteristics, on grooming behavior in both free-head and constrained-head scenarios. Head-raising increases the grooming speed by 300% compared to the situation where the head is constrained. It allows the bees to scrape the antennae 5 times per second. In addition, we proposed a mechanical model based on the morphological data to determine that raising the head increases the contact force by 50%. These findings will facilitate the development of innovative approaches for cleaning extended structures featuring bristly surfaces.

Bamboo is an important building material with natural hygroscopicity, and the mechanism of water effects on its deformation and fracture behavior has not been fully revealed. For this purpose, a novel in-situ testing method was developed in this study, which coupled Acoustic Emission (AE) and Digital Image Correlation (DIC) techniques. This method was used to investigate the effects of various Moisture Content (MC) levels (0, 6%, 15%, and 25%) on the tensile behavior of bamboo. The results showed that as the MC increased from 0 to 25%, the tensile strength of bamboo decreased from 163 to 110 MPa, the Young's modulus dropped from 8.5 to 3.9 GPa, and the elongation increased from 4.3 to 14%. An increase in MC could effectively promote the occurrence of subcritical cracks and micro-interfacial dissociations in bamboo. The synergistic effect of these two factors facilitated strain dispersion, ensuring adaptability to large deformations. Additionally, it was found that an increase in MC could significantly alter the fracture mode. This ingenious synergistic effect in bamboo was revealed for the first time in this study. The mechanisms discovered in this study may provide some important insights into the design and fabrication of advanced biomimetic heterostructures and biomimetic interfacial materials.

Tree knots are generally considered defects in wood, but how the surrounding structures of the defects affects strength of wood has not been studied. Here the mechanical properties of static compression and hole bearing tests were designed for encased knots and intergrown knots, and the strengthening mechanism of streamline tissue and connecting interface was analyzed by finite element modeling. And the two reinforced structures were applied to composite structural holes and connecting holes, which significantly improved open hole compressive strength and hole bearing strength. And the finite element models for two kinds of composite hole were created to analyze how the stress field around the reinforced structure strengthens the composite. Both the experimental results and the finite analysis results show that the streamline structure could effectively improve the compressive properties of composite structural holes, and the connecting interface provided a stable constraint for giving full play to the hole bearing properties of stronger materials. These two structures will provide reference for the structural design of lightweight composites.

Heterogeneous manufacturing is a topic that continues to receive attention. As an emerging manufacturing technology, additive manufacturing can provide strong technical support for heterogeneous manufacturing. In this study, both homogeneous and heterogeneous composite tubular bionic components were fabricated based on the cold metal transition technology, and the influence of deposition current on the microstructure and mechanical properties of the components was studied. The results show that the interface of the as-deposited heterogeneous composite component is well bonded, and there is an obvious mechanical interlocking structure. The compressive yield strength and elongation of the heterogeneous composite components are higher than those of the homogeneous components, and are positively correlated with the deposition current. Due to the fluctuation of element content, there are a large number of fine grain structures at the interface of the heterogeneous composite components, which increases the mechanical properties.

Bioinspired superhydrophobic surfaces have been used for drag reduction. However, the secondary structures and the air cushions on these surfaces could be destructed in a flow, losing the effect of drag reduction. Here, a stainless-steel surface with mushroom-like cross-section (SMC) and diamond cavities (SMC_D) having a drag reduction rate up to 19.37% is developed by 3D printing. The concealed re-entrant structures in SMC_D prevent the infiltration of water into the chamber and form gas cushions, which converts the sliding friction at liquid-solid interface into rolling friction at liquid-gas interface, realizing the drag reduction. Meanwhile, 98.3% of air can be maintained in the chamber in a flow with Reynolds number (Re) of 9?×?10⁵, ensuring the drag reduction in a high-velocity flow. Moreover, the continuous top stainless-steel surface and the supporting mesh network protect the critical re-entrant structures, ensuring the robustness of SMC. With the bioinspired design and one-step additive manufacturing process, SMC holds great potential for large-area production and applications requiring robust drag reduction.

Flexible pressure sensors have excellent prospects in applications of human-machine interfaces, artificial intelligence and human health monitoring due to their bendable and lightweight characteristics compared to rigid pressure sensors. However, arising from the limited compressibility of soft materials and the hardening of microstructures at the device interface, there is always a trade-off between high sensitivity and broad sensing range for most flexible pressure sensors, which results in a gradual saturation response and limits their practical applications. Herein, inspired by the distinct pressure perception function of crocodile receptors, a highly sensitive and wide-range flexible pressure sensor with multiscale microdomes and interlocked architecture is developed via a facile PS-decorated molding method. Combined with interlocked architecture, the multiscale dome-shaped structured interface enhances the compressibility of the material through structural complementarity, increases the contact area between functional materials, which compensates for the stiffness induced by the deformation of dense microscale columns. This effectively mitigates structural hardening across a wide pressure range, leading to the overall high performance of the sensor. As a result, the obtained sensor exhibits a low detection limit of 5 Pa, a high sensitivity of 6.14 kPa^??1, a wide measurement range up to 231 kPa, short response/recovery time of 56 ms/69 ms, outstanding stability over 10,000 cycles. Considering these excellent properties, the sensor shows promising potential in health monitoring, human-computer interaction, wearable electronics. This study presents a strategy for the fabrication of flexible pressure sensors exhibiting high sensitivity and a wide pressure response range.

Surface morphology of Ceratocanthus beetle elytra was investigated for spike surface texture and its geometry using Scanning Electron Microscopy (SEM). Material properties were analyzed for both surface and cross-section of elytra using nano-indentation technique. The spike texture was significantly rigid compared with the non-textured zone; a bi-layer system of E and H was identified at the elytra cross-section. Normal load acting on spike texture during free-fall conditions was estimated analytically and deflection equation was derived. The design of spike texture with conical base was studied for minimization of deflection and volume using the Non-dominated Sorting Genetic Algorithm (NSGA-II) optimization technique, confirming the smart design of the natural solution. The frictional behavior of elytra was studied using fundamental tribology test and the role of the oriented spike texture was investigated for frictional anisotropy. Compression resistance of full beetle was evaluated for both conglobated and non-conglobated configuration and tensile strengths were compared using Brazilian test. Puncture and wear resistance of full elytra were characterized and correlated with its defense mechanism.

In clinical work, many soft medical pipelines are located deep within the body, resulting in a lack of feedback regarding bending or folding conditions, which presents significant challenges for medical staff. To solve the problem, this study innovatively designs a flexible bending sensor, which can be attached to the medical pipelines and monitor the bending conditions. Based on a flexible substrate with secondary microstructures copied from champagne rose petals, the interdigital electrodes are designed to enhance the sensitivity of the sensor due to the amplifying effect. A high sensitivity of 2.209%^?1in a bending strain range of 8.9%, and a stable repeatability for over 6000 cycles under 1.8% bending strain are achieved by the sensor. By integrating the bending sensor, here, the nasogastric tube, femoral vein catheter, and tracheal intubation are used to demonstrate the sensing performance. Additionally, during the measurement, the sensing signals are processed and transformed to the bending angles simultaneously, enabling the direct visualization of the bending conditions of the pipelines. This work proposes innovative applications for bending sensors in medical technology and establishes a foundation for further research on flexible bending sensors.

Recent advances in bone regeneration have introduced the concept of four-dimensional (4D) scaffolds that can undergo morphological and functional changes in response to external stimuli. While several studies have proposed patient-specific designs for defect sites, they often fail to adequately distinguish the advantages of 4D scaffolds over conventional 3D counterparts. This study aimed to investigate the potential benefits of 4D scaffolds in clinically challenging scenarios involving curved defects, where fixation is difficult. We proposed the use of Shape-Memory Polymers (SMPs) as a solution to address critical issues in personalized scaffold fabrication, including dimensional accuracy, measurement error, and manufacturing imprecision. Experimental results demonstrated that the Curved-Layer Fused Deposition Modeling (CLFDM) scaffold, which offers superior conformability to curved defects, achieved significantly higher interfacial contact with the defect area compared to traditional Fused Deposition Modeling (FDM) scaffolds. Specifically, the CLFDM scaffold facilitated bone regeneration of 25.59?±?4.72 mm3, which is more than twice the 9.37?±?1.36 mm3 observed with the 3D FDM scaffold. Furthermore, the 4D CLFDM scaffold achieved 75.38?±?11.65 mm3 of new bone formation after four weeks, approximately three times greater than that of the 3D CLFDM scaffold, regardless of surface micro-roughness. These results underscore that improved geometrical conformity between the scaffold and the defect site enhances cellular infiltration and contributes to more effective bone regeneration. The findings also highlight the promise of 4D scaffolds as a compelling strategy to overcome geometric and dimensional mismatches in the design of patient-specific scaffolds.

In bone tissue engineering, scaffold design must achieve specific mechanical compatibility with implantation sites, critically determining implant performance. This study developed four cylindrical Ti6Al4V bone scaffolds via selective laser melting (SLM), incorporating distinct lattice architectures: Face-Centered Cubic (FCC), Body-Centered Cubic (BCC), Glass Sponge (GS), and Auxetic Structures (AS). Integrated experimental characterization and finite element simulations revealed exceptional mechanical superiority of FCC scaffolds, demonstrating 7-fold greater maximum stress compared to BCC, GS, and AS counterparts. Furthermore, FCC scaffolds exhibited optimal performance metrics including plateau stress (1.2–1.4 GPa), densification strain (0.15–0.25), energy absorption (85–100 MJ/m3), and specific energy absorption (45–55 kJ/kg). These findings confirm that the unique energy dissipation mechanisms inherent to FCC lattice geometry significantly enhance energy absorption efficiency. The study provides a theoretical foundation for developing mechanically adaptive bone implants, particularly advancing clinical applications requiring enhanced energy absorption capabilities.

Osteochondral defects involving both articular cartilage and subchondral bone remain challenging in clinical treatment. Inspired by the zonal organization of native osteochondral tissue and the sophisticated architecture of articular cavity, we designed a biomimetic bilayer scaffold system using 3D printing technology. The scaffold recreates the natural structural and mechanical gradients of the osteochondral interface, featuring a gradient transition from cartilage to bone phase. To enhance the bio-functionality of this biomimetic design, we incorporated the small molecule Kartogenin (KGN), which has shown promising potential in cartilage regeneration by promoting chondrogenic differentiation and inhibiting cartilage degeneration. However, the reparative efficacy of KGN is highly concentration-dependent, and the optimal concentration within complex three-dimensional scaffold environments remains unclear. Through both in vitro and in vivo evaluations of this bio-inspired scaffold system loaded with varying KGN concentrations, we identified that 5 μM KGN (SCS@K5) achieved optimal outcomes. At 12 weeks, the SCS@K5 treatment resulted in better organized osteochondral tissue with improved interface integration relative to other groups. This biomimetic gradient design incorporating KGN release offers a viable approach for osteochondral defect repair.

Formation control remains a critical challenge in cooperative multi-agent systems, particularly for Unmanned Underwater Vehicles (UUVs). Conventional approaches often suffer from several limitations, including reliance on global information, limited adaptability, high computational complexity, and poor scalability. To address these issues, we propose a novel bio-inspired formation control method for UUV swarms, drawing inspiration from the self-organizing behavior of fish schools. Our method integrates three key components: (1) a coordinated motion strategy without predefined targets that enables individual UUVs to align their movements via simple left or right rotations based solely on local neighbor interactions; (2) a target-directed movement strategy that guides UUVs toward specified regions; and (3) a dispersion control strategy that prevents overcrowding by regulating local spatial distributions. Simulation results confirm that the method achieves robust formation control and efficient area coverage using only local perception. Validation in a 9-UUV simulation environment demonstrates the approach’s flexibility, decentralization, and computational efficiency, making it particularly suitable for large-scale swarms with limited sensing and processing capabilities.

Inspired by the collective behaviors observed in bird flocks and fish schools, this paper proposes a novel Decentralized Model Predictive Flocking Control (DMPFC) framework to enable UAV swarms to autonomously track predefined reference trajectories while avoiding collisions and maintaining a stable quasi [Math Processing Error]-lattice formation. Unlike traditional approaches that rely on switching between predefined swarm formations, this framework utilizes identical local interaction rules for each UAV, allowing them to dynamically adjust their control inputs based on the motion states of neighboring UAVs, external environmental factors, and the desired reference trajectory, thereby enabling the swarm to adapt its formation dynamically. Through iterative state updates, the UAVs achieve consensus, allowing the swarm to follow the reference trajectory while self-organizing into a cohesive and stable group structure. To enhance computational efficiency, the framework integrates a closed-form solution for the optimization process, enabling real-time implementation even on computationally constrained micro-quadrotors. Theoretical analysis demonstrates that the proposed method ensures swarm consensus, maintains desired inter-agent distances, and stabilizes the swarm formation. Extensive simulations and real-world experiments validate the approach’s effectiveness and practicality, demonstrating that the proposed method achieves velocity consensus within approximately 200 ms and forms a stable quasi [Math Processing Error]-lattice structure nearly ten times faster than traditional models, with trajectory tracking errors on the order of millimeters. This underscores its potential for robust and efficient UAV swarm coordination in complex scenarios.

Accurate forecasting of wind power is crucial for ensuring the reliable operation of the electrical grid. Due to the impact of various factors, wind power forecasting presents a significant challenge. This paper presents the model that integrates Osprey and adaptive T-distribution dung beetle algorithm for optimizing a convolutional neural network. The CNN-BiLSTM-Attention model combines bidirectional long short-term memory neural networks with an attention mechanism, thereby improving the accuracy of wind power generation predictions. The original data is subjected to Variational Mode Decomposition (VMD) for analysis, taking into account the fluctuations in wind power across different periods. The BiLSTM network with short-term memory processes time-series wind power data, yielding an optimal predictive performance. The integration of the osprey algorithm and adaptive T-distribution within the Dung Beetle Optimization Algorithm was utilized to optimize the hyperparameters of the CNN-BiLSTM-Attention model, thereby enhancing its predictive performance. To assess the efficacy of the CNN-BiLSTM-Attention algorithm, enhanced by Ospreys and adaptive T-distributed dung beetle algorithm, we conducted experiments using the CEC2021 benchmark function. The integrated Osprey and adaptive T-distribution Dung Beetle algorithm has excellent global optimization performance when dealing with complex optimization problems. The fusion of Osprey and the adaptive T-distribution Dung beetle algorithm optimized the CNN-BiLSTM-Attention algorithm as well as other optimization algorithms for ablation experiments. The results show that the improved algorithm performs well in predicting wind power. The experimental findings suggest that the model’s predictive efficiency has enhanced by a minimum of 17.74%.

In the realm of video understanding, the demand for accurate and contextually rich video captioning has surged with the increasing volume and complexity of multimedia content. This research introduces an innovative solution for video captioning by integrating a Convolutional BiLSTM Convolutional Bidirectional Long Short-Term Memory (BiLSTM) constructed Variational Sequence-to-Sequence (CBVSS) approach. The proposed framework is adept at capturing intricate temporal dependencies within video sequences, enabling a more nuanced and contextually relevant description of dynamic scenes. However, optimizing its parameters for improved performance remains a crucial challenge. In response, in this research Golden Eagle Optimization (GEO) a metaheuristic optimization technique is used to fine-tune the Convolutional BiLSTM variational sequence-to-sequence model parameters. The application of GEO aims to enhancing the CBVSS ability to produce more exact and contextually rich video captions. The proposed attains an overall higher Recall of 59.75% and Precision of 63.78% for both datasets. Additionally, the proposed CBVSS method demonstrated superior performance across both datasets, achieving the highest METEOR (25.67) and CIDER (39.87) scores on the ActivityNet dataset, and further outperforming all compared models on the YouCook2 dataset with METEOR (28.67) and CIDER (43.02), highlighting its effectiveness in generating semantically rich and contextually accurate video captions.

Alzheimer’s Disease (AD), a prevalent neurodegenerative disorder characterized by memory loss and cognitive decline, poses significant challenges for individuals and society. Multimodal data fusion has emerged as a promising approach for AD diagnosis, with Graph Convolutional Networks (GCNs) effectively capturing irregular brain information. However, traditional GCN methods face limitations in representing and integrating multimodal data, often resulting in feature mismatch. In this study, we propose a novel Kolmogorov-Arnold Graph Attention Network (KAGAN) model to address this issue through semantic-level alignment. KAGAN incorporates a Multimodal Feature Construction method (MuStaF) to extract structural and functional features from T1- and T2-weighted images, and a Multimodal Graph Adjacency Matrix Construction method (MuGAC) to integrate clinical information, modeling intricate relationships across modalities. Experiments conducted on the ADNI dataset demonstrate the superiority of KAGAN in AD/CN/MCI classification, achieving an accuracy of 98.29 ± 1.21%. This highlights KAGAN’s potential for early AD diagnosis by enabling interactive learning and fusion of multimodal features at the semantic level. The source code of our proposed model and the related datasets are available at https://github.com/sheeprra/KAGAN.