Valvular Heart Disease (VHD), including stenosis and regurgitation, is a significant contributor to global cardiovascular morbidity. Current prosthetic solutions mechanical and bioprosthetic heart valves each present major limitation. Mechanical valves require lifelong anticoagulation due to thrombogenicity, while bioprosthetic valves suffer from structural degeneration and limited durability. Polymeric Heart Valves (PHVs) have emerged as promising alternatives, aiming to integrate the mechanical resilience of synthetic materials with the biocompatibility and hemodynamic performance of natural valves. Recent studies have explored advanced polymers such as Polyhedral Oligomeric Silsesquioxane–Polycarbonate–Urea–Urethane (POSS-PCU), Silicone–Polyurethane Urea (SiPUU), and nanocomposites like Polyvinyl Alcohol (PVA) and SIBS for their enhanced thromboresistance, calcification resistance, and long-term mechanical durability. Complementary to material innovation, fabrication methods such as 3D printing, Melt Electrospinning Writing (MEW), and Focused Rotary Jet Spinning (FRJS) offer patient-specific designs and microstructural control. This review systematically compares traditional and next-generation prostheses, examines mechanical and biological performance, and discusses critical design challenges including porosity, thrombogenicity, and leaflet calcification. Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD) are highlighted for optimizing design and simulating physiological conditions. By presenting recent preclinical progress and manufacturing strategies, this review outlines a translational roadmap toward clinically viable, biomimetic polymeric heart valves capable of addressing the needs of both adult and pediatric patients. Compared to traditional bioprosthetic tissues, advanced polymers offer better resistance to calcification, reduced thrombogenicity, and tunable mechanical properties.

Mechanical loading constitutes a fundamental determinant in the process of bone remodeling. This modeling encompasses the incorporation of mechanical stimuli, the involvement of cellular and molecular constituents, as well as the utilization of sophisticated computational methodologies. Such an approach is imperative for forecasting bone behaviour across varying environmental conditions. In the present study, key findings from bone mechanobiology are reviewed, along with the possibility that Functionally Graded Materials (FGM) enhances osseointegration and lowers the stress-shielding effect during bone remodeling and compared to titanium, FGM improves periprosthetic bone remodeling. To summarise some of the most important findings from computational models of bone mechanobiology, explaining how modifications to the mechanical environment affect implant design, growth of bone, and bone response. The impact that changes related to the mechanical environment have on bone response is examined using computational models and methods such as surface microtopography to determine how an implant’s bone density has increased over time. This review focuses on the refinement of advanced simulation frameworks and their synergy with imaging technologies to strengthen model validation, ultimately resulting in better clinical outcomes in the context of bone health treatments.

Jaundice, common condition in newborns, is characterized by yellowing of the skin and eyes due to elevated levels of bilirubin in the blood. Timely detection and management of jaundice are crucial to prevent potential complications. Traditional jaundice assessment methods rely on visual inspection or invasive blood tests that are subjective and painful for infants, respectively. Although several automated methods for jaundice detection have been developed during the past few years, a limited number of reviews consolidating these developments have been presented till date, making it essential to systematically evaluate and present the existing advancements. This paper fills this gap by providing a thorough survey of automated methods for jaundice detection in neonates. The primary focus of the survey is to review the existing methodologies, techniques, and technologies used for neonatal jaundice detection. The key findings from the review indicate that image-based bilirubinometers and transcutaneous bilirubinometers are promising non-invasive alternatives, and provide a good trade-off between accuracy and ease of use. However, their effectiveness varies with factors like skin pigmentation, gestational age, and measurement site. Spectroscopic and biosensor-based techniques show high sensitivity but need further clinical validation. Despite advancements, several challenges including device calibration, large-scale validation, and regulatory barriers still haunt the researchers. Standardization, regulatory compliances, and seamless integration into healthcare workflows are the key hurdles to be addressed. By consolidating the current knowledge and discussing the challenges and opportunities in this field, this survey aims to contribute to the advancement of automatic jaundice detection and ultimately improve neonatal care.

nspired by bacterial motility mechanisms, Magnetic Helical Miniature Robots (MHMRs) exhibit promising applications in biomedical fields due to their efficient locomotion and compatibility with biological tissues. In this review, we systematically survey the basics of MHMRs, from propulsion mechanism, magnetization and control methods to biomedical applications, aiming to provide readers with an easily understandable overview and fundamental knowledge on implementing MHMRs. The MHMRs are actuated by rotating magnetic fields, achieving steering and rotation through magnetic torque, and converting rotation into forward motion through the helical structure. Magnetization methods for MHMRs are reviewed into three types: attaching magnets, magnetic coatings, and magnetic powder doping. Additionally, this review discusses the control methods for MHMRs, covering imaging techniques, path tracking control—including classical control algorithms and increasingly popular learning-based methods, and swarm control. Subsequently, a comprehensive survey is conducted on the biomedical applications of MHMRs in the treatment of vascular diseases, drug delivery, cell delivery, and their integration with catheters. We finally provide a perspective about future challenges in MHMR research, including enhancing functional design capabilities, developing swarm-assisted independent control mechanisms, refining in vivo imaging techniques, and ensuring robust biocompatibility for safe medical use.

To reduce structural modifications and minimize the impact on legged locomotion, this paper presents SlidBot, a quadruped robot with roller-skating capability, designed to improve movement efficiency on sloped surfaces. Two passive wheels without braking mechanisms are installed on the knee joint and lower leg of the robot. During quadruped movement, these wheels remain off the ground and therefore do not interfere with locomotion. The brakeless design reduces the number of components and simplifies the mechanical structure. When roller skating motion is required, simply adjust the leg posture to make the passive wheel on the lower leg contact the ground. The roller skating mode of the robot can be divided into two-legged roller skating and four-legged roller skating. During two-legged roller skating, the passive wheels of the hind legs support the ground, and the front legs execute backward propulsion to provide power for the robot’s movement. In four-legged roller skating, both the front and hind legs’ passive wheels contact the ground, resulting in a large supporting area and a low center of gravity, which helps maintain stability during high-speed movement and facilitates passage through low-lying environments. This paper outlines the robot design method, establishes a kinematic model, plans the gait and mode-switching method. Simulation and physical results indicate that the robot can perform stable diagonal trotting and roller skating movements. Moreover, on flat terrain, the roller skating motion is more energy-efficient than diagonal trotting, and on slopes, its energy and motion efficiency significantly surpasses that of the diagonal trot. This research offers novel insights for quadruped robot design and can considerably enhance the movement efficiency of quadruped robots on sloped terrains.

Existing control methods for humanoid robots, such as Model Predictive Control (MPC) and Reinforcement Learning (RL), generally lack the modeling and exploitation of rhythmic mechanisms. As a result, they struggle to balance stability, energy efficiency, and gait transition capability during typical rhythmic motions like walking and running. To address this limitation, we propose Walk2Run, a unified control framework inspired by biological rhythmicity. The method introduces control priors based on the frequency modulation observed in human walk–run transitions. Specifically, we extract rhythmic parameters from motion capture data to construct a Rhythm Generator grounded in Central Pattern Generator (CPG) principles, which guides the policy to produce speed-adaptive periodic motion. This rhythmic guidance is further integrated with a constrained reinforcement learning framework using barrier function optimization, enhancing training stability and output feasibility. Experimental results demonstrate that our method outperforms traditional approaches across multiple metrics, achieving more natural rhythmic motion with improved energy efficiency in medium- to high-speed scenarios, while also enhancing gait stability and adaptability to the robotic platform.

This paper presents a template-based control method for achieving diverse trotting motions in quadrupedal systems, with a focus on smooth transitions between walking trot, regular trot, and flying (running) trot. First, we extend the Clock Torque Actuated Spring-Loaded Inverted Pendulum (CT-SLIP) template to three dimensions, creating a comprehensive control framework. A template-based control strategy is then developed to compute joint torques for stable locomotion, along with a detailed approach for transitioning between gaits. To enable the flight phase in the running trot, a projectile motion model is incorporated into the template. For improved turning, we implement a yaw control method that rotates the swing foot plane to enhance stability, enabling higher turning rates while maintaining steady forward motion and balance. To further enhance locomotion stability and performance, a Whole-Body Controller (WBC) is integrated. The proposed method is implemented and rigorously evaluated in the MuJoCo simulator, with experiments testing gait transitions and disturbance rejection. Additionally, comparative studies assess the impacts of both swing foot plane rotation and the WBC on overall system performance. Furthermore, the approach is validated through real hardware experiments on Unitree GO1 quadrupedal robot, successfully demonstrating smooth gait transitions, stable locomotion, and practical applicability in real-world scenarios.

The problem of disturbance rejection in humanoid robots has been properly studied, with most prior work focusing on hip-ankle-stepping compliance control strategies or whole-body inverse dynamics control. This paper presents an adaptive disturbance rejection balance controller based on a Variable-inertia Centroidal Model Predictive Control (ViC-MPC) approach, designed to address both minor disturbances that affect standing balance and major disturbances requiring stepping adjustments. The controller also facilitates reliable balance recovery after stepping adjustments. The humanoid robot is modeled as a spatial variable-inertia ellipsoid, representing the distribution of centroidal dynamics, with the contact wrenches optimized in real-time through a customized MPC formulation. Inspired by capturability-based constraints, we propose an adaptive dynamic stability transition strategy. This strategy is activated based on the Retrospective Horizon Average Centroidal Velocity (RHACV) and the Capture Point (CP), ensuring effective stepping adjustments and disturbance rejection. With the torque-controlled humanoid robot BHR8P, extensive simulation and experimental results demonstrate the effectiveness of the proposed method, highlighting its capability to adapt to and recover from various disturbances with improved stability.

This paper proposes 2.5-dimensional polymer micromachined insect-mimetic wings based on a fluid-structure interaction (FSI) design concept that enables natural deformations like cambering and pitching under fluid forces. Instead of directly employing an analysis for the FSI, an iterative structural Design Window (DW) search is used to reduce the computational cost significantly. A DW search using the iterative method refines the initial design by addressing fabrication challenges and tuning it to meet manufacturability constraints. The successful fabrication and demonstration of the final design solution for a wing demonstrates the effectiveness of the iterative DW search based on the FSI design concept. Furthermore, a pixel model is introduced to convert an unstructured to a structured mesh for the FSI analysis to further reduce the computational cost. The camber and pitching error between the unstructured and structured meshes is minimized to achieve insect-like aerodynamic performance by adjusting the elastic moduli of center and root veins. Finally, an analysis for the FSI is conducted, based on the parameters obtained from the pixel model to evaluate the flight performance on the basis of the lift, camber, and pitching required by an actual insect to maneuver and hover.

In this paper, we proposed a compact, lightweight flapping actuation mechanism and a flight control mechanism for a twin-winged, tailless, hover-capable flapping robot named HiFly-Hummingbird, which has a total mass of 14.4 g and a wingspan of 18.8 cm. A four-bar linkage and gears set were adopted to convert the rotation motion of DC motor into flapping oscillation and amplify the flapping amplitude. As well as, a parallel coupled flight control mechanism was designed to implement the aerodynamic moments generation strategies. The proposed flapping actuation mechanism, with a mass of 2.95 g, has been validated to achieve a 168° amplitude at a frequency of 26 Hz with an asymmetrical stroke deviation of 3.5%, operating at a power consumption of 4.05 W. The parallel coupled control mechanism weights 2.14 g (including three servos). Benefit from the nonlinen inverse kinematics model of the parallel coupled control mechanism, the proposed control mechanism exhibits a roll motion range of ±?10° with an accuracy error of 0.8° and a pitch motion range of ±?12° with an accuracy error of 0.6°. The proposed mechanical systems are beneficial to lightweight design, manufacture and assemble under stringent size, weight and power (SWaP) constraints of flapping wing micro air vehicles (FW-MAVs), and possess favorable efficiency and accuracy. Relying on the hardware control circuit and feed-back attitude control algorithm, the robot hummingbird successfully achieved untethered lifting off and reached a maximum flight altitude of 4 m in several flight tests, demonstrating that the proposed mechanical designs of the flapping robot platform effectively enhances the miniaturization and light-weighting of the hummingbird-like FW-MAVs under the conditions of meeting the propulsion and control requirements for lifting off.

Backswimmers exhibit a high degree of mobility in water, and their different motion patterns have important implications for the design of micro-biomimetic underwater robots. This paper used three-dimensional high-speed cameras to extract the key points on the hind legs. The hind leg motion laws and the deformation laws of the setae were obtained in four motion patterns: rapid forward, cruising, in-motion turning, and in-place turning. The motion laws of each joint on the hind leg are modeled using a Fourier series. A kinematic model of hind legs was established based on the DH method, and the motion characteristics of hind legs under different motion patterns were analyzed. This paper provides basic data and theoretical models for micro-biomimetic robots.

Soft robots, as a modern gateway to unlocking the mysteries of underwater realms, present new complexities. Modeling their behavior when in contact with external forces, whether point-based or distributed, is a primary challenge due to the nature of soft bodies. To obtain a holistic view of the system’s behavior determining the governing dynamics is deemed necessary. This paper proposes a new technique to simulate the dynamic lateral undulation of a soft robotic fish with a cable-driven soft tail. By integrating the rigid finite element method with rigid-body robotics, the model represents the undulation of a finite number of rigid elements connected through a set of torsional spring and damper. Instead of directly modeling external forces, we substitute equivalent joint torques into the system dynamics, allowing us to consider external effects without complicating the model. The resulting model yields valuable insights into the system’s behavior, including propulsive and lateral forces. A comparison with experimental results shows strong agreement, with a tip amplitude error of 10% at 0.8 Hz, 5.25% at 1.6 Hz and 2.54% at 2.2 Hz flapping frequency. These findings illuminate the influence of lateral undulation on the overall dynamics, paving the way for fully autonomous robotic fish.

Inspired by the crucial role of the tail in crocodile locomotion, we propose a novel rigid-flexible coupled tail structure design. The tail design reduces the number of required actuators, enables undulatory propulsion in swimming, and provides additional support during terrestrial crawling. However, when the tail lifts off the ground during land crawling, its flexible underactuated structure tends to oscillate randomly due to minimal damping. These oscillations impart disruptive reaction torques to the body, critically impairing locomotion stability. To tackle this issue, we employed the standard Denavit-Hartenberg (DH) method and Newton-Euler equations to formulate a rigid-flexible coupled dynamic model for the tail, in which distributed elastic forces are embedded as internal forces in the force balance equations. Based on this model, we propose an oscillation suppression strategy based on an energy-optimized Nonlinear Model Predictive Controller (NMPC) with a single joint torque as the control input. This controller solves a constrained multi-objective optimization problem to effectively suppress the underactuated oscillations of the tail. Finally, experimental comparisons validate the accuracy of the dynamic model, and simulations based on this model substantiate the effectiveness of the oscillation suppression strategy.

Gait coordination in lower limbs plays a critical role in maintaining stability of the human body during walking. For transfemoral amputees, the absence of limbs disrupts this coordination, reducing prosthesis control accuracy. Hip-knee coordination mapping offers a feasible solution for lower-limb prosthesis control, involving the generation of a reference trajectory for the knee joint by leveraging information from the hip. However, current reference trajectories are usually derived from static models, which cannot generate reference trajectories robustly when dealing with perturbations. Therefore, this paper introduces a time-dependent model based on the Delayed Feedback Reservoir (DFR) for hip-knee coordination in lower-limb prosthetic control. Experimental results show that DFR outperforms classical gait planning approaches when facing perturbations, achieving a 20% lower Root Mean Square Error (RMSE) and reducing residuals by up to 18.14 degrees. This research contributes to understanding gait mapping approaches and emphasizes the potential of time-dependent models for robust and strong lower-limb prosthetic control. The discovery provides a novel way to enhance the perturbation adaptability of prosthetic control.

This paper presents an innovative design of a Bionic Powered Ankle Prosthesis (BPAP) utilizing a muscle recruitment mechanism-inspired clutch, aimed at achieving biomimetic simulation of ankle muscle function. To address the varying stiffness requirements of the prosthesis across different gaits, the clutch dynamically switch between different rope bundle combinations. The mechanical characteristics during the load traction process of the selected flexible ropes are also studied. A mathematical model of the series elastic drive system is established. The optimization is carried out with the minimum peak power required by the motor as the optimization goal. The experimental results show that by applying a clutch with human muscle recruitment mechanism, the proposed prosthesis can achieve better energy efficiency at different speeds.

This study examines the locomotor biomechanics of the giant panda (Ailuropoda melanoleuca), a species of profound ecological and evolutionary significance. Despite its characteristic slow movement and non-sprinting locomotion, the panda has endured for over 8 million years, offering a unique perspective on the evolution of mammalian locomotion. Through comprehensive gait analysis and ground reaction force measurements, we investigate the functional distinctions between the forelimbs and hind limbs, highlighting the biomechanical underpinnings of its plantigrade locomotion. Our findings reveal how the panda’s limb structure and movement patterns contribute to energy efficiency, particularly during slow locomotion. By comparing these results with those of other large mammals, such as grizzly bears (Ursus arcto), we explore the role of limb mechanics in energy conservation. Additionally, we assess the locomotor performance of pandas across different age groups, shedding light on the maturation of locomotor abilities and the potential adaptive significance of their slow, deliberate movement. This research offers novel insights into the biomechanics of panda locomotion and its evolutionary implications, furthering our understanding of the functional evolution of bear species and informing conservation strategies for this iconic species.

This paper presents a fully integrated platform that leverages hardware, software, and specially formulated O/W emulsions to provide localized mechanical stimuli for manipulating cellular behaviors. The system comprises a hexapole magnetic tweezer device, position-based current calculation software, and biocompatible micro-robots embedded with magnetic microbeads for vibration-driven force generation. High-permeability materials in the tweezer tips, combined with fast-response current regulators, enable rapid and precise force control, ensuring uniform and continuous mechanical stimuli in the pico-newton range. Closed-loop control algorithms automatically adjust coil currents based on the micro-robot’s position, thereby compensating for potential hysteresis and optimizing system stability. Experimental results demonstrate stable operation at frequencies up to 4 Hz, with a theoretical possibility of extending to 8 Hz under a 2 A current, delivering mean forces around 20 pN at 1 Hz with a 57 μm emulsion. Additionally, the platform allows fine-tuning of forces by altering emulsion size or bead concentrations, thereby providing researchers with a versatile approach to study apoptosis, proliferation, and the other mechanotransduction pathways. The biodegradable and cell-friendly emulsion serves as a protective membrane for the magnetic microbeads while effectively mimicking the mechanical properties of living cells. By bridging the gap between precise motion control and continuous vibrational force application, this novel platform offers a promising tool for advancing targeted cellular studies, fostering insights into tissue engineering, and improving cancer therapies.

Intraoral scanning has become integral to digital workflows in dental implantology, offering a more efficient and comfortable alternative to conventional impression techniques. For complete edentulism, accurate scanning is crucial to successful full-arch dental implant rehabilitation. However, the absence of well-defined anatomical landmarks can lead to cumulative errors during merging sequential scans, often surpassing acceptable thresholds. Current mitigation strategies rely on manual adjustments in Computer-Aided Design (CAD) software, a time-intensive process that depends heavily on the operator’s expertise. This study presents a novel segment-match-correct process automation workflow to enhance full-arch intraoral scans’ positioning accuracy and efficiency. By leveraging 3D registration algorithms, the proposed method improves implant positioning accuracy while significantly reducing manual labor. To assess the robustness of this workflow, we simulated four types of noise to evaluate their impact on scanning errors. Our findings demonstrate that the process automation workflow reduces dentist workload from 5-8 minutes per scan to less than 1 min (about 57 seconds) while achieving a lower linear error of 45.16 ± 23.76 \(\upmu \hbox {m}\), outperforming traditional scanning methods. We could replicate linear and angular deviations observed in real-world scans by simulating cumulative errors. This workflow improves the accuracy and efficiency of complete-arch implant rehabilitation and provides a practical solution to reduce cumulative scanning errors. Additionally, the noise simulations offer valuable insights into the origins of these errors, further optimizing IntraOral Scanner (IOS) performance.

A mass on-line control type impact inertial piezoelectric actuator with a bionic wheat structure is proposed in this work. Inspired by the anisotropic friction mechanism of natural wheat awns, a bioinspired mechanism is used to achieve the designed driving strategy based on the asymmetric-mass control method that mimics bidirectional motion characteristics of wheat awn. A lumped parameter theoretical model is established, and the numerical simulation results have verified the designed bionic working principle and revealed the key system parameters. Experimental results show that the prototype has the bi-directional motion ability inherited from anisotropic friction of wheat awn, with theoretically infinite stroke and can easily obtain the required step displacement and velocity by conveniently adjusting the voltage. It can achieve a resolution of 0.7 μm, and a forward and backward maximum velocity of 12.7 μm/s and 90.72 μm/s respectively. In addition, the actuator also has the advantages of good stability, control convenience, and ease of integration. Besides, the actuator is capable of adjusting motion direction via voltage, providing a significant advantage in precise bidirectional control. This study confirms that the proposed mass on-line control type actuator embodies a successful bionic translation from plant morphology to precision engineering, and adds a new member to the family of impact inertial piezoelectric actuators, which completes the last piece of the puzzle for the impact inertial driving mechanism. It promotes the further development of inertial precision driving and control technology and is expected to expand the scope of application. Future work will focus on optimizing performance and developing applications.

Inspired by the aquatic-adapted pit structures of the Cybister beetles that enable high-speed swimming, this study employs warp-knitted technology to fabricate drag-reduction swimwear textiles. Eight distinct fabric morphologies were produced, and a self-developed high-precision dynamic drag measurement device was used to systematically analyze the mechanisms underlying the drag-reduction performance of these biomimetic pit structures. The device incorporates a servomotor, ball screw linkage, and high-precision tension sensor, enabling real-time and accurate detection of fluid drag forces. It effectively overcomes the limitations of traditional indirect measurement methods, including dynamic response lag and insufficient accuracy. Experimental results demonstrate that the hydrophobic small-pit fabric (4^#) achieves an 84% drag reduction at 400 mm/s, outperforming the control sample (warp-knitted fabric 7^#). This significant reduction is attributed to the Cassie state established on the hydrophobic surface, which substantially decreases viscous drag and the microvortices generated by the pit structures, which delay flow separation and effectively minimize pressure drag. Furthermore, small-pit fabrics demonstrate a drag reduction rate 26% to 50% higher than that of large-pit structures, highlighting the critical importance of matching the pit scale to the thickness of the near-wall viscous sublayer for optimal drag reduction. This study establishes a theoretical foundation for the biomimetic design of high-performance drag-reduction swimsuits. The developed drag-measuring device also provides a standardized experimental platform for hydrodynamic studies of flexible materials, supporting a shift from empirical design methodologies to theory-driven approaches in drag-reduction technology and exhibiting significant potential for future advancements.

Rough micro-nano structures and low surface energy chemical compositions are two essential conditions for constructing superhydrophobic surfaces. However, for low surface tension liquids, which are extremely easy to spread and wet on solid surfaces, the design of cantilever structures with internal concavity is the third important parameter to achieve their superomniphobic, whose negative geometrical inflections can effectively lock the solid-liquid-gas three phase contact line, maximize the upward component of capillary force of the suspended droplets, and provide a larger breakthrough pressure for the structured surfaces to avoid the low surface tension liquids from collapsing on the solid surfaces. Based on this, microfabrication was used to prepare mushroom structured surfaces. By precisely controlling the etching parameters, mushroom structures with diameter of 3 μm and circular centre distance of 8 μm were prepared. The mushroom structure not only achieves super-repellent from high surface tension water (72.8 mN/m) to ultra-low surface tension perfluorohexane (10 mN/m), but also achieves complete rebound even to the high-speed impact of liquid droplets, including water droplets with an impact height of 7.9 cm and perfluorohexane with a height of 3 mm. This fabrication technology helps to build a robust superomniphobic surface for use in harsh environments such as high-speed droplet impacts.

The solar interfacial evaporation has a broad application prospect in the fields of steam generation and seawater desalination to deal with the global shortage of fresh-water resources. Bamboo is a great material for solar interface evaporators because of its low thermal conductivity and inherent micro-channel porous structure. In this paper, a novel bamboo-based solar interface evaporator with a bionic ripple wave surface structure has been proposed. The subsequent evaporation experiments have been conducted to investigate the salt resistance, stability and water absorption of the bionic ripple bamboo based solar interface evaporator. The results have exhibited that the bamboo's water absorption has been enhanced after carbonization modification. Besides, it should be pointed out that this bamboo-based evaporator’s evaporation rate has dropped during the prolonged simulated seawater evaporation experiment, yet it remained fairly consistent at approximately 1.626 kg·m^?2·h^?1. The appearance for this experimental phenomenon is the decrease of the floatability of the evaporator constricted by the stored water body absorbed by the evaporator and the deposition of NaCl crystals at the photothermal interface. Besides, compared with the plate-structure evaporator, the salt deposition in the evaporator equipped with the bionic ripple wave surface structure is greatly improved. In regard to its advantages in low cost, environmental friendliness, good salt tolerance and high evaporation rate, the bamboo-based solar interface evaporator with a bionic ripple wave surface structure can provide a potential solution to the global problem of fresh-water shortage.

Marine biofouling is the undesired attachment and formation of marine organisms on surfaces, which adversely affects ship maintenance, economic costs, and ecosystem health. Despite remarkable advancements in antifouling coatings, developing formulations that simultaneously achieve environmental benign and high antifouling performances remains a critical challenge. Herein, drawing inspiration from the natural antifouling mechanisms including the superhydrophobicity of lotus leaves and biochemical defense of coral mucus, we developed a Superhydrophobic Antifouling Coating (SHAC) incorporating coral mucus-derived agents. This biomimetic design synergistically integrates physical anti-adhesion of superhydrophobic surfaces with chemical repellency of antifouling agents, yielding outstanding antifouling performance. The SHAC demonstrates remarkable durability (withstanding 100 abrasion cycles), sustained superhydrophobicity (contact angle?>?150°), and outstanding antifouling efficacy (92% bacterial and 96% algal inhibition). Marine field tests demonstrate significant reduction in fouling organism attachment over 30 days. Our work presents an eco-friendly and high-performance solution to marine biofouling, bridging the gap between sustainability and effectiveness in antifouling technology.

Self-healing (SH) polymer composites are a transformative achievement in polymer material technology that offers significant potential to extend the lifespan and reliability of materials. This work presents a novel approach to developing a hybrid natural-synthetic reinforced polymer composite with SH behavior using urea-free, non-toxic, environment-friendly material encapsulating resin, and hardener within a multicavity microcapsule (MC). This MC offers multiple healing because of its multicavity structure. These Xerogel MCs are integrated into hybrid bamboo/recycled glass fiber reinforced epoxy composite (25 wt% and 40 wt%) and were evaluated for their flexural strength, healing efficiency, moisture absorption, and thermal behavior. The results demonstrated that the composite containing 40 wt% exhibited the highest initial flexural strength and modulus retention after multiple healing cycles, approaching 80.67% and 61.34% respectively at 1st and 2nd cycles of healing efficiency. The behavior of self-healing hybrid composites (SHHC) in different environmental conditions was also investigated. Thermal Analysis TGA and DTA done on hybrid and other SH composites. Scanning electron microscopy shows the surface morphology of Xerogel MCs before and after damage, composite fractured surface, and how Healing Agent (HA) gets released and acquires surface after fracture. To ensure functional groups and chemical reactions between each component of the composite, FTIR analysis confirmed the successful encapsulation of HA inside MC.

Tissue engineering holds promise in developing materials for biological applications, such as bone tissue repair. This study focuses on bioabsorbable and biocompatible polymers like Poly(L-lactic acid) (PLLA), Polyurethane (PU), and Polycaprolactone (PCL), along with nanohydroxyapatite (nHA), an essential osteoconductive ceramic. The main objective was the development and characterization of scaffolds obtained by Rotary Jet Spinning (RJS) using PLLA, PU, and PCL incorporated with nHA, for bone-related applications. The resulting scaffolds exhibited uniform fiber morphology and a rough surface, ideal for effective bone-tissue interaction. The crystallinity indicated the scaffolds’ bioactivity by apatite deposition in simulated body fluid. In addition, in vitro biological assays using preosteoblastic cells showed the biocompatibility of cells based on cell viability and adhesion parameters on the scaffolds. The results underscore the capacity of scaffolds incorporating nHA to promote both cell proliferation and osteoconduction, which are key elements essential for achieving effective bone regeneration.

Hemoglobin A1c (HbA1c), a key biomarker for long-term glucose regulation, is essential for diagnosing and managing diabetes mellitus. However, conventional HbA1c detection methods often suffer from limited sensitivity, narrow detection ranges, slow response times, and poor long-term stability. In this study, we developed a high-performance amperometric biosensor for the selective detection of Fructosyl Valine (FV), a model compound for HbA1c, by immobilizing Fructosyl Amino Acid Oxidase (FAAO) onto a glassy carbon electrode modified with electrospun polyaniline/polyindole-Mn₂O₃ nanofibers. Operating at an applied potential of 0.27 V versus Ag/AgCl, the biosensor achieved a rapid detection time of 2 s for FV concentrations up to 50 μM, with a signal-to-noise ratio of 3. Under optimized conditions (pH 7.0 and 35 °C), the biosensor exhibited a wide linear detection range from 0.1 to 3 mM and a high sensitivity of 38.42 μA/mM. Importantly, the sensor retained approximately 70% of its initial activity after 193 days of storage at 4 °C, demonstrating excellent long-term stability. These results suggest that the FAAO/polyaniline/polyindole-Mn₂O₃ nanocomposite-based biosensor offers a promising platform for sensitive, rapid, and durable detection of HbA1c, providing significant potential for improving diabetes monitoring and management.

A human semicircular canal (HSC) with “cupulolithiasis” (HSCC) causes abnormal perception and vertigo. Based on 3D printing technology and target tracking technology, models of a visualized bionic semicircular canal with cupulolithiasis (BSCC) were generated. The model, with careful scaling parameters, similar biomechanical responses to the vestibular-ocular reflex (VOR), and a similarly long time constant to the HSC, allows us to study the mechanics of the HSCC. The static experiments revealed that the bionic cupula of the BSCC continued to shift due to the effect of the gravity of the otolith after rotation stopped. The frequency broadband experiment indicated that the gain of the BSCC decreased as the phase difference increased, and the increase in otolith mass aggravated this trend. BSCCs can be used as a bionic model to study the pathology of human semicircular canal-related diseases and may promote the development of treatments.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has triggered a global health crisis, necessitating accurate predictive models to forecast disease severity and aid in clinical decision-making. This study introduces an innovative machine learning approach, the bDWPLO-FKNN model, designed to predict the severity of COVID-19 pneumonia in patients. The model incorporates the Differential Weibull Polar Lights Optimizer (DWPLO), an enhancement of the Polar Lights Optimizer (PLO) with the differential evolution operator and the Weibull flight operator, to perform effective feature selection. The DWPLO’s performance was rigorously tested against IEEE CEC 2017 benchmark functions, demonstrating its robust optimization capabilities. The binary version of DWPLO (bDWPLO) was then integrated with the Fuzzy K-Nearest Neighbors (FKNN) algorithm to form the predictive model. Using a dataset from the People’s Hospital Affiliated with Ningbo University, the model was trained to identify patients at risk of developing severe pneumonia due to COVID-19. The bDWPLO-FKNN model exhibited exceptional predictive accuracy, with an accuracy of 84.036% and a specificity of 88.564%. The analysis revealed key predictors, including albumin, albumin to globulin ratio, lactate dehydrogenase, urea nitrogen, gamma-glutamyl transferase, and inorganic phosphorus, which were significantly associated with disease severity. The integration of DWPLO with FKNN not only enhances feature selection but also bolsters the model’s predictive power, providing a valuable tool for clinicians to assess patient risk and allocate healthcare resources effectively during the COVID-19 pandemic.

Segmenting skin lesions is critical for early skin cancer detection. Existing CNN and Transformer-based methods face challenges such as high computational complexity and limited adaptability to variations in lesion sizes. To overcome these limitations, we introduce MSAMamba-UNet, a lightweight model that integrates two novel architectures: Multi-Scale Mamba (MSMamba) and Adaptive Dynamic Gating Block (ADGB). MSMamba utilizes multi-scale decomposition and a parallel hierarchical structure to enhance the delineation of irregular lesion boundaries and sensitivity to small targets. ADGB dynamically selects convolutional kernels with varying receptive fields based on input features, improving the model’s capacity to accommodate diverse lesion textures and scales. Additionally, we introduce a Mix Attention Fusion Block (MAF) to enhance shallow feature representation by integrating parallel channel and pixel attention mechanisms. Extensive evaluation of MSAMamba-UNet on the ISIC 2016, ISIC 2017, and ISIC 2018 datasets demonstrates competitive segmentation accuracy with only 0.056 M parameters and 0.069 GFLOPs. Our experiments revealed that MSAMamba-UNet achieved IoU scores of 85.53%, 85.47%, and 82.22%, as well as DSC scores of 92.20%, 92.17%, and 90.24%, respectively. These results underscore the lightweight design and effectiveness of MSAMamba-UNet.

Pulmonary embolism (PE) can range from minor, asymptomatic blood clots to life-threatening emboli capable of obstructing pulmonary arteries, potentially leading to cardiac arrest and fatal outcomes. Due to this significant mortality risk, risk stratification is essential following PE diagnosis to guide appropriate therapeutic intervention. This study proposes a machine learning-based methodology for PE risk stratification, utilizing clinical data from a cohort of 139 patients. The predictive framework integrates an enhanced binary Honey Badger Algorithm (BCCHBA) with the K-Nearest Neighbor (KNN) classifier. To comprehensively evaluate the performance of the core optimization algorithm (CCHBA), a series of benchmark function tests were conducted. Furthermore, diagnostic validation tests were performed using real-world PE patient data collected from medical facilities, demonstrating the clinical significance and practical utility of the BCCHBA-KNN system. Analysis revealed the critical importance of specific indicators, including neutrophil percentage (NEUT%), systolic blood pressure (SBP), oxygen saturation (SaO₂%), white blood cell count (WBC), and syncope. The classification results demonstrated exceptional performance, with the prediction model achieving 100% sensitivity and 99.09% accuracy. This approach holds promise as a novel and accurate method for assessing PE severity.