Journal of Bionic Engineering

Bioinspired Surface Texturing: From Natural Prototypes to Advanced Applications and Future Perspectives

Haowen Qin, Xiaoliang Shi, Chaohua Wu, Yuxuan Chen, Haobing Hu, Qipeng Huang

Journal of Bionic Engineering. 2026, 23 (4): 1991-2023. DOI: 10.1007/s42235-026-00915-9

Abstract ( 7 )

Friction, wear, and lubrication are pivotal to the efficiency, durability, and sustainability of mechanical systems. Surface texturing serves as a key passive strategy in tribology to modulate interfacial behavior, yet conventional designs often lack versatility and adaptability. Biomimetics offers profound solutions by drawing inspiration from nature’s optimized surfaces, such as the drag-reducing denticles of shark skin, the self-cleaning hierarchical structures of lotus leaves, the damage-tolerant “brick-and-mortar” architecture of nacre, and the water-harvesting patterns of desert beetles. This comprehensive review synthesizes recent advances in bioinspired surface textures, elucidating their underlying physical mechanisms—including turbulent flow control, superhydrophobicity, energy dissipation, and environmental responsiveness. It systematically navigates the entire development chain: from bio-prototype abstraction and physics-based design principles to advanced fabrication techniques such as laser processing, additive manufacturing and rigorous performance evaluation via simulation and experiment. Applications across diverse sectors—mechanical engineering, aerospace, biomedicine, and microsystems—are critically examined, demonstrating significant benefits in friction reduction, wear resistance, and functional integration. Finally, the review outlines future challenges and promising directions, such as smart responsive textures and data-driven inverse design, thereby charting an interdisciplinary research path for developing the next generation of intelligent, multifunctional bioinspired surfaces.

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Decoding Animal Skin for Engineering: Mechanical Function, Morphology, and Emerging Bioinspired Applications

Spondona Kashyap, Arun Y. Patil, Mridusmita Roy Choudhury, H. M. Yeshwanth, Navin Kumar, Manasa Nune, Kirthanashri S. Vasanthan

Journal of Bionic Engineering. 2026, 23 (4): 2024-2044. DOI: 10.1007/s42235-026-00913-x

Abstract ( 3 )

Today, as man becoming more mechanized so the utilization of animals to his social connect is depleting and these animals are diminishing out of the society such as cow, sheep and goat. However, these are preferred in breeding for feedstock such as slaughterhouses who consider it for consumption rather than as part of the family member. Animal skin, as a dynamic and multifunctional biological interface that exhibits considerable morphological and functional diversity among terrestrial, aquatic, and avian species. This review analyzes the unique mechanical properties, surface morphologies, and functional behaviors of skin in many contexts, such as moisture regulation, thermal control, drag reduction, camouflage, and sensory perception. The review connects biological discoveries with bioinspired technologies like electronic skin, synthetic leather, and self-healing coatings by looking at scientific articles, patents, and commercial products. Advanced characterization techniques and 3D bioprinting have improved the ability to copy complex skin structures for use in medicine, industry, and the environment. The review shows that materials inspired by skin could help us use less plastic and cut carbon emissions, which would help the goals of the circular economy. In addition to this, the study emphasizes on translational work of raw material extracted from slaughterhouses into an useful product with Technology Readiness Level (TRL) understanding from end to end. Finally, this article concludes by identifying the gaps in current research and highlighting the potential future opportunities for multidisciplinary innovation based on the ideas of natural design.

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Design of Water Hydraulic-driven Soft Joint and Stiffness-posture Integrated Control for Underwater Snake Robots

Fei Gao, Yinglong Chen, Cheng Zhou, Xinyu Yang

Journal of Bionic Engineering. 2026, 23 (4): 2045-2064. DOI: 10.1007/s42235-026-00907-9

Abstract ( 5 )

To address the poor compliance and vulnerability to collision damage in traditional rigid underwater snake robots, a water hydraulic-driven variable-stiffness soft joint is proposed to enhance their safety and compliant operation capabilities. An actuator module was designed based on the antagonistic mechanism between a fluidic actuator and a mechanical spring. Furthermore, a dedicated hydraulic actuation system was developed to satisfy the strict volume and power consumption constraints of the soft joint. Based on geometric mechanics, a mathematical model of the soft joint was established, utilizing an iterative method to decouple its stiffness and posture. A series of experiments were conducted to validate the proposed design and theoretical model. The results indicate that the soft joint achieves a maximum bending angle of 33.8°. The maximum and minimum bending stiffness values are 350 N·m/rad and 28.1 N·m/rad, respectively, demonstrating a significant stiffness variation ratio of 12.55. This soft joint not only meets the application requirements of underwater snake robots but also exhibits potential for other flexible robotic manipulators with stringent power and spatial limitations. Finally, the proposed stiffness-posture integrated control strategy achieves the decoupled regulation of the joint's stiffness and posture, offering new insights into the compliant operation of underwater flexible robots.

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Biomimetic Stable Walking of Bipedal Robots Based on 3D Divergent Component of Motion

Guanqun Chen, Teng Zhang, Weicong Zheng, Wenbang Chen, Lianzhao Zhang, Fusheng Zha, Wei Guo, Lining Sun

Journal of Bionic Engineering. 2026, 23 (4): 2065-2086. DOI: 10.1007/s42235-026-00905-x

Abstract ( 8 )

Robust dynamic walking of humanoid robots is the foundation for their application in complex scenarios. However, the dynamic movement of humanoid robots on complex terrains (such as uneven ground and stairs) is confronted with challenges of balance maintenance, contact constraints and high computational costs. This paper presents a complete planning and control framework to achieve dynamic stable walking of humanoid robots on complex terrains. Based on biological research, the upper-level planner employs a unified MPC framework of 3D Divergent Component of Motion (DCM) for bio-inspired gait generation of humanoid robots, simulating human walking processes in complex terrains. The lowerlevel controller utilizes a Whole-Body Control (WBC) scheme based on task-space inverse dynamics to optimize the joint torques of the robot. The proposed framework enhances the dynamic performance and robustness of humanoid gait control through decoupled optimization and adaptive frequency adjustment, while maintaining simple yet efficient dimensional scalability. The proposed MPC framework decouples the optimization into three directions. During level-ground walking where the Center of Mass (CoM) height remains constant, the Quadratic Programming (QP) of z-direction is excluded from the optimization process. The approach simplifies the optimization process and reduces computational costs while ensuring real-time system performance. When the robot traverses uneven terrain or climbs stairs, QP-z dynamically optimizes the CoM height in real-time, adaptively adjusts the natural frequency of DCM, and outputs the results to the QP-x and QP-y for coordinated control. Simulation and experiments with humanoid robot ’Dexbot’ demonstrate that proposed control framework significantly enhances the adaptability of humanoid robots in complex terrains, reduces the cost of controller adjustments during terrain transitions, and enables stable dynamic walking across various terrains.

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A Rat Brain-Inspired Model for Quadruped Robot Localization Using Path Integration and Visual Landmarks

Yishen Liao, Shufei Fu, Hejie Yu, Naigong Yu

Journal of Bionic Engineering. 2026, 23 (4): 2087-2101. DOI: 10.1007/s42235-026-00924-8

Abstract ( 6 )

Exceptional rodent navigation relies on the integration of sensory and proprioceptive information within the entorhinalhippocampal system. Inspired by neurophysiological studies, a brain-like localization model based on the rat brain’s cognitive mechanism is proposed. To enhance biomimicry, the quadruped robot is used to simulate the physical structure of a rat. Firstly, a path integral model based on a quadrupedal structure is constructed to solve for the robot’s position in space. Then an entorhinal-hippocampal neurocomputing model is constructed to further process the position information, so as to realize the neural representation of path integral results. Finally, the cumulative error in the movement process is corrected using landmark information. To verify the model, several experiments have been carried out in the 3-D simulation environment. Experimental results show that the proposed method not only can well simulate the process of path integration using proprioception in rats, but also can show excellent localization performance. Quantitatively, the quadruped path integration model maintains a low average trajectory error of 0.2132 m. The integration of visual landmarks further reduces this error by 90.06% and boosts the navigation convergence times by 69.23%. These research advance the research on environmental cognition and navigation in bionic robots.

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Humanoid Robot Jumping Control Based on Centroid Trajectory and Sparse Key Limb Pose Guidance

Xiangji Wang, Teng Zhang, Sen Zhang, Fusheng Zha, Wei Guo, Lining Sun

Journal of Bionic Engineering. 2026, 23 (4): 2102-2118. DOI: 10.1007/s42235-026-00931-9

Abstract ( 5 )

Humanoid robot jumping control is a challenging problem due to its strong nonlinearity and randomness. Although precise Trajectory Optimization (TO) can provide high-quality reference trajectories, its computational cost is prohibitively high, and it is difficult to adapt flexibly to the robot's actual state. In this paper, we propose a jump control framework guided by the centroid and sparse key limb pose trajectories, aimed at enhancing the real-time performance of TO for humanoid robot and achieving high-performance whole-body coordination control. Firstly, the complex dynamics model of the humanoid robot is simplified, and a Single Rigid Body Model (SRBM) is constructed. Using the SRBM, the time series and centroid trajectory of the jumping motion are optimized by incorporating the target foot landing position and complete foot contact force constraints. Next, the sparse whole-body motion trajectory is solved based on the centroid trajectory, extracting the pose of key limbs as the guiding trajectory for the humanoid robot during the jumping motion control process. Finally, based on the dynamic characteristics of each phase in the jumping motion, a Constraint-based Hierarchical Inverse Dynamics Whole Body Controller (CHID-WBC) is constructed to control the jumping motion of the humanoid robot. Simulation and experimental results demonstrate that the proposed control framework effectively enables whole-body coordinated jumping motion of the humanoid robot G1 across multiple operating conditions, validating its effectiveness.

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Rigid-flexible Coupling Wing Design in the Bio-inspired Butterfly-like Micro-Air Vehicles (MAV)

Chen-Chen Li, Ning Dai, Qing-Yun Bai, Hong-Qing Dai, Ce Guo, Stanislav Gorb

Journal of Bionic Engineering. 2026, 23 (4): 2119-2137. DOI: 10.1007/s42235-026-00902-0

Abstract ( 7 )

The insect wing is a rigid-flexible coupled system composed of veins and membranes, exhibiting significant directional variations in stiffness distribution. Calculating force and deformation under dynamic conditions is challenging but remains critical for aerodynamic optimization of flapping-wing aircraft. This study developed a rigid-flexible coupled wing design method inspired by butterfly wings, integrating parametric modeling, anisotropic stiffness optimization, and bidirectional Fluid-structure Interaction (FSI) simulation to quantify unsteady interactions and enable passive deformation for enhanced performance. A parametric model of butterfly wings was established using Bézier curves to describe diverse shape characteristics across butterfly families, linking biological morphology to aerodynamic properties. By defining and adjusting the spanwise and chordwise stiffness distribution of the wings, we analyzed the deformation and forces through simulation, revealing how adaptive bending guide Leading-edge Vortex (LEV) and improve thrust. We found that for a monarch butterfly with a 50 mm span, maintaining a chordwise stiffness of 0.154 N/m and a spanwise stiffness of 1.792 N/m results in excellent aerodynamic efficiency, generating thrust 6 times the wing’s weight—outperforming rigid wings by 34%. Based on this, a bionic butterfly MAV was designed and manufactured, achieving a thrust 1.3 times its own weight in free flight at 3.4 m/s, demonstrating the feasibility and scalability of this design method for robotic applications.

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An Energy-Efficient Propulsion Control Strategy Based on Crest Stay for a Bioinspired Manta Ray Robot

Shumin Ma, Yu Xie, Zhonghua Yin, Yingming Yu, Cheng Xing, Yonghui Cao, Qiaogao Huang, Yong Cao

Journal of Bionic Engineering. 2026, 23 (4): 2138-2153. DOI: 10.1007/s42235-026-00904-y

Abstract ( 6 )

Continuous flapping propulsion in bioinspired underwater robots often leads to high energy consumption and limited endurance during low-speed cruising. Inspired by the crest stay behavior of manta rays, this study proposes a propulsion method that combines a flexible fin design with a control strategy. First, we develop a two-degree-of-freedom flexible pectoral fin that reproduces distributed flexibility and supports independent flapping and twisting. Then, we propose an improved central pattern generator model with an explicit crest stay mechanism. The mechanism is embedded into the phase oscillator through a crest stay condition and equation. Next, we conduct systematic pool experiments to evaluate how crest stay time affects performance under different flapping amplitudes and frequencies. The results show that an appropriate crest stay time increases thrust and reduces energy consumption compared with continuous flapping. Moreover, lake experiments with a full-scale manta ray robot quantitatively validate the energy-efficient effect of the crest stay mechanism, achieving a 31.6% reduction in energy consumption without compromising cruising speed. This study is the first to provide practical evidence of crest stay effectiveness in large scale robots, highlighting its importance for the energy-efficient design of long-endurance underwater vehicles.

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Design and Performance Analysis of an Innovative Closed-Chain-Based Compliant Robotic Leg

Kunting Zhang, Yichen Zhang, Xunju Ma, Yan-an Yao, Jianxu Wu

Journal of Bionic Engineering. 2026, 23 (4): 2154-2169. DOI: 10.1007/s42235-026-00908-8

Abstract ( 6 )

This paper aims to establish a design framework for an innovative Closed-Chain-Based Compliant Robotic Leg (CCRL) that effectively reduces inter-joint interference while enhancing impact tolerance. The decoupling of leg extension-retraction motions from swing motions is achieved through a closed-chain-based mechanism, while the incorporation of elastic elements and passive prismatic joints contributes to its impact-mitigation capabilities. A comprehensive analysis of the closed-chain constraint Jacobian, which includes both actively actuated joints and passive joints, along with a stiffness model, is methodically conducted. The simulation analyses are performed to assess the benefits of the CCRL architecture, highlighting improvements in jump height, reductions in peak torque during jumping, and enhanced joint coordination that facilitates a dynamic and interference-free gait. An experimental prototype is also appropriately developed, and vertical jumping tests are conducted to validate the practicality of the proposed design and its theoretical analysis.

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Introducing Environmental Constraints to Grasping Strategies for Paper-like Flexible Materials Using a Soft Gripper

Yi Dong, Yang Li, Jinjun Duan, Zhendong Dai

Journal of Bionic Engineering. 2026, 23 (4): 2170-2185. DOI: 10.1007/s42235-026-00933-7

Abstract ( 6 )

Robotic manipulation of flexible objects is widely required in both industrial and service applications. Among such objects, paper-like materials exhibit distinct mechanical characteristics compared to cloth, being more sensitive to compressive stress, where minor variations in physical properties can significantly affect grasping. This study systematically investigates grasping strategies for paper-like materials using a universal soft gripper by exploiting environmental constraints. Based on manipulation primitives employed in existing grasping strategies, we proposed systematic grasping strategies for flexible materials by exploiting environmental constraints and analyzed their mechanical and kinematic models. To investigate the influence of materials and working conditions on grasping, an evaluation system for measuring grasping force and success rate was defined and experimentally evaluated. Finally, we summarized the specific workspaces and characteristics of different strategies that can satisfy various task requirements and lead to potential applications in household service robots for grasping planar flexible objects.

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HiLoMa: Hierarchical Loco-Manipulation Control for Quadruped Manipulators with Coordinated Planning and Dynamic Coupling Compensation

Lianzhao Zhang, Guanqun Chen, Fusheng Zha, Wei Guo, Yapeng Shi

Journal of Bionic Engineering. 2026, 23 (4): 2186-2203. DOI: 10.1007/s42235-026-00935-5

Abstract ( 7 )

Quadruped manipulators offer promising capabilities for complex tasks in unstructured environments by combining terrain traversability with dexterous manipulation. However, motion control is challenging because arm-base dynamic coupling can destabilize locomotion and degrade end-effector tracking, particularly when the manipulator mass is non-negligible. Meanwhile, coordinated motion planning is essential to ensure consistent behavior of both subsystems. This paper presents HiLoMa, a novel hierarchical loco-manipulation control framework with coordinated planning and decoupled execution. At the high level, a kinematic Model Predictive Control (MPC) module generates coordinated motion references for the manipulator joints and the quadruped base. At the low level, the manipulator is controlled via inverse dynamics with Proportional-Derivative (PD) feedback, while the quadruped employs a Nonlinear Model Predictive Control (NMPC) cascaded with a Whole-Body Control (WBC). The arm-induced wrench is estimated from the manipulator centroidal momentum dynamics and incorporated into the quadruped controller, enabling anticipatory compensation for coupling effects during execution. Comprehensive simulation and hardware experiments validate the feasibility and effectiveness of the proposed framework, demonstrating improved tracking accuracy, enhanced locomotion stability, and successful execution of practical loco-manipulation tasks.

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Study on Load Reduction and Stability Improvement of Asymmetric Head-Shaped Trans-Medium Vehicle

Teng Long, Ziyu Wang, Baoshou Zhang, Renhe Shi, Haoda Li, Xinhui Tai, Tao Zhang

Journal of Bionic Engineering. 2026, 23 (4): 2204-2223. DOI: 10.1007/s42235-026-00911-z

Abstract ( 7 )

The water exit of a Trans-Medium Vehicle (TMV) with lateral velocity presents problems, including multiphase flow and load nonlinearity, which affect the stability of the TMV water exit. To solve this problem, this paper proposes a TMV with a killer whale inspired head shape. The bio-inspired asymmetric head shape refers to the deflection degree of the TMV head tip towards the lateral velocity direction. The three-dimensional parameterized geometry is constructed via the two-parameter cubic polynomial. The unsteady Reynolds-Averaged Navier–Stokes (RANS) equations and the Renormalization Group (RNG) k - ε turbulence model are adopted for flow field simulation. Then the dynamic solution is achieved through the 6 Degrees of Freedom (DOF) equations. The flow field and hydrodynamic characteristics with different head shapes are investigated to reveal the mechanism of load reduction and stability improvement. The results show that the asymmetric head shape reduces fluid dissipation energy, retaining more energy in the form of longitudinal and lateral kinetic energy, thereby effectively hindering the conversion of energy into rotational kinetic energy. Furthermore, the asymmetric head shape reduces the inflow-surface pressure, shifts the low-pressure zone toward the head, and suppresses the formation of the hairpin vortex head in advance. Compared with the symmetrical head, the asymmetric head shape can achieve a 93.74% suppression of the attitude angle deflection and an 84.01% reduction of angular velocity. The study provides positive implications for load reduction and stability improvement during the TMV water exit.

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Long Term Effective Control of Carp Robots Using the Method of Light and Electric Combining Stimulation

Yong Peng, Xueying Dong, Yanhong Yan, Zhiwang Chen, Xue Li, Yang Zhao, Xiaowei Zhang, Kedong Jiang, Shuhao Sun

Journal of Bionic Engineering. 2026, 23 (4): 2224-2237. DOI: 10.1007/s42235-026-00930-w

Abstract ( 8 )

Biological robots are the creatures that humans manipulate by applying intervention signals with control technology to regulate biological behavior. In order to solve the problems of the fatigue and the adaptability of animals caused by only light stimulation or electric stimulation for a long time, which affect the control effect of biological robots, a new method of light and electric combining stimulation for the motion behavior control of biological robots is proposed in this manuscript. This study took carp as the research object, carried out the experiment on visual organ damage effect caused by light stimulation in carp, phototaxis experiment and electric stimulation underwater control experiment on carp robots, determined blue as the most sensitive light source, and screened the best light stimulation parameters and the best electric stimulation parameters. The underwater control experiment of light and electric combining stimulation on carp robots was carried out according to the frequency ratio of 1:1, 1:2, 2:1, 1:3 and 3:1 of light and electric stimulation. The Animal Tracker plug-in of Image J software was used to track the motion trajectory of carp robots, calculate the forward motion speed and turning angular speed of each group, and make statistics on the control success rate to obtain the optimal frequency ratio of light and electric combining stimulation control and to evaluate the efficiency of the light and electric combining stimulation control method compared with single control for the control of carp robots. The results showed that blue light source had the least effect on visual organ damage and carp had the strongest negative phototaxis to blue light, when the ratio of light and electric combining stimulation with the control times was 1:2, the forward speed and turning angular speed of carp under the control of stimulation for a long time were stable and the downward trend was slow, and the success rate of the control turning could reach 80%, which was 77.8% and 37.2% higher than that of the single control mode of light stimulation and electric stimulation, respectively. The research indicated that the method of the control of carp robots with light and electric combining stimulation in this study is an effective and practical new biological control mode.

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Design of a Bionic Spinei Inspired Cable-driven Hyper-redundant Manipulator with High-precision Inverse Kinematics Algorithm

Huiling Wei, Chengbin Liang, Hui Xiao, Weilin Chen, Mingyou Chen, Yanqin Li, Lufeng Luo

Journal of Bionic Engineering. 2026, 23 (4): 2238-2257. DOI: 10.1007/s42235-026-00929-3

Abstract ( 4 )

Cable-driven hyper-redundant manipulators (CDHRMs) have significant potential for application in narrow space exploration and minimally invasive surgery owing to their high degrees of freedom and flexibility. However, existing designs generally suffer from insufficient stiffness, limited load capacity, and poor positioning accuracy. To address these challenges, we propose a bio-inspired CDHRM modeled after the biomechanical properties of the human spine. The modular design integrates bionic spinal segments and spherical joints to balance compliance with a high payload capacity and enhanced stiffness. For accurate control, we developed a hybrid kinematic model combining a Piecewise Constant Curvature (PCC) approach with quaternion representation. Additionally, to solve high-dimensional inverse kinematics (IK), we propose an improved Particle Swarm Optimization (PSO) algorithm incorporating a k-dimensional tree (KDT). Simulations and prototype experiments validate the proposed system. Compared to conventional methods, our KDT-guided PSO algorithm reduces the average IK solving error by 57% and achieves a 92% convergence rate. Furthermore, the physical prototype demonstrates a fast dynamic response (settling time<0.5 s), high stiffness (average 0.709 N/mm), and high precision (repeatability error ≤ 1.6 mm). These integrated advancements provide an effective rigid-flexible design and control paradigm for narrow-space operations.

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Interaction-Aware Stiffness Design for Muscle-Inspired Tensegrity Exoskeleton

Dunwen Wei, Chenguang Cui, Yi Cao

Journal of Bionic Engineering. 2026, 23 (4): 2258-2272. DOI: 10.1007/s42235-026-00897-8

Abstract ( 6 )

Bionic Ankle Tensegrity Exoskeleton (BATE) effectively maps the human musculoskeletal system, exhibiting excellent self-supporting properties and joint compliance, with biomechanical characteristics highly compatible with human motion. Previous research extensively investigated the mechanism of active muscle strings in BATE, but the influence of passive muscle strings, crucial in musculoskeletal models, on human-exoskeleton interaction remains unclear. Addressing the mismatch between interaction forces/moments and human motion demands in exoskeleton models with constant passive string stiffness, this paper reconstructs the constitutive function of passive muscle strings using the Parallel Elastic (PE) element of the Hill-type muscle model, which effectively simulates the dynamic contraction characteristics of muscletendon tissues. An interaction solution algorithm based on the Stiffness Mapping Method (SMM) is established to optimize the parameters of the biomimetic passive string constitutive function. This optimization achieves stable supporting force output while significantly reducing normal pressure, leading to superior interaction performance. Furthermore, the influence mechanism of passive string stiffness modulation on interaction within the BATE tensegrity exoskeleton is deeply explored, and stiffness modulation strategies for different interaction objectives are proposed.

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Design and Mechanical Performance of a Bio-inspired Three-dimensional Interlocking Lattice Structure

Jie Li, Zhou Huang, Jingqi Li, Canghai Tan, Zhenglei Yu, Lei Wang, Xiao Kang, Xiaoyu Zhang

Journal of Bionic Engineering. 2026, 23 (4): 2273-2287. DOI: 10.1007/s42235-026-00900-2

Abstract ( 4 )

Three-dimensional lattice structures display outstanding mechanical attributes and deformation behaviors, making them viable options in aerospace engineering due to their effectiveness as lightweight load-supporting elements and vibration dampening systems. Inspired by the sturdy framework of Arapaima scales, celebrated for their exceptional mechanical qualities, this research presents a new energy-dissipating three-dimensional lattice configuration. A thorough examination of the compressive responses of this pioneering structure is performed using a diverse methodology that includes theoretical computations, empirical assessments, and computational modeling. Mathematical derivations clarifying the elastic modulus of the structure are developed through detailed stress analysis frameworks. Utilizing selective laser melting technology with 304 stainless steel, three variants of three-dimensional lattice structures are produced: the bio-inspired design, a negative Poisson ratio configuration, and a design incorporating BCC cellular units. A detailed finite element analysis was conducted to explore the deformation process and predict potential failure points. The theoretical estimation, simulation results and experimental data of the elastic modulus are consistent. The findings demonstrate the superior mechanical characteristics and notable energy absorption potential embedded in the innovative three-dimensional lattice structure. These results not only validate the effectiveness of this configuration but also lay a critical groundwork for future innovations in structural designs within the aerospace field.

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Rnee: Design, Development, and Functional Evaluations of a Robotic Knee Prosthesis

Omid Arfaie, Baris Baysal, Ramazan Unal

Journal of Bionic Engineering. 2026, 23 (4): 2288-2307. DOI: 10.1007/s42235-026-00927-5

Abstract ( 6 )

Performing activities of daily living is one of the key challenges in the lives of transfemoral amputees. Although several research studies have been conducted on the design and development of transfemoral prostheses, some critical aspects, such as mechanical efficiency, mobility, mass, and compactness, are still challenges to address in this field. With this aim, in this study, we propose the design, development, and functional evaluation of a robotic knee prosthesis called Rnee. To efficiently generate mechanical power around the knee joint, a crank mechanism integrated with a motorized spring is utilized in the Rnee design. Thanks to utilizing the composite material in Rnee manufacturing, it is at least 10% lighter than one of the lightest robotic knee prostheses in the literature. The size of the developed Rnee is 7% smaller than the average dimension of the human knee. To demonstrate its mobility, a Li-ion battery pack is utilized to carry out 5000 steps of walking and 100 stair-ascending steps with a single charge, which would be sufficient for daily activities. The functional evaluations of Rnee are performed with an able-bodied participant. The results show that the gait cycle can be performed continuously using the Rnee. Despite the limitations of using the apparatus for the able-bodied participant, the kinematic results of Rnee resemble the natural gait cycle kinematics with a correlation coefficient of 82%. Additionally, the Rnee is able to perform the stair-climbing cycle successfully, generating 90% of the torque required for this task according to the mechanical and functional evaluations.

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A Bioinspired Anisotropic Flexible Friction Pair for Asymmetric Friction Regulation in Stick-slip Actuators

Tianwei Liang, Jiru Wang, Hu Huang, Yunhong Liang, Hongwei Zhao

Journal of Bionic Engineering. 2026, 23 (4): 2308-2322. DOI: 10.1007/s42235-026-00934-6

Abstract ( 8 )

The backward phenomenon of the stick-slip piezoelectric actuator seriously affects its positioning accuracy and motion stability. The existing suppression methods, which mostly rely on mechanical structure optimization or closed-loop control, still have certain limitations. This study addresses the issue of backward motion from the perspective of interfacial tribology and proposes a biomimetic flexible friction pair for a stick-slip actuator. Inspired by the anisotropy of the microstructure of reed leaves, the surface morphology was replicated using PDMS, and a flexible biomimetic friction pair was fabricated through plasma treatment. By adjusting the orientation of the microstructures on the driving foot relative to those on the slider surface, four types of anisotropic friction pairs were constructed. The experimental results indicate that biomimetic anisotropic friction pairs are effective in suppressing backward motion, and that specific flexible friction pairs can eliminate the backward phenomenon without increasing mechanical or control complexity. Through microscopic morphology characterization and contact theory analysis, the friction enhancement mechanisms and microscopic mechanical mechanisms of different biomimetic microstructure combinations were revealed. This actuator combines high performance, compact structure and simple control, and is particularly suitable for precision micro-robots, space operations and optical positioning, which have strict requirements for volume, energy consumption and reliability.

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Aeroacoustics Comparison of Owl, Hawk, and NACA0012 Airfoils Using Microphone Measurements

M. Kazemi, M. Mani

Journal of Bionic Engineering. 2026, 23 (4): 2323-2346. DOI: 10.1007/s42235-026-00925-7

Abstract ( 5 )

This study investigates the aeroacoustics performance of an owl-inspired airfoil in comparison with a conventional NACA0012 and a hawk-inspired airfoil, using experimental sound pressure measurements conducted in a semi-acoustic wind tunnel. Tests were carried out over a range of Reynolds numbers (44×103 to 134×103), angles of attack (0°–16°), and two chord lengths (150 mm and 250 mm), using seven microphones. The influence of flow velocity, Reynolds number, and angle of attack on sound pressure level (SPL), root-mean-square pulsating coefficient, and psychoacoustic roughness was thoroughly examined. Results show that the owl airfoil consistently demonstrates lower SPL and roughness values across a wide range of operating conditions, especially at low frequencies (50–315 Hz), which are commonly associated with laminar separation bubble shedding and trailing-edge noise. The owl airfoil’s acoustic advantage becomes particularly evident under high angles of attack and increased Reynolds numbers, where flow separation is more pronounced in the hawk airfoil. At the lowest Reynolds number, the owl airfoil exhibits predominantly laminar flow over the upper surface. With increasing Reynolds number, the boundary layer thickens and undergoes transition to turbulence, which enhances momentum exchange and delays flow separation. In contrast, the hawk airfoil experiences earlier and more pronounced separation as the angle of attack and Reynolds number increase. These findings highlight the superior noise reduction potential of bio-inspired owl airfoil in low-speed aerodynamic applications.

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Foldable Biomimetic Burdock Flower-Inspired Hollow Rotational Structure for Fog Collection

Yanshu Zhong, Muneerah Alomar, Song Lv, Yan Xin, Shangzhen Xie

Journal of Bionic Engineering. 2026, 23 (4): 2347-2357. DOI: 10.1007/s42235-026-00909-7

Abstract ( 7 )

The global distribution of freshwater resources is highly uneven. Addressing global water scarcity requires innovative water harvesting technologies. As a promising complementary strategy, fog harvesting can effectively alleviate this problem. The performance of conventional one/two-dimensional fog collectors is highly susceptible to wind direction due to their lack of adaptive adjustment mechanisms for shifting winds. To tackle this limitation, this work presents a facile strategy for fabricating high-performance and three-dimensional fog collectors inspired by the hierarchical structure of burdock flowers and the mechanics of dynamic paper-cut structures. The design features a hollow, rotatable, and foldable aluminum structure fabricated via laser etching. This structure is functionalized with a Slippery Liquid-Infused Porous Surface (SLIPS) through the infusion of a silicone oil and paraffin wax mixture to facilitate efficient droplet capture and rapid shedding. The resulting collector demonstrates an exceptional fog collection rate of 0.2514 g·cm-2·min-1 and outstanding excellent cycle stability. This work offers an approach of designing scalable and efficient 3D fog collectors, which adapts to varying wind directions to achieve omnidirectional fog capture. Moreover, it offers a compelling combination of low cost, light weight, and compact storability, presenting a promising and sustainable solution to water scarcity in arid, freshwater-limited regions.

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Bio-inspired Grooved Tips for TLF Control in Chemical Centrifugal Pumps: Entropy Generation and Pressure Pulsation Suppression

Haitao Yu, Yunxin Pan, Yangjun Wang, Ziyuan Li, Limei Tian

Journal of Bionic Engineering. 2026, 23 (4): 2358-2375. DOI: 10.1007/s42235-026-00938-2

Abstract ( 3 )

To address excessive energy dissipation and flow instability caused by tip-leakage flow (TLF) in centrifugal pumps, this study designs two bio-inspired triangular grooved tip structures (Bio-seg, Bio-con) based on the shell surface features of Chlamys farreri and Argopecten irradians. The SST k-ω turbulence model combined with entropy generation theory was adopted for steady and unsteady full-flow field numerical simulations of a centrifugal pump conveying shear-thinning EVA latex, with the numerical model validated comprehensively by experiments. Results demonstrate that the Bio-seg structure enables momentum exchange between TLF and trapped fluid via its segmented grooves, weakening the interaction intensity between TLF and the main flow. This thus reduces the entropy generation losses in the impeller and volute by 35.6% and 15.1% respectively (with an overall reduction of 20.2%), and lowers the pressure pulsation amplitudes at the dominant frequency by 29.1% (impeller) and 9.6% (volute) respectively. It also boosts pump head and efficiency by 2.5% and 2.7% versus the original impeller. In contrast, Bio-con exhibits limited effects (3.7% total entropy reduction, 1.0% head and 1.2% efficiency gains) due to the vortex effect in continuous grooves weakening TLF blocking efficiency. This work proposes a novel bio-inspired topological design method for energy loss and pressure pulsation control in centrifugal chemical pumps, providing a structural optimization solution for their efficient and stable operation.

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Nepenthes-inspired Two-stage Structure for Laser-induced Graphene Piezoresistive Sensors with High Sensitivity and Wide Pressure Range

Wentao Wang, Junhao Hu, Baixuan Gao, Wenbo Cong, Bo Zhang, Zhuming Bi, Kaikai Li, Shudong Yu

Journal of Bionic Engineering. 2026, 23 (4): 2376-2388. DOI: 10.1007/s42235-026-00912-y

Abstract ( 5 )

Typical tactile properties possessed by carnivorous plants like Nepenthes enable them to hunt and survive with the superlubricity traps. Inspired by the two-stage structure of the unique peristome in Nepenthes, we propose a bionic manufacturing method includes UV laser patterning, CO2 laser-induced formation, lubricating oil infusion, and face-to-face encapsulation steps, to fabricate high-performance Laser-Induced Graphene (LIG) piezoresistive sensors. It was found that the Nepenthes-inspired electrode has a Water Sliding Angle (WSA) of 9.45° and shows self-cleaning properties for milk, cola, artificial plasma, and chemical solvents. The LIG-based electrode exhibits two-stage topological features, the microgrooves could provide efficient conductive paths, while the numerous micropores in graphene undergoes reversible compression and rebound under external pressure, thereby synergistically enhancing the resistance changes. Experimentally, the Nepenthes-inspired piezoresistive sensor demonstrates excellent sensitivities of 275.83, 93.23, and 4.28 kPa- 1 in linear pressure ranges of 0.15-36, 36–70, and 70–160 kPa, and keeps excellent signal repeatability and stability even after 6000 cycles under the pressure of 100 kPa. After integrating the self-developed wireless sensing system, this Nepenthesinspired sensor could realize real-time monitoring of human respiratory status and joint movements, providing a green, low-cost, and easy-to-operate tech-route for the fabrication of wide-range, high-reliability wearable electronics.

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Biomimetic Composite Structures with Fibre Angle Gradients: Enhancing Energy Absorption via Progressive Failure

Ole Middendorf, Milan Kelch, Jörg Müssig

Journal of Bionic Engineering. 2026, 23 (4): 2389-2400. DOI: 10.1007/s42235-026-00926-6

Abstract ( 8 )

As mobility, safety, and sustainability are increasingly important issues today, this work focuses on energy-absorbing structures with fibre-angle gradients along the specimen length. The aim is to create a gradient of material properties in a composite, leading to a significant increase in energy absorption properties due to a more efficient failure. The methodology is based on the biomimetic techno-pull principle, which led to the abstraction of the dactyl club of the Mantis shrimp (Odontodactylus scyllarus). The fibre angle gradient was realised with three different single-specimen winding patterns and a honeycomb structure, compared with 87° and 0° single-specimen winding patterns and a 0° honeycomb structure. All specimens were made of regenerated cellulose fibre in an epoxy resin matrix and tested under axial compression at a process speed of 1000 mm min?1. Comparing the two structures, the mass-specific energy absorption for the fibre angle gradient structure is significantly higher at 20.77 ±2.33 kJ kg?1 than for the 0° fibre angle structure at 4.98±0.99 kJ kg?1, due to progressive failure.

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Critical Interface/Fiber Energy Ratio Unlocks Optimal Bending Performance in Biological and Bioinspired Crossed-Lamellar Structures

Yingying Li, Simin Liang, Hongmei Ji, Xiaowu Li

Journal of Bionic Engineering. 2026, 23 (4): 2401-2416. DOI: 10.1007/s42235-026-00921-x

Abstract ( 5 )

Although the Crossed-Lamellar Structure (CLS) in mollusk shells achieves remarkable fracture resistance, the optimal design of its organic matrix interface still remains underexplored. The present study reveals that maximizing mechanical performance requires tuning the interface-to-fiber fracture energy ratio (Γi/Γf) to a critical value, rather than simply pursuing the strongest interface. To determine this optimal value, a dual-scale comparative experimental approach is employed, encompassing microscale natural shells and macroscale 3D-printed composites. Natural shells are subjected to thermal degradation to weaken the interfaces, while three-dimensional (3D) printed CLS models are used to geometrically modulate the bonding strength via varying interfacial bridge radii. Both systems reveal a consistent, non-monotonic relationship between interfacial strength and bending performance. A universal critical ratio of Γi/Γf≈0.37± 0.01 is identified, at which the bending strength and fracture work are simultaneously optimized. In natural shells, moderate interface weakening (Γi/Γf ≈0.36) via heat treatment enhances bending performance by activating crack deflection. This mechanism is successfully replicated in synthetic systems by tuning the bridge-fiber radius, achieving peak performance at Γi/Γf≈0.38. Theoretical analysis confirms that this ratio aligns with the criterion for oblique crack deflection at bimaterial interfaces. These findings underscore the importance of interface-fiber energy matching and establish a quantitative design criterion for high-performance bioinspired CLS composites.

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Research on Discipline Competition Team Member Category Prediction Based on the MICOA-Optimized Stacking Model

Cheng Tao, Ying Chen, Peng Min, Huiling Chen, Yuliang Zhang, Zeye Long, Yudi Xie

Journal of Bionic Engineering. 2026, 23 (4): 2417-2442. DOI: 10.1007/s42235-025-00741-5

Abstract ( 5 )

In response to the problem of restricting team members' individuality and comprehensive development in the discipline competition teaching mode, this paper proposes a Competition Team Member Category Prediction (CTMCP) model based on the Multi-strategy Improved Coati Optimization Algorithm (MICOA) optimized Stacking model. For COA, firstly, the cyclone foraging strategy is introduced to expand the population search range; secondly, a defense mechanism against predators is proposed to enhance the ability to balance exploration and exploitation; then, the soft-frost search strategy is adopted to accelerate the algorithm convergence speed; finally, Cauchy mutation is applied to the optimal individual to avoid falling into local optima. The results of multiple sets of simulation experiments on the CEC2017 test functions comparing MICOA with other algorithms demonstrate that MICOA exhibits stronger optimization performance, providing strong support for the hyperparameter tuning of MICOA in machine learning single models and Stacking models. For the research on predicting the categories of competition team members, firstly, factor analysis is used to reduce the dimension of various data of competition team members and define relevant factors, secondly, the clustering result of factors is used as the label for constructing the prediction model of competition team members, then, the original data of the first 3 and first 5 semesters of team members are selected as features, finally, based on the MICOA-optimized machine learning single model, the prediction of team member categories achieves a significant improvement in classification performance. The prediction based on the MICOA-optimized Stacking model further improves the classification performance, validating the effectiveness of the proposed model in predicting the categories of competition team members.

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Chemo-Electro-Mechanical Coupled Simulation of pH-Responsive Swelling in Cationic Hydrogels

Xinying Shao, Jingxi Wang, Ping Li, Yubo Fan

Journal of Bionic Engineering. 2026, 23 (4): 2443-2459. DOI: 10.1007/s42235-026-00923-9

Abstract ( 6 )

The tunable swelling of hydrogels underpins applications in drug delivery, soft actuators, and biomedical sensors. Given the complexity of experimentation, computational simulation has become central to elucidating swelling mechanisms. However, prevailing single-phase models assume macroscopic homogeneity and a single continuum, which limits their ability to capture the multicomponent dynamic responses of hydrogels under pH stimulation. This limitation is especially pronounced for cationic hydrogels (e.g., chitosan-based systems), where simulation must account for ion interactions, charge balance, electric field effects, and dynamic protonation, making multiphysics modeling challenging. Here, we propose a chemo-electro-mechanical coupled framework that integrates chemical diffusion, electric field, solid mechanics, and the Arbitrary Lagrangian-Eulerian method to simulate the swelling behavior of cationic hydrogels. The framework quantifies the influence of fixed charge density, Young’s modulus, pKa, and geometric parameters on swelling kinetics and identifies charge density and pKa as dominant regulators of deformation. Empirical equations derived from parameter analyses establish a quantitative relationship between structural parameters and swelling behavior. Experimental validation shows a prediction error of less than 2%. This work deepens understanding of the swelling mechanisms of cationic hydrogels and provides a robust tool for multiphysics analysis of stimulus-responsive polymers, supporting advances in pH-controlled drug delivery, soft robotics, and biomedical sensing.

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Electrospun Polycaprolactone-Based Nanocomposite Scaffolds Incorporating Salvia Officinalis and Graphene Oxide for Neural Tissue Regeneration

Shadi Emadi, Mehdi Ebrahimian-Hosseinabadi, Anousheh Zargar Kharazi

Journal of Bionic Engineering. 2026, 23 (4): 2460-2473. DOI: 10.1007/s42235-026-00903-z

Abstract ( 5 )

This study aimed to develop and comprehensively characterize electrospun Polycaprolactone (PCL)-based nanocomposite scaffolds incorporating Salvia officinalis (Salvia) and Graphene Oxide (GO) for potential applications in neural tissue regeneration. Four scaffold compositions, namely PCL-20Salvia, PCL-10Salvia, PCL-20Salvia-2GO, and PCL-10Salvia- 2GO, were fabricated using electrospinning. The morphological, structural, and chemical properties of the scaffolds were systematically evaluated by scanning electron microscopy, Fourier-transform infrared spectroscopy, and X-ray diffraction, while mechanical performance was assessed through tensile testing. Surface wettability was analyzed by water contact angle measurements, and in vitro degradation behavior was monitored over 56 days in phosphate-buffered saline to assess mass loss and pH stability. Cytocompatibility was investigated by culturing PC12 neuronal cells on the scaffolds to evaluate cell viability, proliferation, and adhesion. The results demonstrated that the inclusion of GO significantly improved fiber alignment, interfacial uniformity, and mechanical strength, while reducing fiber diameter and enhancing porosity when combined with lower Salvia content. Among the tested formulations, PCL-10Salvia-2GO exhibited the highest tensile strength and Young’s modulus without compromising flexibility, alongside controlled degradation kinetics and maintenance of physiological pH. In vitro assays further confirmed that this scaffold promoted neuronal cell viability, extensive adhesion, and neurite-like extensions, indicating the establishment of a neuroinductive microenvironment. These findings highlight that the synergistic incorporation of bioactive Salvia phytochemicals and functional GO within PCL scaffolds effectively enhances mechanical integrity, structural uniformity, degradation behavior, and cytocompatibility. Consequently, PCL-10Salvia-2GO represents a promising multifunctional scaffold for neural tissue engineering and regenerative medicine, warranting further investigation in preclinical models to establish its translational potential.

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A Bio-inspired Corn Pre-threshing Loosening Mechanism to Reduce Corn Harvest Losses

Zhao Xue, Zhenye Li, Chengyuan Zhang, Zhenzi Zhang, Jun Fu

Journal of Bionic Engineering. 2026, 23 (4): 2474-2488. DOI: 10.1007/s42235-026-00906-w

Abstract ( 6 )

High kernel damage caused by corn mechanical threshing leads to primary threshing loss and subsequent severe loss of the storage process. A novel method for low kernel damage threshing was inspired by the loosen behavior of hands twisting corn ears, and a ripple-inspired corrugated drum was created to implement the loosening process of this method. Three different types of tests were conducted to quantify the effectiveness of the novel method from different perspectives with five different loosening conditions to prove its effectiveness. The result of the mechanical test has demonstrated loosening reduces the detachment force of corn kernels to corncob from the standpoint of the mechanic. The torque test has proven the proposed pre-threshing loosening method decreases the drag torque of corn threshing by a developed instantaneous acceleration threshing bench from the aspect of loading. The threshing test provides corroborating actual threshing performance evidence that loosened corn ears by the created corrugated drum were effective to reduce kernel damage. The best result was achieved when the compression depth was 10 mm, and detachment force, drag torque, and kernel damage rate substantially decreased compared with the non-loosened group, respectively. The proposed method and implementation show tremendous superiority to reduce kernel damage and also reduce subsequent post-harvest losses and have substantial potential for industrialization.

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Thermal–Bioconvective Analysis of Slip-Affected Oldroyd-B Nanofluid Flow Using Artificial Neural Networks

Zeeshan Khan, Raheela Razzaq, Ho-Kin Tang, Cho Tung yip, M. N. Abrar

Journal of Bionic Engineering. 2026, 23 (4): 2489-2509. DOI: 10.1007/s42235-026-00920-y

Abstract ( 6 )

Recent advancements in nanoparticle technology have attracted substantial attention due to their enhanced thermal properties and broad applicability across engineering and thermal sciences. Nanoparticles are widely utilized in energy systems, heating and cooling processes, and material engineering, as well as in biomedical applications such as brain tumor therapy, targeted cell destruction, and cancer treatment. Moreover, the bioconvective behavior of nanoparticles plays a crucial role in various bioscience applications, including biofuel production, enzyme activity, and biosensor design. Motivated by these promising applications, the present study investigates the bioconvective flow of an Oldroyd-B nanofluid in a convectively heated environment using a hybrid Levenberg–Marquardt Backpropagation Neural Network (LMLABPNN) framework. The model incorporates partial slip and thermal effects to accurately capture the underlying flow characteristics. The governing equations are formulated under appropriate physical assumptions and reduced to a system of ordinary differential equations through suitable similarity transformations. The bvp4c solver is then employed to generate high-quality training data for the LMLA-BPNN, enabling reliable prediction of temperature, velocity, and nanoparticle concentration profiles within the bioconvective Oldroyd-B fluid. The performance of the proposed LMLA-BPNN approach is assessed under various parametric conditions and validated against reference numerical solutions. Statistical evaluation techniques—including state transition dynamics, mean square error analysis, regression performance, and error distribution histograms—demonstrate the accuracy, robustness, and reliability of the neural network model in solving the considered physical problem.

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Evaluation of Viscous-Fluid Swallowing Performance Under Biomimetic Motion Timing and Deformation Conditions

Hua Sun, Shanhua Qian, Tengxiao Liu, Yuchao Fan, Xiaomei Wei, Da Bian, Zifen Ni, Jianhao Li

Journal of Bionic Engineering. 2026, 23 (4): 2510-2528. DOI: 10.1007/s42235-026-00932-8

Abstract ( 4 )

Dysphagia is a major health concern in older adults and stroke patients. The complexity of swallowing hampers the development of functional foods and complicates objective evaluation of their swallowing performance. Therefore, an in vitro method that can simulate safe and effective swallowing is urgently needed. In this study, Video Fluoroscopic Swallowing Study (VFSS) from Normal Subject (NS) was used to investigate the motion timing and deformation of soft tissues, including the tongue, Soft Palate (SP), and Posterior Pharyngeal Wall (PPW), to inform the design of a multi-chamber soft-tissue structure. A Finite Element Method (FEM) was then used to obtain time-resolved deformation parameters under pneumatic actuation, enabling the development of a Bionic Swallowing Device (BSD) for in vitro assessment of food swallowing performance. By adjusting the control system parameters to reproduce swallowing in dysphagia patients (DP), the BSD was validated against VFSS. The device was further used to quantify the effects of bolus consistency on the swallowing performance of pea protein solutions. The results showed that oral soft tissues exhibit coordinated motion characterized by tongue-dominant propulsion, early SP closure, and delayed PPW constriction. The multi-chamber model correlated well with VFSS (r=0.94), and the BSD reproduced the swallowing process of DP (r= 0.88). Moreover, BSDbased guidance recommended a 10–15% concentration for DP with timing abnormalities and 10% for DP with combined timing and deformation abnormalities. This study provides valuable insights for novel BSD development and in vitro evaluation of the swallowing performance about functional foods.

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An Improved Multi-objective African Vulture Optimization Algorithm with Beta-Hill Climbing for Task Scheduling in Blockchain-Integrated Fog and Cloud Systems

Vahid Alipour Darbandi, Farhad Soleimanian Gharehchopogh, Mohammad Masdari, Kambiz Majidzadeh, Amin Babazadeh Sangar

Journal of Bionic Engineering. 2026, 23 (4): 2529-2557. DOI: 10.1007/s42235-026-00918-6

Abstract ( 5 )

Given the enormous volume of information generated online, leveraging fog/cloud blockchain networks is a priority. Nevertheless, there are two challenges that providers are struggling to sort out. One is the system’s security against malicious clients, and the other is the designation of the executing unit to accommodate reasonable expenses. Integrating blockchain into the fog/cloud system can be highly beneficial for addressing this issue. In this paper, we propose an adaptive Multiobjective African Vulture Optimization Algorithm (AVOA) that uses the Beta-Hill Climbing algorithm to schedule tasks in Bag-of-Tasks applications across fog, cloud, and blockchain systems. Task scheduling plays a crucial role in resource allocation; therefore, this approach is particularly relevant. To solve the multi-objective problems, the algorithm is adapted to the Pareto dominance mechanism, allowing it to address them with maximum efficiency. To evaluate the performance of the proposed method, a comprehensive experimental study was conducted, demonstrating its superiority in this role.

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LIMRI-Net: A Lightweight Multi-scale and Attention-Based Deep Learning Model for Brain Tumor Classification

Fazal Hadi, Sohaib Asif

Journal of Bionic Engineering. 2026, 23 (4): 2558-2576. DOI: 10.1007/s42235-026-00917-7

Abstract ( 4 )

Brain tumor diagnosis remains a major challenge due to the complex and heterogeneous nature of tumor structures. Accurate and early classification of tumor types is essential for guiding treatment strategies and improving patient outcomes. Although deep learning has shown great potential in medical image analysis, many existing models either require substantial computational resources or fail to generalize effectively across diverse tumor categories. In this study, we propose LIMRI-Net, a lightweight and high-performing Convolutional Neural Network (CNN) for brain tumor classification using Magnetic Resonance Imaging (MRI) scans. The framework integrates standard convolutional layers with multiscale atrous convolutions to capture spatial details across different receptive fields. An inception module further enhances multiscale feature extraction, while a Squeeze-and-Excitation (SE) block adaptively recalibrates channel responses to strengthen discriminative capability without increasing computational overhead. LIMRI-Net was evaluated on a publicly available four-class brain MRI dataset, where it achieved an accuracy of 98.63%. To assess robustness, additional experiments were performed on the Bangladesh MRI dataset, containing 6,056 images across three classes, where the model reached 98.18% accuracy. Remarkably, LIMRI-Net achieved the lowest parameter count (0.42 M), smallest model size, and minimal FLOPs compared to state-of-the-art methods, highlighting its suitability for deployment in resource-constrained clinical environments. Furthermore, Grad-CAM visualizations were employed to highlight the salient regions driving predictions, thereby improving interpretability. These findings demonstrate that LIMRI-Net offers an effective balance of accuracy, efficiency, and transparency, making it a reliable diagnostic support tool for advancing precision in brain tumor classification.

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ResTransNet: A Residual and Transformer-based Architecture for Accurate Oral Cancer Diagnosis from Histopathological Images

Nebras Sobahi, Yağmur Ölmez, Osman Fatih Koparır, Berna Gürler Arı, Adalet Çelebi, Muammer Türkoğlu, Abdul Hafeez Baig, Abdulkadir Sengur

Journal of Bionic Engineering. 2026, 23 (4): 2577-2594. DOI: 10.1007/s42235-026-00928-4

Abstract ( 5 )

Oral cancer has emerged as a critical public health problem, with a rising incidence of cancer, thereby increasing mortality rates as a consequence. The diversity of cancer types, as well as the variability of different imaging modalities, has complicated the process of accurate diagnosis of cancer. This study aims at achieving this objective through the creation of a novel deep learning model. A hybrid model, named ResTransNet, was created by combining Residual Networks models with transformers, which are capable of accurately classifying images based on both local and global features of images. Bayesian optimization was used to optimize the hyperparameters of the model. The model was tested on two different datasets of histopathological images of diverse types of lesions occurring in the oral cavity, comparing the results with conventional Convolutional Neural Networks models as well as Vision Transformers. The ResTransNet model was able to achieve a classification accuracy of 96.21% on the first dataset, as well as 92.11% on the second dataset, thereby proving its efficacy over conventional models. The ResTransNet model has the potential to become a strong diagnostic tool in the accurate diagnosis of cancer, owing to its hybrid ability to improve the process of image classification.

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HMS-CTN: A Hybrid Multi-scale Convolutional Transformer Network with Feature Fusion and Support Vector Machine (SVM) for Enhanced Diabetic Foot Ulcer Detection

Kamran Amjad, Sohaib Asif, Zafran Waheed, Yun Mao, Ying Guo

Journal of Bionic Engineering. 2026, 23 (4): 2595-2614. DOI: 10.1007/s42235-026-00922-w

Abstract ( 6 )

Diabetic foot ulcers (DFUs) are a common complication of diabetes, posing significant challenges for early detection and timely intervention. This paper presents a novel deep learning model, HMS-CTN, specifically designed for automated DFU detection. The proposed model integrates a custom convolutional module, ConvTransNet, which captures intricate spatial hierarchies and contextual dependencies at multiple scales. To further enhance feature extraction, a Multi-Scale Separable Convolutional Module (MS-SCM) is incorporated, along with a transformer encoder, to improve long-range dependency modeling. The MS-SCM captures multi-scale features efficiently, enhancing the model’s ability to recognize both fine-grained and high-level patterns. The transformer encoder improves long-range dependency modeling and global context, refining feature extraction for more accurate and robust predictions, particularly in clinical applications. The model architecture incorporates MobileNet for efficient computation, ensuring a lightweight design without sacrificing performance. A unique feature fusion approach combines outputs from both the MobileNet and ConvTransNet branches, enhancing the model’s ability to capture diverse feature representations. Finally, Support Vector Machines (SVM) are utilized for classification, further improving accuracy and robustness in predicting DFUs. We preprocessed the input images using CLAHE (Contrast Limited Adaptive Histogram Equalization) to enhance image features, particularly in low-contrast areas, which is crucial for improving model performance on medical imaging datasets. Additionally, we optimized CLAHE parameters using the Artificial Bee Colony (ABC) optimization algorithm, which fine-tuned the contrast enhancement process to further improve the model’s accuracy and computational efficiency. We conducted an ablation study to evaluate the contribution of each model component and configuration to the overall performance. Our extensive experiments on benchmark datasets, including thermography and foot ulcer images, demonstrate that HMS-CTN outperforms existing methods, achieving an impressive accuracy of 99.52% on foot images and 94.78% on the thermography dataset. The results indicate that the proposed model offers significant improvements in both accuracy and computational efficiency, making it a promising tool for real-world clinical applications, where quick and reliable DFU diagnosis is essential.

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Explainable AI Driven Lenet-Deep Maxout Network Based Polycystic Ovary Syndrome Detection Using Ultrasound Image

Pavithra Babu, Dr. Lakshmanan laxmanan

Journal of Bionic Engineering. 2026, 23 (4): 2615-2644. DOI: 10.1007/s42235-026-00916-8

Abstract ( 5 )

Polycystic ovary syndrome (PCOS) significantly impacts women of reproductive age and is recognized as a leading cause of female infertility. It involves a disruption in hormone levels, which leads to irregular menstruation and the appearance of multiple small cysts in the ovaries. Timely identification of PCOS plays a key role in managing its manifestations and lowering the risk of associated health issues. Moreover, the efficiency of classical detection models was reduced due to the lack of advanced feature extraction schemes. Therefore, this study introduces an Explainable Artificial Intelligence+LeNet-Deep Maxout Network (Explainable AI +LeDMNet) for PCOS detection. Firstly, input ultrasound image is preprocessed with Gaussian filtering. After that, the segmentation of follicles from the ovary is conducted using Superpixel-based Fast Fuzzy C-Means Clustering (SFFCM), where region-level iterative strength is modified using chord distance. After segmentation, image augmentation techniques, including brightness, rotation, and translation, are applied to the segmented follicle regions to enhance dataset diversity, improve model robustness, and prevent overfitting. Following augmentation, the feature extraction process is performed, which identifies the patterns and correlations to enabling early detection and personalized treatment plans. Finally, the Explainable AI +LeDMNet is devised for PCOS detection. wherein LeDMNet is developed using LeNet and Deep Maxout Network (DMN). The evaluation outcomes expose that, Explainable AI +LeDMNet has gained accuracy, True Positive Rate (TPR), True Negative Rate (TNR), precision, and F1-score as 91.867%, 91.755%, 91.858%, 90.939%, and 90.302%.

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