Biomimetics, Volume 10, Issue 5 (May 2025) – 67 articles

In recent years, there has been development in bio-hybrid actuators that utilize living organisms themselves, as opposed to biomimetics. However, most of the plants and animals used for these purposes are no longer actually alive, as their corpses, parts, or seeds are used. There is research on the use of microorganisms, but it is limited to use in building materials. Here, we focused on plants in terms of their ease of growth with water and light and their ability to change shape significantly from seed through growth. Therefore, we propose a material that incorporates living plants. The objective of this research is to realize the shape change of this material by using the property of plants to grow toward light. In the experiment, we confirmed that plants growing from two devices cross-linked between the devices by controlling the direction of growth using peas. The bridged plants did not break when a mass of up to 575 g was placed on it and indicated a load-bearing capacity of more than 6.6 times from the mass ratio. Then, it is demonstrated that the robot could cross over that. Full article

In this paper, a beetle with excellent flight ability and a large folding ratio of its hind wings is selected as the biomimetic design. We mimicked the geometric patterns formed during the folding process of the hind wings to construct a deployable mechanism while calculating the sector angles and dihedral angles of the origami mechanism. In the expandable structure of thick plates, hinge-like steps are added on the thick plate to effectively avoid interference motion caused by the folding of the thick plate. The kinematic characteristics of two deployable mechanisms were characterized by ADAMS 2018 software to verify the feasibility of the mechanism design. The finite element method is used to analyze the structural performance of the deployable mechanism, and its modal response is analyzed in both unfolded and folded configurations. The aerodynamic generation of a spatially deployable wing is characterized by computational fluid dynamics (CFD) to study the vortex characteristics at different frame rates. Based on the aerodynamic parameters obtained from CFD simulation, a wavelet neural network is introduced to learn and train the aerodynamic parameters. Full article

Motion control of droplets undergoing collisions with solid surface is required in a number of technological and industrial situations. Droplet dynamics after lifting off is often unpredictable, leading to a major problem in many technologies that droplets move in uncontrolled and potentially undesirable ways. Herein, this work shows that well-designed surface chemistry can produce an accurate control of force transmission to impinging droplets, permitting precise controlled droplet rebounce. The non-wetting surfaces (superhydrophobic), which mimics the water-repellent mechanism of lotus leaves via micro-to-nanoscale hierarchical morphology, with patterned “defect” of extreme wettability (hydrophilic), are synthesized by photolithography using only one inexpensive fluorine-free reagent (methyltrichlorosilane). The contact line of impinging droplet during flatting and receding is free to move on the superhydrophobic region and pinned as it meets with the hydrophilic defect, which introduces a net surface tension force allowing patterned droplet deposition, controlled droplet splitting, and directed droplet rebound. The work also achieves controlled vertical rebound of impinging droplets on inclined surfaces by controlling defect’s size, impact position, and impact velocity. This research demonstrates pinning forces as a general strategy to attain sophisticated droplet motions, which opens an avenue in future explorations, such as matter transportation, energy transformation, and object actuation. Full article

In this study, we propose a neuromorphic computing system based on a 3D-NAND flash architecture that utilizes analog input voltages applied through wordlines (WLs). The approach leverages the velocity saturation effect in short-channel MOSFETs, which enables a linear increase in drain current with respect to gate voltage in the saturation region. A NAND flash array with a TANOS (TiN/Al₂O₃/Si₃N₄/SiO₂/poly-Si) gate stack was fabricated, and its electrical and reliability characteristics were evaluated. Output characteristics of short-channel (L = 1 µm) and long-channel (L = 50 µm) devices were compared, confirming the linear behavior of short-channel devices due to velocity saturation. In the proposed system, analog WL voltages serve as inputs, and the summed bitline (BL) currents represent the outputs. Each synaptic weight is implemented using two paired devices, and each WL layer corresponds to a fully connected (FC) layer, enabling efficient vector-matrix multiplication (VMM). MNIST pattern recognition is conducted, demonstrated only a 0.32% accuracy drop for the short-channel device compared to the ideal linear case, and 0.95% degradation under 0.5 V threshold variation, while maintaining robustness. These results highlight the strong potential of 3D-NAND flash memory, which offers high integration density and technological maturity, for neuromorphic computing applications. Full article

Nanotopography refers to the intricate surface characteristics of materials at the sub-micron (<1000 nm) and nanometer (<100 nm) scales. These topographical surface features significantly influence the physical, chemical, and biological properties of biomaterials, affecting their interactions with cells and surrounding tissues. The development of nanostructured surfaces of polymeric nanocomposites has garnered increasing attention in the fields of tissue engineering and regenerative medicine due to their ability to modulate cellular responses and enhance tissue regeneration. Various top-down and bottom-up techniques, including nanolithography, etching, deposition, laser ablation, template-assisted synthesis, and nanografting techniques, are employed to create structured surfaces on biomaterials. Additionally, nanotopographies can be fabricated using polymeric nanocomposites, with or without the integration of organic and inorganic nanomaterials, through advanced methods such as using electrospinning, layer-by-layer (LbL) assembly, sol–gel processing, in situ polymerization, 3D printing, template-assisted methods, and spin coating. The surface topography of polymeric nanocomposite scaffolds can be tailored through the incorporation of organic nanomaterials (e.g., chitosan, dextran, alginate, collagen, polydopamine, cellulose, polypyrrole) and inorganic nanomaterials (e.g., silver, gold, titania, silica, zirconia, iron oxide). The choice of fabrication technique depends on the desired surface features, material properties, and specific biomedical applications. Nanotopographical modifications on biomaterials’ surface play a crucial role in regulating cell behavior, including adhesion, proliferation, differentiation, and migration, which are critical for tissue engineering and repair. For effective tissue regeneration, it is imperative that scaffolds closely mimic the native extracellular matrix (ECM), providing a mechanical framework and topographical cues that replicate matrix elasticity and nanoscale surface features. This ECM biomimicry is vital for responding to biochemical signaling cues, orchestrating cellular functions, metabolic processes, and subsequent tissue organization. The integration of nanotopography within scaffold matrices has emerged as a pivotal regulator in the development of next-generation biomaterials designed to regulate cellular responses for enhanced tissue repair and organization. Additionally, these scaffolds with specific surface topographies, such as grooves (linear channels that guide cell alignment), pillars (protrusions), holes/pits/dots (depressions), fibrous structures (mimicking ECM fibers), and tubular arrays (array of tubular structures), are crucial for regulating cell behavior and promoting tissue repair. This review presents recent advances in the fabrication methodologies used to engineer nanotopographical microenvironments in polymeric nanocomposite tissue scaffolds through the incorporation of nanomaterials and biomolecular functionalization. Furthermore, it discusses how these modifications influence cellular interactions and tissue regeneration. Finally, the review highlights the challenges and future perspectives in nanomaterial-mediated fabrication of nanotopographical polymeric scaffolds for tissue engineering and regenerative medicine. Full article

Spoken Language Identification (SLID)-based applications have become increasingly important in everyday life, driven by advancements in artificial intelligence and machine learning. Multilingual countries utilize the SLID method to facilitate speech detection. This is accomplished by determining the language of the spoken parts using language recognizers. On the other hand, when working with multilingual datasets, the presence of multiple languages that have a shared origin presents a significant challenge for accurately classifying languages using automatic techniques. Further, one more challenge is the significant variance in speech signals caused by factors such as different speakers, content, acoustic settings, language differences, changes in voice modulation based on age and gender, and variations in speech patterns. In this study, we introduce the DBODL-MSLIS approach, which integrates biomimetic optimization techniques inspired by natural intelligence to enhance language classification. The proposed method employs Dung Beetle Optimization (DBO) with Deep Learning, simulating the beetle’s foraging behavior to optimize feature selection and classification performance. The proposed technique integrates speech preprocessing, which encompasses pre-emphasis, windowing, and frame blocking, followed by feature extraction utilizing pitch, energy, Discrete Wavelet Transform (DWT), and Zero crossing rate (ZCR). Further, the selection of features is performed by DBO algorithm, which removes redundant features and helps to improve efficiency and accuracy. Spoken languages are classified using Bayesian optimization (BO) in conjunction with a long short-term memory (LSTM) network. The DBODL-MSLIS technique has been experimentally validated using the IIIT Spoken Language dataset. The results indicate an average accuracy of 95.54% and an F-score of 84.31%. This technique surpasses various other state-of-the-art models, such as SVM, MLP, LDA, DLA-ASLISS, HMHFS-IISLFAS, GA base fusion, and VGG-16. We have evaluated the accuracy of our proposed technique against state-of-the-art biomimetic computing models such as GA, PSO, GWO, DE, and ACO. While ACO achieved up to 89.45% accuracy, our Bayesian Optimization with LSTM outperformed all others, reaching a peak accuracy of 95.55%, demonstrating its effectiveness in enhancing spoken language identification. The suggested technique demonstrates promising potential for practical applications in the field of multi-lingual voice processing. Full article

High-dimensional feature selection is one of the key problems of big data analysis. The binary particle swarm optimization (BPSO) method, when used to achieve feature selection for high-dimensional data problems, can get stuck in local optima, leading to reduced search efficiency and inferior feature selection results. This paper proposes a novel BPSO method with manta ray foraging learning strategies (BPSO-MRFL) to address the challenges of high-dimensional feature selection tasks. The BPSO-MRFL algorithm draws inspiration from the manta ray foraging optimization (MRFO) algorithm and incorporates several distinctive search strategies to enhance its efficiency and effectiveness. These search strategies include chain learning, cyclone learning, and somersault learning. Chain learning allows particles to learn from each other and share information more effectively in order to improve the social learning ability of the population. Cyclone learning introduces a gradual increase over iterations, which helps the BPSO-MRFL algorithm to transition smoothly from exploratory searching to exploitative searching, and it creates a balance between exploration and exploitation. Somersault learning enables particles to adaptively search within a changing search range and allows the algorithm to fine-tune the selected features, which enhances the algorithm’s local search ability and improves the quality of the selected subset. The proposed BPSO-MRFL algorithm was evaluated using 10 high-dimensional small-sample gene expression datasets. The results demonstrate that the proposed BPSO-MRFL algorithm achieves enhanced classification accuracy and feature reduction compared to traditional feature selection methods. Additionally, it exhibits competitive performance compared to other advanced feature selection methods. The BPSO-MRFL algorithm presents a promising approach to feature selection in high-dimensional data mining tasks. Full article

Intradialytic hypotension (IDH) is a critical complication in patients with chronic kidney disease undergoing dialysis, affecting both patient safety and treatment efficacy. This study examines the application of advanced machine learning techniques, combined with metaheuristic optimization methods, to improve predictive models for intradialytic hypotension (IDH) in hemodialysis patients. Given the critical nature of IDH, which can lead to significant complications during dialysis, the development of effective predictive tools is vital for improving patient safety and outcomes. Dialysis session data from 758 patients collected between January 2016 and October 2019 were analyzed. Particle Swarm Optimization, Grey Wolf Optimizer, Pendulum Search Algorithm, and Whale Optimization Algorithm were employed to reduce the feature space, removing approximately 45% of clinical and analytical variables while maintaining high recall for the minority class of patients experiencing hypotension. Among the evaluated models, the XGBoost classifier showed superior performance, achieving a macro F-score of 0.745 with a recall of 0.756 and a precision of 0.718. These results highlight the effectiveness of the combined approach for early identification of patients at risk for IDH, minimizing false negatives, and improving clinical decision-making in nephrology. Full article

Wearable fall-detection systems have received significant research attention during the last years. Fall detection in wearable devices presents key challenges, particularly in balancing high precision with low power consumption—both of which are essential for the continuous monitoring of older adults and individuals with reduced mobility. This study introduces a hybrid system that integrates a threshold-based model for preliminary detection with a deep learning-based approach that combines a CNN (Convolutional Neural Network) for spatial feature extraction with a LSTM (Long Short-Term Memory) model for temporal pattern recognition, aimed at improving classification accuracy. LoRa technology enables long-range, energy-efficient communication, ensuring real-time monitoring across diverse environments. The wearable device operates in ultra-low-power mode, capturing acceleration data at 20 Hz and transmitting a 4-s window when a predefined threshold in the acceleration magnitude is exceeded. The CNN-LSTM classifier refines event identification, significantly reducing false positives. This design extends operational autonomy to 178 h of continuous monitoring. The experimental and systematic evaluation of the prototype achieved a 96.67% detection rate (sensitivity) for simulated falls and a 100% specificity in classifying conventional Activities of Daily Living as non-falls. These results establish the system as a robust and scalable solution, effectively addressing limitations in power efficiency, connectivity, and detection accuracy while enhancing user safety and quality of life. Full article

Shoulder girdle plays a dominant role in coordinating the natural movements of the upper arm. Inverse kinematics, optimization, and data-driven approaches are usually used to conduct motion retargeting. However, these methods do not consider shoulder girdle movement. When the kinematic structure of human and that of exoskeletons share a similar joint configuration, analytical motion retargeting methods can be used for exoskeletons with shoulder girdle mechanism. This paper proposes a vector-based analytical motion retargeting approach for exoskeletons with shoulder girdle mechanism. The approach maps the vectors of the upper limb segments to the joint space using vector-based methods. Simulation results using four different motion descriptions confirm the method’s accuracy and efficiency. Full article

To overcome the problem of insufficient adaptability to the motion environment of lower limb exoskeleton robots, this paper introduces computer vision technology into the motion control of lower limb exoskeleton robots and studies an obstacle-crossing-motion method based on detecting obstacle feature information. Considering the feature information of different obstacles and the distance between obstacles and robots, a trajectory planning method based on direct point matching was used to generate offline adjusted gait trajectory libraries and obstacle-crossing gait trajectory libraries. A lower limb exoskeleton robot obstacle-crossing motion decision-making algorithm based on obstacle feature information is proposed by combining gait constraints and motion constraints, enabling it to select appropriate motion trajectories in the trajectory library. The proposed obstacle-crossing-motion method was validated at three distances between the obstacle and the robot and with the feature information of four obstacles. The experimental results show that the proposed method can select appropriate trajectories from the trajectory library based on the detected obstacle feature information and can safely complete obstacle-crossing motions. Full article

The Black-winged Kite Optimization Algorithm (BKA) is likely to experience a sluggish convergence rate when confronted with the optimization of complex multimodal functions. The fundamental algorithm has a tendency to get stuck in local optima, thus rendering it arduous to identify the global optimal solution. When dealing with large-scale data or high-dimensional optimization challenges, the BKA algorithm entails significant computational expenses, which might lead to excessive memory usage or prolonged running durations. In order to enhance the BKA and tackle these problems, a revised Black-winged Kite Optimization Algorithm (TGBKA) that incorporates the Tent chaos mapping and Gaussian mutation strategies is put forward. The algorithm is simulated and analyzed alongside other swarm intelligence algorithms by utilizing the CEC2017 test function set. The optimization outcomes of the test functions and the function convergence curves indicate that the TGBKA demonstrates superior optimization precision, a quicker convergence speed, as well as robust anti-interference and environmental adaptability. It is also contrasted with numerous similar algorithms via simulation experiments in various scene models for Unmanned Aerial Vehicle (UAV) path planning. In comparison to other algorithms, the TGBKA produces a shorter flight route, a higher convergence speed, and stronger adaptability to complex environments. It is capable of efficiently addressing UAV path planning issues and improving the UAV’s path planning abilities. Full article

Bionic micro air vehicles are currently being popularized for military as well as civilian use and dragonflies display a wealth of skill in their remarkable flight capabilities. This study designs an asymmetric motion flapping-wing mechanism inspired by the dragonfly, using a single actuator to achieve the coupling of stroke and pitch motion. This study simulates the motion of the dragonfly’s wings using the designed mechanism and experimentally validates the motion laws and aerodynamic characteristics of the mechanism. The analysis focuses on the asymmetry in the wing’s stroke and pitch motion and their aerodynamic implications. The flapping-wing mechanism accurately replicates the wing motion of a real dragonfly in flight, and the maximum lift-to-weight ratio can reach up to 230.2%, demonstrating significant aerodynamic benefits. This mechanism provides valuable guidance for the structural design and kinematic control of future flapping-wing vehicles. Full article

Raman spectroscopy is one of the best techniques for obtaining information concerning the physical–chemical interactions between a lipid and a solvent. Phospholipids in water are the main elements of cell membranes and, by means of their chemical and physical structures, their cells can interact with other biological molecules (i.e., proteins and vitamins) and express their own biological functions. Phospholipids, due to their amphiphilic structure, form biomimetic membranes which are useful for studying cellular interactions and drug delivery. Synthetic systems such as DPhPC-based liposomes replicate the key properties of biological membranes. Among the different models, phospholipid mimetic membrane models of lamellar vesicles have been greatly supported. In this work, a biomimetic system, a deuterium solution (50 mM) of the synthetic phospholipid 1,2-diphytanoyl-sn-glycero-3-phosphocholine (DPhDC), is studied using μ-Raman spectroscopy in a wide temperature range from −181.15 °C up to 22.15 °C, including the following temperatures: −181.15 °C, −146.15 °C, −111.15 °C, −76.15 °C, −61.15 °C, −46.15 °C, −31.15 °C, −16.15 °C, −1.15 °C, 14.15 °C, and 22.15 °C. Based on the Raman evidence, phase transitions as a function of temperature are shown and grouped into five classes, where the corresponding Raman modes describe the stretching of the (C−N) bond in the choline head group (gauche) and the asymmetric stretching of the (O−P−O) bond. The acquisition temperature of each Raman spectrum characterizes the rocking mode of the methylene of the acyl chain. These findings enhance our understanding of the role of artificial biomimetic lipids in complex phospholipid membranes and provide valuable insights for optimizing their use in biosensing applications. Although the phase stability of DPhPC is known, the collected Raman data suggest subtle molecular rearrangements, possibly due to hydration and second-order transitions, which are relevant for membrane modeling and biosensing applications. Full article

High-speed trains are some of the most important transportation vehicles requiring human–computer collaboration. This study investigated the effects of different types of icons on recognition performance and cognitive load during frequent observation and sudden takeover tasks in high-speed trains. The results of this study can be used to improve the efficiency of human–computer collaboration tasks and driving safety. In this study, 48 participants were selected for a simulated driving experiment on a high-speed train. The recognition reaction time, operation completion time, number of recognition errors, number of operation errors, SUS scale, and NASA-TLX questionnaire for the icons were all analyzed using analysis of variance (ANOVA) and the nonparametric Mann–Whitney U test. The results show that anthropomorphic icons can reduce the drivers’ visual fatigue and mental load in frequent observation tasks due to the anthropomorphic facial features attracting driver attention through simple lines and improving visual search efficiency. However, for the sudden takeover human–computer collaboration task, the facial features of the anthropomorphic icons were not recognized in a short period of time. Additionally, due to the positive emotions produced by the facial features, the drivers did not perceive the suddenness and danger of the sudden takeover human–computer collaboration task, resulting in the traditional icons being more capable of arousing the drivers’ alertness and helping them take over the task quickly. At the same time, neither type of icon triggered misrecognition or operation for sufficiently skilled drivers. These research results can provide guidance for the design of icons in human–computer collaborative interfaces for different types of driving tasks in high-speed trains, which can help improve the recognition speed, reaction speed, and safety of drivers. Full article

Subsoiling is an effective tillage method for breaking up the plough pan and reducing soil bulk density. However, subsoilers often encounter challenges such as high draft resistance and excessive energy consumption during operation. In this study, the claw toes of the badger and the scales of the pangolin were selected as bionic prototypes, based on which coupling bionic subsoilers were designed. The discrete element method (DEM) was used to simulate and analyze the interactions between soil and both the standard subsoiler and coupling bionic subsoilers. Field experiments were conducted to validate the simulation results. The simulation results showed that the coupling bionic subsoilers reduced the draft force by 7.70–16.02% compared to the standard subsoiler at different working speeds. Additionally, the soil disturbance coefficient of the coupling bionic subsoilers decreased by 5.91–13.57%, and the soil bulkiness was reduced by 2.84–18.41%. The field experiment results showed that coupling bionic subsoilers reduced the average draft force by 11.06% and decreased the soil disturbance area. The field experiments validated the accuracy of DEM simulation results. This study provides valuable insights for designing more efficient subsoilers. Full article

Brain-inspired bionic navigation is a groundbreaking technological approach that emulates the biological navigation systems found in mammalian brains. This innovative method leverages experiences within cognitive space to plan global paths to targets, showcasing remarkable autonomy and adaptability to various environments. This work introduces a novel bionic, goal-oriented path planning approach for mobile robots. First, an experience map is constructed using NeuroSLAM, a bio-inspired simultaneous localization and mapping method. Based on this experience map, a successor representation model is then developed through reinforcement learning, and a goal-oriented predictive map is formulated to address long-term reward estimation challenges. By integrating goal-oriented rewards, the proposed algorithm efficiently plans optimal global paths in complex environments for mobile robots. Our experimental validation demonstrates the method’s effectiveness in experience sequence prediction and goal-oriented global path planning. The comparative results highlight its superior performance over traditional Dijkstra’s algorithm, particularly in terms of adaptability to environmental changes and computational efficiency in optimal global path generation. Full article

The rapidly evolving field of functional yarns has garnered substantial research attention due to their exceptional potential in enabling next-generation electronic textiles for wearable health monitoring, human–machine interfaces, and soft robotics. Despite notable advancements, the development of yarn-based strain sensors that simultaneously achieve high flexibility, stretchability, superior comfort, extended operational stability, and exceptional electrical performance remains a critical challenge, hindered by material limitations and structural design constraints. Here, we present a bioinspired, hierarchically structured core-sheath yarn sensor (CSSYS) engineered through an efficient dip-coating process, which synergistically integrates the two-dimensional conductive MXene nanosheets and one-dimensional silver nanowires (AgNWs). Furthermore, the sensor is encapsulated using a yarn-based protective layer, which not only preserves its inherent flexibility and wearability but also effectively mitigates oxidative degradation of the sensitive materials, thereby significantly enhancing long-term durability. Drawing inspiration from the natural architecture of plant stems—where the inner core provides structural integrity while a flexible outer sheath ensures adaptive protection—the CSSYS exhibits outstanding mechanical and electrical performance, including an ultralow strain detection limit (0.05%), an ultrahigh gauge factor (up to 744.45), rapid response kinetics (80 ms), a broad sensing range (0–230% strain), and exceptional cyclic stability (>20,000 cycles). These remarkable characteristics enable the CSSYS to precisely capture a broad spectrum of physiological signals, ranging from subtle arterial pulsations and respiratory rhythms to large-scale joint movements, demonstrating its immense potential for next-generation wearable health monitoring systems. Full article

High-dimensional complex optimization problems are pervasive in engineering and scientific computing, yet conventional algorithms struggle to meet collaborative optimization requirements due to computational complexity. While Chicken Swarm Optimization (CSO) demonstrates an intuitive understanding and straightforward implementation for low-dimensional problems, it suffers from limitations including a low convergence precision, uneven initial solution distribution, and premature convergence. This study proposes an Adaptive Dynamically Enhanced Variant of Chicken Swarm Optimization (ADVCSO) algorithm. First, to address the uneven initial solution distribution in the original algorithm, we design an elite perturbation initialization strategy based on good point sets, combining low-discrepancy sequences with Gaussian perturbations to significantly improve the search space coverage. Second, targeting the exploration–exploitation imbalance caused by fixed role proportions, a dynamic role allocation mechanism is developed, integrating cosine annealing strategies to adaptively regulate flock proportions and update cycles, thereby enhancing exploration efficiency. Finally, to mitigate the premature convergence induced by single update rules, hybrid mutation strategies are introduced through phased mutation operators and elite dimension inheritance mechanisms, effectively reducing premature convergence risks. Experiments demonstrate that the ADVCSO significantly outperforms state-of-the-art algorithms on 27 of 29 CEC2017 benchmark functions, achieving a 2–3 orders of magnitude improvement in convergence precision over basic CSO. In complex composite scenarios, its convergence accuracy approaches that of the championship algorithm JADE within a 10⁻² magnitude difference. For collaborative multi-subproblem optimization, the ADVCSO exhibits a superior performance in both Multiple Traveling Salesman Problems (MTSPs) and Multiple Knapsack Problems (MKPs), reducing the maximum path length in MTSPs by 6.0% to 358.27 units while enhancing the MKP optimal solution success rate by 62.5%. The proposed algorithm demonstrates an exceptional performance in combinatorial optimization and holds a significant engineering application value. Full article

In this study, a brand-new algorithm called the Comprehensive Adaptive Enterprise Development Optimizer (CAED) is proposed to overcome the drawbacks of the Enterprise Development (ED) algorithm in complex optimization tasks. In particular, it aims to tackle the problems of slow convergence and low precision. To enhance the algorithm’s ability to break free from local optima, a lens imaging reverse learning approach is incorporated. This approach creates reverse solutions by utilizing the concepts of optical imaging. As a result, it expands the search range and boosts the probability of finding superior solutions beyond local optima. Moreover, an environmental sensitivity-driven adaptive inertial weight approach is developed. This approach dynamically modifies the equilibrium between global exploration, which enables the algorithm to search for new promising areas in the solution space, and local development, which is centered on refining the solutions close to the currently best-found areas. To evaluate the efficacy of the CAED, 23 benchmark functions from CEC2005 are chosen for testing. The performance of the CAED is contrasted with that of nine other algorithms, such as the Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO), and the Antlion Optimizer (AOA). Experimental findings show that for unimodal functions, the standard deviation of the CAED is almost 0, which reflects its high accuracy and stability. In the case of multimodal functions, the optimal value obtained by the CAED is notably better than those of other algorithms, further emphasizing its outstanding performance. The CAED algorithm is also applied to engineering optimization challenges, like the design of cantilever beams and three-bar trusses. For the cantilever beam problem, the optimal solution achieved by the CAED is 13.3925, with a standard deviation of merely 0.0098. For the three-bar truss problem, the optimal solution is 259.805047, and the standard deviation is an extremely small 1.11 × 10⁻⁷. These results are much better than those achieved by the traditional ED algorithm and the other comparative algorithms. Overall, through the coordinated implementation of multiple optimization strategies, the CAED algorithm exhibits high precision, strong robustness, and rapid convergence when searching in complex solution spaces. As such, it offers an efficient approach for solving various engineering optimization problems. Full article

This paper presents a bio-inspired rigid–flexible continuum robot driven by flexible shaft tension–torsion synergy, tackling the trade-off between actuation complexity and flexibility in continuum robots. Inspired by the muscular arrangement of octopus arms, enabling versatile multi-degree-of-freedom (DoF) movements, the robot achieves 6-DoF motion and 1-DoF gripper opening and closing movement with only six flexible shafts, simplifying actuation while boosting dexterity. A comprehensive kinetostatic model, grounded in Cosserat rod theory, is developed; this model explicitly incorporates the coupling between the spinal rods and flexible shafts, the distributed gravitational effects of spacer disks, and friction within the guide tubes. Experimental validation using a physical prototype reveals that accounting for spacer disk gravity diminishes the maximum shape prediction error from 20.56% to 0.60% relative to the robot’s total length. Furthermore, shape perception experiments under no-load and 200 g load conditions show average errors of less than 2.01% and 2.61%, respectively. Performance assessments of the distal rigid joint showcased significant dexterity, including a 53° grasping range, 360° continuous rotation, and a pitching range from −40° to +45°. Successful obstacle avoidance and long-distance target reaching experiments further demonstrate the robot’s effectiveness, highlighting its potential for applications in medical and industrial fields. Full article

Insect flight mechanisms are highly efficient and involve complex hinge structures that facilitate amplified wing movement through thoracic deformation. However, in the field of flapping-wing robots, the replication of thoracic skeletal structures has received little attention. In this study, we propose and compare two different hinge models inspired by insect flight: an elastic hinge model (EHM) and an axle hinge model (AHM). Both models were fabricated using 3D printing technology using PLA material. The EHM incorporates flexible structures in both the hinge and lateral scutum regions, allowing for deformation-driven wing motion. In contrast, the AHM employs metal pins in the hinge region to reproduce joint-like articulation, while still permitting elastic deformation in the lateral scutum. To evaluate their performance, we employed an SMA actuator to generate flapping motion, and measured the wing displacement, flapping frequency, and exoskeletal deformation. The experimental results demonstrate that the EHM achieves wing flapping through overall structural flexibility, whereas the AHM provides more defined hinge motion while maintaining exoskeletal elasticity. These findings contribute to our understanding of the role of hinge mechanics in bioinspired flapping-wing robots. Future research will focus on optimizing these mechanisms for higher frequency operation, weight reduction, and better energy efficiency. Full article

The Sparrow Search Algorithm (SSA), proposed by Jiankai Xue in 2020, is a swarm intelligence optimization algorithm that has received extensive attention due to its powerful optimization-seeking ability and rapid convergence. However, similar to other swarm intelligence algorithms, the SSA has the problem of being prone to falling into local optimal solutions during the optimization process, which limits its application effectiveness. To overcome this limitation, this paper proposes a Modified Sparrow Search Algorithm (MSSA), which enhances the algorithm’s performance by integrating three optimization strategies. Specifically, the Latin Hypercube Sampling (LHS) method is employed to achieve a uniform distribution of the initial population, laying a solid foundation for global search. An adaptive weighting mechanism is introduced in the producer update phase to dynamically adjust the search step size, effectively reducing the risk of the algorithm falling into local optima in later iterations. Meanwhile, the cat mapping perturbation and Cauchy mutation operations are integrated to further enhance the algorithm’s global exploration ability and local development efficiency, accelerating the convergence process and improving the quality of the solutions. This study systematically validates the performance of the MSSA through multi-dimensional experiments. The MSSA demonstrates excellent optimization performance on 23 benchmark test functions and the CEC2019 standard test function set. Its application to three practical engineering problems, namely the design of welded beams, reducers, and cantilever beams, successfully verifies the effectiveness of the algorithm in real-world scenarios. By comparing it with deterministic algorithms such as DIRET and BIRMIN, and based on the five-dimensional test functions generated by the GKLS generator, the global optimization ability of the MSSA is thoroughly evaluated. In addition, the successful application of the MSSA to the problem of robot path planning further highlights its application advantages in complex practical scenarios. Experimental results show that, compared with the original SSA, the MSSA has achieved significant improvements in terms of convergence speed, optimization accuracy, and robustness, providing new ideas and methods for the research and practical application of swarm intelligence optimization algorithms. Full article

Natural organisms have evolved highly sophisticated mechanisms for managing water across a broad range of environmental conditions, from arid to highly humid regions. Among these mechanisms, directional liquid transport (DLT) is particularly noteworthy, as it relies on structural designs that facilitate the spontaneous movement of liquids along predefined pathways without the need for external energy sources. The increasing interest in DLT systems is primarily driven by their potential applications in fields such as microfluidics, water harvesting, and biomedical engineering. The focus on DLT is motivated by its ability to inspire efficient, energy-independent liquid transport technologies, which hold significant promise for both fundamental research and practical applications. Notably, wedge-shaped DLT systems have emerged as a particularly promising area of study due to their advantages in terms of manufacturability, liquid collection efficiency, and scalability—attributes that are essential for industrial deployment. This review seeks to explore natural wedge-based DLT systems, providing an in-depth analysis of their underlying principles and their potential for engineering replication. The discussion includes examples from nature, such as desert beetles and spider silk, and explores the theoretical mechanisms governing these systems, including the role of surface energy gradients and Laplace pressure. Additionally, the review highlights advanced fabrication techniques, such as photolithography and laser micromachining, which are crucial for the development of these systems in practical applications. Full article

This study investigates how different levels of extrusion pressure during 3D printing affect the cell viability of human bronchial epithelial (HBE) cells embedded in printed samples. In this study, samples were printed at three levels of extrusion pressure. The cell viability was assessed through live/dead staining via microscopic imaging. The results show that increasing the extrusion pressure from 50 to 100 kPa led to a higher degree of cell death. These results demonstrate how the extrusion pressure affects the viability of HBE cells and provide a basis for future studies on pressure-induced responses in respiratory tissues. Full article

While deep neural networks demonstrate robust performance in visual tasks, the long-tail distribution of real-world data leads to significant recognition accuracy degradation in critical scenarios such as medical human–robot affective interaction, particularly the misidentification of low-frequency negative emotions (e.g., fear and disgust) that may trigger psychological resistance in patients. Here, we propose a method based on dynamic intra-class clustering (DICC) to optimize the class imbalance problem in facial expression recognition tasks. The DICC method dynamically adjusts the distribution of majority classes by clustering them into subclasses and generating pseudo-labels, which helps the model learn more discriminative features and improve classification accuracy. By comparing with existing methods, we demonstrate that the DICC method can help the model achieve superior performance across various facial expression datasets. In this study, we conducted an in-depth evaluation of the DICC method against baseline methods using the FER2013, MMAFEDB, and Emotion-Domestic datasets, achieving improvements in classification accuracy of 1.73%, 1.97%, and 5.48%, respectively. This indicates that the DICC method can effectively enhance classification precision, especially in the recognition of minority class samples. This approach provides a novel perspective for addressing the class imbalance challenge in facial expression recognition and offers a reference for future research and applications in related fields. Full article

Background: Composite resin veneers have gained popularity due to their affordability and minimally invasive application as biomimetic restorations. However, long-term clinical challenges, such as discoloration, wear, and reduced fracture resistance, necessitate their replacement over time. Ceramic veneers, particularly feldspathic and lithium disilicate, offer superior esthetics and durability, as demonstrated by studies showing their high survival rates and enamel-preserving preparation designs. However, while ceramic veneers survive longer than composite resin veneers, ceramic veneers may need to be removed and replaced. Reports vary for using Er:YAG (erbium-doped yttrium aluminum garnet) lasers for the removal of existing veneers. Methods: A review was conducted to evaluate the effectiveness of removing restorative materials with an Er:YAG laser. A clinical study was included, highlighting the conservative removal of aged composite resin veneers using the Er:YAG laser. This method minimizes enamel damage and facilitates efficient debonding. Following laser application, minimally invasive tooth preparation was performed, and feldspathic porcelain veneers were bonded. Results: The review showed positive outcomes whenever the Er:YAG laser was used. In the case study, after a 3-year follow-up, the restorations exhibited optimal function and esthetics. Conclusions: Laser-assisted debonding provides a safe and predictable method for replacing failing composite veneers with ceramic alternatives, aligning with contemporary biomimetic principles. Full article

In this work, the position control of a cable-driven soft robot is proposed through the approximation of its kinematic model. This approximation is derived from artificial learning rules via neural networks and experimentally observed data. To improve the learning process, a combination of active sampling and Model Agnostic Meta Learning is carried out to improve the data based model to be used in the control stage through the inverse velocity kinematics derived from the data based modeling along with a self differentiation procedure to come up with the pseudo inverse of the robot Jacobian. The proposal is verified in a designed and constructed cable-driven soft robot with three actuators and position measurement through a vision system with three-dimensional motion. Some preliminary assessments (tension and repeatability) were performed to validate the robot movement generation, and, finally, a 3D reference trajectory was tracked using the proposed approach, achieving competitive tracking errors. Full article

Research on modular robots for space exploration has primarily focused on reconfiguration, with limited attention given to the maneuverability in space environment, which is essential for harnessing the advantages of reconfiguration. In this paper, a modular snake-like robot (MSR) is designed, which is expected to emulate a snake to navigate complex environments and employ the reconfiguration capability for on-site shape-shifting. To this end, a snake-like motion analysis and planning method is proposed for MSR. Firstly, we explore the feasibility of utilizing modules in realizing snake-like motion, including functional compatibility and structural constraints. Secondly, we analyze the kinematics of MSR and design joint coordination motion schemes to meet the requirements of snake-like motion. Finally, a path planning method based on reinforcement learning is proposed, which fully considers the motion characteristics and the structural constraints. Through motion analysis and planning, a balance between environmental adaptability and versatility can be achieved. Simulations of comparisons and potential applications further demonstrate the significant advantages of MSR in space exploration. Full article

In order to obtain high-performance 3D printed parts, this study focuses on the key performance indicator of impact toughness. The parametric modeling software Rhino 6 is used to design impact specimens, and the laser selective melting equipment DiMetal-100, independently developed by the South China University of Technology, is used to manufacture impact specimens. Subsequently, the CoCrMo alloy parts were annealed using an MXQ1600-40 box-type atmosphere furnace and subjected to impact testing using a cantilever beam impact testing machine XJV-22. Fractal theory was applied to analyze the fractal behavior of the resulting impact fracture surfaces. The research results indicate that the 3D-printed impact specimens exhibited excellent surface quality, characterized by brightness, low roughness, and the absence of significant defects such as warping or deformation. In terms of annealing treatment, lower annealing temperatures did not improve the impact performance of SLM-formed CoCrMo alloy parts but instead led to a decrease in toughness. While increasing the annealing temperature can improve toughness to some extent, the effect is limited. Furthermore, the relationship between impact energy and heat treatment temperature exhibits a U-shaped trend. The fractal dimension analysis shows that the parts annealed in a 1200 °C furnace have the highest fractal dimension and better toughness performance. This study introduces a novel approach by comprehensively integrating advanced 3D printing technology, annealing processes, and fractal theory analysis to systematically investigate the influence of annealing temperature on the impact properties of 3D-printed CoCrMo alloy parts, thereby establishing a solid foundation for the application of high-performance 3D printed parts. Full article