Biomimetics, Volume 10, Issue 5 (May 2025) – 89 articles

In order to solve problems with the original crayfish optimization algorithm (COA), such as reduced diversity, local optimization, and insufficient convergence accuracy, a multi-strategy optimization algorithm for crayfish based on differential evolution, named the ICOA, is proposed. First, the elite chaotic difference strategy is used for population initialization to generate a more uniform crayfish population and increase the quality and diversity of the population. Secondly, the differential evolution strategy and the dimensional variation strategy are introduced to improve the quality of the crayfish population before its iteration and to improve the accuracy of the optimal solution and the local search ability for crayfish at the same time. To enhance the updating approach to crayfish exploration, the Levy flight strategy is adopted. This strategy aims to improve the algorithm’s search range and local search capability, prevent premature convergence, and enhance population stability. Finally, the adaptive parameter strategy is introduced to improve the development stage of crayfish, so as to better balance the global search and local mining ability of the algorithm, and to further enhance the optimization ability of the algorithm, and the ability to jump out of the local optimal. In addition, a comparison with the original COA and two sets of optimization algorithms on the CEC2019, CEC2020, and CEC2022 test sets was verified by Wilcoxon rank sum test. The results show that the proposed ICOA has strong competition. At the same time, the performance of ICOA is tested against different high-performance algorithms on 6 engineering optimization examples, 30 high–low-dimension constraint problems and 2 large-scale NP problems. Numerical experiments results show that ICOA has superior performance on a range of engineering problems and exhibits excellent performance in solving complex optimization problems. Full article

In recent years, metaheuristic algorithms have garnered significant attention for their efficiency in solving complex optimization problems. However, their performance critically depends on maintaining a balance between global exploration and local exploitation; a deficiency in either can result in premature convergence to local optima or low convergence efficiency. To address this challenge, this paper proposes an enhanced ivy algorithm guided by a particle swarm optimization (PSO) mechanism, referred to as IVYPSO. This hybrid approach integrates PSO’s velocity update strategy for global searches with the ivy algorithm’s growth strategy for local exploitation and introduces an ivy-inspired variable to intensify random perturbations. These enhancements collectively improve the algorithm’s ability to escape local optima and enhance the search stability. Furthermore, IVYPSO adaptively selects between local growth and global diffusion strategies based on the fitness difference between the current solution and the global best, thereby improving the solution diversity and convergence accuracy. To assess the effectiveness of IVYPSO, comprehensive experiments were conducted on 26 standard benchmark functions and three real-world engineering optimization problems, with the performance compared against 11 state-of-the-art intelligent optimization algorithms. The results demonstrate that IVYPSO outperformed most competing algorithms on the majority of benchmark functions, exhibiting superior search capability and robustness. In the stability analysis, IVYPSO consistently achieved the global optimum across multiple runs on the three engineering cases with reduced computational time, attaining a 100% success rate (SR), which highlights its strong global optimization ability and excellent repeatability. Full article

In this work, we explore the possibility of using the topology and weight distribution of the connectome of a Drosophila, or fruit fly, as a reservoir for multivariate chaotic time-series prediction. Based on the information taken from the recently released full connectome, we create the connectivity matrix of an Echo State Network. Then, we use only the most connected neurons and implement two possible selection criteria, either preserving or breaking the relative proportion of different neuron classes which are also included in the documented connectome, to obtain a computationally convenient reservoir. We then investigate the performance of such architectures and compare them to state-of-the-art reservoirs. The results show that the connectome-based architecture is significantly more resilient to overfitting compared to the standard implementation, particularly in cases already prone to overfitting. To further isolate the role of topology and synaptic weights, hybrid reservoirs with the connectome topology but random synaptic weights and the connectome weights but random topologies are included in the study, demonstrating that both factors play a role in the increased overfitting resilience. Finally, we perform an experiment where the entire connectome is used as a reservoir. Despite the much higher number of trained parameters, the reservoir remains resilient to overfitting and has a lower normalized error, under 2%, at lower regularisation, compared to all other reservoirs trained with higher regularisation. Full article

Both for design issues and for the study, analysis, and understanding of the interaction of workers with exoskeletons, the study of adverse effects provides criteria to improve the design of more efficient exoskeletons with better ergonomics and long-term usability. In this work, a scoping review was carried out on adverse effects due to the prolonged use of upper-limb exoskeletons, which have been evidenced in the scientific literature. The causes of the effects are described in terms of their impacts on the physiological, psychological, and technological aspects that affect the user. A scoping review of articles of the last ten years on negative effects of upper-extremity exoskeletons for industrial tasks was carried out following the guidelines of the PRISMA^® methodology with three phases: formulation of questions, definition of scopes and exhaustive search in SCOPUS, Web of Science, Science Direct, Taylor & Francis, and PubMed. The selection was made by two review authors with a Cohen’s Kappa coefficient of 0.9530, indicating high agreement. The effectiveness of upper-limb exoskeletons depends on the environment and the task, so an adaptable ergonomic design, field validations, and standards are required to ensure their functionality and acceptance. Use of exoskeletons mainly activates the posterior deltoid and latissimus dorsi and reduces the activity of muscles such as the trapezius, pectoralis major, anterior and middle deltoids, biceps brachii, brachioradialis, and flexor carpi radialis. Full article

This review provides an in-depth examination of the technologies and methods used for the acquisition and processing of kinetic and kinematic variables in human motion analysis. This review analyzes the capabilities and limitations of motion-capture cameras (MCCs), inertial measurement units (IMUs), force platforms, and other prototype technologies. The role of advanced processing techniques, including filtering and transformation methods, and the increasing integration of artificial intelligence (AI) and machine learning (ML) for data classification is also discussed. These advancements enhance the precision and efficiency of biomechanical analyses, paving the way for more accurate assessments of human movement patterns. The review concludes by providing guidelines for the effective application of these technologies in both clinical and research settings, emphasizing the need for comprehensive validation to ensure reliability. This comprehensive overview serves as a valuable resource for researchers and professionals in the field of biomechanics, guiding the selection and application of appropriate technologies and methodologies for human movement analysis. Full article

The starfish optimization algorithm (SFOA) is a metaheuristic evolutionary intelligence algorithm with a great global search capability and strong adaptability. Although the SFOA has a good global search capability, it is not accurate enough in local search and converges slowly. To further enhance this convergence ability and global optimization ability, an enhanced starfish optimization algorithm (SFOAL) is proposed that combines sine chaotic mapping, t-distribution mutation, and logarithmic spiral reverse learning. The SFOAL can remarkably enhance both the global and local convergence capabilities of the algorithm, leading to a more rapid convergence speed and greater stability. In total, 23 benchmark functions and CEC2021 were used to test the development, search, and convergence capabilities of the SFOAL. The SFOAL was compared in detail with other algorithms. The experimental results demonstrated that the overall performance of the SFOAL was better than that of other algorithms, and the joint strategy could effectively balance the development and search capabilities to obtain stronger global and local optimization capabilities. For solving practical problems, the SFOAL was used to optimize the back propagation (BP) neural network to solve the ultra-wideband line-of-sight positioning problem. The results showed that the SFOAL-BP neural network had a smaller average position error compared to the random BP neural network and the SFOA-BP neural network, so it can be used to solve practical application problems. Full article

Long-term manual material handling (MMH) work leads to the trend of the younger onset of work-related musculoskeletal disorders (WMSDs), with low back pain (LBP) being the most common, which causes great trouble for both society and patients. To effectively prevent LBP and provide support for workers engaged in MMH work, wearable lumbar assistive exoskeletons have played a key role in industrial scenarios. This paper divides wearable lumbar assistive exoskeletons into powered, unpowered, and quasi-passive types, systematically reviews the research status of each type of exoskeleton, and compares and discusses the key factors such as driving mode, mechanical structure, control strategy, performance evaluation, and human–machine interaction. It is found that many studies focus on the assistive performance, human–machine coupling coordination, and adaptability of wearable lumbar assistive exoskeletons. At the same time, the analysis results show that there are many types of performance evaluation indicators, but a unified and standardized evaluation method and system are still lacking. This paper analyzes current research findings, identifies existing issues, and provides recommendations for future research. This study provides a theoretical basis and design ideas for the development of wearable lumbar assistive exoskeleton systems. Full article

This paper addresses the problem of observability analysis and enhancement for UAV target localization and sensor bias estimation with bearing-only measurement. Inspired by the compound eye vision, a bio-inspired observability analysis method is proposed for stochastic systems. Furthermore, a performance metric that can be utilized in UAV trajectory optimization for observability enhancement of the target localization system is formulated based on maximum mean discrepancy. The performance metric and the distance of the UAV relative to the target are utilized as objective functions for trajectory optimization. To determine the decision variables (the UAV’s velocity and turn rate) for UAV maneuver decision making, a multi-objective optimization framework is constructed, and is subsequently solved via the nonlinear constrained multi-objective whale optimization algorithm. Finally, the analytical results are validated through numerical simulations and comparative analyses. The proposed method demonstrates superior convergence in both target localization and sensor bias estimation. The nonlinear constrained multi-objective whale optimization algorithm achieves minimal values for both generational distance and inverted generational distance, demonstrating superior convergence and diversity characteristics. Full article

This paper presents the design and control of a quadruped robot equipped with a six-degree-of-freedom (6-DOF) bionic active torso based on a parallel mechanism. Inspired by the compliant and flexible torsos of quadrupedal mammals, the proposed torso structure enhances locomotion performance by enabling coordinated motion between the torso and legs. A complete kinematic model of the bionic torso and the whole body of the quadruped robot is developed. To address the variation in inertial properties caused by torso motion, a model predictive control (MPC) strategy with a variable center of mass (CoM) is proposed for integrated whole-body motion control. Comparative simulations under trot gait are conducted between rigid-torso and active-torso configurations. Results show that the active torso significantly improves gait flexibility, postural stability, and locomotion efficiency. This study provides a new approach to enhancing biomimetic locomotion in quadruped robots through active torso-leg coordination. Full article

An efficient solar water evaporator is an important strategy for addressing the problem of water shortage. Constructing high-performance solar interfacial evaporators through bionic design has become a crucial approach for performance enhancement. Through the study of zebra patterns, it has been found that the black-and-white alternating patterns generate vortices on the surface of the zebra’s skin, thereby reducing the temperature. By utilizing the vortices brought about by the temperature difference, the design of a solar water evaporator is created based on the bionic zebra pattern, so as to improve its water evaporation performance. In this work, green and sustainable wood is used as the base of the evaporator, and the bionic design of zebra stripes is adopted. Meanwhile, the following research is conducted: The wood is cut into thin slices with dimensions of 30 × 30 × 5 mm³, and a delignification treatment is performed. Tannic acid-Fe ions are used as the photothermal material for functionalization. A series of stable patterned water evaporators based on delignification wood loaded with tannic acid-Fe ion complex (TA-Fe³⁺) are successfully prepared. Among them, the wood-based solar water evaporator with 3 mm zebra stripes exhibits excellent photothermal water evaporation performance, achieving a water evaporation rate of 1.44 kg·m⁻²·h⁻¹ under the illumination intensity of one sun. Its water evaporation performance is significantly superior to that of other coating patterns, proving that the bionic design of zebra patterns is effective and can improve water evaporation efficiency. This work provides new insights into the development of safe and environmentally friendly solar interfacial water evaporation materials through bionic design. Full article

Routing and scheduling in Time-Sensitive Networking (TSN) is an NP-hard problem. In this paper, we propose a novel routing and scheduling approach for TSN based on evolutionary algorithm. Specifically, we introduce a flow grouping method that leverages the greatest common divisor to optimize flow aggregation. On this basis, we develop a flow routing strategy that employs a genetic algorithm, where the evaluation function considers not only flow combinability but also path length and network load. By exploiting the non-combinable properties of flows, we effectively reduce the search space for the genetic algorithm. Furthermore, we design a scheduling method based on differential evolution algorithms tailored to TSN’s requirements of zero jitter and no frame loss. We propose a gene coding method and rigorously prove its correctness, which significantly reduces the search space of the differential evolution algorithm. The experimental results demonstrate that our approach enables more flows to traverse along the shortest path compared to both k-shortest path methods and integer linear programming approaches, while achieving a faster execution time in large-scale scheduling scenarios. Full article

Motion perception is a fundamental function of biological visual systems, enabling organisms to navigate dynamic environments, detect threats, and track moving objects. Inspired by the mechanisms of biological motion processing, we propose an Unsupervised Artificial Visual System for motion direction detection. Unlike traditional supervised learning approaches, our model employs unsupervised learning to classify local motion direction detection neurons and group those with similar directional preferences to form macroscopic motion direction detection neurons. The activation of these neurons is proportional to the received input, and the neuron with the highest activation determines the macroscopic motion direction of the object. The proposed system consists of two layers: a local motion direction detection layer and an unsupervised global motion direction detection layer. For local motion detection, we adopt the Local Motion Detection Neuron (LMDN) model proposed in our previous work, which detects motion in eight different directions. The outputs of these neurons serve as inputs to the global motion direction detection layer, which employs a Gaussian Mixture Model (GMM) for unsupervised clustering. GMM, a probabilistic clustering method, effectively classifies local motion detection neurons according to their preferred directions, aligning with biological principles of sensory adaptation and probabilistic neural processing. Through repeated exposure to motion stimuli, our model self-organizes to detect macroscopic motion direction without the need for labeled data. Experimental results demonstrate that the GMM-based global motion detection layer successfully classifies motion direction signals, forming structured motion representations akin to biological visual systems. Furthermore, the system achieves motion direction detection accuracy comparable to previous supervised models while offering a more biologically plausible mechanism. This work highlights the potential of unsupervised learning in artificial vision and contributes to the development of adaptive motion perception models inspired by neural computation. Full article

This paper proposes an adaptive multi-threshold image segmentation method named IBKA-OTSU to address the limitations of existing deep learning-based image segmentation methods, particularly their heavy reliance on large-scale annotated datasets and high computational complexity. The proposed algorithm significantly enhances the capability of complex remote sensing scenarios by systematic improvements to core algorithm components, including population initialization strategy, attack behavior patterns, migration mechanisms, and opposition-based learning strategy. The improved intelligent optimization algorithm is innovatively integrated with the OTSU threshold method to establish a multi-threshold segmentation model specifically designed for remote sensing imagery. Experimental validation using representative samples from the ISPRS Potsdam benchmark dataset demonstrates that our IBKA-optimized OTSU multi-threshold segmentation method outperforms traditional IBKA-optimized pulse coupled neural network (PCNN) approaches in remote sensing image analysis. Quantitative evaluations reveal substantial improvements in the dice coefficient across six randomly selected remote sensing images, achieving performance enhancements of 7.76%, 11.99%, 30.75%, 22.91%, 44.37%, and 18.55%, respectively. This research provides an effective technical solution for intelligently interpreting remote sensing imagery in resource-constrained environments, demonstrating significant theoretical value and practical application potential in engineering implementations. Full article

To address lower limb fatigue in workers engaged in prolonged standing, this study proposes a structural design for a medial-support passive exoskeleton seat. The design incorporates support rods positioned along the medial aspect of the user’s lower limbs and features an adaptive telescopic rod system, enhancing sitting stability and reducing collision risks in workplace environments. Human motion capture technology was used to collect kinematic data of the lower limbs, and a mathematical model of center-of-gravity variation was developed to calculate and optimize the exoskeleton’s structural parameters. Static analysis was performed using ANSYS software (2025 R1) to evaluate the structural integrity of the design. The effectiveness of the exoskeleton seat was validated through surface electromyography (sEMG) experiments, with results showing that the exoskeleton significantly reduces lower limb muscle load by 49.2% to 72.9%. Additionally, force plate experiments demonstrated that the exoskeleton seat improves stability, with a 39.2% reduction in the average displacement of the center of pressure (CoP), confirming its superior postural alignment and balance. The design was also compared with existing exoskeleton chairs, showing comparable or better performance in terms of muscle load reduction, stability, and overall effectiveness. Full article

Dragonflies exhibit remarkable flight capabilities, and their wings feature corrugated structures that are distinct from conventional airfoils. This study investigates the aerodynamic effects of three corrugation parameters on gliding performance at a Reynolds number of 1350 and angles of attack ranging from 0° to 20°: (1) chordwise corrugation position, (2) linear variation in corrugation amplitude toward the trailing edge, and (3) the number of trailing-edge corrugations. The results show that when corrugation structures are positioned closer to the trailing edge, they generate localized vortices in the mid-forward region of the upper surface, thereby enhancing aerodynamic performance. Further studies show that a linear increase in corrugation amplitude toward the trailing edge significantly delays the shedding of the leading-edge vortex (LEV), produces a more coherent LEV, and reduces the number of vortices within the corrugation grooves on the lower surface. Consequently, the lift coefficient is maximized with an enhancement of 28.99%. Additionally, reducing the number of trailing-edge corrugations makes the localized vortices on the upper surface approach the trailing edge and merge into larger, more continuous LEVs. The vortices on the lower surface grooves also decrease in number, and the lift coefficient is maximally increased by 20.09%. Full article

The wing kinematics of birds plays a significant role in their excellent unsteady aerodynamic performance. However, most studies investigate the influence of different kinematic parameters of flapping wings on their aerodynamic performance based on simple harmonic motions, which neglect the aerodynamic effects of the real flapping motion. The purpose of this article was to study the effects of wing kinematics on aerodynamic performance for a pigeon-inspired flapping wing. In this article, the dynamic geometric shape of a flapping wing was reconstructed based on data of the pigeon wing profile. The 3D wingbeat kinematics of a flying pigeon was extracted from the motion trajectories of the wingtip and the wrist during cruise flight. Then, we used a hybrid RANS/LES method to study the effects of wing kinematics on the aerodynamic performance and flow patterns of the pigeon-inspired flapping wing. First, we investigated the effects of dynamic spanwise twisting on the lift and thrust performance of the flapping wing. Numerical results show that the twisting motion weakens the leading-edge vortex (LEV) on the upper surface of the wing during the downstroke by reducing the effective angle of attack, thereby significantly reducing the time-averaged lift and power consumption. Then, we further studied the effects of the 3D sweeping motion on the aerodynamic performance of the flapping wing. Backward sweeping reduces the wing area and weakens the LEV on the lower surface of the wing, which increases the lift and reduces the aerodynamic power consumption significantly during the upstroke, leading to a high lift efficiency. These conclusions are significant for improving the aerodynamic performance of bionic flapping-wing micro air vehicles. Full article

This paper investigates and designs an adaptive landing gear inspired by the passive adaptation mechanism of the praying mantis on intricate landing surfaces to improve the landing safety of unmanned aerial vehicles (UAVs) in complicated terrain situations. A new passive adaptation structure utilizing multiple mutually perpendicular four-bar mechanisms is developed to address the limitations of the typical fixed truss structure landing gear. The system employs a singular laser range sensor locking mechanism, thereby significantly diminishing the control and structural complexity. The design incorporates a parallelogram mechanism to achieve the adaptation of different height differences through the mechanism’s deformation. The buffer damping mechanism and locking mechanism are engineered to augment the safety of the landing process and enhance the energy recovery rate. The circuit design employs the STC32G and Keil C251 microcontroller for development, thus achieving the automatic control of the landing gear. The experimental results demonstrate that the adaptive landing gear suggested in this paper can successfully adjust to the complex landing surface and has a good energy recovery performance. This aids in the advancement of UAVs in the field of complex environment applications and offers a safe, dependable, and creative solution for UAV landing scenarios in complex terrains. Full article

Credit risk assessment plays a critical role in financial risk management, focusing on predicting borrower default to minimize losses and ensure compliance. This study systematically reviews 23 empirical articles published between 2019 and 2023, highlighting the integration of machine learning and optimization techniques, particularly bio-inspired metaheuristics, for feature selection in individual credit risk assessment. These nature-inspired algorithms, derived from biological and ecological processes, align with bio-inspired principles by mimicking natural intelligence to solve complex problems in high-dimensional feature spaces. Unlike prior reviews that adopt broader scopes combining corporate, sovereign, and individual contexts, this work focuses exclusively on methodological strategies for individual credit risk. It categorizes the use of machine learning algorithms, feature selection methods, and metaheuristic optimization techniques, including genetic algorithms, particle swarm optimization, and biogeography-based optimization. To strengthen transparency and comparability, this review also synthesizes classification performance metrics—such as accuracy, AUC, F1-score, and recall—reported across benchmark datasets. Although no unified experimental comparison was conducted due to heterogeneity in study protocols, this structured summary reveals consistent trends in algorithm effectiveness and evaluation practices. The review concludes with practical recommendations and outlines future research directions to improve fairness, scalability, and real-time application in credit risk modeling. Full article

This paper presents a data-driven framework for optimizing energy-efficient locomotion in a bio-inspired soft inchworm robot. Leveraging a feedforward neural network, the proposed approach accurately models the nonlinear relationships between actuation parameters (pressure, frequency) and environmental conditions (surface friction). The neural network achieves superior velocity prediction performance, with a coefficient of determination (R²) of 0.9362 and a root mean squared error (RMSE) of 0.3898, surpassing previously reported models, including linear regression, LASSO, decision trees, and random forests. Particle Swarm Optimization (PSO) is integrated to maximize locomotion efficiency by optimizing the velocity-to-pressure ratio and adaptively minimizing input pressure for target velocities across diverse terrains. Experimental results demonstrate that the framework achieves an average 9.88% reduction in required pressure for efficient movement and a 6.45% reduction for stable locomotion, with the neural network enabling robust adaptation to varying surfaces. This dual optimization strategy ensures both energy savings and adaptive performance, advancing the deployment of soft robots in diverse environments. Full article

Ground walking training using a floating-base lower-limb exoskeleton rehabilitation robot improves patients’ dynamic balance function, thereby increasing their motor and daily life activity capabilities. We propose a balance-directed motion generator (BDMG) based on the principles of deep reinforcement learning. The reward function sub-components pertaining to physiological guidance and compliant assistance were designed to explore motion instructions that are harmoniously aligned with the human body’s balance correction mechanisms. To address the sparse rewards resulting from the above design, we introduce a stepwise training method that adjusts the reward function to control the model’s training direction and exploration difficulty. Based on the aforementioned generator, we construct a training and evaluation process database and design an abnormal command recognizer by extracting samples with diverse feature characteristics. Furthermore, we develop a sample generation optimizer to search for the optimal action combination within a closed space defined by abnormal commands and extremum points of physiological trajectories, thereby enabling the design of an abnormal instruction corrector. To validate the proposed approach, we implement a training simulation environment in MuJoCo and conduct experiments on the developed lower-limb exoskeleton system. Full article

Optimization algorithms serve as a powerful instrument for tackling optimization issues and are highly valuable in the context of engineering design. The coati optimization algorithm (COA) is a novel meta-heuristic algorithm known for its robust search capabilities and rapid convergence rate. However, the effectiveness of the COA is compromised by the homogeneity of its initial population and its reliance on random strategies for prey hunting. To address these issues, a multi-strategy adaptive coati optimization algorithm (MACOA) is presented in this paper. Firstly, Lévy flights are incorporated into the initialization phase to produce high-quality initial solutions. Subsequently, a nonlinear inertia weight factor is integrated into the exploration phase to bolster the algorithm’s global search capabilities and accelerate convergence. Finally, the coati vigilante mechanism is introduced in the exploitation phase to improve the algorithm’s capacity to escape local optima. Comparative experiments with many existing algorithms are conducted using the CEC2017 test functions, and the proposed algorithm is applied to seven representative engineering design problems. MACOA’s average rankings in the three dimensions (30, 50, and 100) were 2.172, 1.897, and 1.759, respectively. The results show improved optimization speed and better performance. Full article

This study introduces an innovative annular sealing groove design inspired by the hierarchical structure of octopus suckers, addressing the limitations of conventional seals under extreme conditions in aerospace engineering. Using finite element analysis, eight bionic configurations with varying groove parameters (width, depth, number) were systematically evaluated under cryogenic (−196.25 °C) and high-pressure (2 MPa) scenarios. Results show that the optimized bionic6 configuration (seven grooves, 0.4 mm width, 0.4 mm depth) achieved a 21.71% improvement in average von Mises stress compared to the original design, demonstrating enhanced leakage resistance. Parameter interaction analysis revealed groove number as the most significant factor affecting performance, followed by width, while depth showed minimal influence. The hierarchical groove architecture effectively mimicked the multi-level sealing mechanism of octopus suckers, reducing leakage paths and improving adaptability to irregular surfaces. This work bridges biological inspiration and engineering application, providing a scalable solution for extreme environments. The identified optimal parameters lay a theoretical foundation for designing high-performance seals in aerospace, cryogenic storage, and advanced manufacturing. Full article

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