Biomimetics, Volume 10, Issue 8 (August 2025) – 77 articles

A miniaturised bionic electronic nose system was developed to solve the problems of expensive equipment and long response time for soil pesticide residue detection. The structure of the bionic electronic nasal cavity is designed based on the spatial structure and olfactory principle of the sturgeon nasal cavity. Through experimental study, the structure of the nasal cavity of the sturgeon was extracted and analyzed. The 3D model of the bionic electronic nasal cavity was constructed and verified by Computational Fluid Dynamics (CFD) simulation. The results show that the gas flow distribution in the bionic chamber is more uniform than that in the ordinary chamber. The airflow velocity near the sensor in the bionic chamber is lower than in the ordinary chamber. The eddy current intensity near the bionic chamber sensor is 2.29 times that of the ordinary chamber, further increasing the contact intensity between odor molecules and the sensor surface and shortening the response time. The 10-fold cross-validation method of K-Nearest Neighbor (K-NN), Random Forest (RF) and Support Vector Machine (SVM) was used to compare the recognition performance of the bionic electronic nasal cavity with that of the ordinary electronic nasal cavity. The results showed that, when the bionic electronic nose detection system identified the concentration of pesticide residues in soil, the recognition rate of the above three recognition algorithms reached 97.3%, significantly higher than that of the comparison chamber. The bionic chamber electronic nose system can improve the detection performance of electronic noses and has a good application prospect in soil pesticide residue detection. Full article

Steady-State Visual Evoked Potentials (SSVEPs) have emerged as an efficient means of interaction in brain–computer interfaces (BCIs), achieving bioinspired efficient language output for individuals with aphasia. Addressing the underutilization of frequency information of SSVEPs and redundant computation by existing transformer-based deep learning methods, this paper analyzes signals from both the time and frequency domains, proposing a stacked encoder–decoder (SED) network architecture based on an xLSTM model and spatial attention mechanism, termed SED-xLSTM, which firstly applies xLSTM to the SSVEP speller field. This model takes the low-channel spectrogram as input and employs the filter bank technique to make full use of harmonic information. By leveraging a gating mechanism, SED-xLSTM effectively extracts and fuses high-dimensional spatial-channel semantic features from SSVEP signals. Experimental results on three public datasets demonstrate the superior performance of SED-xLSTM in terms of classification accuracy and information transfer rate, particularly outperforming existing methods under cross-validation across various temporal scales. Full article

The recycling and remanufacturing of end-of-life (EoL) electric vehicle (EV) batteries are urgent challenges for a circular economy. Disassembly is crucial for handling EoL EV batteries due to their inherent uncertainties and instability. The human–robot collaborative disassembly of EV batteries as a semi-automated approach has been investigated and implemented to increase flexibility and productivity. Unscrewing is one of the primary operations in EV battery disassembly. This paper presents a new method for the robotic unfastening and collecting of screws, increasing disassembly efficiency and freeing human operators from dangerous, tedious, and repetitive work. The design inspiration for this method originated from how human operators unfasten and grasp screws when disassembling objects with an electric tool, along with the fusion of multimodal perception, such as vision and touch. A robotic disassembly system for screws is introduced, which involves a collaborative robot, an electric spindle, a screw collection device, a 3D camera, a six-axis force/torque sensor, and other components. The process of robotic unfastening and collecting screws is proposed by using position and force control. Experiments were carried out to validate the proposed method. The results demonstrate that the screws in EV batteries can be automatically identified, located, unfastened, and removed, indicating potential for the proposed method in the disassembly of EoL EV batteries. Full article

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, and treatment remains challenging due to high recurrence rates, resistance to chemotherapy, and severe side effects. Dasatinib (Das) has shown therapeutic potential against HCC, but its clinical use is limited by poor bioavailability and short half-life (~3–4 h). Here, we developed a hyaluronic acid (HA)-coated sterosome for targeted and sustained delivery of Das to CD44-overexpressing HCC cells. Sterosomes composed of octadecylamine and cholesterol at a 5:5 (v/v) ratio were prepared via thin-film hydration and sonication, yielding stable particles (~90 nm) with high encapsulation efficiency (EE ~72%) for uncoated vesicles and ~58% after HA coating. HA-sterosomes (HA-St-Das) exhibited a uniform size (≈200 nm) and negative surface charge (–26 mV), with improved storage stability and resistance to lyophilization. In vitro release studies demonstrated pH-responsive Das release accelerated under acidic conditions (pH 6.0–5.0), mimicking tumor and lysosomal environments. In HepG2 cells, HA-St-Das exhibited enhanced cytotoxicity (IC50 ~7.0 μM) and prolonged intracellular retention compared to free Das and uncoated carriers. Fluorescence microscopy confirmed receptor-mediated uptake via CD44, leading to gradual and sustained intracellular delivery. Overall, the HA-St-Das system provides biocompatible, targeted, and controlled Das delivery, addressing key limitations of current liver cancer therapies and representing a promising nanomedicine platform for further development. Full article

The multi-strategy optimized dream optimization algorithm (MSDOA) is proposed to address the challenges of inadequate search capability, slow convergence, and susceptibility to local optima in intelligent optimization algorithms applied to UAV three-dimensional path planning, aiming to enhance the global search efficiency and accuracy of UAV path planning algorithms in 3D environments. First, the algorithm utilizes Bernoulli chaotic mapping for population initialization to widen individual search ranges and enhance population diversity. Subsequently, an adaptive perturbation mechanism is incorporated during the exploration phase along with a lens imaging reverse learning strategy to update the population, thereby improving the exploration ability and accelerating convergence while mitigating premature convergence. Lastly, an Adaptive Individual-level Mixed Strategy (AIMS) is developed to conduct a more flexible search process and enhance the algorithm’s global search capability. The performance of the algorithm is evaluated through simulation experiments using the CEC2017 benchmark test functions. The results indicate that the proposed algorithm achieves superior optimization accuracy, faster convergence speed, and enhanced robustness compared to other swarm intelligence algorithms. Specifically, MSDOA ranks first on 28 out of 29 benchmark functions in the CEC2017 test suite, demonstrating its outstanding global search capability and conver-gence performance. Furthermore, UAV path planning simulation experiments conducted across multiple scenario models show that MSDOA exhibits stronger adaptability to complex three-dimensional environments. In the most challenging scenario, compared to the standard DOA, MSDOA reduces the best cost function fitness by 9% and decreases the average cost function fitness by 12%, thereby generating more efficient, smoother, and higher-quality flight paths. Full article

Biomimetic restorative dentistry aims to preserve tooth structure and achieve optimal aesthetic harmony with surrounding dentition. The principles and protocols associated with biomimetic restorative dentistry are designed to enhance the longevity of the restoration. The use of flowable CRs is increasingly common; however, the effect of viscosity on the discoloration has not been clearly established. This in vitro study aimed to assess the color stability of flowable CRs with varying viscosities following immersion in common staining solutions and subsequent repolishing. A total of 250 disc-shaped specimens (8 mm × 2 mm) were prepared from five CRs with different viscosity profiles: high-viscosity (Spectra STHV, Dentsply, Milford, DE, USA), medium-viscosity (Estelite Universal Flow Medium, Tokuyama Dental Co., Tokyo, Japan), bulk-fill (Estelite Bulk-Fill Flow, Tokuyama Dental Co., Tokyo, Japan; SDR Plus, Dentsply, Milford, DE, USA), and packable (Estelite Posterior, Tokuyama Dental Co., Tokyo, Japan). After polymerization and baseline color measurements, specimens were immersed in coffee, tea, cola, red wine, or distilled water for 144 h. Color values were recorded before and after staining, and again following repolishing. Color changes (ΔE₁, ΔE₂, ΔE₃) were calculated using the CIE Lab system and statistically analyzed via two-way ANOVA and Tukey HSD (α = 0.05). Both the CR type and the staining solution substantially affected the color change. SDR Plus exhibited the highest ΔE values. Red wine caused the most discoloration. Repolishing enhanced color in selected groups. Full article

This study aims to optimize bionic hydrofoil propulsion performance and establish design guidelines for efficient transmission mechanisms by comparing three mechanisms (crank-slider, cylindrical cam, and synchronous belt drive). Through 3D modeling, virtual assembly, and ADAMS simulations, dynamic responses of slider displacement and driving force/torque were obtained, revealing that the crank-slider consumes the least energy, followed by the cylindrical cam, with the synchronous belt being the most energy-intensive. Further CFD analysis demonstrated that while the crank-slider generates drag intermittently, the cylindrical cam and synchronous belt sustain continuous thrust. All mechanisms achieve effective water propulsion below their critical frequencies (0.25 Hz, 0.75 Hz, and 1.4 Hz, respectively). Propulsion efficiency peaks at 26.0% (crank-slider) and 24.7% (cylindrical cam) at 0.25 Hz but declines at higher frequencies, whereas the synchronous belt reaches 24.3% efficiency at 1 Hz with superior frequency adaptability. The synchronous belt emerges as the optimal solution for efficient flapping propulsion due to its motion continuity and frequency adaptability. This work elucidates the critical impact of transmission mechanisms on hydrofoil hydrodynamics, providing foundational insights for mechanism design and performance optimization. Full article

Fault-tolerant control for bionic robotic fish presents significant challenges due to the complex dynamics and asymmetric propulsion introduced by joint failures. To address this issue, this paper proposes a fault-tolerant following control framework for multi-joint bionic robotic fish by combining fuzzy control methodologies and dynamic model correction. Firstly, offline fault analysis is conducted based on the dynamic model under multi-variable parameter conditions, quantitatively deriving influence factor functions that characterize the effects of different joint faults on velocity and yaw performance of the robotic fish. Secondly, an adaptive-period yaw filtering algorithm combined with an improved line-of-sight navigation method is employed to accommodate the motion characteristics of bionic robotic fish. Thirdly, a dual-loop following control strategy based on fuzzy algorithms is designed, comprising coordinated velocity and yaw control loops, where velocity and yaw influence factors serve as fuzzy controller inputs with expert experience-based rule construction. Finally, extensive numerical simulations are conducted to verify the effectiveness of the proposed method. The obtained results indicate that the bionic robotic fish can achieve fault-tolerant following control under multiple fault types, offering a valuable solution for underwater operations in complex marine environments. Full article

Osteoporosis compromises bone quality and impairs implant osseointegration. Since an adequate bone bed is essential for implant stability and success, this study evaluated the effects of implant surface functionalization with zoledronic acid (ZOL), alone or combined with ruterpy (TERPY), on peri-implant bone healing in healthy (SHAM) and osteoporotic (OVX) rats. ZOL has antiresorptive properties, while TERPY exhibits osteoinductive potential. The hypothesis was that ZOL + TERPY would act synergistically by inhibiting bone resorption and promoting new bone formation. Sixty-six female Wistar rats (3 months old) were divided into six groups (n = 11) according to systemic condition (SHAM or OVX) and implant type: conventional (CONV), ZOL, or ZOL + TERPY. Surgeries (sham or bilateral ovariectomy) were performed on day 0, and implants were placed in the tibial metaphysis on day 90. Fluorochromes were administered on days 104 (calcein) and 114 (alizarin), and euthanasia was performed on day 118. Samples were analyzed histologically via confocal microscopy and micro-computed tomography (Micro-CT). The ZOL + TERPY groups demonstrated significantly accelerated peri-implant bone repair, showing greater bone formation and organization; improved BV/TV, Tb.N, and I.S.; and reduced Tb.Sp and Po.Tot compared to CONV and ZOL-alone groups. In conclusion, ZOL + TERPY enhances and speeds bone healing, even under osteoporotic conditions. Full article

Chronic skin wounds are difficult to heal or nonhealing. These wounds may become infected and progress to tissue necrosis, potentially leading to limb amputation, sepsis, reduced quality of life, depression, economic burden on the healthcare system, and social isolation. Several clinical strategies, including negative pressure wound therapy, antibiotic-based infection control, and wound debridement, have been developed to treat skin wounds. However, these approaches primarily target local wound conditions and offer only short-term relief, not achieving sustained functional regeneration. Stem cell-based therapy has emerged as an alternative therapeutic method for skin wound treatment owing to its ability to suppress inflammation, stimulate angiogenesis, and promote cellular proliferation. However, the low post-transplantation survival rate of stem cells remains a major limitation. Exosomes, nanosized extracellular vesicles, transport proteins, lipids, mRNAs, and miRNAs and mediate regenerative functions, including anti-inflammatory effects, angiogenesis promotion, and extracellular matrix remodeling. Stem cell-derived exosomes (SC-Exos) offer several advantages over their parent cells, including greater stability, lower immunogenicity, absence of tumorigenic risks, and ease of storage and distribution. These attributes render SC-Exos particularly attractive for cell-free regenerative therapies. In this review, we introduce exosomes derived from various types of stem cells and explore their therapeutic applications in skin wound regeneration. Full article

In this paper, an Enhanced Greylag Goose Optimization Algorithm (EGGO) based on evolutionary game theory is presented to address the limitations of the traditional Greylag Goose Optimization Algorithm (GGO) in global search ability and convergence speed. By incorporating dynamic strategy adjustment from evolutionary game theory, EGGO improves global search efficiency and convergence speed. Furthermore, EGGO employs dynamic grouping, random mutation, and local search enhancement to boost efficiency and robustness. Experimental comparisons on standard test functions and the CEC 2022 benchmark suite show that EGGO outperforms other classic algorithms and variants in convergence precision and speed. Its effectiveness in practical optimization problems is also demonstrated through applications in engineering design, such as the design of tension/compression springs, gear trains, and three-bar trusses. EGGO offers a novel solution for optimization problems and provides a new theoretical foundation and research framework for swarm intelligence algorithms. Full article

Convolutional neural networks (CNNs) and their improved models (like DenseNet-121) have achieved significant results in image classification tasks. However, the performance of these models is still constrained by issues such as hyperparameter optimization and gradient vanishing and exploding. Owing to their unique exploration and exploitation capabilities, evolutionary algorithms offer new avenues for addressing these problems. Simultaneously, to prevent these algorithms from falling into a local optimum during the search process, this study designs a novel interpolation algorithm. To achieve better image classification performance, thus enhancing classification accuracy and boosting model stability, this paper utilizes a hybrid algorithm based on the horned lizard algorithm with quadratic interpolation and the giant armadillo optimization with Newton interpolation (HGAO) to optimize the hyperparameters of DenseNet-121. It is applied to five datasets spanning different domains. The learning rate and dropout rate have notable impacts on the outcomes of the DenseNet-121 model, which are chosen as the hyperparameters to be optimized. Experiments are conducted using the HGAO algorithm on five image datasets and compared with nine state-of-the-art algorithms. The performance of the model is evaluated based on accuracy, precision, recall, and F1-score metrics. The experimental results reveal that the combination of hyperparameters becomes more reasonable after optimization with the HGAO algorithm, thus providing a crucial improvement. In the comparative experiments, the accuracy of the image classification on the training set increased by up to 0.5%, with a maximum reduction in loss of 0.018. On the test set, the accuracy rose by 0.5%, and the loss decreased by 54 points. The HGAO algorithm provides an effective solution for optimizing the DenseNet-121 model. The designed method boosts classification accuracy and model stability, which also dramatically augments hyperparameter optimization effects and resolves gradient difficulties. Full article

This paper presents a bionic extended Kalman filter (EKF) state estimation algorithm for agricultural planters, inspired by the bionic mechanism of migratory birds navigating in complex environments, where migratory birds achieve precise localization behaviors by fusing multi-sensory information (e.g., geomagnetic field, visual landmarks, and somatosensory balance). The algorithm mimics the migratory bird’s ability to integrate multimodal information by fusing laser SLAM, inertial measurement unit (IMU), and GPS data to estimate the position, velocity, and attitude of the planter in real time. Adopting a nonlinear processing approach, the EKF effectively handles nonlinear dynamic characteristics in complex terrain, similar to the adaptive response of a biological nervous system to environmental perturbations. The algorithm demonstrates bio-inspired robustness through the derivation of the nonlinear dynamic teaching model and measurement model and is able to provide high-precision state estimation in complex environments such as mountainous or hilly terrain. Simulation results show that the algorithm significantly improves the navigation accuracy of the planter in unstructured environments. A new method of bio-inspired adaptive state estimation is provided. Full article

The Parrot Optimization Algorithm (PO) represents a contemporary nature-inspired metaheuristic technique formulated through observations of Pyrrhura Molinae parrot behavioral patterns. PO exhibits effective optimization capabilities by achieving equilibrium between exploration and exploitation phases through mimicking foraging behaviors and social interactions. Nevertheless, during iterative progression, the algorithm encounters significant obstacles in preserving population diversity and experiences declining search effectiveness, resulting in early convergence and diminished capacity to identify optimal solutions within intricate optimization landscapes. To overcome these constraints, this work presents the Adaptive Differentiated Parrot Optimization Algorithm (ADPO), which constitutes a substantial enhancement over baseline PO through the implementation of three innovative mechanisms: Mean Differential Variation (MDV), Dimension Learning-Based Hunting (DLH), and Enhanced Adaptive Mutualism (EAM). The MDV mechanism strengthens the exploration capabilities by implementing dual-phase mutation strategies that facilitate extensive search during initial iterations while promoting intensive exploitation near promising solutions during later phases. Additionally, the DLH mechanism prevents premature convergence by enabling dimension-wise adaptive learning from spatial neighbors, expanding search diversity while maintaining coordinated optimization behavior. Finally, the EAM mechanism replaces rigid cooperation with fitness-guided interactions using flexible reference solutions, ensuring optimal balance between intensification and diversification throughout the optimization process. Collectively, these mechanisms significantly improve the algorithm’s exploration, exploitation, and convergence capabilities. Furthermore, ADPO’s effectiveness was comprehensively assessed using benchmark functions from the CEC2017 and CEC2022 suites, comparing performance against 12 advanced algorithms. The results demonstrate ADPO’s exceptional convergence speed, search efficiency, and solution precision. Additionally, ADPO was applied to wind power forecasting through integration with Long Short-Term Memory (LSTM) networks, achieving remarkable improvements over conventional approaches in real-world renewable energy prediction scenarios. Specifically, ADPO outperformed competing algorithms across multiple evaluation metrics, achieving average R² values of 0.9726 in testing phases with exceptional prediction stability. Moreover, ADPO obtained superior Friedman rankings across all comparative evaluations, with values ranging from 1.42 to 2.78, demonstrating clear superiority over classical, contemporary, and recent algorithms. These outcomes validate the proposed enhancements and establish ADPO’s robustness and effectiveness in addressing complex optimization challenges. Full article

Biomimetic design, derived from the study of biological systems, has emerged as a pivotal methodology in contemporary art and design. By systematically integrating the morphological traits, structural principles, and functional mechanisms of living organisms into design thinking, it provides both a novel theoretical perspective and methodological support for modern design practice. This design philosophy draws abundant inspiration from nature’s aesthetics and achieves a profound fusion of organic form and artistic expression. This study systematically traces the theoretical evolution of biomimetic design—from its early phase of direct form-mimicry to today’s holistic, systems-based approach—and clarifies its interdisciplinary logic and developmental trajectory. We examine its applications in public installations, product development, architecture, and fashion. Through a structured analysis of plant-inspired, animal-inspired, and ecosystem-inspired strategies—linked with the aesthetic demands and cultural contexts of design—this study uncovers the underlying mechanisms by which biological models drive innovation. The findings demonstrate that, by organically combining form simulation, function optimization, and ecological awareness, biomimetic design not only elevates the aesthetic value, visual impact, and emotional resonance of design works but also amplifies their social role and cultural significance. Moreover, its interdisciplinary potential in materials innovation, technological integration, and environmental sustainability highlights unique pathways for addressing complex contemporary challenges. This study adopts a methodology that blends case-study analysis and theoretical interpretation. Through an in-depth examination of exemplar projects, it validates that biomimetic design not only achieves a seamless unity of function and form but also offers a robust theoretical framework and practical strategies for sustainable design implementation. These insights advance both the theoretical depth and practical innovation of the design discipline. Full article

Acute ankle sprains frequently lead to chronic ankle instability and muscle atrophy by causing immobilization, which necessitates real-time stiffness modulation for ankle–foot orthoses (AFOs). This paper proposes Active Variable Compression Shoes (AVC-Shoes), an ankle support system inspired by the “heel-lock taping” technique, which employs a wire–fabric compression mechanism to selectively stiffen ankle joints at crucial points in the gait cycle. The experimental results confirmed that AVC-Shoes achieve variable ankle stiffness in all directions, demonstrating dorsiflexion and plantarflexion stiffness ranges of up to 8.3 and 5.9 Nm/rad, respectively. Additionally, preliminary human testing involving three healthy participants revealed that the gastrocnemius muscle activity during the push-off phase in the active compression mode was significantly higher (by 19%) than that in the brace mode. By selectively increasing stiffness at heel strikes, AVC-Shoes represent a promising advancement toward next-generation AFOs capable of stabilizing the ankle while preventing muscle atrophy, which is associated with prolonged brace use. Full article

Biomimetic restorative treatments in pediatric dentistry increase the longevity of the restoration compared to traditional methods and aim to preserve the natural tooth structure. Prefabricated zirconia crowns have been developed as aesthetic alternatives to stainless steel crowns for full-coronal restorations of primary teeth. This study aimed to compare the fracture resistance and microleakage of two different posterior zirconia crown brands—NuSmile^® (USA) and ProfZrCrown^® (Turkey)—cemented with either conventional glass ionomer cement (GIC) or resin-modified glass ionomer cement (RMGIC). Eighty extracted primary molars were divided into four groups (n = 20). Crowns were cemented with Ketac™ Cem Radiopaque (GIC) or Ketac™ Cem Plus (RMGIC), in accordance with the manufacturers’ instructions, and then subjected to thermocycling. Fracture resistance was tested on 40 samples by applying an increasing compressive load until failure, with values recorded in Newtons (N). The remaining 40 samples were immersed in basic fuchsin dye for microleakage testing and evaluated under a stereomicroscope at 30× magnification. The results revealed that the ProfZrCrown^®/RMGIC group exhibited significantly higher fracture resistance compared to the NuSmile^®/RMGIC group (p < 0.05). No statistically significant differences were found among the other groups. Although no significant differences in microleakage were observed among the groups (p > 0.05), crowns cemented with GIC demonstrated higher microleakage levels. Within the limitations of this in vitro study, ProfZrCrown^® may be considered a promising alternative for aesthetic posterior restorations in pediatric dentistry. Full article

A swarm intelligence optimization algorithm called white shark optimizer (WSO) has been proposed and successfully applied in regard to many aspects. In this paper, the location problem of sports facilities is regarded as a multi-objective problem, and the number of residents covered by sports facilities and the Weber problem are introduced as objective functions. A multi-objective white shark optimizer (MOWSO) is proposed, and MOWSO introduced an archived mechanism to store the non-dominated solutions obtained by the algorithm. When the Pareto solutions in the archive overflow, the solutions are removed by calculating the true distance of the Pareto optimal solution. The performance of the MOWSO is verified on CEC 2020 benchmark functions, and the results show that the proposed MOWSO is better than other algorithms in the diversity and distribution of solutions. The MOWSO is applied to solve the rural sports facilities location problem, and a variety of different sports facilities location schemes are obtained. It can provide a variety of options for the location of rural sports facilities, and promote the intelligent design of sports facilities. Full article

Aiming at the cooperative path-planning problem of multiple autonomous underwater vehicles in underwater three-dimensional terrain and dynamic ocean current environments, a hybrid algorithm based on the Improved Multi-Objective Particle Swarm Optimization (IMOPSO) and Dynamic Window (DWA) is proposed. The traditional particle swarm optimization algorithm is prone to falling into local optimization in high-dimensional and complex marine environments. It is difficult to meet multiple constraint conditions, the particle distribution is uneven, and the adaptability to dynamic environments is poor. In response to these problems, a hybrid initialization method based on Chebyshev chaotic mapping, pre-iterative elimination, and boundary particle injection (CPB) is proposed, and the particle swarm optimization algorithm is improved by combining dynamic parameter adjustment and a hybrid perturbation mechanism. On this basis, the Dynamic Window Method (DWA) is introduced as the local path optimization module to achieve real-time avoidance of dynamic obstacles and rolling path correction, thereby constructing a globally and locally coupled hybrid path-planning framework. Finally, cubic spline interpolation is used to smooth the planned path. Considering factors such as path length, smoothness, deflection Angle, and ocean current kinetic energy loss, the dynamic penalty function is adopted to optimize the multi-AUV cooperative collision avoidance and terrain constraints. The simulation results show that the proposed algorithm can effectively plan the dynamic safe path planning of multiple AUVs. By comparing it with other algorithms, the efficiency and security of the proposed algorithm are verified, meeting the navigation requirements in the current environment. Experiments show that the IMOPSO–DWA hybrid algorithm reduces the path length by 15.5%, the threat penalty by 8.3%, and the total fitness by 3.2% compared with the traditional PSO algorithm. Full article

To address the limitations of mobile robots in path planning within farmland-specific environments, this paper proposes a biomimetic model: Multi-strategy Collaborative Evolution Honey Badger Algorithm (MCEHBA), MCEHBA achieves improvements through the following strategies: firstly, integrating a sinusoidal function-based nonlinear convergence factor to dynamically balance global exploration and local exploitation; secondly, combining the differential evolution strategy to enhance population diversity, and utilizing gravity-centred opposition-based learning to improve solution space search efficiency; finally, constructing good point set initialization and decentralized boundary constraint handling strategy to further increase convergence accuracy and speed. This paper validates the effectiveness of the optimization strategy and the performance of MCEHBA through the CEC2017 benchmark function set, and assesses the statistical significance of the results using the Friedman test and Nemenyi test. The findings demonstrate that MCEHBA exhibits excellent optimization capabilities. Additionally, this study applied MCEHBA to solve three engineering application problems and compared its results with six other algorithms, showing that MCEHBA achieved the minimum objective function values in all three cases. Finally, simulation experiments were conducted in three farmland scenarios of varying scales, with comparative tests against three state-of-the-art algorithms. The results indicate that MCEHBA generates paths with minimized total costs, demonstrating superior global convergence and engineering applicability. Full article

Harbor seals, equipped with their uniquely structured whiskers, demonstrate remarkable proficiency in tracking the trajectories of prey within dark and turbid marine environments. This study experimentally investigates the wake-induced vibrations of an elastically supported whisker model placed in the wakes of circular, square, and equilateral triangular cylinders of varying dimensions. Thereafter, a machine learning model is trained to identify and classify these intrinsic responses. The findings reveal a positive correlation between the amplitude of vibration and the total circulation shed by the bluff bodies. In the wake flow fields of triangular and circular cylinders, the mean drag is quite similar. Meanwhile, the whisker’s vibration amplitude and drag fluctuation show that the triangular cylinder is comparable to the square cylinder, and both are higher than the circular cylinder. To classify the wake-generating body shapes based on the hydrodynamic characteristics, hydrodynamic features encompassing vibration amplitudes, fluid forces, and frequency-related information were extracted to train an LSTM-based model, and it was found that the mean drag significantly enhances the model’s flow velocity generalization performance. Full article

In partial shading conditions (PSCs), the power–voltage characteristics of photovoltaic systems exhibit multiple peaks, causing traditional maximum power point tracking (MPPT) algorithms to easily become trapped in local optima and fail to achieve global maximum power point tracking, thereby reducing energy conversion efficiency. Effectively and rapidly locating the global maximum power under complex environmental conditions has become crucial for enhancing MPPT performance in photovoltaic systems. This paper therefore proposes an improved elk herd optimization (IEHO) algorithm to achieve the rapid tracking of the global maximum power point under various weather conditions. The algorithm proposes a position update mechanism guided by the predation risk probability to direct elk herd migration and introduces the triangle walk strategy, thereby enhancing the algorithm’s capability to avoid local optima. Furthermore, IEHO employs a memory-guided redirection strategy to skip redundant calculations of historical duty cycles, significantly improving the convergence speed of MPPT. To validate the algorithm’s performance advantages, the proposed IEHO method is compared with other recognized meta-heuristic algorithms under various weather conditions. The experimental results demonstrate that, across all tested conditions, the proposed IEHO method achieves an average tracking efficiency of 99.99% and an average tracking time of 0.3886 s, outperforming other comparative algorithms. Full article

Snake robots are characterized by their flexibility and environmental adaptability, achieved through various optimized gaits. However, their forward propulsion still requires improvement. This challenge can be addressed by integrating wheels or legs, but these mechanisms often limit the ability of snake robots to perform most optimized gaits. In this article, we develop a novel multimodal snake robot, JiAo-II, with both body-based locomotion and wheeled locomotion to handle complex terrains. The mechanical design and implementation of JiAo-II are presented in detail, with particular emphasis on its innovative elliptical wheels and gear transmission mechanism. Experimental results validate the effectiveness and multifunctionality of JiAo-II across various scenarios, including traversing grasslands, crossing gaps, ascending slopes, navigating pipelines, and climbing cylindrical surfaces. Furthermore, a series of experiments are conducted to evaluate the performance of the wheel–body coordinated locomotion on uneven ground, demonstrating the robustness even without requiring external sensing or sophisticated control strategies. In summary, the proposed multimodal mechanism significantly enhances the locomotion speed, terrain adaptability and robustness of snake robots. Full article

With the improvement in living standards and the aging of the population, the development of thin, light, and unobtrusive electronic skin devices is accelerating. These electronic devices combine the convenience of wearable electronics with the comfort of a skin-like fit. They are used to acquire multimodal physiological signal data from the wearer and real-time transmission of signals for vital signs monitoring, health dynamics warning, and disease prevention. These capabilities impose unique requirements on material selection, signal transmission, and data processing for such electronic devices. Firstly, this review provides a systematic introduction to nanomaterials, conductive hydrogels, and liquid metals, which are currently used in human health monitoring. Then, it introduces the solution to the contradiction between wireless data transmission and flexible electronic skin devices. Then, the latest data processing progress is briefly described. Finally, the latest research advances in electronic skin devices based on medical scenarios are presented, and their current development, challenges faced, and future opportunities in the field of vital signs monitoring are discussed. Full article

Dehydrated human-derived amnion–chorion membranes (ACM), known for their bioactive composition of growth factors and cytokines, have demonstrated potential as a bioactive scaffold in regenerative medicine; however, their clinical application in regenerative endodontic procedures (REPs) remains unexplored. This retrospective study aimed to evaluate the clinical and radiographic outcomes of REPs using ACM compared to collagen matrices (CM) in immature necrotic permanent teeth. Forty-one immature necrotic teeth from 38 patients (mean age: 14.68 ± 7.43 years) were treated with REPs using either ACM (n = 21) or CM (n = 20) scaffolds over a mean follow-up period of 23.23 months. Outcomes assessed included survival, success, root development measured by radiographic root area (RRA), and pulp sensibility. Independent t-tests compared outcomes between groups, while Cox regression and generalized linear models identified predictors of treatment outcomes. Overall survival and success rates were 87.8% and 82.9%, respectively. ACM-treated teeth achieved 90.5% survival and 85.7% success rates, while CM-treated teeth demonstrated 85.0% survival and 80.0% success rates, with no statistically significant differences between groups (p > 0.05). Root development occurred in 85.4% of cases overall, with significant RRA increases of 13.89 ± 13.95% for ACM and 11.24 ± 11.21% for CM (p < 0.05 within each group). Pulp sensibility recovery was observed in 51.2% of treated teeth overall, with 42.9% for ACM-treated teeth and 55.0% for CM-treated teeth (p > 0.05). Notably, ACM-treated teeth demonstrated earlier sensibility recovery compared to those of CM-treated teeth. Age was identified as a significant negative predictor of root development outcomes (p < 0.05). This clinical study demonstrates that both ACM and CM are clinically effective scaffolds for REPs, achieving high survival rates and promoting root development in immature necrotic teeth. While overall success rates were comparable, ACM showed faster sensibility recovery, suggesting potential biological advantages for enhanced tissue regeneration and earlier functional recovery. Full article

Global optimization problems, ubiquitous scientific research, and engineering applications necessitate sophisticated algorithms adept at navigating intricate, high-dimensional search landscapes. The Escape (ESC) algorithm, inspired by the complex dynamics of crowd evacuation behavior—where individuals exhibit calm, herding, or panic responses—offers a compelling nature-inspired paradigm for addressing these challenges. While ESC demonstrates a strong intrinsic balance between exploration and exploitation, opportunities exist to enhance its inter-agent communication and search trajectory diversification. This paper introduces an advanced bio-inspired algorithm, termed Crisscross Escape Algorithm (CCESC), which strategically incorporates a Crisscross (CC) information exchange mechanism. This CC strategy, by promoting multi-directional interaction and information sharing among individuals irrespective of their behavioral group (calm, herding, panic), fosters a richer exploration of the solution space, helps to circumvent local optima, and accelerates convergence towards superior solutions. The CCESC’s performance is extensively validated on the demanding CEC2017 benchmark suites, alongside several standard engineering design problems, and compared against a comprehensive set of prominent metaheuristic algorithms. Experimental results consistently reveal CCESC’s superior or highly competitive performance across a wide array of benchmark functions. Furthermore, CCESC is effectively applied to a complex reservoir production optimization problem, demonstrating its capacity to achieve significantly improved Net Present Value (NPV) over other established methods. This successful application underscores CCESC’s robustness and efficacy as a powerful optimization tool for tackling multifaceted real-world problems, particularly in reservoir production optimization within complex sedimentary environments. Full article

Inspired by the evolutionary optimization of biological load-bearing systems, honeycomb structures are highly valued in applications involving impact protection and lightweight load-bearing due to their outstanding mechanical properties. This study introduces an interesting honeycomb structure known as the hybrid topological cellular honeycomb structure (HTCHS), which integrates four distinctive topological cells. To effectively fabricate HTCHS samples, the research utilized a fused deposition modeling (FDM) process, employing polyethylene terephthalate glycol-modified (PETG) as the matrix material, successfully producing the HTCHS samples. A finite element simulation model for the HTCHS is created using LS-DYNA software(LS-DYNA R11.1.0 software), and its accuracy is confirmed through a comparative analysis of experimental and simulation results. The influence of the topological cell parameters (T₁ to T₄) on compressive energy absorption, specific energy absorption, and peak crushing force through parametric modeling is investigated. The mechanical properties of honeycomb structures vary depending on the cell parameters at different positions, and monotonically increasing the design parameters does not improve the energy absorption capacity of the HTCHS. To enhance the mechanical performance of the HTCHS, the initial periodic cell configurations are transformed into non-periodic designs. A discrete optimization design framework for local parameters of the HTCHS is established, integrating cell coding with the MOPSO algorithm. The feasibility of the optimization results is validated through experimental data, demonstrating that this study offers an effective technical solution for developing a novel generation of cellular honeycomb structures with customizable mechanical properties. Full article

This paper proposes a cascaded state estimation framework based on proprioception for robots. A generalized-momentum-based Kalman filter (GMKF) estimates the ground reaction forces at the feet through joint torques, which are then input into an error-state Kalman filter (ESKF) to obtain the robot’s prior state estimate. The system’s dynamic equations on the Lie group are parameterized using canonical coordinates of the first kind, and variations in the tangent space are mapped to the Lie algebra via the inverse of the right trivialization. The resulting parameterized system state equations, combined with the prior estimates and a sliding window, are formulated as a moving horizon estimation (MHE) problem, which is ultimately solved using a parallel real-time iteration (Para-RTI) technique. The proposed framework operates on manifolds, providing a tightly coupled estimation with higher accuracy and real-time performance, and is better suited to handle the impact noise during foot–ground contact in legged robots. Experiments were conducted on the BQR3 robot, and comparisons with measurements from a Vicon motion capture system validate the superiority and effectiveness of the proposed method. Full article

In this study, the pied kingfisher optimizer (PKO) algorithm is adapted to the uncapacitated facility location problem (UFLP), and its performance is evaluated. The PKO algorithm is binarized with fourteen different transfer functions (TF), and each variant is tested on a total of fifteen different Cap problems. In addition, performance improvement was realized by adding the Levy flight strategy to BinPKO, and this improved method was named BinIPKO. The experimental results show that the TF1 transfer function for BinIPKO performs very well on all problems in terms of both best and mean solution values. The TF2 transfer function performed efficiently on most Cap problems, ranking second only to TF1. Although the other transfer functions provided competitive solutions in some Cap problems, they lagged behind TF1 and TF2 in terms of overall performance. In addition, the performance of BinIPKO was also compared with the well-known PSO and GWO algorithms in the literature, as well as the recently proposed APO and EEFO algorithms, and it was found that BinIPKO performs well overall. In line with this information, it is seen that the IPKO algorithm, especially when used with the TF1 transfer function, provides an effective alternative for UFLP. Full article