Biomimetics

Background: The growing environmental crisis, driven by population increases and rapid urban development, has amplified the need for sustainable and ecological design approaches. Biomimicry, drawing inspiration from nature’s forms, processes, and systems, offers promising solutions in this context. Particularly in landscape architecture, biomimicry supports the integration of esthetics with ecological responsibility. Methods: This study presents a bibliometric analysis using the Scopus database to quantitatively assess the relationship between biomimicry and sustainable/ecological design within landscape architecture. A stepwise search strategy was applied, and the Biblioshiny tool within the version 4.2.1 of Bibliometrix package in RStudio 2024.04.1+748 software was used for data analysis and visualization. Results: A total of 1634 documents were identified under the keyword “biomimicry,” among which 210 addressed sustainability and/or ecological design. However, only three studies explicitly connected biomimicry, sustainable/ecological principles, and landscape architecture. Keyword trends, publication years, and country-level contributions were also examined. Conclusions: The findings highlight a substantial gap in the literature on the integration of biomimicry within sustainable landscape architecture. This underscores the need for further interdisciplinary research and practice that incorporates biomimetic principles to promote ecological innovation in landscape design. Full article

Simultaneous Localization and Mapping (SLAM) is a fundamental technique in mobile robotics, enabling autonomous navigation and environmental reconstruction. However, dynamic elements in real-world scenes—such as walking pedestrians, moving vehicles, and swinging doors—often degrade SLAM performance by introducing unreliable features that cause localization errors. In this paper, we define dynamic regions as areas in the scene containing moving objects, and dynamic features as the visual features extracted from these regions that may adversely affect localization accuracy. Inspired by biological perception strategies that integrate semantic awareness and geometric cues, we propose Instance-level Point-Line SLAM (IPL-SLAM), a robust visual SLAM framework for dynamic environments. The system employs YOLOv8-based instance segmentation to detect potential dynamic regions and construct semantic priors, while simultaneously extracting point and line features using Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features), collectively known as ORB, and Line Segment Detector (LSD) algorithms. Motion consistency checks and angular deviation analysis are applied to filter dynamic features, and pose optimization is conducted using an adaptive-weight error function. A static semantic point cloud map is further constructed to enhance scene understanding. Experimental results on the TUM RGB-D dataset demonstrate that IPL-SLAM significantly outperforms existing dynamic SLAM systems—including DS-SLAM and ORB-SLAM2—in terms of trajectory accuracy and robustness in complex indoor environments. Full article

To enhance the convergence efficiency and solution precision of the Red-billed Blue Magpie Optimizer (RBMO), this study proposes a Multi-Strategy Enhanced Red-billed Blue Magpie Optimizer (MRBMO). The principal methodological innovations encompass three aspects: (1) Development of a novel dynamic boundary constraint handling mechanism that strengthens algorithmic exploration capabilities through adaptive regression strategy adjustment for boundary-transgressing particles; (2) Incorporation of an elite guidance strategy during the predation phase, establishing a guided search framework that integrates historical individual optimal information while employing a Lévy Flight strategy to modulate search step sizes, thereby achieving effective balance between global exploration and local exploitation capabilities; (3) Comprehensive experimental evaluations conducted on the CEC2017 and CEC2022 benchmark test suites demonstrate that MRBMO significantly outperforms classical enhanced algorithms and exhibits competitive performance against state-of-the-art optimizers across 41 standardized test functions. The practical efficacy of the algorithm is further validated through successful applications to four classical engineering design problems, confirming its robust problem-solving capabilities. Full article

Exoskeleton robots function as augmentation systems that establish mechanical couplings with the human body, substantially enhancing the wearer’s biomechanical capabilities through assistive torques. We introduce a lumbar spine-assisted exoskeleton design based on Variable-Stiffness Pneumatic Artificial Muscles (VSPAM) and develop a dynamic adaptation mechanism bridging the pneumatic drive module with human kinematic intent to facilitate human–robot cooperative control. For kinematic intent resolution, we propose a multimodal fusion architecture integrating the VGG16 convolutional network with Long Short-Term Memory (LSTM) networks. By incorporating self-attention mechanisms, we construct a fine-grained relational inference module that leverages multi-head attention weight matrices to capture global spatio-temporal feature dependencies, overcoming local feature constraints inherent in traditional algorithms. We further employ cross-attention mechanisms to achieve deep fusion of visual and kinematic features, establishing aligned intermodal correspondence to mitigate unimodal perception limitations. Experimental validation demonstrates 96.1% ± 1.2% motion classification accuracy, offering a novel technical solution for rehabilitation robotics and industrial assistance. Full article

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