Biomimetics, Volume 11, Issue 5 (May 2026) – 69 articles

Spiking neural networks (SNNs) have emerged as a promising paradigm for energy-efficient neuromorphic computing, particularly when processing asynchronous event streams from dynamic vision sensors (DVSs). However, SNNs often suffer from limited representational capacity and suboptimal feature recalibration compared to their artificial counterparts. To address these challenges, we propose SE-SNN, a novel architecture that integrates Squeeze-and-Excitation (SE) blocks into deep residual SNNs, enabling channel-wise attention without spike generation. Furthermore, we introduce a Robust Parametric Leaky Integrate-and-Fire (RobustPLIF) neuron model with learnable membrane time constant ($\tau$) and firing threshold ($v_{\mathrm{th}}$), allowing adaptive temporal dynamics in each layer. Our model is trained on the CIFAR10-DVS dataset.The experimental results demonstrate that SE-SNN achieves an accuracy of $78.8$% on CIFAR10-DVS with 16 time steps, outperforming baseline SNNs while maintaining biological plausibility and hardware efficiency. Ablation studies confirm the individual contributions of the SE blocks and learnable neuron parameters to the performance gains. Full article

The aim of the study was to evaluate the immediate and aged shear bond strength (SBS) of a dual-cured and a light-cured universal adhesive to primary and permanent dentin. Twenty caries-free primary molars and twenty permanent third molars were selected and sectioned to expose mid-coronal dentin. Specimens were assigned to four groups (n = 10) according to dentition type and adhesive agent used: Group 1—primary teeth/Scotchbond Universal; Group 2—permanent teeth/Scotchbond Universal; Group 3—primary teeth/Futurabond U; and Group 4—permanent teeth/Futurabond U. Two resin micro-rods were fabricated and bonded with the same adhesive on each specimen. The SBS test was applied after 24 h (first micro-rod) and after thermocycling (second micro-rod). Failure modes were assessed under an optical microscope and scanning electron microscopy. Shear bond strength was significantly higher in permanent teeth than in primary teeth (p < 0.001). No statistically significant differences were found between the two adhesives (p = 0.194). Adhesive failures predominated across all groups. Both adhesives exhibited comparable SBS to primary and permanent dentin. These findings support the suitability of both universal adhesives for the restoration of primary and permanent teeth, though long-term clinical studies are warranted. Full article

Polyetheretherketone (PEEK) is widely employed in orthopedic applications due to its bone-mimetic mechanical properties and excellent biocompatibility, establishing it as a promising candidate for bone repair and regeneration. However, the investigation of structural integrity and microstructural features of PEEK implants has remained limited owing to its low atomic number (Z < 8) and hierarchical microstructure. This study presents a comparative characterization of PEEK implants by integrating laboratory X-ray computed tomography (CT) and synchrotron X-ray phase-contrast tomography (SXCT). Laboratory X-ray CT, operating on absorption contrast mechanisms, demonstrates adequate capacity for visualizing macroscopic defects and pore architectures, but exhibits limitations in resolving subtle density variations. In contrast, SXCT, employing phase-contrast imaging, significantly enhances discrimination of ultra-low-contrast features, including interconnected pore networks and localized density gradients. This work elucidates the complementary advantages and inherent limitations of both the imaging modalities for characterizing PEEK-based biomaterials. Furthermore, it demonstrates the potential extensibility of this comparative approach to other polymer composites, offering a methodology to investigate material–tissue interactions in orthopedic research and advancing the development of next-generation implantable devices. Full article

Bone grafting remains limited, and the strategies to design even more structurally complex scaffolds—able to reproduce the hierarchical architecture of bone extracellular matrix—are rapidly growing. In this study, we report the fabrication of a hierarchically structured scaffold produced by layering poly(ε-caprolactone)/gelatin (PCL/Gt) or poly(lactic acid)/gelatin (PLA/Gt) electrospun nanofibers via coaxial electrospinning onto 3D-printed poly(lactic acid) (PLA) scaffolds via fused deposition modeling (FDM). After the printing process, PLA disks (10 × 1 mm, 20% infill, ~80% porosity, pore size ~1.57 mm) were coated with core/shell (PCL/Gt, PLA/Gt) fibers to investigate the in vitro interfacial response of osteoblasts in comparison with monocomponent fibrous coatings (PCL, PLA, Gt). SEM and TEM confirmed that core/shell fibers exhibited bead-free morphologies, with a significant reduction in fiber diameter (≈287–316 nm) and higher interfibrillar porosity compared to monocomponent fibers. FTIR and thermogravimetric analyses indicated the presence of hydrogen bonding between the polyester and gelatin, and the absence of residual solvent after deposition. At the same time, water contact angle measurements confirmed an increase in hydrophilic properties from 80–86° to 120° ascribable to the presence of gelatin. Accordingly, in vitro response of human fetal osteoblasts (hFOB 1.19) exhibited an evident improvement in the case of Gt-based fibrous coatings (i.e., PCL/Gt and PLA/Gt) in terms of early adhesion (4–24 h) and metabolic activity from 3 to 21 days, cell spreading into star-shaped morphologies, formation of extracellular matrix, and mineral phase deposition. In more detail, a remarkable increase in alkaline phosphatase activity was observed in Gt-based coaxial coatings from day 7 onward, with the highest values recorded for PLA/Gt. Overall, we demonstrated that the Gt-based coaxial fibrous coating provided a mix of topological and biochemical cues that synergistically promoted key osteoblast activities at the interface, supporting the regeneration of new bone tissue in highly tailored 3D-printed scaffolds, thus suggesting a promising strategy for personalized regenerative medicine. Full article

Recognizing human actions from static images in complex industrial environments remains challenging due to insufficient feature representation and high computational complexity. This issue is particularly critical in power-grid safety monitoring, where improper worker postures (e.g., bending, climbing, falling) can lead to severe accidents and personal injuries, necessitating automated monitoring systems that operate reliably on resource-constrained edge devices. This study proposes a bio-inspired lightweight recognition framework that integrates an improved YOLO-Pose model with a gated recurrent unit (GRU) network. The scientific motivation is grounded in the observation that the human musculoskeletal system achieves highly efficient motion perception through three key mechanisms: hierarchical muscle coordination providing intrinsic rotation invariance, proprioceptive feedback enabling real-time error correction, and selective neural gating reducing redundant information transmission. These biological principles directly inspire our technical contributions: polar-coordinate encoding provides rotation invariance, three-stage filtering mimics proprioceptive feedback, and GRU gating mirrors selective information propagation. Unlike prior approaches that treat pose-based action recognition as a generic computer vision problem, this work explicitly incorporates anatomical structural constraints into the computational pipeline. The framework addresses three research gaps: (1) existing methods lack biomechanically derived invariance properties; (2) GCN-based approaches use fixed topologies that fail to adapt to occlusion patterns; (3) the trade-off between model complexity and accuracy remains unsatisfactory for edge deployment. Experiments on the self-constructed SKPose dataset demonstrate that the proposed method achieves 95.04% accuracy, outperforming ST-GCN by 3.67 percentage points and 2s-AGCN by 1.94 percentage points, with an inference speed of 48 FPS on 8.7 M parameters in underground power-grid environments and provides practical support for biomimetic perception systems and industrial safety monitoring. Full article

This study investigates the porous structure of Royal Water Lily Leaf vein cross-sections, integrating macroscopic structural observations, quasi-static compression experiments, and finite element simulations to systematically explore the influence of gradient fractal characteristics on mechanical performance and energy absorption behavior. First, the geometric features of the vein cross-sections were extracted through macroscopic measurements, and a parametric model incorporating key variables-porosity, pore ellipticity, and distribution density coefficient-was established. Single-factor analysis reveals that porosity plays a dominant role in determining the overall load-bearing capacity and energy absorption capability; pore ellipticity primarily affects local deformation modes and plateau-stage stability; while the distribution density coefficient significantly regulates the progressive and uniform deformation behavior. Subsequently, a multi-factor coupling model based on the Box–Behnken response surface methodology was developed to investigate the interactions among structural parameters. The results showed that the three variables exhibited significant synergistic effects rather than simple monotonic relationships. Within the investigated range, the optimized configuration (porosity = 30%, ellipticity = 1.6, distribution density coefficient = 1.5) achieved excellent comprehensive performance, with SEA = 115.75 J/kg, MCF = 248.2 N, and CFE = 0.445. Further analysis revealed that the porous vein structure does not exhibit strict self-similar fractal geometry but instead presents a gradient fractal characteristic with hierarchical progression and regional heterogeneity. During compression, the structure undergoes progressive collapse from the inner region outward, enabling staged load-bearing and efficient energy dissipation. These findings provide theoretical support and engineering guidance for the design and optimization of lightweight bioinspired porous energy-absorbing structures. Full article

Alginate hydrogels offer mild ionic gelation and tunable porosity for drug delivery, yet their hydrophilic, macroporous networks suffer from rapid initial burst release of water-soluble payloads. Here we introduce a hierarchical barrier-engineering strategy in which poly(D,L-lactide-co-glycolide)/poly(ethylene glycol) (PLGA/PEG) blend coatings are applied via dip-coating to Ca²⁺-cross-linked alginate beads (~1 mm) and microgels (~100 µm). For beads, three-cycle PLGA/PEG multilayer coating suppressed the initial swelling rate (dQ/dt) by ~50% and reduced 1 h burst release from >85% to ~60%, functioning as an “early-burst buffer” rather than a long-term depot. For microgels, a single PLGA/PEG layer partially attenuated burst release; however, an additional PLGA outer shell (double-barrier architecture) shifted the release-governing mechanism from swelling-dominated to diffusion-barrier-dominated control, limiting 10 min release to <10%. Core–shell formation was verified by confocal laser scanning microscopy (CLSM), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM/EDS), Fourier-transform infrared spectroscopy (FT-IR), and X-ray photoelectron spectroscopy (XPS); thermogravimetric analysis (TGA) showed ~73–79% coating retention after 9 days in phosphate-buffered saline (PBS, 37 °C). A vacuum re-loading process further improved encapsulation efficiency (>50% for beads, >20% for microgels) without compromising gel integrity. In beads, burst control was governed by swelling suppression; in microgels, the additional PLGA shell shifted control to diffusion-barrier-dominated release, demonstrating that barrier architecture must be adapted to particle scale. Full article

Hexapod robots are attractive for operation in cluttered and uneven environments, but their walking stability is strongly affected by the coupled effects of leg morphology and foot-end trajectory planning. In many existing designs, leg-segment proportions, reachable workspace, and swing-phase trajectory smoothness are considered separately, which makes it difficult to clarify how structural parameters and motion planning jointly influence locomotion stability. To address this issue, this study presents a spider-leg-inspired hexapod robot with a simplified three-degree-of-freedom leg configuration. Selected functional characteristics of spider legs, including segmented limb structure and compliant distal contact, were abstracted into an engineering-feasible hexapod platform rather than directly reproducing spider anatomy. A parametric workspace analysis was conducted under a fixed total leg length to compare six candidate femur-to-tibia ratios. Based on forward reach, vertical foot-lifting capability, stride potential, and structural compactness, a 4:6 femur-to-tibia ratio was selected. In addition, an eleventh-order Bézier curve was developed for swing-phase foot trajectory planning and compared with a conventional composite cycloid trajectory under identical tripod-gait conditions. Simulation and straight-line walking experiments showed that the Bézier-based trajectory reduced body-attitude fluctuation and produced smoother angular-velocity variation than the composite cycloid trajectory. The results indicate that the proposed structural design and Bézier-based trajectory can improve flat-ground walking stability of the hexapod robot. This work provides a practical reference for biomimetic structural design and gait-trajectory optimization of multi-legged robots, while further validation on more complex terrain remains necessary. Full article

Human motion provides a valuable source of information for robotic skill acquisition, and Learning from Demonstration (LfD) has been widely adopted as an intuitive paradigm for enabling robots to learn tasks from human demonstrations. However, the lack of an explicit representation of transferable motion knowledge significantly limits the adaptability of LfD when tasks involve varying spatial constraints or environmental configurations. To address this challenge, this paper proposes a motion representation framework based on two fundamental properties of motion and introduces a novel Motion Knowledge Transferring Gaussian Mixture Model (MKT-GMM) for trajectory generalization across related tasks. In the proposed framework, demonstration trajectories from a source task are first collected through kinesthetic teaching and encoded using a Gaussian Mixture Model (GMM), where each Gaussian component represents a local motion primitive. Transferable motion knowledge is captured by jointly preserving the statistical characteristics of individual motion primitives and the geometric relationships between adjacent primitives. For a target task in which only task constraints are specified, the learned motion knowledge is transferred by adapting the GMM parameters through affine transformations combined with constraint-error minimization, enabling feasible trajectories to be generated without additional demonstrations or model retraining. The final motions are reconstructed using Gaussian Mixture Regression (GMR), ensuring smooth and consistent trajectory generation. To further improve the robustness of trajectory transfer, a pseudo via-point mechanism is introduced to automatically generate intermediate constraints when explicit via-points are unavailable. Experiments conducted on a robotic manipulation platform, including handwriting motion learning and pick-and-place tasks under varying task configurations, demonstrate that the proposed method effectively captures transferable motion knowledge and achieves reliable trajectory generalization for previously unseen tasks. Full article

Odor source localization in turbulent environments remains a major challenge for autonomous robots, as odor plumes are highly intermittent, spatially fragmented, and often lack stable concentration gradients. Here, we propose a bio-inspired navigation framework that translates key principles of bumblebee olfactory cognition into robotic decision-making. First, classical conditioning and olfactorily triggered spatial memory experiments demonstrated that bumblebees could form robust odor memories and that training frequency is positively correlated with both proboscis extension response retention and spatial directional preference. Based on these biological findings, a bio-inspired navigation framework, termed Bio-Nav, is constructed by integrating a Partially Observable Markov Decision Process, a Hidden Markov Model, short-term memory, long-term directional reference memory, fuzzy inference, and value iteration. High-fidelity two-dimensional turbulent simulations show that the proposed algorithm substantially outperforms moth-inspired search, Infotaxis, and standard POMDP-based navigation. In 100 Monte Carlo trials, Bio-Nav achieved a success rate of 96.0%, an average of 20.3 search steps, an average path length of 155.1 cm, and a path-to-straight-line distance ratio of 1.6. Even under strong turbulence, the success rate remained above 91%. These results indicate that memory–perception coupling, inspired by bumblebee navigation, provides an effective and robust strategy for odor source localization in complex turbulent environments, offering a generalizable principle for bio-inspired robotic search under uncertainty. Full article

The Compliant Ankle Rehabilitation Robot (CARR) is actuated by four Pneumatic Artificial Muscles (PAMs). These actuators mimic biological muscles, making them highly applicable in robotic systems designed to apply guiding motion to a human joint, but have complex nonlinear dynamic properties making accurate tracking control difficult. This paper presents intelligent modelling and control methods to improve the CARR’s function in a rehabilitation setting. Using the Particle Swarm Optimisation (PSO) algorithm, dynamic models of the actuators are calculated. Two model setups are proposed, a single model and dual model. Model Predictive Control (MPC) was then implemented using these models and experimentally compared with Proportional Integral Derivative (PID) and Iterative Learning Control (ILC). The results showed that PID control was less accurate than the developed control schemes, with evidence of significant chattering, as well as both over- and undershooting the setpoint. ILC performed accurately, but the required learning period and some evidence of overfitting impacted the overall performance. Single-model MPC had low error values on the X axis of the CARR and maintained the most consistent 0 displacement when an axis was intended to stay motionless. Dual-model MPC had the lowest error values on the Y axis, the smoothest motion with the least chattering, and the best performance when both axes were in motion, but showed evidence of overshooting. Based on these results, both MPC implementations have proven to be successful and suitable for future work, and the PSO modelling method is able to produce suitably accurate models for the application. Full article

Alzheimer’s disease (AD) is a serious neurodegenerative disorder that can severely affect behavior and thinking patterns, and is accompanied by frequent memory loss. The early diagnosis of AD is essential, as this can benefit the patient, but detecting AD is a complex process due to the nature of its associated clinical data. Electroencephalography (EEG) serves as a promising and cost-effective technique for analyzing AD-related brain activity patterns. In this work, a consolidated framework for detecting AD using EEG signals and hybrid models is proposed that uses a dataset that is available online. For the feature extraction module, five efficient techniques—Principal Component Analysis (PCA), Kernel Partial Least Squares (KPLS), Kriging Model, Isomap, and K-means clustering—are used. For feature selection, with the help of biomimetics-based concepts, three efficient algorithms are used: hybrid Cuckoo Search Optimization–Rat Swarm Optimization (CSO-RSO), Zebra Optimization (ZOA), and hybrid Gravitational Search Algorithm–Particle Swarm Optimization (GSA-PSO). Four interesting hybrid classifiers are utilized here to detect AD using EEG signals—hybrid Extreme Learning Machine–Adaboost (ELM–Adaboost), hybrid Classification and Regression Trees–Adaboost (CART–Adaboost), and hybrid weighted broad learning system-based Adaboost (HWBLSA), followed by a hybrid machine learning classification model with a soft voting technique—and, finally, these are compared with other standard machine learning classifiers. The highest classification accuracy of 98.71% is found when the Kriging Model feature extraction concept is combined with the hybrid GSA-PSO feature selection method and classified with the ELM–Adaboost classifier. Full article

Soft modular grippers play a significant role in multiple fields due to their excellent adaptability and flexibility. This paper proposes a modular soft modular gripper driven by pneumatically actuated multi-chambers. The designed soft modular gripper features three operational modes, with its modular fingers employing independently controlled dual chambers. The distal and proximal dual-chamber structure enhances the fingertip force of the modular fingers. Based on classical laminated plate theory and incorporating the large deformation characteristics of soft materials, a relationship between the bending centerline of the fingers and the driving pressure is established, providing a theoretical foundation for grasping tasks executed by the soft modular gripper. The Denavit-Hartenberg (D-H) parameter method is utilized to develop the coordinate system of the soft modular gripper, thereby defining its operational workspace. Visual sensing technology is introduced, incorporating improvements to the YOLOv8-based object recognition and localization framework, which enhances recognition accuracy for target objects and ensures grasping stability. Full article

Mechanical engineering systems must sense, inspect, and navigate constrained environments and operate adaptively under uncertainty—requirements that map structurally onto capabilities achieved by biological systems through distributed sensing, morphology-driven locomotion, multimodal perception, and decentralised control. Biomimetics can therefore be interpreted not merely as a source of design inspiration but also as a functional engineering framework relevant to industrial monitoring, inspection, maintenance, and autonomous operation. This study presents a PRISMA 2020-guided systematic mapping review of the biomimetics literature explicitly relevant to mechanical-engineering functions over the decade 2016–2026. A Scopus corpus of 11,114 records was screened through a two-stage abstract-level process. After deduplication and broad relevance filtering, a stricter eligibility audit retained 505 studies assignable to five predefined functional clusters: robotics and access (235 records; 46.5%), mechanical surfaces and tribology (141; 27.9%), sensing and monitoring (106; 21.0%), vision and inspection (14; 2.8%), and control and computation (9; 1.8%). Publication output accelerated markedly after 2022, with 2025 yielding the highest annual count. The principal gap identified is not a shortage of biomimetic concepts, but their limited consolidation into deployable industrial inspection and maintenance architectures. A translational taxonomy connecting biological principles, engineering abstractions, enabling technologies, and mechanical use cases is proposed as an interpretive structuring tool for future research prioritisation and technology-readiness discussion. Full article

Passive thermoregulatory textiles, operating without external energy input, play a crucial role in maintaining the human body within the thermal comfort zone. However, integrating autonomous environmental adaptability with superior wearing comfort into a single textile remains a challenge. In this work, inspired by the autonomous actuation of water lilies, we proposed an intelligent strategy to fabricate thermoregulatory textiles that dynamically adapted to ambient temperature fluctuations, driven by a bidirectional shape memory polymer (SMP). To concurrently achieve robust thermal adaptability and human-body-compatible softness, a crosslinked polyethylene glycol–butyl acrylate (PEG-BA) bidirectional SMP network was engineered. The PEG phase, featuring a broad crystal size distribution, provided the dynamic skeleton for thermally induced actuation, while the incorporation of the BA component tuned the intrinsic softness to match conventional soft textiles. Consequently, the synthesized PEG-BA network exhibited an exceptional bidirectional shape memory effect with a reversible strain of 15.5%, while maintaining high macroscopic softness comparable to that of human skin. By integrating this bidirectional polymer into a garment to form adaptive vents, the smart textile demonstrated the capability to significantly elevate human thermal comfort. Specifically, the vents autonomously open in hot environments to accelerate heat dissipation and close in cool environments to suppress heat loss. Given its exceptional personal thermoregulatory performance and wearing compliance, this proposed strategy exhibits considerable potential for maintaining optimal human comfort against fluctuating environmental conditions. Full article

Gesture recognition based on surface electromyography (sEMG) signals typically involves extracting features from the signals and then incorporating recognition models to increase the accuracy of classification. In this paper, drawing on the properties of sEMG signals and statistical principles, we propose a novel feature extraction method called “multiple mapping”, designed to construct a high-performance representation of sEMG signals. The multiple mapping approach incorporates sequential mappings, including the sliding average power, lg (base-10 logarithm) mapping, a linear compression principle, and a sigmoid normalization function. The sliding average power captures the intensity variations in sEMG signals across various channels, allowing differentiation between the activity levels of distinct muscle groups. lg mapping adjusts the distribution of the sEMG signals to improve their uniformity, enhancing feature stability and facilitating comparisons. The linear compression and sigmoid normalization emphasize the signals’ central characteristics while compressing the extremes at both ends. Then, the sEMG signals obtained from multiple mapping are transformed into Sem grayscale maps, which are subsequently processed using the deep neural network ResNet50 for gesture recognition. Extensive experiments on three public datasets were conducted, and the average recognition accuracy was 95.26%, 90.81%, and 96.72%, respectively, while it was 96.8% in self-collected recognition tasks. The results demonstrate that the multiple mapping method significantly improves the feature extraction performance for sEMG-based gesture recognition, offering a promising direction for applications in prosthetic gesture control and muscle–computer interaction. Full article

This paper presents a novel algebraic saturation-based Proportional–Integral–Derivative (ASB-PID) controller for achieving ultra-fast and well-damped dynamic response in automatic voltage regulator (AVR) systems. The proposed controller incorporates an algebraic saturation-based nonlinear transformation applied to both the error signal and its derivative, enabling adaptive control sensitivity across different operating regions. This formulation preserves high sensitivity near the equilibrium point while effectively limiting excessive control action under large transient deviations, thereby overcoming the inherent trade-off between response speed and overshoot observed in conventional PID-based controllers. To address the highly nonlinear and multimodal tuning problem, the controller parameters are optimally determined using the Animated Oat Optimization Algorithm (AOOA), which provides strong global exploration capability and stable convergence behavior. The effectiveness of AOOA is first validated through comparative analysis with widely used metaheuristic algorithms, including Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA). Furthermore, the proposed controller is benchmarked against recently developed high-performance AVR control strategies, including Gudermannian-PID (G-PID), fractional-order PID (FOPID), and higher-order PID-based controllers. Simulation results demonstrate that the proposed AOOA-optimized ASB-PID controller achieves a rise time of 0.0215 s and a settling time of 0.0383 s with zero overshoot and negligible steady-state error, significantly outperforming both competing optimization algorithms and state-of-the-art control designs. Comprehensive benchmarking further confirms that the proposed method consistently delivers superior performance in terms of speed, stability, and robustness, indicating that it provides an effective, computationally efficient, and scalable solution for high-performance AVR systems and broader nonlinear control applications. Unlike conventional nonlinear PID designs based on hyperbolic or sigmoid mappings, the proposed algebraic formulation provides a more explicit and effective saturation mechanism, enabling a superior balance between transient speed and overshoot suppression without increasing controller complexity. Full article

Biological cellular structures exhibit a high degree of systematic organization in both morphological configuration and functional coordination, providing important biomimetic insights for urban spatial organization. To address issues in traditional high-density residential areas, such as homogeneous spatial structures and insufficient accessibility of public spaces, this study proposes a planning method for five-minute living circle residential areas based on a biomimetic cellular structure within the framework of space syntax theory. Taking a residential area in Wuhan, China, as a case study, a cell-like spatial structure model was constructed. Convex space analysis, axial analysis, and visibility analysis were conducted using Depthmap software to quantitatively evaluate key syntactic indicators, including integration, connectivity, mean depth, and choice. The results show that, compared with the original planning scheme, the biomimetic cellular planning model significantly optimized the spatial structure of the residential area by relying on the functionally synergistic mechanisms of selective permeability of the cell membrane, whole-area permeation of the cytoplasm, central regulation of the nucleus, distributed coordination of organelles, and efficient transport through cellular microfilaments. In the sample living circle, the overall integration increased from 1.27 to 1.64, the mean depth decreased from 3.79 to 3.18, and spatial connectivity increased from 3.74 to 5.44. Meanwhile, the synergy of the road network increased from 0.44 to 0.86, indicating marked improvements in spatial accessibility, connectivity, and the degree of coordination within the spatial structure. In addition, the visibility analysis showed that the pedestrian aggregation capacity of the public core space was enhanced, and the spatial vitality of public activity spaces in the residential area was improved. The findings demonstrate that the spatial organization model based on biomimetic cellular principles can effectively enhance spatial efficiency and social vitality in five-minute living circle residential areas, providing a quantifiable design method and theoretical framework for bio-inspired urban planning. Full article

The way robots represent the environment, make decisions, and express themselves can positively influence human–robot interaction if they clearly communicate their intentions and needs. To improve human–robot communication, biologically inspired models that mimic human communication skills, including task and scenario-specific contextual information, can facilitate mutual understanding and successful task execution. This paper presents a Context-Awareness and Biologically Inspired Behaviour system to generate a more natural human–robot interaction. The architecture combines sensory information processed by a Joint Attention System that prioritises stimuli based on internal processes with task-related motivations to generate context- and goal-adapted verbal and non-verbal interaction. We evaluate the system through a video-based user study that compares two robots with similar appearances but different behaviours, one using the proposed approach and the other not using the internal state and joint attention mechanisms, to make verbal and non-verbal responses. The results show that participants rated the robot endowed with the proposed system as significantly more sociable, agentic, and animated than the robot without it. Additionally, the robot not showing the responses developed in this work was perceived as more disturbing than the robot integrating the proposed system. Full article

So far, space exploration has attracted increasing scientific interest due to the growth of missions promoted by private investment, such as SpaceX, Boeing, Blue Origin, and the recent attention generated by astronomical phenomena such as 3I/ATLAS. However, access to space experimentation remains limited and expensive. For this reason, new approaches to simulate space conditions on Earth are being developed to broaden research opportunities bio-inspired by plant responses to phototropism and geotropism. In this context, Betta Aerospace has continued the development of a microgravity simulation system consisting of a 3-axis clinostat powered by a single motor, continuous external electrical supply, and, in this project, a continuous external liquid supply. The proposed pioneer system was designed as a flexible platform manufactured through reinforced 3D printing, with an approximate size of 30 cm, an estimated payload of 30 kg, and a 24 V supply. Its main goal is to study the effects of simulated microgravity on aquatic organisms while enabling longer observation times in a controlled freshwater environment. Candidate biological samples include Ulva lactuca, Pyropia, Spirulina/Arthrospira, and Chlorella. Preliminary motion tests confirmed continuous operation at 10 rpm. In addition, a simplified static finite element analysis under a 294 N load yielded a maximum von Mises stress of 5.45 × 10⁷ Pa and a maximum displacement of 1.73 mm. Full article

The application of artificial intelligence (AI) technologies in pediatric dentistry has expanded rapidly and is assuming an increasingly important role in the assessment of oral health and diagnostic processes in children. In particular, image-based analyses and data-driven predictive models offer significant potential to enhance diagnostic accuracy and reduce observer-dependent variability. This review addresses AI applications in pediatric dentistry within a structured framework, summarizing the methodological foundations of machine learning and deep learning approaches while critically evaluating the current body of evidence. It further identifies key limitations in the existing literature and outlines future research priorities, aiming to provide a perspective for the safe and sustainable clinical integration of AI in pediatric dentistry. Full article

Understanding the differences between synthetic and natural hydroxyapatite under conditions that mimic the oral environment, particularly the demineralization and remineralization processes of dental enamel, is essential for assessing their suitability as enamel models in biomineralization studies. The present study aims to systematically compare the structural, chemical, and morphological properties of well-crystallized synthetic carbonated hydroxyapatite (CHA) and natural non-biogenic hydroxyapatite (HA) before and after exposure to solutions with demineralizing and remineralizing activity. Two highly informative surface characterization techniques—X-ray photoelectron spectroscopy (XPS) and scanning electron microscopy (SEM)—were employed to examine the resulting surface changes. In addition, powder X-ray diffraction and infrared analyses were used to characterize the initial samples. Demineralization was induced using a lactic acid-based solution, while remineralization was performed through a two-step treatment involving polycarboxybetaine followed by artificial saliva. The results show that natural HA contains an additional fluorapatite phase and a wider range of trace elements (Na, F, Si), leading to a more complex structure. During demineralization, synthetic CHA exhibits more pronounced surface changes and faster dissolution, whereas natural HA demonstrates greater chemical stability. The remineralization process leads to the formation of new surface layers on both materials. Synthetic CHA develops a fine-grained, homogeneous layer enriched in carbonate and hydrated species, while natural HA shows localized crystal growth within structural defects. The results demonstrate that natural HA exhibits greater chemical stability during demineralization and a more enamel-like response during remineralization, whereas synthetic CHA undergoes more pronounced surface restructuring and forms a highly hydrated, carbonate-rich surface layer. Full article

A distinct typing interface using surface electromyography (sEMG) can facilitate silent, hands-free typing by interpreting muscle activity in relation to specific keystrokes. Character-level recognition poses greater challenges than coarse gesture recognition because it is sensitive to subtle temporal variations and overlapping muscle dynamics. Temporal features are essential for typing recognition because keypresses may differ in duration, force, and accompanying hand movements across users. This paper proposes TransTCNet, a two-stage deep neural network architecture with a causal convolutional layer for learning local features and a transformer-based component for learning long-range temporal interactions. We evaluated our network on a publicly available 26-class typing sEMG dataset acquired from 19 individuals. The model achieved a validation accuracy of 96.53%, exceeding the baseline models. Our study revealed generalization among participants, and the AUC values were also high (>0.994) across all classes. The model was highly reliable and exhibited high prediction confidence (>0.9), enabling us to achieve a high training accuracy (97.86%) for real-time filtering decisions. TransTCNet could be suitable for wearable and edge devices due to its efficient architecture and low inference cost. The model’s ability to consistently decode fine-grained neuromuscular signals across users makes it well-suited for real-time applications such as adaptive user interfaces, virtual and augmented reality, prosthetic control, and communication systems. Full article

Erosive wear in pipe elbows subjected to liquid–solid two-phase flow is a major cause of material degradation and service failure in industrial piping systems. In this study, erosion characteristics of pipe elbows were investigated through erosion mapping experiments and numerical simulations. The effects of flow velocity and particle diameter on erosion location and intensity were analyzed. Erosion was found to be mainly concentrated on the outer wall of the elbow within the angular range of 10° to 90°, and both erosion intensity and affected area increased with increasing particle diameter and flow velocity. Dean vortices were shown to play an important role in particle transport and erosion distribution, especially for small particles. Inspired by the ribbed morphology of shells, a biomimetic elbow was further designed and evaluated through an orthogonal numerical study considering flow velocity, particle diameter, rib number, and rib diameter. The results indicate that the ribbed structure can effectively improve erosion resistance by altering particle trajectories, reducing particle impact probability, and dissipating kinetic energy through low-velocity rotating flow between adjacent ribs. This finding provides useful inspiration for addressing erosive wear problems in engineering applications. Full article

This paper proposes a multi-strategy improved pied kingfisher optimizer (MSIPKO), a novel metaheuristic algorithm designed to address constrained optimization problems (COPs). COPs are widely encountered in engineering and industrial applications and are characterized by complex constraints that restrict the feasible solution space and often lead to multiple local optima. To enhance the performance of the original pied kingfisher optimizer (PKO), three strategies are incorporated: (i) a reverse differential crossover mechanism to improve global exploration and maintain population diversity; (ii) an enhanced diving-fishing operator to strengthen local exploitation; and (iii) an improved commensalism phase to enrich search directions and increase robustness. The performance of MSIPKO is evaluated on 12 benchmark functions from the IEEE Congress on Evolutionary Computation 2006 (CEC 2006) test suite and six classical engineering optimization problems. Experimental results demonstrate that MSIPKO outperforms several state-of-the-art algorithms in terms of optimization accuracy, convergence speed, and stability, particularly for high-dimensional, nonlinear, and multi-constrained problems. Moreover, MSIPKO achieves superior or comparable solutions with fewer function evaluations, indicating its high efficiency and adaptability. These results confirm that MSIPKO is a promising tool for solving complex real-world constrained optimization problems. Future work will focus on extending the proposed algorithm to multi-objective and large-scale optimization scenarios. Full article

Ischemic stroke, a major cause of global disability, is characterized by the blockage of an artery leading to reduced cerebral blood flow and subsequent brain injury. Automatic segmentation of ischemic stroke lesions in Computed Tomography Perfusion (CTP) maps is critical for accurate diagnosis, treatment planning, and outcome assessment. However, the accuracy of traditional methods remains limited, with Dice Similarity Coefficient (DSC) values around 68%. To address this challenge, we propose a deep learning-based model inspired by biological systems and brain mechanisms, which emulates natural information processing to enhance ischemic stroke lesion segmentation. The proposed network architecture consists of five graph convolutional layers that automatically extract and classify features from CTP images. We evaluated the model using the ISLES 2018 database, achieving a DSC of 75.41% and a Jaccard Index of 74.52%, representing significant improvements over previous methods. Notably, the proposed approach performs robustly in noisy environments, maintaining accuracy above 60% even at SNR = −4. These results demonstrate the potential of biomimetic-inspired networks for automatic ischemic stroke segmentation. Full article

Currently, small moving object detection remains a highly challenging problem, primarily attributable to four critical factors: limited pixel coverage, blurred texture features, indistinguishability from small-object-like background features (i.e., false positives), and vulnerability to environmental noise interference. The remarkable sensitivity of the Drosophila visual system to small moving objects, which originates from a specialized type of neuron known as “lobula columnar 11” (LC11), has provided inspiration for addressing this challenge. Current bio-inspired visual models have achieved certain advances. However, detection performance against real-world complex dynamic natural environments still requires further improvement. To address the challenge of limited detection accuracy for small moving objects against real-world complex dynamic natural environments, this paper proposes a Motion Small Object Detection (MSOD) model inspired by the Drosophila Vision Small Object Motion Sensitivity (DVSOMS) mechanism, namely DVSOMS-MSOD. The model consists of four stages: The first stage is preliminary processing of visual stimuli, where visual stimuli are perceived, converted to grayscale, and blurred. The second stage is the motion neural pathway, where visual signals are first decomposed into parallel ON and OFF neural pathway signals; then, the neural feedback mechanism is incorporated between the medulla and lobula complex, and the complete Hassenstein–Reichardt correlator (HRC) is integrated into the lobula complex; finally, the LC11 neuron is utilized to detect small moving objects and extract their location information. The third stage is the contrast neural pathway, where visual signals are first processed by the central and surrounding local neighborhoods, then local contrast information is calculated. The fourth stage is the integration of motion and contrast neural pathways, where the mushroom body generates motion trajectories using the location information of small moving objects, and subsequently generates contrast trajectories using the local contrast information and motion trajectories to more finely detect small moving objects. Under real-world complex dynamic natural environment datasets, compared with conventional machine learning methods for moving object detection, the proposed model achieved improvements of 77.82% and 78.70% in detection performance and output quality, respectively, while reducing running time by 10.60%. Compared with bio-inspired visual models for small moving object detection, the proposed model achieved improvements of 28.24% and 43.15% in detection accuracy and detection performance, respectively, but the running time increased by 43.40%. The proposed model demonstrates certain advantages in detection performance, output quality, and detection accuracy; however, its real-time performance still warrants further optimization. Full article

Tree-climbing robots are primarily utilized for pruning and harvesting in tall trees; however, limited structural degrees of freedom (DoFs) reduce their flexibility in complex environments. To improve the flexibility and environmental adaptability of the robots, this study proposes a novel three-armed claw-type tree-climbing robot inspired by gibbons. A 14 DoFs prototype with a total mass of approximately 2.52 kg was developed, comprising three manipulator arms and independently actuated claws. Kinematic models were separately established for the series-connected arms and the parallel-connected moving platform, with accuracy verified through numerical simulations. Based on these models, a control system was implemented, and a physical prototype was tested in field climbing experiments. Grasping tests on surfaces of varying roughness, including moist tree trunks, artificial wood, and smooth steel plates, demonstrated the adaptability of the claw to diverse materials. The robot successfully climbed trunks inclined at 52–90°, supporting a maximum payload of 1.81 kg; each full gait cycle averaged approximately 4 min. These results indicate that the robot can successfully imitate the movements of gibbons during climbing, thereby verifying the feasibility and practical application value of this bionic design in real-world forestry environments. Full article

This study addresses pressure regulation in an induction-motor-driven centrifugal fan and introduces two complementary novelties: a Softsign-PI controller that shapes the tracking error via a Softsign nonlinearity before PI regulation and a hybrid starfish optimization with a differential evolution (hSFOA-DE) scheme for automatically tuning the controller parameters. The approach is evaluated on an experimentally validated nonlinear fan–motor model and benchmarked against modern metaheuristics—starfish optimization algorithm (SFOA), animated oat optimization (AOO), electric eel foraging optimization (EEFO), differential evolution (DE), particle swarm optimization (PSO)—as well as classical tunings—Murrill-based 2-DOF PID, Tyreus–Luyben PID and Ziegler–Nichols PI. Statistical summaries and boxplots indicate superior central tendency with reduced run-to-run variability; fitness–evolution curves show faster convergence; and time-domain performance metrics confirm improved transient and steady-state behaviour. Objective function comparisons further show the lowest values of both the Zwe-Lee Gaing (ZLG) and integral of absolute error (IAE), supporting advantages in robustness and tracking accuracy of the proposed approach. These gains reduce overshoot and cumulative error, which can lessen throttling losses and actuator duty in fan/pump service, suggesting potential energy and maintenance benefits. Full article

Biomaterials play a central role in tissue engineering and regeneration by providing scaffolds that support cell adhesion, proliferation and differentiation while modulating the surrounding microenvironment. They represent promising alternatives to traditional surgical approaches that may lead to complications or tissue damage, and their performance is influenced by chemical composition, mechanical behavior, architecture and interfacial properties, all of which can be precisely tuned through advanced fabrication and surface modification strategies. This review synthesizes evidence from a comprehensive literature search across major scientific databases, focusing on highly cited studies and available clinical data, and examines natural and synthetic biomaterials, their biological responses, functional characteristics, and surface modification methods. Emphasis is placed on Cold Atmospheric Plasma (CAP), which selectively modifies the outermost nanolayer of materials, enhancing hydrophilicity, functional group density, protein adsorption and overall cell–material interactions, as well as improving drug loading capacity. The review also considers stem cell interactions with biomaterials and emerging applications of artificial intelligence (AI) for predicting performance and guiding material optimization. Overall, the analysis highlights that natural matrices provide intrinsic bioactivity, synthetic polymers offer tunable mechanics and degradation profiles, and composite systems integrate these advantages. Advances in technologies such as electrospinning and 3D/4D printing enable precise control over architecture, supporting cell colonization and vascularization. Collectively, developments in CAP treatments and AI-driven design strategies are strengthening the regenerative potential of biomaterials and advancing their clinical translation. Full article