Biomimetics

With the proliferation of social media platforms, misinformation has evolved toward more diverse modalities and complex cross-semantic correlations. Accurately detecting such content, particularly under conditions of semantic inconsistency and uneven modality dependency, remains a critical challenge. To address this issue, we propose a multimodal semantic representation framework named IBKA-MSM, which integrates swarm-intelligence-based optimization with deep neural modeling. The framework first employs an Improved Black-Winged Kite Algorithm (IBKA) for discriminative feature selection, incorporating adaptive step-size control, an elite-memory mechanism enhanced by opposition perturbation, Gaussian-based local exploitation, and population diversity regulation through reinitialization. In addition, a Modality-Generated Loop Verification (MGLV) mechanism is designed to enhance semantic alignment, and a Semantic Confidence Matrix with Modality-Coupled Interaction (SCM-MCI) is introduced to achieve adaptive multimodal fusion. Experimental results demonstrate that IBKA-MSM achieves an accuracy of 95.80%, outperforming mainstream hybrid models. The F1 score is improved by approximately 2.8% compared to PSO and by 1.6% compared to BKA, validating the robustness and strong capability of the proposed framework in maintaining multimodal semantic consistency for fake news detection. Full article

The discovery of the fifth gravitational wave, GW170817, and its electromagnetic counterpart, resulting from the merger of neutron stars by the LIGO and Virgo teams, marked a major milestone in astronomy. It was the first time that gravitational waves and light from the same cosmic event were observed simultaneously. The LIGO detectors in the United States recorded the signal for 100 s, longer than in previous detections. The merging of neutron stars emits both gravitational and electromagnetic waves across all frequencies—from radio to gamma rays. However, pinpointing the exact source remains difficult, requiring rapid sky scanning to locate it. To address this challenge, the Gravitational-Wave Optical Transient Observer (GOTO) project was established. It is specifically designed to detect optical light from transient events associated with gravitational waves, enabling faster follow-up observations and a deeper study of these short-lived astronomical phenomena, which appear and disappear quickly in the universe. In astrophysics, it has become more important to find astronomical transient events like supernovae, gamma-ray bursts, and stellar flares because they are linked to extreme cosmic processes. However, finding these short-lived events in huge sky survey datasets, like those from the GOTO project, is very hard for traditional analysis methods. This study suggests a deep learning methodology employing Convolutional Neural Networks (CNNs) to enhance transient classification. CNNs are based on how biological vision systems work and how they are structured. They mimic how animal brains hierarchically process visual information, making it possible to automatically find complex spatial patterns in astronomical images. Transfer learning and fine-tuning on pretrained ImageNet models are utilized to emulate adaptive learning observed in biological organisms, enabling swift adaptation to new tasks with minimal data. Data augmentation methods like rotation, flipping, and noise injection mimic changes in the environment to improve model generalization. Dropout and different batch sizes are used to stop overfitting, which is similar to how biological systems use redundancy and noise tolerance. Ensemble learning strategies, such as Soft Voting and Weighted Voting, draw inspiration from collective intelligence in biological systems, integrating multiple CNN models to enhance decision-making robustness. Our findings indicate that this bio-inspired framework substantially improves the precision and dependability of transient detection, providing a scalable solution for real-time applications in extensive sky surveys such as GOTO. Full article

The Red-Billed Blue Magpie Optimizer (RBMO) is a recently introduced swarm-based meta-heuristic that has shown strong potential in engineering optimization but remains under-explored. To address its inherent limitations, this paper proposes an Enhanced RBMO (ERBMO) that synergistically incorporates two key strategies: a dominant-group-based two-stage covariance-driven strategy that captures evolutionary trends to improve population quality while reinforcing global exploration, and a Powell mechanism (PM) that eliminates dimensional stagnation and markedly strengthens convergence. Extensive experiments on the CEC 2017 benchmark suite demonstrate that ERBMO outperforms ten basic and improved algorithms in global exploration, local convergence accuracy and robustness, attaining Friedman ranks of 1.931, 1.621, 1.345 and 1.276 at 10D, 30D, 50D and 100D, respectively. Furthermore, empirical studies on practical engineering design problems confirm the algorithm’s capability to consistently deliver high-quality solutions, highlighting its broad applicability to real-world constrained optimization tasks. In future work, we will deploy the algorithm for real-world tasks such as UAV path-planning and resource-scheduling problems. Full article

To investigate the impact of flexible versus rigid bioinspired flapping hydrofoils on the discharge characteristics of suspended particles in raceway aquaculture, this study established a two-way fluid–structure coupling model of a flapping hydrofoil device based on ANSYS Fluent and Transient Structural modules. The research compares the discharge characteristics of hydrofoils with different elastic moduli. The results show that, within a certain range of elastic moduli adjustment, flexible bioinspired hydrofoils exhibit greater surface deformation compared to rigid ones, effectively delaying tail vortex shedding and extending its duration, thus prolonging the range of high flow velocities. During the middle stage of discharge, the escape rate of suspended particles under the influence of flexible bioinspired hydrofoils with 0.05 GPa elastic modulus was 3–4% higher than that of rigid hydrofoils. However, in terms of achieving maximum discharge efficiency and effectiveness, both reached approximately 97.8% with little difference between them. This study highlights the bioinspired principles in hydrofoil design and provides a reference for optimizing flexible hydrofoil discharge characteristics in future research. Full article

This study aimed to evaluate the biomechanics of maxillary first molar teeth following palatal, disto-buccal, and mesio-buccal root amputation. An intact maxillary molar underwent root canal treatment using Reciproc R25 files (VDW, Munich, Germany). The canals were obturated with gutta-percha (DiaDent, Seoul, Republic of Korea) and 2Seal sealer (VDW, Munich, Germany), and the access cavity was restored with composite resin. A high-resolution CBCT scan of an intact maxillary first molar was obtained using a Planmeca Promax 3D Max system (Planmeca Oy, Helsinki, Finland) at 75 kVp and 10 mA. The acquired data were processed in 3D Slicer software (v5.8.0, BSD license, Boston, MA, USA) to segment enamel, dentin, and pulp based on pixel density variations using the three-point cloud method. A baseline intact model and three root-resected models (palatal, disto-buccal, mesio-buccal) were reconstructed in SolidWorks 2021, with resected roots simulated as being sealed with MTA. Finite element analysis was conducted in CosmosWorks to evaluate von Mises stress distribution under a 300 N static occlusal load. Maximum von Mises stresses were detected at occlusal force application sites. Among root dentin tissues, stress values ranked highest after palatal root resection, followed by the mesio-buccal, disto-buccal, and non-resected models. Conclusions: Palatal root amputation of maxillary first molars generated the highest von Mises stresses in root dentin, suggesting a higher biomechanical risk than disto-buccal or mesio-buccal resections. Full article

Background: Developing reliable and cost-effective decellularization methods is critical for advancing tissue engineering and regenerative medicine, particularly in regions with limited access to specialized perfusion systems. Methods: This study standardized a gravity-assisted perfusion protocol for rat liver decellularization, designed to operate without pumps or pressurized equipment. Adult Wistar rat livers were processed through a gravity-driven vascular flushing method and compared with a conventional immersion-based protocol. The resulting scaffolds were evaluated by macroscopic inspection, histological staining (Masson’s trichrome), and residual DNA quantification. Results: The gravity-assisted perfusion method achieved more efficient cellular removal and superior preservation of extracellular matrix (ECM) integrity compared with immersion. Residual DNA levels were 3.7 ng/mg in perfused samples, 209.47 ng/mg in immersed samples, and 331.97 ng/mg in controls, confirming a statistically significant reduction (p < 0.05). Only the perfused group met the accepted threshold for effective decellularization (<50 ng/mg dry tissue). Histological analysis corroborated these findings, showing the absence of nuclei and the preservation of collagen architecture characteristic of a structurally intact ECM. Conclusions: This low-cost, reproducible, and technically simple system enables the generation of high-quality acellular hepatic scaffolds without mechanical pumps. Its accessibility and scalability make it suitable for laboratories with limited infrastructure and educational settings. Moreover, this gravity-assisted approach provides a foundation for future recellularization and preclinical studies aimed at developing bioengineered liver constructs for regenerative and transplant applications. Full article

Background: Quantitatively detecting whether plants exhibit measurable bioelectric differences in the presence of nearby human movement remains challenging, in part because plant signals are low-amplitude, slow, and easily confounded by environmental factors. Methods: We recorded bioelectric activity from 2978 plant samples across three species (basil, salad, tomato) using differential electrode pairs (leaf and soil electrodes) sampling at 142 Hz. Two trained performers executed three specific eurythmic gestures near experimental plants while control plants remained isolated. Random Forest and Convolutional Neural Network classifiers were applied to distinguish the control from treatment conditions using engineered features including spectral, temporal, wavelet, and frequency domain characteristics. Results: Random Forest classification achieved 62.7% accuracy (AUC = 0.67) distinguishing differences in recordings collected near a moving human from control conditions, representing a statistically significant 12.7 percentage point improvement over chance. Individual performer signatures were detectable with 68.2% accuracy, while plant species classification achieved only 44.5% accuracy, indicating minimal species-specific artifacts. Temporal analysis revealed that the plants with repeated exposure exhibited consistently less negative bioelectric amplitudes compared to single-exposure plants. Innovation: We introduce a data-driven approach that pairs standardized, short-window bioelectric recordings with machine-learning classifiers (Random Forest, CNN) to test, in an exploratory manner, whether plant signals differ between human-moving-nearby and isolation conditions. Conclusions: Plants exhibit modest but statistically detectable bioelectric differences in the presence of nearby human movement. Rather than attributing these differences to eurythmic movement itself, the present design can only demonstrate that plant recordings collected within ~1 m of a moving human differ, modestly but statistically, from recordings taken ≥3 m away. The underlying biophysical pathways and specific contributing factors (airflow, VOCs, thermal plumes, vibration, electromagnetic fields) remain unknown. These results should therefore be interpreted as exploratory correlations, not mechanistic evidence of gesture-specific plant sensing. Full article

Defect-rate uncertainty creates cascading operational challenges in multi-stage production, often driving inefficiency and misallocation of labor, materials, and capacity. To confront this, we develop a multi-stage Production Integrated Decision (MsPID) framework that unifies quality inspection and shop-floor decision-making within a single computational model. The framework couples a two-stage sampling inspection policy—used to statistically learn and control defect-rate uncertainty via estimation and rejection rules—with a multi-process, multi-part production decision model. Optimization is carried out with an Improved Grey Wolf Optimizer (IGWO) enhanced with Latin hypercube sampling (LHS) for uniformly diverse initialization; an evolutionary factor mechanism that blends simulated binary crossover (SBX) among three leadership-guided parents (Alpha, Beta, Delta) to strengthen global exploration in early iterations and focus exploitation later; and a greedy, mutation-assisted opposition learning step applied to the lowest-performing quartile of the population to effect leader-informed local refinement and accept only fitness-improving moves. Experiments show the method identifies minimum-cost policies across six single-stage benchmark cases and yields a total profit of 43,800 units in a representative multi-stage scenario, demonstrating strong performance in uncertain environments. Sensitivity analysis further clarifies how recommended decisions adapt to shifts in estimated defect rates, finished product prices, and swap/changeover losses. These results highlight how bio-inspired intelligence can enable adaptive, efficient, and resilient integrated production management at scale. Full article

Efficient coordination among software modules is essential for biomimetic robotic systems, much like the interaction among organs in a biological organism. However, implementing inter-process or inter-module communication in autonomous systems remains a complex and time-consuming task, particularly for new researchers. Simplifying inter-module communication is the central focus of this study. To address this challenge, we propose the DigitalTwinPort framework, a novel communication abstraction inspired by the port-based connectivity of embedded hardware systems. Unlike middleware-dependent solutions such as ROS, the proposed framework provides a lightweight, object-oriented structure that enables unified and scalable communication between software modules and networked devices. The concept is implemented in C++ and validated through an autonomous surface vehicle (ASV) developed for the RobotX Challenge. Results demonstrate that the DigitalTwinPort simplifies the development of distributed systems, reduces configuration overhead, and enhances synchronization between digital and physical components. This work lays the foundation for future digital twin architectures in embodied Internet systems, where software and hardware can interact seamlessly through standardized digital ports. Full article

Cactaceae are plants equipped with spines and adapted to extremely arid environments. In particular, Echinocactus grusonii spines are almost cylindrical structures, which may occasionally present an enlargement of their proximal cross sectional area. In this work, the spines of Echinocactus grusonii were explored as a possible source of biomimetic inspiration for the design and the implantation strategies of self-inserting intraneural interfaces. More specifically, the elastic stability of spines was theoretically studied for structures able to puncture the surface of an external object, as well as for structures unable to pierce it. The biomimicry of Echinocactus grusonii spines suggested an improved insertion strategy for self-inserting intraneural interfaces together with structural changes able to increase their elastic stability. The theoretical approach provided in this work was able to predict an increase of the first buckling threshold up to 39% for not puncturing self-inserting neural interfaces, and up to 59% for puncturing ones. Full article

There is a growing demand for underwater robots to support offshore tasks such as exploration, environmental monitoring, and critical underwater missions. To enhance the performance of these systems, researchers are increasingly turning to biological inspiration to develop robots that understand and adapt the swimming strategies of aquatic animals. Numerical modeling plays a critical role in evaluating and improving the performance of these complex, multi-body robotic systems. However, developing accurate models for multi-body robots that swim freely in three dimensions remains a significant challenge. This study presents the development and validation of a numerical model of a bio-inspired California sea lion (Zalophus californianus) robot. The model was developed to simulate, analyze, and visualize the robot’s body motions in water. The equations of motion were derived in closed form using the Euler–Poincaré formulation, offering advantages for control and stability analysis. Hydrodynamic coefficients essential for estimating fluid forces were computed using computational fluid dynamics (CFD) and strip theory and further refined using a genetic algorithm to reduce the sim-to-real gap. The model demonstrated strong agreement with experiments, accurately predicting the translation and orientation of the robot. This framework provides a validated foundation for simulation, control, and optimization of bio-inspired multi-body systems. Full article

Skin meshing facilitates the greater expansion of donor skin through patterned slits and is widely used for treating extensive burn injuries. However, the actual expansion often falls below manufacturers’ claims. Previous computational analyses using the isotropic Yeoh model have shown that Langer’s line orientation and slit direction significantly affect induced stress and meshing ratios, yet the use of nonlinear anisotropic models that represent collagen fiber alignment corresponding to Langer’s lines remains unexplored. This study employs a nonlinear anisotropic Gasser–Ogden–Holzapfel (GOH) model with slit orientations of 0°, 45°, and 90°, consistent with geometries reported in the literature, to quantify induced stress in skin meshing by incorporating collagen fibers within the dermis layer. The GOH parameters were calibrated to human back skin data uniaxially stretched parallel and perpendicular to Langer’s lines using Levenberg–Marquardt optimization in the GIBBON toolbox (MATLAB R2023a) coupled with FEBio v4.0, achieving a standard deviation of 3% relative to experimental data. The GOH model predicted the highest induced stress at 100% strain for the 45° slit parallel to Langer’s lines and the lowest for the 90° slit perpendicular, exceeding 40 MPa due to absence of damage and rupture modeling but accurately representing anisotropic mesh behavior. Full article

Small object detection, as an important research topic in computer vision, has been widely applied in aerial visual tasks such as remote sensing and UAV imagery. However, due to challenges such as small object size, large-scale variations, and complex backgrounds, existing detection models often struggle to capture fine-grained semantics and high-resolution texture information in aerial scenes, leading to limited performance. To address these issues, this paper proposes an efficient aerial small object detection model, EABI-DETR (Efficient Attention and Bi-level Integration DETR), based on the RT-DETR framework. The proposed model introduces systematic enhancements from three aspects: (1) A lightweight backbone network, C2f-EMA, is developed by integrating the C2f structure with an efficient multi-scale attention (EMA) mechanism. This design jointly models channel semantics and spatial details with minimal computational overhead, thereby strengthening the perception of small objects. (2) A P2-BiFPN bi-directional multi-scale fusion module is further designed to incorporate shallow high-resolution features. Through top-down and bottom-up feature interactions, this module enhances cross-scale information flow and effectively preserves the fine details and textures of small objects. (3) To improve localization robustness, a Focaler-MPDIoU loss function is introduced to better handle hard samples during regression optimization. Experiments conducted on the VisDrone2019 dataset demonstrate that EABI-DETR achieves 53.4% mAP@0.5 and 34.1% mAP@0.5:0.95, outperforming RT-DETR by 6.2% and 5.1%, respectively, while maintaining high inference efficiency. These results confirm the effectiveness of integrating lightweight attention mechanisms and shallow feature fusion for aerial small object detection, offering a new paradigm for efficient UAV-based visual perception. Full article

Animal welfare education requires pedagogical models that bridge conceptual knowledge with practice. This study presents GamifyWELL, a biomimetic, gamified learning environment for students, farmers, and veterinary technicians. Grounded in ecological principles of adaptation, diversification, and niche specialization, the design emulates how living systems evolve through feedback and cooperation. These principles were translated into an instructional model that integrates a core pathway (Pre-Test, Levels 1–4, Post-Test) with optional enrichment tasks and a role-specific Reward Marketplace. Question formats are constant across levels (MCQ, image-based, video-based) while cognitive difficulty increases, culminating in Positive Welfare scenarios. We describe the learning design structure and report preliminary implementation observations using a mixed-methods evaluation plan (pre/post knowledge assessments and engagement indicators). Results from early deployment indicate strong usability and engagement, with high voluntary uptake of enrichment tasks and positive learner feedback on role-tailored rewards; full empirical testing is in progress. Findings support the feasibility and pedagogical promise of biomimetic gamification to enhance knowledge, motivation, and intended practice in animal welfare education. GamifyWELL offers a replicable framework for nature-inspired instructional design that can be extended to allied sustainability domains. Full article

Among the gynecological cancers, ovarian cancer is the most fatal. Despite advancements in modern medicine, the survival rate is abysmally low among ovarian cancer patients. Ovarian cancer poses several unique challenges, like late diagnosis due to the initial vagueness of the symptoms and lack of effective screening protocols. Recently, biomaterials have been explored and utilized extensively for the diagnosis, treatment, and screening of ovarian malignancies. Biomaterials can help bypass the obstacles of traditional chemotherapy and enhance imaging capabilities. They are also indispensable for next-generation biosensors and tumor organoids. Biomaterials inspired by biomimetic strategies that replicate the structural, chemical, and functional properties of natural biological systems have proven to have better functionalities. While numerous review articles have examined biomaterials in oncology, there is a lack of reviews dedicated specifically to their applications in ovarian cancer. This review aims to address this critical gap by providing the first comprehensive overview of the current biomaterial research on ovarian cancer and highlighting key challenges, opportunities, and future directions in this evolving interdisciplinary field. Full article

The Arctic Puffin Optimization (APO) algorithm, proposed in 2024, is a swarm intelligence optimization. Similar to other swarm intelligence optimization algorithms, it suffers from issues such as slow convergence in the early stage, being easy to fall into local optima, and insufficient balance between exploration and exploitation. To address these limitations, an improved APO (IAPO) algorithm incorporating multiple strategies is proposed. Firstly, a mirror opposition-based learning mechanism is introduced to expand the search scope, improving the efficiency of searching for the optimal solution, which enhances the algorithm’s convergence accuracy and optimization speed. Secondly, a dynamic differential evolution strategy with adaptive parameters is integrated to improve the algorithm’s ability to escape local optima and achieve precise optimization. Comparative experimental results between IAPO and eight other optimization algorithms on 20 benchmark functions, as well as CEC2019 and CEC2022 test functions, show that IAPO achieves higher accuracy, faster convergence, and superior robustness, securing first-place average rankings of 1.35, 1.30, 1.25, and 1.08 on the 20 benchmark functions, CEC 2019, 10- and 20-dimensional CEC 2022 test sets, respectively. Finally, simulation experiments were conducted on three engineering optimization design problems. IAPO achieved optimal values of 5.2559 × 10⁻¹, 1.09 × 10³, and 1.49 × 10⁴ for these engineering problems, ranking first in all cases. This further validates the effectiveness and practicality of the IAPO algorithm. Full article

The Crested Porcupine Optimizer (CPO), as a newly emerging swarm intelligence algorithm, demonstrates advantages in balancing global exploration and local exploitation but still suffers from limitations in convergence speed and local exploitation precision. To address these issues, this paper proposes an enhanced variant, the Butterfly Search and Triangular Walk Crested Porcupine Optimizer (BTCPO). The method achieves a dynamic balance between exploration and exploitation by combining triangular walk to boost local exploitation and butterfly search to increase global variety. Experimental results on 23 classical benchmark functions and the CEC2021 test suite show that BTCPO outperforms CPO as well as seven state-of-the-art algorithms (DBO, HBA, BKA, HHO, GWO, GOOSE, and SSA). Specifically, BTCPO achieves the best performance on more than 80% of CEC2021 functions, with convergence speed improved by approximately 25% compared to CPO. Furthermore, BTCPO exhibits higher efficiency and usefulness in engineering design problems such as trusses, welded beams, and cantilever beams. These findings demonstrate the theoretical and practical advantages of BTCPO, making it a workable approach to solving difficult optimization problems. Full article

With the expansion of unmanned aerial vehicles (UAVs) into complex three-dimensional (3D) terrains for reconnaissance, rescue, and related missions, traditional path planning methods struggle to meet multi-constraint and multi-objective requirements. Existing swarm intelligence algorithms, limited by the “no free lunch” theorem, also face challenges when the standard Information Acquisition Optimizer (IAO) is applied to such tasks, including low exploration efficiency in high-dimensional search spaces, rapid loss of population diversity, and improper boundary handling. To address these issues, this study proposes a Multi-Strategy Enhanced Information Acquisition Optimizer (MEIAO). First, a Levy Flight-based information collection strategy is introduced to leverage its combination of short-range local searches and long-distance jumps, thereby broadening global exploration. Second, an adaptive differential evolution operator is designed to dynamically balance exploration and exploitation via a variable mutation factor, while crossover and greedy selection mechanisms help maintain population diversity. Third, a globally guided boundary handling strategy adjusts out-of-bound dimensions to feasible regions, preventing the generation of low-quality paths. Performance was evaluated on the CEC2017 (dim = 30/50/100) and CEC2022 (dim = 10/20) benchmark suites by comparing MEIAO with eight algorithms, including VPPSO and IAO. Based on the mean, standard deviation, Friedman mean rank, and Wilcoxon rank-sum tests, MEIAO demonstrated superior performance in local exploitation of unimodal functions, global exploration of multimodal functions, and complex adaptation on composite functions while exhibiting stronger robustness. Finally, MEIAO was applied to 3D mountainous UAV path planning, where a cost model considering path length, altitude standard deviation, and turning smoothness was established. The experimental results show that MEIAO achieved an average path cost of 253.9190, a 25.7% reduction compared to IAO (341.9324), with the lowest standard deviation (60.6960) among all algorithms. The generated paths were smoother, collision-free, and achieved faster convergence, offering an efficient and reliable solution for UAV operations in complex environments. Full article

Population aging and rising rehabilitation demands highlight the need for advanced assistive devices to improve mobility in individuals with motor impairments. Existing back-support lifting nursing beds often lack adequate human–machine adaptability, safety, and emotional consideration. This study presents a human-centered, data-driven optimization pipeline that integrates behavior–emotion dual recognition, simulation verification, and parameter optimization with user demand mining, biomechanical simulation, and sustainable practices. The design utilizes GreenAI, focusing on low-power algorithms and eco-friendly materials, ensuring energy-efficient AI models and reducing the environmental footprint. A dual-channel data fusion method was developed, combining movement parameters from sit-to-lie transitions with emotional needs extracted from e-commerce reviews using the Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA) models. The fuzzy Kano model prioritized design objectives, identifying multi-position adjustment, joint protection, armrest optimization, and interaction comfort as key targets. An AnyBody-based human–device model quantified muscle (erector spinae, rectus abdominis, trapezius) and hip joint loads during posture changes. Simulations verified the design’s ability to improve load distribution, reduce joint stress, and enhance comfort. The optimized nursing bed demonstrated improved adaptability across user profiles while maintaining functional reliability. This framework offers a scalable paradigm for intelligent rehabilitation equipment design, with potential extension toward AI-driven adaptive control and clinical validation. This sustainable methodology ensures that the device not only meets rehabilitation goals but also contributes to a more environmentally responsible healthcare solution, aligning with global sustainability efforts. Full article