Biomimetics最新文献

Trabecular Metal^TM (TM) dental implants comprise a tantalum (Ta)-based biomimetic open-cell structure designed to replicate the structural, functional, and physiological properties of cancellous bone. Yet, the current literature primarily focuses on the evaluation of osseointegration outcomes surrounding TM implants in uncompromised bone environments and/or brief periods of observation in pre-clinical models. In addition, the performance of TM implants in bony defect environments reconstructed with allogenic grafts and bioactive molecules, such as platelet-rich fibrin (PRF), has not been thoroughly investigated. This longitudinal, randomized clinical trial comprised patients presenting with completely edentulous maxillaries. Guided Bone Regeneration (GBR) was performed using a cortico-cancellous allograft/PRF agglomerate. After 26 weeks, bone biopsies were obtained, followed by the insertion of a TM implant, after which patients were allowed to heal for 52 weeks for assessment of osseointegration. Qualitatively, histomicrographs at 26 weeks confirmed the presence of newly formed bone extending from the periphery of defects and along the direct surface of the allograft. TM implant biopsies at 52 weeks demonstrated osseointegration with bone ongrowth and ingrowth at the interconnected, porous trabecular region. These histological characteristics were consistent across all patients. No metal debris was detected, and the TM implants maintained their porous structure throughout the study period. TM implants placed in PRF-augmented allograft-reconstructed maxillae fostered a conducive environment for osseointegration. By leveraging the open-cell Ta structure, robust new bone formation was achieved without signs of adverse tissue reactions.

The barrier membrane is a key component in guided bone regeneration (GBR); however, there is no current commercially available membrane universally suitable for all clinical situations. The semi-resorbable bioactive barrier membrane derived from a silk fiber sheet (SF), polyvinyl alcohol (PVA), and biphasic calcium phosphate (BCP) was fabricated to provide improved physical, mechanical, and bioactive properties. There were four experimental groups: PVA/SF, 1BCP/PVA/SF, 3BCP/PVA/SF, and 5BCP/PVA/SF. All fabricated membranes appeared white in color with a smooth texture; however, SEM images revealed a rougher top surface compared to the bottom surface. FTIR and DSC validated the presence of the SF and PVA with or without BCP. All membranes displayed high hydrophilicity, except the PVA/SF group, which remained hydrophobic on the bottom surface. The water uptake of all groups reached the plateau phase within 10 min. The degradation rate fell within the range of 5-20% over a three-month period. Both fibroblastic and osteoblastic cells attached and survived on the BCP-incorporated membranes, comparable to those observed in the commercially available ossifying collagen membrane. Among the fabricated membranes, the 3BCP/PVA/SF formulation demonstrated the most favorable physical, mechanical, and biological properties for GBR applications.

The Red-Billed Blue Magpie Optimization (RBMO) algorithm is a metaheuristic method inspired by the foraging behavior of red-billed blue magpies. However, the conventional RBMO often suffers from premature convergence and performance degradation when solving high-dimensional constrained optimization problems due to its over-reliance on population mean vectors. To address these limitations, this study proposes an Improved Red-Billed Blue Magpie Optimization (IRBMO) algorithm through a multi-strategy fusion framework. IRBMO enhances population diversity through Logistic-Tent chaotic mapping, coordinates global and local search capabilities via a dynamic balance factor, and integrates a dual-mode perturbation mechanism that synergizes Jacobi curve strategies with Lévy flight strategies to balance exploration and exploitation. To validate IRBMO's efficacy, comprehensive comparisons with 16 algorithms were conducted on the CEC-2017 (30D, 50D, 100D) and CEC-2022 (10D, 20D) benchmark suites. Subsequently, IRBMO was rigorously evaluated against ten additional competing algorithms across four constrained engineering design problems to validate its practical effectiveness and robustness in real-world optimization scenarios. Finally, IRBMO was applied to 3D UAV path planning, successfully avoiding hazardous zones while outperforming 15 alternative algorithms. Experimental results confirm that IRBMO exhibits statistically significant improvements in robustness, convergence accuracy, and speed compared to classical RBMO and other peers, offering an efficient solution for complex optimization challenges.

Background: Clear aligner therapy (CAT) represents a biomimetic orthodontic approach that uses flexible thermoplastic materials to reproduce the physiological tooth movement and mechanical load distribution of natural tissues. While these materials promote oral hygiene and aesthetic comfort, their long-term biological impact on the caries process remains uncertain. This retrospective study aimed to evaluate changes in the number of decayed teeth (ΔD) before and after clear aligner treatment and to identify duration-dependent risk factors.

Methods: This retrospective study included 362 patients (279 females, 83 males) treated with Invisalign^® aligners between 2020 and 2024. Baseline and post-treatment panoramic radiographs were analyzed to determine decayed tooth counts. Age, sex, and total aligner count were recorded. Non-parametric tests, multivariable regression, and ROC analysis were used to assess predictors of ΔD.

Results: The mean number of decayed teeth increased slightly from 3.54 ± 2.76 to 3.83 ± 2.93 (p < 0.001). Longer treatment duration was independently associated with caries progression (β = +0.0088 per tray, p = 0.0037), and each 10-tray increment increased the odds of new decay by 55% (OR = 1.55, 95% CI: 1.26-1.90). ROC analysis identified ≥42 trays as a clinically relevant threshold (AUC = 0.67).

Conclusions: Clear aligner therapy demonstrated a statistically significant yet clinically small increase in caries incidence, primarily related to treatment duration. As a biomimetic orthodontic approach that integrates mechanical and biological dynamics, extended clear aligner use may alter biofilm-surface interactions and salivary conditions over time. Therefore, preventive strategies-such as professional fluoride applications, strict cleaning protocols, and shorter recall intervals-should be emphasized for long-duration treatments to preserve the biological benefits of this biomimetic system.

As ecological collapse accelerates under the pressures of anthropogenic climate change, adaptation strategies increasingly include technological proxies for nature's functions. But can ecosystem services (ES) be meaningfully replaced by technology? Revisiting this urgent question first posed by Fitter (2013), we assess the extent to which bio-inspired design-particularly biomimetics-has advanced the capacity to support, enhance, or replace natural ES. We convened an interdisciplinary team to synthesize and refine a comprehensive list of 22 ecosystem services, integrating often-overlooked cultural and relational dimensions. Using this framework, we conducted a large-scale analysis of over 68,000 peer-reviewed publications from the biomimetics and bio-inspired design literature between 2004 and 2025, applying AI-assisted classification to evaluate whether, and how, these technologies map onto specific ES functions and benefits. Our findings reveal both promise and profound limitations. Bio-inspired research engages with 20 of the 22 ES, but over 78% of this work concentrates on five technologically tractable functions-biochemicals, disease regulation, waste treatment, fibre/hide/wood, and fuel. Foundational supporting and regulating services such as pollination, soil formation, and nutrient cycling are almost entirely absent. Moreover, only 3% of technologies described in the academic literature aim to support existing systems; the overwhelming emphasis on enhancement (39%) and replacement (58%) suggests a design paradigm skewed toward substitution rather than coexistence. Intangible, co-produced services-particularly those related to culture, identity, and meaning-remain outside the current reach of biomimetic design. This skew reveals a dangerous imbalance: while certain ES can be technologically approximated, the relational, emergent, and systemic qualities of ecosystems elude replication. Technological replacement must not become a substitute for preservation. Instead, bio-inspired design should be mobilized as a tool for adaptation that amplifies and protects the living systems on which human and more-than-human futures depend.

A Multi-Layer Perceptron (MLP), as the basic structure of neural networks, is an important component of various deep learning models such as CNNs, RNNs, and Transformers. Nevertheless, MLP training faces significant challenges, with a large number of saddle points and local minima in its non-convex optimization space, which can easily lead to gradient vanishing and premature convergence. Compared with traditional heuristic algorithms relying on a population-based parallel search, such as GA, GWO, DE, etc., the Besiege and Conquer Algorithm (BCA) employs a one-spot update strategy that provides a certain level of global optimization capability but exhibits clear limitations in search flexibility. Specifically, it lacks fast detection, fast adaptation, and fast convergence. First, the fixed sinusoidal amplitude limits the accuracy of fast detection in complex regions. Second, the combination of a random location and fixed perturbation range limits the fast adaptation of global convergence. Finally, the lack of a hierarchical adjustment under a single parameter (BCB) hinders the dynamic transition from exploration to exploitation, resulting in slow convergence. To address these limitations, this paper proposes a Flexible Besiege and Conquer Algorithm (FBCA), which improves search flexibility and convergence capability through three new mechanisms: (1) the sine-guided soft asymmetric Gaussian perturbation mechanism enhances local micro-exploration, thereby achieving a fast detection response near the global optimum; (2) the exponentially modulated spiral perturbation mechanism adopts an exponential spiral factor for fast adaptation of global convergence; and (3) the nonlinear cognitive coefficient-driven velocity update mechanism improves the convergence performance, realizing a more balanced exploration-exploitation process. In the IEEE CEC 2017 benchmark function test, FBCA ranked first in the comprehensive comparison with 12 state-of-the-art algorithms, with a win rate of 62% over BCA in 100-dimensional problems. It also achieved the best performance in six MLP optimization problems, showing excellent convergence accuracy and robustness, proving its excellent global optimization ability in complex nonlinear MLP optimization training. It demonstrates its application value and potential in optimizing neural networks and deep learning models.

Additive manufacturing has significantly advanced patient-specific medical devices, particularly for hard tissue repair, yet applications in soft tissue remain limited. Existing approaches for 3D-printed soft tissue devices employ mainly biogels and bioinks for regenerative purposes, while synthetic grafts for tendons and ligaments remain non-customizable in shape and mechanics. This study investigates the mechanical performance of 3D-printed thermoplastic polyurethane (TPU) elastomers as a function of printing parameters, informing customizable connective tissue graft designs. Type C dogbone specimens (n = 180) of three replicates each of parameter combinations from material shore hardness, presence of anchoring within the lattice, infill patterns, and infill density were printed and tested following modified ASTM D412 standards for vulcanized rubber and elastomers. The measured mechanical properties are elastic modulus, tensile yield stress, yield strain, ultimate tensile strength, and ultimate strain. Results show that shore hardness and infill density are the strongest predictors of mechanical properties, with positive but modest effects from anchor presence. Infill pattern is only significant through interactions, and its effects depend on other parameters. While all groups underperformed compared to manufacturer-reported TPU strengths and were well below in vitro tendon failure loads, findings highlight material selection and density optimization as critical early considerations for future patient-specific elastomeric graft design.

In human daily-life scenarios, humanoid robots need not only to stand up smoothly but also to autonomously sit down for rest, energy management, and interaction. This capability is crucial for enhancing their autonomy and practicality. However, both sitting and standing involve complex dynamics constraints, diverse initial postures, and unstructured terrains, which make traditional hand-crafted controllers insufficient for multi-scenario demands. Reinforcement Learning (RL), with its generalization ability across high-dimensional state spaces and complex tasks, offers a promising solution for automatically generating motion control policies. Nevertheless, policies trained directly with RL often produce abrupt motions, making it difficult to balance smoothness and stability. To address these challenges, we propose a two-stage reinforcement learning framework: In the first stage, we focus on exploration and train initial policies for both sitting and standing, with relatively weak constraints on smoothness and joint safety, and without introducing noise. In the second stage, we refine the policies by tracking the motion trajectories obtained in the first stage, aiming for smoother transitions. We model the tracking problem as a bi-level optimization, where the tracking precision is dynamically adjusted based on the current tracking error, forming an adaptive curriculum mechanism. We apply this framework to a 1.7 m adult-scale humanoid robot, achieving stable execution in two representative real-world scenarios: sitting down onto a chair, stand up from a chair. Our approach provides a new perspective for the practical deployment of humanoid robots in real-world scenarios.

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.

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.