Biomimetics

Background: Impaired mucus drainage from the paranasal sinuses is often associated with nasal obstruction and reduced airway function in growing patients. Orthopedic maxillary protraction and expansion techniques can enhance airway dynamics, but their underlying fluid–structure mechanisms remain insufficiently understood. Objective: To validate that the Right Angle Maxillary Protraction Appliance (RAMPA), combined with a semi-rapid maxillary expansion (sRME) intraoral device gHu-1, improves mucus drainage by enhancing nasal airflow through nasal cavity expansion. Methods: The effects of RAMPA therapy were analyzed using computational fluid dynamics (CFD) for single-phase (air) and two-phase (air–mucus) flows within the nasal cavity, employing the unsteady RANS turbulence model. Finite element method (FEM) results from prior studies were synthesized to assess changes in the center and radius of maxillary rotation induced by RAMPA-assisted sRME. A male patient (aged 8 years 7 months to 11 years 7 months) treated with extraoral RAMPA and the intraoral appliance (gHu-1) underwent pre- and post-treatment cone-beam computed tomography (CBCT) and ear, nose, and throat (ENT) evaluation. Results: FEM analysis revealed an increased radius and elevated center of maxillary rotation, producing expansion that was more parallel to the palatal plane. CFD simulations showed that nasal cavity expansion increased airflow velocity and pressure drop, enhancing the suction effect that promotes mucus clearance from the frontal sinus. Clinically, nasal passages widened, paranasal opacities resolved, and occlusal and intermolar widths improved. Conclusions: RAMPA combined with sRME improves nasal airflow and maxillary skeletal expansion, facilitating paranasal mucus clearance and offering a promising adjunctive approach for enhancing upper airway function in growing patients.

Skin meshing is widely used to treat extensive burn injuries due to its cost-efficiency and capacity to cover large wound areas. As biomimetics focuses on deriving engineering principles from biological structure–function relationships, this review examines how to optimize skin-meshing expansion and investigates factors contributing to reported discrepancies between clinical and manufacturer-reported expansion ratios. The biology and mechanical behavior of skin layer are discussed, emphasizing the anisotropic properties govern by collagen fiber orientation associated with Langer’s lines in the dermis. The epidermis and hypodermis show isotropic properties and therefore have minimal influence on load-bearing capacity. Surveying 111 studies, the review evaluates which constitutive equations employed for skin modelling is suitable to replicate mechanical behavior of skin meshing undergoing large expansion. Elastic models fail to capture large expansion ratios. Viscoelastic and QLV are excluded due to negligible sliding of collagen fibers at slow strain rates and limited importance of hysteresis. Consequently, hyperelastic models are recognized as more suitable for predicting large deformations. Among these, the structural GOH model, which represents fiber dispersion through a probability-density function, demonstrates strong agreement with experimental data using few parameters; its damage extensions improve prediction of mesh tearing. Additionally, emerging auxetic mesh geometries with negative Poisson ratios are examined, highlighting their potential to achieve greater expansion when combined with suitable structural anisotropic constitutive models, e.g., GOH.

With the explosive growth of data across various fields, effective data preprocessing has become increasingly critical. Evolutionary and swarm intelligence algorithms have shown considerable potential in feature selection. However, their performance often deteriorates in large-scale problems, due to premature convergence and limited exploration ability. To address these limitations, this paper proposes an algorithm named IHBOFS, a biomimetics-inspired optimization framework that integrates multiple adaptive strategies to enhance performance and stability. The introduction of the Good Point Set and Elite Opposition-Based Learning mechanisms provides the population with a well-distributed and diverse initialization. Furthermore, adaptive exploitation–exploration balancing strategies are designed for each subpopulation, effectively mitigating premature convergence. Extensive ablation studies on the CEC2022 benchmark functions verify the effectiveness of these strategies. Considering the discrete nature of feature selection, IHBOFS is further extended with continuous-to-discrete mapping functions and applied to six real-world datasets. Comparative experiments against nine metaheuristic-based methods, including Harris Hawk Optimization (HHO) and Ant Colony Optimization (ACO), demonstrate that IHBOFS achieves an average classification accuracy of 92.57%, confirming its superiority and robustness in high-dimensional feature selection tasks.