Biomimetics最新文献

It seems that the future of building envelopes is moving towards adaptivity and self-regulation, reflecting the growing view that a vital strategy in addressing climate change is understanding buildings as living systems rather than static entities [...].

The increasing demand for sustainable climate control has spurred research into our hydronic conditioning system with a patented radiant ceiling panel (AU 2024227462) inspired by biomimetic methodologies. This study develops a framework that utilizes natural systems for heating and cooling, enhancing system performance and environmental sustainability. Biometric analysis was the primary method for testing these systems, focusing on heat transfer mechanisms modeled after human biology. Findings indicate that the proposed hydronic system excels in cooling mode, achieving an average capacity of 95 W/m² while maintaining thermal comfort levels (PMV) with solar heat gains under 1.5 kW in an 18 m² space. However, in heating mode, the system shows a capacity of 85 W/m² but struggles with vertical air-temperature stratification, especially in the radiant ceiling component. This highlights the potential of biomimetic designs to enhance energy efficiency and comfort in sustainable development. The hydronic panel system parallels the human body in energy transfer; both can emit 75-90 W/m² through radiation. Convection over the panel can increase energy transfer by 50-80%, akin to the human body's heat loss through convection. Notably, natural perspiration facilitates latent energy transfer of 20-25%. When the conditioned panel operates below the dew point, it generates water vapor, boosting cooling capacity by 5-15% and enhancing latent energy transfer. Overall, the heat transfer processes of the hydronic panel mimic certain aspects of human physiology, distinguishing it from conventional HVAC systems.

The global energy transition faces a chasm between current policy commitments (IEA's STEPS) and the deep, rapid transformation required to realize all national net zero pledges (IEA's APC). This perspective addresses the critical innovation and policy gap blocking the APC pathway, where many high-impact, clean technologies remain at low-to-medium Technology Readiness Levels (TRLs 3-6) and lack formal policy support. The insufficient nature of current climate policy nomenclature is highlighted, which often limits Nature-based Solutions (NbS) to incremental projects rather than driving systemic technological change (Bio-inspiration). Then, we propose that a deliberate shift from simple biomimetics (mimicking form) to biomimicry (emulating life cycle sustainability) is the essential proxy for acceleration. Biomimicry inherently targets the grand challenges of resilience, resource efficiency, and multi-functionality that carbon-centric metrics fail to capture. To institutionalize this change, we advocate for the mandatory integration of bio-inspired design into National Determined Contributions (NDCs) by reframing NbS as Nature-based Innovation (NbI) and introducing novel quantitative metrics. Finally, a three-step roadmap to guide this systemic shift is presented, from deployment of prototypes (2025-2028), to scaling evidence and standardization (2029-2035), to consolidation and regenerative integration (2036-2050). Formalizing these principles through policy will de-risk investment, mandate greater R&D rigor, and ensure that the next generation of energy infrastructure is not just carbon-neutral, but truly regenerative, aligning technology deployment with the necessary speed and depth of the APC scenario.

The gradual deterioration of fresco pictorial information presents a formidable obstacle for conservators dedicated to protecting humanity's shared cultural legacy. Currently, scholars in the field of mural conservation predominantly focus on image segmentation techniques as a vital tool for facilitating mural restoration and protection. However, the existing image segmentation methods frequently fall short of delivering optimal segmentation results. To address this issue, this study introduces a novel mural image segmentation approach termed NDFNGO, which integrates a nonlinear differential learning strategy, a decay factor, and a Fractional-order adaptive learning strategy into the Northern Goshawk Optimization (NGO) algorithm to enhance segmentation performance. Firstly, the nonlinear differential learning strategy is incorporated to harness the diversity and adaptability of differential tactics, thereby augmenting the algorithm's global exploration capabilities and effectively improving its ability to pinpoint optimal segmentation threshold regions. Secondly, drawing on the properties of nonlinear functions, a decay factor is proposed to achieve a more harmonious balance between the exploration and exploitation phases. Finally, by integrating historical individual data, the Fractional-order adaptive learning strategy is employed to reinforce the algorithm's exploitation capabilities, thereby further refining the quality of image segmentation. Subsequently, the proposed method was evaluated through tests on twelve mural image segmentation tasks. The results indicate that the NDFNGO algorithm achieves victory rates of 95.85%, 97.9%, 97.9%, and 95.8% in terms of the fitness function metric, PSNR metric, SSIM metric, and FSIM metric, respectively. These findings demonstrate the algorithm's high performance in mural image segmentation, as it retains a significant amount of original image information, thereby underscoring the superiority of the technology proposed in this study for addressing this challenge.

To address the limitations of the traditional Coati Optimization Algorithm (COA), such as insufficient global exploration, poor population cooperation, and low convergence efficiency in global optimization and photovoltaic (PV) model parameter identification, this paper proposes a Multi-strategy Enhanced Coati Optimization Algorithm (MECOA). MECOA improves performance through three core strategies: (1) Elite-guided search, which replaces the single global best solution with an elite pool of three top individuals and incorporates the heavy-tailed property of Lévy flights to balance large-step exploration and small-step exploitation; (2) Horizontal crossover, which simulates biological gene recombination to promote information sharing among individuals and enhance cooperative search efficiency; and (3) Precise elimination, which discards 20% of low-fitness individuals in each generation and generates new individuals around the best solution to improve population quality. Experiments on the CEC2017 (30/50/100-dimensional) and CEC2022 (20-dimensional) benchmark suites demonstrate that MECOA achieves superior performance. On CEC2017, MECOA ranks first with an average rank of 1.87, 2.07, 1.83, outperforming the second-best LSHADE (2.03, 2.43 and 2.63) and the original COA (9.93, 9.93 and 9.96). On CEC2022, MECOA also maintains the leading position with an average rank of 1.58, far surpassing COA (8.92). Statistical analysis using the Wilcoxon rank-sum test (significance level 0.05) confirms the superiority of MECOA. Furthermore, MECOA is applied to parameter identification of single-diode (SDM) and double-diode (DDM) PV models. Experiments based on real measurement data show that the SDM model achieves an RMSE of 9.8610 × 10^-4, which is only 1/20 of that of COA. For the DDM model, the fitted curves almost perfectly overlap with the experimental data, with a total integrated absolute error (IAE) of only 0.021555 A. These results fully validate the effectiveness and reliability of MECOA in solving complex engineering optimization problems, providing a robust and efficient solution for accurate modeling and optimization of PV systems.

Inspired by the adaptive flexible motion coordination of biological systems, this study presents a bioinspired control strategy that enables robotic manipulators to achieve precise and compliant motion-force coordination for embodied intelligence and dexterous interaction in physically constrained environments. To this end, a learning-based motion-force hybrid control (LMFC) framework is proposed, which unifies learning and kinematic-level control to regulate both motion and interaction forces under incomplete or implicit kinematic information, thereby enhancing robustness and precision. The LMFC formulation recasts motion-force coordination as a time-varying quadratic programming (TVQP) problem, seamlessly incorporating multiple practical constraints-including joint limits, end-effector orientation maintenance, and obstacle avoidance-at the acceleration level, while determining control decisions at the velocity level. An RNN-based controller is further designed to integrate adaptive learning and control, enabling online estimation of uncertain kinematic parameters and mitigating joint drift. Simulation and experimental results demonstrate the effectiveness and practicality of the proposed framework, highlighting its potential for adaptive and compliant robotic control in constraint-rich environments.

Passive flow control methods are widely used to reduce drag in wall-bounded flows. A recent numerical study on separating turbulent flows over a bump covered with shark denticles revealed the formation of a reverse pore flow (RPF) beneath the denticle crowns under an adverse pressure gradient (APG). This RPF generates an upstream thrust, leading to drag reduction. Motivated by these findings, the present study investigates a bio-inspired Anisotropic Permeable Propulsive Substrate (APPS) that incorporates key geometric features of the shark denticles, enabling thrust generation by the RPF. The designed APPS is evaluated through both direct numerical simulations of turbulent channel flows at Reτ = 1500 and experiments using 3D-printed structures in a turbulent boundary layer over a flat-plate model subjected to APG and flow separation (at Reθ = 800). Both approaches demonstrate that the APPS successfully reproduces the RPF-induced thrust mechanism of shark denticles. The results further reveal the dependence of the pore flow on pressure gradient and substrate geometry. This work highlights two features of a thrust-generating APPS: a top surface that shields the porous media from the overlying flow while enabling vertical mass exchange, and a bottom region with dominant wall-parallel permeability, which guides the pore flow in the streamwise direction to generate the thrust.

Bamboo has evolved a highly optimized structural system in its culms, which this study transfers into lightweight fiber composite trusses fabricated by coreless filament winding. Focusing on the structural segmentation involving diaphragms of the biological role model, this design principle was integrated into the additive manufacturing process using a multi-stage winding, a tiling approach, and a water-soluble winding fixture. Through a FE-assisted analytical abstraction procedure, the transition to a carbon fiber material system was considered by determining a geometrical configuration optimized for structural mass, bending deflection, and radial buckling. Samples were fabricated from CFRP and experimentally tested in four-point bending. In mass-specific terms, integrating diaphragms into wound fiber composite samples improved failure load by 36%, ultimate load by 62%, and energy absorption by a factor of 7, at a reduction of only 14% in stiffness. Benchmarking against steel and PVC demonstrated superior mass-specific performance, although mōsō bamboo still outperformed all technical solutions, except in energy absorption.

This study introduces an innovative Honey Badger Optimization (HBO) designed to address the Optimal Power Flow (OPF) challenge in electrical power systems. HBO is a unique population-based searching method inspired by the resourceful foraging behavior of honey badgers when hunting for food. In this algorithm, the dynamic search process of honey badgers, characterized by digging and honey-seeking tactics, is divided into two distinct stages, exploration and exploitation. The OPF problem is formulated with objectives including fuel cost minimization and voltage deviation reduction, alongside operational constraints such as generator limits, transformer settings, and line power flows. HBO is applied to the IEEE 30-bus test system, outperforming existing methods such as Particle Swarm Optimization (PSO) and Gray Wolf Optimization (GWO) in both fuel cost reduction and voltage profile enhancement. Results indicate significant improvements in system performance, achieving 38.5% and 22.78% better voltage deviations compared to GWO and PSO, respectively. This demonstrates HBO's efficacy as a robust optimization tool for modern power systems. In addition to the single-objective studies, a multi-objective OPF formulation was investigated to produce the complete Pareto front between fuel cost and voltage deviation objectives. The proposed HBO successfully generated a well-distributed set of trade-off solutions, revealing a clear conflict between economic efficiency and voltage quality. The Pareto analysis demonstrated HBO's strong capability to balance these competing objectives, identify knee-point operating conditions, and provide flexible decision-making options for system operators.

In recent years, robots have started to be used in tasks involving human interaction. For this to be possible, humans must perceive robots as suitable interaction partners. This can be achieved by giving the robots an animate appearance. One of the methods that can be utilised to endow a robot with a lively appearance is giving it the ability to perform expressions on its own, that is, combining multimodal actions to convey information. However, this can become a challenge if the robot has to use gestures and speech simultaneously, as the non-verbal actions need to support the message communicated by the verbal component. In this manuscript, we present a system that, based on a robot's utterances, predicts the corresponding gesture and synchronises it with the speech. A deep learning-based prediction model labels the robot's speech with the types of expressions that should accompany it. Then, a rule-based synchronisation module connects different gestures to the correct parts of the speech. For this, we have tested two different approaches: (i) using a combination of recurrent neural networks and conditional random fields; and (ii) using transformer models. The results show that the proposed system can properly select co-speech gestures under the time constraints imposed by real-world interactions.