Search Results (160)

Accurately predicting the compressive strength of high-performance concrete (HPC) is critical for ensuring structural integrity and promoting sustainable construction practices. However, HPC exhibits highly complex, nonlinear, and multi-factorial interactions among its constituents (such as cement, aggregates, admixtures, and curing conditions), which pose significant challenges to conventional predictive models. Traditional approaches often fail to adequately capture these intricate relationships, resulting in limited prediction accuracy and poor generalization. Moreover, the high dimensionality and noisy nature of HPC mix data increase the risk of model overfitting and convergence to local optima during optimization. To address these challenges, this study proposes a novel bio-inspired hybrid optimization model, AP-IVYPSO-BP, which is specifically designed to handle the nonlinear and complex nature of HPC strength prediction. The model integrates the ivy algorithm (IVYA) with particle swarm optimization (PSO) and incorporates an adaptive probability strategy based on fitness improvement to dynamically balance global exploration and local exploitation. This design effectively mitigates common issues such as premature convergence, slow convergence speed, and weak robustness in traditional metaheuristic algorithms when applied to complex engineering data. The AP-IVYPSO is employed to optimize the weights and biases of a backpropagation neural network (BPNN), thereby enhancing its predictive accuracy and robustness. The model was trained and validated on a dataset comprising 1030 HPC mix samples. Experimental results show that AP-IVYPSO-BP significantly outperforms traditional BPNN, PSO-BP, GA-BP, and IVY-BP models across multiple evaluation metrics. Specifically, it achieved an R² of 0.9542, MAE of 3.0404, and RMSE of 3.7991 on the test set, demonstrating its high accuracy and reliability. These results confirm the potential of the proposed bio-inspired model in the prediction and optimization of concrete strength, offering practical value in civil engineering and materials design. Full article

Automated generative design is increasingly used across engineering disciplines to accelerate innovation and reduce costs. Generative design offers the prospect of simplifying manual design tasks by exploring the efficacy of solutions automatically. However, existing generative design frameworks rely heavily on expensive optimisation procedures and often produce customised solutions, lacking reusable generative rules that transfer across different problems. This work presents a bioinspired generative design algorithm utilising the concept of evolutionary development (EvoDevo). This evolves a set of developmental rules that can be applied to different engineering problems to rapidly develop designs without the need to run full optimisation procedures. In this approach, an initial design is decomposed into simple entities called cells, which independently control their local growth over a development cycle. In biology, the growth of cells is governed by a gene regulatory network (GRN), but there is no single widely accepted model for this in artificial systems. The GRN responds to the state of the cell induced by external stimuli in its environment, which, in this application, is the loading regime on a bridge truss structure (but can be generalised to any engineering structure). Two GRN models are investigated: graph neural network (GNN) and graph-based Cartesian genetic programming (CGP) models. Both GRN models are evolved using a novel genetic search algorithm for parameter search, which can be re-used for other design problems. It is revealed that the CGP-based method produces results similar to those obtained using the GNN-based methods while offering more interpretability. In this work, it is shown that this EvoDevo approach is able to produce near-optimal truss structures via growth mechanisms such as moving vertices or changing edge features. The technique can be set up to provide design automation for a range of engineering design tasks. Full article

The Adaptive Cross Ivy (ACIVY) algorithm is a novel bio-inspired metaheuristic that emulates ivy plant growth behaviors for complex optimization problems. While the original Ivy Optimization Algorithm (IVYA) demonstrates a competitive performance, it suffers from limited inter-individual information exchange, inadequate directional guidance for local optima escape, and abrupt exploration–exploitation transitions. To address these limitations, ACIVY integrates three strategic enhancements: the crisscross strategy, enabling horizontal and vertical crossover operations for improved population diversity; the LightTrack strategy, incorporating positional memory and repulsion mechanisms for effective local optima escape; and the Top-Guided Adaptive Mutation strategy, implementing ranking-based mutation with dynamic selection pools for smooth exploration–exploitation balance. Comprehensive evaluations on the CEC2017 and CEC2022 benchmark suites demonstrate ACIVY’s superior performance against state-of-the-art algorithms across unimodal, multimodal, hybrid, and composite functions. ACIVY achieved outstanding average rankings of 1.25 (CEC2022) and 1.41 (CEC2017 50D), with statistical significance confirmed through Wilcoxon tests. Practical applications in engineering design optimization and UAV path planning further validate ACIVY’s robust performance, consistently delivering optimal solutions across diverse real-world scenarios. The algorithm’s exceptional convergence precision, solution reliability, and computational efficiency establish it as a powerful tool for challenging optimization problems requiring both accuracy and consistency. Full article

Enhancing the thermal performance of building façades is vital for reducing energy demand in hot desert climates, where envelope heat gain increases cooling loads. This study investigates the integration of biomimicry into opaque ventilated façade (OVF) systems as a novel approach to reduce façade surface temperatures. Thirteen bio-inspired façade configurations, modeled after strategies observed in nature, were evaluated using computational fluid dynamics simulations to assess their effectiveness in increasing airflow and reducing inner skin surface temperatures. Results show that all proposed biomimetic solutions outperformed the baseline OVF in terms of thermal performance, with the wide top mound configuration achieving the greatest temperature reduction—up to 5.9 °C below the baseline OVF and 16.4 °C below an unventilated façade. The study introduces an innovative methodology that derives façade design parameters from nature and validates them through simulation. These findings highlight the potential of nature-based solutions to improve building envelope performance in extreme climates. Full article

This paper introduces a novel bio-inspired metaheuristic algorithm, named the sharpbelly fish optimizer (SFO), inspired by the collective ecological behaviors of the sharpbelly fish. The algorithm integrates four biologically motivated strategies—(1) fitness-driven fast swimming, (2) convergence-guided gathering, (3) stagnation-triggered dispersal, and (4) disturbance-induced escape—which synergistically enhance the balance between global exploration and local exploitation. To assess its performance, the proposed SFO is evaluated on the CEC2022 benchmark suite under various dimensions. The experimental results demonstrate that SFO consistently achieves competitive or superior optimization accuracy and convergence speed compared to seven state-of-the-art metaheuristic algorithms. Furthermore, the algorithm is applied to three classical constrained engineering design problems: pressure vessel, speed reducer, and gear train design. In these applications, SFO exhibits strong robustness and solution quality, validating its potential as a general-purpose optimization tool for complex real-world problems. These findings highlight SFO’s effectiveness in tackling nonlinear, constrained, and multimodal optimization tasks, with promising applicability in diverse engineering scenarios. Full article

Background: Vaccines have revolutionized disease prevention, yet their effectiveness is challenged by variable immunogenicity, individual response differences, and emerging variants. Biomimetic strategies, inspired by natural immune processes, offer new avenues to enhance vaccine performance. Objectives: This narrative review examines how bioinspired approaches—grounded in evolutionary medicine, immunology, and host–microbiota interactions—can improve vaccine immunogenicity and long-term protection. We further examine the evolutionary foundations of immune responses, highlighting how an evolutionary perspective can inform the development of durable, broadly protective, and personalized vaccines. Furthermore, mechanistic insights at the molecular and cellular level are explored, including Toll-like receptor (TLR) engagement, dendritic cell activation pathways, and MHC-I/MHC-II-mediated antigen presentation. These mechanisms are often mimicked in biomimetic systems to enhance uptake, processing, and adaptive immune activation. Results: The review highlights how immunosenescence, maternal immunity, genetic variation, and gut microbiota composition influence vaccine responses. Biomimetic platforms—such as nanoparticle carriers and novel adjuvants—enhance antigen presentation, boost adaptive immunity, and may overcome limitations in traditional vaccine approaches. Additionally, co-administration strategies, delivery systems, and microbiota-derived immunomodulators show promise in improving vaccine responsiveness. Conclusions: Integrating biomimetic and evolutionary principles into vaccine design represents a promising path toward safer, longer-lasting, and more effective immunizations Full article

Background: Biomimetic strategies have gained increasing attention for their ability to enhance the delivery, stability, and functionality of nutraceuticals by emulating natural biological systems. However, the literature remains fragmented, often focusing on isolated technologies without integrating regulatory, predictive, or translational perspectives. Objective: This review aims to provide a comprehensive and multidisciplinary synthesis of biomimetic and bio-inspired nanocarrier strategies for nutraceutical delivery, while identifying critical gaps in standardization, scalability, and clinical translation. Results: We present a structured classification matrix that maps biomimetic delivery systems by material type, target site, and bioactive compound class. In addition, we analyze predictive design tools (e.g., PBPK modeling and AI-based formulation), regulatory frameworks (e.g., EFSA, FDA, and GSRS), and risk-driven strategies as underexplored levers to accelerate innovation. The review also integrates ethical and environmental considerations, and highlights emerging trends such as multifunctional hybrid systems and green synthesis routes. Conclusions: By bridging scientific, technological, and regulatory domains, this review offers a novel conceptual and translational roadmap to guide the next generation of biomimetic nutraceutical delivery systems. It addresses key bottlenecks and proposes integrative strategies to enhance design precision, safety, and scalability. Full article

Webbed-foot gliding water entry is a characteristic water-landing strategy employed by swans and other large waterfowls, demonstrating exceptional low-impact loading and remarkable motion stability. These distinctive biomechanical features offer significant potential for informing the design of cross-medium vehicles’ (CMVs’) water-entry systems. To analyze the hydrodynamic mechanisms and flow characteristics during swan webbed-foot gliding entry, the three-dimensional bionic webbed-foot water-entry process was investigated through a computational fluid dynamics (CFD) method coupled with global motion mesh (GMM) technology, with a particular emphasis on elucidating the regulatory effects of entry parameters on dynamic performance. The results demonstrated that the gliding water-entry process can be divided into two distinct phases: stable skipping and surface gliding. During the stable skipping phase, the motion trajectory exhibits quasi-sinusoidal periodic fluctuations, accompanied by multiple water-impact events and significant load variations. In the surface-gliding phase, the kinetic energy of the bionic webbed foot progressively decreases while maintaining relatively stable load characteristics. Increasing the water-entry velocity will enhance impact loads while simultaneously increasing the skipping frequency and distance. Increasing the water-entry angle will primarily intensify the impact load magnitude while slightly reducing the skipping frequency and distance. An optimal pitch angle of 20° provides maximum glide-skip stability for the bio-inspired webbed foot, with angles exceeding 25° or below 15° leading to motion instability. This study on webbed-foot gliding entry behavior provided insights for developing novel bio-inspired entry strategies for cross-medium vehicles, while simultaneously advancing the optimization of impact-mitigation designs in gliding water-entry systems. Full article

Background/Objectives: Optimization algorithms are acknowledged to be critical in various fields and dynamical systems since they provide facilitation in identifying and retrieving the most possible solutions concerning complex problems besides improving efficiency, cutting down on costs, and boosting performance. Metaheuristic optimization algorithms, on the other hand, are inspired by natural phenomena, providing significant benefits related to the applicable solutions for complex optimization problems. Considering that complex optimization problems emerge across various disciplines, their successful applications are possible to be observed in tasks of classification and feature selection tasks, including diagnostic processes of certain health problems based on bio-inspiration. Sepsis continues to pose a significant threat to patient survival, particularly among individuals admitted to intensive care units from emergency departments. Traditional scoring systems, including qSOFA, SIRS, and NEWS, often fall short of delivering the precision necessary for timely and effective clinical decision-making. Methods: In this study, we introduce a novel, interpretable machine learning framework designed to predict in-hospital mortality in sepsis patients upon intensive care unit admission. Utilizing a retrospective dataset from a tertiary university hospital encompassing patient records from January 2019 to June 2024, we extracted comprehensive clinical and laboratory features. To address class imbalance and missing data, we employed the Synthetic Minority Oversampling Technique and systematic imputation methods, respectively. Our hybrid modeling approach integrates ensemble-based ML algorithms with deep learning architectures, optimized through the Red Piranha Optimization algorithm for feature selection and hyperparameter tuning. The proposed model was validated through internal cross-validation and external testing on the MIMIC-III dataset as well. Results: The proposed model demonstrates superior predictive performance over conventional scoring systems, achieving an area under the receiver operating characteristic curve of 0.96, a Brier score of 0.118, and a recall of 81. Conclusions: These results underscore the potential of AI-driven tools to enhance clinical decision-making processes in sepsis management, enabling early interventions and potentially reducing mortality rates. Full article

Amphibious robots require efficient locomotion strategies to enable smooth transitions between terrestrial and aquatic environments. Drawing inspiration from the undulatory movements of aquatic organisms such as cuttlefish and knifefish, this study introduces a bio-inspired propulsion system that emulates natural wave-based locomotion to improve adaptability and propulsion efficiency. A novel mechanism combining crank–rocker and sliding components is proposed to generate wave-like motions in robotic legs and fins, supporting both land crawling and aquatic paddling. By adopting a rigid–flexible coupling design, the system achieves a balance between structural integrity and motion flexibility. The effectiveness of the mechanism is systematically investigated through kinematic modeling, animation-based simulation, and experimental validation. The developed kinematic model captures the principles of wave propagation via the Crank–Slider–Rocker structure, offering insights into motion efficiency and thrust generation. Animation simulations are employed to visually validate the locomotion patterns and assess coordination across the mechanism. A functional prototype is fabricated and tested in both terrestrial and aquatic settings, demonstrating successful amphibious locomotion. The findings confirm the feasibility of the proposed design and underscore its potential in biomimetic robotics and amphibious exploration. Full article

The cambered deflectors in aquacultural facilities are applied to enhance hydrodynamic efficiencies or enable flow fields to be fully developed. Given the anticipated improvements with the bio-inspired profiles or tandem configurations, the hydrodynamics of cambered deflectors with the above features are investigated at $Re=1\times {10}^5$. The relationship between force coefficients and local flow behaviors for both bionic and non-bionic isolated deflectors, as well as tandem deflectors, is revealed using $k-\omega$ SST simulation. The dependencies of force coefficients on gap (G), stagger (S), and inclination angles ($\theta$) in tandem deflectors are illustrated using an updated metamodeling workflow with simulated data. It is demonstrated that the variations of force coefficients over angles of attack are related to flow physics in boundary-layer regions. The non-bionic isolated deflector with the $\theta ={10}^{\circ }$ prevails as the decent performances of $C_L$ and $\gamma$ globally, which is chosen in the following studies. Regarding tandem deflectors, $\theta$ plays a more vital role in drag coefficients ($C_D$) and lift coefficients ($C_L$), while the influence of S is not quite considerable compared to G. Aiming for cost minimizations and lift improvements, an optimized tandem case is obtained and justified with the superiorities in flow fields. This study has provided novel insights into the designs and optimizations of cambered deflectors in aquacultural engineering. Full article

Underwater sensor deployment in military applications requires high precision, yet existing robotic solutions often lack the maneuverability and adaptability required for complex aquatic environments. To address this gap, this study proposes a bio-inspired underwater robot modeled after the marine iguana, which exhibits effective crawling and swimming capabilities. The research aims to develop a compact, multi-functional robot capable of precise sensor deployment and environmental detection. The methodology integrates a biomimetic mechanical design—featuring leg-based crawling, tail-driven swimming, a deployable head mechanism, and buoyancy control—with a multi-sensor control system for navigation and data acquisition. Gait and trajectory planning are optimized using kinematic modeling for both terrestrial and aquatic locomotion. Experimental results demonstrate the robot’s ability to perform accurate underwater sensor deployment, validating its potential for military applications. This work provides a novel approach to underwater deployment robotics, bridging the gap between biological inspiration and functional engineering. Full article

The pressing need to address climate change and environmentally related challenges highlights the importance of reimagining educational approaches to equip students with the skills required for innovation and sustainability. This study proposes a novel holistic pedagogic framework for architectural education that integrates biomimicry, systems thinking, and Bloom’s Revised Taxonomy to advance innovation, sustainability, and transformative learning. Developed through a triangulated methodological approach—combining reflective practitioner inquiry, design-based research, and conceptual model development—the framework draws from multiple theoretical perspectives to create a cognitively structured, interdisciplinary, and ecologically grounded educational model. Bloom’s Taxonomy provides a scaffold for learning progression, while the Function–Structure–Behavior (FSB) schema enhances the establishment of cross-disciplinary bridges to enable students to address complex design challenges. The framework is informed by insights from the literature and patterns observed in bio-inspired studios, student projects, and interdisciplinary workshops. These examples highlight how the approach supports systems thinking, ecological literacy, and ethical decision-making through iterative, experiential, and metacognitive learning. Rather than offering a fixed intervention, the framework is presented as a flexible, adaptable model that aligns learning outcomes with real-world complexity. It enables learners to navigate interdisciplinary knowledge, reflect critically on design processes and co-create regenerative solutions. By positioning nature as mentor, model, and measure, this pedagogic framework reimagines architectural education as a catalyst for sustainability and systemic change in the built environment. Full article

This study presents a novel, bioinspired perching mechanism designed to enhance the landing and takeoff capabilities of flapping wing micro aerial vehicles (FWMAVs). Drawing inspiration from the human hand, the lightweight gripper integrates a compliant claw structure actuated by shape memory alloys (SMAs) that mimic muscle movement. These SMA springs act as compact, lightweight substitutes for traditional actuators like motors or solenoids. The mechanism operates via short electrical impulses that trigger both opening and closing motions. A detailed design process was undertaken to optimize phalange lengths for cylindrical grasping and to select appropriate SMAs for reliable performance. Weighing only 50 g, the gripper leverages the high power-to-weight ratio and flexibility of SMAs, with the springs directly embedded within the phalanges to reduce size and mass while preserving high-force output. Experimental results demonstrate fast actuation and a grasping force of approximately 16 N, enabling the gripper to hold objects of varying shapes and sizes and perform perching, grasping, and carrying tasks. Compared to existing solutions, this mechanism offers a simpler, highly integrated structure with enhanced miniaturization and adaptability, making it especially suitable for low-payload MAV platforms like FWMAVs. Full article

Accurate segmentation of brain tumors in magnetic resonance imaging (MRI) remains a challenging task due to heterogeneous tumor structures, varying intensities across modalities, and limited annotated data. Deep learning has significantly advanced segmentation accuracy; however, it often suffers from sensitivity to hyperparameter settings and limited generalization. To overcome these challenges, bio-inspired metaheuristic algorithms have been increasingly employed to optimize various stages of the deep learning pipeline—including hyperparameter tuning, preprocessing, architectural design, and attention modulation. This review systematically examines developments from 2015 to 2025, focusing on the integration of nature-inspired optimization methods such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and novel hybrids including CJHBA and BioSwarmNet into deep learning-based brain tumor segmentation frameworks. A structured multi-query search strategy was executed using Publish or Perish across Google Scholar and Scopus databases. Following PRISMA guidelines, 3895 records were screened through automated filtering and manual eligibility checks, yielding a curated set of 106 primary studies. Through bibliometric mapping, methodological synthesis, and performance analysis, we highlight trends in algorithm usage, application domains (e.g., preprocessing, architecture search), and segmentation outcomes measured by metrics such as Dice Similarity Coefficient (DSC), Jaccard Index (JI), Hausdorff Distance (HD), and ASSD. Our findings demonstrate that bio-inspired optimization significantly enhances segmentation accuracy and robustness, particularly in multimodal settings involving FLAIR and T1CE modalities. The review concludes by identifying emerging research directions in hybrid optimization, real-time clinical applicability, and explainable AI, providing a roadmap for future exploration in this interdisciplinary domain. Full article