Search Results (94)

This scoping review paper provides an overview of the evolution, the current stage, and the future prospects of fracture studies on composite laminates. A fundamental understanding of composite materials is presented by highlighting the roles of the fiber and matrix, outlining the applications of various synthetic fibers used in current structural sectors. Challenges posed by interlaminar delamination, one of the critical failure modes, are highlighted. This paper systematically discusses the fracture behavior of these laminates under mixed-mode and complex loading conditions. Standardized fracture toughness testing methods, including Mode I Double Cantilever Beam (DCB), Mode II End-Notched Flexure (ENF) and Mixed-Mode Bending (MMB), are initially discussed, which is followed by a decade-wide chronological analysis of fracture mechanics approaches. Key advancements, including toughening mechanisms, Cohesive Zone Modeling (CZM), Virtual Crack Closure Technique (VCCT), Extended Finite Element Method (XFEM) and Digital Image Correlation (DIC), are analyzed. The review also addresses recent trends in fracture studies, such as bio-inspired architecture, self-healing systems, and artificial intelligence in fracture predictions. By mapping the trajectory of past innovations and identifying unresolved challenges, such as scale integration, dataset standardization for AI, and manufacturability of advanced architectures, this review proposes a strategic research roadmap. The major goal is to enable unified multi-scale modeling frameworks that merge physical insights with data learning, paving the way for next-generation composite laminates optimized for resilience, adaptability, and environmental responsibility. Full article

Recent advancements have been made in the precise prediction of protein structures within the Protein Folding Problem (PFP), particularly in relation to minimizing the energy function to achieve stable and biologically relevant protein structures. This problem is classified as NP-hard within computational theory, necessitating the development of various techniques and algorithms. Bio-inspired algorithms have proven effective in addressing NP-hard challenges in practical applications. This study introduces a novel hybrid algorithm, termed GRSABio, which integrates the strategies of Jumping Spider Algorithm (JSOA) with the Golden Ratio Simulated Annealing (GRSA) for peptide prediction. Furthermore, the GRSABio algorithm incorporates a Convolutional Neural Network for fragment prediction (FCNN), forms an enhanced methodology called GRSABio-FCNN. This integrated framework achieves improved structure refinement based on energy for protein prediction. The proposed enhanced GRSABio-FCNN approach was applied to a dataset of 60 peptides. The Wilcoxon and Friedman statistics test were employed to compare the GRSABio-FCNN results against recent state-of-the-art-approaches. The results of these tests indicate that the GRSABio-FCNN approach is competitive with state-of-the-art methods for peptides up to 50 amino acids in length and surpasses leading PFP algorithms for peptides with up to 30 amino acids. Full article

In next-generation Low-Earth-Orbit (LEO) satellite networks, securing inter-satellite communication links (ISLs) through strong authentication is essential due to the network’s dynamic and distributed structure. Traditional authentication systems often struggle in these environments, leading to the adoption of Zero-Trust Security (ZTS) models. However, current ZTS protocols typically introduce high computational overhead, especially as the number of satellite nodes grows, which can impact both security and network performance. To overcome these challenges, a new bio-inspired ZTS framework called Manta Ray Foraging Cost-Optimized Zero-Trust Security (MRFCO-ZTS) has been introduced. This approach uses data-driven learning methods to enhance security across satellite communications. It continuously evaluates access requests by applying a cost function that accounts for risk level, likelihood of attack, and computational delay. The Manta Ray Foraging Optimization (MRFO) algorithm is used to minimize this cost, enabling effective classification of nodes as either trusted or malicious based on historical authentication records and real-time behavior. MRFCO-ZTS improves the accuracy of attacker detection while maintaining secure data exchange between authenticated satellites. Its effectiveness has been tested through numerical simulations under different satellite traffic conditions, with performance measured in terms of security accuracy, latency, and operational efficiency. Full article

Sustainable architectural heritage conservation focuses on preserving historical buildings while promoting environmental sustainability. It involves using eco-friendly materials and methods to ensure that the cultural value of these structures is maintained while minimizing their ecological impact. In this paper, the use of the hydroxyapatite (HAp) in various combinations on masonry samples is presented, with the aim of identifying the ideal solution to be applied to an entire historical building in Banloc monument. The new solution has various advantages: compatibility with historical lime mortars (chemical and physical), increased durability under aggressive environmental conditions, non-invasive and reversible, aligning with conservation ethics, bioinspired material that avoids harmful synthetic additives, preservation of esthetics—minimal visual change to treated surfaces, and nanostructural (determined via SEM and AFM) reinforcement to improve cohesion without altering the porosity. An innovative approach involving hydroxiapatite addition to commercial mortars is developed and presented within this paper. Physico-chemical, mechanical studies, and architectural and economic trends will be addressed in this paper. Some specific tests (reduced water absorption, increased adhesion, high mechanical strength, unchanged chromatic aspect, high contact angle, not dangerous freeze–thaw test, reduced carbonation test), will be presented to evidence the capability of hydroxyapatite to be incorporated into green renovation efforts, strengthen the consolidation layer, and focus on its potential uses as an eco-material in building construction and renovation. The methodology employed in evaluating the comparative performance of hydroxyapatite (HAp)-modified mortar versus standard Baumit MPI25 mortar includes a standard error (SE) analysis computed column-wise across performance indicators. To further substantiate the claim of “optimal performance” at 20% HAp addition, independent samples t-tests were performed. The results of the independent samples t-tests were applied to three performance and cost indicators: Application Cost, Annualized Cost, and Efficiency-Cost-Performance (ECP) Index. This validates the claim that HAp-modified mortar offers superior overall performance when considering efficiency, cost, and durability combined. Full article

Creating grayscale images from a color reality has been an inherent human practice since ancient times, but it became a technological challenge with the advent of the first black-and-white televisions and digital image processing. Decolorization is a process that projects visual information from a three-dimensional feature space to a one-dimensional space, thus reducing the dimensionality of the image while minimizing the loss of information. To achieve this, various strategies have been developed, including the application of color channel weights and the analysis of local and global image contrast, but there is no universal solution. In this paper, we propose a bio-inspired approach that combines findings from neuroscience on the architecture of the visual system and color coding with evidence from studies in the psychology of art. The goal is to simplify the decolorization process and facilitate its control through color-related concepts that are easily understandable to humans. This new method organizes colors in a scale that links activity on the retina with a system of opponent and complementary channels, thus allowing the adjustment of the perception of warmth and coolness in the image. The results show an improvement in chromatic contrast, especially in the warmth and coolness categories, as well as an enhanced ability to preserve subtle contrasts, outperforming other approaches in the Ishihara test used in color blindness detection. In addition, the method offers a computational advantage by reducing the process through direct pixel-level operation. 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

Snake-like robots face critical challenges in energy-efficient locomotion and smooth gait transitions, limiting their real-world deployment. This study introduces a bio-inspired compliant joint design integrated with a hierarchical neural oscillator network and an energy-optimized control framework. The joint mimics biological skeletal flexibility using specialized wheeled mechanisms and adaptive parallel linkages, while the control network enables adaptive gait generation and seamless transitions through a phase-smoothing algorithm. Critically, this work adopts a synergistic design philosophy where mechanical components and control parameters are co-optimized through shared dynamic modeling. The proposed predictive control strategy optimizes locomotion speed while minimizing energy consumption. Experimental simulations demonstrate that the method achieves an 18% higher average forward speed (0.0563 m/s vs. 0.0478 m/s) with 7% lower energy use (0.1952 J vs. 0.2107 J) compared to conventional approaches. Physical prototype testing confirms these improvements under real-world conditions, showing a 12.9% speed increase (0.0531 m/s vs. 0.0470 m/s) and 7.3% energy reduction (0.2147 J vs. 0.2317 J). By unifying mechanical flexibility and adaptive control parameter tuning, this work bridges dynamic performance and energy efficiency, offering a robust solution for unstructured environments. Full article

In recent years, frequent vehicle–bridge pier collision accidents have posed a serious threat to people’s economic and life security. In order to avert the impairment of reinforced concrete bridge piers (RCBPs) under the impact of vehicles, three kinds of Mg–Al alloy AlSi10Mg anti-collision structures designed by selective laser melting (SLM) printing were tested by the numerical simulation method in this study: an ultra-high performance concrete (UHPC) anti-collision structure, a bio-inspired honeycomb column thin-walled structure (BHTS) buffer interlayer, and a UHPC–BHTS composite structure were used to reduce the damage degree of RCBPs caused by vehicle impact. In accordance with the prototype configuration of the pier, a scaled model with a scale ratio of 1:10 was fabricated. Three anti-collision structures were installed on the reinforced concrete (RC) column specimens for the steel ball impact test. The impact simulation under low-energy and high-energy input was carried out successively, and the protective effect of the three anti-collision devices on the RC column was comprehensively evaluated. The outcomes demonstrate that the BHTS buffer interlayer and the UHPC–BHTS composite structure are capable of converting the shear failure of RC columns into bending failure, thereby exerting an efficacious role in safeguarding RC columns. The damage was evaluated under all impact conditions of BHTS and UHPC–BHTS composite structures, and the RC column only suffered slight damage, while the RC column without protective measures and the RC column with the UHPC anti-collision structure alone showed serious damage and collapse behavior. This approach can offer a valuable reference for anti-collision design within analogous projects. Full article

Flapping wing aircrafts have demonstrated unique advantages in military and civil fields due to their bio-inspired flight mechanisms. However, non-uniform wear in driving mechanisms remains a critical reliability concern during prolonged operation. This study presents a stochastic wear prediction framework that systematically integrates joint clearance dynamics, contact force variations, and material interaction parameters. Through accelerated life testing with flight condition simulations, the method establishes quantitative correlations between multi-source variables and wear progression patterns. Experimental validation confirms the framework’s effectiveness in predicting asymmetric wear distribution, with comparative analysis showing significant improvements in prediction accuracy over conventional single-factor models. The results identify three dominant wear contributors: dynamic clearance fluctuations, impact force randomness, and material compatibility limitations. These findings directly support the development of adaptive lubrication systems and wear-resistant material selection guidelines, offering practical solutions for enhancing flapping wing aircrafts’ reliability in complex operational scenarios. Full article

A bio-inspired vehicle with banded fin fluctuation as the propulsion mode is the research topic. However, this propulsion mode suffers from low efficiency and requires the urgent resolution of other issues. In this paper, the kinematic model of the banded fin surface and the numerical calculation model for its hydrodynamic performance are established based on the long dorsal fin propelled by MPF (Media and/or Paired Fin propulsion) mode. Through numerical simulation, the hydrodynamic performance of the banded fin under typical working conditions is explored and its propulsion mechanism is analyzed. By using a method of controlling variables, such as wave number, swing angle, and frequency, where only one independent variable is changed at a time while the others remain constant, the impact on thrust coefficient function and the obtained periodic variation laws governing hydrodynamic performance are studied. Oscillatory thrust is generated by the fin’s motion, where it first captures water through a ‘scoop’ motion and then expels it via a diagonal ‘push’ motion, producing thrust. Due to limitations in fin length and varying oscillation shapes, the effective water-pushing stroke differs, leading to variations in work and creating periodic oscillatory forces. When the variable is the oscillation frequency, the propulsion efficiency of the oscillating fins remains nearly constant when the oscillation frequency is less than or equal to 1 Hz. However, when the oscillation frequency exceeds 1 Hz, the propulsion efficiency decreases as the oscillation frequency increases, and the rate of decrease gradually slows down. The effect of leading-edge suction on hydrodynamic performance was studied by varying the oscillating fin’s angle of attack. The results showed that, compared to the unchamfered configuration, the forward chamfer better utilizes vortex energy, reducing input power and significantly improving propulsion efficiency. Guided by both numerical simulations and experimental results, we design and manufacture a prototype of an underwater banded fin bio-inspired propeller that encompasses shape modeling, mechanical structure design, and control mechanism design. We conduct real water tests to verify feasibility and reliability in terms of forward movement, backward movement, and turning ability, among others. Furthermore, we analyze how varying angle of attack or optimizing front/rear edge shapes can effectively enhance hydrodynamic performance. Full article

Optimization techniques aim to identify optimal solutions for a given problem. In single-objective optimization, the best solution corresponds to the one that maximizes or minimizes the objective function. However, when dealing with multi-objective optimization, particularly when the objectives are conflicting, identifying the best solution becomes significantly more complex. In such cases, exact or analytical methods are often impractical, leading to the widespread use of heuristic and metaheuristic approaches to identify optimal or near-optimal solutions. Recent advancements have led to the development of various nature-inspired metaheuristics designed to address these challenges. Among these, the Whale Optimization Algorithm (WOA) has garnered significant attention. An adapted version of the WOA has been proposed to handle multi-objective optimization problems. This work extends the WOA to tackle multi-objective optimization by incorporating a decomposition-based approach with a cooperative mechanism to approximate Pareto-optimal solutions. The multi-objective problem is decomposed into a series of scalarized subproblems, each with a well-defined neighborhood relationship. Comparative experiments with seven state-of-the-art bio-inspired optimization methods demonstrate that the proposed decomposition-based multi-objective WOA consistently outperforms its counterparts in both real-world applications and widely used benchmark test problems. Full article

The growing reliance on mobile robots has resulted in applications where users have limited or no control over operating conditions. These applications require advanced controllers to ensure the system’s performance by dynamically changing its parameters. Nowadays, online bioinspired controller tuning approaches are among the most successful and innovative tools for dealing with uncertainties and disturbances. Nevertheless, these bioinspired approaches present a main limitation in real-world applications due to the extensive computational resources required in their exhaustive search when evaluating the controller tuning of complex dynamics. This paper develops an online bioinspired controller tuning approach leveraging a surrogate modeling strategy for an omnidirectional mobile robot controller. The polynomial response surface method is incorporated as an identification stage to model the system and predict its behavior in the tuning stage of the indirect adaptive approach. The comparative analysis concerns state-of-the-art controller tuning approaches, such as online, offline robust, and offline non-robust approaches, based on bioinspired optimization. The results show that the proposal reduces its computational load by up to $62.85\%$ while maintaining the controller performance regarding the online approach under adverse uncertainties and disturbances. The proposal also increases the controller performance by up to $93\%$ compared to offline tuning approaches. Then, the proposal retains its competitiveness on mobile robot systems under adverse conditions, while other controller tuning approaches drop it. Furthermore, a posterior comparison against another surrogate tuning approach based on Gaussian process regression corroborates the proposal as the best online controller tuning approach by reducing the competitor’s computational load by up to $91.37\%$ while increasing its performance by $63\%$. Hence, the proposed controller tuning approach decreases the execution time to be applied in the evolution of the control system without deteriorating the closed-loop performance. To the best of the authors’ knowledge, this is the first time that such a controller tuning strategy has been tested on an omnidirectional mobile robot. Full article

This paper introduces a bio-inspired meta-heuristic algorithm, the Besiege and Conquer Algorithm (BCA), developed to tackle complex and high-dimensional optimization problems. Drawing inspiration from the concept of symmetry and guerrilla warfare strategies, the BCA incorporates four core components: besiege, conquer, balance, and feedback. The besiege strategy strengthens exploration, while the conquer strategy enhances exploitation. Balance and feedback mechanisms maintain a dynamic equilibrium between these capabilities, ensuring robust optimization performance. The algorithm’s effectiveness is validated through benchmark test functions, demonstrating superior results in comparison with existing methods, supported by Friedman rankings and Wilcoxon signed-rank tests. Beyond theoretical and experimental validation, the BCA showcases its real-world relevance through applications in engineering design and classification problems, addressing practical challenges. These results underline the algorithm’s strong exploration, exploitation, and convergence capabilities and its potential to contribute meaningfully to diverse real-world domains. Full article

Natural armors found in animals like fish and armadillos offer inspiration for designing protective systems that balance puncture resistance and flexibility. Although segmented armors have been used historically, modern applications are hindered by a limited understanding of their mechanics. This study addresses these challenges by presenting two novel bio-inspired scale structures with overlapping and staggered configurations, modeled after the elasmoid designs found in fish. Their shapes differ significantly from other artificial scales commonly described in the literature, which are typically flat. Instead, these scales feature a support that extends vertically from the substrate, transitioning into an inclined surface that serves as the protective component. Finite element method tests evaluated their performance in puncture resistance and flexibility. The results showed that one type of scale provided better puncture resistance, while the other type offered greater flexibility. These findings highlight how small geometric variations can significantly influence the balance between protection and flexibility. The results offer new insights into the mechanisms of natural armor and propose innovative designs for personal protective equipment, such as bulletproof vests, protective gloves, and fireproof systems. The finite element simulations employed to test the protective systems can also serve as valuable tools for the scientific community to assess and refine designs. Full article

The Material Generation Optimization (MGO) algorithm is an innovative approach inspired by material chemistry which emulates the processes of chemical compound formation and stabilization to thoroughly explore and refine the parameter space. By simulating the bonding processes—such as the formation of ionic and covalent bonds—MGO generates new solution candidates and evaluates their stability, guiding the algorithm toward convergence on optimal parameter values. To improve its search efficiency, this paper introduces an Enhanced Material Generation Optimization (IMGO) algorithm, which integrates a Quadratic Interpolated Learner Process (QILP). Unlike conventional random selection, QILP strategically selects three distinct chemical compounds, resulting in increased diversity, a more thorough exploration of the solution space, and improved resistance to local optima. The adaptable and non-linear adjustments of QILP’s quadratic function allow the algorithm to traverse complex landscapes more effectively. This innovative IMGO, along with the original MGO, is developed to support applications across three phases, showcasing its versatility and enhanced optimization capabilities. Initially, both the original and improved MGO algorithms are evaluated using several mathematical benchmarks from the CEC 2017 test suite and benchmarks to measure their optimization capabilities. Following this, both algorithms are applied to the following three well-known engineering optimization problems: the welded beam design, rolling element bearing design, and pressure vessel design. The simulation results are then compared to various established bio-inspired algorithms, including Artificial Ecosystem Optimization (AEO), Fitness–Distance-Balance AEO (FAEO), Chef-Based Optimization Algorithm (CBOA), Beluga Whale Optimization Algorithm (BWOA), Arithmetic-Trigonometric Optimization Algorithm (ATOA), and Atomic Orbital Searching Algorithm (AOSA). Moreover, MGO and IMGO are tested on a real Egyptian power distribution system to optimize the placement of PV and the capacitor units with the aim of minimizing energy losses. Lastly, the PV parameters estimation problem is successfully solved via IMGO, considering the commercial RTC France cell. Comparative studies demonstrate that the IMGO algorithm not only achieves significant energy loss reduction but also contributes to environmental sustainability by reducing emissions, showcasing its overall effectiveness in practical energy optimization applications. The IMGO algorithm improved the optimization outcomes of 23 benchmark models with an average accuracy enhancement of 65.22% and a consistency of 69.57% compared to the MGO method. Also, the application of IMGO in PV parameter estimation achieved a reduction in computational errors of 27.8% while maintaining superior optimization stability compared to alternative methods. Full article