Search Results (2,148)

Global optimization problems, ubiquitous scientific research, and engineering applications necessitate sophisticated algorithms adept at navigating intricate, high-dimensional search landscapes. The Escape (ESC) algorithm, inspired by the complex dynamics of crowd evacuation behavior—where individuals exhibit calm, herding, or panic responses—offers a compelling nature-inspired paradigm for addressing these challenges. While ESC demonstrates a strong intrinsic balance between exploration and exploitation, opportunities exist to enhance its inter-agent communication and search trajectory diversification. This paper introduces an advanced bio-inspired algorithm, termed Crisscross Escape Algorithm (CCESC), which strategically incorporates a Crisscross (CC) information exchange mechanism. This CC strategy, by promoting multi-directional interaction and information sharing among individuals irrespective of their behavioral group (calm, herding, panic), fosters a richer exploration of the solution space, helps to circumvent local optima, and accelerates convergence towards superior solutions. The CCESC’s performance is extensively validated on the demanding CEC2017 benchmark suites, alongside several standard engineering design problems, and compared against a comprehensive set of prominent metaheuristic algorithms. Experimental results consistently reveal CCESC’s superior or highly competitive performance across a wide array of benchmark functions. Furthermore, CCESC is effectively applied to a complex reservoir production optimization problem, demonstrating its capacity to achieve significantly improved Net Present Value (NPV) over other established methods. This successful application underscores CCESC’s robustness and efficacy as a powerful optimization tool for tackling multifaceted real-world problems, particularly in reservoir production optimization within complex sedimentary environments. Full article

Maximizing hydrocarbon recovery from mature and complex reservoirs is constrained by heterogeneity, sand production, and harsh operational conditions. While polyacrylamide (PAM)-based systems are pivotal in addressing these challenges, a comprehensive synthesis of their transformative evolution and multifunctional capabilities remains overdue. This review critically analyzes advancements in PAM-based materials for enhanced oil recovery (EOR), conformance control, and sand management. We show that nanomaterial integration (e.g., magnetic NPs, nanoclays) significantly augments PAM’s rheological control, thermal and salinity stability, interfacial properties, and wettability alteration. Furthermore, the emergence of supramolecular chemistry has endowed PAM systems with unprecedented resilience, enabling self-healing and adaptive performance under extreme subsurface conditions. The review highlights a crucial paradigm shift towards integrated reservoir management, synergizing these advanced chemical designs with mechanical strategies and leveraging sophisticated monitoring and predictive analytics. Critically, innovations in sustainable and bio-inspired PAM materials offer environmentally responsible solutions with enhanced biodegradability. This synthesis provides a holistic understanding of the state of the art. Despite persistent challenges in scalability and predictability, continually re-engineered PAM systems are positioned as an indispensable and increasingly sustainable cornerstone for future hydrocarbon recovery in the complex energy landscape. Full article

This systematic literature review explores recent advancements in polymer-based composite materials designed for thermal insulation in automotive applications, with a particular focus on sustainability, performance optimization, and scalability. The methodology follows PRISMA 2020 guidelines and includes a comprehensive bibliometric and thematic analysis of 229 peer-reviewed articles published over the past 15 years across major databases (Scopus, Web of Science, ScienceDirect, MDPI). The findings are structured around four central research questions addressing (1) the functional role of insulation in automotive systems; (2) criteria for selecting suitable polymer systems; (3) optimization strategies involving nanostructuring, self-healing, and additive manufacturing; and (4) future research directions involving smart polymers, bioinspired architectures, and AI-driven design. Results show that epoxy resins, polyurethane, silicones, and polymeric foams offer distinct advantages depending on the specific application, yet each presents trade-offs between thermal resistance, recyclability, processing complexity, and ecological impact. Comparative evaluation tables and bibliometric mapping (VOSviewer) reveal an emerging research trend toward hybrid systems that combine bio-based matrices with functional nanofillers. The study concludes that no single material system is universally optimal, but rather that tailored solutions integrating performance, sustainability, and cost-effectiveness are essential for next-generation automotive thermal insulation. Full article

Vascularization remains a critical challenge in tissue engineering, limiting graft survival, integration, and clinical translation. Although bioprinting enables spatial control over vascular architectures, many existing approaches prioritize geometric precision over biological performance. Bioprinted vasculature can be understood as a dynamic and time-dependent system that requires tissue-specific maturation. Within this framework, hydrogel systems act as active microenvironments rather than passive scaffolds. Hydrogel platforms vary from natural matrices and synthetic polymers to bioinspired or stimuli-responsive systems, each offering tunable control over stiffness, degradation, and biochemical signaling needed for vascular maturation. The design requirements of large and small vessels differ in terms of mechanical demands, remodeling capacity, and host integration. A key limitation in current models is the absence of time-resolved evaluation, as critical processes such as lumen formation, pericyte recruitment, and flow-induced remodeling occur progressively and are not captured by static endpoints. Advancements in bioprinting technologies are evaluated based on their capacity to support hydrogel-mediated vascularization across varying length scales and structural complexities. A framework for functional assessment is proposed, and translational challenges related to immunogenicity, scalability, and regulatory requirements are discussed. Such integration of hydrogel-driven biological cues and bioprinting fidelity is critical to advancing vascularized constructs toward clinical translation. Full article

Inspired by the evolutionary optimization of biological load-bearing systems, honeycomb structures are highly valued in applications involving impact protection and lightweight load-bearing due to their outstanding mechanical properties. This study introduces an interesting honeycomb structure known as the hybrid topological cellular honeycomb structure (HTCHS), which integrates four distinctive topological cells. To effectively fabricate HTCHS samples, the research utilized a fused deposition modeling (FDM) process, employing polyethylene terephthalate glycol-modified (PETG) as the matrix material, successfully producing the HTCHS samples. A finite element simulation model for the HTCHS is created using LS-DYNA software(LS-DYNA R11.1.0 software), and its accuracy is confirmed through a comparative analysis of experimental and simulation results. The influence of the topological cell parameters (T₁ to T₄) on compressive energy absorption, specific energy absorption, and peak crushing force through parametric modeling is investigated. The mechanical properties of honeycomb structures vary depending on the cell parameters at different positions, and monotonically increasing the design parameters does not improve the energy absorption capacity of the HTCHS. To enhance the mechanical performance of the HTCHS, the initial periodic cell configurations are transformed into non-periodic designs. A discrete optimization design framework for local parameters of the HTCHS is established, integrating cell coding with the MOPSO algorithm. The feasibility of the optimization results is validated through experimental data, demonstrating that this study offers an effective technical solution for developing a novel generation of cellular honeycomb structures with customizable mechanical properties. Full article

Carbon fiber fabric reinforced composite laminates are widely used in the automotive and aerospace components, which are prone to suffering low velocity impacts. In this paper, helicoidal layups of fabrics inspired by the Bouligand type structure of the dactyl clubs of mantis shrimp are proposed to improve the impact resistance of carbon fiber fabric reinforced composite laminates. Low velocity impact tests and finite element simulation are carried out to investigate the effect of the rotation angle of helicoidal layups on the impact damage behaviors of composite laminates, including impact force response, energy absorption characteristics and damage mechanism. Results show that the simulation results of impact force–time response, absorbed energy–time response, and damage characteristics show good agreements with the experimental results. With the increase in impact energy, the maximum value of impact force, the absorbed energy and the energy absorption ratio for all specimens are all increased. Under all impact energies, the impact damage of specimens with helicoidal layups are lower than that of specimen QI1 (rotation angle of 0°), indicating that the helical layup of woven carbon fabric can sufficiently enhance the impact resistance of the composite material. Furthermore, the impact resistance of specimen HL2 (rotation angle of 12.8°) is the best, because it demonstrates the lowest impact damage and highest impact force under all energies. This work provides a bionic design guideline for the high impact performance of carbon fiber fabric reinforced composite laminate. Full article

The Mount Wudang architectural complex, recognized as a UNESCO World Cultural Heritage site, extensively utilizes green schist as the building material in its rock temple structures. Due to prolonged exposure to weathering and moisture, effective surface protection of these stones is crucial for their preservation. Inspired by the lotus leaf, femtosecond laser fabrication of bioinspired micro/nanostructures offers a promising approach for imparting hydrophobicity to stone surfaces. However, green schist is a typical heterogeneous material primarily composed of quartz, chlorite, and muscovite, and it contains metal elements, such as Fe and Ni. These pronounced compositional differences complicate laser–material interactions, posing considerable challenges to the formation of stable and uniform micro/nanostructures. To address this issue, we performed systematic femtosecond laser scanning experiments on green schist surfaces using a 100 kHz, 40 μJ laser with a 30 μm spot diameter, fabricating microgrooves under various process conditions. Surface morphology and EDS mapping analyses were conducted to elucidate the ablation responses of quartz, chlorite, and muscovite under different groove spacings (100 μm, 80 μm, 60 μm, and 40 μm) and scan repetitions (1, 2, 4, 6, 8, 10). The results revealed distinct differences in energy absorption, material ejection, and surface reorganization among these minerals, significantly influencing the formation mechanisms of laser-induced structures. Based on optimized parameters (60 μm spacing, 2–6 passes), robust and repeatable micro/nanostructures were successfully produced, yielding superhydrophobic performance with contact angles exceeding 155°. This work offers a novel strategy for interface control in heterogeneous natural stone materials and provides a theoretical and technical foundation for the protection and functional modification of green schist in heritage conservation. Full article

Photoelectrochemical (PEC) sensors have garnered increasing attention due to their high sensitivity, low background signal, and rapid response. The incorporation of hydrogels into PEC platforms has significantly expanded their analytical capabilities by introducing features such as biocompatibility, tunable porosity, antifouling behavior, and mechanical flexibility. This review systematically categorizes hydrogel materials into four main types—nucleic acid-based, synthetic polymer, natural polymer, and carbon-based—and summarizes their functional roles in PEC sensors, including structural support, responsive amplification, antifouling interface construction, flexible electrolyte integration, and visual signal output. Representative applications are highlighted, ranging from the detection of ions, small biomolecules, and biomacromolecules to environmental pollutants, photodetectors, and flexible bioelectronic devices. Finally, key challenges—such as improving fabrication scalability, enhancing operational stability, integrating emerging photoactive materials, and advancing bio-inspired system design—are discussed to guide the future development of hydrogel-enhanced PEC sensing technologies. Full article

The design of ergonomic tool handles is crucial for user comfort and performance, yet conventional stiff materials often lead to uneven pressure distribution and discomfort. This study investigates the application of 3D-printed cellular metamaterials with tunable stiffness, specifically gyroid structures, to enhance the ergonomic and haptic properties of tool handles. We employed finite element analysis to simulate finger–handle interactions and conducted subjective comfort evaluations with participants using a foxtail saw with handles of varying gyroid infill densities and a rigid PLA handle. Numerical results demonstrated that handles with medium stiffness significantly reduced peak contact pressures and promoted a more uniform pressure distribution compared to the stiff PLA handle. The softest gyroid handle, while compliant, exhibited excessive deformation, potentially compromising stability. Subjective comfort ratings corroborated these findings, with medium-stiffness handles receiving the highest scores for overall comfort, fit, and force transmission. These results highlight that a plateau-like mechanical response of the 3D-printed cellular metamaterial handle, inversely bioinspired by human soft tissue, effectively balances pressure redistribution and grip stability. This bioinspired design approach offers a promising direction for developing user-centered products that mitigate fatigue and discomfort in force-intensive tasks. Full article

Composite materials are widely used in aerospace, automotive, biomedical, and renewable energy sectors due to their high strength-to-weight ratio and design flexibility. However, their anisotropic and layered nature makes structural analysis and failure prediction challenging. Traditional methods require solving complex interlaminar stress–strain equations, demanding significant computational resources. This paper presents a bio-inspired machine learning approach, based on human reasoning, to accelerate predictions and reduce dependence on computationally intensive Finite Element Analysis (FEA). An artificial neural network model was developed to rapidly estimate key parameters—laminate thickness, total weight, maximum stress, displacement, deformation, and failure criteria—based on stacking sequence and geometry for a desired load case. Although validated using a specific composite beam, the methodology demonstrates potential for broader use in rapid structural assessment, with prediction deviations under 15% compared to FEA results. The time savings are particularly significant—while conventional FEA can take several hours or even days, the ANN model delivers accurate predictions within seconds. The approach significantly reduces computational time while maintaining precision. Moreover, with further refinement, this logic-driven model could be effectively applied to aircraft maintenance, enabling faster decision-making and improved structural reliability assessment. Full article

The balance between exploration and exploitation is a fundamental element in the design and performance of bio-inspired optimization algorithms. However, to date, its conceptual evolution and its treatment in the scientific literature have not been systematically characterized from a bibliometric approach. This study performs an exhaustive analysis of the scientific production on the balance between exploration and exploitation using records extracted from the Web of Science (WoS) database. The processing and analysis of the data were carried out through the combined use of Bibliometrix (R package) and VOSviewer, tools that made it possible to quantify productivity, map collaborative networks, and visualize emerging thematic trends. The results show a sustained growth in the volume of publications over the last decade, as well as the consolidation of academic collaboration networks and the emergence of new thematic lines in the field. In particular, metaheuristic algorithms have demonstrated a significant and growing impact, constituting a fundamental pillar in the advancement and methodological diversification of the exploration–exploitation balance. This work provides a quantitative framework and a structured view of the evolution of research, identifies the main actors and trends, and raises opportunities for future lines of research in the field of optimization using metaheuristics, the most prominent instantiation of bio-inspired optimization algorithms. Full article

To enable intelligent inspection of underground cable systems, this study presents a gecko-inspired quadruped robot that integrates multi-degree-of-freedom motion with a deep learning-based visual detection system. Inspired by the gecko’s flexible spine and leg structure, the robot exhibits strong adaptability to confined and uneven tunnel environments. The motion system is modeled using the standard Denavit–Hartenberg (D–H) method, with both forward and inverse kinematics derived analytically. A zero-impact foot trajectory is employed to achieve stable gait planning. For defect detection, the robot incorporates a binocular vision module and an enhanced YOLOv8 framework. The key improvements include a lightweight feature fusion structure (SlimNeck), a multidimensional coordinate attention (MCA) mechanism, and a refined MPDIoU loss function, which collectively improve the detection accuracy of subtle defects such as insulation aging, micro-cracks, and surface contamination. A variety of data augmentation techniques—such as brightness adjustment, Gaussian noise, and occlusion simulation—are applied to enhance robustness under complex lighting and environmental conditions. The experimental results validate the effectiveness of the proposed system in both kinematic control and vision-based defect recognition. This work demonstrates the potential of integrating bio-inspired mechanical design with intelligent visual perception to support practical, efficient cable inspection in confined underground environments. Full article

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

The rapid advancement of underwater wireless communication technologies is critical to unlocking the full potential of marine resource exploration and environmental monitoring. This paper reviews recent progress in three primary modalities: underwater acoustic communication, radio frequency (RF) communication, and underwater optical wireless communication (UWOC), each designed to address specific challenges posed by complex underwater environments. Acoustic communication, while effective for long-range transmission, is constrained by ambient noise and high latency; recent innovations in noise reduction and data rate enhancement have notably improved its reliability. RF communication offers high-speed, short-range capabilities in shallow waters, but still faces challenges in hardware miniaturization and accurate channel modeling. UWOC has emerged as a promising solution, enabling multi-gigabit data rates over medium distances through advanced modulation techniques and turbulence mitigation. Additionally, bio-inspired approaches such as electric field communication provide energy-efficient and robust alternatives under turbid conditions. This paper further examines the practical integration of these technologies in underwater platforms, including autonomous underwater vehicles (AUVs), highlighting trade-offs between energy efficiency, system complexity, and communication performance. By synthesizing recent advancements, this review outlines the advantages and limitations of current underwater communication methods and their real-world applications, offering insights to guide the future development of underwater communication systems for robotic and vehicular platforms. Full article

Robotic assembly of electrical connectors enables the automation of high-efficiency production of electronic products. A rigid gripper is adopted as the end-effector by the majority of existing works with a force–torque sensor installed at the wrist, which suffers from very limited perception capability of the manipulated objects. Moreover, the grasping and movement actions, as well as the inconsistency between the robot base and the end-effector frame, tend to result in angular misalignment, usually leading to assembly failure. Bio-inspired by the human finger, we designed a tactile finger in this paper with three characteristics: (1) Compliance: A soft ‘skin’ layer provides passive compliance for plenty of manipulation actions, thus increasing the tolerance for alignment errors. (2) Tactile Perception: Two types of sensing elements are embedded into the soft skin to tactilely sense the involved contact status. (3) Enhanced manipulation force: A rigid fingernail is designed to enhance the manipulation force and enable potential delicate operations. Moreover, a tactile-based alignment algorithm is proposed to search for the optimal orientation angle about the z axis. In the application of U-disk insertion, the three characteristics are validated and a success rate of 100% is achieved, whose generalization capability is also validated through the assembly of three types of electrical connectors. Full article