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Drawing inspiration from the nitrate reductase enzymes, which catalyze nitrate to nitrite in nature, here a bio‐inspired, reduced molybdenum oxide (MoOx) shell is introduced that is grown on top of a dendritic nickel foam core (NiNF). The resulting MoOx/NiNF material is prepared via a facile, two‐step electrodeposition strategy using commercially available, low‐cost precursors. This catalytic material displays a remarkable faradaic efficiency (FE) of 99% at −0.5 V versus RHE and a high ammonia (NH3) yield rate of up to 4.29 mmol h−1 cm−2 at −1.0 V versus RHE in neutral media. Most importantly, MoOx/NiNF exhibits exceptional stability for the nitrate reduction reaction (NO3RR), maintaining operation for over 3100 h at a high current density of −650 mA cm−2, with a yield rate of 2.6 mmol h−1 cm−2 and a stable average NH3 FE of ≈83%. Through combined XPS and in situ Raman spectroscopy it is shown that the pronounced affinity of MoOx/NiNF for nitrate is associated with a substantial presence of oxygen vacancies within the material. [ABSTRACT FROM AUTHOR]

Highlights The engineering structures with cutouts should consider both buckling and stiffness. Inspired by the multi‐level leaf veins, in this paper, the skeleton of the grid‐honeycomb sandwich structure was designed in the hierarchical form, and combined with the cutout characteristics of the side walls of the railway vehicle body, the multi‐objective stiffness and mass optimization of the skeleton layout under buckling constraints was achieved. The cutout honeycomb sandwich structure was designed with orthogonal grids in the stiffness design area and curved grids based on the quadratic polynomial in the buckling design area. The optimization model also included the linear angle hierarchical grid, the constant angle hierarchical grid, and the single‐level orthogonal grid. The multi‐objective optimization framework based on the Radial Basis Function (RBF) surrogate model and the Non‐dominated Sorting Genetic Algorithm (NSGA‐II) was developed. The optimization results indicated that the hierarchical grid provided the more extensive design space. The optimal solution of the quadratic polynomial hierarchical grid had the highest stiffness and stiffness per unit mass compared to the rest of the grid layouts, which were 23.28% and 30.47% higher than the single‐level orthogonal grid, respectively. Finally, the multi‐order eigenvalue buckling was analyzed based on the optimal structure. Layout of hierarchical grids proposed inspired by multi‐level leaf veins. Orthogonal grids ensure stiffness and curved grids inhibit buckling at cutouts. Apply the hierarchical grids as skeletons to honeycomb sandwich structures. Stiffness and mass optimization subject to buckling constraints is performed. [ABSTRACT FROM AUTHOR]

Recent technological advancements have significantly expanded both networks and data, thereby introducing new forms of attacks that pose considerable challenges to intrusion detection and network security. With intruders deploying increasingly diverse attack vectors, the need for robust Intrusion Detection Systems (IDSes) has become paramount. IDS serves as a crucial tool for monitoring network traffic to uphold the integrity, confidentiality, and availability of systems. Despite the integration of Machine Learning (ML) and Deep Learning (DL) algorithms into IDS frameworks, achieving higher accuracy levels while minimizing false alarms remains a challenging task, especially when handling large datasets. In response to this challenge, researchers have turned to bio-inspired algorithms as potential solutions to enhance IDS models. This paper undertakes a comprehensive literature review focusing on augmenting the security of Internet of Things (IoT) networks by integrating bio-inspired methodologies with ML and DL techniques. Among 145 published articles, 25 relevant studies were selected to address the defined research objectives. The findings underscore the efficacy of combining bio-inspired techniques with ML and DL approaches in enhancing IDS performance, highlighting their potential to bolster IoT network security. Furthermore, the review incorporates a comparative analysis of the selected articles, considering various factors, and outlines ongoing challenges and future directions in integrating bio-inspired techniques with ML and DL algorithms. [ABSTRACT FROM AUTHOR]

Concurrently achieving mechanical robustness, low hysteresis, and high transparency are essential for ionogels to enhance their reliability and satisfy the requirements in soft electronics. Fabricating ionogels comprising these characteristics presents a considerable challenge. Herein, inspired by the structure of neural networks, a new strategy for in situ formation of dense urea moieties aggregated domains is proposed to achieve topology‐tailoring polyurea ionogels. Initially, leveraging the pronounced disparity in reactivity of the isocyanate (─NCO) groups between isophorone diisocyanate (IPDI) and NCO‐terminated prepolymer (PPGTD), IPDI preferentially reacts with deblocked trifunctional latent curing agents, resulting in the formation of dense urea moieties aggregated domains. Thereafter, these domains are interconnected via PPGTD to establish polymer networks in which the ionic liquid is uniformly dispersed, forming neural networks like ionogels. Attributed to this unique design strategy, the polyurea ionogel demonstrates remarkable properties, including high strength (0.6–2.4 MPa), excellent toughness (0.9–4.3 MJ m−3), low hysteresis (6.6–11.6%), high transparency (>92%), along with enhanced fatigue and puncture resistance. Furthermore, the polyurea ionogels exhibit outstanding versatility, enabling their in strain sensors, flexible electroluminescence devices, and nanogenerators. This strategy contributes to the design of ionogels with unparalleled combinatory properties, catering to the diverse demands of soft iontronics. [ABSTRACT FROM AUTHOR]

Complex robotic systems, such as humanoid robot hands, soft robots, and walking robots, pose a challenging control problem due to their high dimensionality and heavy non-linearities. Conventional model-based feedback controllers demonstrate robustness and stability but struggle to cope with the escalating system design and tuning complexity accompanying larger dimensions. In contrast, data-driven methods such as artificial neural networks excel at representing high-dimensional data but lack robustness, generalization, and real-time adaptiveness. In response to these challenges, researchers are directing their focus to biological paradigms, drawing inspiration from the remarkable control capabilities inherent in the human body. This has motivated the exploration of new control methods aimed at closely emulating the motor functions of the brain given the current insights in neuroscience. Recent investigation into these Brain-Inspired control techniques have yielded promising results, notably in tasks involving trajectory tracking and robot locomotion. This paper presents a comprehensive review of the foremost trends in biomimetic brain-inspired controlmethods to tackle the intricacies associated with controlling complex robotic systems. [ABSTRACT FROM AUTHOR]

The Polymer Electrolyte Membrane Water Electrolyser (PEMWE) has gained significant interest among various electrolysis methods due to its ability to produce highly purified, compressed hydrogen. The spatial configuration of bipolar plates and their flow channel patterns play a critical role in the efficiency and longevity of the PEM water electrolyser. Optimally designed flow channels ensure uniform pressure and velocity distribution across the stack, enabling high-pressure operation and facilitating high current densities. This study uses flow channel geometry inspired by authentic vine leaf patterns found in biomass, based on various plant leaves, including Soybean, Victoria Amazonica, Water Lily, Nelumbo Nucifera, Kiwi, and Acalypha Hispida leaves, as a novel channel pattern to design a PEM bipolar plate with a circular cross-section area of 13.85 c m 2 . The proposed bipolar design is further analysed with COMSOL Multiphysics to integrate the conservation of mass and momentum, molecular diffusion (Maxwell–Stefan), charge transfer equations, and other fabrication factors into a cohesive single-domain model. The simulation results showed that the novel designs have the most uniform velocity profile, lower pressure drop, superior pressure distribution, and heightened mixture homogeneity compared to the traditional serpentine models. [ABSTRACT FROM AUTHOR]

[Display omitted] • We proposed an SPA based on a Kresling-like pattern with a rigid skeleton by observing the cloning and moving behaviors from salps. • An extensible inserting structure is innovatively designed to replace the creases in traditional Kresling patterns. • The SPA can perform an axial contraction motion without twisting and a controllable bending motion. • The number of layers of Kresling-like patterns is changeable by adding or reducing skeleton components. • The elongation can reach to above 162% and the output force can reach to above 6.36 N. Biomimetic soft pneumatic actuators (SPA) with Kresling origami patterns have unique advantages over conventional rigid robots, owing to their adaptability and safety. Inspired by cloning and moving behaviors observed from salps, we proposed an SPA based on a Kresling-like pattern with a rigid skeleton. The elongation and output force were tested, and the effectiveness of the applications with the SPA was evaluated. The proposed SPA consists of rigid skeletons and a soft skin. The rigid skeletons are constructed using layers of Kresling-like patterns, while a novel extensible inserting structure is devised to replace the folds found in conventional Kresling patterns. This innovative approach ensures that the SPA exhibits axial contraction/expansion motion without any twisting movement. To mimic the bionic characteristics of swimming and ingesting progress of salps, the proposed SPA can perform an axial contraction motion without twisting and a controllable bending motion based on multi-layered Kresling-like patterns; to mimic the cloning and releasing life phenomena of salps, the number of layers of Kresling-like patterns is changeable by adding or reducing skeleton components according to the practical needs. The experimental elongation results on the SPA with multiple layers of Kresling-like patterns show that the elongation can increase to above 162% by adding layers; the experimental output force results show that the three-layer SPA can provide 6.36 N output force at an air flow rate of 10 L/min, and the output force will continue to increase as the number of layers of Kresling-like pattern increases or the air flow rate increases. Further, we demonstrate the applications of the SPA in soft grippers, scissor grippers, claw grippers and pipe crawlers. Our proposed SPA can avoid twisting in the radial contraction motion with high elongation and output force, and provide the practical guidance for bio-inspired soft robotic applications. [ABSTRACT FROM AUTHOR]

Recently, solving complex real-world challenges has become a significant and vital task, many of these challenges involve combinatorial issues where optimal solutions are desired. Traditional optimization strategies have proven to be efficient for small-scale problems. However, when it comes to larger problems, such as those in the finance or business domains, a meta-heuristic search algorithm, like the Spotted Hyena optimization (SHO), is implemented. However, the SHO algorithm faces a challenge in selecting the optimal cluster head, resulting in low efficiency. To address this issue, a new bio-inspired optimization strategy called a novel Fuzzy Based Spotted Hyena Optimizer (FSHO) is proposed. This novel algorithm aims to solve the problem of optimum clustering and provides a solution to real-world engineering challenges. In addition, the Spotted Hyena algorithm is combined with Fuzzy C-Means to enhance its exploration capability, making it well-suited for optimization tasks. The implementation of this strategy is carried out using MATLAB, and its performance is evaluated using 29 benchmark functions, as well as by addressing various complex and computationally expensive engineering issues. Comparing the cost analysis of existing techniques such as FFA, AVOA, MGO, and GTO, the proposed FSHO achieves significantly better results with a cost reduction of 1.2%, as opposed to 5%, 3%, 2.7%, and 1.5% achieved by the other techniques, respectively. This method not only enhances performance but also provides cost-effective analysis with a low convergence rate. [ABSTRACT FROM AUTHOR]

Supply chain efficiency is a major challenge in today's business environment, where efficient resource allocation and coordination of activities are essential for competitive advantage. Traditional efficiency strategies often struggle for resources for the complex and dynamic network. In response, bio-inspired metaheuristic algorithms have emerged as powerful tools to solve these optimization problems. Referring to the random search nature of metaheuristic algorithms and emphasizing that no metaheuristic algorithm is the best optimizer for all optimization applications, the No Free Lunch (NFL) theorem encourages researchers to design newer algorithms to be able to provide more effective solutions to optimization problems. Motivated by the NFL theorem, the innovation and novelty of this paper is in designing a new meta-heuristic algorithm called Bobcat Optimization Algorithm (BOA) that imitates the natural behavior of bobcats in the wild. The basic inspiration of BOA is derived from the hunting strategy of bobcats during the attack towards the prey and the chase process between them. The theory of BOA is stated and then mathematically modeled in two phases (i) exploration based on the simulation of the bobcat's position change while moving towards the prey and (ii) exploitation based on simulating the bobcat's position change during the chase process to catch the prey. The performance of BOA is evaluated in optimization to handle the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100, as well as to address CEC 2020. The optimization results show that BOA has a high ability in exploration, exploitation, and balance them during the search process in order to achieve a suitable solution for optimization problems. The results obtained from BOA are compared with the performance of twelve well-known metaheuristic algorithms. The findings show that BOA has been successful in handling the CEC 2017 test suite in 89.65, 79.31, 93.10, and 89.65% of the functions for the problem dimension equal to 10, 30, 50, and 100, respectively. Also, the findings show that in order to handle the CEC 2020 test suite, BOA has been successful in 100% of the functions of this test suite. The statistical analysis confirms that BOA has a significant statistical superiority in the competition with the compared algorithms. Also, in order to analyze the efficiency of BOA in dealing with real world applications, twenty-two constrained optimization problems from CEC 2011 test suite and four engineering design problems have been selected. The findings show that BOA has been successful in 90.90% of CEC2011 test suite optimization problems and in 100% of engineering design problems. In addition, the efficiency of BOA to handle SCM applications has been challenged to solve ten case studies in the field of sustainable lot size optimization. The findings show that BOA has successfully provided superior performance in 100% of the case studies compared to competitor algorithms. [ABSTRACT FROM AUTHOR]

Metasurfaces are sub‐wavelength nanostructures with the potential to overcome the limitations of traditional optics. Existing dielectric metasurfaces, however, are often limited to geometric primitives, which hamper their application in emergent hybrid metasurfaces. Here, a simple fabrication scheme for non‐primitive metasurfaces that addresses this limitation is introduced. Due to their broken out‐of‐plane symmetry, these nanostructures provide a new dimension in the design space. Taking inspiration from bio‐photonic systems in nature, using these elements complex hybrid metasurfaces are designed that encompass an ordered and a disordered phase. The capabilities of this hybrid optical systems are illustrated by generating configurable structural colors with extra ordinary resolution. Furthermore, the local control of light over a broad bandwidth is demonstrated and a maximum coupling efficiency of more than 97% is achieved. [ABSTRACT FROM AUTHOR]

The navigational accuracy of sea animals and trans-ocean birds provides inspiration in using geo-magnetic field (GMF) for realizing strategic truly autonomous underwater vehicles (AUV) capable of determining their absolute position on earth, without the aid of ship-referenced acoustic baseline systems. Supervised Machine Learning algorithms are applied on the GMF intensity data obtained from NOAA World Magnetic Model for a 900 km2 within the Indian mineral exploratory area in the Central Indian Ocean, with a resolution of 50 m, considering the sensitivity of commercially available magnetometers. It is identified that, for AUVs equipped with magnetometers with a detection sensitivity of 0.1 nT, the supervisory random forest regression and decision tree algorithm trained with priori GMF data, could provide trajectory guidance to AUVs with an absolute mean position accuracy in 2D plane, with reference to the last known position from Integrated Navigation system aided initially with GPS and with acoustic positioning in underwater. Circular Error Probable (CEP 50) of 53 m and 56 m, respectively. The scalar GMF anomaly navigation demonstrated to be a viable GPS-alternative navigation system could be extended to larger areas with inclination and declination vectors, as unique identifiers. [ABSTRACT FROM AUTHOR]

Summary: Environmental disasters such as earthquakes, tsunamis, floods, and landslides disrupt natural resources, human casualties, and so on. Early warning and alert of such events will help for preparedness to avoid human and economic causalities. Managing a disaster or emergency scenario is a difficult endeavor. These episodes of mass devastation, whether caused by natural disasters or man‐made disasters, result in massive losses of money, property, and lives as a result of inadequate response by the government and management authorities. As a result, actions must be taken to avoid these circumstances from occurring by discovering the causes of these catastrophes in advance and providing swift rescue procedures once the crisis happens. In a disaster situation, communication plays a vital role, and a lot of people turn towards it as a source of information conveyance/delivery for controlling the situation. Even these communication networks are damaged and incur tremendous losses during disasters. All the communication networks, including mobile wireless sensor networks (WSNs) deployed for communication, also suffer huge damage and face many challenges during disasters. Nature appears to deal with these issues well. WSN is inspired by several biological systems and processes that have intrinsically attractive properties such as adaptation, self‐organization, and self‐healing. In this paper, we proposed an enhanced bio‐inspired energy‐efficient localization (EBEEL) routing for Mobile WSN. The main focus of the proposed EBEEL algorithm is to overcome the challenges faced by peer‐existing routing protocols such as data aggregation, limited bandwidth, scalability, data redundancy, energy consumption, packet drops, load balancing, and Quality of Service (QoS). [ABSTRACT FROM AUTHOR]

The honeycomb structure is a topological structure with excellent performance that stems from the properties of the basic units of the structure. Different structural features of basic units may lead to different mechanical characteristics in the whole part. In this study, a novel hexagonal cell body topology structure (NH) was designed and manufactured by the fused deposition modeling (FDM) technique to explore the effects on mechanical properties. The tensile and impact performance of the NH structure were compared with the regular hexagonal honeycomb structure (HH), and the influence of different unit single-cell sizes on the impact performance of the NH structure was investigated. The force transmission of the basic units of the NH structure was revealed through finite element analysis. The results indicate that both the tensile and impact performances of the NH structure have been improved compared to the HH structure. The improvement is due to the better force transmission capability of the basic units of the NH structure, leading to a more uniform stress distribution. Moreover, excessively large or small single-cell sizes of the NH structure will reduce the overall structure's impact resistance. The overall structure achieves optimal impact resistance when the single-cell size is around 1.2 mm. [ABSTRACT FROM AUTHOR]

The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Management (SCM), which is characterized by elevated risks due to inadequate accountability and transparency. To address these challenges and improve operations in green manufacturing, optimization algorithms play a crucial role in supporting decision-making processes. In this study, we propose a solution to the green lot size optimization issue by leveraging bio-inspired algorithms, notably the Stork Optimization Algorithm (SOA). The SOA draws inspiration from the hunting and winter migration strategies employed by storks in nature. The theoretical framework of SOA is elaborated and mathematically modeled through two distinct phases: exploration, based on migration simulation, and exploitation, based on hunting strategy simulation. To tackle the green lot size optimization issue, our methodology involved gathering real-world data, which was then transformed into a simplified function with multiple constraints aimed at optimizing total costs and minimizing CO emissions. This function served as input for the SOA model. Subsequently, the SOA model was applied to identify the optimal lot size that strikes a balance between cost-effectiveness and sustainability. Through extensive experimentation, we compared the performance of SOA with twelve established metaheuristic algorithms, consistently demonstrating that SOA outperformed the others. This study's contribution lies in providing an effective solution to the sustainable lot-size optimization dilemma, thereby reducing environmental impact and enhancing supply chain efficiency. The simulation findings underscore that SOA consistently achieves superior outcomes compared to existing optimization methodologies, making it a promising approach for green manufacturing and sustainable supply chain management. [ABSTRACT FROM AUTHOR]

The metal-matrix composites (MMCs) with biomimetic bricks-and-mortar architectures have been experimentally demonstrated to exhibit excellent strength-ductility match. Here, biomimetic bricks-and-mortar architecture mimicking masonry bonds was introduced in numerical models. By translating perpendicular layers on stack bond model, 1/2 running and running bond models were established. The results reveal that elongation of running bond model is the highest (4.77%), which is ∼ 1.5 times as that of stack type model. The strength of these models is similar (330 ± 1 MPa). However, it is the trade-off between load bearing capacity and fracture of SiC particles. In the stack bond model, over a small junction layer area led to a relatively straight crack path and thus lower elongation. On the contrary, running bond model shows a zigzag main crack. So, the main crack deflects frequently with high energy consumption. Furthermore, crack deflection into matrix cell increases propagation resistance, leading to the highest elongation in the running bond model. Therefore, the biomimetic bricks-and-mortar architecture delays and deflects main crack propagation. These findings have significant implication for the architecture design of advanced composite materials. [ABSTRACT FROM AUTHOR]

Introduction: Biomimetic soft pneumatic actuators (SPA) with Kresling origami patterns have unique advantages over conventional rigid robots, owing to their adaptability and safety. Objectives: Inspired by cloning and moving behaviors observed from salps, we proposed an SPA based on a Kresling-like pattern with a rigid skeleton. The elongation and output force were tested, and the effectiveness of the applications with the SPA was evaluated. Methods: The proposed SPA consists of rigid skeletons and a soft skin. The rigid skeletons are constructed using layers of Kresling-like patterns, while a novel extensible inserting structure is devised to replace the folds found in conventional Kresling patterns. This innovative approach ensures that the SPA exhibits axial contraction/expansion motion without any twisting movement. To mimic the bionic characteristics of swimming and ingesting progress of salps, the proposed SPA can perform an axial contraction motion without twisting and a controllable bending motion based on multi-layered Kresling-like patterns; to mimic the cloning and releasing life phenomena of salps, the number of layers of Kresling-like patterns is changeable by adding or reducing skeleton components according to the practical needs. Results: The experimental elongation results on the SPA with multiple layers of Kresling-like patterns show that the elongation can increase to above 162% by adding layers; the experimental output force results show that the three-layer SPA can provide 6.36 N output force at an air flow rate of 10 L/min, and the output force will continue to increase as the number of layers of Kresling-like pattern increases or the air flow rate increases. Further, we demonstrate the applications of the SPA in soft grippers, scissor grippers, claw grippers and pipe crawlers. Conclusion: Our proposed SPA can avoid twisting in the radial contraction motion with high elongation and output force, and provide the practical guidance for bio-inspired soft robotic applications.

Abstract Supply chain efficiency is a major challenge in today’s business environment, where efficient resource allocation and coordination of activities are essential for competitive advantage. Traditional efficiency strategies often struggle for resources for the complex and dynamic network. In response, bio-inspired metaheuristic algorithms have emerged as powerful tools to solve these optimization problems. Referring to the random search nature of metaheuristic algorithms and emphasizing that no metaheuristic algorithm is the best optimizer for all optimization applications, the No Free Lunch (NFL) theorem encourages researchers to design newer algorithms to be able to provide more effective solutions to optimization problems. Motivated by the NFL theorem, the innovation and novelty of this paper is in designing a new meta-heuristic algorithm called Bobcat Optimization Algorithm (BOA) that imitates the natural behavior of bobcats in the wild. The basic inspiration of BOA is derived from the hunting strategy of bobcats during the attack towards the prey and the chase process between them. The theory of BOA is stated and then mathematically modeled in two phases (i) exploration based on the simulation of the bobcat’s position change while moving towards the prey and (ii) exploitation based on simulating the bobcat’s position change during the chase process to catch the prey. The performance of BOA is evaluated in optimization to handle the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100, as well as to address CEC 2020. The optimization results show that BOA has a high ability in exploration, exploitation, and balance them during the search process in order to achieve a suitable solution for optimization problems. The results obtained from BOA are compared with the performance of twelve well-known metaheuristic algorithms. The findings show that BOA has been successful in handling the CEC 2017 test suite in 89.65, 79.31, 93.10, and 89.65% of the functions for the problem dimension equal to 10, 30, 50, and 100, respectively. Also, the findings show that in order to handle the CEC 2020 test suite, BOA has been successful in 100% of the functions of this test suite. The statistical analysis confirms that BOA has a significant statistical superiority in the competition with the compared algorithms. Also, in order to analyze the efficiency of BOA in dealing with real world applications, twenty-two constrained optimization problems from CEC 2011 test suite and four engineering design problems have been selected. The findings show that BOA has been successful in 90.90% of CEC2011 test suite optimization problems and in 100% of engineering design problems. In addition, the efficiency of BOA to handle SCM applications has been challenged to solve ten case studies in the field of sustainable lot size optimization. The findings show that BOA has successfully provided superior performance in 100% of the case studies compared to competitor algorithms.

Abstract The navigational accuracy of sea animals and trans-ocean birds provides inspiration in using geo-magnetic field (GMF) for realizing strategic truly autonomous underwater vehicles (AUV) capable of determining their absolute position on earth, without the aid of ship-referenced acoustic baseline systems. Supervised Machine Learning algorithms are applied on the GMF intensity data obtained from NOAA World Magnetic Model for a 900 km2 within the Indian mineral exploratory area in the Central Indian Ocean, with a resolution of 50 m, considering the sensitivity of commercially available magnetometers. It is identified that, for AUVs equipped with magnetometers with a detection sensitivity of 0.1 nT, the supervisory random forest regression and decision tree algorithm trained with priori GMF data, could provide trajectory guidance to AUVs with an absolute mean position accuracy in 2D plane, with reference to the last known position from Integrated Navigation system aided initially with GPS and with acoustic positioning in underwater. Circular Error Probable (CEP 50) of 53 m and 56 m, respectively. The scalar GMF anomaly navigation demonstrated to be a viable GPS-alternative navigation system could be extended to larger areas with inclination and declination vectors, as unique identifiers.

A low-order physics-based model to simulate the unsteady flow response to airfoils undergoing large-amplitude variations of the camber is presented in this paper. Potential-flow theory adapted for unsteady airfoils and numerical methods using discrete-vortex elements are combined to obtain rapid predictions of flow behavior and force evolution. To elude the inherent restriction of thin-airfoil theory to small flow disturbances, a time-varying chord line is proposed in this work over which to satisfy the appropriate boundary condition, enabling large deformations of the camber line to be modeled. Computational fluid dynamics simulations are performed to assess the accuracy of the low-order model for a wide range of dynamic trailing-edge flap deflections. By allowing the chord line to rotate with trailing-edge deflections, aerodynamic loads predictions are greatly enhanced as compared to the classical approach where the chord line is fixed. This is especially evident for large-amplitude deformations. [ABSTRACT FROM AUTHOR]

The escalating challenge of antibiotic resistance has driven the innovation of new antibacterial and antifouling materials. Recent developments focus on nature-inspired topographical engineering and nanostructured surfaces to combat resistant bacteria. This review discusses these advances, emphasizing the potential of nanoantibiotics and biopolymers. Nanoantibiotics revitalize drug effectiveness by encapsulating them in nanoparticles, presenting a new strategy to fight pathogens. Biopolymers, eco-friendly and biodegradable, emerge as a sustainable alternative, with applications in food safety and beyond. The exploration of these materials signifies a leap in design, fabrication, and the possibility of cost-effective, large-scale production, highlighting a promising avenue for commercial applications to tackle antibiotic resistance and biofouling effectively.

AbstractHierarchical tubes have been proved to be excellent energy absorbers under axial compression. However, there are limited studies exploring their oblique crushing performance. Therefore, the oblique crushing characteristics of hierarchical tubes, bio-inspired hierarchical multi-cell square tubes (BHMS), hierarchical multi-cell square tubes (HMSA and HMSB), have been numerically studied. The influencing factors of loading angle and hierarchical order on deformation modes, force-displacement curves, and energy absorption (EA) behaviors were explored. It is found that BHMS tubes present the superior crushing performance among these hierarchical square tubes. Moreover, BHMS tubes display the excellent crushing characteristics in comparison with single square tubes. Specifically, the specific EA of BHMS-3 tubes is 213%, 212%, 66%, and 42% greater than that of single square tubes at loading angles of 0°, 10°, 20°, and 30°. The findings demonstrate the priority of hierarchical square tubes under oblique crushing, and provide the reference and insight for the design of energy absorbers. [ABSTRACT FROM AUTHOR]

Bipolar flow channel plate design, which is a key component and performance qualifier of fuel cells, is an extensively investigated area. Recently, bio-inspired flow fields (FFs) have been designed to increase the overall performance as an alternative to traditional FFs. In the present study, performance evaluation of the various bio-inspired FFs was carried out in PEM fuel cell and direct methanol fuel cell modes. The effects of the main performance characteristics including the clamping force of the short stack assembly, air flow rate, and operating temperature on the cell power were investigated for both PEMFC and DMFC modes. The highest performance output was ensured with the Morus leaf-inspired FF (MLFF) at PEMFC mode at 55 °C with a peak power density of 313 mW/cm2. Besides, the highest performance for DMFC mode was achieved at 35.8 mW/cm2 with the Ficus Carica leaf-inspired FF (FCLFF). • Bio-inspired flow fields were fabricated considering Morus and Ficus Carica leaves. • Fabricated BPs were tested for both PEM and direct-methanol fuel cell modes. • Gas flow rate, operating temperature, and clamping force conditions were regarded. • In PEMFC mode, Morus leaf-inspired field yielded maximum power with 313 mW/cm2. • In DMFC mode, Ficus Carica leaf-inspired field had maximum power with 35.8 mW/cm2. [ABSTRACT FROM AUTHOR]

Electronic skins are expected to replicate a human‐like tactile sense, which significantly detects surface information, including geometry, material, and temperature. Although most texture features can be sensed in the horizontal direction, the lack of effective approaches for detecting vertical properties limits the development of artificial skin based on tactile sensors. In this study, an all‐printed finger‐inspired tactile sensor array is developed to realize the 3D detection and reconstruction of microscale structures. A beam structure with a suspended multilayer membrane is proposed, and a tactile sensor array of 12 units arranged in a dual‐column layout is developed. This architecture enables the tactile sensor array to obtain comprehensive geometric information of micro‐textures, including 3D morphology and clearance characteristics, and optimizes the 3D reconstruction patterns by self‐calibration. Moreover, an innovative screen‐printing technology incorporating multilayer printing and sacrificial‐layer techniques is adopted to print the entire device. In additon, a Braille recognition system utilizing this tactile sensor array is developed to interpret Shakespeare's quotes printed in Grade 2 Braille. The abovementioned demonstrations reveal an attractive future vision for endowing bioinspired robots with the unique capability of touching and feeling the microscale real world and reconstructing it in the cyber world. [ABSTRACT FROM AUTHOR]

Mussel byssus offers bio‐inspired designs for advanced adhesives, coatings, and supramolecular hydrogels. Solidification of secreted proteins into mechanically robust fibers is triggered by a pH jump from acidic fluid condensates to basic seawater conditions driving protein crosslinking by metal coordination with 3,4‐dihydroxyphenylalanine (DOPA). Knowledge of the dynamic and localized pH changes during secretion is currently lacking. Yet, this information is crucial for controlling the gelation and properties of mussel‐inspired DOPA‐modified materials. Here, an iridium oxide‐based pH microsensor combining high mechanical stability and spatial resolution is used to measure the acidic pH during byssus secretion and to map mussel‐inspired hydrogels revealing compositional heterogeneity resulting in mechanically distinct regions. Within the hydrogel, high pH values >12 are measured which are detrimental to DOPA due to its strong propensity for oxidation, which adversely alters gel properties. This demonstrates the need to improve preparation methods of mussel‐inspired adhesive materials to more closely mimic native processes. [ABSTRACT FROM AUTHOR]

The gyroid structure is a bio-inspired structure that was discovered in butterfly wings. The geometric design of the gyroid structure in butterfly wings offers a unique combination of strength and flexibility. This study investigated sandwich panels consisting of a 3D-printed gyroid structure core and carbon fiber-reinforced polymer (CFRP) facing skin. A filament fused fabrication 3D printer machine was used to print the gyroid cores with three different relative densities, namely 10%, 15%, and 20%. Polylactic acid (PLA) was used as the printing material for the gyroid. The gyroid structure was then sandwiched and joined by an epoxy resin between CFRP laminates. Polyurethane foam (PUF) was filled into the gyroid core to fill the cavity on the core for another set of samples. Flexural and compression tests were performed on the samples to investigate the mechanical behavior of the sandwiches. Moreover, the two-parameter Weibull distribution was used to evaluate the results statistically. As a result, the sandwich-specific facing stress and core shear strength from the three-point bending test of the composites increased with the increase in sandwich density. Core density controls the flexural characteristics of the sandwich. Adding PUF improves the deflection at the maximum stress and the sustained load after fracture of the sandwich. Compression strength, modulus, and energy absorbed by gyroid core sandwiches and their specific properties are higher than the PUF-filled gyroid core sandwiches at equal sandwich density. [ABSTRACT FROM AUTHOR]

Many species of plants and animals show an ability to resist fouling with surface topographies tailored to their environments. The mollusk species Dosinia juvenilis has demonstrated the ability to resist the accumulation of fouling on its outer surface. Understanding the functional mechanism employed by nature represents a significant opportunity for the persistent challenges of many industrial and consumer applications. Using a biomimetic approach, this study investigates the underlying hydrodynamic mechanisms of fouling resistance through Large Eddy simulations of a turbulent boundary layer above a novel ribletted surface topography bio-inspired by the Dosinia juvenilis. The results indicate a maximum drag reduction of 6.8% relative to a flat surface. The flow statistics near the surface are analogous to those observed for other ribletted surfaces in that the appropriately sized riblets effectively reduce the spanwise and wall-normal velocity fluctuations near the surface. This study supports the understanding that nature employs ribletted surfaces toward multiple functionalities including the considered drag reduction and fouling resistance. [ABSTRACT FROM AUTHOR]

The applications of Si 3 N 4 ceramics are hindered by their brittleness and susceptibility to fracture, despite their high strength and wear resistance. To address this challenge, layered ceramics were prepared using lamination and plasma sintering techniques, inspired by the structure of shell pearl layers. The bio-inspired layered ceramics were alternately arranged with Si 3 N 4 as the matrix layer (hard) and BN as the interfacial layer (soft). During the study, ceramic specimens with different microstructures were obtained by modulating the layer thickness ratio (ratio of Si 3 N 4 to BN) and sintering temperature. The microstructure of the specimens was characterized using SEM (Scanning Electron Microscope) and EDS (Energy Dispersive Spectrometer). The mechanical of various specimens were assessed by measuring the flexural strength and fracture toughness. The results of the investigation revealed that the mechanical characteristics of the samples reached their optimum state (with a toughness of 13.51 ± 1.56 MPa m1/2 and a strength of 429 ± 4.28 MPa) when the ratio of Si 3 N 4 to BN was 5:1, and the sintering temperature was maintained at 2000 °C. [ABSTRACT FROM AUTHOR]

Biostructures found in nature exhibit remarkable strength, toughness, and damage resistance, achieved over millions of years. Observing nature closely might help develop laminates that resemble natural structures more closely, potentially improving strength and mimicking natural principles. Bio-inspired Carbon Fiber-Reinforced Polymers (CFRP) investigated thus far exhibit consistent pitch angles between layers, whereas natural structures display gradual variations in pitch angle rather than consistency. Therefore, this study explores helicoidal CFRP laminates, focusing on the Non-Linear Rotation Angle (NLRA) or gradual variation to enhance composite material performance. In addition, it compares the strength and failure mechanisms of the gradual configuration with conventional helicoidal and unidirectional (UD) laminates, serving as references while conducting transverse tensile tests (out-of-plane tensile). The findings highlight the potential of conventional and gradual helicoidal structures in reinforcing CFRP laminates, increasing the failure load compared to unidirectional CFRP laminate by about 5% and 17%, respectively. In addition, utilizing bio-inspired configurations has shown promising improvements in toughness compared to traditional unidirectional laminates, as evidenced by the increased displacement at failure. The numerical and experimental analyses revealed a shift in crack path when utilizing the bio-inspired helicoidal stacking sequence. Validated by experimental data, this alteration demonstrates longer and more intricate crack propagation, ultimately leading to increased transverse strength. [ABSTRACT FROM AUTHOR]

Bio-inspired gyroid triply periodic minimum surface (TPMS) lattice structures have been the focus of research in automotive engineering because they can absorb a lot of energy and have wider plateau ranges. The main challenge is determining the optimal energy absorption capacity and accurately capturing plastic plateau areas using finite element analysis (FEA). Using nTop's Boolean subtraction method, this study combined walled TPMS gyroid structures with a normal TPMS gyroid lattice. This made a composite TPMS gyroid lattice (CTG) with relative densities ranging from 14% to 54%. Using ideaMaker 4.2.3 (3DRaise Pro 2) software and the fused deposition modeling (FDM) Raise3D Pro 2 3D printer to print polylactic acid (PLA) bioplastics in 1.75 mm filament made it possible to slice computer-aided design (CAD) models and fabricate 36 lattice samples precisely using a layer-by-layer technique. Shimadzu 100 kN testing equipment was utilized for the mechanical compression experiments. The finite element approach validates the results of mechanical compression testing. Further, a composite CTG was examined using a field emission scanning electron microscope (FE-SEM) before and after compression testing. The composite TPMS gyroid lattice showed potential as shock absorbers for vehicles with relative densities of 33%, 38%, and 54%. The Gibson–Ashby model showed that the composite TPMS gyroid lattice deformed mainly by bending, and the size effect was seen when the relative densities were less than 15%. The lattice's relative density had a significant impact on its ability to absorb energy. The research also explored the use of these innovative foam-like composite TPMS gyroid lattices in high-speed crash box scenarios to potentially enhance vehicle safety and performance. The structures have tremendous potential to improve vehicle safety by acting as advanced shock absorbers, which are particularly effective at higher relative densities. [ABSTRACT FROM AUTHOR]

This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the unique hunting behavior of frilled lizards in their natural habitat. FLO draws its inspiration from the sit-and-wait hunting strategy of these lizards. The algorithm's core principles are meticulously detailed and mathematically structured into two distinct phases: (i) an exploration phase, which mimics the lizard's sudden attack on its prey, and (ii) an exploitation phase, which simulates the lizard's retreat to the treetops after feeding. To assess FLO's efficacy in addressing optimization problems, its performance is rigorously tested on fifty-two standard benchmark functions. These functions include unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions, as well as the challenging CEC 2017 test suite. FLO's performance is benchmarked against twelve established metaheuristic algorithms, providing a comprehensive comparative analysis. The simulation results demonstrate that FLO excels in both exploration and exploitation, effectively balancing these two critical aspects throughout the search process. This balanced approach enables FLO to outperform several competing algorithms in numerous test cases. Additionally, FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems, further validating its robustness and versatility in solving real-world optimization challenges. Overall, the study highlights FLO's superior performance and its potential as a powerful tool for tackling a wide range of optimization problems. [ABSTRACT FROM AUTHOR]

Abstract\nHIGHLIGHTSNature-built structures effectively sustain higher loads without failing, even though they are made of weak organic materials. The laminated construction mode in various fish scales provides them toughness and flexibility. The present manuscript aims to carry out the free vibration analysis of double- and cross-helicoidal laminated composite plates inspired by the scales of fish. The study has been carried out using the recently proposed finite element-based higher-order zigzag theory. The influence of aspect ratio, side-to-thickness ratio, skew angle, and end conditions on the free vibration behavior of double- and cross-helicoidal plates is depicted. The mode shapes of the plates are studied in detail. The free vibration behavior of skew plates is also studied for the first time. An improved performance of the helicoidal plate compared to the conventional lamination scheme has been obtained.Free vibration behavior of cross- and double-helicoidal plates is studied.The study has been carried out in the framework of HOZT.Cross-helicoidal plates exhibit either stiff behavior or similar to quasi-isotropic plates.Increasing the pitch angle increases the stiffness of the helicoidal plate.Free vibration behavior of cross- and double-helicoidal plates is studied.The study has been carried out in the framework of HOZT.Cross-helicoidal plates exhibit either stiff behavior or similar to quasi-isotropic plates.Increasing the pitch angle increases the stiffness of the helicoidal plate. [ABSTRACT FROM AUTHOR]

Designers have been fascinated by exploring new geometries made by high-performance structures. In more specific terms, biological systems have always been proven to be characterised by sophisticated structures with adapting properties to nature challenges. Insightful analyses have shown how these natural structures are dominated by characteristics such as high energy absorption and elevated strength-weight proportion. Fractal geometries are examples of bio-inspired mathematical objects whose complex 3D structures can be obtained only by advanced manufacturing systems, such as additive manufacturing (AM). This study investigates the feasibility and energy absorption properties of a novel fractal structure based on a 3D Greek cross (3D-CFS). The structure was designed with different volume fractions and produced by powder bed fusion (PBF) AM processes in polyamide (PA12) and thermoplastic polyurethane (TPU). The 3D-CFS properties are investigated under quasi-static and dynamic compression tests. The analysis revealed that for certain geometrical parameters, the manufacturing of the structures is constrained by the sintered powder entrapped in the structure. However, in the case of powder-free structures, the results showed a high impact resistance and cushioning capability. Overall, in terms of specific energy absorption (SEA), the TPU structures showed values between 2.5 and 3.5 kJ/kg, while PA12 ones are between 7.5 and 17.4 kJ/kg, making the 3D-CFS structure compatible with personal protective equipment (PPE) applications. Compared to the literature data on cellular structures made by AM, 3D-CFS performs considerably better. Also, PA12 3D-CFS is better, with a SEA value up to 170% higher than that of a typical material employed for head PPE (e.g. EPS-60 SEA equal to 2.76 kJ/kg). In contrast, TPU 3D-CFS looks more promising in the case of multiple impact conditions. [ABSTRACT FROM AUTHOR]

Recent advancements in biomimetics have spurred significant innovations in prosthetic limb development by leveraging the intricate designs and mechanisms found in nature. Biomimetics, also known as "nature-inspired engineering", involves studying and emulating biological systems to address complex human challenges. This comprehensive review provides insights into the latest trends in biomimetic prosthetics, focusing on leveraging knowledge from natural biomechanics, sensory feedback mechanisms, and control systems to closely mimic biological appendages. Highlighted breakthroughs include the integration of cutting-edge materials and manufacturing techniques such as 3D printing, facilitating seamless anatomical integration of prosthetic limbs. Additionally, the incorporation of neural interfaces and sensory feedback systems enhances control and movement, while technologies like 3D scanning enable personalized customization, optimizing comfort and functionality for individual users. Ongoing research efforts in biomimetics hold promise for further advancements, offering enhanced mobility and integration for individuals with limb loss or impairment. This review illuminates the dynamic landscape of biomimetic prosthetic technology, emphasizing its transformative potential in rehabilitation and assistive technologies. It envisions a future where prosthetic solutions seamlessly integrate with the human body, augmenting both mobility and quality of life. [ABSTRACT FROM AUTHOR]

Bio‐inspired micro soft robots mimic biologically specific body structures and movement mechanisms by utilizing bionic principles. Because of its soft body, it can adapt to complex external environments. However, the existing polymer material system is single and the fabrication process is limited. How to further reduce soft robot size has become a key issue. In this work, a Programming Magnetic Elastic Composites (PMEC) is proposed for 3D printing to manufacture micro soft robots. The PMEC has the advantage of changing the direction of the internal magnetic field in response to changes in the external magnetic field. A Microsoft robot is designed and fabricated using PMEC inspired by an inchworm. Numerical simulations are also used to study the effect of soft robot size parameters on local stresses and optimize the structure. The results show that the microscale soft robot can crawl with a load of 2 times its weight at a speed of 6.67 mm s−1, crawl on slopes from 0° to 90°, and crawl over obstacles with a maximum height of 7 mm. In addition, active adaptation of soft robots to complex tunnel models based on external stimuli is achieved by magnetic field control. [ABSTRACT FROM AUTHOR]

This paper presents a novel nonlinear anti-vibration ring with deformable crescent-shaped cross-sections (NAVR-DCCS) inspired by the petiole of palm leaf. The proposed NAVR-DCCS exhibits markedly enhanced nonlinear quasi-zero stiffness through deformable cross-sections, which endow it with advantageous vibration isolation attributes. A comprehensive investigation of the structural nonlinearities and dynamic behaviors of the NAVR-DCCS is undertaken, with emphasis on the principle of cross-sectional deformation and its nonlinear stiffness properties. This study explores the influence of pertinent parameters on the nonlinear characteristics and displacement transmissibility. Tensile-compression testing and transmissibility measurements are conducted to verify theoretical calculations, and the experimental results are found to be in congruity with theoretical predictions. The beneficial nonlinear characteristics of the NAVR-DCCS hold promise for providing a passive vibration isolation methodology, representing a potentially innovative solution with broad-reaching applicability and utility across diverse research domains. [ABSTRACT FROM AUTHOR]

This paper introduces a novel nature-inspired optimization algorithm called the Addax Optimization Algorithm (AOA), which emulates the natural behavior of addax in the wild. The core inspiration for AOA is drawn from the addax's foraging strategy and digging skills. The theoretical foundation of AOA is expounded and mathematically modeled in two phases: (i) exploration based on modeling addax position change during foraging and (ii) exploitation based on addax position change modeling during digging. The efficiency of AOA in handling real-world engineering applications is evaluated on four engineering design problems. The optimization results show that AOA is achieved effective solutions for optimization problems with its high ability in exploration, exploitation, and establishing a balance between them during the search process. The outcomes derived from applying AOA are compared with the performance of twelve well-known optimization algorithms. The simulation results show that AOA is provided superior performance compared to competitor algorithms, by achieving better results and ranking as the first best optimizer. The simulation findings show that the proposed AOA approach has an effective performance for handling optimization tasks in engineering applications. [ABSTRACT FROM AUTHOR]

Complex robotic systems, such as humanoid robot hands, soft robots, and walking robots, pose a challenging control problem due to their high dimensionality and heavy non-linearities. Conventional model-based feedback controllers demonstrate robustness and stability but struggle to cope with the escalating system design and tuning complexity accompanying larger dimensions. In contrast, data-driven methods such as artificial neural networks excel at representing high-dimensional data but lack robustness, generalization, and real-time adaptiveness. In response to these challenges, researchers are directing their focus to biological paradigms, drawing inspiration from the remarkable control capabilities inherent in the human body. This has motivated the exploration of new control methods aimed at closely emulating the motor functions of the brain given the current insights in neuroscience. Recent investigation into these Brain-Inspired control techniques have yielded promising results, notably in tasks involving trajectory tracking and robot locomotion. This paper presents a comprehensive review of the foremost trends in biomimetic brain-inspired control methods to tackle the intricacies associated with controlling complex robotic systems.

Underwater superhydrophobic surfaces stand as a promising frontier in materials science, holding immense potential for applications in underwater infrastructure, vehicles, pipelines, robots, and sensors. Despite this potential, widespread commercial adoption of these surfaces faces limitations, primarily rooted in challenges related to material durability and the stability of the air plastron during prolonged submersion. Factors such as pressure, flow, and temperature further complicate the operational viability of underwater superhydrophobic technology. This comprehensive review navigates the evolving landscape of underwater superhydrophobic technology, providing a deep dive into the introduction, advancements, and innovations in design, fabrication, and testing techniques. Recent breakthroughs in nanotechnology, magnetic‐responsive coatings, additive manufacturing, and machine learning are highlighted, showcasing the diverse avenues of progress. Notable research endeavors concentrate on enhancing the longevity of plastrons, the fundamental element governing superhydrophobic behavior. The review explores the multifaceted applications of superhydrophobic coatings in the underwater environment, encompassing areas such as drag reduction, anti‐biofouling, and corrosion resistance. A critical examination of commercial offerings in the superhydrophobic coating landscape offers a current perspective on available solutions. In conclusion, the review provides valuable insights and forward‐looking recommendations to propel the field of underwater superhydrophobicity toward new dimensions of innovation and practical utility. [ABSTRACT FROM AUTHOR]

The device was invented to sense the variations in the deployed Environment, which takes different dimensions in its functionality. The basic operation does not change; it is the same surveillance, gathering and alert. Human intervention in all places is not possible and efficient monitoring in all circumstances cannot be done manually. Wireless Sensor Networks give the solution for the above-specified problem, continuous efficient monitoring and alerting on time without human intervention in any circumstances. Deploying WSNs and extracting efficient performance requires a strong technology backup. However, it's a machine in logical connectivity that may face several technical issues while transferring the gathered data. It has to be rectified, and efficient service to be provided in the deployed application. Finding the safest and fastest path is one of the critical tasks in the WSN. To achieve that, Bio-Inspired Computing will be used as a tool for designing the protocol to attain the fastest reliable network, even if it deals with the other problems in WSN, such as efficient energy utilization from the battery. In such a way, the Paper is proposed based on the exceptional and exciting behavior of one of the living things, which shook the entire world for almost two years in all aspects, and even a few countries are still seeking a way to come back from its impact, the reason for global lockdown, born with the crown, founder of social distancing, which is popularly known as "Corona" the virus. [ABSTRACT FROM AUTHOR]