Biomimetics

Cyclists are vulnerable road users and often suffer head-neck injuries in car–cyclist accidents. Wearing a helmet is currently the most prevalent protection method against such injuries. Today, there is an ongoing debate about the ability of helmets to protect the cyclists’ head-neck from injury. In the current study, we numerically reconstructed five real-world car–cyclist impact accidents, incorporating previously developed finite element models of four cyclist helmets to evaluate their protective performances. We made comparative head-neck injury predictions for unhelmeted and helmeted cyclists. The results show that helmets could clearly lower the risk of severe (AIS 4+) brain injury and skull fracture, as assessed by the predicted head injury criterion (HIC), while a relatively limited decrease in AIS 4+ brain injury risk can be achieved in terms of the analysis of CSDM_0.25. Assessment using the maximum principal strain (MPS_0.98) and head impact power (HIP) criteria suggests that helmets could lower the risk of diffuse axonal injury and subdural hematoma of the cyclist. The helmet efficacy in neck protection depends on the impact scenario. Therefore, wearing a helmet does not seem to cause a significant neck injury risk level increase to the cyclist. Our work presents important insights into the helmet’s efficacy in protecting the head-neck of cyclists and motivates further optimization of protective equipment. Full article

This paper presents the design and testing of an arched foot with several biomimetic features, including five individual MTP (toe) joints, four individual midfoot joints, and plantar fascia. The creation of a triple-arched foot represents a step further in bio-inspired design compared to other published designs. The arched structure creates flexibility that is similar to human feet with a vertical deflection of up to 12 mm. The individual toe joints enable abduction–adduction in the forefoot and therefore a natural pronation motion. Adult female bone data was obtained and converted into a CAD model to accurately identify the location of bones, joints, and arches. An analytical model is presented that gives the relationship between the vertical stiffness and horizontal stiffness of the longitudinal arches and therefore allows the optimization of stiffness elements. Experimental tests have demonstrated a vertical arch stiffness of 76 N/mm which is similar to adult human feet. The range of movement of the foot is similar to human feet with the following values: dorsi-plantarflexion (28°/37°), inversion-eversion (30°/15°), and abduction–adduction (30°/39°). Tests have also demonstrated a three-point contact with the ground that is similar to human feet. Full article

We introduce two new search strategies to further improve the performance of vegetation evolution (VEGE) for solving continuous optimization problems. Specifically, the first strategy, named the dynamic maturity strategy, allows individuals with better fitness to have a higher probability of generating more seed individuals. Here, all individuals will first become allocated to generate a fixed number of seeds, and then the remaining number of allocatable seeds will be distributed competitively according to their fitness. Since VEGE performs poorly in getting rid of local optima, we propose the diverse mutation strategy as the second search operator with several different mutation methods to increase the diversity of seed individuals. In other words, each generated seed individual will randomly choose one of the methods to mutate with a lower probability. To evaluate the performances of the two proposed strategies, we run our proposal (VEGE + two strategies), VEGE, and another seven advanced evolutionary algorithms (EAs) on the CEC2013 benchmark functions and seven popular engineering problems. Finally, we analyze the respective contributions of these two strategies to VEGE. The experimental and statistical results confirmed that our proposal can significantly accelerate convergence and improve the convergence accuracy of the conventional VEGE in most optimization problems. Full article

Bio-inspired (biomimetic) materials, which are inspired by living organisms, offer exciting opportunities for the development of advanced functionalities. Among them, bio-inspired superhydrophobic surfaces have attracted considerable interest due to their potential applications in self-cleaning surfaces and reducing fluid resistance. Although the mechanism of superhydrophobicity is understood to be the free energy barrier between the Cassie and Wenzel states, the solid-surface technology to control the free energy barrier is still unclear. Therefore, previous studies have fabricated solid surfaces with desired properties through trial and error by measuring contact angles. In contrast, our study directly evaluates the free energy barrier using molecular simulations and attempts to relate it to solid-surface parameters. Through a series of simulations, we explore the behavior of water droplets on surfaces with varying values of surface pillar spacing and surface pillar height. The results show that the free energy barrier increases significantly as the pillar spacing decreases and/or as the pillar height increases. Our study goes beyond traditional approaches by exploring the relationship between free energy barriers, surface parameters, and hydrophobicity, providing a more direct and quantified method to evaluate surface hydrophobicity. This knowledge contributes significantly to material design by providing valuable insights into the relationship between surface parameters, free energy barriers, and hydrophilicity/hydrophobicity. Full article

The prevalence of multidrug-resistant (MDR) bacteria has recently increased dramatically, seriously endangering human health. Herein, amoxicillin (Amoxi)-conjugated gold nanoparticles (AuNPs) were created as a novel drug delivery system to overcome MDR bacteria. MDR bacteria were isolated from a variety of infection sources. Phenotype, biotype, and 16S rRNA gene analyses were used for isolate identification. Additionally, Juniperus excelsa was used for the production of AuNPs. The conjugation of AuNPs with Amoxi using sodium tri-polyphosphate (TPP) as a linker to produce Amoxi-TPP-AuNPs was studied. The AuNP and Amoxi-TPP-AuNP diameters ranged from 15.99 to 24.71 nm, with spherical and hexagonal shapes. A total of 83% of amoxicillin was released from Amoxi-TPP-AuNPs after 12 h, and after 3 days, 90% of the medication was released. The Amoxi-TPP-AuNPs exhibited superior antibacterial effectiveness against MRSA and MDR E. coli strains. Amoxi-TPP-AuNPs had MICs of 3.6–8 µg mL⁻¹ against the tested bacteria. This is 37.5–83 fold higher compared to values reported in the literature. Amoxi-TPP-AuNPs exhibit a remarkable ability against MRSA and E. coli strains. These results demonstrate the applicability of Amoxi-TPP-AuNPs as a drug delivery system to improve therapeutic action. Full article

The flexibility of insect wings should be considered in the design of bionic micro flapping-wing aircraft. The honeybee is an ideal biomimetic object because its wings are small and possess a concise vein pattern. In this paper, we focus on resilin, an important flexible factor in honeybees’ forewings. Both resilin joints and resilin stripes are considered in the finite element model, and their mechanical behaviors are studied comprehensively. Resilin was found to increase the static deflections in chordwise and spanwise directions by 1.4 times and 1.9 times, respectively. In modal analysis, natural frequencies of the first bending and first torsional modes were found to be decreased significantly—especially the latter, which was reduced from 500 Hz to 217 Hz—in terms of resilin joints and stripes, closely approaching flapping frequency. As a result, the rotational angle amplitude in dynamic responses is remarkable, with an amplification ratio of about six. It was also found that resilin joints and stripes together lead to well-cambered sections and improve the stress concentrations in dynamic deformation. As resilin is widespread in insect wings, the study could help our understanding of the flexible mechanism of wing structure and inspire the development of flexible airfoils. Full article

This work aimed to compare the effect of four new toothpastes (P1–P4) based on pure and biomimetic substituted nano-hydroxyapatites (HAPs) on remineralization of human enamel. Artificially demineralized enamel slices were daily treated for ten days with different toothpastes according to the experimental design. Tooth enamel surfaces were investigated using atomic force microscope (AFM) images and surface roughness (Ra) determined before and after treatment. The surface roughness of enamel slices was statistically analyzed by one-way ANOVA and Bonferroni’s multiple comparison test. X-ray diffraction (XRD) and Fourier transform infrared (FTIR) data revealed the HAP structure with crystal sizes between 28 and 33 nm and crystallinity between 29 and 37%. The average size of HAP particles was found to be between 30 and 40 nm. The Ra values indicated that P3 (HAP-Mg-Zn-Sr-Si) toothpaste was the most effective after 10 days of treatment, leading to the lowest mean roughness. The P3 and P2 (HAP) toothpastes were found to be effective in promoting remineralization. Specifically, their effectiveness can be ranked as follows: P3 = P2 > P4 (HAP-Mg-Zn-Si) > P1 (HAP-Zn), considering both the chemical composition and the size of their constitutive nanoparticles. The proposed toothpastes might be used successfully to treat early tooth decay. Full article

In recent research, fake news detection in social networking using Machine Learning (ML) and Deep Learning (DL) models has gained immense attention. The current research article presents the Bio-inspired Artificial Intelligence with Natural Language Processing Deceptive Content Detection (BAINLP-DCD) technique for social networking. The goal of the proposed BAINLP-DCD technique is to detect the presence of deceptive or fake content on social media. In order to accomplish this, the BAINLP-DCD algorithm applies data preprocessing to transform the input dataset into a meaningful format. For deceptive content detection, the BAINLP-DCD technique uses a Multi-Head Self-attention Bi-directional Long Short-Term Memory (MHS-BiLSTM) model. Finally, the African Vulture Optimization Algorithm (AVOA) is applied for the selection of optimum hyperparameters of the MHS-BiLSTM model. The proposed BAINLP-DCD algorithm was validated through simulation using two benchmark fake news datasets. The experimental outcomes portrayed the enhanced performance of the BAINLP-DCD technique, with maximum accuracy values of 92.19% and 92.56% on the BuzzFeed and PolitiFact datasets, respectively. Full article

In this paper, a new concept of extra-durable and sustainable wind turbine blades is presented. The two critical materials science challenges of the development of wind energy now are the necessity to prevent the degradation of wind turbine blades for several decades, and, on the other side, to provide a solution for the recyclability and sustainability of blades. In preliminary studies by DTU Wind, it was demonstrated that practically all typical wind turbine blade degradation mechanisms (e.g., coating detachment, buckling, spar cap/shell adhesive joint degradation, trailing edge failure, etc.) have their roots in interface degradation. The concept presented in this work includes the development of bio-inspired dual-mechanism-based interface adhesives (combining mechanical interlocking of fibers and chemical adhesion), which ensures, on the one side, extra-strong attachment during the operation time, and on the other side, possible adhesive joint separation for re-use of the blade parts. The general approach and physical mechanisms of adhesive strengthening and separation are described. Full article

Reactive oxygen species are implicated in several human diseases, including neurodegenerative disorders, cardiovascular dysfunction, inflammation, hereditary diseases, and ageing. Mn^III–salen complexes are superoxide dismutase (SOD) and catalase (CAT) mimetics, which have shown beneficial effects in various models for oxidative stress. These properties make them well-suited as potential therapeutic agents for oxidative stress diseases. Here, we report the synthesis of the novel glycoconjugates of salen complex, EUK-108, with glucose and galactose. We found that the complexes showed a SOD-like activity higher than EUK-108, as well as peroxidase and catalase activities. We also investigated the conjugate activities in the presence of Ricinus communis agglutinin (RCA₁₂₀) lectin. The hybrid protein–galactose–EUK-108 system showed an increased SOD-like activity similar to the native SOD1. Full article

Nanozymes represent a category of nano-biomaterial artificial enzymes distinguished by their remarkable catalytic potency, stability, cost-effectiveness, biocompatibility, and degradability. These attributes position them as premier biomaterials with extensive applicability across medical, industrial, technological, and biological domains. Following the discovery of ferromagnetic nanoparticles with peroxidase-mimicking capabilities, extensive research endeavors have been dedicated to advancing nanozyme utilization. Their capacity to emulate the functions of natural enzymes has captivated researchers, prompting in-depth investigations into their attributes and potential applications. This exploration has yielded insights and innovations in various areas, including detection mechanisms, biosensing techniques, and device development. Nanozymes exhibit diverse compositions, sizes, and forms, resembling molecular entities such as proteins and tissue-based glucose. Their rapid impact on the body necessitates a comprehensive understanding of their intricate interplay. As each day witnesses the emergence of novel methodologies and technologies, the integration of nanozymes continues to surge, promising enhanced comprehension in the times ahead. This review centers on the expansive deployment and advancement of nanozyme materials, encompassing biomedical, biotechnological, and environmental contexts. Full article

Depth estimation is an ill-posed problem; objects of different shapes or dimensions, even if at different distances, may project to the same image on the retina. Our brain uses several cues for depth estimation, including monocular cues such as motion parallax and binocular cues such as diplopia. However, it remains unclear how the computations required for depth estimation are implemented in biologically plausible ways. State-of-the-art approaches to depth estimation based on deep neural networks implicitly describe the brain as a hierarchical feature detector. Instead, in this paper we propose an alternative approach that casts depth estimation as a problem of active inference. We show that depth can be inferred by inverting a hierarchical generative model that simultaneously predicts the eyes’ projections from a 2D belief over an object. Model inversion consists of a series of biologically plausible homogeneous transformations based on Predictive Coding principles. Under the plausible assumption of a nonuniform fovea resolution, depth estimation favors an active vision strategy that fixates the object with the eyes, rendering the depth belief more accurate. This strategy is not realized by first fixating on a target and then estimating the depth; instead, it combines the two processes through action–perception cycles, with a similar mechanism of the saccades during object recognition. The proposed approach requires only local (top-down and bottom-up) message passing, which can be implemented in biologically plausible neural circuits. Full article

A promising method for improving the functional properties of calcium-phosphate coatings is the incorporation of various antibacterial additives into their structure. The microbial contamination of a superficial wound is inevitable, even if the rules of asepsis and antisepsis are optimally applied. One of the main problems is that bacteria often become resistant to antibiotics over time. However, this does not apply to certain elements, chemical compounds and drugs with antimicrobial properties. In this study, the fabrication and properties of zinc-containing calcium-phosphate coatings that were formed via micro-arc oxidation from three different electrolyte solutions are investigated. The first electrolyte is based on calcium oxide, the second on hydroxyapatite and the third on calcium acetate. By adding zinc oxide to the three electrolyte solutions, antibacterial properties of the coatings are achieved. Although the same amount of zinc oxide has been added to each electrolyte solution, the zinc concentration in the coatings obtained vary greatly. Furthermore, this study investigates the morphology, structure and chemical composition of the coatings. The antibacterial properties of the zinc-containing coatings were tested toward three strains of bacteria—Staphylococcus aureus, methicillin-resistant Staphylococcus aureus and Pseudomonas aeruginosa. Coatings of calcium acetate and zinc oxide contained the highest amount of zinc and displayed the highest zinc release. Moreover, coatings containing hydroxyapatite and zinc oxide show the highest antibacterial activity toward Pseudomonas aeruginosa, and coatings containing calcium acetate and zinc oxide show the highest antibacterial activities toward Staphylococcus aureus and methicillin-resistant Staphylococcus aureus. Full article

As an important branch of robotics, soft robots have the advantages of strong flexibility, a simple structure, and high safety. These characteristics enable soft robots to be widely used in various fields such as biomedicine, military reconnaissance, and micro space exploration. However, contemporary soft crawling robots still face problems such as the single drive mode and complex external equipment. In this study, we propose an innovative design of an inchworm-like soft crawling robot utilizing the synergistic interaction of electricity and moisture for its hybrid dual-drive locomotion. The legs of the soft robot are mainly made of GO-CNT/PE composite film, which can convert its own volume expansion into a corresponding bending motion after being stimulated by electricity or moisture. Unlike other drive methods, it requires less power and precision from external devices. The combination of the two driving methods greatly improves the environmental adaptability of the soft robot, and we developed visible light as the driving method on the basis of the dual drive. Finally, we also verified the robot’s excellent load capacity, climbing ability, and optical drive effect, which laid the foundation for the application of soft robots in the future. Full article

The field of regenerative medicine is constantly advancing and aims to repair, regenerate, or substitute impaired or unhealthy tissues and organs using cutting-edge approaches such as stem cell-based therapies, gene therapy, and tissue engineering. Nevertheless, incorporating artificial intelligence (AI) technologies has opened new doors for research in this field. AI refers to the ability of machines to perform tasks that typically require human intelligence in ways such as learning the patterns in the data and applying that to the new data without being explicitly programmed. AI has the potential to improve and accelerate various aspects of regenerative medicine research and development, particularly, although not exclusively, when complex patterns are involved. This review paper provides an overview of AI in the context of regenerative medicine, discusses its potential applications with a focus on personalized medicine, and highlights the challenges and opportunities in this field. Full article

The Hunger Games Search (HGS) is an innovative optimizer that operates without relying on gradients and utilizes a population-based approach. It draws inspiration from the collaborative foraging activities observed in social animals in their natural habitats. However, despite its notable strengths, HGS is subject to limitations, including inadequate diversity, premature convergence, and susceptibility to local optima. To overcome these challenges, this study introduces two adjusted strategies to enhance the original HGS algorithm. The first adaptive strategy combines the Logarithmic Spiral (LS) technique with Opposition-based Learning (OBL), resulting in the LS-OBL approach. This strategy plays a pivotal role in reducing the search space and maintaining population diversity within HGS, effectively augmenting the algorithm’s exploration capabilities. The second adaptive strategy, the dynamic Rosenbrock Method (RM), contributes to HGS by adjusting the search direction and step size. This adjustment enables HGS to escape from suboptimal solutions and enhances its convergence accuracy. Combined, these two strategies form the improved algorithm proposed in this study, referred to as RLHGS. To assess the efficacy of the introduced strategies, specific experiments are designed to evaluate the impact of LS-OBL and RM on enhancing HGS performance. The experimental results unequivocally demonstrate that integrating these two strategies significantly enhances the capabilities of HGS. Furthermore, RLHGS is compared against eight state-of-the-art algorithms using 23 well-established benchmark functions and the CEC2020 test suite. The experimental results consistently indicate that RLHGS outperforms the other algorithms, securing the top rank in both test suites. This compelling evidence substantiates the superior functionality and performance of RLHGS compared to its counterparts. Moreover, RLHGS is applied to address four constrained real-world engineering optimization problems. The final results underscore the effectiveness of RLHGS in tackling such problems, further supporting its value as an efficient optimization method. Full article

From insects to arachnids to bacteria, the surfaces of lakes and ponds are teaming with life. Many modes of locomotion are employed by these organisms to navigate along the air–water interface, including the use of lipid-laden excretions that can locally change the surface tension of the water and induce a Marangoni flow. In this paper, we improved the speed and maneuverability of a miniature remote-controlled robot that mimics insect locomotion using an onboard tank of isopropyl alcohol and a series of servomotors to control both the rate and location of alcohol release to both propel and steer the robot across the water. Here, we studied the effect of a series of design changes to the foam rubber footpads, which float the robot and are integral in efficiently converting the alcohol-induced surface tension gradients into propulsive forces and effective maneuvering. Two designs were studied: a two-footpad design and a single-footpad design. In the case of two footpads, the gap between the two footpads was varied to investigate its impact on straight-line speed, propulsion efficiency, and maneuverability. An optimal design was found with a small but finite gap between the two pads of 7.5 mm. In the second design, a single footpad without a central gap was studied. This footpad had a rectangular cut-out in the rear to capture the alcohol. Footpads with wider and shallower cut-outs were found to optimize efficiency. This observation was reinforced by the predictions of a simple theoretical mechanical model. Overall, the optimized single-footpad robot outperformed the two-footpad robot, producing a 30% improvement in speed and a 400% improvement in maneuverability. Full article

Attachment to the substrate is an important phenomenon that determines the survival of many organisms. Most insects utilize wet adhesion to support attachment, which is characterized by fluids that are secreted into the interface between the tarsus and the substrates. Previous research has investigated the composition and function of tarsal secretions of different insect groups, showing that the secretions are likely viscous emulsions that contribute to attachment by generating capillary and viscous adhesion, leveling surface roughness and providing self-cleaning of the adhesive systems. Details of the structural organization of these secretions are, however, largely unknown. Here, we analyzed footprints originating from the arolium and euplantulae of the stick insect Medauroidea extradentata using cryo-scanning electron microscopy (cryo-SEM) and white light interferometry (WLI). The secretion was investigated with cryo-SEM, revealing four morphologically distinguishable components. The 3D WLI measurements of the droplet shapes and volumes over time revealed distinctly different evaporation rates for different types of droplets. Our results indicate that the subfunctionalization of the tarsal secretion is facilitated by morphologically distinct components, which are likely a result of different proportions of components within the emulsion. Understanding these components and their functions may aid in gaining insights for developing adaptive and multifunctional biomimetic adhesive systems. Full article

In recent years, disease attacks have posed continuous threats to agriculture and caused substantial losses in the economy. Thus, early detection and classification could minimize the spread of disease and help to improve yield. Meanwhile, deep learning has emerged as the significant approach to detecting and classifying images. The classification performed using the deep learning approach mainly relies on large datasets to prevent overfitting problems. The Automatic Segmentation and Hyper Parameter Optimization Artificial Rabbits Algorithm (AS-HPOARA) is developed to overcome the above-stated issues. It aims to improve plant leaf disease classification. The Plant Village dataset is used to assess the proposed AS-HPOARA approach. Z-score normalization is performed to normalize the images using the dataset’s mean and standard deviation. Three augmentation techniques are used in this work to balance the training images: rotation, scaling, and translation. Before classification, image augmentation reduces overfitting problems and improves the classification accuracy. Modified UNet employs a more significant number of fully connected layers to better represent deeply buried characteristics; it is considered for segmentation. To convert the images from one domain to another in a paired manner, the classification is performed by HPO-based ARA, where the training data get increased and the statistical bias is eliminated to improve the classification accuracy. The model complexity is minimized by tuning the hyperparameters that reduce the overfitting issue. Accuracy, precision, recall, and F1 score are utilized to analyze AS-HPOARA’s performance. Compared to the existing CGAN-DenseNet121 and RAHC_GAN, the reported results show that the accuracy of AS-HPOARA for ten classes is high at 99.7%. Full article