Biomimetics

The ability to control and manipulate biological fluids within microchannels is a fundamental challenge in biological diagnosis and pharmaceutical analyses, particularly when buffers with very high ionic strength are used. In this study, we investigate the numerical and experimental study of fluidic biochips driven by ac electrothermal flow for controlling and manipulating biological samples inside a microchannel, e.g., for fluid-driven and manipulation purposes such as concentrating and mixing. By appropriately switching the voltage on the electrode structures and inducing AC electrothermal forces within the channel, a fluidic network with pumping and manipulation capabilities can be achieved, enabling the control of fluid velocity/direction and also fluid rotation. By using finite element analysis, coupled physics of electrical, thermal, fluidic fields, and molecular diffusion transport were solved. AC electrothermal flow was studied for pumping and mixing applications, and the optimal model was extracted. The microfluidic chip was fabricated using two processes: electrode structure development on the chip and silicon mold fabrication in a cleanroom. PDMS was prepared as the microchannel material and bonded to the electrode structure. After implementing the chip holder and excitation circuit, a biological buffer with varying ionic strengths (0.2, 0.4, and 0.6 [S/m]) was prepared, mixed with fluorescent particles, and loaded into the microfluidic chip. Experimental results demonstrated the efficiency of the proposed chip for biological applications, showing that stronger flows were generated with increasing fluid conductivity and excitation voltage. The system behavior was characterized using an impedance analyzer. Frequency response analysis revealed that for a solution with an electrical conductivity of 0.6 [S/m], the fluid velocity remained almost constant within a frequency range of 100 kHz to 10 MHz. Overall, the experimental results showed good agreement with the simulation outcomes. Full article

The properties of the large unilamellar vesicles (LUVs) from 1,2-dimyristoyl-sn-glycero-3-phosphatidylcholine (DMPC), modified by lipopolysaccharides (LPS) from Salmonella enterica sv. Enteritidis, which mimics Gram-negative bacteria, were studied by various physical methods. LPS, in the range of 0/20/50 % w/w relative to the lipid, had a regulatory role in the structure of the LUVs toward the lower size, low polydispersity, and over-a-month size stability due to the lower negative zeta potential. The addition of LPS resulted in increased density, which determined the ultrasound velocity and the specific adiabatic compressibility. In a 0.5/1/2 mg/mL concentration range, the total lipid content did not significantly affect the size of LUVs and influenced the density-related attributes similarly to the LPS content. A positive correlation was found between temperature and vesicle size, and a negative correlation was found between temperature and density and compressibility—except for the anomaly behavior at 25 °C, around the melting point of DMPC. Full article

Optimization algorithms play a crucial role in solving complex problems across various fields, including global optimization and feature selection (FS). This paper presents the enhanced polar lights optimization with cryptobiosis and differential evolution (CPLODE), a novel improvement upon the original polar lights optimization (PLO) algorithm. CPLODE integrates a cryptobiosis mechanism and differential evolution (DE) operators to enhance PLO’s search capabilities. The original PLO’s particle collision strategy is replaced with DE’s mutation and crossover operators, enabling a more effective global exploration and using a dynamic crossover rate to improve convergence. Furthermore, a cryptobiosis mechanism records and reuses historically successful solutions, thereby improving the greedy selection process. The experimental results on 29 CEC 2017 benchmark functions demonstrate CPLODE’s superior performance compared to eight classical optimization algorithms, with higher average ranks and faster convergence. Moreover, CPLODE achieved competitive results in feature selection on ten real-world datasets, outperforming several well-known binary metaheuristic algorithms in classification accuracy and feature reduction. These results highlight CPLODE’s effectiveness for both global optimization and feature selection. Full article

Wrist movements play a crucial role in upper-limb motor tasks. As prosthetic and robotic hand technologies have evolved, increasing attention has been focused on replicating the anatomy and functionality of the wrist. Closely imitating the biomechanics and movement mechanisms of human limbs is expected to enhance the overall performance of bionic robotic hands. This study presents the design of a tendon-driven bionic spherical robot wrist, utilizing two pairs of cables that mimic antagonist muscle pairs. The cables are actuated by pulleys driven by servo motors, allowing for two primary wrist motions: flexion–extension and ulnar–radial deviation. The performance Please confirm if the “1583 Iiyama” is necessary. Same as belowof the proposed robot wrist is validated through manipulation experiments using a prototype, demonstrating its capability to achieve a full range of motion for both ulnar and radial deviation. This wrist mechanism is expected to be integrated into robotic systems, enabling greater flexibility and more human-like movement capabilities. Full article

(1) Background: At present, the bio-inspired visual neural models have made significant achievements in detecting the motion direction of the translating object. Variable contrast in the figure-ground and environmental noise interference, however, have a strong influence on the existing model. The responses of the lobula plate tangential cell (LPTC) neurons of Drosophila are robust and stable in the face of variable contrast in the figure-ground and environmental noise interference, which provides an excellent paradigm for addressing these challenges. (2) Methods: To resolve these challenges, we propose a bio-inspired visual neural model, which consists of four stages. Firstly, the photoreceptors (R1–R6) are utilized to perceive the change in luminance. Secondly, the change in luminance is divided into parallel ON and OFF pathways based on the lamina monopolar cell (LMC), and the spatial denoising and the spatio-temporal lateral inhibition (LI) mechanisms can suppress environmental noise and improve motion boundaries, respectively. Thirdly, the non-linear instantaneous feedback mechanism in divisive contrast normalization is adopted to reduce local contrast sensitivity; further, the parallel ON and OFF contrast pathways are activated. Finally, the parallel motion and contrast pathways converge on the LPTC in the lobula complex. (3) Results: By comparing numerous experimental simulations with state-of-the-art (SotA) bio-inspired models, we can draw four conclusions. Firstly, the effectiveness of the contrast neural computation and the spatial denoising mechanism is verified by the ablation study. Secondly, this model can robustly detect the motion direction of the translating object against variable contrast in the figure-ground and environmental noise interference. Specifically, the average detection success rate of the proposed bio-inspired model under the pure and real-world complex noise datasets was increased by 5.38% and 5.30%. Thirdly, this model can effectively reduce the fluctuation in this model response against variable contrast in the figure-ground and environmental noise interference, which shows the stability of this model; specifically, the average inter-quartile range of the coefficient of variation in the proposed bio-inspired model under the pure and real-world complex noise datasets was reduced by 38.77% and 47.84%, respectively. The average decline ratio of the sum of the coefficient of variation in the proposed bio-inspired model under the pure and real-world complex noise datasets was 57.03% and 67.47%, respectively. Finally, the robustness and stability of this model are further verified by comparing other early visual pre-processing mechanisms and engineering denoising methods. (4) Conclusions: This model can robustly and steadily detect the motion direction of the translating object under variable contrast in the figure-ground and environmental noise interference. Full article

With the increasing application of robots in human-centred environments, there is increasing motivation for incorporating some degree of human-like social competences. Fields such as psychology and cognitive science not only provide guidance on the types of behaviour that could and should be exhibited by the robots, they may also indicate the manner in which these behaviours can be achieved. The domain of social child–robot interaction (sCRI) provides a number of challenges and opportunities in this regard; the application to an educational context allows child-learning outcomes to be characterised as a result of robot social behaviours. One such social behaviour that is readily (and unconsciously) used by humans is behavioural alignment, in which the behaviours expressed by one person adapts to that of their interaction partner, and vice versa. In this paper, the role that robot non-verbal behavioural alignment for their interaction partner can play in the facilitation of learning outcomes for the child is examined. This behavioural alignment is facilitated by a human memory-inspired learning algorithm that adapts in real-time over the course of an interaction. A large touchscreen is employed as a mediating device between a child and a robot. Collaborative sCRI is emphasised, with the touchscreen providing a common set of interaction affordances for both child and robot. The results show that an adaptive robot is capable of engaging in behavioural alignment, and indicate that this leads to greater learning gains for the children. This study demonstrates the specific contribution that behavioural alignment makes in improving learning outcomes for children when employed by social robot interaction partners in educational contexts. Full article

Diabetes mellitus (DM) is a fatal metabolic disease characterized by persistent hyperglycemia. In recent studies, mesenchymal stem cell (MSC)-derived exosomes, which are being investigated clinically as a cell-free therapy for various diseases, have gained attention due to their biomimetic properties that closely resemble natural cellular communication systems. These MSC-derived exosomes inherit the regenerative and protective effects from MSCs, inducing pancreatic β-cell proliferation and inhibiting apoptosis, as well as ameliorating insulin resistance by suppressing the release of various inflammatory cytokines. Consequently, MSC-derived exosomes have attracted attention as a novel treatment for DM as an alternative to stem cell therapy. In this review, we will introduce the potential of MSC-derived exosomes for the treatment of DM by discussing the studies that have used MSC-derived exosomes to treat DM, which have shown therapeutic effects in both type 1 and type 2 DM. Full article

A future unmanned system needs the ability to perceive, decide and control in an open dynamic environment. In order to fulfill this requirement, it needs to construct a method with a universal environmental perception ability. Moreover, this perceptual process needs to be interpretable and understandable, so that future interactions between unmanned systems and humans can be unimpeded. However, current mainstream DNN (deep learning neural network)-based AI (artificial intelligence) is a ‘black box’. We cannot interpret or understand how the decision is made by these AIs. An SNN (spiking neural network), which is more similar to a biological brain than a DNN, has the potential to implement interpretable or understandable AI. In this work, we propose a neuron group-based structural learning method for an SNN to better capture the spatial and temporal information from the external environment, and propose a time-slicing scheme to better interpret the spatial and temporal information of responses generated by an SNN. Results show that our method indeed helps to enhance the environment perception ability of the SNN, and possesses a certain degree of robustness, enhancing the potential to build an interpretable or understandable AI in the future. Full article

The Whale Optimization Algorithm (WOA) is recognized for its simplicity, few control parameters, and effective local optima avoidance. However, it struggles with global search efficiency and slow convergence. This paper introduces the Improved WOA (ImWOA) to overcome these challenges. Initially, ImWOA utilizes a dynamic elastic boundary optimization strategy, which leverages boundary information and the current optimal position to guide solutions that exceed the boundaries back within permissible limits, gradually converging towards the optimal solution. Subsequently, ImWOA integrates an advanced random searching strategy that equilibrates global and local searches by focusing on the current optimal location and the mean position of all individuals. Lastly, a combined mutation mechanism is employed to enhance population diversity, prevent the algorithm from stagnating in local optima, and consequently augment its overall search capability. Performance evaluations on CEC2017 benchmark functions show ImWOA outperforming five metaheuristic algorithms and three WOA variants in optimization accuracy, stability, and convergence speed. ImWOA excelled in 25 out of 29 test functions in 30D and 26 out of 29 in 100D scenarios. Furthermore, its efficacy in addressing complex challenges is corroborated by real-world applications in reducer design, vehicle side impact design, and welded beam design, highlighting its potential utility across various engineering domains. Full article

Wood plastic composites (WPCs) offer a means to reduce the carbon footprint by incorporating natural fibers to enhance the mechanical properties. However, there is limited information on the mechanical properties of these materials under hostile conditions. This study evaluated composites of polypropylene (PP), polystyrene (PS), and polylactic acid (PLA) processed via extrusion and injection molding. Tests were conducted on tensile and flexural strength and modulus, heat deflection temperature (HDT), and creep analysis under varying relative humidity conditions (10% and 90%) and water immersion, followed by freeze—thaw cycles. The addition of fibers generally improved the mechanical properties but increased water absorption. HDT and creep were dependent on the crystallinity of the composites. PLA and PS demonstrated a superior overall performance, except for their impact properties, where PP was slightly better than PLA. Full article

The gas (or plastron) trapped between micro/nano-scale surface textures, such as that on superhydrophobic surfaces, is crucial for many engineering applications, including drag reduction, heat and mass transfer enhancement, anti-biofouling, anti-icing, and self-cleaning. However, the longevity of the plastron is significantly affected by gas diffusion, a process where gas molecules slowly diffuse into the ambient liquid. In this work, we demonstrated that plastron longevity could be extended using a gas-soluble and gas-permeable polydimethylsiloxane (PDMS) surface. We performed experiments for PDMS surfaces consisting of micro-posts and micro-holes. We measured the plastron longevity in undersaturated liquids by an optical method. Our results showed that the plastron longevity increased with increasing the thickness of the PDMS surface, suggesting that gas initially dissolved between polymer chains was transferred to the liquid, delaying the wetting transition. Numerical simulations confirmed that a thicker PDMS material released more gas across the PDMS–liquid interface, resulting in a higher gas concentration near the plastron. Furthermore, we found that plastron longevity increased with increasing pressure differences across the PDMS material, indicating that the plastron was replenished by the gas injected through the PDMS. With increasing pressure, the mass flux caused by gas injection surpassed the mass flux caused by the diffusion of gas from plastron to liquid. Overall, our results provide new solutions for extending plastron longevity and will have significant impacts on engineering applications where a stable plastron is desired. Full article

The human wrist affects the ability to efficiently perform many manipulation tasks. Despite this, most upper-limb prostheses are focused on the hand’s mobility, which makes users compensate for the lost wrist mobility with complex manipulation strategies relying on the mobility of other body parts. In this context, research on wrist prostheses is still open to new contributions, even though a number of such prostheses are already present in the literature and on the market. In particular, the potential uses of parallel mechanisms in wrist prosthesis design have not been fully explored yet. In this work, after recalling the mobility characteristics of human wrists and reviewing the literature both on wrist prostheses and parallel mechanisms, a number of parallel architectures employable in a wrist prosthesis are selected. Then, with reference to the design requirements of this prosthesis type, the dimensional synthesis and kinetostatic analysis of the selected architectures are addressed. The results of this work are new wrist prosthesis architectures together with the analysis of their kinetostatic performances. These findings complete the first step of a research project aimed at developing new concepts for mechatronic wrists. Full article

Treating the surfaces of dental implants in an alkaline medium allows us to obtain microstructures of sodium titanate crystals that favor the appearance of apatite in the physiological environment, producing osteoconductive surfaces. In this research, 385 discs made of titanium used in dental implants underwent different NaOH treatments with a 6M concentration at 600 °C and cooling rates of 20, 50, 75, and 115 °C/h. Using high-resolution electron microscopy, the microstructures were observed, and the different crystal sizes were determined and compared with control samples (those without biomimetic treatment). Roughness, wettability, surface energy and the sodium content of the surface were determined. The different surfaces were cultured with human osteoblastic cells; cell adhesion was determined at 3 and 14 days, and the degree of mineralization was determined at 14 days via alkaline phosphatase levels. Variations in the microstructure and size of sodium titanate crystals in NaOH solutions rich (1 g/L) or low in calcium (approximately 100 ppm) were determined. The results show that as the cooling rate increases, the size of the crystals decreases (from 0.4 μm to 0.8 μm) except for the case of 115 °C/h, when the rate is too fast for crystalline nucleation to occur on the surface of the titanium. The thermochemical treatment does not influence the roughness or the cooling rate since a Sa of 0.21 μm is maintained. However, the presence of titanate causes a decrease in the contact angle from 70° to 42° and, in turn, causes an increase in the total surface energy from 35 to 49.5 mJ/m², with the polar component standing out in this energy increase. No variations were observed in the thermochemical treatments in the presence of sodium, which was around 1200 ppm. It was observed that as the size of the crystals decreases, cell adhesion increases at 3 days and decreases at 14 days. This is because finer crystals on the surface are already in the mineralization process, as demonstrated using the level of alkaline phosphatase that is maximal for the cooling rate of 75 °C/h. It was possible to confirm that the variations in the concentrated NaOH solutions with different calcium contents did not affect the crystal sizes or the microstructure of the surface. This research makes it possible to obtain dental implants with different mineralization speeds depending on the cooling rate applied. Full article

This research centers around cast steel 20Mn, which is the material utilized for the ear-picking roller of a corn harvester. The study delves into methods of enhancing its hydrophobicity and wear resistance. Fiber laser-processing technology was employed to fabricate pangolin bionic micro-textures on the material surface, and PVD technology was utilized to deposit a TiN coating. The wear resistance of the modified surface was investigated via the reciprocating dry sliding wear method, while its hydrophobicity was concurrently examined. The results demonstrate that the laser texturing technology and TiN coating managed to reduce the friction coefficient of the sample surface by 20% and 30.9%, respectively. This can be chiefly attributed to the significant effects of the modified surface in augmenting hardness, diminishing the contact area of the friction surface, lowering shear stress, and entrapping wear debris. When the pangolin texture and TiN coating work in concert, the abrasive and fatigue wear between the two surfaces is conspicuously mitigated, and the friction coefficient is reduced by 38.09%. Moreover, the experiment also reveals that a superhydrophobic surface can be achieved by fabricating the pangolin micro-textures. Full article

With the advancement of the Internet, social media platforms have gradually become powerful in spreading crisis-related content. Identifying informative tweets associated with natural disasters is beneficial for the rescue operation. When faced with massive text data, choosing the pivotal features, reducing the calculation expense, and increasing the model classification performance is a significant challenge. Therefore, this study proposes a multi-strategy improved black-winged kite algorithm (MSBKA) for feature selection of natural disaster tweets classification based on the wrapper method’s principle. Firstly, BKA is improved by utilizing the enhanced Circle mapping, integrating the hierarchical reverse learning, and introducing the Nelder–Mead method. Then, MSBKA is combined with the excellent classifier SVM (RBF kernel function) to construct a hybrid model. Finally, the MSBKA-SVM model performs feature selection and tweet classification tasks. The empirical analysis of the data from four natural disasters shows that the proposed model has achieved an accuracy of 0.8822. Compared with GA, PSO, SSA, and BKA, the accuracy is increased by 4.34%, 2.13%, 2.94%, and 6.35%, respectively. This research proves that the MSBKA-SVM model can play a supporting role in reducing disaster risk. Full article

In this preliminary study, the long-term effects of calcium chloride crosslinking concentration on viability of 16HBE14o- human bronchial epithelial cells embedded in alginate-extracellular matrix (ECM) or alginate–methylcellulose–ECM hydrogels have been investigated. There is currently a limited understanding regarding the effects of crosslinking solution concentration on lung epithelial cells embedded in hydrogel. Furthermore, the effects of calcium chloride concentration in crosslinking solutions on other cell types have not been reported regarding whether the addition of viscosity and stiffness tuning agents such as methylcellulose will alter the responses of cells to changes in calcium chloride concentration in crosslinking solutions. While there were no significant effects of calcium chloride concentration on cell viability in alginate–ECM hydrogels, there is a decrease in cell viability in alginate–methylcellulose–ECM hydrogels crosslinked with 300 mM calcium chloride crosslinking solution. These findings have implications in the maintenance of 16HBE14o- 3D cultures with respect to the gelation of alginate with high concentrations of ionic crosslinking solution. Full article

Biomimetics aims to learn from living systems to develop innovative technical artefacts. As it transcends disciplinary boundaries and needs to integrate both biological and technological knowledge, a domain ontology for biomimetics would be highly desirable. So far, several terminological resources have been designed to support the biomimetic development process. This paper examines nine resources for Biologically Inspired Design and biomimetics, including taxonomies, thesauri, and ontologies. Their benefits and limitations for structuring or organising biomimetic knowledge are evaluated against nine criteria, including availability, clarity, and machine readability. Our analysis shows that existing terminological resources have little to no potential for reuse due to inconsistent structure, ambiguous class labels, lack of standardisation, and lack of availability. Furthermore, no resource adequately represents biomimetic knowledge, as all resources suffer from limitations in content representation, reusability, or infrastructure. In particular, an adequate domain ontology for supporting biomimetic development is lacking; we discuss the desiderata for such an ontology. Full article

Stereo-orientation selectivity is a fundamental neural mechanism in the brain that plays a crucial role in perception. However, due to the recognition process of high-dimensional spatial information commonly occurring in high-order cortex, we still know little about the mechanisms underlying stereo-orientation selectivity and lack a modeling strategy. A classical explanation for the mechanism of two-dimensional orientation selectivity within the primary visual cortex is based on the Hubel-Wiesel model, a cascading neural connection structure. The local-to-global information aggregation thought within the Hubel-Wiesel model not only contributed to neurophysiology but also inspired the development of computer vision fields. In this paper, we provide a clear and efficient conceptual understanding of stereo-orientation selectivity and propose a quantitative explanation for its generation based on the thought of local-to-global information aggregation within the Hubel-Wiesel model and develop an artificial visual system (AVS) for stereo-orientation recognition. Our approach involves modeling depth selective cells to receive depth information, simple stereo-orientation selective cells for combining distinct depth information inputs to generate various local stereo-orientation selectivity, and complex stereo-orientation selective cells responsible for integrating the same local information to generate global stereo-orientation selectivity. Simulation results demonstrate that our AVS is effective in stereo-orientation recognition and robust against spatial noise jitters. AVS achieved an overall over 90% accuracy on noise data in orientation recognition tasks, significantly outperforming deep models. In addition, the AVS contributes to enhancing deep models’ performance, robustness, and stability in 3D object recognition tasks. Notably, AVS enhanced the TransNeXt model in improving its overall performance from 73.1% to 97.2% on the 3D-MNIST dataset and from 56.1% to 86.4% on the 3D-Fashion-MNIST dataset. Our explanation for the generation of stereo-orientation selectivity offers a reliable, explainable, and robust approach for extracting spatial features and provides a straightforward modeling method for neural computation research. Full article