Biomimetics最新文献

This paper proposes a Q-learning-driven butterfly optimization algorithm (QLBOA) by integrating the Q-learning mechanism of reinforcement learning into the butterfly optimization algorithm (BOA). In order to improve the overall optimization ability of the algorithm, enhance the optimization accuracy, and prevent the algorithm from falling into a local optimum, the Gaussian mutation mechanism with dynamic variance was introduced, and the migration mutation mechanism was also used to enhance the population diversity of the algorithm. Eighteen benchmark functions were used to compare the proposed method with five classical metaheuristic algorithms and three BOA variable optimization methods. The QLBOA was used to solve the green vehicle routing problem with time windows considering customer preferences. The influence of decision makers' subjective preferences and weight factors on fuel consumption, carbon emissions, penalty cost, and total cost are analyzed. Compared with three classical optimization algorithms, the experimental results show that the proposed QLBOA has a generally superior performance.

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.

Biomimetics research on living systems attempts to transfer their properties and functions to engineering applications [...].

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.

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.

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.

(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.

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.

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.

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.