Biomimetics最新文献

Numerous efforts have been invested in previous algorithms to expose and enhance blood vessel (BV) visibility derived from clinical coronary angiography (CAG) procedures, such as noise reduction, segmentation, and background subtraction. Yet, the visibility of the BVs and their luminal content, particularly the small ones, is still limited. We propose a novel visibility enhancement algorithm, whose main body is inspired by a line completion mechanism of the visual system, i.e., lateral interactions. It facilitates the enhancement of the BVs along with simultaneous noise reduction. In addition, we developed a specific algorithm component that allows better visibility of small BVs and the various CAG tools utilized during the procedure. It is accomplished by enhancing the BVs' fine resolutions, located in the coarse resolutions at the BV zone. The visibility of the most significant clinical features during the CAG procedure was evaluated and qualitatively compared by the consensus of two cardiologists (MM and JE) to the algorithm's results. These included the visibility of the whole frame, the coronary BVs as well as the small ones, the main obstructive lesions within the BVs, and the various angiography interventional tools utilized during the procedure. The algorithm succeeded in producing better visibility of all these features, even under low-contrast or low-radiation conditions. Despite its major advantages, the algorithm also caused the appearance of disturbing vertebral and bony artifacts, which could somewhat lower diagnostic accuracy. Yet, viewing the processed images from multiple angles and not just from a single one and evaluating the cine mode usually overcomes this drawback. Thus, our novel algorithm potentially leads to a better clinical diagnosis, improved procedural capabilities, and a successful outcome.

In recent years, humanoid robot technology has been developing rapidly due to the need for robots to collaborate with humans or replace them in various tasks, requiring them to operate in complex human environments and placing high demands on their mobility. Developing humanoid robots with human-like walking and hopping abilities has become a key research focus, as these capabilities enable robots to move and perform tasks more efficiently in diverse and unpredictable environments, with significant applications in daily life, industrial operations, and disaster rescue. Currently, methods based on hybrid zero dynamics and reinforcement learning have been employed to enhance the walking and hopping capabilities of humanoid robots; however, model predictive control (MPC) presents two significant advantages: it can adapt to more complex task requirements and environmental conditions, and it allows for various walking and hopping patterns without extensive training and redesign. The objective of this study is to develop a bipedal robot controller using shooting method-based MPC to achieve human-like walking and hopping abilities, aiming to address the limitations of the existing methods and provide a new approach to enhancing robot mobility.

This study designs and develops a wearable exoskeleton piano assistance system for individuals recovering from neurological injuries, aiming to help users regain the ability to perform complex tasks such as playing the piano. While soft robotic exoskeletons have proven effective in rehabilitation therapy and daily activity assistance, challenges remain in performing highly dexterous tasks due to structural complexity and insufficient motion accuracy. To address these issues, we developed a modular division method based on multi-domain mapping and a top-down process model. This method integrates the functional domain, structural domain, and user needs domain, and explores the principles and methods for creating functional construction modules, overcoming the limitations of traditional top-down approaches in design flexibility. By closely combining layout constraints with the design model, this method significantly improves the accuracy and efficiency of module configuration, offering a new path for the development of piano practice assistance devices. The results demonstrate that this device innovatively combines piano practice with rehabilitation training and through the introduction of ontological modeling methods, resolves the challenges of multidimensional needs mapping. Based on five user requirements (P), we calculated the corresponding demand weight (K), making the design more aligned with user needs. The device excels in enhancing motion accuracy, interactivity, and comfort, filling the gap in traditional piano assistance devices in terms of multi-functionality and high adaptability, and offering new ideas for the design and promotion of intelligent assistive devices. Simulation analysis, combined with the motion trajectory of the finger's proximal joint, calculates that 60° is the maximum bending angle for the aforementioned joint. Physical validation confirms the device's superior performance in terms of reliability and high-precision motion reproduction, meeting the requirements for piano-assisted training. Through multi-domain mapping, the top-down process model, and modular design, this research effectively breaks through the design flexibility and functional adaptability bottleneck of traditional piano assistance devices while integrating neurological rehabilitation with music education, opening up a new application path for intelligent assistive devices in the fields of rehabilitation medicine and arts education, and providing a solution for cross-disciplinary technology fusion and innovative development.

The RIME algorithm is a novel physical-based meta-heuristic algorithm with a strong ability to solve global optimization problems and address challenges in engineering applications. It implements exploration and exploitation behaviors by constructing a rime-ice growth process. However, RIME comes with a couple of disadvantages: a limited exploratory capability, slow convergence, and inherent asymmetry between exploration and exploitation. An improved version with more efficiency and adaptability to solve these issues now comes in the form of Hybrid Estimation Rime-ice Optimization, in short, HERIME. A probabilistic model-based sampling approach of the estimated distribution algorithm is utilized to enhance the quality of the RIME population and boost its global exploration capability. A roulette-based fitness distance balanced selection strategy is used to strengthen the hard-rime phase of RIME to effectively enhance the balance between the exploitation and exploration phases of the optimization process. We validate HERIME using 41 functions from the IEEE CEC2017 and IEEE CEC2022 test suites and compare its optimization accuracy, convergence, and stability with four classical and recent metaheuristic algorithms as well as five advanced algorithms to reveal the fact that the proposed algorithm outperforms all of them. Statistical research using the Friedman test and Wilcoxon rank sum test also confirms its excellent performance. Moreover, ablation experiments validate the effectiveness of each strategy individually. Thus, the experimental results show that HERIME has better search efficiency and optimization accuracy and is effective in dealing with global optimization problems.

Cardiovascular diseases such as myocardial infarction or limb ischemia are characterized by regression of blood vessels. Local delivery of growth factors (GFs) involved in angiogenesis such as fibroblast blast growth factor-2 (FGF-2) has been shown to trigger collateral neovasculature and might lead to a therapeutic strategy. In vivo, heparin, a sulfated polysaccharide present in abundance in the extracellular matrix (ECM), has been shown to function as a local reservoir for FGF-2 by binding FGF-2 and other morphogens and it plays a role in the evolution of GF gradients. To access injectable biomaterials that can mimic such natural electrostatic interactions between soluble signals and macromolecules and mechanically tunable environments, the backbone of agarose, a thermogelling marine-algae-derived polysaccharide, was modified with sulfate, phosphate, and carboxylic moieties and the interaction and release of FGF-2 from these functionalized hydrogels was assessed by ELISA in vitro and CAM assay in ovo. Our findings show that FGF-2 remains active after release, and FGF-2 release profiles can be influenced by sulfated and phosphorylated agarose, and in turn, promote varied blood vessel formation kinetics. These modified agaroses offer a simple approach to mimicking electrostatic interactions experienced by GFs in the extracellular environment and provide a platform to probe the role of these interactions in the modulation of growth factor activity and may find utility as an injectable gel for promoting angiogenesis and as bioinks in 3D bioprinting.

In this contribution, a methodology for the optimal tuning of controllers of complex systems based on meta-heuristic techniques is proposed. Two bio-inspired meta-heuristic optimization algorithms -the Antlion Optimizer (ALO) and the Whale Optimization Algorithm (WOA)- have been applied to two different dynamic systems: the Hoop & Ball electromechanical system, a system where a linearized description is adequate; and to a Wind Turbine-Generator-Rectifier, as an example of a complex non-linear dynamic system. The performance of the ALO and WOA techniques for the tuning of conventional PID controllers is evaluated in relation to the number of agents nS and the maximum number of iterations nMaxIter; given the stochastic nature of both methods, repeatability is also addressed. Finally, the computational effort required for their implementation is considered. By analyzing the obtained metrics, it is observed that both methods provide comparable results for the two systems considered and, therefore, the ALO and WOA techniques can complement each other by exploiting the advantages of each of them in controller tuning.

In this study, we designed a biomimetic artificial visual system (AVS) inspired by biological visual system that can process RGB images. Our approach begins by mimicking the photoreceptor cone cells to simulate the initial input processing followed by a learnable dendritic neuron model to replicate ganglion cells that integrate outputs from bipolar and horizontal cell simulations. To handle multi-channel integration, we utilize a nonlearnable dendritic neuron model to simulate the lateral geniculate nucleus (LGN), which consolidates outputs across color channels, an essential function in biological multi-channel processing. Cross-validation experiments show that AVS demonstrates strong generalization across varied object-background configurations, achieving accuracy where traditional models like EfN-B0, ResNet50, and ConvNeXt typically fall short. Additionally, our results across different training-to-testing data ratios reveal that AVS maintains over 96% test accuracy even with limited training data, underscoring its robustness in low-data scenarios. This demonstrates the practical advantage of the AVS model in applications where large-scale annotated datasets are unavailable or expensive to curate. This AVS model not only advances biologically inspired multi-channel processing but also provides a practical framework for efficient, integrated visual processing in computational models.

Multirotor drones are widely used in fields such as environmental monitoring, agricultural inspection, and package delivery, but they still face numerous challenges in durability and aerial operation capabilities. To address these issues, this paper presents a biomimetic leg-claw mechanism (LCM) inspired by the biomechanics of birds. The claw of the LCM adopts a bistable gripper design that can rapidly close through external impact or actively close via the coordination of internal mechanisms. Additionally, its foldable, parallelogram-shaped legs bend under external forces, stretching the main tendon. A ratchet and pawl mechanism at the knee joint locks the leg in the bent position, thereby enhancing the gripping force of the claw. This paper calculates and experimentally verifies the degrees of freedom in different states, the forces required to open and close the gripper, the application scenarios of active and passive grasping, and the maximum load capacity of the mechanism. Furthermore, perching experiments demonstrate that the LCM enables the drone to perch stably on objects of varying diameters.

Inspired by our recent success in designing CO₂-phobic and CO₂-philic domains on nano-MgO for effective CO₂ adsorption, our ongoing efforts focus on incorporating dopants into pristine MgO to further enhance its CO₂ adsorption capabilities. However, a clear set of guidelines for dopant selection and a holistic understanding of the underlying mechanisms is still lacking. In our investigation, we combined first-principles calculations with experimental approaches to explore the crystal and electronic structural changes in MgO doped with high-valence elements (Al, C, Si, and Ti) and their interactions with CO₂. Our findings unveiled two distinct mechanisms for CO₂ capture: Ti-driven catalytic CO₂ decomposition and CO₂ polarization induced by Al, C, and Si. Ti doping induced outward Ti atom displacement and structural distortion, facilitating CO₂ dissociation, whereas C doping substantially bolstered the electron donation capacity and CO₂ adsorption energy. Pristine and C-doped MgO engaged CO₂ through surface O atoms, while Al-, Si-, and Ti-doped MgO predominantly relied on dopant-O atom interactions. Our comprehensive research, integrating computational modeling and experimental work supported by scanning electron microscopy and thermal gravimetric analysis, confirmed the superior CO₂ adsorption capabilities of C-doped MgO. This yielded profound insights into the mechanisms and principles that govern dopant selection and design.

In this study, we describe the development of hydrogel formulations composed of micelles loading two natural antioxidants-resveratrol and rutin-and the evaluation of the effect of a by-product on the rheological and textural properties of the developed semi-solids. This approach aims to associate the advantages of hydrogels for topical administration of drugs and of lipid micelles that mimic skin composition for the delivery of poorly water-soluble compounds in combination therapy. Biomimetic micelles composed of L-α-phosphatidylcholine loaded with two distinct polyphenols (one non-flavonoid and one flavonoid) were produced using hot shear homogenisation followed by the ultrasonication method. All developed micelles were dispersed in a carbomer 940-based hydrogel to obtain three distinct semi-solid formulations, which were then characterised by analysing the thermal, rheological and textural properties. Olive pomace-based hydrogels were also produced to contain the same micelles as an alternative to respond to the needs of zero waste and circular economy. The thermograms showed no changes in the typical profiles of micelles when loaded into the hydrogels. The rheological analysis confirmed that the produced hydrogels achieved the ideal properties of a semi-solid product for topical administration. The viscosity values of the hydrogels loaded with olive pomace (hydrogels A) proved to be lower than the hydrogels without olive pomace (hydrogels B), with this ingredient having a considerable effect in reducing the viscosity of the final formulation, yet without compromising the firmness and cohesiveness of the gels. The texture analysis of both hydrogels A and B also exhibited the typical behaviour expected of a semi-solid system.