Biomimetics最新文献

Motorised arm supports for individuals with severe arm muscle weakness require precise compensation for arm weight and elevated passive joint impedance (e.g., joint stiffness as a result of muscle atrophy and fibrosis). Estimating these parameters in vivo, along with the arm's centre of mass, is challenging, and human evaluations of assistance can be subjective. To address this, a dummy arm was designed to replicate the human arm's anthropometrics, degrees of freedom, adjustable segment masses, and passive elbow joint impedance (eJimp). This study presents the design, anthropometrics, and verification of the dummy arm. It successfully mimics the human arm's range of motion, mass, and centre of mass. The dummy arm also demonstrates the ability to replicate various eJimp torque-angle profiles. Additionally, it allows for the tuning of the segment masses, centres of mass, and eJimp to match a representative desired target population. This simple, cost-effective tool has proven valuable for the development and verification of the Duchenne ARm ORthosis (DAROR), a motorised arm support, or 'exoskeleton'. This study includes recommendations for practical applications and provides insights into optimising design specifications based on the final design. It supplements the CAD design, enhancing the dummy arm's application for future arm-assistive devices.

This paper introduces a novel approach to robotic assistance in bottle opening using the dual-arm robot TIAGo++. The solution enhances accessibility by addressing the needs of individuals with injuries or disabilities who may require help with common manipulation tasks. The aim of this paper is to propose a method involving vision, manipulation, and learning techniques to effectively address the task of bottle opening. The process begins with the acquisition of bottle and cap positions using an RGB-D camera and computer vision. Subsequently, the robot picks the bottle with one gripper and grips the cap with the other, each by planning safe trajectories. Then, the opening procedure is executed via a position and force control scheme that ensures both grippers follow the unscrewing path defined by the cap thread. Within the control loop, force sensor information is employed to control the vertical axis movements, while gripper rotation control is achieved through a Deep Reinforcement Learning (DRL) algorithm trained to determine the optimal angle increments for rotation. The results demonstrate the successful training of the learning agent. The experiments confirm the effectiveness of the proposed method in bottle opening with the TIAGo++ robot, showcasing the practical viability of the approach.

In this study, we investigate the impact of O₂ plasma treatment on the performance of Al/TaO_X/Al-based resistive random-access memory (RRAM) devices, focusing on applications in neuromorphic systems. Comparative analysis using scanning electron microscopy and X-ray photoelectron spectroscopy confirmed the differences in chemical composition between O₂-plasma-treated and untreated RRAM cells. Direct-current measurements showed that O₂-plasma-treated RRAM cells exhibited significant improvements over untreated RRAM cells, including higher on/off ratios, improved uniformity and distribution, longer retention times, and enhanced durability. The conduction mechanism is investigated by current-voltage (I-V) curve fitting. In addition, paired-pulse facilitation (PPF) is observed using partial short-term memory. Furthermore, 3- and 4-bit weight tuning with auto-pulse-tuning algorithms was achieved to improve the controllability of the synapse weight for the neuromorphic system, maintaining retention times exceeding 10³ s in the multiple states. Neuromorphic simulation with an MNIST dataset is conducted to evaluate the synaptic device.

Cystic Fibrosis (CF) is a life-shortening, genetic disease that affects approximately 145,000 people worldwide. CF causes a dehydrated mucus layer in the lungs, leading to damaging infection and inflammation that eventually result in death. Nanoparticles (NPs), drug delivery vehicles intended for inhalation, have become a recent source of interest for treating CF and CF-related conditions, and many formulations have been created thus far. This paper is intended to provide an overview of CF and the effect it has on the lungs, the barriers in using NP drug delivery vehicles for treatment, and three common material class choices for these NP formulations: metals, polymers, and lipids. The materials to be discussed include gold, silver, and iron oxide metallic NPs; polyethylene glycol, chitosan, poly lactic-co-glycolic acid, and alginate polymeric NPs; and lipid-based NPs. The novelty of this review comes from a less specific focus on nanoparticle examples, with the focus instead being on the general theory behind material function, why or how a material might be used, and how it may be preferable to other materials used in treating CF. Finally, this paper ends with a short discussion of the two FDA-approved NPs for treatment of CF-related conditions and a recommendation for the future usage of NPs in people with Cystic Fibrosis (pwCF).

The whale optimization algorithm has several advantages, such as simple operation, few control parameters, and a strong ability to jump out of the local optimum, and has been used to solve various practical optimization problems. In order to improve its convergence speed and solution quality, a reinforced whale optimization algorithm (RWOA) was designed. Firstly, an opposition-based learning strategy is used to generate other optima based on the best optimal solution found during the algorithm's iteration, which can increase the diversity of the optimal solution and accelerate the convergence speed. Secondly, a dynamic adaptive coefficient is introduced in the two stages of prey and bubble net, which can balance exploration and exploitation. Finally, a kind of individual information-reinforced mechanism is utilized during the encircling prey stage to improve the solution quality. The performance of the RWOA is validated using 23 benchmark test functions, 29 CEC-2017 test functions, and 12 CEC-2022 test functions. Experiment results demonstrate that the RWOA exhibits better convergence accuracy and algorithm stability than the WOA on 20 benchmark test functions, 21 CEC-2017 test functions, and 8 CEC-2022 test functions, separately. Wilcoxon's rank sum test shows that there are significant statistical differences between the RWOA and other algorithms.

Feature selection (FS) is a classic and challenging optimization task in most machine learning and data mining projects. Recently, researchers have attempted to develop more effective methods by using metaheuristic methods in FS. To increase population diversity and further improve the effectiveness of the beluga whale optimization (BWO) algorithm, in this paper, we propose a multi-strategies improved BWO (MSBWO), which incorporates improved circle mapping and dynamic opposition-based learning (ICMDOBL) population initialization as well as elite pool (EP), step-adaptive Lévy flight and spiral updating position (SLFSUP), and golden sine algorithm (Gold-SA) strategies. Among them, ICMDOBL contributes to increasing the diversity during the search process and reducing the risk of falling into local optima. The EP technique also enhances the algorithm's ability to escape from local optima. The SLFSUP, which is distinguished from the original BWO, aims to increase the rigor and accuracy of the development of local spaces. Gold-SA is introduced to improve the quality of the solutions. The hybrid performance of MSBWO was evaluated comprehensively on IEEE CEC2005 test functions, including a qualitative analysis and comparisons with other conventional methods as well as state-of-the-art (SOTA) metaheuristic approaches that were introduced in 2024. The results demonstrate that MSBWO is superior to other algorithms in terms of accuracy and maintains a better balance between exploration and exploitation. Moreover, according to the proposed continuous MSBWO, the binary MSBWO variant (BMSBWO) and other binary optimizers obtained by the mapping function were evaluated on ten UCI datasets with a random forest (RF) classifier. Consequently, BMSBWO has proven very competitive in terms of classification precision and feature reduction.

The erosion phenomena of the natural stone in cultural heritage are induced by various sources. Consequently, the development of multifunctional protective materials that combine two or more useful properties is an effective strategy in addressing the synergistic effects of various erosion mechanisms. A multifunctional coating, consisting of a silane-based precursor and zinc oxide (ZnO) nanoparticles (NPs), is produced and tested for the protection of limestone. The hybrid coating combines the following three properties: superhydrophobicity, including water-repellency, photocatalytic self-cleaning and biocidal activity. The relative concentration of the NPs (0.8% w/w), used for the suggested composite coating, is carefully selected according to wetting studies, colourimetric measurements and durability (tape peeling) tests. The non-wetting state is evidenced on the surface of the composite coating by the large contact angle of water drops (≈153°) and the small contact angle hysteresis (≈5°), which gives rise to a physical self-cleaning scenario (lotus effect). The photocatalytic chemical self-cleaning is shown with the removal of methylene blue, induced by UV-A radiation. Moreover, it is shown that the suggested coating hinders the incubation of E. coli and S. aureus, as the inhibitions are 94.8 and 99.9%, respectively. Finally, preliminary studies reveal the chemical stability of the suggested coating.

The prediction of total ionospheric electron content (TEC) is of great significance for space weather monitoring and wireless communication. Recently, deep learning models have become increasingly popular in TEC prediction. However, these deep learning models usually contain a large number of hyperparameters. Finding the optimal hyperparameters (also known as hyperparameter optimization) is currently a great challenge, directly affecting the predictive performance of the deep learning models. The Beluga Whale Optimization (BWO) algorithm is a swarm intelligence optimization algorithm that can be used to optimize hyperparameters of deep learning models. However, it is easy to fall into local minima. This paper analyzed the drawbacks of BWO and proposed an improved BWO algorithm, named FAMBWO (Firefly Assisted Multi-strategy Beluga Whale Optimization). Our proposed FAMBWO was compared with 11 state-of-the-art swarm intelligence optimization algorithms on 30 benchmark functions, and the results showed that our improved algorithm had faster convergence speed and better solutions on almost all benchmark functions. Then we proposed an automated machine learning framework FAMBWO-MA-BiLSTM for TEC prediction, where MA-BiLSTM is for TEC prediction and FAMBWO for hyperparameters optimization. We compared it with grid search, random search, Bayesian optimization algorithm and beluga whale optimization algorithm. Results showed that the MA-BiLSTM model optimized by FAMBWO is significantly better than the MA-BiLSTM model optimized by grid search, random search, Bayesian optimization algorithm, and BWO.

As industrial informatization progresses, virtual simulation technologies are increasingly demonstrating their potential in industrial applications. These systems utilize various sensors to capture real-time factory data, which are then transmitted to servers via communication interfaces to construct corresponding digital models. This integration facilitates tasks such as monitoring and prediction, enabling more accurate and convenient production scheduling and forecasting. This is particularly significant for flexible or mixed-flow production modes. Bionic optimization algorithms have demonstrated strong performance in factory scheduling and operations. Centered around these algorithms, researchers have explored various strategies to enhance efficiency and optimize processes within manufacturing environments.This study introduces an efficient migratory bird optimization algorithm designed to address production scheduling challenges in an assembly shop with mold quantity constraints. The research aims to minimize the maximum completion time in a batch flow mixed assembly flow shop scheduling problem, incorporating variable batch partitioning strategies. A tailored virtual simulation framework supports this objective. The algorithm employs a two-stage encoding mechanism for batch partitioning and sequencing, adapted to the unique constraints of each production stage. To enhance the search performance of the neighborhood structure, the study identifies and analyzes optimization strategies for batch partitioning and sequencing, and incorporates an adaptive neighborhood structure adjustment strategy. A competition mechanism is also designed to enhance the algorithm's optimization efficiency. Simulation experiments of varying scales demonstrate the effectiveness of the variable batch partitioning strategy, showing a 5-6% improvement over equal batch strategies. Results across different scales and parameters confirm the robustness of the algorithm.

Depictions of robots as romantic partners for humans are frequent in popular culture. As robots become part of human society, they will gradually assume the role of partners for humans whenever necessary, as assistants, collaborators, or companions. Companion robots are supposed to provide social contact to those who would not have it otherwise. These companion robots are usually not designed to fulfill one of the most important human needs: the one for romantic and intimate contact. Human-robot intimacy remains a vastly unexplored territory. In this article, we review the state-of-the-art research in intimate robotics. We discuss major issues limiting the acceptance of robots as intimate partners, the public perception of robots in intimate roles, and the possible influence of cross-cultural differences in these domains. We also discuss the possible negative effects human-robot intimacy may have on human-human contact. Most importantly, we propose a new term "intimate companion robots" to reduce the negative connotations of the other terms that have been used so far and improve the social perception of research in this domain. With this article, we provide an outlook on prospects for the development of intimate companion robots, considering the specific context of their use.