Search Results (2,173)

Surface electromyography (sEMG) non-invasively captures the electrical activity generated by muscle contractions, offering valuable insights into motion intentions. While sEMG has been widely applied to general gesture recognition in rehabilitation, there has been limited exploration of specific, intricate daily tasks, such as the pouring action. Pouring is a common yet complex movement requiring precise muscle coordination and control, making it an ideal focus for rehabilitation studies. This research proposes a granular computing-based deep learning approach utilizing ConvMixer architecture enhanced with feature fusion and granular computing to improve gesture recognition accuracy. Our findings indicate that the addition of hand-crafted features significantly improves model performance; specifically, the ConvMixer model’s accuracy improved from 0.9512 to 0.9929. These results highlight the potential of our approach in rehabilitation technologies and assistive systems for restoring motor functions in daily activities. Full article

This paper addresses the critical challenge of optimizing the energy efficiency of induction motors, which are pivotal components across diverse industrial sectors due to their substantial energy consumption. Given the non-measurable internal parameters of induction motors, parameter identification becomes a complex, multidimensional optimization problem characterized by highly nonlinear and multimodal error surfaces. Traditional optimization algorithms often weaken, yielding suboptimal results due to an inadequate balance between the exploration and exploitation phases. To overcome these limitations, this study introduces an Adaptive Weight Grey Wolf Optimizer (AWGWO) to enhance the accuracy and reliability of induction motor parameter estimation. The AWGWO incorporates an adaptive weight mechanism that dynamically adjusts the exploration and exploitation balance, effectively mitigating issues such as premature convergence to local optima. Extensive simulation validation was conducted across various induction motor models, including eight commercial motors, and demonstrated that AWGWO consistently outperforms state-of-the-art algorithms in terms of convergence speed, solution accuracy, and robustness in multimodal optimization landscapes. The AWGWO consistently exhibited faster convergence, significantly reducing premature convergence. Moreover, the adaptive weight mechanism enabled a more effective balance between exploration and exploitation, leading to higher accuracy in parameter estimation. Comparative analyses reveal that AWGWO outperforms existing algorithms not only in achieving lower error rates, but also in maintaining stability. This study significantly contributes to progress in the field by providing an effective tool for induction motor parameterization, thereby offering potential improvements in energy efficiency. Full article

To create an orthodontic bracket material combining the favourable properties of ceramic and polymer while minimising their limitations, graded porous ceramic scaffolds were created using unidirectional gelation-freeze casting, following which the pores were infiltrated with polymer. Two processing parameters were investigated: (1) sedimentation times of 0, 8, and 24 h, with ceramic solid loading of 20 vol.% and 2.5 wt.% gelatine concentration, and (2) ceramic solid loadings of 15, 20, and 25 vol.% with a fixed 2.5 wt.% gelatine concentration and an 8 h sedimentation time. The graded ceramic structures demonstrated porosity gradients ranging from 9.86 to 63.84 vol.%, except those with 25 vol.% ceramic solid loading at 8 h sedimentation. The Al₂O₃-UDMA/TEGDMA composites had compressive strengths of 60.25 to 120.92 MPa, modulus of elasticity of 19.84 to 35.29 GPa, and fracture toughness of 0.78 to 1.78 MPa·m^1/2. The values observed were between those of dense ceramic and pure polymer. Statistical analysis was conducted using Excel^® 2019 (Microsoft^®, Washington, DC, USA). Means, standard deviations, and 95% confidence intervals (CI) were calculated at a significance level of α = 0.05, alongside polynomial regression to evaluate relationships between variables. Composites with 20 vol.% ceramic solid loading at 8 h sedimentation displayed promising potential for further clinical validation. Full article

An improved black-winged kite algorithm with multiple strategies (BKAIM) is proposed in this paper to address two critical limitations in the original black-winged kite optimization algorithm (BKA): the restricted search capability caused by the low-quality initial population and the reduced population diversity resulting from blind following behavior during the migration phase. Our enhancement implements three strategic modifications across different algorithm stages. During initialization, an opposition-based learning strategy was incorporated to generate a higher-quality initial population. For the migration phase, a differential mutation strategy was integrated to facilitate information exchange among population members, mitigate the tendency of blind leader-following behavior, enhance convergence precision, and achieve an optimal balance between exploration and exploitation capabilities. Regarding boundary handling, the conventional absorption boundary method was replaced with a random boundary approach to increase population diversity and subsequently improve the algorithm’s search capabilities. Comprehensive testing was conducted on four benchmark function sets (CEC2017, CEC2019, CEC2021, and CEC2022) to validate the effectiveness of the improved algorithm. Detailed convergence analysis and Wilcoxon rank-sum test comparisons with other algorithms demonstrated BKAIM’s superior convergence performance and robustness. Furthermore, the support vector machine (SVM) model was optimized by BKAIM for grade identification of Dendrobium huoshanense based on near-infrared spectral data, thereby confirming its effectiveness in practical applications. Full article

Transfer learning is the act of using the data or knowledge in a problem to help solve different but related problems. In a brain computer interface (BCI), it is important to deal with individual differences between topics and/or tasks. A kind of capsule decision neural network (CDNN) based on transfer learning is proposed. In order to solve the problem of feature distortion caused by EEG feature extraction algorithm, a deep capsule decision network was constructed. The architecture includes multiple primary capsules to form a hidden layer, and the connection between the advanced capsule and the primary capsule is determined by the neural decision routing algorithm. Unlike the dynamic routing algorithm that iteratively calculates the similarity between primary capsules and advanced capsules, the neural decision network computes the relationship between each capsule in the deep and shallow hidden layers in a probabilistic manner. At the same time, the distribution of the EEG covariance matrix is aligned in Riemann space, and the regional adaptive method is further introduced to improve the independent decoding ability of the capsule decision neural network for the subject’s EEG signals. Experiments on two motor imagery EEG datasets show that CDNN outperforms several of the most advanced transfer learning methods. Full article

This paper reviews the research progress of bionic soft robot technology learned from octopuses. The number of related research papers increased from 760 in 2021 to 1170 in 2024 (Google Scholar query), with a growth rate of 53.95% in the past five years. These studies mainly explore how humans can learn from the physiological characteristics of octopuses for sensor design, actuator development, processor architecture optimization, and intelligent optimization algorithms. The tentacle structure and nervous system of octopus have high flexibility and distributed control capabilities, which is an important reference for the design of soft robots. In terms of sensor technology, flexible strain sensors and suction cup sensors inspired by octopuses achieve accurate environmental perception and interaction. Actuator design uses octopus muscle fibers and movement patterns to develop various driving methods, including pneumatic, hydraulic and electric systems, which greatly improves the robot’s motion performance. In addition, the distributed nervous system of octopuses inspires multi-processor architecture and intelligent optimization algorithms. This paper also introduces the concept of expected functional safety for the first time to explore the safe design of soft robots in failure or unknown situations. Currently, there are more and more bionic soft robot technologies that draw on octopuses, and their application areas are constantly expanding. In the future, with further research on the physiological characteristics of octopuses and the integration of artificial intelligence and materials science, octopus soft robots are expected to show greater potential in adapting to complex environments, human–computer interaction, and medical applications. Full article

The development of efficient directional liquid transport systems has become a central focus in numerous research and engineering fields. Natural organisms have evolved intricate structures that facilitate the controlled movement of liquids on planar surfaces. These natural mechanisms offer insights into creating sustainable, energy-efficient technologies that mimic these natural adaptations. The purpose of biomimetic directional liquid transport is to harness the principles found in nature to design systems that can autonomously manage the flow of liquids. One of the core objectives is to achieve efficient liquid directional movement without the need for external energy sources or mechanical pumps. In this article, we review the typical models of natural systems with directional liquid transport on planar surfaces. Next, we reveal the physical mechanism by which surface chemical gradients, wettability gradients, and geometric gradients synergically drive liquid directional motion. Then, we introduce the breakthroughs of bionic surface engineering strategies in water harvesting, directional liquid transport and recent advancements in engineering applications. Finally, we give a conclusion and future perspectives on the development of directional liquid transport. Full article

This paper introduces the Enhanced Team-Oriented Swarm Optimization (ETOSO) algorithm, a novel refinement of the Team-Oriented Swarm Optimization (TOSO) algorithm aimed at addressing the stagnation problem commonly encountered in nature-inspired optimization approaches. ETOSO enhances TOSO by integrating innovative strategies for exploration and exploitation, resulting in a simplified algorithm that demonstrates superior performance across a broad spectrum of benchmark functions, particularly in high-dimensional search spaces. A comprehensive comparative evaluation and statistical tests against 26 established nature-inspired optimization algorithms (NIOAs) across 15 benchmark functions and dimensions (D = 2, 5, 10, 30, 50, 100, 200) confirm ETOSO’s superiority relative to solution accuracy, convergence speed, computational complexity, and consistency. Full article

The honeycomb configuration has been widely adopted in numerous sectors owing to its superior strength-to-weight ratio, rigidity, and outstanding energy absorption properties, attracting substantial academic attention and research interest. This study introduces a biomimetic modular honeycomb configuration inspired by the variable-density biological enhancement characteristics of tree stem tissues. This study examined the out-of-plane compressive behavior and mechanical characteristics of modular honeycomb structures. A numerical model of the modular honeycomb was constructed utilizing finite element technology, enabling simulation studies at varying impact velocities. The improved weight-bearing and impact-absorbing properties of modular honeycomb structures are investigated using theoretical analysis and computer simulations. It also scrutinizes the effects of boundary and matching conditions on the honeycomb’s performance. The results indicate that adjusting the thickness of the walls in both the matrix honeycomb and sub-honeycomb structures can substantially improve their resistance to low-velocity out-of-plane compression impacts. Furthermore, the energy absorption capacity of modular honeycombs during high-velocity impacts is significantly influenced by multiple factors: the impact velocity, the density of the honeycomb structure, and the distribution of wall thickness within the sub-honeycomb and the primary honeycomb matrix. Notably, the modular honeycomb with an optimally designed structure demonstrates superior high-speed impact resistance compared to conventional honeycombs of equivalent density. These insights underscore the potential for advanced honeycomb designs to further advance material performance in structural applications. Full article

Designing effective drug therapies requires balancing competing objectives, such as therapeutic efficacy, safety, and cost efficiency—a task that poses significant challenges for conventional optimization methods. To address this, we propose the multi-objective spider–wasp optimizer (MOSWO), a novel approach uniquely emulating the cooperative predation dynamics between spiders and wasps observed in nature. MOSWO integrates adaptive mechanisms for exploration and exploitation to resolve complex trade-offs in multiobjective drug design. Unlike existing approaches, the algorithm employs a dynamic population-partitioning strategy inspired by predator–prey interactions, enabling efficient Pareto frontier discovery. We validate MOSWO’s performance through extensive experiments on synthetic benchmarks and real-world case studies spanning antiviral and antibiotic therapies. Results demonstrate that MOSWO surpasses state-of-the-art methods (NSGA-II, MOEA/D, MOGWO, and MOPSO), achieving 11% higher hypervolume scores, 8% lower inverted generational distance scores, 9% higher spread scores, a 30% faster convergence, and superior robustness against noisy biological datasets. The framework’s adaptability to diverse therapeutic scenarios underscores its potential as a transformative tool for computational pharmacology. Full article

To better address the issue of multi-threshold image segmentation, this paper proposes a hybrid adaptive crayfish optimization algorithm with differential evolution for color multi-threshold image segmentation (ACOADE). Due to the insufficient convergence ability of the crayfish optimization algorithm in later stages, it is challenging to find a more optimal solution for optimization. ACOADE optimizes the maximum foraging quantity parameter p and introduces an adaptive foraging quantity adjustment strategy to enhance the randomness of the algorithm. Furthermore, the core formula of the differential evolution (DE) algorithm is incorporated to balance ACOADE’s exploration and exploitation capabilities better. To validate the optimization performance of ACOADE, the IEEE CEC2020 test function was selected for experimentation, and eight other algorithms were chosen for comparison. To verify the effectiveness of ACOADE for threshold image segmentation, the Kapur entropy method and Otsu method were used as objective functions for image segmentation and compared with eight other algorithms. Subsequently, the peak signal-to-noise ratio (PSNR), feature similarity index measure (FSIM), structural similarity index measure (SSIM), and Wilcoxon test were employed to evaluate the quality of the segmented images. The results indicated that ACOADE exhibited significant advantages in terms of objective function value, image quality metrics, convergence, and robustness. Full article

The root walls of immature permanent teeth are often weak, thin, and short, making regenerative endodontic treatment (RET) necessary. The goal of RET is to create a favorable environment for further root development. A biomimetic approach is essential for thorough disinfection, followed by the preservation and potential stimulation of stem cells from surrounding tissue to enable root regeneration and continued development. The objective of this study was to assess temperature changes on the external root surface, structural alterations in the internal root walls following irradiation with a 940 nm diode laser, and the biocompatibility of stem cells from the apical papilla (SCAPs). Irradiation was performed with varying output powers (0.5 W, 1 W, 1.5 W, and 2 W) in continuous mode for 5 s over four consecutive cycles. Thermographic measurements during irradiation, the micro-CT analysis of root samples, and mitochondrial activity of SCAPs were evaluated. The heating effect correlated directly with a higher output power and thinner root walls. A 1 W output power was found to be safe for immature teeth, particularly in the apical third of the root, while 1.5 W could be safely used for mature mandibular incisors. Diode laser irradiation at 1 W and 1.5 W significantly stimulated SCAPs’ mitochondrial activity within 24 h post-irradiation, indicating a potential photobiostimulatory effect. However, no significant changes were observed at lower (0.5 W) and higher (2 W) output powers. The area of open tubular space inside the root canal was significantly reduced after irradiation, regardless of the applied power. Additionally, irradiation contributed to the demineralization of the dentin on the inner root walls. Future studies should explore the impact of irrigants used between irradiation cycles, the potential benefits of conical laser tips for more even energy distribution, and a thorough analysis of how disinfection protocols affect both the dentin structure and stem cell viability. Full article

Nanomaterials have revolutionized various biological applications, including cosmeceuticals, enabling the development of smart nanocarriers for enhanced skin delivery. This review focuses on the role of nanotechnologies in skincare and treatments, providing a concise overview of smart nanocarriers, including thermo-, pH-, and multi-stimuli-sensitive systems, focusing on their design, fabrication, and applications in cosmeceuticals. These nanocarriers offer controlled release of active ingredients, addressing challenges like poor skin penetration and ingredient instability. This work discusses the unique properties and advantages of various nanocarrier types, highlighting their potential in addressing diverse skin concerns. Furthermore, we address the critical aspect of biocompatibility, examining potential health risks associated with nanomaterials. Finally, this review highlights current challenges, including the precise control of drug release, scalability, and the transition from in vitro to in vivo applications. We also discuss future perspectives such as the integration of digital technologies and artificial intelligence for personalized skincare to further advance the technology of smart nanocarriers in cosmeceuticals. Full article

Underwater multi-agent systems face critical hydrodynamic constraints that significantly degrade the performance of conventional constraint optimization algorithms in dynamic fluid environments. To meet the needs of underwater multi-agent applications, a fish recognition-inspired optimization method (FROM) is proposed in this paper. The proposed method introduces the characteristics of fish recognition. There are two major improvements in the proposed method: the neighbor topology improvement based on vision recognition and the learning strategies improvement based on hydrodynamic recognition. The computational complexity of the proposed algorithm was analyzed, and it was found to be acceptable. The statistical analysis of the experimental results shows that the FROM algorithm performs better than other algorithms in terms of minimum, maximum, standard deviation, mean, and median values calculated from objective functions. With solid experiment results, we conclude that the proposed FROM algorithm is a better solution to solve multi-agent decision-making problems with fluid environment constraints. Full article