Search Results (782)

MXenes, a rapidly emerging family of two-dimensional transition metal carbides and nitrides, have attracted considerable attention in recent years for their potential in next-generation energy storage technologies. In solid-state batteries (SSBs), they combine metallic-level conductivity (>10³ S cm⁻¹), adjustable surface terminations, and mechanical resilience, which makes them suitable for diverse functions within the cell architecture. Current studies have shown that MXene-based anodes can deliver reversible lithium storage with Coulombic efficiencies approaching ~98% over 500 cycles, while their use as conductive additives in cathodes significantly improves electron transport and rate capability. As interfacial layers or structural scaffolds, MXenes effectively buffer volume fluctuations and suppress lithium dendrite growth, contributing to extended cycle life. In solid polymer and composite electrolytes, MXene fillers have been reported to increase Li⁺ conductivity to the 10⁻³–10⁻² S cm⁻¹ range and enhance Li⁺ transference numbers (up to ~0.76), thereby improving both ionic transport and mechanical stability. Beyond established Ti-based systems, double transition metal MXenes (e.g., Mo₂TiC₂, Mo₂Ti₂C₃) and hybrid heterostructures offer expanded opportunities for tailoring interfacial chemistry and optimizing energy density. Despite these advances, large-scale deployment remains constrained by high synthesis costs (often exceeding USD 200–400 kg⁻¹ for Ti₃C₂T_x at lab scale), restacking effects, and stability concerns, highlighting the need for greener etching processes, robust quality control, and integration with existing gigafactory production lines. Addressing these challenges will be crucial for enabling MXene-based SSBs to transition from laboratory prototypes to commercially viable, safe, and high-performance energy storage systems. Beyond summarizing performance, this review elucidates the mechanistic roles of MXenes in SSBs—linking lithiophilicity, field homogenization, and interphase formation to dendrite suppression at Li|SSE interfaces, and termination-assisted salt dissociation, segmental-motion facilitation, and MWS polarization to enhanced electrolyte conductivity—thereby providing a clear design rationale for practical implementation. Full article

The advancement of sports science has heightened demands for precise monitoring of athletes’ technical movements, physiological status, and performance. Optical fiber sensing (OFS) technology, with its unique advantages including high sensitivity, immunity to electromagnetic interference, capability for distributed sensing, and strong biocompatibility, demonstrates significant application potential in sports science. This review systematically examines the technical principles, innovative breakthroughs, and practical application cases of optical fiber sensors in various domains: monitoring key human physiological parameters such as respiration, heart rate, and body temperature; capturing motion and analyzing movement covering muscle activity, joint angles, and gait; integrating within smart sports equipment and protective gear; and monitoring sports apparatus and environments. The value of OFS technology is further analyzed in areas including sports biomechanics analysis, training load monitoring, injury prevention, and rehabilitation optimization. Concurrently, current technical bottlenecks such as the need for enhanced sensitivity, advancements in flexible packaging technologies, cost control, system integration, and miniaturization are discussed. Future development trends involving the integration of OFS with artificial intelligence, the Internet of Things, and new materials are explored, aiming to provide a theoretical foundation for sports medicine and training optimization. Full article

Optically levitated particles in high vacuum offer an exceptionally isolated mechanical platform for photonic control. Effective cooling of their center-of-mass motion is essential not only for enabling ultrasensitive precision sensing but also for opening access to the quantum regime where macroscopic superposition and nonclassical states can be realized. In this review, we present a comprehensive overview of recent advances in active feedback cooling, based on real-time photonic modulation, and passive feedback cooling, driven by optomechanical interactions within optical resonators. We highlight key experimental milestones, including ground state cooling in one and two dimensions, and discuss the emerging applications of these systems in force sensing, inertial metrology, and macroscopic quantum state preparation. Particular attention is given to novel proposals for probing quantum gravity, detecting dark matter and dark energy candidates, and exploring high-frequency gravitational waves. These advancements establish levitated optomechanical systems as a powerful platform for both high-precision metrology and the investigation of fundamental quantum phenomena. Finally, we discuss the current challenges and future prospects in cooling multiple degrees of freedom, device integration, and scalability toward future quantum technologies. Full article

Increasing global food production to address challenges from population growth, labor shortages, and climate change necessitates a significant enhancement of agricultural sustainability. Autonomous agricultural machinery, a recognized application of precision agriculture, offers a promising solution to boost productivity, resource efficiency, and environmental sustainability. This study presents a systematic review of autonomous driving technologies for agricultural machinery based on 506 rigorously selected publications. The review emphasizes three core aspects: navigation reliability assurance, motion control mechanisms for both vehicles and implements, and actuator fault-tolerance strategies in complex agricultural environments. Applications in farmland, orchards, and livestock farming demonstrate substantial potential. This study also discusses current challenges and future development trends. It aims to provide a reference and technical guidance for the engineering implementation of intelligent agricultural machinery and to support sustainable agricultural transformation. Full article

This study presents a comparative analysis of horizontal-axis wind turbines (HAWTs) equipped with and without bio-inspired active oscillating vortex generators (VGs). The experimental investigation examines key aspects of mechanical integration and the resulting variations in aerodynamic behavior, demonstrating measurable improvements in electrical power output. The VGs were designed and implemented using servomechanisms and embedded control systems to enable oscillatory motion during operation. Experimental findings were validated against CFD simulations, indicating that the use of VGs increases annual energy production efficiency by 16.7%, primarily due to the stabilization of wake turbulence. While a reduction in output voltage was observed at wind speeds below 5 m/s, the VGs exhibited enhanced performance under variable wind conditions. These results highlight the potential of combining biomimetic design principles with electronically actuated flow-control devices to advance HAWT technology, improving energy efficiency and contributing to operational sustainability. Full article

Brain–Computer Interfaces (BCIs) offer a non-invasive pathway for restoring motor function, particularly for individuals with limb loss. This review explored the effectiveness of Electroencephalography (EEG) and function Near-Infrared Spectroscopy (fNIRS) in decoding Motor Imagery (MI) movements for both offline and online BCI systems. EEG has been the dominant non-invasive neuroimaging modality due to its high temporal resolution and accessibility; however, it is limited by high susceptibility to electrical noise and motion artifacts, particularly in real-world settings. fNIRS offers improved robustness to electrical and motion noise, making it increasingly viable in prosthetic control tasks; however, it has an inherent physiological delay. The review categorizes experimental approaches based on modality, paradigm, and study type, highlighting the methods used for signal acquisition, feature extraction, and classification. Results show that while offline studies achieve higher classification accuracy due to fewer time constraints and richer data processing, recent advancements in machine learning—particularly deep learning—have improved the feasibility of online MI decoding. Hybrid EEG–fNIRS systems further enhance performance by combining the temporal precision of EEG with the spatial specificity of fNIRS. Overall, the review finds that predicting online imagined movement is feasible, though still less reliable than motor execution, and continued improvements in neuroimaging integration and classification methods are essential for real-world BCI applications. Broader dissemination of recent advancements in MI-based BCI research is expected to stimulate further interdisciplinary collaboration among roboticists, neuroscientists, and clinicians, accelerating progress toward practical and transformative neuroprosthetic technologies. Full article

This study addresses the agricultural requirement for flexible adjustment of planting spacing in seed breeding corn, designing an active rotating in-film hole-forming mechanism driven by an independent motor. The mechanism allows flexible regulation of planting spacing by adjusting the motor speed. The study first optimized the structure of the hole-forming device, selecting a rhombic duckbill as its core component and analyzing its motion trajectory and hole-forming shape. Single-factor experiments were conducted to determine the structural parameter ranges affecting film hole length. Using discrete element and multibody dynamics co-simulation, experiments were carried out with duckbill number, duckbill bottom width, and duckbill bottom height as experimental factors, and film hole length as the response variable, employing a three-factor, three-level orthogonal experimental method. Simulation results indicated that the factors influencing film hole length, in descending order of impact, were duckbill number, duckbill bottom height, and duckbill bottom width. The optimized best structural parameters were: 9 duckbills, bottom height of 351 mm, and bottom width of 22 mm, ensuring film hole length control within the range of 25–40 mm, meeting planting requirements, preventing weed growth, and ensuring a seed growth environment. Furrow testing validated the adaptability and planting performance of the mechanism under different spacing conditions, providing a theoretical basis and practical reference for the promotion of small-scale breeding and the sowing technology on the film for field seed production. Full article

Additive manufacturing (AM) is gaining prominence in architecture, engineering, and construction (AEC). Within this context, robotic additive manufacturing (RAM) has emerged as a promising solution, offering enhanced flexibility and motion control for fabricating complex geometries and performing on-site production. However, it also introduces new, complex manufacturing processes that impact the design, making the control of manufacturing variables important for achieving accurate and feasible architectural results. In this sense, this study presents a systematic review of the state of the art in RAM for AEC, with a focus on extrusion-based 3D printing using flexible robotic arms and materials such as thermoplastics and paste-based mixtures (cementitious and earth-based compositions). This review includes 142 peer-reviewed journal and conference papers published between 2014 and 2025. It maps key research subfields, geographic trends, and RAM technology evolution, complemented by a bibliometric analysis of co-authorship and keyword networks. This review identifies four key areas of research: process, design, materials, and equipment. Most studies come from North America, Europe, and Asia, with clay emerging as a material receiving growing attention in construction within the RAM field. However, challenges like scalability, programming complexity, and AI integration still limit broader implementation. Full article

Autonomous vehicles (AVs) are increasingly becoming a reality, enabled by advances in sensing technologies, intelligent control systems, and real-time data processing. For AVs to operate safely and effectively, they must maintain a reliable perception of their surroundings and internal state. However, sensor failures, whether due to noise, malfunction, or degradation, can compromise this perception and lead to incorrect localization or unsafe decisions by the autonomous control system. While modern AV systems often combine data from multiple sensors to mitigate such risks through sensor fusion techniques (e.g., Kalman filtering), the extent to which these systems remain resilient under faulty conditions remains an open question. This work presents a simulation-based fault injection framework to assess the impact of sensor failures on AVs’ behavior. The framework enables structured testing of autonomous driving software under controlled fault conditions, allowing researchers to observe how specific sensor failures affect system performance. To demonstrate its applicability, an experimental campaign was conducted using the CARLA simulator integrated with the Autoware autonomous driving stack. A multi-segment urban driving scenario was executed using a modified version of CARLA’s Scenario Runner to support Autoware-based evaluations. Faults were injected simulating LiDAR, GNSS, and IMU sensor failures in different route scenarios. The fault types considered in this study include silent sensor failures and severe noise. The results obtained by emulating sensor failures in our chosen system under test, Autoware, show that faults in LiDAR and IMU gyroscope have the most critical impact, often leading to erratic motion and collisions. In contrast, faults in GNSS and IMU accelerometers were well tolerated. This demonstrates the ability of the framework to investigate the fault-tolerance of AVs in the presence of critical sensor failures. Full article

Our paper introduces a novel idea of a virtual mouse character driven by gesture detection, eye-tracking, and voice monitoring. This system uses cutting-edge computer vision and machine learning technology to let users command and control the mouse pointer using eye motions, voice commands, or hand gestures. This system’s main goal is to provide users who want a more natural, hands-free approach to interacting with their computers as well as those with impairments that limit their bodily motions, such as those with paralysis—with an easy and engaging interface. The system improves accessibility and usability by combining many input modalities, therefore providing a flexible answer for numerous users. While the speech recognition function permits hands-free operation via voice instructions, the eye-tracking component detects and responds to the user’s gaze, therefore providing exact cursor control. Gesture recognition enhances these features even further by letting users use their hands simply to execute mouse operations. This technology not only enhances personal user experience for people with impairments but also marks a major development in human–computer interaction. It shows how computer vision and machine learning may be used to provide more inclusive and flexible user interfaces, therefore improving the accessibility and efficiency of computer usage for everyone. Full article

Orchard mobile robots (OMR) represent a critical research focus in the field of modern intelligent agricultural equipment, offering the potential to significantly enhance operational efficiency through the integration of path planning and tracking control navigation methods. However, the inherent complexity of orchard environments presents substantial challenges for robotic systems. Researchers have extensively investigated the robustness of various path planning and tracking control techniques for OMR in complex scenes, aiming to improve the robots’ security, stability, efficiency, and adaptability. This paper provides a comprehensive review of the state-of-the-art path planning and tracking control strategies for OMR in such environments. First, it discusses the advances in both global and local path planning methods designed for OMR navigating through complex orchard scenes. Second, it examines tracking control approaches in the context of different motion models, with an emphasis on the application characteristics and current trends in various scene types. Finally, the paper highlights the technical challenges faced by OMR in autonomous tasks within these complex environments and emphasizes the need for further research into navigation technologies that integrate artificial intelligence with end-to-end control systems. This fusion is identified as a promising direction for achieving efficient autonomous operations in orchard environments. Full article

Accurately decoding human locomotion intention from physiological signals remains a significant hurdle for the seamless control of advanced rehabilitation devices like exoskeletons and intelligent prosthetics. Conventional recognition methods often falter, exhibiting limited accuracy and struggling to capture the complex, nonlinear dynamics inherent in biological data streams. Addressing these critical limitations, this study introduces a novel framework for lower-limb motion intent recognition, integrating Kernel Principal Component Analysis (KPCA) with a Support Vector Machine (SVM) optimized via an Improved Sparrow Search Algorithm (ISSA). Our approach commences by constructing a comprehensive high-dimensional feature space from synchronized surface electromyography (sEMG) and inertial measurement unit (IMU) data—a potent combination reflecting both muscle activation and limb kinematics. Critically, KPCA is employed for nonlinear dimensionality reduction; leveraging the power of kernel functions, it transcends the linear constraints of traditional PCA to extract low-dimensional principal components that retain significantly more discriminative information. Furthermore, the Sparrow Search Algorithm (SSA) undergoes three strategic enhancements: chaotic opposition-based learning for superior population diversity, adaptive dynamic weighting to adeptly balance exploration and exploitation, and hybrid mutation strategies to effectively mitigate premature convergence. This enhanced ISSA meticulously optimizes the SVM hyperparameters, ensuring robust classification performance. Experimental validation, conducted on a challenging 13-class lower-limb motion dataset, compellingly demonstrates the superiority of the proposed KPCA-ISSA-SVM architecture. It achieves a remarkable recognition accuracy of 95.35% offline and 93.3% online, substantially outperforming conventional PCA-SVM (91.85%) and standalone SVM (89.76%) benchmarks. This work provides a robust and significantly more accurate solution for intention perception in human–machine systems, paving the way for more intuitive and effective rehabilitation technologies by adeptly handling the nonlinear coupling characteristics of sEMG-IMU data and complex motion patterns. Full article

A mill relining manipulator is key maintenance equipment for liners exchanged and operated by workers inside a grinding mill. To improve the operation efficiency and safety, real-time path planning and end deformation compensation should be performed prior to actual execution. This paper proposes a five-dimensional digital twin framework to realize virtual–real interaction between a physical manipulator and virtual model. First, a real-time digital twin scene is established based on OpenGL. The involved technologies include scene rendering, a camera system, the light design, model importation, joint control, and data transmission. Next, different solving methods are introduced into the service space for relining tasks, including a kinematics model, collision detection, path planning, and end deformation compensation. Finally, a user application is developed to realize real-time condition monitoring and simulation analysis visualization. Through comparison experiments, the superiority of the proposed path planning algorithm is demonstrated. In the case of a long-distance relining task, the planning time and path length of the proposed algorithm are 1.7 s and 15,299 mm, respectively. For motion smoothness, the joint change curve exhibits no abrupt variation. In addition, the experimental results between original and modified end trajectories further verified the effectiveness and feasibility of the proposed end effector compensation method. This study can also be extended to other heavy-duty manipulators to realize intelligent automation. Full article

Advancements in multi-rotor quadcopter technology and sensing capabilities have led to their increased utilisation for last-mile delivery. However, battery capacity constraints limit their use in extended-distance delivery scenarios. A visual servoing implementation is first proposed that leverages a CUDA-accelerated tag detection algorithm for real-time pose estimation of the target. A new approach is then developed to enhance quadcopter package collection by implementing a control scheme to attenuate aggressive load-swing in a payload arm that shifts from horizontal to vertical after obtaining a vertically mounted payload. The motion of the payload arm imposes a shift in the system’s centre of mass, leading to a possible instability. A non-linear control scheme is then introduced to address this problem through attenuation of the residual energy from payload oscillation. The performance of the visual servoing approach is validated through both numerical simulations and a physical quadcopter implementation, along with the performance of the load-swing attenuation through numerical simulations. Full article

Intra-fractional respiratory management represents one of the greatest challenges of modern cancer radiotherapy (RT), as significant breathing-induced lesion motion might affect target coverage and organs at risk (OARs) sparing, jeopardizing oncological and toxicity outcomes. The detrimental effects on dosage of uncompensated organ motion are exacerbated in RT with charged particles (e.g., protons and carbon ions), due to their higher ballistic selectivity. The simplest strategies to counteract this phenomenon are the use of larger treatment margins and reductions in or control of respiration (e.g., by means of compression belts, breath hold). Gating and tracking, which synchronize beam delivery with the respiratory signal, also represent widely adopted solutions. When tracking the tumor itself or surrogates, invasive procedures (e.g., marker implantation), an unnecessary imaging dose (e.g., in X-ray-based fluoroscopy), or expensive equipment (e.g., magnetic resonance imaging, MRI) is usually required. When chest and abdomen excursions are measured to infer internal tumor displacement, the additional devices needed to perform this task, such as pressure sensors or surface cameras, present inherent limitations that can impair the procedure itself. In this context, radars have intrigued the radiation oncology community, being inexpensive, non-invasive, contactless, and insensitive to obstacles. Even if real-world clinical implementation is still lagging behind, there is a growing body of research unraveling the potential of these devices in this field. The purpose of this narrative review is to provide an overview of the studies that have delved into the potential of radar-based technologies for motion-adaptive photon and particle RT applications. Full article