Search Results (690)

In the context of Industry 5.0, human–robot collaboration increasingly demands intuitive, safe, and sensorless interaction for tasks such as hand-guided teaching and concurrent manipulation. However, conventional admittance control systems are prone to instability due to abrupt changes in human arm stiffness and their reliance on accurate dynamic models. To address these challenges, this paper proposes a sensorless external force estimation and variable admittance control method that models robot dynamic uncertainties and interaction forces as normally distributed stochastic quantities. An improved particle swarm optimization algorithm is introduced to calibrate the variance parameters, enhancing estimation accuracy and robustness. Furthermore, an energy-based variable admittance control strategy is developed, which preserves system passivity by adaptively adjusting inertia and damping gains based on real-time energy variations. The proposed method was validated on a redundant robot platform. Experimental results show that the external force and torque estimation errors remain below 3 N and 3 N.m, respectively, with lower detection delays and errors than those of a first-order generalized momentum observer in collision detection. Variable admittance experiments demonstrate that the system maintains passivity and stable interaction even under sudden arm stiffness changes. The approach is well-suited for industrial applications requiring safe, sensorless, and compliant human–robot collaboration. Full article

Surface Acoustic Wave (SAW) technology, long central to analog signal processing and RF filtering, is undergoing a major renewal. Driven by advances that decouple SAWs from traditional piezoelectric materials and fixed-function devices, the field is gaining unprecedented control over acoustic, optical, and electronic interactions at the micro and nanoscale. This review synthesizes these developments across four fronts: new physical mechanisms for SAW manipulation, emerging material platforms, ranging from thin films to 2D systems, along with reconfigurable device architectures and circuits, and the expanding landscape of applications they enable. Optical methods are reshaping how SAWs are generated and controlled, bypassing the limits of conventional electromechanical coupling. Coherent optical excitation of high-Q SAW cavities via Brillouin-like optomechanical interactions now grants access to modes in non-piezoelectric substrates such as diamond and silicon, while on-chip SAW excitation in photonic waveguides through backward stimulated Brillouin scattering opens new integrated sensing routes. In parallel, magneto-acoustic experiments have revealed nonreciprocal SAW diffraction from resonant scattering in magnetoelastic gratings. On the device side, ZnO thin-film transistors integrated on LiNbO₃ exploit acoustoelectric coupling to realize voltage-tunable phase shifters; UHF Z-shaped delay lines achieve high sensitivity in a compact footprint; and parametric synthesis of wideband, multi-stage lattice filters targets 5G-class performance. Atomistic simulations show that SAW propagation in 2D MXene films can be engineered via surface terminations, while aerosol jet printing and SAW-assisted particle patterning provide agile, cleanroom-light fabrication of microfluidic and magnetic components. These advances enable applications ranging from hybrid quantum systems and quantum links to lab-on-a-chip particle control, SBS-based and UHF sensing, reconfigurable RF front-ends, and soft robotic actuators based on patterned magnetic composites. At the same time, optical techniques offer non-contact probes of dissipation, and MXenes and other emerging materials open new regimes of acoustic control. Conclusively, they are transforming SAW technology into a versatile, programmable platform for mediating complex interactions in next-generation electronic, photonic, and quantum systems. Full article

Precise manipulation and directed transport of micro- and nano-particles are cornerstones of emerging lab-on-a-chip technologies. Traditional optofluidic systems that combine optical tweezers with microfluidic channels enable long-range transport. However, they rely on fixed physical boundaries that lack reconfigurability. To bridge this gap, we propose a reconfigurable virtual optical waveguide (VOW) based on a discretized beam-shaping strategy. By superposing two orthogonally polarized shaped beams, we construct interference-free optical channels without physical boundaries. This platform enables programmable transport along complex trajectories, including space-filling Hilbert curves that maximize interaction path length, and shields the transport channel from perturbations induced by surrounding particles. Crucially, the VOW offers multi-dimensional sorting capabilities: (i) it performs precise size-dependent sieving via tunable channel widths, and (ii) it functions as an intrinsic material filter by stably guiding scattering-dominated particles (e.g., gold) while rejecting gradient-dominated dielectric ones. This work establishes a versatile, contactless strategy for adaptive optical logistics and on-chip material purification. Full article

Nano-tweezers, especially those based on photonic crystals and plasmonic structures, are powerful tools for trapping, manipulating, or accelerating nano-sized objects. However, the precise control of the inter-distance between trapped nanoparticles has rarely been considered. In this paper, we propose a mirror-symmetric optical conveyor belt, in which each unit contains three graded nano-slots. Through the optimized design of spacing between these nano-slots, the structure generates multiple trapping centers, enabling wavelength-selective control over trapping positions. The results show that, through dynamically shifting excitation wavelengths, the programmable bidirectional optical manipulation of nanoparticles can be achieved. Also, the inter-distance between trapped particles can be tuned with subwavelength precision. The proposed structure provides a versatile solution for lab-on-a-chip systems, especially for systems aiming to study the interactions between objects. Full article

The Mpemba effect refers to the counterintuitive situation in which a system initially farther from equilibrium can relax faster than one that starts closer to it. In quantum systems, the effect is enriched by the presence of coherent dynamics, dissipation, and metastable manifolds associated with long-lived Liouvillian modes. Here we demonstrate a giant Mpemba effect in open quantum systems, where relaxation can be either hyper-accelerated or dramatically slowed depending on the initial state. We focus on weakly-coupled particle-conserving bosonic networks, each of which independently relaxes rapidly to a unique stationary state. When a weak coherent interaction is introduced, the composite system typically develops slow metastable modes and a hierarchy of relaxation timescales. We show that by tailoring the interaction Hamiltonian, these slow modes can be effectively suppressed for a broad class of initial states satisfying a minimal global requirement, enabling ultrafast relaxation even when the system starts far from equilibrium. Conversely, other initial states—sometimes arbitrarily close to the stationary state—may remain trapped in the metastable manifold and decay anomalously slowly. This mechanism provides a general route to engineer giant Mpemba effects, offering new possibilities for controlling dissipative dynamics, accelerating state preparation, and manipulating relaxation processes in complex quantum devices. Full article

In this article, the motion control of ferromagnetic particles through varying a non-invasive magnetic field is addressed. Within an experimental test bench, three experiments are proposed to verify motion control, which consist of control of the distance between electromagnets, retention of particles over the flow, and manipulation of the direction of particle flow at a “Y”-type bifurcation emulating an “OR” gate. At each experimental stage, instrumented test benches were integrated with current, distance, and flow sensors, enabling measurement and feedback of the system’s physical variables. These benches were configured using pulse-width-modulation (PWM) and Proportional–Integral–Derivative (PID) controllers to regulate the current supplied to the electromagnets and, thereby, control the intensity of the induced electromagnetic field according to the requirements of each experiment. Different study cases were defined to analyze the operational limits of the system by varying the current influencing the electromagnetic field and the configuration of the electromagnets. The results describe the response of the magnetic field, the induced force, and the behavior of the suspended particles under each condition, providing elements to characterize the performance of the electromagnetic system in operational scenarios and contributing to the understanding of the phenomena associated with the non-invasive manipulation of ferromagnetic particles by means of controlled magnetic fields. Full article

The manipulation of host cellular metabolism is a key strategy for flaviviruses like Japanese encephalitis virus (JEV) to establish a productive infection. This study identifies the host NADase CD38 as a central regulator of this process. Using a CRISPR/Cas9-generated CD38 knockout (KO) TM3 cell model, we found that CD38 deficiency significantly restricted the production of infectious viral particles. While loss of CD38 also partially impaired viral entry, our central finding is that CD38 primarily promotes JEV infection by suppressing a host-intrinsic metabolic defense. We show that CD38 deficiency leads to a surge in intracellular NAD+, which sustains SIRT1 activity and inactivates p53, thereby blocking the mitochondrial apoptosis required for viral propagation. The dominance of this metabolic axis was confirmed through bidirectional pharmacological interventions; while SIRT1 inhibition using EX527 restored JEV replication, SIRT1 activation using SRT1720 suppressed it in wild-type cells. Our work reveals that JEV hijacks the CD38-NAD+-SIRT1-p53 axis to overcome host metabolic defenses in reproductive cell models, establishing CD38 as a promising therapeutic target. Full article

In ultrasonic nondestructive testing, maintaining the ultrasonic sensor in normal contact with curved surfaces is pivotal for acquiring valid defect signals. Replacing manual operation with a robotic arm ensures stable signal collection, while stable and fast trajectory planning for complex curved-surface tracking remains a key challenge. This research investigates gesture-driven robotic trajectory planning and impact optimization via the particle swarm optimization (PSO) algorithm in the robot joint space for rapid and smooth movement. Gesture trajectories are acquired via a Leap Motion device, with unified mapping established through spatial transformations among gesture, simulation, and experimental robot spaces. PSO is utilized to optimize trajectories, enhancing accuracy and controllability. Median filtering is applied to trajectory coordinate data to suppress errors from hand tremor and sensor limitations, followed by introducing a surface normal offset to generate pose matrices at each trajectory point. Systematic comparison of interpolation methods (polynomial, cubic spline, circular, cubic B-spline) reveals that cubic B-spline interpolation achieves the shortest execution time under angular acceleration constraints. The results show that PSO optimizes point-to-point trajectories based on 5-5-5 polynomial interpolation, with impact force and execution time as objectives, yielding the optimal trajectory with minimal time under acceleration constraints. This research provides valuable methodological references for robotic manipulator trajectory planning and optimization in complex curved-surface ultrasonic testing. Full article

This study investigated the combined effects of leaching agents and iron distribution on the migration behavior of pollutants in municipal solid waste incineration bottom ash (MSWI-BA). A column leaching experiment was designed where the control group (CK) employed deionized water with uniformly distributed iron. This baseline was systematically compared against treatment groups involving two leaching agents (Na₂CO₃, Na₂SO₄) and three iron distribution scenarios (Top, Bottom, and Removal). Compared to the CK, the introduction of Na₂CO₃ significantly intensified pollutant mobilization: the abundance of microplastics (MPs) increased by 49.33%, chloride leaching rose by 189.99%, and heavy metal (HM) concentrations (Cu, Cr, Pb, As) surged by 2.0–40.6 times. Furthermore, iron distribution played a critical regulatory role; specifically, manipulating iron placement further elevated MP abundance by 80.2% and chloride leaching by 191.03%. Morphological analysis indicated that MPs primarily existed as transparent or yellow particles, films, and fibers, characteristics that remained stable across treatments. Crucially, these findings offer engineering insights for real-world scenarios: retaining a bottom iron-rich layer during stockpiling can act as a reactive barrier to intercept pollutants, whereas carbonate-rich landfill environments require pH-buffering to mitigate MP co-migration. This study provides a theoretical basis for optimizing pretreatment processes (e.g., coordinated washing and magnetic separation) to ensure the safe resource recovery of BA. Full article

In recent years, passive cell manipulation in microfluidic devices has emerged as a crucial tool for biomedical and biotechnological applications, allowing control over cell positioning and behavior without the need for chemical labels or complex external forces. However, achieving precise and tunable modulation of cell dynamics remains a challenge, particularly with low-cost and non-invasive methods. In this work, we present a novel approach that leverages controlled acousto-mechanical perturbations (AMPs) to modulate cell arrangement and behavior in microchannels. By coupling a smartphone-driven audio speaker with a microfluidic device, acoustic signals are converted into mechanical vibrations of the tubing, generating AMPs that interact with hydrodynamically driven flows. Experiments with yeast cells and silica beads under different flow conditions revealed that acoustic stimulation induced periodic flow dynamics, with yeast cells showing tunable, flow-dependent responses while inert particles exhibited weak and stable modulation. Frequency-domain analysis highlighted a dominant response synchronized with the applied acoustic protocol, accompanied by higher-frequency components characteristic of acoustic actuation. These results demonstrate that simple, low-cost acoustic actuation revealed distinct dynamical responses between rigid inert particles and deformable biological cells and enable label-free cellular manipulation. The proposed platform offers a versatile, non-invasive, and accessible approach for controlled cell manipulation in microfluidics. Full article

The absolute positioning accuracy of industrial manipulators is frequently bottlenecked by the interplay of geometric tolerances and complex, unmodeled non-geometric parameter drifts. Traditional static kinematic models, predicated on rigid-body assumptions, often struggle to characterize these state-dependent dynamic behaviors. To bridge this gap, this study introduces a PSO-Driven Neuro-Symbolic State-Space Framework incorporating Adaptive Wavelet Networks, drawing inspiration from two biological principles: the collective swarm intelligence observed in bird flocking and fish schooling, and the localized receptive field structure of mammalian visual cortex neurons. By reformulating calibration as a latent state estimation problem, we model kinematic parameters as stochastic states. Crucially, the observation model fuses symbolic Denavit–Hartenberg (D–H) predictions with an Adaptive Wavelet Network (AWNN). The AWNN utilizes Mexican Hat kernels, whose morphology mirrors the center-surround antagonism of cortical receptive fields, and leverages their precise time–frequency localization to effectively learn complex, configuration-dependent residuals. The framework employs a robust decoupled strategy. First, Particle Swarm Optimization (PSO) executes meta-optimization to autonomously determine hyperparameters, thereby mitigating initialization sensitivity. Second, a recursive inference engine estimates the hybrid states. Third, a global batch optimization refines the symbolic parameters against a frozen non-geometric error field. Experimental validation on an ABB IRB 120 robot (400 datasets) yielded a test RMSE of 0.73 mm. Compared to the standard Levenberg–Marquardt method, our approach reduced the RMSE by 40.16% and the maximum error by 35.71% (down to 0.99 mm). Moreover, it outperforms the state-of-the-art RPSO-DCFNN baseline by 12.05% while maintaining high computational efficiency (convergence within 20.15 s). These findings underscore the superiority of the proposed bio-inspired state-space fusion strategy for high-precision industrial applications. Full article

Particulate matter (PM) emissions are common in technological processes, and effective mitigation requires gas pre-treatment before high-efficiency filtration to reduce fine and ultrafine PM that are particularly dangerous to the human health. This study evaluates a multichannel electric field manipulator (agglomerator) as a flow pre-treatment stage and investigates the aerodynamic conditions that govern particle–gas flow distribution and variation in trajectories and dynamics at different flow rates. These factors provide meaningful assumptions about the possible behavior of particles in the flow, and they are critical for optimizing an agglomeration and its intensity. Such phenomena can have an impact on the probability of agglomeration in the manipulator channels, i.e., the adherence of small particles into larger ones, and this allows for improving the design and operating conditions of the apparatus. Gas flow velocities and pressure were analyzed experimentally at various cross-sectional points in the inlet and outlet ducts at inflow rates of 3.4 L/s and 50 L/s. The static inlet pressure of the manipulator ranged from 8 Pa to 178 Pa. This study provides new insights into flow pre-treatment using the electric field mechanism in a multichannel modular apparatus and provides a reasonable understanding of the necessary characteristics of gas flow distribution to support subsequent improvements targeting higher agglomeration. Full article

With the rapid development of artificial intelligence and robotics, the application of robotics in the chemical domain is driving a transformation toward intelligent and large-scale research in chemistry and material science. However, sample weighing and synthesis reactions constitute critical stages in chemical experiments, which presents significant challenges for robotic gripping of reagent tubes to achieve precise measurements and collision-free path planning autonomously. Therefore, this study aims to address automation of manipulation in chemical experiments, achieving collision-free path planning and optimization under multi-objective constraints. Specifically, the trajectory planning problem for such tasks is formulated as a multi-objective optimization to minimize motion time, joint jerk and energy consumption. Then, an improved optimized multi-objective particle swarm optimization (OMOPSO) algorithm that incorporates seventh-order polynomial interpolation is proposed to improve the smoothness of robotic motion trajectory. A uniform Pareto front is obtained through a reference vector-guided leader selection mechanism, and an update strategy based on $\epsilon$-domination, and inflection point selection is proposed to balance the convergence and diversity of the solution set. Finally, simulation results and demonstrations on a manipulation platform have fully validated the feasibility and practicality of the proposed method, which further provides a reference for robotic execution of chemical experiments. Full article

Accurate perception of deformable objects on the lunar surface is essential for autonomous construction and in situ resource utilization (ISRU) missions. However, the lack of representative lunar imagery limits the development of data-driven models for pose estimation and manipulation. We present SynBag 1.0, a large-scale synthetic dataset designed for training and benchmarking six-degree-of-freedom (6-DoF) pose estimation algorithms on regolith-filled construction bags. SynBag 1.0 employs rigid-body representations of bag meshes designed to approximate deformable structures with varied levels of feature richness. The dataset was generated using a novel framework titled MoonBot Studio, built in Unreal Engine 5 with physically consistent lunar lighting, low-gravity dynamics, and dynamic dust occlusion modeled through Niagara particle systems. SynBag 1.0 contains approximately 180,000 labeled samples, including RGB images, dense depth maps, instance segmentation masks, and ground-truth 6-DoF poses in a near-BOP format. To verify dataset usability and annotation consistency, we perform zero-shot 6-DoF pose estimation using a state-of-the-art model on a representative subset of the dataset. Variations span solar azimuth, camera height, elevation, dust state, surface degradation, occlusion level, and terrain type. SynBag 1.0 establishes one of the first open, physically grounded datasets for 6-DoF-object perception in lunar construction and provides a scalable basis for future datasets incorporating soft-body simulation and robotic grasping. Full article

High-resolution contact localization and three-axis force estimation are crucial for human–robot interaction and precision manipulation, yet the sensing area is limited by channel density and wiring cost. Sparse strain readout makes joint estimation of location and three-axis force challenging due to cross-axis coupling and nonlinear responses, while dense arrays or extensive calibration increase complexity. We present a sparse strain-node tactile interface device (SSTID) whose three-module layout is optimized via particle swarm optimization to maximize informative response overlap, enabling contact localization $(x,y)$ and three-axis force $(F_x,F_y,F_z)$ estimation using only nine strain channels. We further propose a strain-node contact-state decoding framework (SCDF) implemented with a lightweight multilayer perceptron and trained via a two-stage sim-to-real strategy, including FEM pretraining followed by few-shot real-data adaptation. Experiments demonstrate accurate contact-state decoding with full-workspace characterization, supporting low-cost and scalable deployment of sparse tactile interfaces. Full article