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ABSTRACT Electromagnetic (EM) fields have been used in technologies such as communication, imaging, and energy transfer. In recent years, there has been growing interest in exploiting EM fields for the actuation of functional materials, enabling applications in soft robotics, biomedical devices, active metamaterials, and shape‐morphing systems. These materials are often composites that incorporate EM‐responsive components, granting them a remarkable versatility in responsiveness. Specifically, EM fields can induce actuation through static magnetic force and torque, Lorentz forces, or thermal effects via eddy currents and magnetic hysteresis losses. In addition, EM fields can be harnessed for sensing, wireless communication, and power transfer, extending their role far beyond actuation. The coexistence of such diverse mechanisms makes EM one of the most powerful and integrative external stimuli for multifunctional materials. This review provides the first holistic overview of EM‐active material systems. We systematically organize recent progress in EM‐based actuation, sensing, communication, and wireless power transfer, highlighting the fundamental principles, experimental demonstrations, and emerging design strategies. Approaches that integrate multiple EM‐driven functionalities and the role of optimization and machine learning in advancing design and control are discussed. By consolidating these advances, this review establishes a roadmap for the development of next‐generation EM‐enabled intelligent materials and devices. 

In the realm of computational science and engineering, constructing models that reflect real-world phenomena requires solving partial differential equations (PDEs) with different conditions. Recent advancements in neural operators, such as deep operator network (DeepONet), which learn mappings between infinite-dimensional function spaces, promise efficient computation of PDE solutions for a new condition in a single forward pass. However, classical DeepONet entails quadratic complexity concerning input dimensions during evaluation. Given the progress in quantum algorithms and hardware, here we propose to utilize quantum computing to accelerate DeepONet evaluations, yielding complexity that is linear in input dimensions. Our proposed quantum DeepONet integrates unary encoding and orthogonal quantum layers. We benchmark our quantum DeepONet using a variety of PDEs, including the antiderivative operator, advection equation, and Burgers' equation. We demonstrate the method's efficacy in both ideal and noisy conditions. Furthermore, we show that our quantum DeepONet can also be informed by physics, minimizing its reliance on extensive data collection. Quantum DeepONet will be particularly advantageous in applications in outer loop problems which require exploring parameter space and solving the corresponding PDEs, such as uncertainty quantification and optimal experimental design. 

The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying full‐field heterogeneous elastic properties of soft materials using traditional engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in data‐driven models for learning full‐field mechanical responses, such as displacement and strain, from experimental or synthetic data. However, research studies on inferring full‐field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, a physics‐informed machine learning approach is proposed to identify the elasticity map in nonlinear, large deformation hyperelastic materials. This study reports the prediction accuracies and computational efficiency of physics‐informed neural networks (PINNs) in inferring the heterogeneous elasticity maps across materials with structural complexity that closely resemble real tissue microstructure, such as brain, tricuspid valve, and breast cancer tissues. Further, the improved architecture is applied to three hyperelastic constitutive models: Neo‐Hookean, Mooney Rivlin, and Gent. The improved network architecture consistently produces accurate estimations of heterogeneous elasticity maps, even when there is up to 10% noise present in the training data. 

Abstract Active metamaterials are a type of metamaterial with tunable properties enabled by structural reconfigurations. Existing active metamaterials often achieve only a limited number of structural reconfigurations upon the application of an external load across the entire structure. Here, a selective actuation strategy is proposed for inhomogeneous deformations of magneto‐mechanical metamaterials, which allows for the integration of multiple elastic wave‐tuning functionalities into a single metamaterial design. Central to this actuation strategy is that a magnetic field is applied to specific unit cells instead of the entire metamaterial, and the unit cell can transform between two geometrically distinct shapes, which exhibit very different mechanical responses to elastic wave excitations. The numerical simulations and experiments demonstrate that the tunable response of the unit cell, coupled with inhomogeneous deformation achieved through selective actuation, unlocks multifunctional capabilities of magneto‐mechanical metamaterials such as tunable elastic wave transmittance, elastic waveguide, and vibration isolation. The proposed selective actuation strategy offers a simple but effective way to control the tunable properties and thus enhances the programmability of magneto‐mechanical metamaterials, which also expands the application space of magneto‐mechanical metamaterials in elastic wave manipulation. 

ObjectiveTo identify lifting actions and count the number of lifts performed in videos based on robust class prediction and a streamlined process for reliable real-time monitoring of lifting tasks. BackgroundTraditional methods for recognizing lifting actions often rely on deep learning classifiers applied to human motion data collected from wearable sensors. Despite their high performance, these methods can be difficult to implement on systems with limited hardware resources. MethodThe proposed method follows a five-stage process: (1) BlazePose, a real-time pose estimation model, detects key joints of the human body. (2) These joints are preprocessed by smoothing, centering, and scaling techniques. (3) Kinematic features are extracted from the preprocessed joints. (4) Video frames are classified as lifting or nonlifting using rank-altered kinematic feature pairs. (5) A lifting counting algorithm counts the number of lifts based on the class predictions. ResultsNine rank-altered kinematic feature pairs are identified as key pairs. These pairs were used to construct an ensemble classifier, which achieved 0.89 or above in classification metrics, including accuracy, precision, recall, and F1 score. This classifier showed an accuracy of 0.90 in lifting counting and a latency of 0.06 ms, which is at least 12.5 times faster than baseline classifiers. ConclusionThis study demonstrates that computer vision-based kinematic features could be adopted to effectively and efficiently recognize lifting actions. ApplicationThe proposed method could be deployed on various platforms, including mobile devices and embedded systems, to monitor lifting tasks in real-time for the proactive prevention of work-related low-back injuries. 

Abstract Navigating the complex and high‐flow environment of human vasculature remains a major challenge for conventional endovascular tools and externally actuated tethered systems. While catheter‐based approaches are the clinical standard, their limited steerability and force transmission hinder access to tortuous or distal vessels, especially in the brain. Untethered robotic systems have emerged as a promising alternative for enhanced flexibility and reachability. However, most designs struggle against the high, pulsatile blood flow in human arteries. Here, the study presents a magnetically actuated milli‐spinner robot that overcomes existing limitations in navigating complex and high‐flow vasculature. Capable of swimming at 23 cm·s⁻¹(73 body lengths per second), the milli‐spinner enables rapid, stable navigation through complex vasculature. This performance is driven by its hollow cylindrical structure with integrated helical fins and slits, which together generate a spinning‐induced flow field that enhances propulsion efficiency and allows the robot to maintain stability and control even in dynamic, pulsatile blood flow environments. In addition to its navigation capabilities, the milli‐spinner enables multifunctional treatment, including localized suction and shear for efficient clot removal, targeted drug delivery, and in situ embolization for aneurysm treatment. These features establish the milli‐spinner as a versatile and powerful platform for next‐generation, untethered endovascular interventions.