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We demonstrated a nonvolatile electrically reconfigurable metasurface based on low-loss phase-change materials Sb₂Se₃with phase-only (~0.25π) modulation in the free-space. The tunable metasurface is robust against reversible switching over 1,000 times. 

Brain–computer interfaces (BCIs) are neural prosthetics that enable closed-loop electrophysiology procedures. These devices are currently used in fundamental neurophysiology research, and they are moving toward clinical viability for neural rehabilitation. State-of-the-art BCI experiments have often been performed using tethered (wired) setups in controlled laboratory settings. Wired tethers simplify power and data interfaces but restrict the duration and types of experiments that are possible, particularly for the study of sensorimotor pathways in freely behaving animals. To eliminate tethers, there is significant ongoing research to develop fully wireless BCIs having wireless uplink of broadband neural recordings and wireless recharging for long-duration deployment, but significant challenges persist. BCIs must deliver complex functionality while complying with tightly coupled constraints in size, weight, power, noise, and biocompatibility. In this article, we provide an overview of recent progress in wireless BCIs and a detailed presentation of two emerging technologies that are advancing the state of the art: ultralow-power wireless backscatter communication and adaptive inductive resonant (AIR) wireless power transfer (WPT). 

Modeling of power distribution system components that are valid for a wide range of frequencies are crucial for highly accurate modeling of electromagnetic transient (EMT) events. This has recently become of interest due to the improvements needed for the resilient operation of distribution systems. Vector fitting (VF) is a very popular and commonly used algorithm for wide band representations of power system components in EMT simulations. In this research, we present a new multi-input rational approximation algorithm (MIAAA) and illustrate its advantages with respect to VF using examples of approximations of admittance matrices discussed in the literature. We show that MIAAA not only outperforms VF in terms of achieving better accuracy using lesser number of poles, but also has no numerical issues achieving convergence. In contrast to VF, MIAAA is not sensitive to the location of input sample points and it does not require good estimates for the location of the desired approximation poles. The novelty of this research work is the use of recent mathematical results to solve existing challenges in distribution system modeling and to develop rational approximations for power system models that intend to be optimal in terms of accuracy and performance.