In this paper, a reflective metasurface is designed, fabricated, and experimentally demonstrated to generate an orbital angular momentum (OAM) vortex wave in radio frequency domain. Theoretical formula of phase-shift distribution is deduced and used to design the metasurface producing vortex radio waves. The prototype of a practical configuration is designed, fabricated, and measured to validate the theoretical analysis at 5.8 GHz. The simulated and experimental results verify that the vortex waves with different OAM mode numbers can be flexibly generated by using sub-wavelength reflective metasurfaces. The proposed method and metasurface pave a way to generate the OAM vortex waves for radio and microwave wireless communication applications.

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In this paper, a mechanically reconfigurable circular array with single-arm spiral antennas (SASAs) is designed, fabricated, and experimentally demonstrated to generate broadband circularly polarized orbital angular momentum (OAM) vortex waves in radio frequency domain. With the symmetrical and broadband properties of single-arm spiral antennas, the vortex waves with different OAM modes can be mechanically reconfigurable generated in a wide band from 3.4 GHz to 4.7 GHz. The prototype of the circular array is proposed, conducted, and fabricated to validate the theoretical analysis. The simulated and experimental results verify that different OAM modes can be effectively generated by rotating the spiral arms of single-arm spiral antennas with corresponding degrees, which greatly simplify the feeding network. The proposed method paves a reconfigurable way to generate multiple OAM vortex waves with spin angular momentum (SAM) in radio and microwave satellite communication applications.

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This study develops a two-part hidden Markov model (HMM) for analyzing semicontinuous longitudinal data in the presence of missing covariates. The proposed model manages a semicontinuous variable by splitting it into two random variables: a binary indicator for determining the occurrence of excess zeros at all occasions and a continuous random variable for examining its actual level. For the continuous longitudinal response, an HMM is proposed to describe the relationship between the observation and unobservable finite-state transition processes. The HMM consists of two major components. The first component is a transition model for investigating how potential covariates influence the probabilities of transitioning from one hidden state to another. The second component is a conditional regression model for examining the state-specific effects of covariates on the response. A shared random effect is introduced to each part of the model to accommodate possible unobservable heterogeneity among observation processes and the nonignorability of missing covariates. A Bayesian adaptive least absolute shrinkage and selection operator (lasso) procedure is developed to conduct simultaneous variable selection and estimation.The proposed methodology is applied to a study on the Alzheimer's Disease Neuroimaging Initiative dataset. New insights into the pathology of Alzheimer's disease and its potential risk factors are obtained. K E Y W O R D SBayesian adaptive lasso, hidden Markov models, nonignorable missing covariates, semicontinuous data, two-part models