A Multiplexed 32 × 32 2D Matrix Array Transducer for Flexible Sub-Aperture Volumetric Ultrasound Imaging.

A fully-sampled two-dimensional (2D) matrix array ultrasonic transducer is essential for fast and accurate three-dimensional (3D) volumetric ultrasound imaging. However, these arrays, usually consisting of thousands of elements, not only face challenges of poor performance and complex wiring due to high-density elements and small element sizes but also put high requirements for electronic systems. Current commercially available fully-sampled matrix arrays, dividing the aperture into four fixed sub-apertures to reduce system channels through multiplexing are widely used. However, the fixed sub-aperture configuration limits imaging flexibility and the gaps between sub-apertures lead to reduced imaging quality. In this study, we propose a high-performance multiplexed matrix array by the design of 1-3 piezocomposite and gapless sub-aperture configuration, as well as optimized matching layer materials. Furthermore, we introduce a sub-aperture volumetric imaging method based on the designed matrix array, enabling high-quality and flexible 3D ultrasound imaging with a low-cost 256-channel system. The influence of imaging parameters, including the number of sub-apertures and steering angle on imaging quality was investigated by simulation, in vitro and in vivo imaging experiments. The fabricated matrix array has a center frequency of 3.4 MHz and a -6 dB bandwidth of above 70%. The proposed sub-aperture volumetric imaging method demonstrated a 10% improvement in spatial resolution, a 19% increase in signal-to-noise ratio, and a 57.7% increase in contrast-to-noise ratio compared with the fixed sub-aperture array imaging method. This study provides a new strategy for high-quality volumetric ultrasound imaging with a low-cost system.

Modeling Event Propagation via Graph Biased Temporal Point Process.

Temporal point process is widely used for sequential data modeling. In this article, we focus on the problem of modeling sequential event propagation in graph, such as retweeting by social network users and news transmitting between websites. Given a collection of event propagation sequences, the conventional point process model considers only the event history, i.e., embed event history into a vector, not the latent graph structure. We propose a graph biased temporal point process (GBTPP) leveraging the structural information from graph representation learning, where the direct influence between nodes and indirect influence from event history is modeled. Moreover, the learned node embedding vector is also integrated into the embedded event history as side information. Experiments on a synthetic data set and two real-world data sets show the efficacy of our model compared with conventional methods and state-of-the-art ones.

Electric field induced hydrogenation of silicene.

An alternative approach for hydrogenation of silicene is proposed through applying an external electric field in order to reduce the reaction energy barrier based on density functional theory calculations. It is found that a positive perpendicular electric field F can act as a catalyst to reduce the energy barrier of H2 dissociative adsorption on silicene, which facilitates the hydrogenation of silicene. In addition, it is found that the barrier decreases as F increases, and when F is above 0.05 a.u. (1 a.u. = 5.14 × 10(11) V m(-1)), the barrier is quite low and hydrogenation of silicene can take place efficiently at room temperature. The catalytic effect of the electric field on hydrogenation of silicene is induced by the redistribution of atomic charge under the electric field, which would change the chemical activity of silicene significantly.