scispace - formally typeset
X

Xiang Zhang

Researcher at Baylor College of Medicine

Publications -  3483
Citations -  144843

Xiang Zhang is an academic researcher from Baylor College of Medicine. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 154, co-authored 1733 publications receiving 117576 citations. Previous affiliations of Xiang Zhang include University of California, Berkeley & University of Texas MD Anderson Cancer Center.

Papers
More filters
Journal ArticleDOI

Extraordinary optical transmission induced by excitation of a magnetic plasmon propagation mode in a diatomic chain of slit-hole resonators

TL;DR: In this article, the magnetic-resonance nanostructures in a metal surface were used to realize extraordinary optical transmission (EOT) in a one-dimensional diatomic chain of slit-hole resonator (SHR).
Journal ArticleDOI

Stable Casimir equilibria and quantum trapping

TL;DR: By coating one object with a low–refractive index thin film, it is shown that the Casimir interaction between two objects of the same material can be reversed at short distances and preserved at long distances so that two objects can remain without contact at a specific distance.
Journal ArticleDOI

Value of three-dimensional hysterosalpingo-contrast sonography with SonoVue in the assessment of tubal patency.

TL;DR: To investigate the accuracy of transvaginal three‐dimensional hysterosalpingo‐contrast sonography using SonoVue (3D SoniVue‐HyCoSy) in the assessment of Fallopian tubal patency, a high-resolution diffraction-based method is used.
Journal ArticleDOI

A cohesive zone model for predicting delamination suppression in z-pinned laminates

TL;DR: In this paper, a cohesive zone model based finite element analysis of delamination resistance of z-pin reinforced double cantilever beam (DCB) is presented, where each zpin bridging force is governed by a traction-separation law derived from a meso-mechanical model of the pin pullout process.
Proceedings ArticleDOI

Dual-Regularized One-Class Collaborative Filtering

TL;DR: This paper addresses the ambiguity challenge by integrating two state-of-the-art one-class collaborative filtering methods to enjoy the best of both worlds, and tackles the sparseness challenge by exploiting the side information from both users and items.