Skip To Main Content
Jie Ying on Muck Rack

Jie Ying

As seen in: Color Research & Application, Nature, Wiley Online Library, arXiv, Frontiers, Taylor & Francis Online, ACS Publications, Virology Journal, Royal Society of Chemistry, Chemistry World and
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile

Get in touch with Jie

Contact Jie, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Articles

Efficient nitrogen elimination and microbial community characteristics in a sulfur-based autotrophic bioreactor at high nitrate loading

|
By Yuan Gao Verified, Jie Ying
| Taylor & Francis Online
ABSTRACT This study systematically evaluated the nitrogen removal performance and microbial community characteristics of the sulfur-based autotrophic denitrification (SAD) process under various influent loads (15–140 mg N/L). After 22 days acclimation at 15 mg N/L, the system achieved >90% stable NO3−-N removal. Notably, the system demonstrated exceptional stability at a high load of 140 mg N/L, with an average total nitrogen removal rate of 98% (peaking at 99%).
Open in Who Shared Issue with article?

Computer Science > Robotics

|
By Susheela Sharma, Jie Ying
| arXiv
Estimating deformable object states remains a fundamental challenge in robotics and simulation. We propose a novel factor graph-based framework for probabilistic mesh state estimation of deformable objects. The method directly updates a tetrahedral mesh, a rich and physically-grounded representation of an environment, by combining physics priors, noisy sensor measurements, and temporal smoothness constraints within a unified probabilistic formulation.
Open in Who Shared Issue with article?

Computer Science > Computer Vision and Pattern Recognition

|
By Muhammad Abdullah Verified, Jie Ying
| arXiv
Vision foundation models (VFMs) are valuable in data-scarce domains such as surgery, where a single pretrained backbone can provide rich representations for many downstream tasks. Yet the dominant self-supervised pretraining paradigm uses only RGB images, leaving readily available complementary signals, such as depth maps, unused.
Open in Who Shared Issue with article?
See all 51 articles