About

Rui Ni is an associate professor in the Department of Mechanical Engineering at Johns Hopkins University. His research focuses on the fundamental science of turbulence and multi-phase flows that involve more than one phase, such as liquid, solid, or gas. His work has applications in next-generation energy systems, environmental engineering, and physiological flows in the human body. Ni earned his PhD in physics from the Chinese University of Hong Kong in 2011. He worked as a postdoctoral scholar at Yale University and Wesleyan University before joining Penn State University in 2015, where he held the Kenneth Kuan-Yun Kuo Early Career Professorship in mechanical engineering. He has received several awards and honors, including a grant from the Gordon and Betty Moore Foundation's Experimental Physics Investigators Initiative, the NSF CAREER Award in fluid dynamics, and the ACS-PRF New Investigator Award.

Research topics

• Physics
• Mechanics
• Virology
• Statistical physics
• Thermodynamics
• Medicine

Selected publications

• Integrative analysis of physiology, transcriptomics, and metabolomics unveils mechanisms in soybean (Glycine max (Linn.) Merr) response to alkaline stress

  Plant Science · 2026-02-20 · 1 citations

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  PublisherDOI

• An effective detection model based on YOLO for pore defects in additive manufacturing

  Scientific Reports · 2026-03-21

  articleOpen access1st author

  Microscopic imaging serves as a crucial method for assessing the quality of selective laser melting (SLM). Traditional approaches rely on manual inspection, which limits their efficiency and reproducibility. To address the demand for defect detection and analysis, this paper proposes a synergistic method for analyzing pore defects in microscopic images, integrating image segmentation with polynomial fitting. We designed a high-performance image segmentation model. Its capabilities are enhanced through an adaptive curved learning rate adjustment strategy, an attention-based feature extraction module, and a lightweight feature fusion network. Additionally, the model automatically calculates and quantifies the pixel proportion of pore defects within micrographs. Experiments conducted on a constructed SLM pore defect microscopic image dataset demonstrated excellent performance, enabling effective calibration and quantification of defect information. Chebyshev polynomials are employed to fit the nonlinear relationship between key process parameters and porosity. Based on these results, we conducted an in-depth analysis of how different process parameters influence pore defect formation, revealing the intrinsic correlation between process parameters and defects. This study provides an effective automated detection and analysis tool for SLM quality assessment and analysis.

  PublisherOA PDFDOI

• p62/ <scp>SQSTM1</scp> Condensation Modulates Mitochondrial Clustering to Participate in Mitochondrial Quality Control

  Aging Cell · 2026-02-01

  articleOpen access

  Mitochondrial quality control is tightly associated with aging-related neurodegenerative diseases such as Parkinson's disease, Alzheimer's disease, amyotrophic lateral sclerosis (ALS), and frontotemporal dementia (FTD). Previous studies reported that ALS/FTD-associated protein p62 drives "mitochondrial clustering" (perinuclear clustering of fragmented and swollen mitochondria) during PINK1/Parkin-mediated mitophagy, but the underlying molecular mechanism, especially the precise role of p62 in mitochondrial clustering during mitophagy and the potential relationship between the mitochondrial quality control mediated by p62 and disease pathogenesis of ALS/FTD, remains unclear. Here, using cell biology in combination with an optogenetic tool, we show that the phase separation (condensation) of p62 mediates the clustering of damaged mitochondria to form "grape-like" clusters during PINK1/Parkin-mediated mitophagy, which is tightly associated with aging-related neurodegenerative diseases. In addition, our data suggest this mitochondrial clustering process is an arrest mechanism driven by p62 condensation (beyond the function of other autophagy receptors in mitophagy), which acts as a "brake" to reduce the surface area of dysfunctional mitochondria within cytoplasm for minimizing mitochondrial turnover in cells. Moreover, ALS/FTD-related pathological mutations perturb p62 condensation, thereby inhibiting mitochondrial clustering and destroying the "brake" machinery of mitochondrial quality control. Together, our data highlight how p62 condensation modulates organelle quality control in cell biology, and the important role of p62 condensation in both physiology and pathology.

  PublisherDOI

• FATE (Fish Aquarium with a Turbulent Environment): a turbulence-control facility for quantifying fish–flow interactions and collective behavior

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-27

  articleOpen accessSenior author

  Abstract Despite its ubiquity in natural flows, the effects of turbulence on fish locomotion and behavior remain poorly understood. The prevailing hypothesis is that these effects depend on the spatial and temporal scales of the turbulence relative to the fish’s size and swimming speed. But in conventional facilities, turbulence usually increases with mean flow, which forces higher swimming speeds and can leave these relative scales unchanged. We therefore present a novel experimental facility that leverages a jet array to decouple the turbulence from the mean flow and systematically control its scales. This approach allows the ratio of turbulent to fish inertial scales to be varied over an order of magnitude, providing a controlled framework for quantifying fish–turbulence interactions. The facility also supports experiments probing strategies fish may use to cope with turbulence, including collective behaviors. Insights from this work have broader implications for ecological studies and engineering applications, including the design of effective fishways and bio-inspired underwater vehicles.

  PublisherDOI

• Neural Network Classifier for Ti6Al4V Selective Laser Melting Process Classification via Elephant Herding Optimization with Multi-Learning

  Applied Sciences · 2026-02-10

  articleOpen access

  Classification techniques, reliant on annotated data for autonomous decision training, have become pivotal tools in diverse domains. These techniques rely on models like Backpropagation Neural Networks (BPNNs). However, BPNNs frequently trap local optima, leading to suboptimal classification accuracy, and its convergence speed is relatively slow, which affects efficiency in complex and non-linear process data classification applications. Existing optimization algorithms struggle to balance global exploration and local exploitation when adjusting BPNNs. Addressing these limitations, this paper proposes a BP classifier based on an Elephant Herding Optimization with Multi-Learning strategy (MLEHO), termed MLEHO-BPC. The proposed MLEHO establishes a triple learning framework. Firstly, a collective learning stage incorporates two different adaptive operators into the original algorithm to strengthen global exploration. Subsequently, a group learning stage is designed, integrating exemplar, deskmate, and random learning methods to enhance convergence efficiency. Finally, a tutorship learning stage, guided by fitness value discrimination, empowers the algorithm to escape local optima. Benchmark function tests confirm MLEHO’s superiority in convergence speed and stability over comparative algorithms. Furthermore, MLEHO replaces traditional gradient descent, reformulating the BPNN’s update mechanism to optimize weights and thresholds. Validated on classification datasets and a Ti6Al4V process classification problem, MLEHO-BPC demonstrates exceptional classification accuracy and robustness against other algorithm classifiers.

  PublisherDOI

• Dynamics of bubble collision and coalescence in three-dimensional turbulent flows

  Journal of Fluid Mechanics · 2025-10-01 · 3 citations

  articleOpen accessSenior authorCorresponding

  Turbulence exhibits a striking duality: it drives concentrated substances apart, enhancing mixing and transport, while simultaneously drawing particles and bubbles into collisions. Little experimental data exist to clarify the latter process due to challenges in techniques for resolving bubble pairs from afar to coalescence via turbulent entrainment, film drainage and rupture. In this work, we tracked pairs of bubbles across nearly four orders of magnitude in spatial resolution, capturing the entire dynamics of collision and coalescence. The resulting statistics show that critical variables exhibit scalings with bubble size in ways that are different from some classical models, which were developed based on assumptions that bubble collision and coalescence only mirror the key scales of the surrounding turbulence. Furthermore, contrary to classical models which suggest that coalescence favours slow collision velocity, we find a ‘Goldilocks zone’ of relative velocities for bubble coalescence, where there is an optimal coalescence velocity that is neither too high nor too low. This zone arises from the competition between bubble–bubble and bubble–eddy interactions. Incorporating this zone into the new model yields excellent agreement with experimental results, laying a foundation for better predictions for many multiphase flow systems.

  PublisherOA PDFDOI

• Research on Driver Fatigue Detection Method Based on Convolutional Neural Networks Using Eye Features

  2025-09-19

  article1st authorCorresponding

  Addressing the performance bottleneck of traditional fatigue detection methods in eye microstructure recognition, this study designs a multi-scale fatigue state recognition model based on convolutional neural networks. Utilizing dual-channel thermal maps as input, the model integrates SE attention mechanisms with inter-frame sliding prediction strategies to enhance dynamic eye-openingclosing variation modeling capabilities on a typical CNN architecture. Experiments conducted on a dataset comprising 67,200 frames demonstrate that the proposed model achieves 92.8% accuracy, 90.4% recall, and an F1-score of 0.913. These results represent an average improvement of 6.5% over shallow CNNs and 11.1% over SVM, effectively enhancing the stability and precision of fatigue detection.

  PublisherDOI

• Scripted Role-Playing Games and Emotional Deficit among Contemporary Chinese Youth

  Highlights in Art and Design · 2025-11-27

  articleOpen access1st authorCorresponding

  This article examines the rise of immersive entertainment in China, with a particular focus on live-action role-playing games (LARPs) and their commodification of emotional labor. The study aimed to explore how players experience and negotiate scripted intimacy within these games. Data were collected through ten unstructured interviews lasting 60–90 minutes, and thematic analysis was applied to identify recurring patterns in participants’ accounts. Findings show that players actively invest emotions to gain access to intense and scripted experiences unavailable in daily life. While most respondents distinguish between fictional and real interactions, some reported difficulty detaching from game-induced emotions, leading to psychological burdens. The analysis further reveals that the popularity of LARPs reflects a wider emotional deficit in contemporary Chinese society, where intimacy is increasingly mediated by commodified scripts. This research contributes to debates on emotional and affective labor by situating LARPs within the logic of capitalist commodification, highlighting both the cultural significance and the psychological risks of immersive entertainment.

  PublisherOA PDFDOI

• Author response for "Ti6Al4V single-track formation optimization for selective laser melting using a modified crow search algorithm"

  2025-11-12

  peer-review1st authorCorresponding
  PublisherDOI

• Effect of lycium barbarum polysaccharide on ameliorating high-fat diet-damaged spermatogenesis via PCSK9 and TLR4 signaling pathways in obese mice

  Molecular Biology Reports · 2025-08-18

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Recent grants

• CAREER: Multiscale experimental framework on dynamics of deformable particles in turbulent dispersed multiphase flow

  NSF · $448k · 2018–2023

• Turbulent multiphase flow with interfacial mass and heat transfer: linking microscopic physics to macroscopic mixing

  NSF · $134k · 2018–2021

• Turbulent multiphase flow with interfacial mass and heat transfer: linking microscopic physics to macroscopic mixing

  NSF · $150k · 2017–2018

• CAREER: Multiscale experimental framework on dynamics of deformable particles in turbulent dispersed multiphase flow

  NSF · $508k · 2017–2019

Frequent coauthors

• Ke‐Qing Xia

  29 shared

• Nicholas T. Ouellette

  Mechanics' Institute

  23 shared

• Ashwanth Salibindla

  Johns Hopkins University

  23 shared

• Shiyong Tan

  Johns Hopkins University

  21 shared

• Ashik Ullah Mohammad Masuk

  Johns Hopkins University

  21 shared

• Yuanlin Wu

  Daping Hospital

  20 shared

• Yanping Li

  Beijing Shijitan Hospital

  20 shared

• Greg Voth

  19 shared

Awards & honors

• Gordon and Betty Moore Foundation's Experimental Physics Inv…
• NSF CAREER Award in fluid dynamics
• ACS-PRF New Investigator Award
• Kenneth Kuan-Yun Kuo Early Career Professorship in mechanica…