About

Kate Ren is an Assistant Professor in the Department of Marketing and Logistics at the Fisher College of Business. She joined the college after earning her Ph.D. in Supply Chain Management from the University of Maryland, where she received the Ann G. Wylie Dissertation Award. Her research centers on the supply chain-marketing interface, with a focus on solving real-world problems in retail operations. She primarily investigates the impact of omnichannel operations on firms’ financial and operational performance. Dr. Ren has collaborated closely with several leading North American retailers to collect proprietary datasets and analyze them using big-data analytics. Her research has been published in several leading journals, including Production and Operations Management, Journal of Operations Management, and Journal of Business Logistics. In addition to her research, Dr. Ren teaches core undergraduate and working professional MBA courses in logistics and supply chain management, covering topics such as inventory management, logistics, e-commerce, and the interaction between online and offline marketing.

Research topics

• Artificial Intelligence
• Computer Science
• Geography
• Remote sensing
• Algorithm
• Data Mining
• Optics
• Materials science
• Physics
• Computer vision

Selected publications

• Association between frailty and postoperative delirium after transcatheter aortic valve replacement: a meta-analysis

  Frontiers in Psychiatry · 2026-05-21

  articleOpen accessSenior author

  Background Postoperative delirium (POD) is a common complication following transcatheter aortic valve replacement (TAVR) and is associated with adverse outcomes in older patients. Frailty, a multidimensional geriatric syndrome, has been increasingly recognized as a potential risk factor for POD. However, existing evidence remains inconsistent. This meta-analysis aimed to evaluate the association between frailty and POD after TAVR. Methods A systematic search of PubMed, Embase, and Web of Science was conducted from inception to January 22, 2026. Cohort studies evaluating the association between preprocedural frailty and POD after TAVR were included. Odds ratios (ORs) with 95% confidence intervals (CIs) were pooled using a random-effects model accounting for the influence of potential heterogeneity. Results Ten cohort studies involving 7,702 patients were included. Frailty was present in 2,062 (26.8%) patients, and 786 (10.2%) developed POD. Pooled analysis showed that frailty was significantly associated with an increased risk of POD after TAVR (OR: 2.17, 95% CI: 1.60–2.95; I 2 = 55%). The association was stronger in studies with sample size ≥ 500 compared with < 500 (OR: 2.74 vs. 1.38; p for subgroup difference < 0.001). The effect estimates were consistent across subgroups stratified by study design, age, sex, frailty assessment methods, follow-up duration, analytic models, and study quality (all p for subgroup difference > 0.05). Notably, studies using CAM-ICU to diagnose POD showed a stronger association than those using DSM criteria or other methods (OR: 3.60 vs. 1.56 and 2.53; p = 0.006). Meta-regression identified sample size as a significant source of heterogeneity ( p = 0.02). Conclusions Frailty is associated with an increased risk of POD after TAVR. These findings highlight the importance of frailty assessment for perioperative risk stratification and support targeted strategies to prevent delirium in high-risk patients undergoing TAVR. Systematic review registration https://www.crd.york.ac.uk/prospero/ , identifier CRD420261352173.

  PublisherDOI

• Validation of a health economics evaluation framework for minimally invasive congenital heart disease clinical pathways: A multi-center disparity analysis

  Vascular Investigation and Therapy · 2025-07-01

  articleOpen access

  BACKGROUND: Significant regional disparities exist in implementing minimally invasive congenital heart disease (CHD) clinical pathways across China, leading to inefficient resource utilization and increased healthcare costs. METHODS: Using the Sino-CHD registry ( n = 7001 patients from 13 centers), we applied a novel four-domain health economics evaluation framework to assess: (1) medical efficiency (hospitalization duration, intensive care unit stay, and same-day discharge [SDD] rates); (2) treatment-related metrics (surgical duration and transfusion rates); (3) medical quality metrics (mortality and complications); and (4) medical cost (material/drug expenditures). Comparative analyses examined interventional procedures ( n = 4677) versus open surgical ( n = 2324) approaches for atrial/ventricular septal defects and patent ductus arteriosus. RESULTS: Critical disparities emerged that Interventional cohorts demonstrated 20% SDD rates (range: 0%–99.5%), inversely correlating with hospitalization duration ( P < 0.001). Surgical duration varied nearly 4-fold (24.7–86.5 min), while material costs drove 51%–80% of total expenditures (25,979 CNY–40,929 CNY). Open surgery cohorts exhibited 3-fold variation in transfusion rates (0%–75%) and major complications (0%–5.3%). Material costs constituted 55%–74% of total expenses (29,656 CNY–49,097 CNY). Visual analytics revealed preoperative testing completion and SDD adoption as primary modulators of hospitalization duration. CONCLUSIONS: This validated framework quantifies actionable pathway implementation gaps, highlighting cost-containment opportunities through standardized preoperative workflows, blood conservation protocols, and value-based device selection. The system enables targeted quality improvement in the resource-limited settings.

  PublisherDOI

• YOLOv5-CE-DAFF: A polyp detection model based on CBAM-ECA attention mechanism and dropout-based adaptive feature fusion

  Soft Computing · 2025-09-01 · 2 citations

  article
  PublisherDOI

• Combined subspace low rank learning and nonlocal low rank estimation for spectral super-resolution of multispectral remote sensing images

  International Journal of Digital Earth · 2025-10-06 · 1 citations

  articleOpen accessSenior author

  Spectral Super-Resolution (SR) of Multispectral Images (MSI) enhances spectral resolution in MSI's non-overlapping regions by utilizing the overlapping regions with Hyperspectral Images (HSI). This technique effectively addresses the trade-off between spectral resolution and spatial coverage in remote sensing imagery, attracting significant research attention. However, traditional spectral SR methods based on sparse representation and deep learning primarily exploit local similarities in HSI, neglecting prevalent non-local spatial patterns, thus limiting reconstruction performance. In this paper, we propose a Combined Subspace Low Rank Learning and NonLocal Low Rank Estimation (CSLNLE) method for MSI spectral SR, capturing both global spectral correlations and nonlocal spatial-spectral self-similarity. The CSLNLE method decomposes the target HSI into a low-rank dictionary of subspaces and associated coefficient matrices. Through low-rank learning, we derive dictionaries from overlapping HSI-MSI regions and estimate coefficients for non-overlapping MSI regions using a nonlocal tensor multi-rank prior. Specifically, images are partitioned into patches, clustered by similarity, and coefficient matrices are estimated via low-tensor multi-rank decomposition within each group. Spectral SR experiments on simulated and real datasets using seven current benchmark methods validate the superiority of our proposed method.

  PublisherDOI

• Quantum Transport in a 12-nm MOSFET Using FDTD

  2025-07-13

  article1st authorCorresponding

  Quantum transport is investigated in an N-channel enhancement metal-oxide-semiconductor field-effect transistor (MOSFET). With the presence of tunneling potential distributions in a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 2}-\mathbf{n m}$</tex> channel, transmission functions and Fermi-Dirac distributions with different external drain-source and gate-source voltages are obtained. Current-voltage characteristics of the MOSFET are calculated based on the transmission functions and Fermi-Dirac distributions. To unveil the essence of current generations in the MOSFET channel, wave nature of electrons in nanoscale devices are simulated using the finite-difference time-domain (FDTD) method. The electron wavefunction is assumed to be a sinusoidal wave in a Gaussian envelope. Electron scattering through different potential distributions in the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 2}-\mathbf{n m}$</tex> channel is explored through FDTD numerical simulations.

  PublisherDOI

• FDTD in Computational Electromagnetics and Quantum Transport

  2025-01-07 · 2 citations

  article1st authorCorresponding

  The finite-difference time-domain (FDTD) method used in computational electromagnetics (CEM) and quantum transport are investigated. In CEM, FDTD is applied to coupled electric and magnetic fields based on the Maxwell's equations. In quantum transport, FDTD is implemented to coupled real and imaginary components of the electron wavefunction based on the time dependent Schrödinger equation. To have a better understanding of CEM and quantum transport, FDTD formulations and simulations of plane wave propagation in free space and electron scattering in metal-oxide-semiconductor field-effect transistors (MOSFETs) are presented. A comprehensive observation of electromagnetic and quantum techniques can be obtained through discretization of the time-dependent Maxwell's equations and Schrödinger equation.

  PublisherDOI

• The lane detection algorithm for row classification based on dynamic shape perception and feature alignment

  2025-06-30

  article

  To address the issue of semantic feature misalignment caused by downsampling and residual connection, a new lane detection algorithm for row classification based on dynamic shape perception and feature alignment is proposed in this paper. Firstly, this paper designs the Attention-Guided feature alignment pyramid (AGFA-FPN), which solves the misalignment problem in the traditional feature pyramid network by using the Attention-Based feature alignment module (AFAM) to accurately align features at different levels. At the same time, CA_CBAM parallel attention mechanism and Enhanced Up-Sampling Module (EUM) are used to pay attention to the retention of details in the up-sampling process, so as to improve the expression ability of features and the validity of semantic information, so as to improve the detection accuracy of the model. Finally, we propose a dynamic shape perception loss function, which enables the model to flexibly perceive the shapes of different lane lines and ensure accurate and robust lane line recognition in different driving scenarios. The experimental results show that our algorithm achieves 96.16% accuracy on the Tusimple dataset, and on the more challenging CULane dataset, the F1 score reaches 74.7%, especially in the curve scenario, the score is also as high as 72.3%. It is worth mentioning that the proposed model with ResNet-18 as the backbone network has a reasoning speed of 300+ FPS while maintaining high performance, which fully demonstrates the effectiveness of the algorithm.

  PublisherDOI

• Electron Scattering in an 8-nm FinFET Using FDTD

  2025-11-04

  article1st authorCorresponding

  Electron scattering is explored in a nanoscale Fin field-effect transistor (FinFET) with a channel length of 8 nm. This work mainly focuses on the quantum transport behavior within the FinFET channel, incorporating potential distributions to examine their influence on charge carrier dynamics. Transmission functions and Fermi-Dirac distributions are evaluated under varying external bias voltages, providing insight into the resulting current-voltage (I-V) characteristics of the nano device. To gain a deeper understanding of current formations in the FinFET channel, the electron wave behavior is analyzed using time-domain numerical simulations based on the finite-difference time-domain (FDTD) method. The propagation and scattering of this wave packet through various potential landscapes within the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$8-\text{nm}$</tex> channel are computed, enabling detailed visualization of tunneling phenomena. These simulations offer a quantum-mechanical perspective on transport processes in FinFETs at the atomic scale.

  PublisherDOI

• Med: A Multi-Encoder-Decoder Architecture for Mixed-Frequency Data Forecasting

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Development of Ternary Hydrogel Electrolytes for Superior Gel Thermocells: Exceptional Anti‐Drying, Anti‐Freezing, and Mechanical Robustness

  Advanced Materials · 2025-03-03 · 41 citations

  articleOpen access

  Abstract Gel thermocells (GTCs) provide a safe, facile, and scalable solution for harvesting waste heat to power ubiquitous electronics. However, achieving a harmonious integration of high power density, wide‐temperature‐range stability, and mechanical robustness in GTCs remains a significant challenge. In this work, a novel ternary gel thermocell (TGTC) is proposed and fabricated by integrating ferro/ferricyanide (Fe(CN) 6 3−/4− ) redox couples, thermosensitive crystallizing agents guanidinium chloride (GdmCl), and supporting electrolytes lithium chloride (LiCl) into natural nanocellulose hydrogels to enhance overall performance. GdmCl selectively induces Fe(CN) 6 4− crystallization, increasing the concentration difference of redox pairs, resulting in improving thermopower and significantly increased fiber friction, while LiCl rapidly balances charges through electromigration promoting efficient ion transport and reconstructing hydrogen bond networks, contributing to an excellent output power density and the capture of water molecules, which are further elucidated by simulations, achieving synchronous enhancement of anti‐drying, anti‐freezing and mechanical properties. Consequently, the TGTC achieves a remarkable thermopower of 3.42 mV K −1 , a maximum power density of 2.8 mW m −2 K −2 , multiple continuous stable cycles at −20 °C, and an impressive strength of 3.06 MPa. Notably, this study elucidates the design principles and underlying mechanisms of ternary gel electrolytes, offering a practical strategy for advancing GTC technology.

  PublisherOA PDFDOI

Frequent coauthors

• Forhad Hossain

  Kyushu University

  49 shared

• Ahmed Hassebo

  Wentworth Institute of Technology

  49 shared

• Sheikh Sharif Iqbal

  King Fahd University of Petroleum and Minerals

  49 shared

• Gang Yang

  Xi’an University

  19 shared

• Weiwei Sun

  Ningbo University

  19 shared

• Xiangchao Meng

  17 shared

• Robert J. Burkholder

  The Ohio State University

  15 shared

• Garth A. Gibson

  Carnegie Mellon University

  13 shared

Education

• Ph.D.

  Robert H. Smith School of Business at the University of Maryland

Awards & honors

• Ann G. Wylie Dissertation Award