A5036935998- Advanced Statistical Process Monitoring
- Data-Driven Disease Surveillance
- Nanoplatforms for cancer theranostics
- Fault Detection and Control Systems
- Statistical Methods and Inference
- COVID-19 epidemiological studies
- Carbon and Quantum Dots Applications
- Mathematical and Theoretical Epidemiology and Ecology Models
- Medical Malpractice and Liability Issues
- Metal-Organic Frameworks: Synthesis and Applications
- Ethics in Clinical Research
- Pericarditis and Cardiac Tamponade
- Metabolism, Diabetes, and Cancer
- Stochastic processes and financial applications
- Esophageal and GI Pathology
- Influenza Virus Research Studies
- Advanced X-ray and CT Imaging
- Atrial Fibrillation Management and Outcomes
- Advanced Statistical Methods and Models
- Ethics and Legal Issues in Pediatric Healthcare
- Amino Acid Enzymes and Metabolism
- Graphene and Nanomaterials Applications
- Stochastic Gradient Optimization Techniques
- Financial Distress and Bankruptcy Prediction
- Cardiac Structural Anomalies and Repair
Xinxiang Medical University
2022-2024
University of Pittsburgh
2022-2024
Johns Hopkins University
2019
Glucose is a sugar crucial for human health since it participates in many biochemical reactions. It produces adenosine 5'-triphosphate (ATP) and nucleosides through glucose metabolic pentose phosphate pathways. These processes require transporter proteins to assist transferring across cells, the most notable ones are transporter-2 (GLUT-2) sodium/glucose cotransporter 1 (SGLT1). enters small intestinal epithelial cells from lumen by crossing brush boundary membrane via SGLT1 cotransporter....
Nanoscale metal-organic frameworks (MOFs) offer high biocompatibility, nanomaterial permeability, substantial specific surface area, and well-defined pores. These properties make MOFs valuable in biomedical applications, including biological targeting drug delivery. They also play a critical role tumor diagnosis treatment, cell targeting, identification, imaging, therapeutic methods such as delivery, photothermal effects, photodynamic therapy, immunogenic death. The diversity of with...
The SEIR (susceptible-exposed-infected-recovered) model has become a valuable tool for studying infectious disease dynamics and predicting the spread of diseases, particularly concerning COVID pandemic. However, existing models often oversimplify population characteristics fail to account differences in sensitivity social contact rates that can vary significantly among individuals. To address these limitations, we have developed new multi-feature considers heterogeneity health conditions...
Exactly and asymptotically optimal algorithms are developed for robust detection of changes in nonstationary processes. In processes, the distribution data after change varies with time. The decision maker does not have access to precise information on post-change distribution. It is shown that if post-change, family has a least favorable well-defined sense, then designed using laws optimal. This first result which an exactly robust-optimal solution obtained setting where law also allowed be...
The problem of robust quickest change detection (QCD) in non-stationary processes under a multi-stream setting is studied. In classical QCD theory, optimal solutions are developed to detect sudden the distribution stationary data. Most studies have focused on single-stream processes, data both before and after varies with time not precisely known. multi-dimension even complicates such issues. It shown that if family for each dimension or stream has least favorable law (LFL) well-defined...
The credit spread is a key indicator in bond investments, offering valuable insights for fixed-income investors to devise effective trading strategies. This study proposes novel forecasting model leveraging ensemble learning techniques. To enhance predictive accuracy, feature selection method based on mutual information incorporated. Empirical results demonstrate that the proposed methodology delivers superior accuracy predictions. Additionally, we present forecast of future trends using...
The problem of quickest detection a change in the distribution sequence random variables is studied. objective to detect with minimum possible delay, subject constraints on rate false alarms and cost observations used decision-making process. post-change data known only within family. It shown that if family has least favorable well-defined sense, then computationally efficient algorithm can be designed uses an on-off observation control strategy save observations. In addition, robustly...
Evaluating the statistical error in estimate coming from a stochastic approximation (SA) algorithm is useful for confidence region calculation and determination of stopping times. Robbins-Monro (RM) type gradient descent widely used method SA. Knowledge probability distribution SA process analysis. Currently, however, only asymptotic has been studied this setting theories, while functions finite-sample regime have not clearly depicted. We developed to finite sample based on surrogate...
SEIR (susceptible-exposed-infected-recovered) model has been widely used to study infectious disease dynamics. For instance, there have many applications of analyzing the spread COVID provide suggestions on pandemic/epidemic interventions. Nonetheless, existing models simplify population, regardless different demographic features and activities related disease. This paper provides a comprehensive enhance prediction quality effectiveness intervention strategies. The new estimates exposed...
Optimal algorithms are developed for robust detection of changes in non-stationary processes. These processes which the distribution data after change varies with time. The decision-maker does not have access to precise information on post-change distribution. It is shown that if family has a least favorable well-defined sense, then designed using distributions and optimal. Non-stationary encountered public health monitoring space military applications. applied real simulated show their...