Digital Commons Network^™

Voice-Activated Self-Monitoring Application (Vois): User Acceptance And Satisfaction In The Field, Hyunkyoung Oh, Li Yang, Tala Abu Zahra, Shiyu Tian, Min Sook Park, Jake Luo, Sheikh Iqbal Ahamed, Evelyn Chan, Jeffrey Whittle

Computer Science Faculty Research and Publications

This paper illustrates the processes and results of user acceptance and satisfaction tests for the voice-activated self-monitoring (VoiS) application conducted in the field. VoiS was designed and developed for individuals with diabetes (DM) and hypertension (HTN) to support their routine and convenient self-management using a smart speaker platform. VoiS is also accessible on users’ mobile devices to visualize user-generated data. A total of nine adults with DM and HTN participated and were asked to use the VoiS system at home for a week to identify its acceptability in real-world conditions. Participants completed phone interviews to report operational errors and a …

Digital Health Using Data Science, Padmapriya Velupillai Mekandan, Paramita Basak Upama, Masud Rabbani, Amity Ali, Sheikh Iqbal Ahamed

Computer Science Faculty Research and Publications

The digitization of healthcare has led to an unprecedented growth in health-related data, offering new opportunities to transform clinical decision-making, disease prediction, and patient engagement. However, extracting actionable insights from diverse data sources such as electronic health records, wearable devices, and mobile health apps requires a fusion of domain knowledge in healthcare and technical expertise in data science. This paper presents a structured, interdisciplinary framework for digital health that emphasizes practical strategies for data acquisition, preprocessing, feature engineering, machine learning, and ethical data use. The model promotes a holistic understanding of digital health challenges and opportunities, preparing future professionals to …

Secure Ios Mhealth Apps Development: An Ide-Embedded Framework For Hipaa-Aware Coding, Bajlur Rashid, Md Abdul Barek, Mostafizur Rahman, Sharmin Yeasmin, Hossain Shahriar, Sheikh Iqbal Ahamed

Computer Science Faculty Research and Publications

With the rapid growth of technology, accessing digital health records has become increasingly easier. Especially mobile health technology like mHealth apps help users to manage their health information, as well as store, share and access medical records and treatment information. Along with this huge advancement, mHealth apps are increasingly at risk of exposing protected health information (PHI) when security measures are not adequately implemented. The Health Insurance Portability and Accountability Act (HIPAA) ensures the secure handling of PHI, and mHealth applications are required to comply with its standards. But it is unfortunate to note that many mobile and mHealth app …

Synthesis And Design Of Clpp Activators As Novel Antibiotics, Schyler Odum

Theses and Dissertations (ETD)

Purpose. Design and synthesize novel compounds to treat Staphylococcus aureus infections by targeting the dysregulation of the Casein Protease P, ClpP. Methods. Utilize established chemical methods to synthesize ureadepsipeptides, small-molecule ClpP activators, and ureadepsipeptide hybrids. Assess their effectiveness by using minimum inhibitory concentration assays and in vitro biochemical assays for ClpP activation. Explore their potential as antibiotics through mitochondrial toxicity tests, glucose/galactose assays, and biophysical measurements related to metabolism and clearance, including thermal shift and surface plasmon resonance assays. Results. Synthesized and tested 33 novel compounds, comprising a total of 80 synthetic steps. Conclusion. The three-part conclusion from the dissertation …

Multi-Source Remote Sensing–Based Soil Moisture Prediction Using Machine Learning, Niraj Neupane

Electronic Theses and Dissertations

Soil moisture (SM) plays a central role in climatic and environmental processes, influencing shear strength of soil, agricultural productivity, land–atmosphere interactions, and hydrologic functioning. However, accurately estimating SM across diverse climatic regions remains challenging due to spatial heterogeneity, limited in situ measurements, and inconsistencies in sensor resolution. Machine learning (ML) and remote sensing offer promising avenues for improving SM prediction, yet many existing approaches struggle with generalization across climatic gradients and often fail to capture temporal variability. This study integrates multi-source satellite and climate datasets, including SMAP L4_SM, MODIS land surface temperature, Daymet meteorological variables, and in situ observations from …

Quantifying Avian And Vegetation Communities On Agricultural Conservation Easement Wetlands In Eastern South Dakota, Katherine Millman

Electronic Theses and Dissertations

The Prairie Pothole Region of eastern South Dakota contains numerous small depressional wetlands, colloquially known as potholes, that provide important ecosystem services, including water storage, water quality improvement, and wildlife habitat. The Agricultural Conservation Easement Program (ACEP) was established by the U.S. Department of Agriculture to protect and restore wetlands and grasslands on private land through easements such as Wetland Reserve Easements. Although ACEP requires management practices intended to maintain or improve habitat quality, few studies have evaluated their effectiveness at supporting wildlife communities and vegetation integrity. To address this gap, we evaluated wetlands at 30 ACEP sites and 20 …

Enhancing Alfalfa Persistence In Rangelands: Evaluating Morphological Traits Of Medicago Sativa Subsp. Falcata Germplasm, Abdulallah Mohammed Alomair

Electronic Theses and Dissertations

Rangelands cover nearly half of the Earth’s surface, representing about 54% (79 million km2), and occur in different types and climates worldwide. Alfalfa (Medicago sativa L.) is a major component in rangelands and covers about (320 thousand km2), representing about 0.4% of total global rangeland area. As a critical perennial legume crop, alfalfa can improve soil health by fixing approximately 336 kg/ha/year of nitrogen and sequestering up to 614 kg/ha/year of carbon, while enhancing livestock production due to its high content level of vitamins, magnesium, minerals, fiber, and protein. However, environmental stresses such as drought, freezing, and grazing limit alfalfa …

Enhancing Channel Data Savings And Information Transfer Efficiency In Ultrasound Imaging, Sai Konda, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

Ultrasound is a popular imaging technique mainly due to its non-invasive nature. And so, it is being used in a variety of applications. Due to plane wave imaging technique in ultrasound, frame rate of ultrasound imaging has the potential for being very high. Due to which, many channel data frames are being generated within a few seconds. As a result, tasks such as storing data frames and transferring them from front end ultrasonic system to processing computers are presenting significant challenges. Our current research work minimized these issues. We proposed and implemented: (a) Data encoding technique - We combined every …

Key Brain Region Identification In Obesity Prediction With Structural Mri And Probabilistic Uncertainty Aware Model, Walia Farzana, Megan A. Witherow, Ahmed Temtam, Liangsuo Ma, Melanie Bean, F. Gerry Moeller, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Objectives/Goals: Predictive performance alone may not determine a model’s clinical utility. Neurobiological changes in obesity alter brain structures, but traditional voxel-based morphometry is limited to group-level analysis. We propose a probabilistic model with uncertainty heatmaps to improve interpretability and personalized prediction. Methods/Study Population: The data for this study are sourced from the Human Connectome Project (HCP), with approval from the Washington University in St. Louis Institutional Review Board. We preprocessed raw T1-weighted structural MRI scans from 525 patients using an automated pipeline. The dataset is divided into training (357 cases), calibration (63 cases), and testing (105 cases). Our probabilistic model …

A Fast Framework For Generating Radioactive Mixture Spectra And Its Application To Remote High-Performance Mixture Identification, Chiman Kwan, Bulent Ayhan, Adam Stavola, Kazi Aminul Islam, Hongfang Zhang, Jiang Li

Electrical & Computer Engineering Faculty Publications

Remote detection of radioactive materials in mixtures using handheld or portal detectors remains a challenge because of factors such as low concentration, environmental interference, sensor noise, and other complications. This work introduces a fast framework for generating realistic mixture spectra. Moreover, we present mixture isotope identification using data generated by the fast framework. Researchers have examined a range of conventional and recent algorithms within the fields of machine learning and deep learning. An application to uranium enrichment-level prediction has been included. Extensive simulation experiments validated the efficacy of the proposed framework.

Three Essays On Sustainability Economics, Hélène Flore Nguemgaing

Graduate Theses, Dissertations, and Problem Reports (ETD)

As global economies face increasing resource dependencies and supply chain challenges, it becomes essential to understand the vulnerabilities that threaten economic stability. This dissertation investigates three empirical issues in agribusiness and critical minerals using econometric models to estimate the following: 1) the impact of supply chain disruptions on stock market returns during COVID-19; 2) the extraction of rare earth elements from acid mine drainage treatment by investigating the impacts of ownership and legislation in West Virginia and Pennsylvania; 3) China-to�FOBb (Free-on-Board) price transmission in rare earth elements markets: a copula-based dynamic tail dependence analysis.

The first essay evaluates how events …

Spectrum Of The Lakes: Using Satellite Remote Sensing To Unveil Water Color In Minnesota's Sentinel Lakes For Water Quality Monitoring, Andrew J. Dooley, Wondwosen M. Seyoum, Catherine M. O'Reilly

Faculty Publications - Geography, Geology, and the Environment

Traditional water quality monitoring methods often face limitations of equipment costs and labor demands, requiring innovative approaches to effectively assess the ecological health of inland lakes across vast areas. This research explores the application of free, publicly available water color chromaticity analysis for midcontinent lakes in Minnesota, USA. We examined water color variations in Minnesota’s Sentinel Lakes using Landsat 8 OLI data to analyze surface reflectance samples collected from the deepest area within each lake during the late summer, corresponding to peak annual insolation and trophic activity. The median dominant visible wavelength was used to characterize water color. Results indicate …

Multitec: A Data-Driven Multimodal Short Video Detection Framework For Healthcare Misinformation On Tiktok, Lanyu Shang, Yang Zhang, Yawen Deng, Dong Wang

Computer Science Faculty Works

With the prevalence of social media and short video sharing platforms (e.g., TikTok, YouTube Shorts), the proliferation of healthcare misinformation has become a widespread and concerning issue that threatens public health and undermines trust in mass media. This paper focuses on an important problem of detecting multimodal healthcare misinformation in short videos on TikTok. Our objective is to accurately identify misleading healthcare information that is jointly conveyed by the visual, audio, and textual content within the TikTok short videos. Three critical challenges exist in solving our problem: i) how to effectively extract information from distractive and manipulated visual content in …

Development And Evaluation Of Machine Learning Models For Early Pediatric Sepsis Prediction, Ancita M. Andrade

Browse all Theses and Dissertations

Sepsis is a leading cause of pediatric mortality, claiming more lives in the United States annually than all childhood cancers combined. Early identification in Emergency Departments (EDs) remains challenging, as the current Phoenix criteria establishes an updated international consensus definition for sepsis, however is not designed for use as a screening tool. This study aimed to develop predictive models identifying pediatric patients at risk of sepsis within 24 hours of admission. Multiple tree-based and deep learning models were trained utilizing clinical and laboratory data from the initial four hours of presentation. Both the LightGBM and LSTM architectures demonstrated superior performance, …

Wearable Sensor Data Analysis For Machine Learning-Based Detection Of Posture And Autonomic Responses, Chaitanya Vardhini Anumula

Browse all Theses and Dissertations

This study investigates how Iyengar yoga postures influence autonomic nervous system (ANS) activity by analyzing multimodal physiological signals collected via wearable sensors. The physiological mechanisms underlying Iyengar yoga’s therapeutic effects remain under-explored at the granular, pose-level. Using data collected from 16 participants, this research evaluates whether machine learning models can distinguish between baseline, parasympathetic-dominant, and sympathetic-dominant states based on wrist-worn sensor data. The goals were to explore whether subtle postural variations elicit measurable autonomic responses and to identify which sensor features most effectively capture these changes. Participants performed a sequence of yoga poses while wearing synchronized sensors measuring electrodermal activity …

Subjective Readiness Forecasting Using Supervised Machine Learning And Wearable Device Data, Nathaniel Michael Weiland

Browse all Theses and Dissertations

Recent advances in wearable technology allow continuous monitoring of physiological and behavioral data, opening new opportunities for real-time assessments of readiness and well-being. However, creating predictive models that generalize across diverse users remains challenging, especially in high-stakes settings like the military, where preventable injuries, illnesses, and stress-related performance declines are frequent. This research assesses the feasibility of using supervised machine learning models trained on wearable device data to predict subjective readiness indicators—recovery, stress, injury, and illness. Data from over 10,000 users in the OHWS (Optimizing the Human Weapons System) program combined daily check ins with physiological metrics from Garmin, Polar, …

Dataset Generation For Routing Policy Study In Ad Hoc Wireless Networks, Vishnu Vishnu Priya

Browse all Theses and Dissertations

Ad Hoc wireless networks, with their decentralized architecture and dynamic topology, present challenges in reliable and energy-efficient routing. While machine learning (ML) and reinforcement learning (RL) offer promising solutions, progress is limited by the lack of realistic, high-fidelity datasets. This research introduces a simulation-based framework for generating four diverse datasets representing combinations of node mobility (mobile vs. static) and spatial distribution (random vs. clustered). Each dataset captures critical metrics such as Signal-to-Interference-plus-Noise Ratio (SINR), bottleneck rate, and power consumption across multi-hop paths. A lookahead-based greedy routing algorithm with scenario-aware power control is implemented to emulate practical behavior. Supervised ML models, …

Biosensing Applications Of Thz Rotational Spectroscopy: Sensing And Analysis Of Exogenous And Endogenous Compounds In Exhaled Breath, Daniel J. Tyree

Browse all Theses and Dissertations

Exhaled human breath contains a wealth of volatile molecular species which bear an imprint of compounds dissolved in blood. Assessing trace amounts of these species in exhaled breath requires a highly sensitive and selective method. Terahertz (THz) rotational spectroscopy satisfies these needs by detecting numerous and narrow, molecule specific spectral features with feature intensity dependent on the quantity of absorbing molecules, molecular structure, and experimental parameters. Expanding the applicability of THz sensing to biological gases requires systems to measure this spectral data, reliable analysis of the spectra, and demonstration of the utility of biological data extracted from the spectra. To …

A Governance-Centric Framework For Strengthening Healthcare Cybersecurity: A Systems Perspective, Sujatha Alla, Sai Gireesh Komaragiri, Teresa Duvall, Satluk Karahan, Nagesh Bheesetty, Vijay Kumar Chattu

Engineering Management & Systems Engineering Faculty Publications

Healthcare systems face unprecedented security and privacy challenges due to increasing digitization and interconnectedness. This paper provides a comprehensive analysis of these challenges by examining various cyberattacks, defensive mechanisms, and governance frameworks within modern healthcare infrastructure. The research systematically categorizes prevalent security threats, such as ransomware, insider threats, and data breaches, identifying vulnerabilities specific to healthcare systems. Furthermore, the study evaluates current defensive strategies, including encryption techniques, access control systems, and intrusion detection tools, assessing their effectiveness against complex cyber threats. A key focus is placed on governance structures and their role in cybersecurity resilience. The research explores how regulatory …

A Unionid Mussel Biodiversity Hotspot Experiencing Unexplained Declines: Evaluating The Influence Of Chemical Stressors Using Caged Juveniles, W. Aaron Wilson, Christine Bergeron, Jennifer Archambault, Jason M. Unrine, Jess Jones, Braven Beaty, Damian Shea, Peter R. Lazaro, Jody L. Callihan, Jennifer J. Rogers

Plant and Soil Sciences Faculty Publications

Unionid mussel populations in a section of the Clinch River in Virginia, USA, has declined substantially, but the causes of the decline remain unknown. To investigate this zone of decline (ZOD), we deployed juvenile freshwater mussels (Villosa iris in 2012 and Lampsilis fasciola in 2013) in both cages and silos at sites within the Clinch River System. We analyzed mussel tissues for trace element and organic contaminant concentrations, shells for trace elements, and environmental media (total water, dissolved water, particulate sediment, and bedload sediment) for both inorganic and organic contaminants. We found a few differences between mussels deployed in cages …

Development And Application Of Gpu Based Monte Carlo Simulation Techniques In Radiation Medicine, Satzhan Sitmukhambetov

Physics Dissertations - Archive

Cancer remains a significant public health challenge, with treatments like radiation therapy forming a cornerstone of modern care. To address this challenge, advanced computer simulations can improve radiotherapy in two primary ways. From one hand, mechanistic simulations help elucidate the fundamental radiobiological working principles, which could contribute to the clinical advancements from a bottom-up framework. On the other hand, modality simulations could help design better clinical systems with better detection and therapeutic capabilities. This research enhances these critical simulation tools by introducing two significant developments.

First, this work introduces a metaphase DNA model into a microscopic Monte Carlo simulation framework …

Multitask Learning For Named Entity Recognition And Relationship Extraction, Adrienne D. Hembrick

Theses and Dissertations

Information Extraction (IE) is a fundamental task in Natural Language Processing (NLP), involving the identification of structured information from unstructured text. Two core components of IE—Named Entity Recognition (NER) and Relation Extraction (RE)—are widely used to extract key concepts and the relationships between them across various domains. However, the sequential dependency of RE on the output of NER makes it vulnerable to error propagation: inaccuracies in entity recognition can negatively affect downstream relation extraction.

To mitigate this issue, Multitask Learning (MTL) has been proposed as an approach that jointly models NER and RE, aiming to improve overall performance and reduce …

Patient Subset Classification Using Encoded Embeddings And Knowledge Graph Retrieval-Augmented Generation, Benjamin A. Holmes

Browse all Theses and Dissertations

The widespread adoption of electronic medical records has created a vast reservoir of clinical data that can be leveraged to better understand how interventions relate to patient outcomes. Much of this information, however, exists as unstructured free-text, posing significant challenges for traditional statistical and machine-learning methods. Solving these challenges would allow the extraction of specific patient subpopulations (clinically relevant cohorts of individuals who share overlapping symptoms, risk factors, or diagnostic criteria), which could be used in precision medicine. Despite this promise, extracting these subpopulations from unstructured medical notes is an ongoing challenge due to the variability of clinical language and …

A Secure Ml-Assisted Framework For Resilient And Efficient Prediction Of Physiotherapy Sequence In Bilateral Carpal Tunnel Syndrome, Pratik Pandurang Kharat

Browse all Theses and Dissertations

Bilateral idiopathic carpal tunnel syndrome (CTS) is a neuromuscular disorder characterized by compression of the median nerve at both wrists, leading to symptoms such as pain, numbness, tingling, and muscle weakness. Unlike unilateral cases, bilateral idiopathic CTS presents distinct therapeutic challenges due to the simultaneous involvement of both hands and the lack of an identifiable underlying cause. This study explores the application of machine learning techniques to predict the optimal sequence of physiotherapeutic interventions Stretching followed by Myofascial Mobilization (S/M) or the reverse (M/S) in female patients with bilateral idiopathic CTS and right hand dominance. Data were drawn from a …

Smartphone Based Non Invasive Real Time White Blood Cell Counter Leveraging Blue Light And Static Magnetic Field, Nafi Us Sabbir Sabith, Masud Rabbani, Kazi Shafiul Alam, Sheikh Iqbal Ahamed

Computer Science Faculty Research and Publications

White blood cells (WBCs), also known as leukocytes, are one of the most significant parts of the immune system. They generate antibodies, protect the body from illnesses, and heal wounds. Accurate estimation of WBCs is key for diagnosing cancer, infections, leukemia, lymphoma, and other diseases. However, the widely used Complete Blood Count (CBC) test presents challenges, including prick anxiety, discomfort, and logistical inconvenience to patients. This study introduces a ubiquous White Blood Cell counting system, UbiWhite, a novel smartphone-based, non-invasive system for real-time WBC counting from fingertip videos. Our system uses optical and magnetic techniques to provide accurate WBC counts …

Causal Discovery On The Effect Of Antipsychotic Drugs On Delirium Patients In The Icu Using Large Observational Ehr Dataset, Riddhiman Adib, Md. Osman Gani, Sheikh Iqbal Ahamed, Mohammad Adibuzzaman

Computer Science Faculty Research and Publications

Delirium occurs in about 80% of cases in the Intensive Care Unit (ICU) and is associated with an extended hospital stay, increased mortality, and other complications. Delirium lacks biomarker-based diagnosis and is frequently treated with antipsychotic drugs (APD), despite numerous studies debating its efficacy. Since randomized controlled trials (RCT) are expensive and time-consuming, we approach the research question of estimating the efficacy and safety outcomes of APD in treating delirium through retrospective cohort analysis. We employed the Causal inference framework to explore the underlying causal model for Delirium patient cohort. We focus on building a structural causal model for delirium …

Light Absorbing Aerosol Interactions With Warm Clouds, Shreya Joshi

Dissertations, Master's Theses and Master's Reports

Atmospheric particles such as biomass-burning aerosols, including black carbon - are critical in altering the Earth's energy balance through interactions with light and clouds. Biomass-burning aerosols also contain organic carbon, a diverse mix of organic compounds. As these aerosols age, they evolve by interacting with other atmospheric species, including cloud water, leading to changes in particle morphology and the development of coatings that impact their lifetime and ability to interact with clouds and light.

Some biomass-burning aerosols serve as cloud condensation nuclei. When cloud water droplets form, the properties of the nucleating particles can change. Many clouds evaporate before precipitating, …

Great Lakes And Its Regional Climate In The Future, Miraj Bhakta Kayastha

Dissertations, Master's Theses and Master's Reports

The overarching objective of this dissertation is to enhance our predictive understanding of the Great Lakes regional climate by utilizing advanced modeling techniques and provide comprehensive insights into the future of the Great Lakes. This dissertation focuses on four areas pertinent to the future of the Great Lakes hydroclimate and extreme events, beginning with the derivation of robust water level projections for the Great Lakes in Chapter 2. Using a regional climate modeling system with a two-way coupled three-dimensional (3D) hydrodynamic lake and ice model for the Great lakes, we analyze long-term future changes in the over-lake precipitation, lake evaporation, …

Near-Infrared Fluorescent Cyanine Probes For Monitoring Nad(P)H Dynamics In Living Cells And Drosophila Larvae, May Waters

Dissertations, Master's Theses and Master's Reports

Nicotinamide adenine dinucleotide (NAD+/NADH) plays a critical role in cellular metabolism, with abnormally elevated NADH levels linked to disrupted metabolic processes and cancer. In this study, we developed near-infrared fluorescent probes to selectively measure NADH concentrations. We present five cyanine-based probes, which were optimized by modifying the connection bridges to fine tune electronics. Two NAD(P)H-biosensing probes consisting of 1,3,3-trimethyl-3H-indolium and 3-quinolinium acceptors, linked by thiophene, A, and 3,4-ethylenedioxythiophene, B, bridges are detailed. They exhibit near-infrared fluorescence at 742.1 nm and 727.2 nm for probes A and B, respectively, and generate absorbance signals at 690.6 nm and 685.9 nm. Probe A …

Utilizing Biometrics And Blockchain For Enhanced Security Of Remote Patient Monitoring (Rpm) Data Sharing, Amaturrahman Raihanah Medlock

Dissertations, Master's Theses and Master's Reports

Advancements in Artificial Intelligence (AI) and Internet of Medical Things (IoMT) technologies have significantly revolutionized the conventional healthcare systems. Through the integration of smart devices, medical sensors, and communication technology, IoMT provides real-time patient’s monitoring data for healthcare providers, thus promoting accurate and timely clinical decisions for patient-centric care. The current healthcare sector is evolving to a connected ecosystem with connectivity and intelligence. While it also incurs increasing security and privacy concerns as integrating IoMT generated patient monitoring data into healthcare information systems. Both blockchain and biometrics are measures that have established reputable names in the security realm. When evaluating …