Digital Commons Network^™

A Multi-Scale Finite Element Method For Investigating Fiber Remodeling In Hypertrophic Cardiomyopathy, Mohammad Mehri, Kenneth S. Campbell, Lik Chuan Lee, Jonathan F. Wenk

Mechanical Engineering Faculty Publications

A significant hallmark of hypertrophic cardiomyopathy (HCM) is fiber disarray, which is associated with various cardiac events such as heart failure. Quantifying fiber disarray remains critical for understanding the disease’s complex pathophysiology. This study investigates the role of heterogeneous HCM-induced cellular abnormalities in the development of fiber disarray and their subsequent impact on cardiac pumping function. Fiber disarray is predicted using a stress-based law to reorient myofibers and collagen within a multiscale finite element cardiac modeling framework, MyoFE. Specifically, the model is used to quantify the distinct impacts of heterogeneous distributions of hypercontractility, hypocontractility, and fibrosis on fiber disarray development …

In Vitro Microfluidics System For Mechanobiologic Investigations, Michael A. Daanen

Honors Theses

Mechanobiology is an emerging field that aims to study the relationships between mechanical forces and cellular behavior. Such mechanobiological relationships are especially critical in the cardiovascular system, where endothelial cells lining the vasculature align in response to fluid shear stresses from blood flow (Sinha, 2016). Implanted flow devices, diabetes, chronic hypertension, and various other diseases and pathophysiologies alter vessel geometries and flow dynamics. Such alterations impact fluid shear stress and endothelial cell behavior, leading to microcirculatory dysfunction, vessel leakage, and organ failure (Poredos, 2021; Papadaki, 1999; Leitschuh, 1987). To simulate dysfunctional endothelial cell behavior in vitro and evaluate novel therapeutic …

Evaluation Of Heavy Metal Bioaccumulation And Growth In Hemp From Chicken Manure, Vermicompost, And Wastewater Sludge With Biochar And Health Risk Assessment, Chutiphan Sangsoda

Chulalongkorn University Theses and Dissertations (Chula ETD)

Hemp (Cannabis sativa L.) has become an economically valuable crop with a wide range of benefits. To promote more sustainable agricultural systems, the use of recycled substrates has increasingly replaced traditional fertilizers. However, substrates such as dry chicken manure, vermicompost, and wastewater sludge (WWS) combined with biochar often contain heavy metal contaminants, which may pose health risks through exposure to hemp. This study investigated three cultivars Rosella, Superwoman S1, and Red Robin over a 120-day period. The objectives were: (1) to compare the effects of dry chicken manure, vermicompost, and wastewater sludge (with and without biochar) on soil nutrient quality …

Interaction Between Extreme Temperature Events And Air Pollution On Mortality In Thailand : A Nationwide Time Series Study, Phichet Khunthong

Chulalongkorn University Theses and Dissertations (Chula ETD)

Climate change has increased the frequency and intensity of extreme temperature events (ETEs; heat and cold waves), concomitantly affecting PM2.5 fluctuation. However, limited research has investigated the interaction of ETEs and PM2.5 on mortality, particularly in tropical regions where the interaction could influence differently from cold countries. A two-stage time-series using distributed lag non-linear models was employed to estimate region-specific relative risks and 95% confidence intervals (RRs and 95% CIs) of ETEs and PM2.5 on daily cardiovascular and respiratory mortality. The relative excess risk due to interaction (RERI) was examined to quantify additive effects. Cold waves had the greatest effect …

A Novel Low-Complex Optimized Resource Allocation Algorithm Using Gwo Optimization Technique In Energy Scavenging For Wban, M. Samir Abou El-Seoud, Amira Olazan, Albashir A. Youssef

Computer Science

In wireless body area networks (WBANs) powered by energy scavenging, effectively managing renewable energy is critical to ensuring delay-sensitive services. This paper proposes a novel low-complex optimized resource allocation algorithm using the Grey Wolf Optimization (GWO) technique to allocate resources, specifically energy and communication channels, and maximize user utility while guaranteeing the worst-case delay. To achieve this, firstly, a formulation of a user utility optimization problem that accounts for the stochastic nature of energy scavenging and consumption without requiring prior knowledge of these processes is performed. Utilizing GWO optimization techniques, optimization problems broke down into four sub-problems: battery management, collection …

Mitigation Of Antibiotic Resistance Using Ultraviolet Light-Emitting Diodes For Water Treatment, Nicole Heyniger, Patrick J. Mcnamara, Anthony D. Kappell, Brandon Schultz, Troy Skwor, Brooke K. Mayer

Civil and Environmental Engineering Faculty Research and Publications

The presence of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in the environment is a growing issue, which has been exacerbated by the overuse and misuse of antibiotics in health care and agricultural systems. One means of mitigating antibiotic resistance is through drinking water and wastewater treatment, specifically during disinfection processes. Ultraviolet light-emitting diodes (UV-LEDs) are an emerging disinfection technology featuring adjustable peak wavelength emissions. We assessed the use of UV-LED for treating waterborne ARB (Escherichia coli and Aeromonas hydrophila) and ARGs (intracellular and extracellular) compared to conventional low-pressure UV (LP-UV). Overall, less efficient reduction (bacterial inactivation and/or gene …

Separating The Impacts Of A Corrosion Inhibitor From Copper Corrosion Products On Antibiotic Resistance In Drinking Water, Veronika Folvarska, Maya Adelgren, Emily Lou Lamartina, Ryan J. Newton, Yin Wang, Patrick J. Mcnamara

Civil and Environmental Engineering Faculty Research and Publications

Antibiotic resistance is a growing threat to public health, and environmental factors, including metals in drinking water distribution systems, are increasingly recognized as contributors to the spread of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs). Zinc orthophosphate, a common corrosion inhibitor, and copper corrosion products (CuO and Cu₂O) are frequently present in drinking water systems. While each has been shown to increase ARB and ARGs individually, their combined effects remain unknown. The objective of this study was to evaluate the combined impact of copper corrosion products and the corrosion inhibitor zinc orthophosphate on antibiotic resistance. Two …

The Genetic Role Of Pcsk 1, Lisbeth Aquino

Student Research Design & Innovation Symposium

Proprotein convertase subtilisin/Kexin type 1 (PCSK 1) is a rare inherited metabolic disorder associated with severe digestive issues, delayed puberty, and obesity. The cause of this genetic mutation occurs when a person inherits two copies of an autosomal recessive gene. Recessive traits are more common in families who marry close relatives. When the PCSK 1 gene is negatively impacted, it can impair the enzyme function, worsening the disease. As the gene is being mutated, it can create other mutations such as N309K, RCV001374497-rs143209024, and c.1095 + 1G > A. The N309K gene affects the active site of an enzyme that causes …

Deep Learning-Driven Biometric Security: Advancing Liveness Detection And Anti-Spoofing Techniques, Banafsheh Adami

Graduate Theses, Dissertations, and Problem Reports (ETD)

Biometric authentication has become a key part of our everyday lives—from unlocking smartphones with a fingerprint or face to verifying identities in banks and airports. These systems rely on our unique physical or behavioral traits, making them both convenient and secure. Unlike passwords, biometrics cannot be forgotten or stolen in the traditional sense. However, they are not without risk. One of the biggest concerns is spoofing: attempts by attackers to fool systems using fake biometric traits, such as silicone fingerprints or AI-generated videos.

As generative AI tools become more powerful and accessible, the ability to create convincing fake biometric data …

Knowledge Integrated Deep Learning For Enhanced Fault Diagnosis And Prognosis For Rotating Machinery And Its Applications On Marine Systems, Yunsheng Su

Dissertations, Master's Theses and Master's Reports

This dissertation develops a knowledge-informed deep learning framework for robust fault diagnosis and prognosis across various engineered systems, including bearings, lithium-ion batteries, and mooring systems in wave energy converters (WECs). A deep transfer learning (DTL) method is first developed to improve bearing fault diagnosis by fusing data from multiple sources and extracting key features using convolutional neural networks (CNNs). Building upon this, a knowledge-informed deep network (KIDN) framework is proposed, integrating physics-based features with deep learning to enhance diagnostic accuracy and generalizability. A constrained Gaussian process (CGP) model is further employed to guide adaptive network design and reduce model tuning …

A Hybrid Model To Assess Commercial Port Resilience In Taiwan, Po-Hsing Tseng, James J.H. Liou

Journal of Marine Science and Technology–Taiwan

Since 2021, international commercial ports have faced unprecedented challenges (e.g., COVID-19 pandemic, Suez Canal congestion), impacting daily life, work, human well-being, properties, the environment, and socio-economic activities. The adaptation capability resilience of international commercial ports to respond to external changes has become crucial. This study develops an expert knowledge-based hybrid multi-criteria decision-making model integrating Best-Worst Method (BWM), Rough Dombi Aggregator, and Combined Compromise Solution (CoCoSo) to evaluate port resilience capabilities, using Taiwan's international commercial ports as a case study. We propose four evaluation dimensions encompassing 13 indicators: detection capability, resistance capability, resource integration capability, and recovery capability. The BWM determined …

Long-Term Traffic Prediction Using Deep Learning Long Short-Term Memory, Ange-Lionel Toba, Sameer Kulkarni, Wael Khallouli, Timothy Pennington

School of Cybersecurity Faculty Publications

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue …

Security Enhancement In Aav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Autonomous Aerial Vehicles (AAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These AAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

Leaf-Based Varietal Categorization Of Sweetpotato (Ipomoea Batatas L. Lam.), A Potentially Healthful Vegetable, Using Image Processing And K-Means Clustering, Shahidul Islam, Md Towfiqur Rahman, Md Hamidul Rahman, Abdul Momin

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Sweetpotato (Ipomoea batatas Lam) leaves contain higher concentrations of phenolic compounds, flavonoids, and carotenoids that are remarkable in health promotion. However, the nutrient content in sweetpotato leaves varies from variety to variety, and leaf shape and color are the key identifying factors for the varietal classification of sweetpotatoes. So, detecting sweetpotato leaves is essential for the in-situ identification of sweetpotato varieties and for developing intelligent agricultural systems. This study aimed to create a leaf-shape-based varietal classification technique for sweetpotato using image processing techniques coupled with a K-means clustering algorithm. 38 leaf images (RGB) of two sweetpotato cultivars were collected …

In-Season Nitrogen Mmanagement: Leveraging Data Visualization For Precision Agriculture, Chathurika Harshani Narayana

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Effective nitrogen management is vital for sustainable agriculture, impacting both crop yield and environmental health. Traditional methods often use fixed application rates set before planting, which do not adapt to changing crop needs during the season. This can lead to over- or under-application, reducing efficiency and sustainability. While modern tools like sensors, satellites, and UAVs provide valuable real-time data on crop and field conditions, integrating and using this data to guide timely nitrogen decisions remains a major challenge. In-season nitrogen management offers a solution by allowing for dynamic adjustments to nitrogen applications, addressing crop needs as they arise. This approach …

Improvement Of Cellular Pattern Organization And Clarity Through Centrifugal Force, Lauren E. Mehanna, James D. Boyd, Shelley Remus-Williams, Nicole M. Racca, Dawson P. Spraggins, Martha E. Grady, Brad J. Berron

Chemical and Materials Engineering Faculty Publications

Rapid and strategic cell placement is necessary for high throughput tissue fabrication. Current adhesive cell patterning systems rely on fluidic shear flow to remove cells outside of the patterned regions, but limitations in washing complexity and uniformity prevent adhesive patterns from being widely applied. Centrifugation is commonly used to study the adhesive strength of cells to various substrates; however, the approach has not been applied to selective cell adhesion systems to create highly organized cell patterns. This study shows centrifugation as a promising method to wash cellular patterns after selective binding of cells to the surface has taken place. After …

Integrating Protein Language Model And Molecular Dynamics Simulations To Discover Antibiofouling Peptides, Ibrahim A. Imam, Shea Bailey, Duolin Wang, Shuai Zeng, Dong Xu, Qing Shao

Chemical and Materials Engineering Faculty Publications

Antibiofouling peptide materials prevent the nonspecific adsorption of proteins on devices, enabling them to perform their designed functions as desired in complex biological environments. Due to their importance, research on antibiofouling peptide materials has been one of the central subjects of interfacial engineering. However, only a few antibiofouling peptide sequences have been developed. This narrow scope of antibiofouling peptide materials limits their capacity to adapt to the broad spectrum of application scenarios. To address this issue, we searched for antibiofouling peptides in the vast sequence pool of the microbiome library using a combination of deep learning-based high-throughput search and molecular …

From Equitable Access To Equitable Usage: Moving Towards Data-Driven, Evidence-Based Cycling Assets Management, Kewei Ren, Yunping Liang, Chun-Hsing Ho

Durham School of Architectural Engineering and Construction: Faculty Publications

Cycling is increasingly recognized for its wide range economic, environmental, and health benefits as a mobility option. However, disparities in the provision and the quality of bicycle infrastructure persists. Ensuring fair access and usage of these benefits for all presents a significant challenge. As a result, establishing effective methodologies to evaluate cycling equity is critically important. This paper synthesizes research on infrastructure asset management with a focus on equity considerations in bicycle infrastructure. A systematic review of 19 North American studies critically examines how existing research categorizes vulnerable populations and assesses fairness. The results reveal that most existing analyses focus …

Modeling Ion-Specific Effects In Polyelectrolyte Brushes: A Modified Poisson-Nernst-Planck Model, William J. Ceely, Marina Chugunova, Ali Nadim, James D. Sterling

Business and Information Technology Faculty Research & Creative Works

Polyelectrolyte brushes consist of a set of charged linear macromolecules, each tethered at one end to a surface. An example is the glycocalyx which refers to hair-like negatively charged sugar molecules that coat the outside membrane of all cells. We consider the transport and equilibrium distribution of ions and the resulting electrical potential when such a brush is immersed in a salt buffer containing monovalent cations (sodium and/or potassium). The Gouy-Chapman model for ion screening at a charged surface captures the effects of the Coulombic force that drives ion electrophoresis and diffusion but neglects non-Coulombic forces and ion pairing. By …

Online Parameter Adaptation Of Lqr Controllers Via Rls For Prosthetic Joint Control: Experimental Validation On A Quanser Qube-Servo 2 Platform, Cynthia Lopez-Jordan

Theses and Dissertations

This thesis develops and experimentally validates an online adaptive Linear Quadratic Regulator (LQR) control method for prosthetic joint systems using Recursive Least Squares (RLS)-based real-time parameter estimation on the Quanser QUBE-Servo 2 platform. Traditional LQR controllers assume a fxed system model, which limits adaptability and results in reduced tracking accuracy, poor robustness, and loss of optimal performance when applied to dynamically changing prosthetic joints, infuenced by load variations, user gait changes, and mechanical wear. Limited experimental validation exists for combining RLS with online LQR adaptation in prosthetic-like systems.

To address these limitations, an RLS-driven Adaptive LQR framework was implemented to …

Improving System-Level Outcomes Via Artificial Intelligence Decision Support In Kidney Utilization, Casey I. Canfield, Cihan H. Dagli, Daniel Burton Shank, Krista Lentine, Mark Schnitzler, Henry Randall, V. Sriram Siddhardh Nadendla, Brendon Cummiskey

Engineering Management and Systems Engineering Faculty Research & Creative Works

Transplantation provides patients suffering from end-stage kidney disease a better quality of life and long-term survival. However, over 20% of deceased donor kidneys are not utilized and never transplanted. While this is sometimes medically appropriate, this also reflects missed opportunities. We are designing Artificial Intelligence decision support for the kidney offer process to support both demand at the transplant center and supply at the organ procurement organization. This includes (1) developing deep learning models, (2) evaluating the effect of explainable interfaces, (3) improving fairness in the model output, (4) identifying factors that influence adoption decisions, and (5) conducting a randomized …

Augmenting Missing Sensor Data For Robust Human Activity Recognition, Suryakangeyan Kandasamy Gowdaman

Master's Projects

Applications of ubiquitous computing, including health monitoring, sports analytics, and ambient-assisted living, rely on Human Activity Recognition (HAR) using wearable sensors. However, model robustness is challenged by missing sensor values, class imbalance, inter-subject variability, and temporal noise. This work proposes a complete HAR pipeline that addresses these challenges through sampling, time-series augmentation, dynamic feature handling, and GAN-PCA-based imputation. Built on the DeepSense architecture, the model integrates convolutional feature extraction with bi-GRUs for temporal modeling. The system is evaluated using 5-fold cross-validation, subject-aware holdout, and LOSEO strategies on the Opportunity dataset. Results demonstrate consistent accuracy across folds and strong generalization to …

Energy Considerations For Large Pre-Trained Neural Networks, Leo Mei

Master's Projects

In recent years, neural network models have achieved phenomenal performance due to the increasing parameters and complexity of model architectures. However, these advancements come with high environmental costs as they require massive computational resources and consume substantial amounts of electricity, leading to high carbon emissions. Previous studies have demonstrated that substantial redundancies exist in large pre-trained models, and reducing these redundancies through compression would not compromise model performance. While these studies focused on retaining comparable model performance, the direct impact of compression on energy consumption when training models appears to have received little attention. By quantifying the energy usage associated …

Link Failure Localization In Hierarchical, Multidomain Optical Networks, Martin Mihailov Bojinov

Master's Projects

Optical networks transport data encoded on light signals over optical fiber cables. Individual optical networks are managed by domain administrators, such as service providers, vendors, or regional bodies. Multidomain optical networking explores the possibility of enabling seamless data transmission across domain boundaries. In this project, we explore how we can perform network monitoring in a hierarchical multidomain optical network while preserving domain security and autonomy. This is done by allocating dedicated monitoring trails across various broker abstractions of our network topologies. We propose three heuristic functions that expedite trail selection. Of the three heuristics, our least monitored algorithm performs the …

Suicidal Ideation Detection On Reddit Using Llm-Annotated Data And Graph Neural Networks, Ikbal Singh Gurdev Singh Dhanjal

Master's Projects

Suicide is the fourth leading cause of death among people aged 15-29. More than 720, 000 people commit suicide every year. During the COVID-19 pandemic, we saw an increase in people seeking out mental health support on anonymous forums like Reddit. These anonymous forums allow people to express their suicidal ideation without judgment and give them a support structure that not everyone has. The aim of this project is to detect suicidal ideation using Reddit. In this work, we propose SIRGEL (Suicidal Ideation on Reddit using Graph Embeddings and LLMs), a dual-pipeline approach that combines large language model (LLM)- based …

Indiana Stem Teachers’ Perceptions Of The Shift From In-Person To Virtual Training During The Covid-19 Pandemic, Steve Heinold

Indiana STEM Education Conference

The COVID-19 pandemic brought uncertainty to Indiana teachers in 2020. School closings caused disruptions to teachers’ classroom settings, ongoing training, and social and mental well-being (Pozo-Rico et al., 2020). Indiana GEAR UP’s commitment to teacher professional development for STEM educators led to a shift from their in-person summer teacher training sessions to virtual, just months after the school shutdowns in spring 2020. Participant evaluations showed that teachers appreciated the virtual training options and were able to learn new strategies to apply to their new virtual learning spaces. The online training sessions also had drawbacks. This research brief describes recommendations to …

Automatic Cat Caretaker, Connor Mcclenathan, Ryan Anderson, Andrew Tate

Williams Honors College, Honors Research Projects

This project will involve developing and constructing a self-cleaning litter box with feeding and watering functions attached. The machine will have a user interface for setting both the feeding and watering times, as well as how much to fill the food and water bowls at the same time. The project will involve the use of motors for the cleaning, feeding, and watering functions, sensors to detect when to perform those functions, and a microcontroller to process all the data and to tell the motors when to perform their respective functions. This project's goal is to make the task of caring …

Monitoring A Small Network With Snmp, Dillon Taynor

Williams Honors College, Honors Research Projects

This project takes a look at the implementation of three distinct free Network Monitoring Systems (NMSs) and their usage of monitoring a small, virtualized network using the Simple Network Management Protocol (SNMP). The 3 free NMSs are PRTG, LibreNMS, and Zabbix. The network is virtualized within Cisco Modeling Labs. A literature review on relevant media is conducted alongside a review of the three NMSs based on installation difficulty, user friendliness, reporting ability, and their ability to respond to changes in the network. A brief discussion on the difference between an SNMPv2 and SNMPv3 packet is included as well.

Effects Of Soil Properties On Cp Performance, Cecilia Segretario

Williams Honors College, Honors Research Projects

This research aims to evaluate the affect of various soil properties on the effectiveness of cathodic protection (CP) in preventing underground pipeline corrosion. Metal coupons of common pipeline materials with applied CP will be mounted in several separate controlled soil environments to manipulate real world underground corrosion scenarios. Each soil environment will have different physical properties by mixing the soil with a prepared chemical solution. The physical properties to be evaluated include pH, resistivity, and moisture content. At the end of a corrosion duration, weight loss method will be used to evaluate the corrosion rate over that period. Additionally, the …

Cochlear Electrode Insertion Training Model, Sarah Powell, Kaelyn E. Kraley, Nathan J. Smith

Williams Honors College, Honors Research Projects

Cochlear implant surgery is a delicate procedure performed by Otolaryngologists (ENTs) to implant an electronic device into the inner ear to provide a sense of sound for people who are profoundly deaf or hard of hearing. The current practices of training involve cadavers and 3D-printed models. Cadavers are commonly used but are expensive, single-use, and do not provide visual and haptic feedback, which are essential for medical students. 3D printed models are less commonly used and are hard to fabricate and not as realistic. If medical students are not properly trained for this delicate procedure, then risks are significantly increased …