Digital Commons Network^™

Radial And Carotid Arterial Pulse Signals For Assessing Cardiovascular Function At Rest And During Post-Exercise Recovery In A Heart Transplant Patient: A Case Study, Md Mahfuzur Rahman, Mamun Hasan, Jennifer F. May, John M. Herre, Leryn Reynolds, Zhili Hao

Mechanical & Aerospace Engineering Faculty Publications

Aim: This study investigates the feasibility of using radial and carotid arterial pulse signals to assess cardiovascular (CV) function at rest and during post-exercise recovery in a heart transplant (HTx) patient. Method: Two micro-fabricated tactile sensors were used to simultaneously acquire arterial pulse signals at the radial artery (RA) and carotid artery (CA). Measurements were taken at rest and at multiple time points post-exercise on three subjects: an HTx patient, a percutaneous coronary intervention (PCI; coronary stent) patient and a healthy control. An SDOF-TF-based time-frequency analysis algorithm was applied to extract a comprehensive set of CV parameters, including heart rate …

A Computational Approach To Graduated Centrifuge Profiles For Partial Gravity Planetary Environments, Cabell W. Jones

Mechanical & Aerospace Engineering Faculty Publications

Missions such as NASA’s Artemis and SpaceX’s Starship aspire towards creating permanent settlements beyond Earth. In order to achieve this goal, it must be considered that the gravity present on planetary surfaces such as the Moon (0.165 g) and Mars (0.378 g) may be insufficient to support human physiology for long term habitation. On near-Earth missions, issues such as muscular atrophy, neuro-ocular changes, cardiopulmonary variations, and bone mineral density loss have been observed proportional to the duration spent in microgravity. Further studies have suggested that the severity of these conditions scales linearly with decreasing gravity, meaning that astronaut health can …

Enhancing The Performance Of Disk-Based Key-Value Stores: From Learned Index Acceleration To I/O-Efficient Hybrid Caching, Sujit Maharjan

Computer Science and Engineering Dissertations - Archive

The exponential growth of data in modern computing environments has rendered the efficient extraction of information from massive datasets a critical systemic requirement. Key-value (KV) storage systems serve as the backbone for these operations; however, their performance is consistently bottlenecked by two primary functional requirements: identifying the data's location and managing the physical cost of accessing the storage device. Data locations are typically identified via an index, while disk I/O is minimized through caching. This dissertation presents LearnedStore, TurboIndex, and ReadBooster, which break these performance bottlenecks by introducing architectural modifications to the index and cache. LearnedStore accelerates operations by adapting …

Adaptive Synchronization In Digital Twin–Enabled Iot Systems: A Unified Framework For Energy, Fidelity, And Latency Trade-Offs, Uzma Zehra

Computer Science and Engineering Theses

Digital twin technology has emerged as a foundational paradigm for enabling real-time monitoring, analysis, and control in Internet of Things (IoT) systems by maintaining virtual representations of physical processes. Its effectiveness, however, critically depends on timely and accurate synchronization between distributed sensing devices and their corresponding digital counterparts. Frequent synchronization improves reconstruction fidelity and system responsiveness but incurs significant communication energy consumption and network latency. In contrast, infrequent synchronization conserves communication resources but can lead to stale or inaccurate digital twin states, particularly in environments with rapidly changing dynamics. These opposing effects give rise to a fundamental trade-off among energy …

Utility-Privacy-Communication Trade-Offs In Federated Financial Fraud Detection Under Non-Iid Client Heterogeneity, Baraa I. Farhan

Polytechnic Journal

Financial fraud detection is difficult because fraudulent transactions are rare, unevenly distributed, and sensitive. This study evaluates a lightweight multilayer perceptron under Federated Averaging (FedAvg), Federated Proximal optimization (FedProx), and Federated Adam (FedAdam), using three seeds and validation-selected decision thresholds. Corrected R4 experiments cover the Credit Card Fraud Detection benchmark, the larger Credit Card Transactions dataset, PaySim as a stand-alone synthetic stress test, and the Bank Account Fraud (BAF) Dataset Suite. On Credit Card, FedProx with 20 clients and alpha = 0.5 achieved F1 = 0.789 +/- 0.010. On Transactions, the best tested federated setting used 10 clients and achieved …

Enhancing Deep Reinforcement Learning With Expert Demonstrations For Mobile Robot Navigation In Unstructured Off-Road Environments, Dulitha Dabare

Theses: Doctorates and Masters

Advancements in the field of Deep Learning has ushered in a boom in autonomous navigation research. Most of the work being conducted in this space, however, has focused on on-road urban navigation scenarios, with unstructured outdoor terrain navigation receiving much more limited attention. Given the wide range of applications that exist for legged and wheeled ground robots in off-road environments in areas such as agriculture, mining and disaster recovery, there is a growing need for research work to improve the navigational capabilities of mobile robots deployed in these challenging environments.

A promising candidate for application to these navigation challenges in …

Generative Imaging For Computational Pathology, Md Jillur Rahman Saurav

Computer Science and Engineering Dissertations

Hematoxylin and eosin (H&E) staining remains central to cancer diagnosis, providing morphological information essential for pathological assessment. Immunohistochemistry (IHC) and newer multiplexed imaging technologies complement H&E by revealing molecular information critical for accurate tumor subtyping and treatment decisions. In practice, however, H&E and IHC are obtained from different consecutive sections that are not spatially aligned, comprehensive multiplexed panels are expensive and tissue-consumptive, and not all stains are available at every clinical site, limiting comprehensive molecular profiling and the full diagnostic potential of these technologies in clinical practice. This dissertation addresses these gaps through three complementary generative deep learning studies in …

Exploring Relational Farming Futures For Responsible Innovation In Precision Agriculture Through Q-Method And Speculative Design, Bhavna Joshi, Maaz Gardezi, Edward Prutzer, Awais Hameed Khan, Asim Zia, Benjamin E. K. Ryan, Ali Dadkhah, Donna M. Rizzo, John T. Mcmaine, David Clay

Biosystems and Agricultural Engineering Faculty Publications

This article draws on concepts of speculative design and responsible innovation to examine the possible impacts of data-driven precision agriculture on farmers and farm work. We use an innovative mixed-method approach to research design. Using artistic depiction of futures of precision agriculture in design workshop and in-depth semistructured interviews conducted with the farmers in Vermont and South Dakota, we explain how farmers perceive different futures of precision agriculture. Specifically, in our design workshop, we combined visual arts and social science methods, namely Q-method, to explore existing and new relationships between humans, technologies, and environment in the design, deployment, and use …

Validity And Reliability Of Force Insoles To Measure Center Of Pressure During Return-To-Sport Testing, Delaney Mcneese, Charles Eisner, Rachel Todd, Brian W. Noehren, Meredith K. Owen

Biomedical Engineering Faculty Publications

Center of pressure is a valuable biomechanical variable, predicting joint loading contributions during movement and giving insight into compensatory patterns. The purpose of this study was to assess the validity and reliability of force insoles in calculating vertical ground reaction force and center of pressure during return-to-sport jump testing. Ten healthy individuals performed double- and single-leg vertical and horizontal jumps on an instrumented treadmill while wearing instrumented force insoles. Vertical ground reaction force and anterior–posterior and medial–lateral center of pressure were collected at peak vertical ground reaction force from both devices. Repeat testing occurred 7 ± 5 days following the …

Non-Contact Optical Spectroscopy For Metabolic And Vascular Characterizations Of Orthotopic Tongue Cancer Models In Vivo, Md Zahid Hasan, Jing Yan, Sumit Sarker, Pranto Soumik Saha, Caigang Zhu

Biomedical Engineering Faculty Publications

Most tissue optical spectroscopy platforms use a fiber probe for light delivery and collection, while the inconsistent probe-sample contact could induce significant distortions in the measured optical signals, which consequently bring analysis errors. Moreover, it will be practically difficult to use a fiber probe for measurements in some cases such as oral cancer investigations using small animal models. To address the critical challenge, we report a portable, lens-based, optical spectroscopy device capable of quantifying key vascular and metabolic parameters in vivo without probe-sample contact. We combined lenses based diffuse reflectance and fluorescence spectroscopy into one portable platform to enable multi-parametric …

Combined Autofluorescence And Diffuse Reflectance Spectroscopy For Rapid Metabolic And Vascular Characterizations Of Orthotopic Tongue Tumors In Vivo, Pranto Soumik Saha, Jing Yan, Sumit Sarker, Zahid Hasan, Caigang Zhu

Biomedical Engineering Faculty Publications

Precise label-free quantification of tissue metabolic and vascular dynamics in vivo represents a critical challenge for cancer therapy prediction and longitudinal treatment assessment. In this study, we demonstrated a portable autofluorescence and diffuse reflectance spectroscopy device along with novel spectroscopic algorithms to quantify tissue vascular and metabolic parameters of orthotopic head and neck cancer models in vivo. Tissue-mimicking phantom studies were used to verify the dual-modal optical spectroscopy and easy-to-use spectroscopic algorithms for rapid and accurate estimation of tissue oxygen saturation, total hemoglobin contents, and intrinsic optical redox ratio. Animal studies were conducted to demonstrate the feasibility of our …

Noninvasive Diffuse Optical Monitoring Of Cerebral Blood Flow And Oxygenation Responses To Intermittent Hypoxia In Neonatal Rats, Pegah Safavi, Mehrana Mohtasebi, Chowdhury Azimul Haque, Faezeh Akbari, Xuhui Liu, Yiqi Yuan, Li Chen, Lei Chen, Guoqiang Yu

Biomedical Engineering Faculty Publications

Significance: Intermittent hypoxia (IH) is common in preterm neonates and can cause hypoxic–ischemic brain injury. Simultaneous monitoring of cerebral blood flow (CBF) and oxygenation is essential to detect oxygen delivery-extraction mismatches and guide intervention.

Aim: We aimed to adapt and test an innovative diffuse speckle contrast flow oximetry (DSCFO) system for continuous monitoring of cerebral hemodynamics during IH in neonatal rats, a model approximating human neonates.

Approach: Two compact laser diodes and a miniature CMOS camera were integrated into a fiber-free probe for continuous monitoring of changes in relative CBF (rCBF) and oxy- and deoxy-hemoglobin concentrations (Δ[HbO₂] and …

Computational Study Of Rotating Detonation Combustors, Aditya Balasubramaniam

Mechanical and Aerospace Engineering Theses

Rotating detonation combustors (RDCs) are pressure-gain combustion devices that sustain one or more continuously rotating detonation waves, offering potential thermodynamic and performance advantages over conventional deflagration-based systems. Their behavior depends strongly on combustor geometry and operating conditions. Understanding these effects is therefore essential for the design and optimization of practical RDCs. Accordingly, this thesis numerically investigates annular RDCs with two primary objectives: (1) to evaluate the effects of propellant mass flux and (2) to assess the influence of annular width on detonation-wave dynamics and combustor performance.

A finite-volume framework is used to solve the compressible reactive Euler equations with hydrogen–air …

Scalable Quantum Network Routing Through Reinforcement Learning And Resource Optimization, Tasdiqul Islam

Computer Science and Engineering Dissertations

Long-distance quantum communication depends on distributing high-delity entanglement across quantum repeaters. Entangled states are fragile: they decohere in memory, are consumed when used, and lose delity after each swap. Quantum routing therefore diers from classical routing: an algorithm must decide not only the path, but when to generate, store, swap, and consume entanglement before they lose their usefulness. This dissertation studies scalable resource allocation and routing for quantum networks under delity, memory, and concurrency constraints. It rst addresses re- peater deployment with heuristics that nd near-optimal locations while cutting com- putation from days to seconds versus integer linear programming (ILP). …

Electron Beam Irradiation Effects On Bulk Metals: A Comparative Study Of Polycrystalline Versus Single-Crystalline Structures, A. A. Elmustafa, N. A. Sultana, A. H. Al-Allaq, M. Ojha, Y. S. Mohammed, J. Vennekate, H. Baumgart

Mechanical & Aerospace Engineering Faculty Publications

This study investigates the effects of electron beam (e-beam) irradiation on the mechanical and structural properties of eight bulk metallic samples, comprising both polycrystalline (PC) and single-crystalline (SC) forms of Ni, Cr, V, and Ti. These metals were evaluated as potential candidates for beam exit windows in high-power (MW-class) particle accelerators. The primary objective is to identify metals capable of withstanding the conditions of high-power/MW-class e-beam accelerators and serve effectively as exit windows. Selection criteria were based on each metal’s intrinsic properties, power dissipation capability, and irradiation-induced changes in mechanical behavior, including hardness, elastic modulus, and defect density. Comprehensive characterization …

From Comparison To Integration: Building Energy Simulation Tool Variability And The Case For Intelligent Retrofit Workflows, Amir Safari, Dalya Ismael, Mahsa Safari, James Freihaut

Engineering Technology Faculty Publications

As the urgency to address climate change and modernize energy infrastructure grows, the building sector plays a key role in improving energy efficiency and reducing carbon emissions. This study evaluates five energy retrofit strategies for Building 101 at The Navy Yard in Philadelphia, comparing two real-world proposals from energy service companies with three simulation-based packages derived from Building Energy Simulation (BES) tools. The study examined whether advanced BES tools provide greater accuracy and decision-making value compared to simpler alternatives. Electricity savings ranged from 5 % to 40 %, gas savings from 29.7 % to 61 %, and annual cost reductions …

Multidentate Surfactant-Dependent Synthesis Of Giant Iridium Superstructures, Ramjee Balasubramanian, Maeren E. Hill

Chemistry & Biochemistry Faculty Publications

Giant superstructures of iridium, comprised of smaller spherical and slightly larger anisotropic nanoparticles, were synthesized by reducing iridium chloride in the presence of resorcinarene, a class of tetrameric macrocyclic polyphenols, with sodium borohydride in ethanol in 30 min under mild conditions. These superstructures were characterized by a range of techniques including TEM, HRTEM, EDS and other spectroscopic methods. The impact of the macrocyclic surfactant, its features and reaction conditions in dictating the formation of giant iridium superstructures was evaluated. The packing density of nanoparticles in these giant iridium superstructures could be altered qualitatively by varying the duration of the reaction, …

Array Signal Processing And Machine Learning In 5g/6g Networks, Roopesh Kumar Polaganga

Electrical Engineering Dissertations - Archive

This dissertation investigates advanced methodologies in Array Signal Processing (ASP) and Machine Learning (ML) to enhance the performance, efficiency, and intelligence of next-generation wireless networks, with a primary focus on 5G and emerging 6G systems. As wireless networks face rapid traffic growth, increasingly heterogeneous service requirements, and more complex propagation environments, conventional design and optimization approaches become insufficient to meet evolving demands in reliability, capacity, spectral efficiency, and energy efficiency. On the network intelligence side, this work develops data-driven frameworks for causal discovery, scheduler enhancement, session-duration prediction, and Radio Resource Control (RRC) state optimization using real-world telecommunication network data. On …

Ensemble Dependence Of Mechanical Relaxation In Molecular Dynamics Simulation, Mason E. Hyde

Open Access Master's Theses

This thesis investigates whether the selection of thermodynamic ensemble introduces quantifiable differences in the stress relaxation modulus G(t), storage modulus G”(w), loss modulus G”(w), and phase angle d(w) computed from equilibrium molecular dynamics simulations of the AAA-1 asphalt model. A 72-molecule, 12-component representation of AAA-1 bitumen was simulated under three ensembles (NPT, NVT, and NVE) at three temperatures (400.15 K, 443.15 K, and 533.15 K) with three independent replications at each condition, yielding 27 total simulations. Stress autocorrelation functions were computed from each production trajectory and Fourier-transformed to obtain frequency-dependent rheological properties.

The computed properties …

Evaluating Training Effectiveness And Learning Transfer In The Kentucky Transportation Cabinet Safety Academy, Amanda L. Casolare

Theses and Dissertations--Civil Engineering

Effective evaluation of safety training is crucial to ensure that learning translates into safe work practices, particularly in high-risk public sector environments. The Kentucky Transportation Cabinet (KYTC) is developing a Safety Academy to enhance hazard recognition, risk management, and safety leadership within the organization. However, the Safety Academy has not yet been implemented, and formal evaluation methods for training have not been established. This study aimed to examine evidence-based training evaluation techniques and develop recommendations for evaluating the KYTC Safety Academy prior to program implementation.

This study employed an applied pre-implementation research design grounded in a comprehensive review of safety …

Mental Health And Help-Seeking In Engineering Education: Beliefs, Norms, And Intervention Pathways For Undergraduate Students, Matthew D. Whitwer

Theses and Dissertations--Chemical and Materials Engineering

Engineering education is at a critical juncture where supporting student mental health is essential for fostering persistence, equity, and the development of a resilient and innovative workforce. While engineering students experience high levels of distress and low rates of mental health treatment seeking (known as a mental health treatment gap), there is a gap in knowledge around how this compares to that of students in other fields of study. Further, while efforts have been made to improve the mental health treatment gap in engineering, they are limited by a lack of actionable, theory-based targets. Therefore, this dissertation aims to characterize …

A Specification-Driven Framework For Self-Supervised Learning In Specialized Vision Domains, Mahmut S. Gokmen

Theses and Dissertations--Computer Science

Self-supervised learning (SSL) has emerged as a principled approach to visual representation learning that derives supervisory signal directly from unlabeled data, enabling foundation models to be trained at scale without manual annotation. Deployments in medical imaging and biometric recognition have demonstrated the potential of this paradigm, yet the assumptions that make SSL effective on natural image benchmarks fail systematically in specialized domains. Generic SSL pipelines encode a tacit assumption that the most informative correspondence is spatial proximity within a single acquisition. In specialized domains this assumption breaks at the level of the data-generating process: the signal that carries domain-specific information …

Linking S‑Pillar Empirical Criteria With Boundary And Finite Element Models For Underground Stone Pillar Analysis, Benjamin Diddle

Theses and Dissertations--Mining Engineering

The objective of this thesis is to strengthen the relationship between empirically based stone pillar design methods and two numerical modeling techniques by calibrating boundary element and finite element models to the S‑Pillar empirical criteria and observed pillar behavior in underground stone mines. The first component of the research investigates the displacement‑discontinuity boundary element method implemented in LaModel, quantifying the influence of the lamination thickness and element size on stone pillar safety factor predictions. Results showed that optimal thickness depends on overburden modulus, while larger element sizes introduce significant SF differences from S-Pillar values once the grid size exceeds 5 …

From Floodplain To Floodplain: Analyzing Stream And Riparian Restoration Methodologies And Impacts, Noah P. Lane

Theses and Dissertations--Biosystems and Agricultural Engineering

Stream restoration is a relatively new scientific field. Currently, there is not a definitive methodology for performing restorations that all professionals in industry and academia can agree upon. This has allowed for multiple ideologies to be developed and altered that vary by geographic location and the professional using them. In addition to the methodologies used, the publicly available design guidance published by state government organizations also varies by regulatory authority, method of analysis, ecoregion, and restoration objectives. In some cases, design guidance is replicated from a different geographical area and may not satisfy local needs or existing site conditions. Since …

Shock The System: Pouring Water On The Cpuc’S Income-Graduated Flat Rates In Favor Of More Effective Rate Structures, Benjamin Hill

Dickinson Law Review (2017-Present)

The volume of rate cases in the United States continues to increase, resulting in constant price hikes for utility customers. The battle between corporate profit and consumer financial satisfaction will likely continue. Considering these diametrically opposed interests, the California Public Utility Commission, at the behest of the California legislature, decreased the rate consumers pay for usage and added fixed fees based on income to utility bills. The broader goals of the Commission’s model are to maintain utility profitability, alleviate the financial burden consumers face, promote energy conservation, and develop energy efficient technology to combat greenhouse gas emissions. This Comment will …

Social Safety And Social Security: Validating Context-Specific Instruments For Slums, Liss D. Romero, Katherine Palacio, Zacheous Ako Abang, Valeria Silgado, Humberto Llinas, Leidy González, Erika Frydenlund, Daniel Bolivar, Jose J. Padilla

VMASC Publications

Social safety and social security are concepts that help explain a community's well-being by assessing how it manages and mitigate existing, or perceived, risks. However, these terms are often conflated, which can limit understanding, particularly in areas with unstable living conditions. This study explores both concepts in the context of residents living in slums. As the number of displaced people grows, slums and informal settlements are becoming increasingly common worldwide, making it essential to clarify these concepts. Residents of slums face numerous hazards, including crime, violence, inadequate housing, overcrowding, and limited access to essential services. To measure social safety and …

Machine Learning For Predicting Prosthetic Limb Movements, Jessica Alexandra Cegarra Arraiz

Theses and Dissertations

This thesis develops and evaluates a deep learning-based prediction model capable of identifying intended limb movement from surfaced electromyography (sEMG) signals using sequence learning techniques. sEMG signals change over time due to multiple factors such as muscle fatigue or user variability. Traditional prosthetics control methods rely on static feature extraction, ignoring how signals change over time, thereby limiting their ability to capture the temporal changes of muscle activity. As a result, these approaches often lead to poor accuracy, robustness, and generalization. Limited experimental validation has been conducted on sequence-based machine learning approaches using temporal sEMG data from publicly available datasets …

Edge Guided Channel Attention In Fsrcnn: A Novel Approach For Depth Super Resolution, Yagneshkumar Jayantilal Parmar, Paresh M. Dholakia

Mansoura Engineering Journal

Depth images from low-cost sensors often suffer from blurred edges and structural distortions when processed with standard super-resolution models. While FSRCNN is efficient for RGB images, it struggles to handle the unique geometric requirements of depth maps. To solve this, we propose the Edge Guided Channel Attention FSRCNN (EGCA FSRCNN). This method incorporates an edge-guided modulation mechanism to preserve object boundaries and a Squeeze and Excitation (SE) block to focus on critical structural features. A major benefit of this framework is the use of frozen, pretrained FSRCNN weights, which bypasses the requirement for retraining. Our evaluation on the UTKinect, Middlebury, …

Trident-Bladder: A Resolution-Aware Multimodal System For Robust, Calibrated Bladder Cancer Diagnosis Across Endoscopy, Histopathology, And Liquid Biopsy, K. Sankar, S. Venkata Lakshmi, Gummadi Reshma, V. Gokula Krishnan, M. Ganesan, V. Vijayaraja

Mansoura Engineering Journal

Bladder cancer diagnosis demands the integration of cystoscopy, histopathology, and biomarker testing, yet the majority of AI systems continue to be modality, specific and sensitive to acquisition variations. TRIDENT, Bladder is a resolution, aware multimodal framework that we have developed to address this issue. The method combines cystoscopic lesion segmentation, whole, slide image (WSI) molecular subtype prediction, and urine miRNAclinical data through reliability, weighted fusion and a utility, aware decision policy. The endoscopic branch features a quality, gated super, resolution component, resulting in mDice 87.5, sensitivity 92.1%, precision 91.4%, boundary, F1 85.3%, and ECE 0.023 at 32 FPS. mDice on …

Process Modeling, Operability Analysis, And Techno-Economic Optimization Of Soecs For Sustainable H2 Production, Krishna Murthy Busam

Graduate Theses, Dissertations, and Problem Reports (ETD)

The global imperative to decarbonize energy systems has intensified research into sustainable hydrogen production pathways, as hydrogen emerges as a critical energy carrier for sectors difficult to electrify directly. Among water electrolysis technologies, solid oxide electrolyzer cells operating at elevated temperatures offer the highest theoretical efficiency by utilizing thermal energy to reduce electrical energy requirements, yet comprehensive frameworks integrating electrochemical modeling with process operability analysis and techno-economic optimization remain scarce in the literature. This dissertation develops a process systems framework for proton-conducting solid oxide electrolyzer cells that bridges the gap between fundamental electrochemical phenomena and industrial-scale process economics. At the …