Digital Commons Network^™

Heat Transfer During Air-Water Two Phase Flow, Saumitra K. Deshpande

Theses

The effects of air injection on the heat transfer coefficient for the liquid flow in two-phase in microchannels are studied. Different manifolds and microchannel configurations are used for studying the effects of air injection. The manifold has a uniform gap of 400μm between the heater chip and the cover. A manifold is designed and manufactured to provide different air water flow patterns for different combinations of liquid and air flow rates. The effect of these flow rates on the heat transfer coefficient are studied. The flow rate for air injected is kept very small with respect to the flow rate …

Modeling Impact Of Simulated Myocardial Perfusion On Cardiac Ablation For Arrhythmia, Brendan Cappon

Theses

Bioheat transfer is the study of heat transfer applied in anatomy and physiology, which is relevant to maintaining homeostatic conditions from metabolic activity, thermal exposures, and various treatments and diagnostic methods. Blood flow significantly impacts heat transfer throughout the body, facilitating the diffusion of heat. Models have been developed to quantify bioheat transfer and the effect of blood flow through tissue for many biological functions and medical procedures, one of which is radiofrequency ablation for cardiac arrhythmia. Significant improvements to models and assumptions are still required as success rates for this procedure remain low for various arrhythmia types. The research …

Development And Optimization Of Electric-Double-Layer Gated Transistors Based On Low-Dimensional Materials: Gate-All-Around Inas Nanowire Arrays And Graphene Channels, John Wyatt Morrell

Theses

Electric-double-layer (EDL) gating is a promising alternative to conventional gating methods for field-effect transistors (FETs), with potential applications spanning current logic, future logic, and neuromorphic computing architectures. This technique employs mobile ions in an electrically insulating but ionically conductive medium to generate strong local electric fields at the gate-to-channel interface, achieving field magnitudes on the order of 1 V/nm. These intense local fields enable significant charge carrier accumulation in the semiconducting channel, positioning EDL gating as a strong candidate for device applications. In this study, we investigate the use of low-dimensional channel materials, including two-dimensional graphene and one-dimensional InAs nanowires, …

Artificial Neurovascular Networks To Simulate Energy Distribution To Spiking Neural Networks, William Bottom

Theses

Biological neurons are known to be much more energy efficient than modern computers, with the human brain only consuming about 20 Watts. An Artificial Neural Network can take hours or days to train on high-performance and energy-consuming GPUs, so networks that can achieve similar performance at a fraction of the energy cost are very sought after. Spiking Neural Networks, which mimic biological neurons more closely than Artificial Neural Networks (ANNs), are a potential option for the energy problem in Neural Networks. By converting an ANN to an SNN and applying energy-saving methods/architectures, the equivalent SNN network can achieve similar performance …

An Automated Approach To Detect Cyber Threats With Smart Contracts In Blockchain Systems., Alia Mohammad Ahli

Theses

Blockchain is becoming one of the main components of the modern world due to its rapid development. Considering its main characteristics as a collaborative distributed database that provides transparency and scalability, blockchain development and application have grown dramatically over the past decade. While the blockchain is a key feature and is used in the industry, it is subject to many risk factors and attacks. Advanced technologies that can successfully detect and mitigate vulnerabilities must be developed for solving these security challenges. This thesis presents the development of a machine learning based approach to discover vulnerabilities in blockchain-based systems. It begins …

Enhancing Medical Cybersecurity: A Machine Learning Solution For Intrusion Detection In Iomt Devices, Afra Yaqoob

Theses

In the past few decades, the healthcare sector has undergone major technological advancements, which have included the adoption of Internet of Medical Things (IoMT), and networked medical devices. These advancements have changed the manner in which patient care is provisioned, enabling for more accurate diagnosis, personalized treatment plans, among other benefits. However, as healthcare providers are growing more reliant on interconnected systems, they have also been exposed to critical cybersecurity vulnerabilities. Medical devices have now become targets for cyberattacks, threatening patient data and its security and endangering patient's health and safety. This thesis examines the affects that inadequate cybersecurity measures, …

Using Machine Learning To Predict Energy Consumption In Commercial Buildings In The Uae, Saif Nasser Aljasmi

Theses

The ever-growing demand for energy necessitates a shift towards efficient management practices. Buildings, responsible for a significant portion of global energy consumption, present a prime opportunity for optimization. Building Energy Management Systems (BEMS) have emerged as a crucial tool in this endeavor, offering a comprehensive platform for monitoring and integrating various building systems. However, a key functionality within BEMS, energy consumption prediction, has faced limitations in accuracy. This stagnation hinders the full potential of BEMS for achieving optimal energy management strategies. Traditional methods for energy prediction often rely on historical data and lack the sophistication to capture the complex and …

Analyzing The Effects Of Economic Factors On Residential Real Estate Market Dynamics Of Dubai: Using Machine Learning Methods, Zarrina Shokirova

Theses

Dubai’s residential real estate market is known for its rapid growth and dynamic nature, making it an intriguing subject for analyzing the impact of various economic factors. This thesis examines how key economic elements such as GDP growth, inflation rates, and foreign investment influence property dynamics in Dubai. By synthesizing current research, this study aims to understand how these economic conditions affect real estate values, demand, and supply. Additionally, modern analytical methods, including decision trees, logistic regression, and random forest models, are employed to predict market trends and uncover significant patterns. This research contributes to both academic understanding and practical …

Temporal Analysis And Prediction Of Air Quality Based On Environmental Data, Mizbah Syed

Theses

Air pollution is one of the most pressing environmental challenges of the modern era, significantly impacting public health, ecosystems, and economic stability. Pollutants such as PM2.5, PM10, and NO₂—largely driven by urbanization, industrialization, and vehicular emissions—have exacerbated global health issues, including respiratory illnesses, cardiovascular diseases, and premature mortality. According to the World Health Organization, air pollution contributes to over 7 million deaths annually, with economic losses amounting to billions of dollars. Beyond its health and financial toll, air pollution worsens climate change, underscoring the urgent need for accurate air quality index (AQI) prediction to enable timely interventions and evidence-based policymaking. …

Introducing The Second-Order Features Adjoint Sensitivity Analysis Methodology For Neural Ordinary Differential Equations—I: Mathematical Framework, Dan Gabriel Cacuci

Faculty Publications

This work introduces the mathematical framework of the novel “First-Order Features Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations” (1st-FASAM-NODE). The 1st-FASAM-NODE methodology produces and computes most efficiently the exact expressions of all of the first-order sensitivities of NODE-decoder responses with respect to the parameters underlying the NODE’s decoder, hidden layers, and encoder, after having optimized the NODE-net to represent the physical system under consideration. Building on the 1st-FASAM-NODE, this work subsequently introduces the mathematical framework of the novel “Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations (2nd-FASAM-NODE)”. The 2nd-FASAM-NODE methodology efficiently computes the exact expressions …

A Graph Motif Adversarial Attack For Fault Detection In Power Distribution Systems, Dibaloke Chanda, Nasim Yahyasoltani

Computer Science Faculty Research and Publications

Fault detection is an integral part of the protection system in a power distribution network. Due to advanced computational capabilities, deep learning-based algorithms can significantly outperform traditional methods. However, these deep learning models are prone to adversarial attacks which are not well-addressed as traditional cyber attacks in distribution systems. More specifically, to capture the structure of distribution systems, graph neural networks (GNNs) are employed. Leveraging the backdoor attack model, we propose a novel graph-based adversarial attack algorithm for fault detection in power systems. It is further shown that the adaptable structure of GNN can make them vulnerable to adversarial attacks …

Chemogenetic Neuronal Silencing Decouples C-Jun Activation From Cell Death In The Temporal Cortex, Melissa A Rudy, Trent A Watkins, Joanna L Jankowsky

Faculty, Staff and Students Publications

Initial symptoms of neurodegenerative diseases are often defined by the loss of the most vulnerable neural populations specific to each disorder. In the early stages of Alzheimer's disease, vulnerable circuits in the temporal lobe exhibit diminished activity prior to overt degeneration. It remains unclear whether these functional changes contribute to regional vulnerability or are simply a consequence of pathology. We previously found that entorhinal neurons in the temporal cortex undergo cell death following transient suppression of electrical activity, suggesting a causal role for activity disruption in neurodegeneration. Here we demonstrate that electrical arrest of this circuit stimulates the injury-response transcription …

How The European Union Should Respond To Trump's Tariffs, Ignacio García Bercero, Petros C. Mavroidis, André Sapir

Faculty Scholarship

President-elect Trump has threatened to increase United States tariffs to 60 percent on imports from China and 10 percent to 20 percent on imports from other trading partners, including the European Union. In doing so the US would be ignoring its World Trade Organisation commitments and would also rollback the substantial liberalisation that has taken place during the past 80 years, with a potentially major negative impact on the world economy.

In response to this threat, the EU should pursue a three-pronged strategy.

First, the EU should engage bilaterally with the US to seek to avoid the imposition of tariffs. …

Ai Regulation Has Its Own Alignment Problem: The Technical And Institutional Feasibility Of Disclosure, Registration, Licensing, And Auditing, Neel Guha, Christie M. Lawrence, Lindsey A. Gailmard, Kit T. Rodolfa, Faiz Surani, Rishi Bommasani, Inioluwa Deborah Raji, Mariano-Florentino Cuéllar, Colleen Honigsberg, Percy Liang, Daniel E. Ho

Faculty Scholarship

Calls for regulating artificial intelligence (“AI”) are widespread, but there remains little consensus on both the specific harms that regulation can and should address and the appropriate regulatory actions to take. Computer scientists propose technical solutions that may be infeasible or illegal; lawyers propose regulation that may be technically impossible; and commentators propose policies that may backfire. AI regulation, in that sense, has its own alignment problem, in which proposed interventions are often misaligned with societal values.This Article assesses the alignment and technical and institutional feasibility of four dominant proposals for AI regulation in the United States: disclosure, registration, licensing, …

Finite-Time Control Strategies For Rendezvous And Proximity Operations, John Tamotsu Akagi

All Graduate Theses and Dissertations, Fall 2023 to Present

One common metric to use when designing a controller is the time that the system will take to reach the desired state. Unfortunately, many approaches to developing controllers only guarantee that the system will approach, but not exactly reach, the desired state. This can become a limitation in time-sensitive situations where rapid and complete convergence is necessary. One group of control methods, known as finite-time control, does guarantee both faster convergence and that the desired state will be reached, but often fails to define exactly what time that will occur, how much control will be used to get there, and …

The Effect Of Indaziflam On Seed Production In Perennial Cool-Season Grasses, Benson F. Israelsen

All Graduate Theses and Dissertations, Fall 2023 to Present

Annual invasive grass weeds are a major problem in grass seed production as they contaminate seed lots and decrease the quality. Few effective herbicides are available to control invasive grass weeds in seed production systems. Indaziflam is a pre-emergence herbicide capable of providing annual weed control for 3-5 years after application. Indaziflam is not labeled for use in seed production as little is known of the effect it will have. The purpose of this study was to measure total seed yield, seed germination, seed mass, and seedling vigor when applying 0, 40, 77, and 93 g ai ha^-1 of …

Channel Dynamics In Response To Compounding Hydrogeomorphic Disturbances In A Steep Burned Watershed, Paxton Ridgway

All Graduate Theses and Dissertations, Fall 2023 to Present

Wildfires significantly increase sediment input to rivers through various pathways, including post-fire debris flows. Understanding how mountain rivers respond to these sediment inputs is crucial for predicting and managing downstream impacts on infrastructure, water security, and aquatic habitats. While the causes and characteristics of post-fire debris flows have been extensively studied, the morphological changes in downstream fluvial channels remain poorly understood. This study uses scripted spatial analysis toolsets, high-resolution pre- and post-fire DEMs, repeat cross-sectional surveys, time-lapse footage, and pebble counts to document channel responses to sediment supply in a burned steep mountain stream in the upper Colorado River Basin, …

Assessing Canopy Features, Its Shading Factor, And Their Influence In The Estimation Of Evapotranspiration In Almond Orchards Using Uas Information And The Two-Source Energy Balance Model, Moises R. Duran-Gomez

All Graduate Theses and Dissertations, Fall 2023 to Present

This study explored how canopy biomass and shadows affect evapotranspiration (ET) estimates, a crucial factor for irrigation management in almond orchards in California’s Central Valley. Data from 24 drone flights over three almond plots in 2021 and 2022 were analyzed using the Two-Source Energy Balance (TSEB) model, combined with meteorological data from EC-towers. The study found that filtering out shadows improved ET estimation, particularly in young orchards, and identified the importance of using new Leaf Area Index (LAI) models for better accuracy. This research is part of the T-REX initiative, which integrates drone and ground-based data to enhance water use …

Evaluation Of 3d Beach Profiles: A New Method For Monitoring Changes In Beach Morphology And Grain Size, Kalinda Ellen Roberts

Graduate Masters Theses

Beach profiles are a simple and affordable approach to monitoring changes in beach morphology. However, traditional methods offer limited resolution and dimension. Recent advancements in technologies, particularly structure-from-motion (SfM) and grain size detection models (GSDMs), can be integrated into conventional beach profiling techniques, enabling more detailed spatial and temporal analysis of slope and grain sizes. This research introduces a new technique for coastal monitoring, termed “3D Beach Profiles,” which integrates SfM technology to create high-resolution three-dimensional digital surface models (DSMs) and orthomosaics of beach surfaces. These digital surfaces enable the extraction of beach slopes with orders of magnitude more data …

Training-Informed Air Traffic Control Workforce Scheduling, Eli Winett

Theses and Dissertations

The Radar Control Facility (RCF) at Eglin Air Force Base creates monthly workforce schedules. Adherence to regulations must be verified manually and no evaluation criteria exist to compare schedules. A mixed-integer program model for air traffic controllers allocates workers across 19 positions for a user-defined number of weeks of 24-hour operations. Across eight consecutive months, this model produced schedules to nearly eliminate all shifts with critical shortages from the reference schedules. Without civilian overtime work, gains in positional coverage require a 35% increase in overtime work for military workers and 35% reduction in monthly training instances. We recommend users adopt …

Mitigating Code Reuse Attacks On Risc-V Binaries: Minimizing Gadget Availability Using The Compressed Extension, Heitor Vieira

Theses and Dissertations

Embedded systems are vital in civilian and military applications, requiring high performance and security. The open RISC-V Instruction Set Architecture (ISA) offers significant advantages, including security through community review and strategic independence in microchip supplies. Brazil’s recent partnership with RISC-V highlights its potential for national technological sovereignty. However, RISC-V is not inherently resistant to code reuse attacks (CRAs), highlighting the need to integrate security measures early in development. The RISC-V Compressed extension, while beneficial for optimizing performance and code flexibility, introduces security trade-offs. As RISC-V adoption grows, particularly in critical systems, addressing these security challenges from the start is crucial …

Dual-Channel Side Channel Attack: Improved Aes Key Decryption By Combining Power And Electromagnetic Side Channels With Convolutional Neural Networks, Sean P. O'Neill

Theses and Dissertations

This research introduces a novel DL approach for SCA that combines power consumption and EM signals to enhance encryption key deduction by leveraging a dual-channel CNN architecture. A new dataset, consisting of simultaneous power and EM signal collections during 128-bitAES encryption, was developed to train and evaluate the model’s effectiveness. The combined approach achieved an 88% reduction in traces needed, from 50 traces to 6, for encryption key classification, outperforming traditional methods such as random forest, DPA, DEMA,and individual side channel CNN models. These findings highlight the potential of integrating multiple side channels in SCA to improve performance without the …

“Time To Rebuild The Healthcare Workforce”: Addressing Socio-Cultural And Psychological Perspectives Of Men’S Presence In Nursing And Midwifery, Sarmad Muhammad Soomar, Salima Farooq, Sarosh Tahar Khymani, Nargis Asad

Journal of Asian Midwives (JAM)

Background: There is a global scarcity of healthcare workers, particularly nurses and midwives, who make up more than half of the current workforce associated with “care”, which is the essence of nursing and midwifery in patient and family care. This is always considered as a profession for women in most of the global workforce. In a patriarchal society, nursing and midwifery limits the choice to be adopted by men. Based on male nurses and midwives’ experiences and stories, it was aimed to explore the present literature to see the citations related to this area, which have been cited to address …

Recovery Resiliency Of Interdependent Power Systems Infrastructure Subject To Extreme Events, Partha P. Sarker

Graduate Theses and Dissertations (2019 - present)

When Hurricane Maria struck the island of Puerto Rico on September 20, 2017, it devastated the island’s aging power systems infrastructure and inflicted an island-wide power outage that left Puerto Rico in total darkness for an entire week before the system slowly started to recover. This unprecedented failure of the critical power systems infrastructure exacerbated the failure of other critical infrastructures or lifeline systems of the island. This research explores and quantifies the relationships or interdependencies that exist between the power systems and other critical infrastructure systems by investigating the post-hurricane recovery data of these lifeline systems. Subsequently, the research …

Three-Dimensional Environmentally Sustainable Neuromorphic Computing System Based On Natural Organic Memristor, Mohammed Rafeeq Khan

Graduate Theses and Dissertations (2019 - present)

A three-dimensional neuromorphic (3D) computing architecture based on environmentally sustainable natural organic honey memristors is proposed in this thesis. A set of comprehensive and experimental results indicate that the proposed systems exhibit remarkable inference accuracy, consistently surpassing the 90% threshold, even with different challenges such as device variations and nonlinearity. This study also considers four different conductance drift situations, the effects of analog-to-digital converter (ADC) quantization, and multiple algorithms, such as VGG8 and DenseNet-40. The deliverable of this thesis will test the stability of the proposed systems and explore their potential applications and scalability in real-world situations.

Mesoporous Silica Administration As A New Strategy In The Management Of Warfarin Toxicity: An In-Vitro And In-Vivo Study, Fatemeh Farjadian, Fatemeh Parsi, Reza Heidari, Khatereh Zarkesh, Hamid Reza Mohammadi, Soliman Mohammadi-Samani, Lobat Tayebi

School of Dentistry Faculty Research and Publications

Purpose:

Warfarin is one of the most widely used anticoagulants that functions by inhibiting vitamin K epoxide reductase. Warfarin overdose, whether intentional or unintentional, can cause life-threatening bleeding. Here, we present a novel warfarin adsorbent based on mesoporous silica that could serve as an antidote to warfarin toxicity.

Methods:

Amino-functionalized mesoporous silica (MS-NH2) was synthesized based on the co-condensation method through a soft template technique followed by template removal. The prepared structure and functional group were studied by Fourier transform infrared spectroscopy (FT-IR), and X-ray diffraction (XRD). Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) checked the morphology. The …

Metabolic Shifts In Ratio Of Ucocn To Cocn Toward Bone Resorption Contribute To Age-Dependent Bone Loss In Male Mice, Matthew Bernhard, Obinna Okorie, Wei-Ju Louis Tseng, Mengcun Chen, Julia Danon, Mingshu Cui, Elisabeth Lashbrooks, Yanmei Yang, Bin Wang

Center for Translational Medicine Faculty Papers

The study of the senile osteoporosis in men still lags significantly behind that in women. The changes of protein molecule levels and their relationships with bone loss remain poorly understood. In the present study, we used C57BL/6J male mice at ages from 3 to 24 mo to delineate the mechanisms of aging effects on bone loss. We used the microcomputed tomography, mechanical testing, histomorphometry assays, and detection of serum levels of undercarboxylated osteocalcin (ucOcn) and carboxylated osteocalcin (cOcn) to assess bone mass changes and their relationships with the ratios of ucOcn-to-cOcn in mice from different age groups. The results showed …

Eigenbin Compression For Reducing Photon-Counting Ct Data Size, Taly Gilat Schmidt, Zhye Yin, Jingwu Yao, Jiahua Fan

Biomedical Engineering Faculty Research and Publications

Background

Photon-counting CT (PCCT) systems acquire multiple spectral measurements at high spatial resolution, providing numerous image quality benefits while also increasing the amount of data that must be transferred through the gantry slip ring.

Purpose

This study proposes a lossy method to compress photon-counting CT data using eigenvector analysis, with the goal of providing image quality sufficient for applications that require a rapid initial reconstruction, such as to confirm anatomical coverage, scan quality, and to support automated advanced applications. The eigenbin compression method was experimentally evaluated on a clinical silicon PCCT prototype system.

Methods

The proposed eigenbin method performs principal …

Practice Unveils Weaknesses: Analysing The Implementation Of Regulations On Seafarers’ Work/Rest Hours, Bikram S. Bhatia

World Maritime University Ph.D. Dissertations

Seafarers’ fatigue risk management is rooted in regulations on manning and work/rest hours. These regulations establish a minimum fatigue management standard achieved through work/rest hours compliance. Seafarers must maintain daily records of their work/rest hours and report any non-compliance to their company. However, adjustments to these records have been reported in the literature. Such malpractice obscures instances of non- compliance and hinders the feedback necessary for resolving these issues, including flag State review and amendments to minimum safe manning documents. It compromises the integrity of fatigue risk management and underscores the need for further research.

This study examined the implementation …

Work-Stealing Scheduler For Parallel Cache-Adaptive Algorithms, Chuqi Jiang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Modern computing systems with hierarchical memory structures, such as multiple cache levels, main memory, and external storage, pose significant challenges in optimizing memory usage for algorithm efficiency. Traditional models like the Disk Access Model (DAM) and advancements such as cache-oblivious algorithms have focused on minimizing memory transfers without requiring explicit knowledge of memory hierarchy parameters. However, these approaches assume fixed memory sizes and exclusive cache access, limiting their applicability in real-world, shared-memory environments where memory allocations fluctuate. To address these limitations, cache-adaptive algorithms were developed to dynamically adjust to changing memory profiles, enabling near-optimal performance even in multi-process systems. While …