Physical Sciences and Mathematics Commons^™

Single-Shot Mev-Resolution Hard X-Ray Spectrograph For Cavity-Based X-Ray Free Electron Laser, Keshab Kauchha

Dissertations

The Cavity-Based X-ray Free Electron Laser (CBXFEL) is a possible future direction in the development of fully coherent hard X-ray sources of high spectral brilliance, a narrow spectral bandwidth of ≃ 1 − 100 meV, and a high repetition rate of ≃ 1 MHz. A diagnostic tool is required to measure CBXFEL spectra with a meV resolution on a shot-to-shot basis.

The CBXFEL hard X-ray spectrograph is designed to image 9.831 keV X-rays in a ≃ 200 meV spectral window and with a spectral resolution of a few meV using an LCLS XFEL (Linac Coherent Light Source X-ray Free Electron …

Experimental Evaluation Of A Recrosslinkable Co2-Resistant Micro-Sized Preformed Particle Gel For Co2 Sweep Efficiency Improvement In Reservoirs With Super-K Channels, Adel Alotibi, Tao Song, Ali Al Brahim, Baojun Bai, Thomas P. Schuman

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

A recross linkable CO₂-resistant branched preformed particle gel (CO₂-BRPPG) was developed for controlling CO₂ injection conformance, particularly in reservoirs with super-permeable channels. Previous work focused on a millimeter-sized CO₂-BRPPG in open fractures, but its performance in high-permeability channels with pore throat networks remained unexplored. This study used a sand pack model to evaluate a micro-sized CO₂-BRPPG under varying conditions of salinity, gel concentration, and pH. at ambient conditions, the equilibrium swelling ratio (ESR) of the gel reached 76 times its original size. This ratio decreased with increasing salinity but remained stable …

Neutrosophic Cox Proportional Hazards Model For Robust Variable Selection In Survival Analysis, Ibrahim Yasser, Aya A. Abd El-Khalek, A. A. Salama, Doaa A. Abdo

Neutrosophic Systems with Applications

This paper introduces a novel approach for variable selection in survival analysis by integrating neutrosophic logic into the Cox Proportional Hazards (Cox PH) model to address the limitations of recent studies related to high dimensionality. Neutrosophic logic, is a mathematical framework that allows for uncertainty, indeterminacy, and inconsistency, and particularly well suited for handling the complexity and often-ambiguous nature of biological data. By incorporating neutrosophic sets into the Cox PH model, we aim to enhance model robustness, improve variable selection, and address the curse of dimensionality. We compare the performance of the neutrosophic-enhanced Cox PH model with traditional variable selection …

Multi-Criteria Decision-Making Approach Based On Correlation Coefficient For Multi-Polar Interval-Valued Neutrosophic Soft Set, Hamza Naveed, Saalam Ali

Neutrosophic Systems with Applications

The correlation coefficient between two factors is crucial in statistical computation, indicating the extent and evolution of the appropriate link. The precision of applicability evaluations frequently relies on the thoroughness and caliber of data obtained from a certain dataset. Statistical research sometimes entails data marked by intrinsic trade-offs and uncertainty. This study seeks to present m-polar interval-valued neutrosophic soft sets (mPIVNSSs) through the integration of m-polar fuzzy sets with interval-valued neutrosophic soft sets. The suggested mPIVNSS structure is a significantly generalized version of m-polar neutrosophic soft sets and serves as a substantial extension of interval-valued neutrosophic soft sets. In this …

Analysis Of Bck/Bci-Algebras Based On Bipolar Complex Intuitionistic Fuzzy Soft Ideals, Zeeshan Ali

Neutrosophic Systems with Applications

In this article, we design an informative and reliable technique of bipolar complex intuitionistic fuzzy soft sets with numerous operational laws by merging the model of soft sets, complex fuzzy sets, and bipolar intuitionistic fuzzy sets to handle imprecise data. In addition, an ideal in a BCK-algebra is derived based on bipolar complex intuitionistic fuzzy soft set theory are proposed which can capture the information of hesitancy, vagueness, and non-membership information within the circumstance of BCK-algebra. Moreover, we design union, intersection, AND, and OR based on bipolar complex intuitionistic fuzzy soft ideal and simplify it with the help of numerous …

Comparative Analysis Of Multi-Criteria Techniques In Neutrosophic Environment And Their Applications To Economic Condition Assessment, Asmaa Elsayed, Mai Mohamed

Neutrosophic Systems with Applications

In economic decision-making, the challenge of evaluating multiple, often conflicting criteria necessitates advanced Multi-Criteria Decision-Making (MCDM) techniques. Traditional methods can struggle with the inherent uncertainty, ambiguity, and imprecision of real-world data. This paper addresses these challenges by investigating the effectiveness of various MCDM techniques within neutrosophic environments, with a particular focus on the Criteria-wise Alternatives Ranking and Correlation Analysis for Composite Scoring (CARCACS) method. Neutrosophic sets, which incorporate truth, falsity, and indeterminacy, provide a robust framework for addressing the vagueness and inconsistencies found in economic indicators such as GDP growth, employment levels, inflation rates, trade balances, investment activity, and government …

Patterns Of Interactions In Human-Machine Teams, Kazuhiko Momose

Theses and Dissertations

Increasingly capable machines, including Artificial Intelligence (AI) agents are playing a more important role in a wide range of applications, including human daily activities and safety-critical systems. They can benefit even more when humans and such machines agents work together as a team by leveraging each other's strengths and complementing each other to enhance overall performance. To design high-performing teams, it is critical to analyze the team dynamics and understand how humans and machines interact with each other. Collaboration, Coordination, and Cooperation (3Cs) are terms typically used to describe the behavior of teams. However, these terms tend to be used …

Machine Learning Optimized Depolymerization Of Pet (Polyethylene Terephthalate) Via Glycolysis, Mehwash Aamir

Theses and Dissertations

Over 90% of the approximately 1 million PET bottles sold every minute end up in landfills or oceans, where they can persist for centuries. Plastic pollution urgently needs sustainable management and recycling solutions to mitigate the environmental impact of PET waste. From all techniques to recycle plastic waste, catalytic glycolysis stands out for its rapid reaction time, high monomer yields, lower costs, and enhanced durability.

In this work glycolysis of PET was evaluated under microwave conditions using 1,5,7-Triazabicyclo [4.4.0] dec5-ene (TBD) and 1,8-Diazabicyclo [5.4.0] undec-7-ene (DBU) as catalysts, being DBU as the best catalyst for further analysis. The effect of …

Classifying Supersonic Frequencies For Active Acoustic Side-Channel Exploitation, Destin Hinkel

Graduate Theses and Dissertations (2019 - present)

Computing side-channel research explores the manner in which physical emanations from systems can be used to reconstruct data. Acoustic side-channels are those physical emanations that produce a sonic frequency that is subsonic, supersonic, or considered in the range of human hearing [1]. Acoustic side-channel attacks (SCAs) are typically performed passively: a listening device captures aural frequencies from a machine via a microphone that are transmitted to the attacker for analysis [1]–[3]. Machine learning models have been presented to classify individual keystrokes according to variations in acoustic frequency [4]. Furthermore, the SonarSnoop framework presents a novel active approach that involves both …

Hybrid Deep Learning-Based Model For Eclipse Attack Detection On Ethereum Network, Dhanasak Bhumichai

Graduate Theses and Dissertations (2019 - present)

An eclipse attack is a significant cyber threat targeting the network layer of blockchain platforms. Detecting eclipse attacks is challenging for several reasons. First, there are no available datasets for training and testing models. Second, comprehensive studies identifying features to detect eclipse attacks are lacking. Additionally, the amount of eclipse network traffic is much smaller than that of normal network traffic, which leads to imbalanced samples. Moreover, the characteristics of eclipse network traffic closely resemble those of normal traffic, causing overlapping samples, which makes it challenging for traditional classifiers to learn how to identify eclipse attacks. To address these challenges, …

Exploring Post-Covid-19 Health Effects And Features With Advanced Machine Learning Techniques, Muhammad N. Islam, Md S. Islam, Nahid H. Shourav, Iftiaqur Rahman, Faiz A. Faisal, Md M. Islam, Iqbal H. Sarker

Research outputs 2022 to 2026

COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state. To achieve these objectives, we have identified the post-COVID-19 health factors and …

Llm Potentiality And Awareness: A Position Paper From The Perspective Of Trustworthy And Responsible Ai Modeling, Iqbal H. Sarker

Research outputs 2022 to 2026

Large language models (LLMs) are an exciting breakthrough in the rapidly growing field of artificial intelligence (AI), offering unparalleled potential in a variety of application domains such as finance, business, healthcare, cybersecurity, and so on. However, concerns regarding their trustworthiness and ethical implications have become increasingly prominent as these models are considered black-box and continue to progress. This position paper explores the potentiality of LLM from diverse perspectives as well as the associated risk factors with awareness. Towards this, we highlight not only the technical challenges but also the ethical implications and societal impacts associated with LLM deployment emphasizing fairness, …

Locally Varying Geostatistical Machine Learning For Spatial Prediction, Francky Fouedjio, Emet Arya

Research outputs 2022 to 2026

Machine learning methods dealing with the spatial auto-correlation of the response variable have garnered significant attention in the context of spatial prediction. Nonetheless, under these methods, the relationship between the response variable and explanatory variables is assumed to be homogeneous throughout the entire study area. This assumption, known as spatial stationarity, is very questionable in real-world situations due to the influence of contextual factors. Therefore, allowing the relationship between the target variable and predictor variables to vary spatially within the study region is more reasonable. However, existing machine learning techniques accounting for the spatially varying relationship between the dependent variable …

Forward And Backward Private Searchable Encryption For Cloud-Assisted Industrial Iot, Tianqi Peng, Bei Gong, Shanshan Tu, Abdallah Namoun, Sami Alshmrany, Muhammad Waqas, Hisham Alasmary, Sheng Chen

Research outputs 2022 to 2026

In the cloud-assisted industrial Internet of Things (IIoT), since the cloud server is not always trusted, the leakage of data privacy becomes a critical problem. Dynamic symmetric searchable encryption (DSSE) allows for the secure retrieval of outsourced data stored on cloud servers while ensuring data privacy. Forward privacy and backward privacy are necessary security requirements for DSSE. However, most existing schemes either trade the server’s large storage overhead for forward privacy or trade efficiency/overhead for weak backward privacy. These schemes cannot fully meet the security requirements of cloud-assisted IIoT systems. We propose a fast and firmly secure SSE scheme called …

Phypo: Priority-Based Hybrid Task Partitioning And Offloading In Mobile Computing Using Automated Machine Learning, Shehr Bano, Ghulam Abbas, Muhammad Bilal, Ziaul Haq Abbas, Zaiwar Ali, Muhammad Waqas

Research outputs 2022 to 2026

With the increasing demand for mobile computing, the requirement for intelligent resource management has also increased. Cloud computing lessens the energy consumption of user equipment, but it increases the latency of the system. Whereas edge computing reduces the latency along with the energy consumption, it has limited resources and cannot process bigger tasks. To resolve these issues, a Priority-based Hybrid task Partitioning and Offloading (PHyPO) scheme is introduced in this paper, which prioritizes the tasks with high time sensitivity and offloads them intelligently. It also calculates the optimal number of partitions a task can be divided into. The utility of …

The Temperature Versus Orbital Period Relation Of Am Cvns: Insights From Their Donors, Colin W. Macrie, Liliana Rivera Sandoval, Yuri Cavecchi, Tin Long Sunny Wong, Manuel Pichardo Marcano

Physics & Astronomy Faculty Publications

We studied the spectral energy distribution of 22 known AM CVns with orbital periods (Porb) larger than 35 minutes using multiwavelength public photometric data to estimate the effective temperature of the accreting white dwarf. We find an infrared (IR) excess in all systems when compared to a single blackbody, both when the disk should be extended and when it should be truncated by the accretor's magnetic field. This suggests a dominant contribution from the donor to the IR flux. When fitting two blackbodies, the temperature of the hot component decreases with Porb, as expected by evolutionary models. …

D-Optimal Joint Best Linear Unbiased Predictors In Progressively Type-Ii Ordered Statistics, Tamim Alam

Open Access Theses & Dissertations

Reliability and life-testing experiments play a crucial role in understanding the longevity and performance of systems and components, particularly in high-stakes applications such as engineering, manufacturing, and quality control. In this thesis, we focus on the prediction of future unobserved failure times by employing joint predictors based on progressively Type-II censored data obtained from such life-testing experiments. Specifically, we derive explicit analytical expressions for the joint best linear unbiased predictors (BLUPs) of two future order statistics under the D-optimality criterion. The derivation involves minimizing the determinant of the variance-covariance matrix of the predictors within the context of progressively Type-II censored …

Thermal Inequity Along The Us-Mexico Border: An Analysis Of El Paso, Texas, Mia Alexandra Trevino

Open Access Theses & Dissertations

The Urban Heat Island effect (UHI) combined with rising average global temperatures invariably results in thermal inequality, the unequal distribution of temperature patterns by different socioeconomic and sociodemographic groups, resulting in an array of negative public health impacts. This thesis research focused on the historical factors that contribute to UHI and tree cover in historically underserved neighborhoods in El Paso, Texas. The impact of maps from 1933-1954 created by the Home Ownerâ??s Loan Corporation (HOLC), a federal selective lending program, resulted in decades of disinvestment and inequality especially in redlined areas. Geospatial analyses of environmental and socioeconomic indicators such as …

Optimization And Purification Of Magnesium Borides Fabricated By Combustion Synthesis And By High-Temperature Sintering, Miguel Joaquin Camarena

Open Access Theses & Dissertations

Hydrogen as a fuel provides several benefits over the use of fossil fuels; however, one challenge in utilizing hydrogen as an energy carrier revolves around its storage. Achieving sufficient volumetric hydrogen density in a storage solution will facilitate hydrogenâ??s adoption for use in both stationary and mobile applications. Solid-state hydrogen storage provides a promising pathway to solving this problem. However, the hydrogenation of these materials is characterized by slow kinetics and extreme thermodynamic conditions. Magnesium borohydride (Mg(BH4)2) is a promising material in hydrogen storage due to its reversible properties and a theoretical hydrogen capacity of 14.9 wt.%. To synthesize this …

Functionalized Carbon Quantum Dots: A Promising Nanomaterial For Investigating Their Potential In Neurodegenerative Disease Treatment, Sherin M. El Morsy

Open Access Theses & Dissertations

Neurodegenerative diseases such as Parkinson's Disease (PD), Alzheimer's Disease (AD), and Huntington's Disease (HD) remain incurable, despite extensive research into therapeutic development. In the United States of America, the annual cost of PD and AD in terms of patient care and lost productivity is approximately $200 billion. Consequently, there is a critical and unaddressed need for the development of innovative drugs and drug delivery systems that can decelerate, stop, or ideally reverse neurodegenerative diseases. The dissertation presented here focuses on devising strategies to target PD through multiple mechanisms, including the inhibition of protein aggregation and the reduction of oxidative stress. …

Redefining Approaches For Measuring Landscape Subsidence And Permafrost Degradation In Arctic Tundra Environments, Tabatha Lynn Fuson

Open Access Theses & Dissertations

As climate change accelerates in the Arctic, the degradation of permafrost is leading to significant landscape transformation in tundra landscapes. This dissertation investigates the multifaceted responses of permafrost systems to warming, focusing on the dynamics of surface elevation changes and active layer thickness (ALT) across the North Slope of Alaska. In this study, I explore the capacity of repeat Terrestrial Laser Scanning (TLS) technology for modeling tundra features and detecting surface subsidence, specifically how different climate and landscape conditions during scanning impact TLS model precision. We also compare TLS model precision estimates to TLS model accuracy by comparing elevation values …

Assessing Sedimentation Impact Through Dem, Point Cloud, And Discharge Analysis, Shandell Thomas

Open Access Theses & Dissertations

This study focused on five arroyos flowing into the Rio Grande, in the stretch between Caballo Dam and Las Cruces, New Mexico. Drone imagery was acquired from study areas in each arroyo. Photogrammetry was used to create high-resolution digital elevation models (DEMs), point clouds, drone imagery and potential discharges were acquired for each arroyo. Investigation of seven different methods resulted in the selection of the Terrain Ruggedness Map (TRM) as a predictor of surface roughness, along with high-resolution DEMâ??s to illustrate small and low-relief channel features. The five arroyos with varying sediment sizes and characteristics were studied to understand how …

Rockets, Rovers, And Rocks, Oh My!, Tara Lynn Sweeney

Open Access Theses & Dissertations

This body of work undertakes three distinct projects, all with the ultimate goal of improving the future efforts of human space exploration.Chapter 2 is a seismic analysis of rocket launch and landing operations. Being able to identify and understand anthropogenically induced seismic signals in comparison to naturally occurring seismic signals is an important step in characterizing and monitoring both types of signals on the Moon. Relatively few terrestrial studies have been performed to explore this, but there is a growing opportunity to record, discern, and understand the differences between anthropogenic and naturally occurring seismic signals due to the increasing pace …

Enhancing Predictive Accuracy In Noisy Data: A Robust Gradient Boosting Approach, Gabriela Alexa Acuna

Open Access Theses & Dissertations

This thesis aims to enhance the gradient boosting technique, a well-known machine learning method, in a regression setup. Gradient boosting techniques implement sequential weak learners, in this case, shallow trees that contribute to the model with a small percentage. The traditional gradient boosting approach uses regression trees that choose thresholds based on minimizing variance and makes predictions based on the mean of the target variable for the observations contained within each node. The residual sum of squares (RSS) and mean metrics are sensitive to outliers, which makes the model's predictions less robust. Outliers influence the predictions, resulting in a model …

Towards Robust And Fair Vision Learning In Open-World Environments, Thanh-Dat Truong

Graduate Theses and Dissertations

The rapid increase of large-scale data and high-performance computational hardware has promoted the development of data-driven machine vision approaches. Advanced deep learning approaches have achieved remarkable performance in various vision problems and are closing the capability gap between artificial intelligence (AI) and humans. However, towards the ultimate goal of AI, which replicates human ability in visual perception tasks, the machine vision learning methods still need to address several ill-posed challenges. First, while the current vision learning methods often rely on large-scale annotated data, the data annotation process is a costly and time-consuming process. Second, the unfaired predictions produced by vision …

Neural Network Architecture Search Enabled Wide-Deep Learning (Nas-Wd) Integrated Hyperspectral Imaging Understanding For Woody Breast In Poultry Processing, Chaitanya Kumar Reddy Pallerla

Graduate Theses and Dissertations

The development and implementation of a Wide & Deep (WD) learning model tailored for classification and regression tasks utilizing spectral data provides a robust solution to evaluate woody breast (WB) conditions in poultry fillets. This process begins with thorough data preprocessing, which includes loading spectral and classification datasets, imputing missing values with medians, and splitting the data into training and testing sets to ensure rigorous model evaluation. The WD model architecture integrates wide linear models and deep neural networks to harness the strengths of both approaches. The wide component excels at memorizing sparse feature interactions, while the deep component captures …

Microwave-Assisted Green Synthesis Of Spiroindolinopyranopyrazoles, Esther Owusu

Theses and Dissertations

Green Chemistry, usually known as sustainable chemistry, is widely implemented worldwide to safeguard human health and the environment. Recent investigations in organic synthesis and chemical transformation have demonstrated that green chemistry techniques are significantly reducing both chemical waste and reaction time. In order to fulfill this ecological requirement, it is crucial to lessen the utilization of perilous solvents and noxious chemicals, while also creating innovative reaction methods that decrease the production of trash. The implementation of such methodologies can greatly enhance the field of green chemistry. A more environmentally friendly synthetic method was utilized to generate spiro chemicals and their …

Policies And Price Controls On The Research And Development Of Orphan Drugs In The United States And The European Union, Bena Pearl Filipczak Smith

Master's Theses

There is substantive literature surrounding the impact of price controls on the research and development (R&D) of new pharmaceutical products. The European Union (EU) and United States (US) are often studied in contrast to examine the influence of price controls as the US has fewer pharmaceutical price controls than the EU.

We find moderate evidence that the US spent more on annual domestic pharmaceutical R&D than the EU between 2004 and 2021, on average, before and after adjusting for GDP growth per capita and year. We find strong evidence that the US increased annual domestic R&D spending at a faster …

Fitting Quality Of Nmr Relaxation Data To Differentiate Asphalt Binders, Rebecca M. Herndon, Kevin Lai, Magdy Abdelrahman, Klaus Woelk

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Asphalt binder performance grades (PGs) are important metrics in designing pavements for effective transportation infrastructure. the PG system relies on the binder's stiffness and is determined through energy- and time-intensive physical testing. Physical properties, like stiffness, can also be determined by spin–lattice NMR relaxometry, a non-destructive chemical method. NMR relaxometry can quantify the molecular mobility of materials by determining relaxation times from exponential decays of excited nuclear magnetization. While relaxation times have been used to determine physical properties of materials, a quantitative relation to the PG grades of asphalt binder is yet to be established. in this study, T1 NMR …

This Year In Space: The Astounding Astronomical Events Of 2024, Celeste Wicks

The Synapse: Intercollegiate science magazine

No abstract provided.