Physical Sciences and Mathematics Commons^™

Type I Error Rate Controlling Procedures For Multiple Hypotheses Testing, Beibei Li

Dissertations

This dissertation addresses several different but related topics arising in the field of multiple testing, including weighted procedures and graphical approaches for controlling the familywise error rate (FWER), and stepwise procedures with control of the false discovery rate (FDR) for discrete data. It consists of three major parts.

The first part investigates weighted procedures for controlling the FWER. In many statistical applications, hypotheses may be differentially weighted according to their different importance. Many weighted multiple testing procedures (wMTPs) have been developed for controlling the FWER. Among these procedures, two weighted Holm procedures are commonly used in practice: one is based …

Development Of Novel Mass Spectrometric Methods For Reaction Screening, Oligosaccharide Detection, And Nitrosamine Quantitation, Qi Wang

Dissertations

Benefitting from its high detection sensitivity and specificity, mass spectrometry (MS) has become a powerful technique in academia and industry. The aim of this dissertation study is to develop new mass spectrometric methods for organic reaction screening, detection of oligosaccharide/glycan in complex matrices, and nitrosamine absolute quantitation.

First, an electrochemistry/mass spectrometry (EC/MS) platform is built to generate an N-cyclopropylaniline radical cation electrochemically and to monitor its reactivity toward alkenes, which leads to the discovery of a new redox neutral reaction of intermolecular [3 + 2] annulation of N-cyclopropylanilines and alkenes. Net redox neutral electrosynthesis is quite rare in synthetic organic …

Adversarially Robust And Accurate Machine Learning For Image Classification, Yanan Yang

Dissertations

Machine learning techniques in medical imaging systems are accurate, but minor perturbations in the data known as adversarial attacks can fool them. These attacks make the systems vulnerable to fraud and deception, and thus a significant challenge has been posed in practice. This dissertation presents the gradient-free trained sign activation networks to detect and deter adversarial attacks on medical imaging AI (Artificial Intelligence) systems. Experimental results show a higher distortion value is required to attack the proposed model than other state-of-the-art models on brain MRI (Magnetic resonance imaging), Chest X-ray, and histopathology image datasets. Moreover, the proposed models outperform the …

A New Model For Predicting The Drag And Lift Forces Of Turbulent Newtonian Flow On Arbitrarily Shaped Shells On The Seafloor, Carley R. Walker, James V. Lambers, Julian Simeonov

Dissertations

Currently, all forecasts of currents, waves, and seafloor evolution are limited by a lack of fundamental knowledge and the parameterization of small-scale processes at the seafloor-ocean interface. Commonly used Euler-Lagrange models for sediment transport require parameterizations of the drag and lift forces acting on the particles. However, current parameterizations for these forces only work for spherical particles. In this dissertation we propose a new method for predicting the drag and lift forces on arbitrarily shaped objects at arbitrary orientations with respect to the direction of flow that will ultimately provide models for predicting the sediment sorting processes that lead to …

Bis(Tryptophan) Amphiphiles: Design, Synthesis And Efficacy As Antimicrobial Agents, Michael Mckeever

Dissertations

Amphiphiles play important roles in nature. These molecules contain both hydrophilic and hydrophobic regions, leading to some astonishing properties. The lipid bilayer of the cell membrane is a fascinating organization of amphiphilic phospholipids. Natural and synthetic amphiphiles, such as antimicrobial peptides, interact with the cell membrane. Such interactions can impact transport of molecules across the cell membrane, disrupting cell functions. In this work, a library of tryptophan-containing amphiphiles was synthesized and their antimicrobial properties were explored.

First, a library of bis(tryptophan) amphiphiles was synthesized. Preparation included a coupling reaction of a diamine with tryptophan residues, via their carboxy-termini, at …

Hydrolytically Degradable Thermosets With Tunable Degradation Profiles Via Ketal-Based Crosslinks, Benjamin Alameda

Dissertations

Thermoset polymer networks are ubiquitous in the construction of high-performance materials due to their excellent mechanical properties, solvent resistance, and thermomechanical performance. However, the crosslinked structure that instills these materials with favorable performance also makes them incredibly resistant to degradation and are nearly impossible to recycle – adding to the ever-growing problem of plastic pollution. Hydrolytically degradable thermosets have emerged as a potentially sustainable alternative to traditional thermosets by affording networks that are inherently degradable in aqueous environments. This dissertation focuses on the development of hydrolytically degradable thermoset networks with tunable degradation behavior through the implementation of ketal-based crosslinks. Given …

A Remote Sensing And Machine Learning-Based Approach To Forecast The Onset Of Harmful Algal Bloom (Red Tides), Moein Izadi

Dissertations

In the last few decades, harmful algal blooms (HABs, also known as “red tides”) have become one of the most detrimental natural phenomena all around the world especially in Florida’s coastal areas due to local environmental factors and global warming in a larger scale. Karenia brevis produces toxins that have harmful effects on humans, fisheries, and ecosystems. In this study, I developed and compared the efficiency of state-of-the-art machine learning models (e.g., XGBoost, Random Forest, and Support Vector Machine) in predicting the occurrence of HABs. In the proposed models, the K. brevis abundance is used as the target, and 10 …

Unsupervised Learning With Word Embeddings Captures Quiescent Knowledge From Covid-19 And Materials Science Literature, Tasnim H. Gharaibeh

Dissertations

Millions of scientific papers are produced each year and the scientific literature is continuing to grow at a head-spinning speed. Thus, massive scientific knowledge exists in solid text, but all these publications make it difficult, if not impossible, for researchers to keep in up to date with discoveries, even within a narrow scientific area. This massive amount of information also makes it difficult to find implicit and hidden connections, relationships, and dependencies within the information that may guide the direction of future research or lead to valuable new insights. So, there is a need for algorithms or models that can …

Irregular Orbital Domination In Graphs, Peter E. Broe

Dissertations

In recent decades, domination in graphs has become a popular area of study due in large degree to its applications to modern society and the mathematical beauty of the topic. While this area evidently began with the work of Claude Berge in 1958 and of Oystein Ore in 1962, domination did not become an active area of research until 1977 with the appearance of a survey paper by Ernest Cockayne and Stephen Hedetniemi. Since then a large number of variations of domination have surfaced and provided numerous applications to different areas of science and real-life problems. Among these variations are …

Management Of Data Brokers In Support Of Smart Community Applications, Shadha Tabatabai

Dissertations

The widespread use of smart devices has led to the Internet of Things (IoT) revolution. Big data generated by billions of devices must be analyzed to make better decisions. However, this introduces security, communication, and processing problems. To solve these problems, we develop algorithms to enhance the work of brokers. We focus our efforts on three problems.

In the first problem, brokers are used in the cloud along with Software Defined Network (SDN) switches. We formulate minimizing brokers’ load difference within a reconfiguration budget with the constraint of indivisible topics as an Integer Linear Programming (ILP) problem. We show that …

Experimental Insights Into The Origin Of Microcrystalline Calcites, Mohammed Hashim

Dissertations

A significant proportion of modern marine calcium carbonate sediments is dominated by metastable aragonite and high Mg calcite that either dissolves or stabilizes to low-Mg-calcite (calcite) or dolomite during diagenesis. Sediment dissolution and stabilization have implications for the CaCO3 budget in the ocean and carbon burial rates. Yet, the diagenetic conditions that promote each process and their relative importance are poorly understood. Further, stabilization most commonly produces calcite microcrystals that exhibit various textures and host micropores. Despite their ubiquity in the rock record, the controls on microcrystal textures remain unclear. Here, laboratory experiments were used to investigate aragonite-to-calcite stabilization as …

The Global Rise Of Online Devices, Cyber Crime And Cyber Defense: Enhancing Ethical Actions, Counter Measures, Cyber Strategy, And Approaches, Naresh Kshetri

Dissertations

The rise of online devices, online users, online shopping, online gaming, and online teaching has ultimately given rise to online attacks and online crimes. As cases of COVID-19 seem to increase day by day, so do online crimes and attacks (as many sectors and organizations went 100% online). Technological advancements and cyber warfare already generated many ethical issues, as internet users increasingly need ethical cyber defense strategies.

Individual internet users have challenges on their end; and on the other end, nation states (some secretly, some openly), are investing in robot weapons and autonomous weapons systems (AWS). New technologies have combined …

Healing Earth In A Time Of Crisis: Curriculum For Integral Ecology, Caleb Steindam

Dissertations

This intrinsic multiple case study examined secondary- and university-level educators’ experiences teaching with Healing Earth, a curriculum developed by the International Jesuit Ecology Project at Loyola University Chicago, which merges scientific, social, spiritual, and ethical analyses of pressing ecological issues. Based on the conceptual framework of integral ecology, Healing Earth is a response to Pope Francis’s (2015a) call for “a new way of thinking about human beings, life, society and our relationship with nature” (§215).

This study primarily consisted of in-depth interviews with educators who have used Healing Earth in a variety of secondary and post-secondary Catholic educational contexts. A …

Rhodium-Catalyzed Decarbonylation Of Aroyl Chlorides, Wiktoria M. Koza

Dissertations

The development of efficient strategies for the synthesis of aryl–halogen bonds is highly desirable due to the prevalence of these moieties in pharmaceuticals, agrochemicals, and organic synthesis. Although there are numerous applications of aryl chlorides in chemistry, an efficient strategy for the preparation of these molecules is underdeveloped. Transition metal-catalyzed decarbonylation provides an efficient and selective approach for aryl–halogen bond formation. There has been significant progress in the development of new decarbonylation strategies, particularly involving aldehydes for the synthesis of new carbon–hydrogen (C–H) bonds or for cross-coupling reactions. However, transition metal-catalyzed decarbonylation methods for carbon–halogen (C–X) bond formation have been …

Dark Patterns: Effect On Overall User Experience And Site Revisitation, Deon Soul Calawen

Dissertations

Dark patterns are user interfaces purposefully designed to manipulate users into doing something they might not otherwise do for the benefit of an online service. This study investigates the impact of dark patterns on overall user experience and site revisitation in the context of airline websites. In order to assess potential dark pattern effects, two versions of the same airline website were compared: a dark version containing dark pattern elements and a bright version free of manipulative interfaces. User experience for both websites were assessed quantitatively through a survey containing a User Experience Questionnaire (UEQ) and a System Usability Scale …

An Analysis On Network Flow-Based Iot Botnet Detection Using Weka, Cian Porteous

Dissertations

Botnets pose a significant and growing risk to modern networks. Detection of botnets remains an important area of open research in order to prevent the proliferation of botnets and to mitigate the damage that can be caused by botnets that have already been established. Botnet detection can be broadly categorised into two main categories: signature-based detection and anomaly-based detection. This paper sets out to measure the accuracy, false-positive rate, and false-negative rate of four algorithms that are available in Weka for anomaly-based detection of a dataset of HTTP and IRC botnet data. The algorithms that were selected to detect botnets …

Evaluating The Performance Of Vision Transformer Architecture For Deepfake Image Classification, Devesan Govindasamy

Dissertations

Deepfake classification has seen some impressive results lately, with the experimentation of various deep learning methodologies, researchers were able to design some state-of-the art techniques. This study attempts to use an existing technology “Transformers” in the field of Natural Language Processing (NLP) which has been a de-facto standard in text processing for the purposes of Computer Vision. Transformers use a mechanism called “self-attention”, which is different from CNN and LSTM. This study uses a novel technique that considers images as 16x16 words (Dosovitskiy et al., 2021) to train a deep neural network with “self-attention” blocks to detect deepfakes. It creates …

Measuring And Comparing Social Bias In Static And Contextual Word Embeddings, Alan Cueva Mora

Dissertations

Word embeddings have been considered one of the biggest breakthroughs of deep learning for natural language processing. They are learned numerical vector representations of words where similar words have similar representations. Contextual word embeddings are the promising second-generation of word embeddings assigning a representation to a word based on its context. This can result in different representations for the same word depending on the context (e.g. river bank and commercial bank). There is evidence of social bias (human-like implicit biases based on gender, race, and other social constructs) in word embeddings. While detecting bias in static (classical or non-contextual) word …