Physical Sciences and Mathematics Commons^™

The Role Of Elevation As A Control On Salt Marsh-Upland Ecotones Along The Mississippi Gulf Coast, Carlton P. Anderson

Dissertations

Salt marshes are critical ecological gems that serve as habitat for many specialized species of flora and fauna, while providing a natural buffer to human populations from tropical cyclone impacts. However, rises growth of coastal populations has led to greater strain on the natural services these ecosystems provide. To better understand and project the future of coastal evolution, the physical, mechanical, and biological feedbacks between the natural and human coupled systems need to be more defined and thoroughly understood. Human encroachment towards naturally retreating shorelines will lead to coastal communities being more susceptible to nuisance tidal flooding and minor tropical …

On Prediction Of Early Signs Of Alzheimer’S— A Machine Learning Framework, Abdalrahman Alsaedi

Dissertations

Dementia is a collective term used to indicate a loss of memory functions with the presence of at least one additional loss of a major cognitive ability that hinders a person’s previous level of functioning. Studies show that dementia is highly age- associated and that the most common cause of dementia is Alzheimer’s disease. Early recognition of Alzheimer’s disease, before irreversible damage to the brain has already occurred, is paramount to slowing or preventing the disease. Therefore, algorithms for the prediction of early signs of dementia are essential. Machine learning approach has been reported to use several data sources such …

On Problems In Random Structures, Ryan Cushman

Dissertations

This work addresses two problems in optimizing substructures within larger random structures. In the first, we study the triangle-packing number v(G), which is the maximum size of a set of edge-disjoint triangles in a graph. In particular we study this parameter for the random graph G(n,m). We analyze a random process called the online triangle packing process in order to bound v(G). The lower bound on v(G(n,m)) that this produces allows for the verification of a conjecture of Tuza for G( …

Institutional Changes In Western Michigan University For Incorporation Of Education For Sustainability, Saman Khan

Dissertations

Higher Education institutions (HEIs) have the potential to be significant contributors in the pursuit of a sustainable world through the incorporation of Education for Sustainability (EFS). However, HEIs are entrenched with structures and values that are often resistant to change. The literature reveals that instructors’ beliefs and institutional contexts are the two main factors that impact the implementation of institutional change for sustainability education. Western Michigan University (WMU) has created new required curriculum “WMU Essential Studies” (WES) for undergraduate students and they have targeted sustainability as an essential learning outcome, by integrating and applying it in content courses. Using a …

An Integrated Approach For The Assessment And Monitoring Of Land Deformation In The Riyadh City And Its Surroundings For Identifying The Controlling Factors, Abdulaziz Ibrahim Aljammaz

Dissertations

An integrated approach (remote sensing, field, geodesy, geology, and hydrogeology) was conducted to assess and monitor land deformation at Riyadh city and its surroundings. Two settings were investigated. The first, Jinadriyah area (1.5 km²), an urban area located east of Riyadh city, was selected to assess deformation associated with urbanization over desert lands. The second location was Al Kharj area (19,790 km²) to the south of Riyadh city, a reclaimed desert land that was selected to assess the deformation associated with agricultural groundwater extraction from fossil aquifers. My investigations revealed high subsidence rates (up to -30 …

From Multi-Prime To Subset Labelings Of Graphs, Bethel I. Mcgrew

Dissertations

A graph labeling is an assignment of labels (elements of some set) to the vertices or edges (or both) of a graph G. If only the vertices of G are labeled, then the resulting graph is a vertex-labeled graph. If only the edges are labeled, the resulting graph is an edge-labeled graph. The concept was first introduced in the 19th century when Arthur Cayley established Cayley’s Tree Formula, which proved that there are n^n-2 distinct labeled trees of order n. Since then, it has grown into a popular research area.

In this study, we first review several types …

Advancing The Use Of Quality Control Tools For High-Density Data In Modern Manufacturing, Romina Dastoorian

Dissertations

In modern manufacturing, advanced metrology systems are continually being incorporated into quality control (QC) systems to provide high-density (HD) datasets. These datasets can contain millions of measurements that can be used to represent a part’s whole geometry. While integrating HD datasets into QC systems has brought several opportunities to enhance the performance of QC systems, it has resulted in new challenges in this area as well. While significant amounts of research have been performed in this area, the QC research community still strives to tackle these challenges. This study identifies key challenges regarding incorporating HD datasets into QC systems. Specifically, …

Dolomite Stoichiometry As A Proxy For Fluid Chemistry, Cameron J. Manche

Dissertations

The origin of massive dolomites is a contentious issue in geology. Because modern dolomite is rare, interpretation of ancient dolomites requires the use of proxy resources, most of which provide ambiguous information. High-temperature dolomite synthesis experiments have shown that dolomite stoichiometry, the degree of cation ordering, and reaction rate are controlled by Mg:Ca, temperature, molarity, and salinity of the formative solution. Thus, it is posited here that dolomite stoichiometry may be a useful proxy for fluid chemistry, as are stable isotope and trace element composition of ancient dolomites. High-frequency sample intervals (cm-scale) from the Cretaceous Upper Glen Rose Formation (Comanche …

Assessing The Real-Time Lagrangian Predictability Of The Operational Navy Coastal Ocean Model In The Gulf Of Mexico, Lea Kristen Locke

Dissertations

This study quantitatively assesses the drift predictive skill of Fleet Numerical Meteorology and Oceanography Center’s (FNMOC’s) operational ocean models which are used to support a wide range of military and civilian applications. Overall, the findings of this work support the recommendation of spatial filtering for regional-scale ocean model velocity fields used in deep-water drift applications. In conjunction with filtering, the use of a pure particle drift algorithm is suggested for short-term forecasts and a drift algorithm including a sub-grid scale, random flight, parameterization for predictions requiring extended forecast predictions.

Drift prediction skill is quantified through metrics of in-cloud percentage, distance …

Microplankton Dynamics In The River-Dominated Mississippi Bight, Adam D. Boyette

Dissertations

The Mississippi Bight (MSB) is a river-dominated continental margin influenced by multiple large river systems, including the Mississippi River, Alabama and Tombigbee rivers via Mobile Bay, and numerous smaller rivers, creeks, and bayous. This is part of a biologically-rich ecosystem that supports the second largest fishery industry by volume in the United States. Despite our understanding of the linkages between primary production with higher trophic levels, there remains limited studies quantifying these trophic interactions in this system. Microplankton (µm) community dynamics and trophic connectivity between primary producers and heterotrophic protists represent a critical nexus influencing overall biological productivity in this …

Construct Linear Quasi-Interpolants On Infinite Intervals, Johara Farah Albaliwi

Dissertations

In solving the data interpolation problem, which is fundamental in data analysis, we typically deal with the data samples spread in a finite interval [a, b], which results in the operations involving finite-dimensional matrices. There are many interesting results developed under this framework. However, when the data samples are given from an infinite interval [a, ∞) (for certain special types of real-world applications), many existing results would not work anymore due to the special properties of the infinite data samples. A new framework should be established to support the infinite data samples.

In this dissertation, we develop a special tool …

Matrix Product Structure Of A Permuted Quasi Cyclic Code And Its Dual, Perian Perdhiku

Dissertations

In my Dissertation I will work mostly with Permuted Quasi Cyclic Codes. They are a generalization of Cyclic Codes, one of the most important families of Linear Codes in Coding Theory. Linear Codes are very useful in error detection and correction. Error Detection and Correction is a technique that first detects the corrupted data sent from some transmitter over unreliable communication channels and then corrects the errors and reconstructs the original data. Unlike linear codes, cyclic codes are used to correct errors where the pattern is not clear and the error occurs in a short segment of …

Ready To Engage? Urban Middle School Teachers’ Responsiveness To Targeted Engagement Interventions On Their Virtual Instructional Practices: An Action Research Study, Svetlana Nikic

Dissertations

Teachers’ effectiveness is associated with their instructional practices and is ultimately linked to students’ learning outcomes. In order to impact teachers’ effectiveness, schools focus substantial effort and resources on professional development led by an assumption that teachers’ classroom practices can be improved through targeted interventions. Even if this premise is correct, little information is available about how much a teacher’s practice may change through interventions, or which aspects of instructional practice are more receptive to improving teacher effectiveness (Garret et al., 2019).

This study took place at an urban middle school and examined teachers’ responsiveness to targeted engagement intervention in …

Instantaneous Frequency Estimation And Signal Separation Using Fractional Continuous Wavelet Transform, Abdelbaset R. Zeyani

Dissertations

In the signal processing field, time-frequency representations (TFR's) have intensively been improved to provide effective and powerful tools for reliable signal analysis. One of the most valuable and frequently used tools is Fourier transform (FT) which has been used to study the frequency content of stationary signals in the Fourier domain (FD). However, FT is not sufficient to study the frequency of non-stationary signals. For this particular type of signals to be best analyzed, some transforms such as the short time Fourier transform (STFT) and the continuous wavelet transform (CWT) have been introduced to provide us with a signal representation …

Geo-Spatial Modeling Of Carbon Sequestration Assessment In Date Palm, Abu Dhabi: An Integrated Approach Of Fieldwork, Remote Sensing, And Gis, Basam Saeed Dahy

Dissertations

The United Arab Emirates (UAE) has undertaken huge efforts to green the desert and afforestation projects (planted mainly with date palms) hence, reducing its carbon footprint, which have never been accounted for, because of lack of implemented mechanisms and tools to assess the amount of biomass and carbon stock (CS) sequestered by plants in the country. The purpose of this dissertation is to implement a new approach towards assessing the carbon sequestered by date palm (DP) plantations in Abu Dhabi, in both their biomass compartment as well as the soils under beneath, using geospatial technologies (RS and GIS) assessed by …

Exploiting Bert And Roberta To Improve Performance For Aspect Based Sentiment Analysis, Gagan Reddy Narayanaswamy

Dissertations

Sentiment Analysis also known as opinion mining is a type of text research that analyses people’s opinions expressed in written language. Sentiment analysis brings together various research areas such as Natural Language Processing (NLP), Data Mining, and Text Mining, and is fast becoming of major importance to companies and organizations as it is started to incorporate online commerce data for analysis. Often the data on which sentiment analysis is performed will be reviews. The data can range from reviews of a small product to a big multinational corporation. The goal of performing sentiment analysis is to extract information from those …

Combination Of Facebook Prophet And Attention-Based Lstm With Multi- Source Data For Indian Stock Market Prediction, Pavan Nagesh

Dissertations

The stock market prediction has been the subject of interest to various researchers and analysts due to its highly unpredictable nature and serves as a perfect example for time series forecasting. Over the years deep learning models such as Long-Term Short-Term Memory and statistical models such as Autoregressive Integrated Moving Average have shown promising results in predicting future stock prices. But the results from these models cannot be generalized as they fail to incorporate the dynamics of the market and influence of several external factors such as political, social, investor's emotion, etc on stock markets. Recently Facebook’s creation of the …

Feature Augmentation For Improved Topic Modeling Of Youtube Lecture Videos Using Latent Dirichlet Allocation, Nakul Srikumar

Dissertations

Application of Topic Models in text mining of educational data and more specifically, the text data obtained from lecture videos, is an area of research which is largely unexplored yet holds great potential. This work seeks to find empirical evidence for an improvement in Topic Modeling by pre- extracting bigram tokens and adding them as additional features in the Latent Dirichlet Allocation (LDA) algorithm, a widely-recognized topic modeling technique. The dataset considered for analysis is a collection of transcripts of video lectures on Machine Learning scraped from YouTube. Using the cosine similarity distance measure as a metric, the experiment showed …

Evaluating The Performance Of Transformer Architecture Over Attention Architecture On Image Captioning, Deepti Balasubramaniam

Dissertations

Over the last few decades computer vision and Natural Language processing has shown tremendous improvement in different tasks such as image captioning, video captioning, machine translation etc using deep learning models. However, there were not much researches related to image captioning based on transformers and how it outperforms other models that were implemented for image captioning. In this study will be designing a simple encoder-decoder model, attention model and transformer model for image captioning using Flickr8K dataset where will be discussing about the hyperparameters of the model, type of pre-trained model used and how long the model has been trained. …

Finetuning Bert And Xlnet For Sentiment Analysis Of Stock Market Tweets Using Mixout And Dropout Regularization, Shubham Jangir

Dissertations

Sentiment analysis is also known as Opinion mining or emotional mining which aims to identify the way in which sentiments are expressed in text and written data. Sentiment analysis combines different study areas such as Natural Language Processing (NLP), Data Mining, and Text Mining, and is quickly becoming a key concern for businesses and organizations, especially as online commerce data is being used for analysis. Twitter is also becoming a popular microblogging and social networking platform today for information among people as they contribute their opinions, thoughts, and attitudes on social media platforms over the years. Because of the large …

Improving A Network Intrusion Detection System’S Efficiency Using Model-Based Data Augmentation, Vinicius Waterkemper Lodetti

Dissertations

A network intrusion detection system (NIDS) is one important element to mitigate cybersecurity risks, the NIDS allow for detecting anomalies in a network which may be a cyberattack to a corporate network environment. A NIDS can be seen as a classification problem where the ultimate goal is to distinguish between malicious traffic among a majority of benign traffic. Researches on NIDS are often performed using outdated datasets that don’t represent the actual cyberspace. Datasets such as the CICIDS2018 address this gap by being generated from attacks and an infrastructure that reflects an up-to-date scenario.

A problem may arise when machine …

An Evaluation On The Performance Of Code Generated With Webassembly Compilers, Raymond Phelan

Dissertations

WebAssembly is a new technology that is revolutionizing the web. Essentially it is a low-level binary instruction set that can be run on browsers, servers or stand-alone environments. Many programming languages either currently have, or are working on, compilers that will compile the language into WebAssembly. This means that applications written in languages like C++ or Rust can now be run on the web, directly in a browser or other environment. However, as we will highlight in this research, the quality of code generated by the different WebAssembly compilers varies and causes performance issues. This research paper aims to evaluate …

A Hybrid Neural Network For Stock Price Direction Forecasting, Daniel Devine

Dissertations

The volatility of stock markets makes them notoriously difficult to predict and is the reason that many investors sell out at the wrong time. Contrary to the efficient market hypothesis (EMH) and the random walk theory, contribution to the study of machine learning models for stock price forecasting has shown evidence of stock markets predictability with varying degrees of success. Contemporary approaches have sought to use a hybrid of convolutional neural network (CNN) for its feature extraction capabilities and long short-term memory (LSTM) neural network for its time series prediction. This comparative study aims to determine the predictability of stock …

Identifying Significant Features For Player Evaluation In Nfl Comparing Anns And Traditional Models, Ronan Walsh

Dissertations

The evaluation of player performance in sports is popular and important in modern sports, enabling teams to use real data in the construction of their rosters. This dissertation proposes to apply machine learning algorithms to predicting the player evaluations from a leading NFL analytics company who use a combination of statistics and expert evaluation. In addition, it will investigate what features are significant in the evaluation of a position. Data for the dissertation is obtained from multiple online sources - Pro Football Reference and Pro Football Focus (the the NFL analytics company). These data sets are combined and analysed before …

Identifying Roles Of Software Developers From Their Answers On Stack Overflow, Dean Power

Dissertations

Stack Overflow is the world’s largest community of software developers. Users ask and answer questions on various tagged topics of software development. The set of questions a site user answers is representative of their knowledge base, or “wheelhouse”. It is proposed that clustering users by their wheelhouse yields communities of similar software developers by skill-set. These communities represent the different roles within software development and could be used as the basis to define roles at any point in time in an ever-evolving landscape of software development. A network graph of site users, linked if they answered questions on the same …

Can Generative Adversarial Networks Help Us Fight Financial Fraud?, Sean Mciver

Dissertations

Transactional fraud datasets exhibit extreme class imbalance. Learners cannot make accurate generalizations without sufficient data. Researchers can account for imbalance at the data level, algorithmic level or both. This paper focuses on techniques at the data level. We evaluate the evidence of the optimal technique and potential enhancements. Global fraud losses totalled more than 80 % of the UK’s GDP in 2019. The improvement of preprocessing is inherently valuable in fighting these losses. Synthetic minority oversampling technique (SMOTE) and extensions of SMOTE are currently the most common preprocessing strategies. SMOTE oversamples the minority classes by randomly generating a point between …

Performance Comparison Between A Distributed Particle Swarm Algorithm And A Centralised Algorithm, Ciarán O’Loughlin

Dissertations

Particle Swarm optimisation (PSO) is a particular form of swarm intelligence, which itself is an innovative intelligent paradigm for solving optimization problems. PSO is generally used to find a global optimum in a single optimisation function. This typically occurs on one node(machine) but there has been a significant body of research into creating distributed implementations of the PSO algorithm. Such research has often focused on the creation and performance of the distributed implementation in an isolated manner or compared to different distributed algorithms.

This research piece aims to bridge a gap in the existing literature, by testing a distributed implementation …

Event-Driven Servers Using Asynchronous, Non-Blocking Network I/O: Performance Evaluation Of Kqueue And Epoll, Lorcan Leonard

Dissertations

This research project evaluates the performance of kqueue and epoll in the context of event-driven servers. The evaluation is done through benchmarking and tracing which are used to measure throughput and execution time respectively. The experiment is repeated for both a virtualised and native server environment. The results from the experiment are statistically analysed and compared. These results show significant differences between kqueue and epoll, and a profound impact of virtualisation as a variable.

Identification Of New Pathways For Co Oxidation On Rh(111) & Spatial And Structural Control Of O-Induced Reconstruction Of Ag(111), Marie Turano

Dissertations

Understanding the fundamentals of oxygen surface structures under a variety of conditions is pivotal to determining reactivity of heterogeneous catalysis. Exposure of catalytically active metal surfaces to high oxygen coverages results in a myriad of surface structures. A further complication is the formation of subsurface oxygen (Osub) or oxygen present in the near subsurface region of the metal. It is known to form in transition metals yet the absorption of oxygen and resultant formation of Osub is not equivalent across all catalytically relevant metals. As a result, it is difficult to predict the stability and efficacy of the formation of …

Development Of Small Molecule Inhibitors Of Therapeutic Target Enzymes: Paths To Discover Novel Antimicrobials, Thahani Shifna Habeeb Mohammad

Dissertations

The rising antimicrobial resistance to antibiotics is a major global problem, which has been exacerbated by the inappropriate use of antibiotics. The effectiveness of frequently prescribed penicillin derivatives and β-lactamase inhibitors are compromised by the evolution of bacterial β-lactamases and antibiotic-resistant bacteria. Consequently, design and synthesis of small-molecule inhibitors of identified novel antibiotic targets is an urgent unmet medical need. We previously demonstrated that N-functionalized α-aminocyclobutanones can act as peptidomimetic enzyme inhibitors, including inhibition of a key esterase in Francisella Tularensis. The carbonyl of a cyclobutanone is electrophilic due to ring strain, therefore cyclobutanone derivatives can serve as transition state …