Physical Sciences and Mathematics Commons^™

Hypervalent Iodine Compounds With Carboxylate And Tetrazolate Ligands, Avichal Vaish

Chemistry Theses and Dissertations

In modern organic chemistry, hypervalent (HV) iodine(III) compounds are frequently used as oxidizing agents but application of λ³-iodanes in polymer and material chemistry is still underexplored. This dissertation describes the preparation of dynamic and self-healing materials by employing ligand exchange reactions involving HV iodine(III) compounds of the type ArIL₂ (Ar = Aryl, L = ligand, e.g., carboxylate or (pseudo)halide). These compounds can undergo ligand exchange reactions in presence of nucleophiles (Nu^-) to form ArINu₂. Diacetoxyiodo benzene was successfully employed as a crosslinker to prepare dynamic and self-healing gels derived from carboxylate-containing polymers. Furthermore, …

Cell-Trappable Chemiluminescent Probes For Monitoring Hydrogen Sulfide In Living Cells, Briley Bezner

Chemistry Theses and Dissertations

Hydrogen sulfide (H2S) is an important biological signaling molecule that has been recognized alongside nitric oxide and carbon monoxide as being a small gasotransmitter that is enzymatically produced and impacts multiple physiological functions. To detect hydrogen sulfide, there has been a focus on developing fluorescent probes to target particular analytes; however, fluorescent probes lack sensitivity and depth penetration due to background autofluorescence and light scattering. Chemiluminescence does not require light excitation, which greatly reduces the amount of autofluorescence and photoactivation.

In order to detect hydrogen sulfide in living systems with high sensitivity, a series of sterically stabilized 1,2-dioxetane chemiluminescent reduction-reaction …

Model Selection And Experimental Design Of Biological Networks With Algebraic Geometry, Anyu Zhang

Mathematics Theses and Dissertations

Model selection based on experimental data is an essential challenge in biological data science. In decades, the volume of biological data from varied sources, including laboratory experiments, field observations, and patient health records has seen an unprecedented increase. Mainly when collecting data is expensive or time-consuming, as it is often in the case with clinical trials and biomolecular experiments, the problem of selecting information-rich data becomes crucial for creating relevant models.

Motivated by certain geometric relationships between data, we partitioned input data sets, especially data sets that correspond to a unique basis, into equivalence classes with the same basis to …

Evaluation Of The Mechanisms And Effectiveness Of Nano-Hydroxides, Wood And Dairy Manure-Derived Biochars To Remove Fluoride And Heavy Metals From Water, Anna Rose Wallace, Wenjie Sun Dr, Chunming Su Dr

Civil and Environmental Engineering Theses and Dissertations

The development of effective treatment processes for the removal contaminants, such as fluoride and heavy metals, from polluted water have been urgently needed due to serious environmental health and safety concerns. In this dissertation, a variety of materials including various (hydro)oxide nanomaterials, biochars and surface modified biochar were studied to evaluate their effectiveness and mechanism on removing fluoride or mixed heavy metals from water.

In the Chapter 2, this study investigated the adsorptive removal of fluoride from water using various (hydro)oxide nanomaterials, focusing on ferrihydrite, hydroxyapatite (HAP) and brucite, which have the potential to be used as sorbents for surface …

Stochastic Orthogonalization And Its Application To Machine Learning, Yu Hong

Electrical Engineering Theses and Dissertations

Orthogonal transformations have driven many great achievements in signal processing. They simplify computation and stabilize convergence during parameter training. Researchers have introduced orthogonality to machine learning recently and have obtained some encouraging results. In this thesis, three new orthogonal constraint algorithms based on a stochastic version of an SVD-based cost are proposed, which are suited to training large-scale matrices in convolutional neural networks. We have observed better performance in comparison with other orthogonal algorithms for convolutional neural networks.

Inference Of Heterogeneity In Meta-Analysis Of Rare Binary Events And Rss-Structured Cluster Randomized Studies, Chiyu Zhang

Statistical Science Theses and Dissertations

This dissertation contains two topics: (1) A Comparative Study of Statistical Methods for Quantifying and Testing Between-study Heterogeneity in Meta-analysis with Focus on Rare Binary Events; (2) Estimation of Variances in Cluster Randomized Designs Using Ranked Set Sampling.

Meta-analysis, the statistical procedure for combining results from multiple studies, has been widely used in medical research to evaluate intervention efficacy and safety. In many practical situations, the variation of treatment effects among the collected studies, often measured by the heterogeneity parameter, may exist and can greatly affect the inference about effect sizes. Comparative studies have been done for only one or …

Polyarylenes: Synthesis And Characterization Towards Advanced Applications, Stephen Budy

Chemistry Theses and Dissertations

A series of semi-fluorinated and non-fluorinated Diels−Alder step-growth polyarylene polymers and co-polymers were synthesized via typical oil bath heating (days/weeks) and more rapid microwave-assisted polymerization (hours). The polymers were characterized by multi-nuclear (¹H, ¹³C, and ¹⁹F) NMR and ATR−FTIR spectroscopy, thermal analysis (TGA, DSC, and DMA), GPC, XRD, water contact analysis (WCA), and refractive index (RI) measurements. The NMR spectra indicated a mixture of para and meta conformations through the polymer backbone increasing to more para with greater fluorine content. Thermal gravimetric analysis revealed the fluorine-containing polyarylenes possessed the highest char yields at almost 80% at …

Constraints On Parton Distribution Functions Imposed By Hadronic Experiments, Boting Wang

Physics Theses and Dissertations

The theoretical uncertainties of the Large Hadron Collider (LHC) observables are decreasing with the increasing statistics of the LHC experiments, and it is becoming more and more important to reduce the uncertainties in the knowledge of the nucleon structure. The latest LHC high-energy experiments, future experimental proposals, and computational tools are expected to enhance the knowledge of the nucleon structure. However, the global analysis that assesses their impact on Parton Distribution Functions (PDFs) knowledge is computationally expensive due to the corresponding large size of data. I developed a new approach that can make a quick preliminary evaluation to help the …

Automating Software Changes Via Recommendation Systems, Xiaoyu Liu

Computer Science and Engineering Theses and Dissertations

As the complexity of software systems is growing tremendously, it came with increasingly sophisticated data provided during development. The systematic and large-scale accumulation of software engineering data opened up new opportunities that infer information appropriately can be helpful to software development in a given context. This type of intelligent software development tools came to be known as recommendation systems.

Recommendation Systems in Software Change (RSSCs) share commonalities with conventional recommendation systems: mainly in their usage model, the usual reliance on data mining, and in the predictive nature of their functionality. So a major challenge for designing RSSCs is to automatically …

Identifying Customer Churn In After-Market Operations Using Machine Learning Algorithms, Vitaly Briker, Richard Farrow, William Trevino, Brent Allen

SMU Data Science Review

This paper presents a comparative study on machine learning methods as they are applied to product associations, future purchase predictions, and predictions of customer churn in aftermarket operations. Association rules are used help to identify patterns across products and find correlations in customer purchase behaviour. Studying customer behaviour as it pertains to Recency, Frequency, and Monetary Value (RFM) helps inform customer segmentation and identifies customers with propensity to churn. Lastly, Flowserve’s customer purchase history enables the establishment of churn thresholds for each customer group and assists in constructing a model to predict future churners. The aim of this model is …

Personalized Detection Of Anxiety Provoking News Events Using Semantic Network Analysis, Jacquelyn Cheun Phd, Luay Dajani, Quentin B. Thomas

SMU Data Science Review

In the age of hyper-connectivity, 24/7 news cycles, and instant news alerts via social media, mental health researchers don't have a way to automatically detect news content which is associated with triggering anxiety or depression in mental health patients. Using the Associated Press news wire, a semantic network was built with 1,056 news articles containing over 500,000 connections across multiple topics to provide a personalized algorithm which detects problematic news content for a given reader. We make use of Semantic Network Analysis to surface the relationship between news article text and anxiety in readers who struggle with mental health disorders. …

A Data Science Approach To Defining A Data Scientist, Andy Ho, An Nguyen, Jodi L. Pafford, Robert Slater

SMU Data Science Review

In this paper, we present a common definition and list of skills for a Data Scientist using online job postings. The overlap and ambiguity of various roles such as data scientist, data engineer, data analyst, software engineer, database administrator, and statistician motivate the problem. To arrive at a single Data Scientist definition, we collect over 8,000 job postings from Indeed.com for the six job titles. Each corpus contains text on job qualifications, skills, responsibilities, educational preferences, and requirements. Our data science methodology and analysis rendered the single definition of a data scientist: A data scientist codes, collaborates, and communicates – …

Achieving Optimal Horizontal Drill Operations, Daniel J. Serna, James Vasquez, Donald Markley

SMU Data Science Review

In this paper, we present a novel method of predicting the onset of a slide event in horizontal drilling operations. Horizontal drilling operations attempt to create a well through a subsurface as quickly as possible by rotating a drill through the subsurface. A slide event occurs when the drill begins to inefficiently rotate through the subsurface, resulting in a significantly reduced rate of penetration. Slide events can be prevented, or significantly reduced in their impact, when their onset is accurately predicted. We present a method of accurately predicting the onset of slide events with a time-series based predictive model that …

A Data Driven Approach To Forecast Demand, Hannah Kosinovsky, Sita Daggubati, Kumar Ramasundaram, Brent Allen

SMU Data Science Review

Abstract. In this paper, we present a model and methodology for accurately predicting the following quarter’s sales volume of individual products given the previous five years of sales data. Forecasting product demand for a single supplier is complicated by seasonal demand variation, business cycle impacts, and customer churn. We developed a novel prediction using machine learning methodology, based upon a Dense neural network (DNN) model that implicitly considers cyclical demand variation and explicitly considers customer churn while minimizing the least absolute error between predicted demand and actual sales. Using parts sales data for a supplier to the oil and gas …

Big-Data Talent Analytics In The Public Sector: A Promotion And Firing Model Of Employees At Federal Agencies, Rabih Neouchi

Operations Research and Engineering Management Theses and Dissertations

Talent analytics is a relatively new area of focus to researchers working in analytics and data science. Talent Analytics has the potential to help companies make many informed critical decisions around talent acquisition, promotion and retention. This work investigates data science to predict “shiny star” employees in the U.S. public sector, defined as top-notch performers over the years of a given time span. Its scope falls within talent analytics, also called people analytics, a relatively new research area.

We clean a data set made available by the U.S. Office of Personnel Management (OPM) and present two models to predict the …

A Machine Learning Model For Clustering Securities, Vanessa Torres, Travis Deason, Michael Landrum, Nibhrat Lohria

SMU Data Science Review

In this paper, we evaluate the self-declared industry classifications and industry relationships between companies listed on either the Nasdaq or the New York Stock Exchange (NYSE) markets. Large corporations typically operate in multiple industries simultaneously; however, for investment purposes they are classified as belonging to a single industry. This simple classification obscures the actual industries within which a company operates, and, therefore, the investment risks of that company.
By using Natural Language Processing (NLP) techniques on Security and Exchange Commission (SEC) filings, we obtained self-defined industry classifications per company. Using clustering techniques such as Hierarchical Agglomerative and k-means clustering we …

Predicting Wind Turbine Blade Erosion Using Machine Learning, Casey Martinez, Festus Asare Yeboah, Scott Herford, Matt Brzezinski, Viswanath Puttagunta

SMU Data Science Review

Using time-series data and turbine blade inspection assessments, we present a classification model in order to predict remaining turbine blade life in wind turbines. Capturing the kinetic energy of wind requires complex mechanical systems, which require sophisticated maintenance and planning strategies. There are many traditional approaches to monitoring the internal gearbox and generator, but the condition of turbine blades can be difficult to measure and access. Accurate and cost- effective estimates of turbine blade life cycles will drive optimal investments in repairs and improve overall performance. These measures will drive down costs as well as provide cheap and clean electricity …

Machine Learning In Support Of Electric Distribution Asset Failure Prediction, Robert D. Flamenbaum, Thomas Pompo, Christopher Havenstein, Jade Thiemsuwan

SMU Data Science Review

In this paper, we present novel approaches to predicting as- set failure in the electric distribution system. Failures in overhead power lines and their associated equipment in particular, pose significant finan- cial and environmental threats to electric utilities. Electric device failure furthermore poses a burden on customers and can pose serious risk to life and livelihood. Working with asset data acquired from an electric utility in Southern California, and incorporating environmental and geospatial data from around the region, we applied a Random Forest methodology to predict which overhead distribution lines are most vulnerable to fail- ure. Our results provide evidence …

Identifying Undervalued Players In Fantasy Football, Christopher D. Morgan, Caroll Rodriguez, Korey Macvittie, Robert Slater, Daniel W. Engels

SMU Data Science Review

In this paper we present a model to predict player performance in fantasy football. In particular, identifying high-performance players can prove to be a difficult problem, as there are on occasion players capable of high performance whose past metrics give no indication of this capacity. These "sleepers"' are often undervalued, and the acquisition of such players can have notable impact on a fantasy football team's overall performance. We constructed a regression model that accounts for players' past performance and athletic metrics to predict their future performance. The model we built performs favorably in predicting athlete performance in relation to other …

Machine Learning Predicts Aperiodic Laboratory Earthquakes, Olha Tanyuk, Daniel Davieau, Charles South, Daniel W. Engels

SMU Data Science Review

In this paper we find a pattern of aperiodic seismic signals that precede earthquakes at any time in a laboratory earthquake’s cycle using a small window of time. We use a data set that comes from a classic laboratory experiment having several stick-slip displacements (earthquakes), a type of experiment which has been studied as a simulation of seismologic faults for decades. This data exhibits similar behavior to natural earthquakes, so the same approach may work in predicting the timing of them. Here we show that by applying random forest machine learning technique to the acoustic signal emitted by a laboratory …

Longitudinal Analysis With Modes Of Operation For Aes, Dana Geislinger, Cory Thigpen, Daniel W. Engels

SMU Data Science Review

In this paper, we present an empirical evaluation of the randomness of the ciphertext blocks generated by the Advanced Encryption Standard (AES) cipher in Counter (CTR) mode and in Cipher Block Chaining (CBC) mode. Vulnerabilities have been found in the AES cipher that may lead to a reduction in the randomness of the generated ciphertext blocks that can result in a practical attack on the cipher. We evaluate the randomness of the AES ciphertext using the standard key length and NIST randomness tests. We evaluate the randomness through a longitudinal analysis on 200 billion ciphertext blocks using logistic regression and …

Pristine Sentence Translation: A New Approach To A Timeless Problem, Meenu Ahluwalia, Brian Coari, Ben Brock

SMU Data Science Review

Abstract.

Pristine Sentence Translation (PST) is a new approach to language translation based upon sentence-level granularity. Traditional translation approaches, including those utilizing advanced machine learning or neural network-based approaches, translate on a word-by-word or phrase-by-phrase basis; thereby, potentially missing the context or meaning of the complete sentence. Instead of these piecewise translations, PST utilizes deep learning and predictive modeling techniques to translate complete sentences from their source language into their target language. With these approaches we were able to translate sentences that closely conveyed the meaning of the original sentences. Our results demonstrated that PST’s method of translating an entire …

Forecasting Localized Weather-Based Photovoltaic Energy Production, Kevin Chang, Afreen Siddiqui, Robert Slater

SMU Data Science Review

Photovoltaic (PV) power system performance can vary from nominal specifications when put in application, making it difficult to accurately estimate real power generation at a localized level. As the usage and efficiency of PV systems has increased in recent years, the amount of power contributed to the national power grid from solar irradiation has also increased significantly. However, solar power installations are subject to variances in efficiency and output, driven by differences in system size, local weather, and atmospheric condition changes. With a significant install base in today's world, combined with extensive solar irradiance and meteorological data, the variables exist …

Assessing Diagenetic Conditions In The Late Triassic Chinle Formation Through Petrographic And Geochemical Analysis Of Phytosaur Teeth, John Fortner

Earth Sciences Theses and Dissertations

The Upper Triassic (Norian; ~227-208 Ma) Chinle Formation of Petrified Forest (PeFo) National Park was deposited during a time of major tectonic breakup and profound changes in atmospheric circulation and faunal compositions. Little has been done however to assess whether surficial paleoenvironmental trends related to deterioration of the Late Triassic Megamonsoon resulted in similar trends in the early diagenetic environment. Here, we use phytosaur tooth dentin as a proxy for diagenetic conditions in the Chinle of PEFO, as its greater susceptibility to chemical alteration relative to enamel makes it an excellent substrate from which changing diagenetic conditions may be studied …

Parallel Multipole Expansion Algorithms And Their Biology Applications, Jiahui Chen

Mathematics Theses and Dissertations

N-body pairwise interactions are ubiquitous in scientific areas such as astrophysics, fluids mechanics, electrical engineering, molecular biology, etc. Computing these interactions using direct sum of an O(N) cost is expensive, whereas multipole expansion methods, such as the fast multipole method (FMM) or treecode, can reduce the cost to O(N) or O(N log N). This thesis focuses on developing numerical algorithms of Cartesian FMM and treecode, as well as using these algorithms to directly or implicitly solve biological problems involving pairwise interactions. This thesis consists of the following topics. 1) A cyclic parallel scheme is developed to handle the load balancing …

Sample Size Calculation Of Clinical Trials With Correlated Outcomes, Dateng Li

Statistical Science Theses and Dissertations

In this thesis, we investigate sample size calculation for three kinds of clinical trials: (1). Randomized controlled trials (RCTs) with longitudinal count outcomes; (2). Cluster randomized trials (CRTs) with count outcomes; (3). CRTs with multiple binary co-primary endpoints.

A Search For Boosted Low Mass Resonances Decaying To The Bb̅ Final State And Produced In Association With A Jet At √S = 13 Tev With The Atlas Detector, Matthew Feickert

Physics Theses and Dissertations

A search in the high momentum regime for new resonances, produced in association with a jet, decaying into a pair of bottom quarks is presented using an integrated luminosity of 80.5 fb^-1 of proton-proton collisions at center-of-mass energy √s = 13 TeV recorded by the ATLAS detector at the Large Hadron Collider. The search was performed for low mass resonances, including the Standard Model Higgs boson and leptophobic Z' dark matter mediators, in the mass range of 100 GeV to 200 GeV. For the Standard Model Higgs boson, the observed signal strength is μH = 5.8 ± 3.1 (stat.) …

Massive Elementary Particles In The Standard Model And Its Supersymmetric Triplet Higgs Extension, Keping Xie

Physics Theses and Dissertations

In this dissertation, we focus on massive elementary particles in the Standard Model and its supersymmetric triplet Higgs extension.

In the first part, we start with a review of electroweak (EW) sector in the Standard Model. Motivated by nonzero neutrino masses, we consider triplet scalars in addition to the Standard Model. The vacuum expectation values of scalar triplets are strongly constrained by the $\rho$ parameter, extracted from electroweak precision measurements. Therefore, we introduce a custodial symmetry to weaken this constraint and obtain the well-known Georgi-Machacek (GM) Model. The GM model still requires fine-tuning to satisfy the $\rho$ parameter constraint. It …

A Likelihood Search For Low-Mass Dark Matter Via Inelastic Scattering In Supercdms, Daniel Jardin

Physics Theses and Dissertations

An abundance of evidence suggests that most of the Universe is composed of nonluminous matter. This "dark matter” is believed to be a new elementary particle and experiments around the world are attempting to directly detect rare collisions with terrestrial detectors.

The properties of dark matter have yet to be identified, thus efforts are ongoing to explore a range of possible masses and interaction cross-sections. For the latter, experiments can increase exposure by scaling up the detector mass and operating for a longer time. To search for dark matter with less mass than a nucleon, new technologies and analysis techniques …

Investigation Of Fundamental Principles Of Rigid Body Impact Mechanics, Khalid Alluhydan

Mechanical Engineering Research Theses and Dissertations

In impact mechanics, the collision between two or more bodies is a common, yet a very challenging problem. Producing analytical solutions that can predict the post-collision motion of the colliding bodies require consistent modeling of the dynamics of the colliding bodies. This dissertation presents a new method for solving the two and multibody impact problems that can be used to predict the post-collision motion of the colliding bodies. Also, we solve the rigid body collision problem of planar kinematic chains with multiple contacts with external surfaces.

In the first part of this dissertation, we study planar collisions of Balls and …