Physical Sciences and Mathematics Commons^™

Sustainable Approaches To Photo-, Thermo-, And Electro-Chemical Reactions Of Amines, James David Sitter

Theses and Dissertations

In recent years, sustainable routes for chemical synthesis have garnered renewed interest. As the world continues to fight pollution and rising costs, it is imperative that new routes of synthesis that continuously fight the issues of pollution and waste are discovered, defined and explored. While traditional synthesis routes that require energy input typically require the burning of fossil fuels to produce heat, the amount of resources that go into the production of these fossil fuels and the sequestration of its waste to minimize its impact on the carbon footprint is enormous. Furthermore, sustainable chemistry research aims to decrease and minimize …

Utilizing Deep Learning Methods In The Identification And Synthesis Of Gene Regulations, Jiandong Wang

Theses and Dissertations

Gene expression is the fundamental differentiation and development process of life. Although all cells in an organism have essentially the same DNA, cell types and activities vary due to changes in gene expression. Gene expression can be influenced by many gene regulations. RNA editing contributes to the variety of RNA and proteins by allowing single nucleotide substitution. Reverse transcription can alter the expression status of genes by inducing genetic diversity and polymorphism via novel insertions, deletions, and recombination events. Gene regulation is critical to normal development because it enables cells to respond rapidly to environmental changes. However, identifying gene regulations …

Exploring The Current Training Of Undergraduate Geology Students And Teaching Spatial Skills To Improve Student Outcomes, Ann Marie Klyce

Theses and Dissertations

Spatial skills, which represent the ability to mentally manipulate objects (Schneider & McGrew, 2012; Atit et al., 2020) have been shown to be correlated with entrance, persistence and success in STEM (Shea et al., 2001; Wai et al., 2009). Specifically, these skills have been shown to be necessary to geologists and geoscientists (Hegarty, 2014; Gagnier et al., 2016). While we recognize the importance of these skills, explicit training in them is rarely offered (NRC 2006). Consequently, cognitive scientists and discipline based education researchers have begun concerted efforts to offer training in spatial skills to improve student outcomes (e.g. Uttal et …

Sparse Partitioned Empirical Bayes Ecm Algorithms For High-Dimensional Linear Mixed Effects And Heteroscedastic Regression, Anja Zgodic

Theses and Dissertations

Variable selection methods in both the frequentist and Bayesian frameworks are powerful techniques that provide prediction and inference in high-dimensional linear regression models. These methods often assume independence between observations and normally distributed errors with the same variance. In practice, these two assumptions are often violated. To mitigate this, we develop efficient and powerful Bayesian approaches for linear mixed modeling and heteroscedastic linear regression. These method offers increased flexibility through the development of empirical Bayes estimators for hyperparameters, with computationally efficient estimation through the Expectation Conditional-Minimization (ECM) algorithm. The novelty of these approaches lies in the partitioning and parameter expansion, …

Structure Property Investigation And Applications Of Self-Assembled Triphenylamine Bis Urea Macrocycles, Md Faizul Islam

Theses and Dissertations

Porous organic crystals can be advantageous as nanoreactors and in sensing, separations, and storage. The Shimizu group utilizes bifurcated urea hydrogen bonding to direct the assembly small building block into pillars and columns affording functional porous crystals. Recently, our group has examined the incorporation of triphenylamines within the framework of bis-urea macrocycles. These macrocycles assemble into columns that contain the solvent of crystallization bound within the channels. Heating the crystals leads to removal of the solvent and activates the host for guest exchange by single-crystal-to-single[1]crystal transformations. Different electron accepting molecules can be encapsulated inside the pore of the host …

Molecular Devices For Measuring Weak Solvophobic Interactions And The Development Of Pedagogical Tools For Organic Chemistry Students, Alexander N. Manzewitsch

Theses and Dissertations

The main topics of this dissertation are: 1) the development and discussion of a new molecular balance for measuring alkyl-alkyl interactions in a wide array of organic solvents, 2) the development of linear solvation energy relationships between solvent interaction parameters and solvent accessible surface area (SASA) in CH-arene interactions, and 3) the development of online resources for helping undergraduate students perform organic chemistry mechanisms problems well.

The solvophobic effect, that is the forced association between two solutes due to intermolecular attraction of the solvent molecules, is ubiquitous in organic chemistry and responsible for many significant phenomena thereof. However, there is …

Impact Of Applying Visual Design Principles To Boardwork In A Mathematics Classroom, Jennifer Rose Canizales

Theses and Dissertations

Though black boards and white boards have been a fundamental tool in the classroom for over a century, little research has been done on how to best design and present information using these boards. My study takes visual design principles and applies them to boardwork in a mathematics classroom to better organize and clarify the content. This research shows that students notice boardwork, have strong opinions on what makes boardwork good, and that the application of design principles on boards has a significant impact on students and the teacher. Students felt their cognitive load was lightened and that they were …

Role Of The Cytosolic Chaperonin Cct In The Folding Of Novel Substrates, Theresa M. Smith

Theses and Dissertations

All cells depend on properly folded proteins for survival and function. Misfolding of proteins results in loss of critical functions and may trigger the misfolding of other nearby proteins leading to toxic aggregation. While many proteins can fold on their own, others with complicated domain structures require assistance from protein folding machines called chaperones. The most complex and highly specialized of all chaperones is the eukaryotic chaperonin complex CCT which is necessary for the folding of a wide variety of essential proteins. These include the cytoskeletal proteins actin and tubulin as well as the Gβ subunit of the G protein …

Hierarchical Joint Entity Recognition And Relation Extraction Of Contextual Entities In Family History Records, Daniel Segrera

Theses and Dissertations

Entity extraction is an important step in document understanding. Higher accuracy entity extraction on fine-grained entities can be achieved by combining the utility of Named Entity Recognition (NER) and Relation Extraction (RE) models. In this paper, a cascading model is proposed that implements NER and Relation extraction. This model utilizes relations between entities to infer context-dependent fine-grain named entities in text corpora. The RE module runs independent of the NER module, which reduces error accumulation from sequential steps. This process improves on the fine-grained NER F1-score of existing state-of-the-art from .4753 to .8563 on our data, albeit on a strictly …

Fast And Accurate 3d Object Reconstruction For Cargo Load Planning, Adam R. Nasi

Theses and Dissertations

Cargo load planning involves efficiently packing objects into aircraft subject to constraints such as space and weight distribution. Currently, this is performed manually by loadmasters. The United States Air Force is investigating ways to automate this process in order to improve airlift operational readiness while saving money. The first step in such a process would be generating 3D reconstructions of cargo objects to be used by a load planning algorithm. To that end, this thesis presents a novel method for fast, scaled, and accurate 3D reconstruction of cargo objects. This method can scan a 2.5m×3m×2m object in less than 10 …

Temporal And Spatial Variability Of Specific Energy Consumption For Atmospheric Water Generators, Anthony T. Brenes

Theses and Dissertations

Atmospheric Water Generators (AWG) produce potable water from the moisture in the air, providing a potentially viable water source in austere locations or emergency response scenarios. In this study, the operating constraints of three existing commercially available AWG devices are investigated, compared to historical weather data from across the continental United States. Utilizing linear regression modeling and weather station data for the years of 1985-2019, the monthly and spatial trends of energy demand to produce water from these devices are evaluated. Energy and water production efficiencies for the devices are highly dependent on environmental conditions with relative humidity and temperature …

Automated Additive Layering Of Vat Polymerized Plastic Organic Scintillators, Chandler J. Moore

Theses and Dissertations

The current technology for fast neutron detection imaging is limited in achieving the required high spatial resolution, strong neutron discrimination, and practical time of manufacturing. Traditional fabrication methods require days of thermal polymerization and hundreds of man-hours to produce average resolution pixelated scintillator arrays. The present work helps to eliminate this limitation by developing an additive manufacturing technique to construct such detectors for use in in dual particle imaging applications. In this work, fast-, light-curing resins are used in a prototype automated assembly machine, capable of layering of individual light-cured resin layers and optical segmentation with a self-bonded specular reflector, …

Student Performance In Traditional In-Person Vs. Online Sections Of An Introductory Graduate Mathematics Course, Lauran E. Kittle

Theses and Dissertations

The growth of technology impacts nearly every aspect of everyday life, to include education and learning. The availability of distance learning (online) classes has increased drastically in the last few decades, expanding access to education for millions of people. However, it is imperative to consider exactly how the growth of technology impacts education – whether it is a positive, negative, or neutral impact. Previous research comparing distance learning and in-residence (traditional) classes have widely mixed, disparate conclusions. This type of research, two-stage analysis, and modeling has yet to be conducted on a graduate school level. For this reason, a detailed …

Modeling Radiation Exposure On Flight Missions To Analyze Aircrew Risk, Camila V. Quintero Hilsaca

Theses and Dissertations

GCR and SPE comprise the majority of the ionizing radiation experienced in the upper atmosphere within flight-altitude environments. Although previous studies have analyzed radiation doses from single sources on civilian flight operations, there is a lack of research focused on dose received by military personnel during flight from both sources simultaneously. In-flight radiation environments are modeled through the MCNP6 for two separate aircraft, an Air Force A-10 and a Boeing 737. Particle fluence values for galactic cosmic rays and solar particle events for four separate flight paths are determined using the CARI-7A software and the SIRE2 toolkit, respectively. MCNP6 code …

Uncertainty Quantification In Federated Learning For Persistent Post-Traumatic Headache, Byungmoo Brian Kim

Theses and Dissertations

A post-traumatic headache (PTH), resulting from a mild traumatic brain injury (mTBI), potentially develops into persistent post-traumatic headache (PPTH). Although no known cure for PPTH exists, research has shown that receiving treatment at earlier stages of PTH lowers the risk of patients developing PPTH. Previous studies have shown machine learning (ML) models capable of predicting a patient’s PTH progression, but none have considered the issue of protecting patient privacy. Due to patient privacy, ML models only have access to data within the institution. Federated learning (FL) harnesses data from separate institutions without sacrificing patient privacy as institutions can run ML …

Predicting Success Of Pilot Training Candidates Using Interpretable Machine Learning, Alexandra S. King

Theses and Dissertations

The United States Air Force (USAF) has struggled with a sustained pilot shortage over the past several years; senior military and government leaders have been working towards a solution to the problem, with no noticeable improvements. Both attrition of more experienced pilots as well as wash out rates within pilot training contribute to this issue. This research focuses on pilot training attrition. Improving the process for selecting pilot candidates can reduce the number of candidates who fail. This research uses historical specialized undergraduate pilot training (SUPT) data and leverages select machine learning techniques to determine which factors are associated with …

Examining Failures Of Kc-135 Boom Assemblies Using Survival Analysis, Benjamin D. Miller

Theses and Dissertations

The purposes of this study are to confirm the applicability of survival analysis for predicting recurrent failures of a component of a military aircraft and to provide practical insights to maintenance managers and mission planners. The results of this study also can help the United States Department of Defense improve the CBM+ program. This study was able to predict recurrent failures of the component using Nelson-Aalen cumulative estimates. In addition, this study used a Cox proportional hazards regression model with shared frailty for measuring the effect of covariates on recurrent failures and unidentified heterogeneity in the model, which warranted future …

Temporal Convolutional Neural Networks For Device Discrimination, Ryan T. Zacher

Theses and Dissertations

This research uses TCN modifications to CNN classifiers, specifically dilation, causal padding, and residual blocks, to focus on temporal features and improve existing DNA analysis processes. Dilation significantly improves classification accuracy, even detecting features where no other models were able to. The smallest improvement shown is a 3-6dB reduction in SNR to reach a 90% classification accuracy. The maximum improvement is shown in the data-delivery region of the Cisco dataset, with the dilated model being the only model to exceed 90% classification accuracy. The other TCN modifications are shown to have no beneficial effect on the models.

Entering Hyperspace: Conditional Hyperspectral Reflectance Image Generation Using Convolutional Neural Networks, Bret M. Wagner

Theses and Dissertations

The field of remote sensing continues to expand in both commercial and defense domains. Development of advanced space based EOIR sensors has driven corresponding demand for sensor data for algorithm development. The AFIT Sensor and Scene Emulation Tool (ASSET) produces realistic synthetic electro-optical and infrared (EO/IR) data with absolute truth for the purpose of clutter suppression, target detection, and tracking algorithm development. This thesis presents a novel model which transforms panchromatic images into realistic hyperspectral reflectance images. The direct application of this model is to allows users to generate hyperspectral background images as inputs to ASSET allowing users to benefit …

Hierarchical Federated Learning On Healthcare Data: An Application To Parkinson's Disease, Brandon J. Harvill

Theses and Dissertations

Federated learning (FL) is a budding machine learning (ML) technique that seeks to keep sensitive data private, while overcoming the difficulties of Big Data. Specifically, FL trains machine learning models over a distributed network of devices, while keeping the data local to each device. We apply FL to a Parkinson’s Disease (PD) telemonitoring dataset where physiological data is gathered from various modalities to determine the PD severity level in patients. We seek to optimally combine the information across multiple modalities to assess the accuracy of our FL approach, and compare to traditional ”centralized” statistical and deep learning models.

Air Force Cadet To Career Field Matching Problem, Ian P. Macdonald

Theses and Dissertations

This research examines the Cadet to Air Force Specialty Code (AFSC) Matching Problem (CAMP). Currently, the matching problem occurs annually at the Air Force Personnel Center (AFPC) using an integer program and value focused thinking approach. This paper presents a novel method to match cadets with AFSCs using a generalized structure of the Hospitals Residents problem with special emphasis on lower quotas. This paper also examines the United States Army Matching problem and compares it to the techniques and constraints applied to solve the CAMP. The research culminates in the presentation of three algorithms created to solve the CAMP and …

Improving Accessibility And Efficiency Of Analytic Provenance Tools For Reverse Engineering, Caleb W. Richardson

Theses and Dissertations

Reverse engineering is a vital technique for identifying and mitigating cyber threats. Yet, despite its importance, reverse engineering is a time-consuming process. Provenance tools help to improve the workflow of reverse engineers by providing an accessible method of viewing their flow through a binary. The current state-of-theart provenance tool for reverse engineering software called SensorRE, leverages an external server, web browser, and a large array of javascript libraries. This thesis presents Provenance Ninja, a software reverse engineering tool developed in Python that runs directly within Binary Ninja. Provenance Ninja captures reverse engineers’ provenance data and provides an interactive graph within …

Pulsed Power Neutron Production With Deuterated Polymer Accelerator Targets, Anthony O. Hagey

Theses and Dissertations

This document presents an investigation of the effect of deuterated polyethylene accelerator targets on the neutron fluence from a local mass injection dense plasma focus driven by the United States Naval Research Laboratory’s Hawk pulsed-power generator. After successful production of thin targets, the acquisition of thicker targets, and testing inside Hawk, it was found that the presence of a deuterated polyethylene target increased the neutron fluence. Results suggested that fluence can significantly increase with the presence of a deuterated target vs a nondeuterated target. Additive manufacturing printing was used as a production method in order to determine if deuterated accelerator …

Regular Simplices Within Doubly Transitive Equiangular Tight Frames, Evan C. Lake

Theses and Dissertations

An equiangular tight frame (ETF) yields an optimal way to pack a given number of lines into a given space of lesser dimension. Every ETF has minimal coherence, and this makes it potentially useful for compressed sensing. But, its usefulness also depends on its spark: the size of the smallest linearly dependent subsequence of the ETF. When formed into a sensing matrix, a larger spark means a lower chance that information is lost when sensing a sparse vector. Spark is difficult to compute in general, but if an ETF contains a regular simplex, then every such simplex is a linearly …

Induced Correlation And Its Effects In The Performance Of Fused Classification Systems, Mary K. Collins

Theses and Dissertations

Classification systems are abundant in modern-day life. The United States Air Force uses classification systems across many applications such as radar, satellite, and infrared sensing just to name a few. Combining classification systems allows an opportunity to get more accurate results. Using the known information from already built and tested systems that can be mathematically combined can give insight into the performance of the fused system without having to build a combined system. Leveraging this can save time, resources, and money. This work examines the correlation effects of fusing two classifier systems, each with only two labels, using the Boolean …

Air Force Digital Badges, Jacob Chan

Theses and Dissertations

The Air Force talent management and force development systems are antiquated. Airmen records are often stored on different Air Force information systems. Existing records sometimes lack granularity and context to recognize Airmen skills. Digital badges are a newer technology utilized by academia and industry to recognize member skills. However, military badging research is sparse and existing studies do not provide sufficient evidence on the value of digital badging to the Air Force. The studies: (1) lack background research on badging; (2) do not provide quantitative data on the effects of badging; and (3) issued badges through commercial entities which may …

The Electromagnetic Bayonet: Development Of A Scientific Computing Method For Aperture Antenna Optimization, Michael P. Ingold

Theses and Dissertations

The quiet zone of a radar range is the region over which a transmitted EM field approximates a uniform plane wave to within some finite error tolerance. Any target to be measured must physically fit within this quiet zone to prevent excess measurement error. Compact radar ranges offer significant operational advantages for performing RCS measurements but their quiet zone sizes are constrained by space limitations. In this work, a scientific computing approach is used to investigate whether equivalent-current transmitters can be designed that generate larger quiet zones than a conventional version at short range. A time-domain near-field solver, JefimenkoModels, was …

Cellphone-Acoustics Based Suas Detection And Tracking, Ryan D. Clendening

Theses and Dissertations

Small Unmanned Aerial Systems (sUAS) are an easily accessible technology that has become an increasingly large threat to US critical systems. This threatening technology demands using fault-tolerant, low-cost, replaceable, and accurate sensing resources, which counter the ubiquitous nature of sUAS [1]. Therefore, the methods developed in this thesis detect and track sUAS using easily accessible sensing resources, such as cellphones. First, we develop an acoustics sensor network-based sUAS detection methodology. In the latter effort, a deep learning model is trained using the acoustics data from the data collection to predict sUAS range from a cellphone. Combined, these two efforts demonstrate …

Adaptation Of Network Flow Problems For Course Of Action Generation, Alexander N. Stephens

Theses and Dissertations

This thesis introduces two methods to generate Courses of Action (COA) in distributed warfare scenarios: the Wargaming Commodity Course of Action Automated Method Under Uncertainty (WCCAAM-U2) and Dynamic Transshipment Problem (DTP)-generated COAs. Previous work by Deberry et al. used a Multi-Commodity Flow Problem (MCFP) to generate COAs for single-period wargame scenarios with known enemy force amounts. In WCCAAM-U2, we adapt an MCFP to work in situations where only intelligence estimates of enemy forces are known. Compared to two other COA-generation methods, the WCCAAAM-U2 COA outperforms the next highest-performing COA by 307% when compared by a ratio of objective success rate …

Monocular Vision And Machine Learning For Pose Estimation, Quang Ngoc Tran

Theses and Dissertations

This thesis introduces a monocular vision-based approach for 6 DoF pose estimation on a known object. The proposed solution is to use a CNN to find known features of an object in an image. These known features, together with their known locations, are used by a PnP algorithm to estimate the pose of the target object with respect to the camera. The primary difficulty with CNN-based methods is needing to generate a large amount of training data to effectively create the CNN. To overcome this difficulty, a 3D model of the real-world object is created and used in a visualization …