Physical Sciences and Mathematics Commons^™

Estimating Weighted Panel Sizes For Primary Care Providers: An Assessment Of Clustering And Novel Methods Of Panel Size Estimation On Electronic Medical Records, Martin A. Lavallee

Theses and Dissertations

Primary Care is on the frontlines of healthcare, thus they see the most diverse set of patients. In order to achieve high functioning primary care, a practice must establish empanelment, the pairing of patients to providers. Enumeration of empanelment, or estimating panel sizes, helps ensure that the demands of the patients demand the supply of providers and optimize the balance of primary care resources to improve quality of care. Further we can adjust panel sizes by using patient-level data on healthcare utilization and complexity extracted from the electronic medial record to determine the amount of care or burden of work …

Mathematical Models Of Infection Prevention Programs In Hospital Settings, Kelly A. Reagan

Theses and Dissertations

Hospitals play a vital role in providing for the healthcare needs of a community. Patients can develop hospital-acquired infections (HAIs) during their hospitalization due to exposure to foreign bacteria, viruses, and fungi. Infection prevention programs target and reduce HAIs, but implementing the infection prevention programs often comes with a cost. The goal of my research is to use mathematical models to quantify the impact of infection prevention programs on cases of HAIs and total healthcare costs. First, I use a Markov chain model to quantify how one infection prevention program reduces general HAIs in the hospital. Then, I calculate the …

Synthesis And Modeling Of Manganese Ferrite Nanoparticles For Magnetic Hyperthermia Applications, Margaret E. Thornton

Theses and Dissertations

Biologically targeted magnetic hyperthermia (MH) is a promising cancer therapeutic that is both non-invasive and has the potential to serve as a single-modality cancer treatment. MH operates through the elevation of temperatures between 40-43 °C to induce apoptosis in malignant tumor cells, while the small size of the magnetic nanoparticles preserves the healthy surrounding tissue. At present, MH is limited by low heating efficiency and heterogenous outcomes, and treatment requires direct-injection of the nanoparticles, excluding deep-tissue and metastatic tumors from the therapy. The most popular candidates for MH are the spinel ferrites iron oxide and manganese iron oxide (MFO). These …

Progress Towards Next Generation Modulators Of Ctbp's Oncogenic Effects, Jacqueline L. West

Theses and Dissertations

Human C-terminal binding proteins (CtBP1 and CtBP2) are transcriptional coregulators of multiple genes in the human genome, including tumor suppressor genes (e.g., Bik, PTEN, BRCA1, and E-cadherin) as well as oncogenes (e.g. MDR1 and Tiam1). Both homologues of CtBP are overexpressed in many types of cancer, including breast cancer (92%), ovarian cancer (83%), colorectal cancer (64%), hepatocellular carcinoma (60%), gastric cancer, prostate cancer, and pancreatic adenocarcinoma. Further, expression levels of CtBP correlate with worse prognostic outcomes and more aggressive tumor features because it promotes proliferation, epithelial-mesenchymal transition, and cancer stem cell self-renewal activity.

Our laboratory has identified a lead inhibitor …

Automated Conjecturing On The Independence Number And Minimum Degree Of Diameter-2-Critical Graphs, Joshua R. Forkin

Theses and Dissertations

A diameter-2-critical (D2C) graph is a graph with diameter two such that removing any edge increases the diameter or disconnects the graph. In this paper, we look at other lesser-studied properties of D2C graphs, focusing mainly on their independence number and minimum degree. We show that there exist D2C graphs with minimum degree strictly larger than their independence number, and that this gap can be arbitrarily large. We also exhibit D2C graphs with maximum number of common neighbors strictly greater than their independence number, and that this gap can be arbitrarily large. Furthermore, we exhibit a D2C graph whose number …

Periodic Trends In The Infrared And Optical Absorption Spectra Of Metal Chalcogenide Clusters, Alain Ward

Theses and Dissertations

We have investigated the Optical absorption, Infrared spectra, Binding Energies, and various other cluster properties to determine the existence of periodic trend for Transition Metal Chalcogenide Clusters ligated with CO ligands. We were motivated to answer the question of whether periodic behavior can be observed in properties of octahedral metal-chalcogenide clusters. We have used the Amsterdam Density Functional code to calculate the electronic structure of Transition Metal Chalcogenide Clusters using gradient-corrected density functional theory. We determined the existence of several periodic trends in properties of octahedral Transition Metal Chalcogenide Clusters TM₆Se₈(CO)₆. To investigate these …

Direction Modulated Brachytherapy For The Treatment Of Cervical Cancer, Dylan Richeson

Theses and Dissertations

Purpose: To evaluate and compare the performance of 9 experimental DMBT tandem models of varying physical dimensions in relation to 24 previously planned HDR cervical cancer treatment plans from multiple institutions that used conventional tandem and rings or ovoid applicators.

Methods and Materials: The DMBT tandem is designed to be used concurrently with IGABT and is made from an MRI-compatible tungsten-alloy rod with 6 channels grooved out of its periphery. 9 experimental DMBT tandem prototypes were provided. Each of the models was of equal lengths but varied in thickness, channel diameter size, and circle channel diameter size. Replanning …

Incorporating Ontological Information In Biomedical Entity Linking Of Phrases In Clinical Text, Evan French

Theses and Dissertations

Biomedical Entity Linking (BEL) is the task of mapping spans of text within biomedical documents to normalized, unique identifiers within an ontology. Translational application of BEL on clinical notes has enormous potential for augmenting discretely captured data in electronic health records, but the existing paradigm for evaluating BEL systems developed in academia is not well aligned with real-world use cases. In this work, we demonstrate a proof of concept for incorporating ontological similarity into the training and evaluation of BEL systems to begin to rectify this misalignment. This thesis has two primary components: 1) a comprehensive literature review and 2) …

Deep Learning Augmentation For Medical Image Analysis, Fouzia Altaf

Theses: Doctorates and Masters

Deep learning is at the center of the current rise of computer aided diagnosis in medical imaging. This technology has the ability to mimic extremely complex mathematical functions for predictive tasks. These functions are encoded as computational models that are learned directly from data. Deep learning models are known to achieve human-level accuracy for predictive tasks. However, such a performance requires that the model is trained on a huge amount of training data. For computer aided diagnosis tasks, the relevant training data needs to be carefully annotated by medical experts. This process is laborious and expensive, which generally results in …

Machine Infelicity In A Poignant Visitor Setting: Comparing Human And Ai’S Ability To Analyze Discourse, Martin Maccarthy, Hairong Shan

Research outputs 2014 to 2021

This study compares the efficacy of computer and human analytics in a commemorative setting. Both deductive and inductive reasoning are compared using the same data across both methods. The data comprises 2490 non-repeated, non-dialogical social media comments from the popular touristic site Tripadvisor. Included in the analysis is participant observation at two Anzac commemorative sites, one in Western Australia and one in Northern France. The data is then processed using both Leximancer V4.51 and Dialectic Thematic Analysis. The findings demonstrate artificial intelligence (AI) was incapable of insight beyond metric-driven content analysis. While fully deduced by human analysis the metamodel was …

Identifying Key Elements To Assess Patient’S Acceptability Of Neurorehabilitation In Stroke Survivors–A Delphi Method, Manonita Ghosh, Kaoru Nosaka, Lisa Whitehead, Kazunori Nosaka

Research outputs 2014 to 2021

Purpose: Assessing patient acceptability of treatment is a clinical concern. No guidance exists to determine the best way to measure acceptability in stroke neurorehabilitation. This study identifies key elements to measure patient’s acceptance of stroke neurorehabilitation by establishing expert consensus.

>Materials and methods: A four-phase Delphi method with a three-round electronic-based survey was conducted. Experts were considered as stroke survivors or their caregivers and professionals in stroke neurorehabilitation. A twenty-five-item list was sourced from a literature review and discussion with a consumer panel (n = 22). In Round-1 (n = 118) and Round-2 (n = 80), experts …

A Truly Spatial Random Forests Algorithm For Geoscience Data Analysis And Modelling, Hassan Talebi, Luk J. M. Peeters, Alex Otto, Raimon Tolosana-Delgado

Research outputs 2014 to 2021

Spatial data mining helps to find hidden but potentially informative patterns from large and high-dimensional geoscience data. Non-spatial learners generally look at the observations based on their relationships in the feature space, which means that they cannot consider spatial relationships between regionalised variables. This study introduces a novel spatial random forests technique based on higher-order spatial statistics for analysis and modelling of spatial data. Unlike the classical random forests algorithm that uses pixelwise spectral information as predictors, the proposed spatial random forests algorithm uses the local spatial-spectral information (i.e., vectorised spatial patterns) to learn intrinsic heterogeneity, spatial dependencies, and complex …

Evaluating Essential Processes And Forecast Requirements For Meteotsunami-Induced Coastal Flooding, Chenfu Huang, Eric Anderson, Yi Liu, Gangfeng Ma, Greg Mann, Pengfei Xue

Civil & Environmental Engineering Faculty Publications

Meteotsunamis pose a unique threat to coastal communities and often lead to damage of coastal infrastructure, deluge of nearby property, and loss of life and injury. The Great Lakes are a known hot-spot of meteotsunami activity and serve as an important region for investigation of essential hydrodynamic processes and model forecast requirements in meteotsunami-induced coastal flooding. For this work, we developed an advanced hydrodynamic model and evaluate key model attributes and dynamic processes, including: (1) coastal model grid resolution and wetting and drying process in low-lying zones, (2) coastal infrastructure, including breakwaters and associated submerging and overtopping processes, (3) annual/seasonal …

Integrating Deep Learning And Hydrodynamic Modeling To Improve The Great Lakes Forecast, Pengfei Xue, Aditya Wagh, Gangfeng Ma, Yilin Wang, Yongchao Yang, Tao Liu, Chenfu Huang

Civil & Environmental Engineering Faculty Publications

The Laurentian Great Lakes, one of the world’s largest surface freshwater systems, pose a modeling challenge in seasonal forecast and climate projection. While physics-based hydrodynamic modeling is a fundamental approach, improving the forecast accuracy remains critical. In recent years, machine learning (ML) has quickly emerged in geoscience applications, but its application to the Great Lakes hydrodynamic prediction is still in its early stages. This work is the first one to explore a deep learning approach to predicting spatiotemporal distributions of the lake surface temperature (LST) in the Great Lakes. Our study shows that the Long Short-Term Memory (LSTM) neural network, …

More Amazon Than Mafia: Analysing A Ddos Stresser Service As Organised Cybercrime, Roberto Musotto, David S. Wall

Research outputs 2014 to 2021

© 2020, The Author(s). The internet mafia trope has shaped our knowledge about organised crime groups online, yet the evidence is largely speculative and the logic often flawed. This paper adds to current knowledge by exploring the development, operation and demise of an online criminal group as a case study. In this article we analyse a DDoS (Distributed Denial of Service) stresser (also known as booter) which sells its services online to enable offenders to launch attacks. Using Social Network Analysis to explore the service operations and payment systems, our findings show a central business model that is similar to …

Reducing Print Time While Minimizing Loss In Mechanical Properties In Consumer Fdm Parts, Long Le, Mitchel A. Rabsatt, Hamid Eisazadeh, Mona Torabizadeh

Mechanical & Aerospace Engineering Faculty Publications

Fused deposition modeling (FDM), one of various additive manufacturing (AM) technologies, offers a useful and accessible tool for prototyping and manufacturing small volume functional parts. Polylactic acid (PLA) is among the commonly used materials for this process. This study explores the mechanical properties and print time of additively manufactured PLA with consideration to various process parameters. The objective of this study is to optimize the process parameters for the fastest print time possible while minimizing the loss in ultimate strength. Design of experiments (DOE) was employed using a split-plot design with five factors. Analysis of variance (ANOVA) was employed to …

Synthesis Of Functional Chalcone Library For Use In Aza- Dipyrromethene Dye Systems, Kendal Crawley

Murray State Theses and Dissertations

Over the past decade, Aza-Bodipy dyes (ADPs), a derivative of the extensively studied BF₂-dipyrromethene (BODIPY), have become a growing field of research due to their unique photophysical properties, high yielding synthesis, and relative stability. They possess a unique spectral window of 600 to 750 nm which is the red to near-IR region, evidenced by the blue color of many of these dyes. Increasing interest in these dyes is due to this near-IR window which is important for many applications.^1-4 ADP chromophores also have a common core motif to that of phthalocyanines which allow for coordination at their …

A Monolithic 3d Printed Axisymmetric Co-Flow Single And Compound Emulsion Generator, Amirreza Ghaznavi, Yang Lin, Mark Douvidzon, Adam Szmelter, Alannah Rodrigues, Malik Blackman, David Eddington, Tal Carmon, Lev Deych, Lan Yang, Jie Xu

Publications and Research

We report a microfluidic droplet generator which can produce single and compound droplets using a 3D axisymmetric co-flow structure. The design considered for the fabrication of the device integrated a user-friendly and cost-effective 3D printing process. To verify the performance of the device, single and compound emulsions of deionized water and mineral oil were generated and their features such as size, generation frequency, and emulsion structures were successfully characterized. In addition, the generation of bio emulsions such as alginate and collagen aqueous droplets in mineral oil was demonstrated in this study. Overall, the monolithic 3D printed axisymmetric droplet generator could …

Trapped Surfaces, Topology Of Black Holes, And The Positive Mass Theorem, Lan-Hsuan Huang, Dan A. Lee

Publications and Research

No abstract provided.

Satellite Evidence Of Canopy-Height Dependence Of Forest Drought Resistance In Southwestern China, Peipei Xu, Wei Fang, Tao Zhou, Hu Li, Xiang Zhao, Spencer Berman, Ting Zhang, Chuixiang Yi

Publications and Research

The frequency and intensity of drought events are increasing with warming climate, which has resulted in worldwide forest mortality. Previous studies have reached a general consensus on the size-dependency of forest resistance to drought, but further understanding at a local scale remains ambiguous with conflicting evidence. In this study, we assessed the impact of canopy height on forest drought resistance in the broadleaf deciduous forest of southwestern China for the 2010 extreme drought event using linear regression and a random forest (RF) model. Drought condition was quantified with standardized precipitation evapotranspiration index (SPEI) and drought resistance was measured with the …

Combinatorial Optimization With Photonics-Inspired Clock Models, Mostafa Honari-Latifpour, Matthew S. Mills, Mohammad-Ali Miri

Publications and Research

NP-hard combinatorial optimization problems are in general hard problems that their computational complexity grows faster than polynomial scaling with the size of the problem. Thus, over the years there has been a great interest in developing unconventional methods and algorithms for solving such problems. Here, inspired by the nonlinear optical process of q-photon down-conversion, in which a photon is converted into q degenerate lower energy photons, we introduce a nonlinear dynamical model that builds on coupled single-variable phase oscillators and allows for efficiently approximating the ground state of the classical q-state planar Potts Hamiltonian. This reduces the exhaustive search in …

Automatic Cephalometric Landmark Detection On X-Ray Images Using Object Detection, Cheng-Ho King, Yin-Lin Wang, Chia-Ling Tsai

Publications and Research

We propose a new deep convolutional cephalometric landmark detection framework for orthodontic treatment. Our proposed method consists of two major steps: landmark detection using a deep neural network for object detection, and landmark repair to ensure one instance per landmark class. For landmark detection, we modify the loss function of the backbone network YOLOv3 to eliminate the constrains on the bounding box and incorporate attention mechanism to improve the detection accuracy. For landmark repair, a triangle mesh is generated from the average face to eliminate superfluous instances, followed by estimation of missing landmarks from the detected ones using Laplacian Mesh. …

A Novel Tropical Geometry-Based Interpretable Machine Learning Method: Pilot Application To Delivery Of Advanced Heart Failure Therapies, Heming Yao, Harm Derkson, Jessica R. Golbus, Justin Zhang, Keith D. Aaronson, Jonathan Gryak, Kayvan Najarian

Publications and Research

Abstract—A model’s interpretability is essential to many practical applications such as clinical decision support systems. In this paper, a novel interpretable machine learning method is presented, which can model the relationship between input variables and responses in humanly understandable rules. The method is built by applying tropical geometry to fuzzy inference systems, wherein variable encoding functions and salient rules can be discovered by supervised learning. Experiments using synthetic datasets were conducted to demonstrate the performance and capacity of the proposed algorithm in classification and rule discovery. Furthermore, we present a pilot application in identifying heart failure patients that are eligible …

Technical Note: Novel Estimates Of The Leaf Relative Uptake Rate Of Carbonyl Sulfide From Optimality Theory, Georg Wohlfahrt, Albin Hammerle, Felix M. Spielmann, Florian Kitz, Chuixiang Yi

Publications and Research

In order to estimate the gross primary productivity (GPP) of terrestrial ecosystems from the canopy uptake of carbonyl sulfide (COS), the leaf relative uptake rate (LRU) of COS with respect to carbon dioxide needs to be known a priori. Currently, the variability of the LRU between plant species in different biomes of the world is poorly understood, making the choice of an appropriate LRU uncertain and hampering further progress towards developing COS as an alternative tracer of GPP. Here we propose a novel approach for estimating LRU based on plant optimality principles, validate it against in situ leaf gas exchange …

Attitudes About Cybersecurity Articulation Agreements And Transfer Students: A Statewide Survey Of Faculty Members And Advisors, Brian K. Payne, Tracy Vandecar-Burdin, Daniela Cigularova

Sociology & Criminal Justice Faculty Publications

In this study, cybersecurity faculty and academic advisors from community colleges and 4-year universities in the southeast region of the United States completed a survey assessing attitudes about and support for articulation agreements and related transfer policies. Hypothesizing that professional structures shape attitudes and experiences, the researchers conducted an exploratory quantitative study with primarily descriptive analyses. The results reveal differences in attitudes between community college and 4-year stakeholders and between faculty and academic advisors. The results of this study are discussed in relation to faculty and advisor training and communication.

Civiic: Cybercrime In Virginia: Impacts On Industry And Citizens Final Report, Randy Gainey, Tancy Vandecar-Burdin, Jay Albanese, Thomas Dearden, James Hawdon, Katalin Parti

Sociology & Criminal Justice Faculty Publications

[First paragraph] Victimization from cybercrime is a major concern in Virginia, the US, and the world. As individuals and businesses spend more time online, it becomes increasingly important to understand cybercrime and how to protect against it. Such an understanding is dependent on valid and reliable baseline data that identifies the specific nature, extent, and outcomes of cybercrime activity. A better understanding of cybercrime activity is needed to target and prevent it more effectively, minimize its consequences, and provide support for both individual and corporate victims. Before that can occur, however, better baseline data are required, and this project was …

Text Reply Latencies For Mental Illness Screening, Ml Tlachac, Samuel S. Ogden

Information Systems and Analytics Department Faculty Conference Proceedings

Campus research presentation poster presented at Celebrating Academic Excellence at Bryant University.

Stereotype Threat Impact On Mobile Depression Screening, Ml Tlachac

Information Systems and Analytics Department Faculty Conference Proceedings

Campus research presentation poster presented at Celebrating Academic Excellence at Bryant University.

Particle Identification And Tracking In Real Time Using Machine Learning On Fpga, F. Barbosa, L. Belfore, C. Dickover, C. Fanelli, S. Furletov, Y. Furletova, L. Jokhovets, D. Lawrence, D. Romanov

Electrical & Computer Engineering Faculty Publications

This project is a multi-disciplinary endeavour between Physics, Electrical Engineering, and Computer Engineering. The purpose is to develop and implement an FPGA(*) based Machine Learning algorithm for real-time particle identification, filtering, and data reduction. This is important research that can be applied to streaming readout systems being developed now at JLab and other facilities. Real-time data processing is a frontier field in experimental physics, especially in HEP. The application of FPGAs at the trigger level is used by many current and planned experiments (CMS, LHCb, Belle2, PANDA). Usually they use conventional processing algorithms. LHCb has implemented ML elements for real-time …

Deeppose: Detecting Gps Spoofing Attack Via Deep Recurrent Neural Network, Peng Jiang, Hongyi Wu, Chunsheng Xin

Electrical & Computer Engineering Faculty Publications

The Global Positioning System (GPS) has become a foundation for most location-based services and navigation systems, such as autonomous vehicles, drones, ships, and wearable devices. However, it is a challenge to verify if the reported geographic locations are valid due to various GPS spoofing tools. Pervasive tools, such as Fake GPS, Lockito, and software-defined radio, enable ordinary users to hijack and report fake GPS coordinates and cheat the monitoring server without being detected. Furthermore, it is also a challenge to get accurate sensor readings on mobile devices because of the high noise level introduced by commercial motion sensors. To this …