Digital Commons Network^™

A Comparison Of Two Hormonal Fertility Monitoring Systems For Ovulation Detection: A Pilot Study, Qiyan Mu, Richard J. Fehring

College of Nursing Faculty Research and Publications

Background and Objectives: Accuracy in detecting ovulation and estimating the fertile window in the menstrual cycle is essential for women to avoid or achieve pregnancy. There has been a rapid growth in fertility apps and home ovulation testing kits in recent years. Nevertheless, there lacks information on how well these apps perform in helping users understand their fertility in the menstrual cycle. This pilot study aimed to evaluate and compare the beginning, peak, and length of the fertile window as determined by a new luteinizing hormone (LH) fertility tracking app with the Clearblue Fertility Monitor (CBFM).

Materials and Methods: A …

Case Reports From Women Using A Quantitative Hormone Monitor To Track The Perimenopause Transition, Maria Meyers, Richard J. Fehring, Mary Schneider

College of Nursing Faculty Research and Publications

The fertility tracking of menstrual cycles during perimenopause with a quantitative hormone monitor is a novel undertaking. Women in regular menstrual cycles have been tracking their fertility using different biomarkers since the 1960′s. Presently, there are newer electronic hormonal devices used to track fertility that provide more exact and objective data to help delineate the fertile time frame of a woman’s cycle. These devices measure quantitative levels of estrogen, the luteinizing hormone, progesterone, and follicle-stimulating hormone, all of which occur at varying levels during the menstrual cycle. As women advance toward menopause, their cycles vary in length, and their hormones …

Acoustic-To-Articulatory Speech Inversion Features For Mispronunciation Detection Of/ɹ/In Child Speech Sound Disorders, Nina R. Benway, Yashish M. Siriwardena, Jonathan L. Preston, Elaine Hitchcock, Tara Mcallister, Carol Espy-Wilson

Department of Communication Sciences and Disorders Faculty Scholarship and Creative Works

Acoustic-to-articulatory speech inversion could enhance automated clinical mispronunciation detection to provide detailed articulatory feedback unattainable by formant-based mispronunciation detection algorithms; however, it is unclear the extent to which a speech inversion system trained on adult speech performs in the context of (1) child and (2) clinical speech. In the absence of an articulatory dataset in children with rhotic speech sound disorders, we show that classifiers trained on tract variables from acoustic-to-articulatory speech inversion meet or exceed the performance of state-of-the-art features when predicting clinician judgment of rhoticity.

Mwirgan: Unsupervised Visible-To Mwir Image Translation With Generative Adversarial Network, Mohammad Shahab Uddin, Chiman Kwan, Jiang Li

Electrical & Computer Engineering Faculty Publications

Unsupervised image-to-image translation techniques have been used in many applications, including visible-to-Long-Wave Infrared (visible-to-LWIR) image translation, but very few papers have explored visible-to-Mid-Wave Infrared (visible-to-MWIR) image translation. In this paper, we investigated unsupervised visible-to-MWIR image translation using generative adversarial networks (GANs). We proposed a new model named MWIRGAN for visible-to-MWIR image translation in a fully unsupervised manner. We utilized a perceptual loss to leverage shape identification and location changes of the objects in the translation. The experimental results showed that MWIRGAN was capable of visible-to-MWIR image translation while preserving the object’s shape with proper enhancement in the translated images and …

The Effect Of The Width Of The Incident Pulse To The Dielectric Transition Layer In The Scattering Of An Electromagnetic Pulse — A Qubit Lattice Algorithm Simulation, George Vahala, Linda Vahala, Abhay K. Ram, Min Soe

Electrical & Computer Engineering Faculty Publications

The effect of the thickness of the dielectric boundary layer that connects a material of refractive index n₁ to another of index n₂is considered for the propagation of an electromagnetic pulse. A qubit lattice algorithm (QLA), which consists of a specially chosen non-commuting sequence of collision and streaming operators acting on a basis set of qubits, is theoretically determined that recovers the Maxwell equations to second-order in a small parameter ϵ. For very thin boundary layer the scattering properties of the pulse mimics that found from the Fresnel jump conditions for a plane wave - except that …

View Synthesis With Scene Recognition For Cross-View Image Localization, Uddom Lee, Peng Jiang, Hongyi Wu, Chunsheng Xin

Electrical & Computer Engineering Faculty Publications

Image-based localization has been widely used for autonomous vehicles, robotics, augmented reality, etc., and this is carried out by matching a query image taken from a cell phone or vehicle dashcam to a large scale of geo-tagged reference images, such as satellite/aerial images or Google Street Views. However, the problem remains challenging due to the inconsistency between the query images and the large-scale reference datasets regarding various light and weather conditions. To tackle this issue, this work proposes a novel view synthesis framework equipped with deep generative models, which can merge the unique features from the outdated reference dataset with …

Security Of Internet Of Things (Iot) Using Federated Learning And Deep Learning — Recent Advancements, Issues And Prospects, Vinay Gugueoth, Sunitha Safavat, Sachin Shetty

Electrical & Computer Engineering Faculty Publications

There is a great demand for an efficient security framework which can secure IoT systems from potential adversarial attacks. However, it is challenging to design a suitable security model for IoT considering the dynamic and distributed nature of IoT. This motivates the researchers to focus more on investigating the role of machine learning (ML) in the designing of security models. A brief analysis of different ML algorithms for IoT security is discussed along with the advantages and limitations of ML algorithms. Existing studies state that ML algorithms suffer from the problem of high computational overhead and risk of privacy leakage. …

Toward Real-Time, Robust Wearable Sensor Fall Detection Using Deep Learning Methods: A Feasibility Study, Haben Yhdego, Christopher Paolini, Michel Audette

Electrical & Computer Engineering Faculty Publications

Real-time fall detection using a wearable sensor remains a challenging problem due to high gait variability. Furthermore, finding the type of sensor to use and the optimal location of the sensors are also essential factors for real-time fall-detection systems. This work presents real-time fall-detection methods using deep learning models. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. First, we developed and compared different data-segmentation techniques for sliding windows. Next, we implemented various techniques to balance the datasets because collecting fall datasets in the real-time setting has …

Adaptive Critic Network For Person Tracking Using 3d Skeleton Data, Joseph G. Zalameda, Alex Glandon, Khan M. Iftekharuddin, Mohammad S. Alam (Ed.), Vijayan K. Asari (Ed.)

Electrical & Computer Engineering Faculty Publications

Analysis of human gait using 3-dimensional co-occurrence skeleton joints extracted from Lidar sensor data has been shown a viable method for predicting person identity. The co-occurrence based networks rely on the spatial changes between frames of each joint in the skeleton data sequence. Normally, this data is obtained using a Lidar skeleton extraction method to estimate these co-occurrence features from raw Lidar frames, which can be prone to incorrect joint estimations when part of the body is occluded. These datasets can also be time consuming and expensive to collect and typically offer a small number of samples for training and …

Deep-Learning-Based Classification Of Digitally Modulated Signals Using Capsule Networks And Cyclic Cumulants, John A. Snoap, Dimitrie C. Popescu, James A. Latshaw, Chad M. Spooner

Electrical & Computer Engineering Faculty Publications

This paper presents a novel deep-learning (DL)-based approach for classifying digitally modulated signals, which involves the use of capsule networks (CAPs) together with the cyclic cumulant (CC) features of the signals. These were blindly estimated using cyclostationary signal processing (CSP) and were then input into the CAP for training and classification. The classification performance and the generalization abilities of the proposed approach were tested using two distinct datasets that contained the same types of digitally modulated signals, but had distinct generation parameters. The results showed that the classification of digitally modulated signals using CAPs and CCs proposed in the paper …

A Survey Of Using Machine Learning In Iot Security And The Challenges Faced By Researchers, Khawlah M. Harahsheh, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The Internet of Things (IoT) has become more popular in the last 15 years as it has significantly improved and gained control in multiple fields. We are nowadays surrounded by billions of IoT devices that directly integrate with our lives, some of them are at the center of our homes, and others control sensitive data such as military fields, healthcare, and datacenters, among others. This popularity makes factories and companies compete to produce and develop many types of those devices without caring about how secure they are. On the other hand, IoT is considered a good insecure environment for cyber …

Comparison Of Machine Learning Methods For Classification Of Alexithymia In Individuals With And Without Autism From Eye-Tracking Data, Furkan Iigin, Megan A. Witherow, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Alexithymia describes a psychological state where individuals struggle with feeling and expressing their emotions. Individuals with alexithymia may also have a more difficult time understanding the emotions of others and may express atypical attention to the eyes when recognizing emotions. This is known to affect individuals with Autism Spectrum Disorder (ASD) differently than neurotypical (NT) individuals. Using a public data set of eye-tracking data from seventy individuals with and without autism who have been assessed for alexithymia, we train multiple traditional machine learning models for alexithymia classification including support vector machines, logistic regression, decision trees, random forest, and multilayer perceptron. …

Technology Adoption Of Computer-Aided Instruction In Healthcare: A Structured Review, Queenie Kate Cabanilla, Frevy Teofilo-Orencia, Rentor Cafino, Armando T. Isla Jr., Jehan Grace Maglaya, Xavier-Lewis Palmer, Lucas Potter, Dave E. Marcial, Lemuel Clark Velasco

Electrical & Computer Engineering Faculty Publications

Computer-Aided Instruction (CAI) is one of the interactive teaching methods that electronically presents instructional resources and enhances learner performance. In health settings, using CAI is one of the important ways to improve learners' knowledge and usefulness in their healthcare specialization yet there is still a lack of research that offers a comprehensive synthesis of investigating into the adoption of CAI in healthcare. This research aims to provide a comprehensive review of related literatures on the enablers and barriers for technology adoption of CAI in healthcare. 31 journals were analyzed and revealed that several studies were utilizing the Unified Theory of …

Implications Of Cyberbiosecurity In Advanced Agriculture, Simone Stephen, Keitavius Alexander, Lucas Potter, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

The world is currently undergoing a rapid digital transformation sometimes referred to as the fourth industrial revolution. During this transformation, it is increasingly clear that many scientific fields are not prepared for this change. One specific area is agriculture. As the sector which creates global food supply, this critical infrastructure requires detailed assessment and research via newly developed technologies (Millett et al, 2019; Peccoud et al, 2018) . Despite its fundamental significance to modern civilization, many aspects of industrial agriculture have not yet adapted to the digital world. This is evident in the many vulnerabilities currently present within agricultural systems, …

A Reflection Of Typology And Verification Flaws In Consideration Of Biocybersecurity/Cyberbiosecurity: Just Another Gap In The Wall, Luke Potter, Kim Mossberg, Xavier Palmer

Electrical & Computer Engineering Faculty Publications

Verification is central to any process in a functional and enduring cyber-secure organization. This verification is how the validity or accuracy of a state of being is assessed (Schlick, 1936; Balci, 1998). Conversely, breakdown in verification procedures is core to the interruption of normal operations for an organization. A key problem for organizations that utilize biology as an interlock within their systems is that personnel lack sufficient ability to verify all practically relevant biological information for procedures such as a nurse logging a blood draw, or a molecular biology technician preparing agar to culture microbes for study. This has several …

Conceptualizing Socio-Emotional Disablism: A Duoethnography Of Crohn’S Disease And Ocd During Covid-19, Kristen L. Cole, Brandi Lawless

Faculty Research, Scholarly, and Creative Activity

We utilize duoethnography to help readers better understand the constitutive nature of disability in the contexts of chronic illness and mental health. By doing so, we articulate the concept of socio-emotional disablism, which expands structural and psycho-emotional theories of disablism to account for the ways socio-material interactions trigger emotion-work—the communicative labor and fatigue that accompanies negotiating our own emotions as well as the emotions of others in disabling contexts—thus limiting relational and ideological engagement with disability. We conclude by discussing what the concept of socio-emotional disablism teaches us about how to build more inclusive social futures through deeper interpersonal engagement …

Learning From The First Citizen Science Association Virtual Conference, Abigail Hils, Skye Kelty, Angel Hjarding, Linda Silka, Alison Cawood

Faculty Research, Scholarly, and Creative Activity

The Citizen Science Association (CSA) is a member-driven organization that connects people with interest in community/citizen science (c*science) from a wide range of backgrounds, disciplines, and experiences. In response to the COVID-19 pandemic, the bi-annual CSA conference pivoted away from an in-person format to a virtual format. CitSciVirtual: Local, Global, Connected occurred throughout May 2021 and brought together more than 700 attendees from 36 countries. The conference prioritized interactive experiences for attendees, including 16 collaborative poster sessions featuring 240 virtual posters, 55 workshops to learn and practice new skills, and 7 social events. This paper summarizes the impacts of the …

Shifting Mediates Gendered Racial Microaggressions And Perceived Racism Among Asian American Women, Glenn Gamst, Christine Ma-Kellams, Lawrence S. Meyers, Leticia Arellano-Morales

Faculty Research, Scholarly, and Creative Activity

Introduction: Microaggressive attacks on Asian American women increased during the COVID-19 pandemic. The present study tested whether Asian American women's shifting, a coping strategy employed by some women of color to alter their self-presentation in response to perceived racism, mediated the association between gendered racial microaggressions and self-perceived subtle and blatant racism. Methods: A convenience sample of 253 Asian American adult women completed the gendered racial microaggressions scale for Asian American women (GRMSAAW), the Asian American women's shifting scale (AsAWSS), and the subtle and blatant racism scale for Asian American college students (SABR-A2). Results: Results from a structural equation model …

Employing The Houseless As Corporate Social Responsibility, Nicholas A. Smith, Larry R. Martinez, Shi (Tracy) Xu, Anna Mattila, Lisa Yixing Gao

Psychology Faculty Publications and Presentations

Purpose

Many hospitality organizations see the benefits of engaging in corporate social responsibility (CSR), which can take many forms. This study aims to examine one relatively unique form of CSR: hiring individuals experiencing houselessness. This research aimed to investigate the impact of hiring individuals experiencing houselessness on customers’ behavioral intentions, attitudes toward an organization and perceptions of CSR actions.

Design/methodology/approach

Across two experiments, this study investigated the impact of employing individuals experiencing houselessness on customers’ perceptions of the employee and organization using organizational legitimacy theory.

Findings

Results demonstrate that employees known to be houseless elicited more positive employee and organizational …

The Daily Association Between Affect And Alcohol Use: A Meta-Analysis Of Individual Participant Data, Jonas Dora, Marilyn Piccirillo, Kelly Arbeau, Stephen Armeli, Marc Auriacombe, Bruce Bartholow, Adriene M. Beltz, Shari M. Blumenstock, Cynthia Mohr, Multiple Additional Authors

Psychology Faculty Publications and Presentations

Influential psychological theories hypothesize that people consume alcohol in response to the experience of both negative and positive emotions. Despite two decades of daily diary and ecological momentary assessment research, it remains unclear whether people consume more alcohol on days they experience higher negative and positive affect in everyday life. In this preregistered meta-analysis, we synthesized the evidence for these daily associations between affect and alcohol use. We included individual participant data from 69 studies (N = 12,394), which used daily and momentary surveys to assess affect and the number of alcoholic drinks consumed. Results indicate that people are not …

Precision Medicine Approach To Alzheimer’S Disease: Rationale And Implications, Dale E. Bredesen, Kat Toups, Ann Hathaway, Deborha Gordon, Henrianna Chung, Cyrus Raji, Alan Boyd, Benjamin D. Hill, Sharon Hausman-Cohen, Mouna Attarha, Won Jong Chwa, Alexei Kurakin, Michael Jarrett

University Faculty and Staff Publications

The neurodegenerative disease field has enjoyed extremely limited success in the development of effective therapeutics. One potential reason is the lack of disease models that yield accurate predictions and optimal therapeutic targets. Standard clinical trials have pre-determined a single treatment modality, which may be unrelated to the primary drivers of neurodegeneration. Recent proof-of-concept clinical trials using a precision medicine approach suggest a new model of Alzheimer’s disease (AD) as a chronic innate encephalitis that creates a network insufficiency. Identifying and addressing the multiple potential contributors to cognitive decline for each patient may represent a more effective strategy. Here we review …

Felon Disenfranchisement: An Attack On Democracy In The United States, Katie Rodrigues

Student Publications and Presentations

No abstract provided.

Regulation, Targets And Functions Of Csk., Shudong Zhu, Hui Wang, Kamakshi Ranjan, Dianzheng Zhang

PCOM Scholarly Works

The Src family kinases (SFK) plays an important role in multiple signal transduction pathways. Aberrant activation of SFKs leads to diseases such as cancer, blood disorders, and bone pathologies. By phosphorylating and inactivating SFKs, the C-terminal Src kinase (CSK) serves as the key negative regulator of SFKs. Similar to Src, CSK is composed of SH3, SH2, and a catalytic kinase domain. However, while the Src kinase domain is intrinsically active, the CSK kinase domain is intrinsically inactive. Multiple lines of evidence indicate that CSK is involved in various physiological processes including DNA repair, permeability of intestinal epithelial cells (IECs), synaptic …

Incidence Of Lung Cancer Among Healthcare Workers In Hunan Province And Analysis Of Related Risk Factors., Jinzhang Xiao, Sishi Li, Shudong Zhu, Kamakshi Ranjan, Dianzheng Zhang

PCOM Scholarly Works

BACKGROUND: The prevalence of lung cancer, a major type of malignant tumor, has been increasing over the years greatly impacting the health of Chinese residents. This study investigates the epidemiological characteristics of lung cancer among healthcare workers in the Hunan Province, as well as the occupational risk factors.

METHODS: The data analyzed in this study was collected from the largest tumor hospital in the province: the Hunan Provincial Tumor Hospital affiliated with Central South University, School of Medicine. The data collected encompasses input collected between the years of 2004 to 2013 of the population of healthcare workers who were hospitalized …

Cost-Effectiveness Analysis Of Overground Robotic Training Versus Conventional Locomotor Training In People With Spinal Cord Injury, Daniel Pinto, Allen W. Heinemann, Shuo-Hsiu Chang, Susan Charlifue, Edelle C. Field-Fote, Catherine L. Furbish, Arun Jayaraman, Candy Tefertiller, Heather B. Taylor, Dustin D. French

Physical Therapy Faculty Research and Publications

Background

Few, if any estimates of cost-effectiveness for locomotor training strategies following spinal cord injury (SCI) are available. The purpose of this study was to estimate the cost-effectiveness of locomotor training strategies following spinal cord injury (overground robotic locomotor training versus conventional locomotor training) by injury status (complete versus incomplete) using a practice-based cohort.

Methods

A probabilistic cost-effectiveness analysis was conducted using a prospective, practice-based cohort from four participating Spinal Cord Injury Model System sites. Conventional locomotor training strategies (conventional training) were compared to overground robotic locomotor training (overground robotic training). Conventional locomotor training included treadmill-based training with body weight …

Patellofemoral Contact Forces And Knee Gait Mechanics 3 Months After Acl Reconstruction Are Associated With Cartilage Degradation 24 Months After Surgery, J. R. Williams, Kelsey Neal, Abdulmajeed Alfayyadh, Jacob J. Capin, Ashutosh Khandha, Kurt Manal, Lynn Snyder-Mackler, Thomas S. Buchanan

Physical Therapy Faculty Research and Publications

Objective: Evaluate patellofemoral cartilage health, as assessed by quantitative magnetic resonance imaging (qMRI) T₂ relaxation times, 24-months after ACL reconstruction (ACLR) and determine if they were associated with patellofemoral contact forces and knee mechanics during gait 3 months after surgery.

Design: Thirty individuals completed motion analysis during overground walking at a self-selected speed 3 months after ACLR. An EMG-driven neuromusculoskeletal model was used to determine muscle forces, which were then used in a previously described model to estimate patellofemoral contact forces. Biomechanical variables of interest included peak patellofemoral contact force, peak knee flexion angle and moment, and walking speed. …

Using Wearable Technology To Quantify Physical Activity Recovery: Secondary Report From The After (App-Facilitated Tele-Rehabilitation) Program For Covid-19 Survivors Randomized Study, Laura Churchill, Mary Morrow, Jacob J. Capin, Sarah Jolley, Kristine Hare, Samantha Mawhinney, Jennifer E. Stevens-Lapsley, Kristine M. Erlandson

Physical Therapy Faculty Research and Publications

Background: Knowledge on physical activity recovery after COVID-19 survival is limited. The AFTER (App-Facilitated Tele-Rehabilitation) program for COVID-19 survivors randomized participants, following hospital discharge, to either education and unstructured physical activity or a telerehabilitation program. Step count data were collected as a secondary outcome, and we found no significant differences in total step count trajectories between groups at 6 weeks. Further step count data were not analyzed.

Objective: The purpose of this analysis was to examine step count trajectories and correlates among all participants (combined into a single group) across the 12-week study period.

Methods: Linear mixed …

Seeing Themselves: White Preservice Teachers As Raced Individuals And As Members Of Their Future School Communities, Laurie Koth

Theses and Dissertations

Students of color, of poverty, with disabilities continue to slide into the school to prison pipeline (STPP.) Very often, an antecedent step is harsh exclusionary discipline that removes them from the classroom. Teachers, who play a major role in the decision whether to keep students in or eject them from the classroom and the school, are among multiple forces driving students toward or away from the STPP. A salient feature of the issue is that nearly 80% of teachers are white and their most vulnerable students are not. Rooted in critical race and care theories, and buttressed by self determination …

Evaluating System Usability, Workload Suitability, And User Experience Of Game-Based Virtual Reality In Spaceflight Education And Training, Lana Laskey, Joseph R. Keebler

Journal of Aviation/Aerospace Education & Research

Game-based instruction and immersive virtual reality are enhanced pedagogical methods beneficial in training environments involving complex disciplines, ranging from medical applications to construction engineering technology. This study investigated the use of game-based virtual reality (GBVR) when applied to the complex field of spaceflight education and training. As modern society places increasing demand on space-based amenities, the need for proficient satellite operators will also increase, requiring more accessible and advanced training options. Satellite ground control training scenarios, immersed in the GBVR environment, were developed and deployed to university student participants. Multiple validated scales were employed to measure the GBVR system regarding …

A Deep Bilstm Machine Learning Method For Flight Delay Prediction Classification, Desmond B. Bisandu, Irene Moulitsas

Journal of Aviation/Aerospace Education & Research

This paper proposes a classification approach for flight delays using Bidirectional Long Short-Term Memory (BiLSTM) and Long Short-Term Memory (LSTM) models. Flight delays are a major issue in the airline industry, causing inconvenience to passengers and financial losses to airlines. The BiLSTM and LSTM models, powerful deep learning techniques, have shown promising results in a classification task. In this study, we collected a dataset from the United States (US) Bureau of Transportation Statistics (BTS) of flight on-time performance information and used it to train and test the BiLSTM and LSTM models. We set three criteria for selecting highly important features …