Digital Commons Network^™

Bayesian And Frequentist Approaches To Rescuing Disrupted Trials, Cornelia Ursula Kunz, Sergey Tarima, Gary L. Rosner, Richard Emsley, Madeline Bauer, Christopher Jennison, James L. Rosenberger, Nigel Stallard, Sarah Zohar, Nancy Flournoy

Biostatistics Faculty Publications

The COVID-19 pandemic impacted clinical trials in ways never expected. However, similar challenges should now be expected going forward. These challenges made us aware of statistical problems arising from other types of disruptions that had not previously captured the attention of the statistical community. This article describes some frequentist and Bayesian statistical tools that can be used with future disruptions and illuminates issues that could benefit from more statistical research. Disruptions may threaten a clinical trial’s validity. Here, we address two resultant challenges: (a) performing an unplanned analysis with options to stop and/or change the sample size; and (b) changes …

Individual-Network Based Predictions Of Microbial Interaction Signatures For Response To Biological Therapies In Ibd Patients, Federico Melograna, Padhmanand Sudhakar, Behnam Yousefi, Clara Caenepeel, Gwen Falony, Sara Vieira-Silva, Sreenikhitha Krishnamoorthy, David Fardo, Bram Verstockt, Jeroen Raes, Severine Vermeire, Kristel Van Steen

Biostatistics Faculty Publications

Inflammatory Bowel Disease (IBD), which includes Ulcerative Colitis (UC) and Crohn’s Disease (CD), is marked by dysbiosis of the gut microbiome. Despite therapeutic interventions with biological agents like Vedolizumab, Ustekinumab, and anti-TNF agents, the variability in clinical, histological, and molecular responses remains significant due to inter-individual and inter-population differences. This study introduces a novel approach using Individual Specific Networks (ISNs) derived from faecal microbial measurements of IBD patients across multiple cohorts. These ISNs, constructed from baseline and follow-up data post-treatment, successfully predict therapeutic outcomes based on endoscopic remission criteria. Our analysis revealed that ISNs characterised by core gut microbial families, …

Stigma And Efficacy Beliefs Regarding Opioid Use Disorder Treatment And Naloxone In Communities Participating In The Healing Communities Study Intervention, Nicky Lewis, Barry Eggleston, Redonna K. Chandler, Dawn Goddard-Eckrich, Jamie E. Luster, Dacia Beard, Emma Rodgers, Rouba A. Chahine, Philip M. Westgate, Shoshana N. Benjamin, Janae Holloway, Thomas Clarke, R. Craig Lefebvre, Michael D. Stein, Donald W. Helme, Jennifer Reynolds, Sharon L. Walsh, Darcy Freedman, Nabila El-Bassel, Kara Stephens, Anita Silwal, Michelle R. Lofwall, Janet E. Childerhose, Hilary L. Surratt, Brooke N. Crockett, Amy L. Farmer, James L. David, Laura Fanucchi, Judy Harness, Ben Wilburn, Kelli Bursey, Kristin Mattson, Sarah Mann, Rebecca D. Jackson, Aimee Shadwick, Katherine Calver, Deborah Chassler, Jennifer Kimball, Nancy Regan, Jeffrey H. Samet, Rachel Sword-Cruz, Michael D. Slater

Biostatistics Faculty Publications

Background The HEALing Communities Study (HCS) included health campaigns as part of a community-engaged intervention to reduce opioid-related overdose deaths in 67 highly impacted communities across Kentucky, Massachusetts, New York, and Ohio. Five campaigns were developed with community input to provide information on opioid use disorder (OUD) and overdose prevention, reduce stigma, and build demand for evidence-based practices (EBPs). An evaluation examined the recognition of campaign messages about naloxone and whether stigma and efficacy beliefs regarding OUD treatment and naloxone changed in HCS intervention communities.

Methods Data were collected through surveys offered on Facebook/Instagram to members of communities participating in …

Investigating The Effects Math Achievement Has On A Student’S Attitude Toward Physics, Katherine Weivoda

Graduate Research Papers

The purpose of this project was to examine the correlation between math achievement and both physics attitudes and specific topics of beginner level physics. This project was studied because of the common issue of students having negative attitudes toward physics. Causes for negative attitudes toward physics are typically researched by surveys. An online survey was given to students at the University of Northern Iowa. Questions about physics attitudes, physics topics attitudes, and math attitudes were asked along with chances for them to explain their thinking. A total of 72 students were categorized by math achievement by both the number of …

The Impact On Growth Of The Scientific And Engineering Practices Throughout The Semester In 9th Grade Earth Science, Mallory Wills-Howe

Graduate Research Papers

The goal of my Non Thesis Masters Project is the development of three instructional units that emphasize the Scientific and Engineering Practices (SEPs) and give students the opportunity to show growth in those SEPs. The three instructional units were implemented in a 9th grade Earth Science classroom over the course of the semester. In all three units students focused on the following SEPs: Asking questions, Analyzing and Interpreting Data, and Engaging in an Argument from Evidence. The content and Next Generation Science Standards that were addressed included: Unit One- Earths Changes addressing NGSS (HS-ESS2-1), Unit Two- Earths History addressing NGSS …

Gamification And Engagement In A 3rd Grade Classroom, Ellen Braet

Graduate Research Papers

Structural gamification is the addition of elements traditionally found in games into a classroom environment. This research examines the effects of gamification on student engagement in 3rd grade in a math classroom. In this mixed-methods research, a group of 3rd grade students were taught both gamified and non-gamified lessons. At the end of each lesson, the students were given surveys to assess their thoughts on both lesson formats and were observed for on-task and off-task behaviors. This research found that gamified lessons were more effective at improving engagement compared to non-gamified lessons. Future research on gamification in elementary classrooms was …

Evaluating The Impact Of Model-Evidence-Link Charts In Seventh-Grade Student’S Ability To Engage In Scientific Argumentation Over Plant Reproductive Structures, Josephine Libby

Graduate Research Papers

The MEL has been utilized in a variety of topics, but not plant reproductive structures. The resource would allow students to form connections between provided scientific text and provided models. The goal is to have students understand the text and connect it to the correct model over the model based on a common misconception.

The study also investigated whether the use of these models would gain student confidence in the area of scientific argumentation. Scientific argumentation is a scientific practice a part of the Next Generation Science Standards that have been adopted by the state of Iowa. The middle school …

Quality Inclusive Classrooms And Integrating The Project Approach With Children With Special Needs, Marie Elizabeth Helmrich

Graduate Research Papers

This literature review aims to identify what elements define quality preschool inclusion. The Inclusive Classroom Profile (ICP) is a measure discussed for quality improvement purposes. The challenges that arise with quality inclusion are also explored. The Project Approach (PA) is explained, including the benefits of the approach, along with the successes and challenges it presents when implementing it in an inclusive classroom. The conclusion of this literature review includes recommendations for educational policies and practices and a recommendation for future research regarding quality inclusion and the implementation of the PA.

Measurement: The Forgotten Unit: Introduction To Quantitative Data Acquisition In Ninth Grade Physical Science, James F. Duff

Graduate Research Papers

What if you have determined that the students you teach need foundational knowledge and skills that are not addressed in the Next Generation Science Standards (NGSS)? My Ninth Grade Physical Science class provides an introduction to the data intensive subjects of subsequent secondary science classes. Sound data requires accurate measurements. Students in my classes have struggled with layering the metric system of measurement on top of the Imperial system and the NGSS lacks a standard that directly addresses measurement with metric units.

I used the Backward Design process of Wiggins & McTighe (2005) to construct a unit centered on the …

Neural Correlates Of Audiovisual Narrative Speech Perception In Children And Adults On The Autism Spectrum: A Functional Magnetic Resonance Imaging Study, Lars A. Ross, Sophie Molholm, John S. Butler, Victor A. Del Bene, Tufikameni Brima, John J. Foxe

Articles

Autistic individuals show substantially reduced benefit from observing visual articulations during audiovisual speech perception, a multisensory integration deficit that is particularly relevant to social communication. This has mostly been studied using simple syllabic or word-level stimuli and it remains unclear how altered lower-level multisensory integration translates to the processing of more complex natural multisensory stimulus environments in autism. Here, functional neuroimaging was used to examine neural correlates of audiovisual gain (AV-gain) in 41 autistic individuals to those of 41 age-matched non-autistic controls when presented with a complex audiovisual narrative. Participants were presented with continuous narration of a story in auditory-alone, …

Advanced Network And Graph Theoretical Approaches To Urban Traffic Control And Optimization, Peirong Wang

Civil Engineering Dissertations - Archive

This article-based dissertation consists of four complete manuscripts related to using proposed advanced network and graph theoretical approaches to optimize traffic control. Manuscript One addresses the integration of Connected Automated Vehicles (CAVs) and human-driven vehicles in heterogeneous traffic networks. Traditional models assume uniform driving behaviors, which are now inadequate. To address this, an integer programming formulation for a vehicle-oriented traffic dynamics model in space-time networks is introduced. This model distinguishes between individual vehicles and their unique driving behaviors, capturing diverse attributes of heterogeneous traffic flow. The manuscript demonstrates how the model can estimate the propagation of backward shockwaves for scheduled …

Experimental And Numerical Analyses Of Percussion Driven Earth Anchors Under Monotonic Pullout Tests, Mehran Azizian

Civil Engineering Dissertations - Archive

This dissertation provides a thorough investigation into the behavior and performance of Percussion-Driven Earth Anchors (PDEAs) during monotonic pullout tests. PDEAs are becoming more commonly utilized in geotechnical engineering because of their effectiveness in installation, ability to bear loads immediately, and minimal disruption to the environment. Despite their advantages, a detailed understanding of their pullout capacity and failure mechanisms remains limited.

The research encompasses both experimental and numerical analyses to bridge this knowledge gap. Experimental tests were conducted using Digital Image Correlation (DIC) techniques to obtain precise measurements of soil and anchor interaction and figure out the failure mechanism. Photogrammetry …

Analysing Natural Language Processing Techniques: A Comparative Study Of Nltk, Spacy, Bert, And Distilbert On Customer Query Datasets., Patrizia De Camillis

ICT

This study examines the role of sentiment analysis in customer queries, emphasising its impact on brand perception and the risks of poor query management. It compares the performance of NLP models—NLTK, spaCy, BERT, and DistilBERT—on customer query and feedback data. The findings show that BERT and DistilBERT produce similar results, often categorising queries as neutral, indicating their strength in handling diverse sentiments. NLTK and spaCy also share performance patterns. The research offers insights into the capabilities and limitations of these models in sentiment analysis.

Evaluating The Performance Of Different Long Short-Term Memory Networks (Lstm’S) On Financial Timeseries Data Using Mean Squared Error In Order To Identify The Optimum Lstm Variant For Regression Performance On Financial Timeseries Data., Patrick O’ Connor

ICT

This study explores the use of Long Short Term Memory (LSTM) networks, a variant of Recurrent Neural Networks (RNNs), in the context of financial forecasting, specifically oil price prediction. The research follows the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology and tests six different LSTM variants. The models are evaluated based on Mean Squared Error (MSE), aiming to determine the optimal parameter settings for each LSTM type. Among the variants tested, the Gated Recurrent Unit (GRU) emerged as the highest performer, achieving an MSE of 0.100. This was surprising, as simpler variants outperformed more complex ones, suggesting that simpler …

Ml Predictive Model For Earthquakes Integrating Mass, Distance, Gravity, And Magnitude., Aadarsh Kushwaha

ICT

This research investigates the application of machine learning regression models to improve earthquake prediction by integrating geophysical and astronomical factors such as Earth-Moon gravitational forces, their varying distances, and localized gravity fluctuations. Using data from 2011 to 2024, sourced from the US Geological Survey (USGS) and web scraping, the study tested models across four dataset proportions (25%, 50%, 80%, and 100%) with a 70:30 train-test split. The XGBRegressor model emerged as the best performer, achieving an R² score of 0.8706 on training data and 0.8632 on test data, along with a Mean Squared Error (MSE) of 0.1114 and Mean Absolute …

Maize Crop Pests And Diseases Classification Using Hybrid Models., Diana Flora Namaemba

ICT

This research focuses on improving the detection and classification of maize crop pests and diseases to enhance agricultural yield and food security. A dataset comprising 5389 images of maize conditions (healthy, pest-affected, and disease-affected) across seven classes was used. The images underwent preprocessing, including resizing to 299x299, class balancing using augmentation techniques, and noise reduction with Gaussian filtering.

Feature extraction utilised EfficientNetB0 and InceptionV3 architectures, with PCA employed for feature selection. Classification was conducted using a Support Vector Machine (SVM) with a One-vs-One strategy, alongside a baseline 2D CNN model. Data engineering included label encoding, standardisation, and an 80:10:10 train-test-validation …

Statistical And Machine Learning Techniques For Predicting Solar Power Generation In A Microgrid., Conor Dillon

ICT

This study investigates statistical and machine learning models for forecasting solar power generation in microgrids, focusing on the solar installation at Powell-Focht Bioengineering Hall, UC San Diego. Accurate predictions are critical due to the variability of solar energy, aiming to optimise microgrid operations and solar power efficiency. The research compares the performance of SARIMAX, LSTM, Random Forest, and ANN models using meteorological and solar power time series data. It finds that current meteorological inputs, especially solar radiation, enhance short-term forecasting accuracy over reliance on historical patterns.

The Random Forest Auto Regressor (RFAR) outperformed other models in 10-day-ahead solar power forecasting, …

Supply Chain Optimisation With Machine Learning And Neural Networks: Applications To Demand Planning, Supply Planning, And Inventory Planning., Laurence Cully

ICT

This thesis explores the impact of machine learning (ML) on supply chain planning, particularly in demand forecasting, supply planning, and inventory optimisation. By analysing literature on supply chain management, data flow, and the intersection of ML and competitive advantage, the author contextualises the research within a globalised market's demands. Case studies, interviews with industry professionals, and raw data collection provide empirical support for evaluating the research objectives and documenting the integration of ML in supply chain processes.

The findings reveal that optimised ML models, particularly those using model stacking (autoregressors, GRUs, and Random Forests), significantly outperform traditional demand forecasting methods, …

Random Walk Methods For Geometry Representation Agnostic Transport, Dario R. Seyb

Dartmouth College Ph.D Dissertations

In computer graphics, we use geometry representations to model a wide range of virtual scenes—from the fantastical worlds shown in animated movies to intricate mechanical parts.
These representations provide the context for transport problems—light transport is used to produce images of virtual scenes and diffusive transport to simulate distributions of quantities like heat.

There are many types of representations each with their own advantages.
For example, explicit ones make it easy to directly manipulate surfaces, while implicit representations allow for intuitive modeling by non-technical users and straightforward integration into machine learning systems.
Unfortunately, many algorithms that work on these digital …

Exploring Group Behavior Through Evolutionary Game Theory: Disease Dynamics, Cooperation, And Reputation, Alina Glaubitz

Dartmouth College Ph.D Dissertations

Cooperation is fundamental in the natural world and crucial to modern society, particularly in managing public health crises such as infectious diseases. This thesis investigates the evolutionary dynamics of cooperation, focusing on its role in com- bating infectious diseases, exemplified by the recent COVID-19 pandemic. We first examine the social dilemma posed by non-pharmaceutical interventions, such as social distancing and face covering. We integrate these interventions into models of disease dynamics, revealing an oscillatory tragedy of the commons: as infection rates fall, public adherence to social distancing wanes, leading to subsequent infection surges. Expanding the model to include less effective …

Halide-Assisted Growth Of Transition Metal Dichalcogenides, Vinaayak Sivam Balasubramaniam

Dissertations, Master's Theses and Master's Reports

Monolayers of transition metal dichalcogenides (TMDCs) have attracted significant attention as the rare two-dimensional (2D) semiconducting materials with a direct energy band gap. Chemical vapour deposition (CVD) is one of the scalable techniques to grow atomically thin TMDC monolayers in high quality, but it requires high growth temperature. Herein we report the growth of MoS2, WS2 and MoSe2 by a one-step halide-assisted CVD method using NaCl and KCl as the catalysts. These halides could reduce the growth temperature of TMDCS by reacting with the precursors (TMDC powders) to form volatile intermediate compounds as the growth species. We use optical microscopy, …

Response Of Open Volcanic Systems To Static Stress Changes, Aimee Zimmerman

Dissertations, Master's Theses and Master's Reports

Large earthquakes of magnitude 7.0 or greater can alter volcanic emissions and even trigger eruptions at nearby volcanoes. The stress changes imparted by large earthquakes can cause otherwise stable conditions within the magmatic system to be disturbed, potentially inducing volcanic activity. The extent of the triggering potential is still unknown due to insufficient understanding of the mechanisms involved. We model static stress changes for 174 earthquake-volcano pairs from 2000-2023 at volcanoes within 500 km, computing changes in both the normal stress perpendicular to the direction of horizontal maximum stress and dilatational strain. We then use thermal satellite data to measure …

Statically Controlled Synchronized Lane Architectures, Scott K. Pomerville

Dissertations, Master's Theses and Master's Reports

Modern superscalar processors dominate the field of computing. While dynamic execution allows for versatility in code, these processors are complex. Statically scheduled code has historically enabled simpler processor designs, but static scheduling cannot account for variables that are unknown at compile time. Furthermore, static scheduling has many inefficiencies, such as the need to insert a large number of nops for code in traditional Very Long Instruction Word (VLIW) processors. In this dissertation, we explore a novel architectural approach for statically scheduled code by breaking the code into several synchronous instruction streams. By representing code in a fundamentally new way, we …

Exploring Consequences Of Statutory Law Through Lightweight Modeling, Joshua T. Alele-Beals

Dissertations, Master's Theses and Master's Reports

The complexity of statutory legislation often leads to confusion and disagreement in interpretation, even among legal experts, leaving ordinary citizens disempowered. While technology offers potential solutions, it can unintentionally deepen the divide between citizens and the law, contributing to a sense of alienation. Our work aims to address this gap by proposing an alternative application of technology: a digital sandbox that empowers legislators, lawyers, judges, and citizens to collaboratively define and explore the consequences of legislation.

We employ a “lightweight” computational modeling approach, using automated analysis to uncover hidden assumptions and potential unintended outcomes. Our case study focuses on expungement …

A Physicochemical Characterization Of Protein Aggregates: Influence Of Lipid Membranes And Surfaces, Connor J. Hall

Dissertations, Master's Theses and Master's Reports

Protein aggregation is a hallmark of neurodegenerative diseases (NDs). Although there has been a greater emphasis in the past decade or more on the correlation between the physicochemical characteristics of aggregates and their cellular toxicity, it is still unclear what specific features of an aggregate render it toxic. To better understand that, there has been a renewed focus on the characterization of unique biochemical and biophysical properties of protein aggregates. Elucidation of these unique characteristics can help to better understand disease pathogenesis in vivo. Furthermore, the lipid-rich nature of the brain sets it apart from all other organs, and numerous …

Enhancing Students’ User Experience With A Code Critiquer, Laura E. Albrant

Dissertations, Master's Theses and Master's Reports

This thesis explores the role of human factors in the realm of code critiquers and students’ experiences with them. Across three studies, the work utilized Design Thinking to improve the user experience of WebTA for introductory engineering students learning MATLAB. The first two studies gathered observational and interview data to empathize, define, and ideate a new user interface (UI). Said UI was prototyped and then tested with the third study. Overall, the surveys’ data suggests that most students found the new design to be more appealing, useful, and purposeful; however, there is still plenty of room for improvement. Additionally, analysis …

Enhancing Resilience Of Critical Infrastructural Systems Against Severe Weather Conditions, Miaomiao Li

Dissertations, Master's Theses and Master's Reports

As climate change intensifies natural hazards and extreme weather events, critical infrastructure systems like power networks and pavements are increasingly vulnerable to damage. While significant progress has been made in studying individual hazards for specific systems, there remains a need for comprehensive frameworks addressing parametrized fragility for key components in critical infrastructure and their application in system risk and resilience evaluation. This dissertation investigates the failure mechanisms and fragility of power systems and pavements under extreme conditions, including winter storms, hurricanes, heatwaves, and flooding. Using structural analysis, numerical modeling, and statistical methods, this work evaluates system vulnerabilities, develops fragility models, …

Constructing The 4th Hawc Catalog Of Very-High-Energy Sources Using An Automated Likelihood Pipeline Search, Samuel J. Groetsch

Dissertations, Master's Theses and Master's Reports

In this thesis, I present an analysis pipeline that allows one to search for and identify sources of very-high-energy (VHE) γ-ray emission in sky maps created from data collected with the High Altitude Water-Cherenkov (HAWC) Observatory. The HAWC Observatory is located at about 4100 m elevation just below Pico de Orizaba, the highest mountain in Mexico also known as Citlaltepetl (from the Nahuatl words citlal(in) = star, and tepetl = mountain). The observatory is an array of several hundred detector stations that measure signals from particle showers, so-called extensive air showers (EAS), that are initiated in interactions of primary cosmic …

Governing Dynamic Watersheds Under Static Institutions – Examining How Local Governments Respond To Floods And Droughts In The United States, Jonathon R. Loos

Dartmouth College Ph.D Dissertations

The U.S. has seen a shift towards decentralized watershed governance in recent decades that has increased the delegation of management responsibilities to local governments and community organizations. This shift has precipitated the emergence of multilevel watershed governance systems (e.g. national, state, regional, local management levels) that are hypothesized to be more adaptive and responsive to local needs. However, multilevel governance systems risk complicating and overburdening the role of local governments within watershed management, and little is known about how local governments address socio-ecological change within multilevel institutions. Working within several U.S. watershed geographies, this dissertation seeks to interrogate theories of …

Entropy-Based Spatial Heterogeneity Analysis In Pathological Images For Diagnostic Applications, Rahul Suresh, Thi Nguyet Que Nguyen, Pascaline Bouzy, Nicholas Stone, Karin Jirstrom, Arman Rahman, William Gallagher, Aidan D. Meade

Conference papers

Clinical pathological diagnosis and prognosis for cancer is often confounded by spatial tissue heterogeneity. This study investigates the utility of entropy as a robust quantitative metric of spatial disorder within Fourier Transform Infrared (FTIR) chemical images of breast cancer tissue. The use of entropy is grounded in its capacity to encapsulate the complexities of pixel-wise spectral intensity distributions, thus providing a detailed assessment of the spatial variations in biochemistry within tissue samples. Here we explore the use of Shannon's entropy as a single image-based metric of spectral biochemical heterogeneity within FTIR chemical images of breast cancer tissue. This metric was …