Digital Commons Network^™

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 …

An Investigation Into The Role Of Machine Learning And Deep Learning Models As A Means Of Leveraging The Ever-Expanding Volume Of Astronomical Data To Automate Stellar Classification., Gerard Heraghty

ICT

This research investigates the use of machine learning and neural network models for automated stellar classification in large astronomical surveys, addressing challenges posed by the increasing volume of data. Using the MK scheme as the classification standard, the study focused on spectroscopic data and balanced the dataset using SMOTE techniques to handle class imbalances. Various models, including Random Forest, SVM, MLP, and CNN, were trained and compared for classifying MK main and sub-classes. CNN achieved the highest accuracy (93.86%) for main class classification, while SVM excelled at sub-class classification (63.23%) on balanced datasets. However, when tested on real-world SDSS data, …

Leveraging Uncertainty To Understand The Causes And Consequences Of Observed Climate Change, Alexander Reid Gottlieb

Dartmouth College Ph.D Dissertations

Detecting and attributing climate change to date is essential to understanding the harm global warming has caused and the threat it poses in the future. Yet detecting and attributing historical climate change and its attendant impacts is challenging owing to observational uncertainties in the historical record, the difficulty of isolating the forced response from unforced variability, and the methodological ambiguity on how to best map climate variability and change onto climate impacts. In this thesis, I use the study system of Northern Hemisphere snow to demonstrate how synthesizing a wide array of observational and climate model data with methods from …

An Analysis Of Crash Factors On Total Property Damage, Erica Peters

Honors Program Theses

Vehicle accidents occur across the world every day. Rain or shine, night or day accidents will always occur. While each accident has its own set of unique characteristics, a statistical data analysis can help identify important factors, trends and overall patterns that explain and predict the total property damage incurred after a car crash. As a result, the primary goal of this research is to employ a regression analysis to identify which environmental and collision variables are most important in explaining and predicting the dependent variable – total property damage that results from auto accidents.

This research will also explore …

The Copyright Work Of Authorship, Hemnes, Thomas

Santa Clara High Technology Law Journal

The “work of authorship” lies at the heart of the Copyright Act of 1976. It is what copyright protects. Central though the concept is, the Act never defines what a work of authorship might be. According to the Act, it can be perceived in tangible fixations, but is distinct from the fixations. The Act also provides examples: writings, drawings, computer programs, but never describes how these might be distinguished from their fixations. Unlike the Patent Act, where “metes and bounds” of a patentable invention are defined by a patent’s claims, the Copyright Act provides no guidance as to what the …

Reaching Past Rucho: A Constitutional Tort For Money Damages Against Individuals Who Draw Gerrymandered Districts, Turner, Sam

Santa Clara Law Review

The Supreme Court in Rucho v. Common Cause held that the issue of partisan gerrymandering—that is, the drawing of political districts in a way that favors the party in power— presented a political question that was outside the competency of the courts to solve, at least through constitutional law. This article argues that Rucho does not close the door to judicial action in the face of partisan gerrymandering but instead closes the door only to the remedy proposed in the case. As with practically all major constitutional cases in recent memory, the Rucho plaintiffs were seeking relief that was equitable …

Analysis Of Implementation Of Student Homeless Programs Per County School District In The State Of Georgia, Hannah Watson

MPA/MPP/MPFM Capstone Projects

This report investigates the implementation of federal and state policies concerning homeless students within school districts across Georgia. This analysis focuses on understanding how these policies are translated into practical strategies to meet the unique needs of homeless students, encompassing identification, enrollment, and provision of essential support services that result in proof of academic success. Through a combination of policy assessment techniques of current county school district procedures and services offered to students experiencing homelessness and a descriptive assessment of the contents of the current policies through the lens of public administration theories; we will develop a series of key …

The Impact Of Actuarial Valuation Reporting On State Pension Funds, Samantha Montgomery

MPA/MPP/MPFM Capstone Projects

The U.S. is currently experiencing a public pension funding crisis that has become the topic of news outlets around the country and political talking points for legislators. It is estimated at the end of fiscal year 2023 that the average funded ratio for a public pension program is 78.1%, which equates to total unfunded pension liabilities for state retirement systems being approximately $1.4 trillion (Pension Plan Funded Ratio Rankings 2023, 2024). Pension systems across the U.S. are managed by boards and pension agencies that are charged with fiduciary responsibility to manage the pension funds in a manner that focuses on …

Plant Root Hair Adhesion Mediated Through Variation In Sodium Ion Concentration, Natasha Mulenga

Honors Program Theses

This study investigates the effects of sodium ion (Na+) concentration on the adhesion of Brassica Rapa root hairs using atomic force microscopy (AFM). Plant root hair adhesion to soil particles plays a crucial role in anchoring plants and facilitating nutrient uptake. Previous research has shown that calcium ions (Ca2+) cross-link pectin molecules in the cell wall, increasing rigidity and reducing adhesion. As Na+ binds to pectin without cross-linking, we expected that root hairs exposed to sodium chloride (NaCl) solutions would exhibit longer adhesive interactions and similar binding forces compared to those exposed to calcium chloride (CaCl2) solutions of equal concentrations. …

Behind Closed Doors: How Some Civil Settlements May Harm The Public Good, Madison Donahue-Wolfe

University of San Francisco Law Review

No abstract provided.

Genetic Diversity Of Paxistima Canbyi A. Gray: Conserving A Rare Plant Species Endemic To The Eastern United States, Isaac Buabeng

Master’s Theses

Premise: The glacial cycles of the Pleistocene period likely had a profound impact on the genetic diversity and phylogeographic patterns of numerous plant taxa in North America. However, the genetic diversity of rare, limestone-endemic woody species has not been highlighted. This study assesses genetic diversity in Paxistima canbyi A. Gray (Canby’s mountain lover or rat stripper), a rare limestone endemic small woody plant.

Methods: We investigated the genetic diversity of P. canbyi using genomic Single Nucleotide Polymorphism (SNP) data from 165 individuals from 14 populations sampled from across the entire range distribution. We first; de novo assembled the SNP …