Digital Commons Network^™

Flood Risk Prediction System For Irish River Networks, Fernando Adalberto Naatz Heringer, Hugo Rodrigues De Freitas

ICT

Flooding costs the Irish economy more than 300 million euros annually; however, the hydrometric and meteorological data needed to predict dangerous river rises is already collected and freely available. This project applies machine learning to a decade of hourly observations from OPW Station 09001 on the River Liffey at Leixlip and Met Eireann Casement Aerodrome to build a flood risk prediction system with a 72-hour forecast horizon.

A two-phase modelling pipeline was built following the CRISP-DM framework. Phase 1 compared LSTM and GRU recurrent neural networks on the task of forecasting the river level in metres 72 hours ahead. LSTM …

A Comparative Analysis Of Supervised Machine Learning Techniques For Predicting It Incident Resolution Time, Ammad Hussain, Hussnain Yaqoob

ICT

IT support teams are facing the persistent challenge in estimating how long an incident will take to resolve. The lack of reliable predictions, planning decisions around staffing, escalation and user communication are largely guesswork. This project set out to address that gap by building a machine learning solution capable of predicting incident resolution time from details available at the point a ticket is logged.

Using the UCI IT Incident Event Log dataset, which contains 141,712 records from a real service management system, the project followed the CRISP-DM framework across six phases from business understanding through to deployment. After thorough data …

A Comprehensive Study Of Sarima And Xgboost Models For Short-Term Traffic Volume Forecasting At A Melbourne Intersection (Victoria), Australia, Douglas Vinicius Dierings, Felipe Fontanive Marques

ICT

Traffic congestion is an increasing challenge in modern cities, impacting transportation efficiency and urban sustainability. This project focuses on short-term traffic volume forecasting, using 15-minute Sydney Coordinated Adaptive Traffic System (SCATS) data collected from the State of Victoria, Australia. The study compares two forecasting approaches: SARIMA, a statistical time-series model, and XGBoost, a decision-tree machine-learning model. Historical traffic data from 2022 to 2024 was analysed to identify traffic patterns, seasonal trends, and peak traffic periods. The models were evaluated for their effectiveness in handling urban traffic behaviour. The results of this study aim to support smarter traffic management, infrastructure planning, …

Identifying New Programme Development Viability, Grant Goodwin

ICT

Higher Education Institutions (HEIs) face the challenge of developing programmes that respond to changing labour market needs while providing graduates with relevant employment opportunities and ensuring institutional financial viability. Rapid technological and economic changes make it increasingly important for HEIs to use reliable data to identify emerging skills and workforce demands. This project explores the use of machine learning and nationally collected employment data to support evidence-based decision-making in higher education programme development. By analysing patterns and relationships within labour market data, machine learning models can help identify areas of growing demand and provide insights into the potential alignment between …

Seasonal Dublin Airport Passenger Arrivals Forecast For The Economy Sector Of Dublin Airport, Roberto Tavares De Oliveira Neto

ICT

Dublin Airport plays a significant role in Ireland’s tourism and commercial sectors, with passenger demand directly influencing retail, hospitality, car rental and other airport-related businesses. Accurate forecasting of passenger numbers can support these sectors in planning marketing activities, managing stock levels and making evidence-based operational decisions. This project develops a monthly forecasting framework for passenger arrivals at Dublin Airport using data from the Central Statistics Office (CSO). Building on previous exploratory data analysis, the study evaluates five time series forecasting approaches: ARIMA, SARIMA, Holt-Winters Triple Exponential Smoothing, TBATS, and an automated model comparison pipeline using PyCaret. The models are assessed …

Machine Learning For Early Obesity Risk Prediction, Giuseppe Cutugno

ICT

Obesity is a major global public health concern, with significant implications for individual health, healthcare systems, and wider economic outcomes. Early identification of obesity risk can support preventive strategies and encourage healthier lifestyle choices before health conditions become more severe. This project develops a machine learning framework for estimating obesity levels using data relating to individuals’ physical characteristics, eating habits, lifestyle behaviours, and demographic factors. Following the CRISP-DM framework, the project examines the structure and limitations of the dataset, including its mixture of objective measurements, survey-based variables, and synthetically generated observations. Two modelling approaches are considered, one including BMI as …

Predicting The Next Recession With Machine Learning, David Heffernan

ICT

Economic recessions can have significant consequences for businesses, households, financial markets, and policymakers, creating a need for improved understanding of the factors associated with economic downturns. This project analyses historical US economic and financial data to examine patterns associated with previous recessionary periods and assess whether machine learning can be used to identify recession risk. The study uses a two-tier analytical approach, combining a macroeconomic dataset covering 1960 to the present with a more detailed dataset from 1987 onwards that incorporates financial markets, house prices, and consumer confidence. Building on previous exploratory analysis, the project develops and evaluates three machine …

Understanding Host Behaviour And Property Review Characteristics Using New York City Airbnb Dataset, Anna Szkwara

ICT

This project applies strategic thinking and the CRISP-DM methodology to analyse Airbnb listings in New York City and identify patterns associated with host performance and market vulnerability. Airbnb has become a major global accommodation platform, creating opportunities for individual hosts while also increasing competition within the short-term rental market. The project focuses on whether data-driven analysis can identify factors that may influence host success and provide useful insights for hosts with less experience or expertise than larger hospitality organisations. The dataset contains 85 variables, many of which require careful evaluation due to redundancy, high cardinality, sensitivity, or limited relevance to …

Predicting Product Returns In Online Retail, Mihaela Cristina Stefanache

ICT

Product returns are a significant challenge for online retailers, contributing to additional operational costs and affecting inventory management and profitability. This project develops and evaluates three machine learning models to predict product returns for a UK online retailer, building on the data understanding and preparation completed in the previous CA2 capstone project. Following the CRISP-DM framework, the study extends the analysis through enhanced exploratory data analysis, feature engineering, model development, hyperparameter optimisation, cross-validation, and model interpretability. Logistic Regression, a Tuned Random Forest, and XGBoost are evaluated, with the Tuned Random Forest achieving the strongest overall performance, obtaining an F1 score …

The Impact Of Strategic Planning On Teachers' Perceptions Of Their Practice: A Mixed-Methods Study, Vivian Robert

Selected Full Text Dissertations, 2011-

Strategic planning is a vital endeavor for school districts across New York State. Often, however, significant human and financial capital is invested in these processes without investigating their impact beyond immediate student outcomes. While student success is the ultimate priority, little attention has been paid to the effect strategic planning has on the practitioners charged with implementing it, specifically teachers. This phenomenological mixed-method case study investigates how strategic planning influences teachers’ perceptions of their practice and identifies how districts can better engage educators to ensure the successful realization of district goals. In this study, district leaders participated in semi-structured interviews, …

Characterizing Cyber Intrusions In Critical Infrastructure Networks Using Discrete-Event Simulation, Lawrence M. Dilworth

Dissertations, Master's Theses and Master's Reports

Over the past two decades, cybersecurity compliance frameworks such as the North American Electric Reliability Corporation Critical Infrastructure Protection (CIP) have introduced prescriptive measures for protecting power system networks, emphasizing restricted access, segmentation, and minimizing routable exposure. While effective for baseline cyber hygiene, these approaches do not capture system-level risks or adversarial propagation across interconnected infrastructure. In contrast, Cyber-Informed Engineering (CIE), advanced by Idaho National Laboratory, embeds security in system design by considering threat vectors and physical constraints.

Despite CIP guidance, many deployments rely on IP-routable, bidirectional communication that enables handshaking, allowing adversaries to infer reachable targets. This work presents …

Modular Synthesis Of Conjugated Aromatic Systems Via Palladium-Catalyzed Cross-Coupling Reactions, Joden Russell Robinson

Dissertations, Master's Theses and Master's Reports

This thesis describes the development of a modular synthetic route to extended conjugated aromatic systems through iterative palladium-catalyzed Sonogashira crosscoupling reactions. The work was motivated by the challenge of preparing a discrete conjugated molecule in a controlled manner. The synthetic strategy used complementary protected alkyne functionalities that could be selectively activated. This enabled sequential deprotection and coupling reactions to predictably extend the molecular scaffold. Using this approach, a series of conjugated intermediates was prepared and exponentially extended. Spectroscopic characterization by NMR and mass spectrometry supported the structures of the isolated products and the success of the iterative elongation strategy. Overall, …

Development Of Stronger, More Extrudable 6xxx Series Alloys For Automotive Applications, Eli A. Harma

Dissertations, Master's Theses and Master's Reports

6xxx alloys are widely used in automotive extrusion structures for their high specific strength and formability. Advanced designs require greater formability and strength, creating an opportunity for high-strength, formable alloys. The 6xxx series forms β”-Mg₅Si₆ precipitates during age hardening and develops a fibrous microstructure during extrusion, both strengthening the alloy. Increasing Mg and Si to form more β” decreases formability. Thus, modifying texture can increase strength without reducing formability. Current alloys like 6005A add Mn and Cr to form dispersoids that inhibit recrystallization and promote strengthening textures; however, high Mn and Cr concentrations reduce formability. Replacing Mn …

Aqueous Iron Electrowinning: From Electrolyte Engineering To The Unexplored Potential Of Acetate Systems, Chodwell N. Verenga

Dissertations, Master's Theses and Master's Reports

Iron electrowinning and electrodeposition sit at the intersection of classical metallurgy and developing sustainable manufacturing. With the iron and steel industry responsible for 7 - 9% of global CO₂ emissions, low-temperature electrochemical pathways for iron production have garnered significant research interest. This review examines the fundamental electrochemical principles governing iron deposition, the thermodynamic constraints imposed by the iron-water system, and the kinetic challenges posed by the parasitic hydrogen evolution reaction (HER). The three key electrolyte systems are objectively evaluated: acidic sulfate and chloride electrolytes, alkaline suspension electrolytes and water-in-salt electrolytes. Their operating parameters, faradaic efficiencies, energy consumption and deposit characteristics …

First-Principles And Thermodynamic Modeling Of Hydrogen Storage In Mxenes, Yi Zhi Chu

Dissertations, Master's Theses and Master's Reports

Hydrogen storage is a critical component of the emerging hydrogen economy, playing a central role in enabling the global transition from fossil fuels to a sustainable, green energy system. With advances in materials research, increasing attention has been directed toward the development of promising hydrogen storage materials. Due to their diverse and advantageous physicochemical properties, MXenes have attracted significant interest in this regard. A fundamental understanding of the hydrogen interactions with the MXenes structure is crucial for explaining and predicting their hydrogen storage performance. In this work, first-principles density functional theory (DFT) combined with a revised thermodynamic model is employed …

Designing With Foresight: Premortem And Human-Centered Evaluation Of Emerging Technologies, Anusha Kannan

Dissertations, Master's Theses and Master's Reports

Artificial intelligence is increasingly embedded in safety-critical systems, including transportation, where people must make fast decisions under uncertainty and high risk. As AI takes on a larger role in supporting human judgment, researchers and designers must evaluate more than just its technical performance, they must ask whether people can understand the system and use it effectively in high-risk settings.

This dissertation presents two distinct evaluation methodologies. First, the Human-Centered Evaluation, examines how intelligent in-vehicle alert systems influence driver decision behavior across two experiments. In Experiment 1 (n=24) and Experiment 2 (n=26), participants completed a driving simulator study in which our …

Comparison Of Fixed Tilt And Single Axis Tracking Solar Photovoltaics During Winter, Isobel R. Bowker

Dissertations, Master's Theses and Master's Reports

As solar projects are built in northern climates, it is necessary to understand how snow cover impacts power generation. While there are validated models that predict snow shedding for fixed tilt projects, there is not one for single axis tracking systems. This report aims to characterize the differences between fixed tilt and single axis tracking systems under the same snow conditions. Generation data was collected over four winter months for fixed tilt and tracking systems at Michigan Technological University. Models were created for both systems using weather information from the site, in which losses were set to zero to create …

Design, Modeling, And Experimental Development Of Nanoscale Confinement Structures On Planar Silicon-Based Microelectrode Arrays For Single-Entity Electrochemical Sensing, Parinaz Eskandari

Dissertations, Master's Theses and Master's Reports

Electrochemical sensing is widely used for chemical and biological detection due to its high sensitivity, label-free operation, and compatibility with miniaturized electronic systems. However, conventional microelectrode platforms operate in an ensemble-averaged regime in which the measured current represents the collective response of many molecules interacting with the electrode surface. This ensemble averaging masks localized nanoscale electrochemical events and limits the ability to detect rare interactions, such as single molecules or nanoparticles. Achieving single-entity electrochemical detection therefore requires strategies that confine electrochemical reactions to nanoscale regions while maintaining compatibility with scalable planar microfabrication.

This dissertation investigates nanoscale electrochemical confinement on planar …

Experimental Rate Feedback Control Of A Model-Scale Hourglass-Shaped Heaving Point Absorber, James R. Halverson

Dissertations, Master's Theses and Master's Reports

Buoy geometry greatly affects a point absorber wave energy converter's dynamic response to waves. Finding the optimal buoy shape and control method remains an open research area focused on maximizing the conversion of wave kinetic energy into electricity. This work presents an experimental comparison of closed-loop energy extraction between a cylindrical and a truncated cone buoy, both with the same submerged volume, across various wave frequencies and amplitudes. To ensure a fair comparison, the optimal rate feedback gain is calculated for each buoy at each wave condition. Multiple metrics, including power output, capture width, and actuator force, are used to …

Numerical And Experimental Evaluation Of A Simple, Low-Energy Lunar Volatile Separation Technology, Eleanor L. Zimmermann

Dissertations, Master's Theses and Master's Reports

In-situ resource utilization (ISRU) is the practice of finding, extracting, and using the local resources found on extraterrestrial bodies, such as the Moon and Mars. ISRU enables in-situ production of mission consumables—such as rocket fuel and potable water—which will be critical for establishing a sustainable human presence on the Moon, a goal NASA intends to accomplish by 2028 with the Artemis missions. The separation and liquefaction of locally extracted volatiles—such as water, CO₂, and Methane—is an essential component in lunar surface sustainability, but there are very few technologies being developed for this purpose. This report introduces the Radiative …

The Excess Path Length Distribution: A Stochastic Model For Sample-Based Path Planners, Chaz B. Cornwall

Dissertations, Master's Theses and Master's Reports

Through random sampling, sample-based path planners enable autonomous agents to quickly find paths without human intervention. However, due to the paths' randomness, sample-based path planners currently require additional verification, partially nullifying agents' ability to act autonomously. I set out to characterize this uncertainty so humans know what to expect from these path planners and know how to alter the path planner to desired specifications. To ensure the results are theoretical as well as practical, I first create a stochastic model of path length uncertainty using the trade-off between sampling time and optimality. By leveraging this model, my proposed algorithm reduces …

Shm For Incipient Buckling Detection In Overloaded Structures Using Nonlinear System Identification Algorithms On Redundant Data Sets, Padmanabh Shridhar Desai

Dissertations, Master's Theses and Master's Reports

Structural health monitoring (SHM) detects and characterizes damage to predict failure, but failures such as buckling, which are not predicated on traditional damage modalities, are harder to detect. Direct load measurements are costly and difficult to implement (especially for dead loads) as they require copious numbers of sensors, which must be installed before loading is present. Vibration-based detection is a good alternative because it can infer global structural characteristics with relatively few sensors. This dissertation presents an SHM approach for detecting incipient buckling in structures under excessive loads based on non-linear models fit from measurements of small lateral vibrations due …

Mechanical Fractionation Of Wheat Middlings For Application In Pyrolysis And Animal Feed, Okwudili I. Obiakor

Dissertations, Master's Theses and Master's Reports

Wheat middlings are grain byproducts that are underused for bioenergy production despite having a high lignocellulose content. Its use as a feedstock for catalytic pyrolysis depends on effective and sustainable fractionation to enhance efficiency and product yield. This study tested mechanical pre-fractionation (sieving and air classification) to reduce ash content of certain fractions to < 1% for catalytic pyrolysis and enrich nutrient levels of other fractions for feed applications. Wheat middlings were separated into 11 fractions using screen sizes from 106 µm to 1651 µm, and the composition of each fraction was characterized (ash, protein, structural carbohydrates, lignin, starch, and simple sugars). For cyclone separation, a full factorial design was used to study the effect of air velocity (15,18, 20 m/s) and feed rate (33, 35, 38 kg/h) on the amount of material collected in the underflow and overflow, and the composition of these fractions was characterized. Sieving showed a pronounced segregation of ash content, from 4.5% to 6.5% in wheat middlings fractions, with 5.7% being the lowest for coarse fractions which has higher lignocellulose content, and 4.5% in the fine fraction (150-212 µm). The same defined enrichment was observed in all nutrients except for protein. Starch enrichment zone were defined in the fine fractions (212 µm and below), for structural carbohydrates and lignin in the coarse fractions (600 µm and above), and for ash in coarse fractions (1180-1651 µm). In contrast, protein content was not significantly different between various fractions (p value=0.787). The fiber rich fraction (> 600 µm), suitable for pyrolysis, which have a composite ash value of 6.0% can be sorted and mixed with intermediate fractions to reduce ash to 5.3% and the remaining fractions rich in digestible nutrients used for animal feed formulation. Compared to sieving, cyclone separations did not …

Measurement Of Wave Elevation Using Piezo-Electric Hydrophone, Ace D. Hobbs

Dissertations, Master's Theses and Master's Reports

Water surface waves exert a pressure oscillation that decreases with depth. Pressure measurements can be used to estimate wave elevation and frequency, which can then be used to (1) implement feedback for wave energy converter control strategies and (2) quantify the potential for converting waterbed pressure fluctuations into electrical energy. This report examines the use of an off-the-shelf piezo-electric hydrophone to estimate wave elevation from pressure data. Experiments were carried out in a wave tank with various wave amplitudes, frequencies, and hydrophone depths. Wave gauges, with a resolution of $\pm 0.5$ mm, measured the wave height above the hydrophone. The …

Experimental Characterization Of Photonic Crystal Based Invisibility Cloak Under Tm-Polarized Microwaves, Muhammad Danyal

Dissertations, Master's Theses and Master's Reports

Electromagnetic invisibility cloaks guide waves around an object so that the transmitted wavefront remains undisturbed. Most experimental microwave cloaking studies have focused on transverse electric (TE) polarization due to established measurement techniques. In this thesis, the experimental realization and characterization of a dielectric photonic crystal cloak operating under transverse magnetic (TM) polarization are presented. A measurement system operating in the X-band was developed to map the electric field distribution of the transmitted waves. Cloaking performance was first evaluated qualitatively by comparing numerical and experimental field distributions, where restoration of a flat wavefront indicated effective cloaking. Quantitative evaluation was performed by …

Computational And Ai Frameworks For Identifying Key Regulatory Genes And Their Target Genes In Plants And Humans, Md Khairul Islam

Dissertations, Master's Theses and Master's Reports

This dissertation presents computational and AI-driven frameworks for identifying key regulatory genes and their downstream targets across plant and human biological systems. Three studies address distinct challenges in genomic regulation using advanced machine learning and bioinformatics approaches.

The first study introduces DyGAF (Dynamic Gene Attention Focus), a dual-attention transformer framework that identifies and ranks disease-relevant biomarker genes by simultaneously modeling independent molecular responses and interdependent regulatory network behavior. Two attention models provide complementary perspectives on gene importance and are fused through a novel combination metric. Applied to COVID-19 nasopharyngeal swab profiles, the attention-weighted representations achieved 94.23% classification accuracy, high sensitivity, …

Changing Snow Hydrology In The Great Lakes And Resilience Through Adaptive Management, Aradea R. Hakim

Dissertations, Master's Theses and Master's Reports

A number of studies have found that global snow cover is generally decreasing, but a closer examination at regional scales shows greater complexity. In the Great Lakes basin, contrasting trends have been identified. While snow cover is generally decreasing basinwide, leeward (downwind) regions have experienced increases in snowfall and snow cover. Changes in snow dynamics are also associated with shifts in runoff timing and volume due to earlier snowmelt and changing precipitation patterns. Streamflow analyses at gauges across the Great Lakes basin indicate trends toward earlier peak runoff and longer runoff periods, although these patterns vary among watersheds and lake …

Machine Learning And Multi-Scale Optimization For Control And Energy Management In Connected And Automated Vehicle Propulsion Systems, Joshua D. Orlando

Dissertations, Master's Theses and Master's Reports

This dissertation presents a multi-scale optimization framework leveraging machine learning (ML) to enhance energy efficiency in connected and automated vehicle (CAV) propulsion systems. As transportation transitions toward hybridization and automation, the integration of vehicle-to-everything (V2X) connectivity and advanced control algorithms offers unprecedented opportunities for energy reduction. This research addresses three critical scales of vehicle energy management: multiple vehicle-level coordination, component-level powertrain dynamics, and real-time vehicle parameter estimation.

First, the research investigates the energy consumption characteristics of heterogeneous propulsion systems—ranging from internal combustion engines to battery electric vehicles across light- and heavy-duty sectors—on arterial roadways. Utilizing Particle Swarm Optimization (PSO) and …

Application Of Reactive Power Theory To Wave Energy Converters Under Multi-Frequency Excitation: Analysis And Experimentation, Mckenna R. Collins

Dissertations, Master's Theses and Master's Reports

There is abundant energy surging in the oceans across the globe. However, there have been technological challenges in extracting that energy for use. Wave energy converter (WEC) technology is being developed to find innovative ways to convert wave kinetic energy into electricity, but there are still efficiency obstacles that inhibit large-scale use. Focusing on heave-point absorber control theory, this thesis continues the work to improve the efficiency of WECs. This is done by leveraging a superposition of linear mass-spring-damper models to analyze a WEC under multi-frequency excitation and applying proportional-derivative gains to each linear model. However, to analyze this system, …

Open Source Tools For Ecological Research, Alex P. Riebe

Dissertations, Master's Theses and Master's Reports

This thesis presents the development of an open source wireless sensor network for hibernacula manipulation with an emphasis on accessibility and reproducibility. It addresses the design of the electrical hardware, guidance on antennas and RF implementation, and design of an application-specific communication protocol, all with the explicit goal of enabling ecologists and other conservationists to be able to manufacture, deploy, operate, and maintain the system for their research. The resulting sensor network designed in this thesis is to control the temperature inside bat hibernacula during the winter to study the relationship between temperature and bat mortality rate due to White …