Digital Commons Network^™

Assessment Of The Feasibility Of Utilizing Plastic Waste In Infrastructure To Promote Circular Economy In Bangladesh, Md Lutfor Rahman

Civil Engineering Dissertations - Archive

This study presents a comprehensive assessment of municipal solid waste (MSW) and plastic waste characterization across Bangladesh, with a focus on circular economy opportunities and the long-term economic viability of plastic road. Using on-site waste characterization data for 13 major cities from four geographic zones of Bangladesh —Northwest, Northeast, Southwest, and Southeast—the research identifies food waste as the dominant component of the national waste stream (67.3%), followed by plastic (12.4%), textiles (7.8%), and paper (4.7%). Among plastics, low-density polyethylene (LDPE) overwhelmingly dominates (89.7%), highlighting the continued prevalence of single-use flexible plastics.

A life cycle cost analysis (LCCA) was conducted comparing …

Implementation And Performance Evaluation Of Plastic Road Mix Design In Parking Lot, Ishraq Faruk

Civil Engineering Dissertations - Archive

The uncontrolled proliferation of plastic waste has emerged as a global environmental crisis, with high-density polyethylene (HDPE) constituting a substantial fraction of the waste stream due to its extensive use in packaging and its resistance to degradation. In parallel, asphalt pavements particularly in urban infrastructure such as parking lots experience accelerated deterioration from raveling, rutting, and cracking as a result of thermal cycling, repetitive loading, and inadequate drainage. These converging challenges present an opportunity for innovative, circular-economy-based sustainable pavement technologies that simultaneously improve surface durability and mitigate plastic waste accumulation.

This research investigates the full-scale implementation and monitoring of HDPE …

Transforming Lives For Eternity: 2024-2025 Year End Summary, Southern Adventist University

Strategic Plans

Southern Adventist University's Strategic Plan Year End Summary for the year 2024-2025.

Deep Learning For Irish Garden Bird Identification: Exploring The Role Of Cnn-Lstm In Video-Based Recognition, Antonina Dolynenko

ICT

Bird populations are widely used as indicators of ecosystem health, but traditional monitoring based on manual observation is labour-intensive and difficult to scale. Recent advances in deep learning and low-cost edge hardware offer new opportunities for automated, real-time bird identification in gardens and other local habitats. This thesis investigates whether video-based deep learning models can reliably classify common Irish garden birds from short motion-triggered clips and how temporal modelling compares to image-based models.

A primary dataset of 20-second clips was collected in a private garden in Ireland using a Raspberry Pi with a high-resolution camera and a YOLO-based trigger to …

Premier League Results Predictions, Laura Consuegra

ICT

This project applies machine learning techniques to predict outcomes in the English Premier League, one of the most prestigious and widely followed football competitions worldwide. By analysing historical and real-time match data, including performance metrics such as goals scored, shots on target, and cards received, predictive models are developed to forecast match results with higher accuracy. The study evaluates the effectiveness of these models and explores the influence of key statistical features on team performance. The findings aim to provide strategic insights for fans, bookmakers, and coaching staff, supporting performance evaluation, tactical decision-making, and a deeper understanding of the factors …

Clustering And Predictive Modelling Of Cryptocurrencies: An Empirical Study Of Lead–Lag Dynamics And Forecasting Performance., Viktor Varga

ICT

Cryptocurrency markets pose analytical challenges due to their volatility, non-stationarity, and complex interdependencies. Traditional econometric models have struggled with these features, while machine learning models often face overfitting and limited interpretability. This thesis has developed an integrated framework combining clustering, lead–lag detection, and predictive modelling to examine whether structural and temporal dependencies in cryptocurrency markets can improve forecasting performance.

Hourly OHLCV data for a selection of liquid cryptocurrencies from the Binance API over a thirteen-month period has been analysed. Dynamic Time Warping (DTW) has been used to identify asset clusters, while cross-correlation, Granger causality, and Vector Autoregression (VAR) have been …

Understanding Model Behaviour And Interpretability In Time Series Forecasting: A Deep Dive Into Lstm And Gru With Xai Techniques., Federico Ariton

ICT

Time-series forecasting is widely used in data analytics, yet the interpretability of deep learning models remains a key challenge. This study compares Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models using a dual interpretability framework that combines attention mechanisms and SHAP analysis. Two feature sets were evaluated across assets with different volatility regimes: Solana and Shiba Inu (high volatility), Bitcoin (moderate volatility), and Apple (low volatility). Results show that autoregressive features achieved the lowest forecasting errors, while volatility- and momentum-based indicators provided stronger interpretability. GRU performed best in moderate-volatility conditions, whereas LSTM demonstrated more consistent performance across varying …

Traditional Vs Deep Learning Approaches For Efficient Electricity Consumption Prediction., Lucas Sant’Ana

ICT

Accurate forecasting of electricity demand is critical for reliable energy planning, resource allocation, and policy design. Traditional statistical models, such as ARIMA, SARIMA, and ARIMAX, have been widely applied but remain constrained by linear assumptions, limited temporal memory, and inflexibility in handling multiple exogenous drivers. In this study, we systematically compare these approaches with multivariate Long Short-Term Memory (LSTM) networks, which are capable of capturing nonlinear dependencies, long-term temporal dynamics, and multivariate interactions. Historical electricity consumption data were combined with weather variables, including temperature, wind speed, and rainfall, and pre-processed through cleaning, scaling, and temporal alignment. Statistical baselines and deep …

Credit Card Fraud Detection., Sonia Ndonga

ICT

This capstone project applies machine learning to detect credit card fraud, addressing a critical financial threat to banks and payment providers. Using an anonymised dataset of 284,807 transactions, which is highly imbalanced with only 0.172% fraudulent cases, three models—Logistic Regression, Random Forest, and Gradient Boosting—were developed and evaluated. The pipeline incorporates data preprocessing, feature engineering, hyperparameter tuning, cross-validation, and interpretability analysis using SHAP values, SHAPASH, and permutation importance. Random Forest achieved the highest performance with an ROC AUC of 0.97 and Average Precision of 0.66. The study also considers fairness, threshold optimisation, and practical deployment strategies, providing a robust automated …

Enhancing Fake News And Rumor Detection Using Metadata, Context, And User Interaction With A Hybrid Gcn-Transformer., Lucas Romulo Zuin Gigli

ICT

The increasing influence of social media has significantly impacted how news spreads among users, making the detection of misinformation, including fake news and rumours, a critical task. Previous research has explored content-based and metadata-driven approaches, while recent advancements have leveraged Graph Neural Networks (GNNs) such as GCNs, GATs, and SAGE to analyse user interactions and propagation patterns. This thesis investigates the effectiveness of a Hybrid Graph-Transformer Convolutional Neural Network (GCN-Transformer) for fake news detection, combining graph-based learning with Transformer attention mechanisms. Through in-depth data analysis and advanced detection techniques, this model aims to enhance predictive performance and mitigate the spread …

Analysis Of Customer Churn In The Banking Sector And Its Mitigating Factors., Fiachra O Callaghan

ICT

This project investigates customer churn within the banking sector, analysing a dataset of 10,000 customers to identify patterns and potential drivers of attrition. By leveraging data analytics and visualization techniques, we aim to understand the demographic and behavioural factors influencing customer decisions to leave for competing banks. The study applies the CRISP-DM framework to structure the analysis, enabling iterative refinement of objectives and hypotheses as insights emerge. Findings indicate that certain customer segments, such as those aged 40–60, are more prone to churn, highlighting opportunities for targeted retention strategies, including fee reductions and tailored loyalty programmes. By integrating academic research …

Credit Card Default Prediction Using Machine Learning., Yassine Zohair

ICT

This project investigates the use of machine learning to predict credit card payment defaults, aiming to help banks and credit card companies mitigate financial losses. Using historical customer data, including demographics, income, education, and previous payment behaviour, three machine learning algorithms were implemented to forecast the likelihood of default. Techniques such as cross-validation, hyperparameter tuning, and SHAPASH were applied to improve model performance and interpretability. Accurate prediction of potential defaulters enables financial institutions to take proactive measures, such as adjusting credit limits or providing targeted financial guidance, thereby enhancing risk management and customer retention.

Leaf Classification Using Convolutional Neural Networks And Vision Transformers, Louis Wilkie

ICT

This study investigates deep learning techniques for plant image classification using four publicly available datasets: Folio, Flavia, Swedish, and Sugarcane. Three modelling approaches—Convolutional Neural Networks (CNN), Vision Transformers (ViT), and hybrid CNN-ViT ensembles—are evaluated. Models leverage PyTorch implementations and pretrained weights from the Hugging Face hub, with hyperparameter tuning applied to optimize accuracy. Transfer learning with pretrained ViT models achieved the highest performance, attaining 100% accuracy on Folio, Flavia, and Swedish datasets, and 94.07% on Sugarcane. CNN and CNN-ViT models showed comparable results, but pretrained CNN-ViTs were more resource-efficient. The findings highlight the importance of evaluating multiple pretrained architectures to …

Enhancing Uk Electricity Price Forecasting Using Deep Learning., Stephen Cooke

ICT

Accurate short-term electricity price forecasting (EPF) is crucial for efficient operation of the UK’s multi-layered power market, impacting generators, traders, the ESO, and policymakers. Prices are highly volatile and non-linear due to renewables, demand fluctuations, and market coupling across Day-Ahead, Intraday, and Balancing Mechanism venues. Traditional statistical models often fail under such dynamics, while machine learning and deep learning approaches—particularly LSTM, GRU, and hybrid architectures—effectively capture temporal dependencies and exogenous drivers. Empirical evidence shows that these models outperform classical baselines, enabling more accurate scheduling, risk management, and financial savings.

Evaluating The Effectiveness Of Variational Mode Decomposition(Vmd)-Enhanced Hybrid Models For Forecasting Volatile Price And Indicator Time Series., Danny Allen

ICT

Forecasting volatile time series, such as commodity prices and market volatility indices, is critical for financial and operational decision-making. This study evaluates the impact of Variational Mode Decomposition (VMD) on hybrid forecasting models combining ARIMA, GARCH, and LSTM. Using gold, platinum, and VIX datasets, models were assessed via MAE, RMSE, and computational time. Results show that VMD improves ARIMA forecasts, and achieves optimal performance when combined with ARIMA-LSTM using performance weighting reducing MAE by 0.62-3.81 and reducing RMSE by 1.48-6.20. Conversely, VMD integration with ARIMA-GARCH and LSTM decreased accuracy. While VMD enhances predictive performance, it increases computational time substantially, increasing …

Exploring The Determinants Of Life Expectancy At Birth: Predicting And Forecasting Global Health Trends Using Statistical, Machine Learning, And Deep Learning Models., Emma Rath

ICT

Accurate life expectancy forecasting is essential for health policy planning, yet research comparing statistical, machine learning, and deep learning approaches under real-world constraints remains limited. This study evaluates ARIMA/ARIMAX, tree-based, and neural network models using Irish and global datasets, considering small samples, missing data, and COVID-19 shocks. ARIMAX with lagged socioeconomic variables outperformed LSTM and other ML/DL methods. Income-based stratification improved predictive accuracy and interpretability, with SHAP analysis highlighting GDP per capita for developed countries and school enrolment and trade indicators for developing contexts. Results provide practical guidance for policymakers and establish limits for model complexity under constrained health data.

Improving The Completeness Of Food Composition Databases Using Predictive Analysis., Carla Arenhart

ICT

This study investigates the use of machine learning regression models to impute missing micronutrient values in Food Composition Databases (FCDBs), focusing on the FAO/INFOODS dataset. A cascading prediction methodology leverages nutrient interdependencies to systematically estimate missing values. Four models—Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machines (GBM), and Deep Neural Networks (DNN)—were evaluated using MAE, MSE, RMSE, and R². RF and GBM achieved the highest predictive accuracy for protein, phosphorus, calcium, and magnesium, demonstrating that ML-based predictive analytics can provide a more reliable alternative to traditional imputation methods. These findings support improved dietary assessments, nutritional research, and data-driven …

Test Stand Development: An Analysis Of Excavation Forces Within A Simulated Lunar Environment, Faith D. Gaertner

Dissertations, Master's Theses and Master's Reports

Space exploration has rapidly developed in recent years as scientists look to the lunar surface for water ice in permanently shadowed regions. The excavation and decomposition of water outside of Earth’s atmosphere could provide in-situ resources for propellant manufacturing and sustaining life. However, without informed design parameters for lunar exploration and infrastructure development, time will be wasted on systems unfit for the harsh environment. Testing facilities that simulate the environmental conditions are required to better understand the forces these complex systems may face while extraterrestrial.

The force test stand is a unique mechanism that collects tensile and compressive force data …

Near-Infrared Hemicyanine And Bodipy Sensors For Monitoring Mitochondrial Viscosity In Live Cancer Cells, Drosophila Melanogaster Larvae, And Adpkd Kidney Tissue, Joseph Peters

Dissertations, Master's Theses and Master's Reports

Two near-infrared fluorescent sensors, sensor A and sensor B, were synthesized and characterized. Sensor A was a hemicyanine derived structure, and sensor B was a BODIPY derived sensor. The sensors were tested to measure their ability to monitor viscosity, and sensor A was used to monitor viscosity in live cells. Both sensors were found to be effective in monitoring the viscosity level through optical measurements with glycerol. Sensor A was found to be successful in monitoring mitochondrial viscosity in HeLa MBA-MD-453 and MDA-MB-231 cell lines. It was also successful in monitoring viscosity in Drosophila melanogaster larvae as well as …

Life Cycle Assessment Of Renewable Hydrocarbon Fuels Produced By The New Cool Gas To Liquid (Cool Gtl) Process, Elochukwu Okoye

Dissertations, Master's Theses and Master's Reports

The Cool Gas-to-Liquid (Cool GTL) process converts biogas and captured CO₂ into renewable hydrocarbon fuels, contributing to GHG emissions reduction in transportation. This report explains a Life Cycle Assessment (LCA) study that was completed to evaluate the environmental impact of different feedstocks, hydrogen sources, and electricity inputs. The analysis considers biogas from food waste, manure, and landfill sources, as well as biogenic, fossil, and direct air capture (DAC) CO₂ feedstocks. Results show that biogas pathways provide the greatest GHG reductions, with the best case (food and manure biogas + solar hydrogen + solar electricity) achieving a net-negative GWP …

Life Cycle Assessment And Life Cycle Cost Assessment Of A Floating Treatment Wetland In A Wastewater Lagoon Located In Copper Harbor, Michigan, Erin Browne

Dissertations, Master's Theses and Master's Reports

A full-scale floating treatment wetland was implemented in a wastewater treatment lagoon in Copper Harbor, Michigan. Copper Harbor is a small, rural community located in a cold climate. Many studies have focused on the performance of floating treatment wetlands in mesocosm studies in warmer climates, but there is not a lot of research conducted on the feasibility and performance of full-scale floating treatment wetlands in wastewater lagoons, specifically in cold climates. The 2024 treatment season was the first season with the floating treatment wetland implemented in the wastewater lagoon. A preliminary assessment of the cost and environmental impacts of a …

Process To Design And Analyze Dynamic Environment Test Fixtures (Pdadye), Cora J. Taylor

Dissertations, Master's Theses and Master's Reports

Vibration qualification testing is an important requirement in many industries. In-lab testing is intended to evaluate a component’s life expectancy by exposing it to a dynamic environment similar to what it may experience in its field condition. To successfully perform these tests, it is imperative that the field boundary conditions (BCs) of the component of interest are replicated as closely as possible in the lab. One approach to achieving accurate dynamic environment testing is to design a fixture that sufficiently represents the impedance of the next-structure (field structure). The process to design and analyze dynamic environment test fixtures (PDADyE) is …

Evaluating Challenges And Solutions For Buckthorn Classification In Shadowed Environments, Moses Tangwam

Dissertations, Master's Theses and Master's Reports

Invasive species, such as buckthorns, pose significant ecological threats by displacing native vegetation and reducing biodiversity. This study examines the impact of shadows on the classification accuracy of buckthorns using drone-based multispectral imagery collected in a forested area near Michigan Technological University. Shadows impacted approximately 70% of the imagery, notably distorting reflectance in key spectral bands such as near-infrared (NIR) and red edge. The study evaluated vegetation indices like NDVI and the performance of machine learning models, specifically the Random Forest classifier, under these shadowed conditions. Conventional shadow correction techniques, including histogram normalization, Shadow Index (SI), and Inverse Distance Weighting …

Developing Inexpensive Sensors That Can Detect Roof Collapse From Snow Loads, Edward Sandy

Dissertations, Master's Theses and Master's Reports

In cold regions in the United States, residential timber structures are susceptible to roof collapses due to heavy snow accumulation. The recent updates in ASCE 7-22 have introduced reliability-targeted ground snow loads, leading to significant increases in design snow loads for certain areas. Despite this development, existing buildings in these regions designed under historical code requirements may be under-designed for snow load. This fact provides motivation to owners of these structures to pursue effective monitoring and mitigation strategies to prevent structural failures. This research focuses on the development of an early warning system utilizing cost-effective slope monitoring sensors to estimate …

Energy Equity In India’S Renewable Energy Transition: A Policy Design Agenda, Aritra Chakrabarty

Dissertations, Master's Theses and Master's Reports

India’s transition to renewable energy has prioritized large-scale, grid-based electrification, often overlooking energy equity, particularly in rural communities. Many households would benefit more from decentralized solutions than from grid extension, yet current policies neither guarantee equitable access nor address pre-existing social and economic inequalities in energy distribution. This dissertation examines the sensitivity of India’s renewable energy policies to energy equity, with a specific focus on gender equity in rural last-mile connectivity. The study is guided by the fundamental question: How can implementation of solar energy program alleviate the lives of rural communities? Using a mixed-method approach that combines policy …

Investigation Of Acoustic Attenuation Methods As A Noise Control Device In Hydraulics, Sophia R. Bollin

Dissertations, Master's Theses and Master's Reports

This research covers a variety of hydraulic configurations to characterize wave propagation and transmission loss. This is mathematically calculated via a transfer matrix that separates the acoustic pressure waves into upstream and downstream. Although there is a solid mathematical basis (ASTM E2611) for these calculations, a range of tests were performed to compare the accuracy of these calculations to real-time data. The main set of tests were performed on a two-part impedance tube setup with a sample section in the center. A total of 13 dynamic pressure transducers (DPT’s) are placed along the bench to record the pressure throughout testing. …

Cyanine-Based Dual-Emission Sensor For Ratiometric Nad(P)H Imaging In Cells, Renal Tissues, And Entire Organisms, Peter Agyemang

Dissertations, Master's Theses and Master's Reports

Monitoring the dynamic fluctuations of NADH and NADPH, key coenzymes in Intracellular redox homeostasis and bioenergetics, are essential for understanding both normal physiology and disease pathology. However, precise, real-time imaging of NAD(P)H levels in complex biological systems remains a major challenge due to interference from endogenous fluorescence and the limitations of intensity-based sensors. We present a newly engineered dual-emission fluorescent sensor, sensor A, which overcomes these obstacles by offering ratiometric detection with high specificity and sensitivity. The sensor features a 3-quinolinium moiety conjugated to a cyanine dye scaffold via an amine linker, enabling selective reactivity toward NAD(P)H. Upon reduction, …

A Seismological Investigation Of Ground Deformation From Meteotsunami And Seiche Activity On Lake Michigan, Bryan Lowney

Dissertations, Master's Theses and Master's Reports

The mechanisms behind the formation and propagation of meteotsunamis and seiches—anomalous water waves generated by meteorological events that share similar spatial, temporal, and behavioral characteristics with seismically-generated tsunamis—are poorly understood. Small-scale ground tilt (microradians-nanoradians) triggered by these anomalous water waves in Lake Michigan is investigated with an array of 10 near-shore (< 100 km) seismometers installed from 2012-2014 by calculating tilt from seismic recordings and comparing it with data from relatively nearby (10s of km) NOAA water level stations in Holland and Ludington, Michigan. After determining an appropriate period band from 15 minutes to four days to isolate these events, we show that potential meteotsunami and seiche activity on eastern Lake Michigan can be deduced from long-term trends (multiple weeks) in tilt and water level time series. Exploratory in nature, the methods and observations presented in this paper serve as an initial step in quantifying the potential for nearshore seismometers to monitor shoreline inundation. Integrating these observations with meteorological data could provide insights into the origins of these hazards by linking them to the specific weather events that generated them. This approach offers a novel method for improving the understanding and potential future mitigation of meteotsunami and seiche impacts in the Great Lakes region.

Design And Development Of Fluorescent Probes For Sensing And Bioimaging Of Nad(P)H, Intracellular Viscosity, And So2, Dilka Liyana Arachchige

Dissertations, Master's Theses and Master's Reports

Understanding the dynamic biochemical and biophysical changes within living systems is vital for advancing disease diagnosis and therapeutic development. This work presents the design and development of innovative fluorescent probes, based on cyanine and coumarin scaffolds, for the highly sensitive and selective detection of key metabolic and biophysical markers, NAD(P)H, mitochondrial viscosity, and sulfur dioxide (SO₂) in live cells and fruit fly larvae. A series of cyanine-based probes were engineered to undergo strong fluorescence enhancement upon reduction by NAD(P)H, enabling real-time visualization of metabolic fluctuations in cancer cells and Drosophila larvae. These probes exhibit excellent sensitivity, mitochondrial targeting capability, and …

From Riches To Ruin: An Exploration Of The Conglomerate Mining Company's Compressor House, Jill T. Muraski

Dissertations, Master's Theses and Master's Reports

This archaeological investigation revealed new insights about the story of the Lake District Copper Mining Industry through the Conglomerate Mining Company’s compressor house building. This structure once housed the air compressor powering the mine’s rock drills. Combining archaeological and historical data, this research is one of the first investigations into a compressor house at a mine location. Excavations were undertaken in order to investigate the physical structure and remains of the building. This thesis integrates fieldwork and historical data to understand the changes to the social and working landscape of the Keweenaw, such as the change in ethnic groups, and …