Digital Commons Network^™

Removal And Monitoring Of Ammophila Arenaria In A Previously Restored Foredune Site In Humboldt Coastal Nature Center, Lucas Benjamin Griffin, Haylee Lynn Kimball, Maya Contreras, Ava Christenson

Environmental Science & Management Senior Capstones

Coastal dunes are important ecosystems that provide vital habitat for rare and endangered species as well as vital ecosystem services. However, since the late nineteenth century, coastal dune ecosystems have been invaded and altered by Ammophila arenaria, which is a beach grass species native to Europe. The dunes within the Humboldt Coastal Nature Center are one such example of this. This property is currently managed by the non-profit organization Friends of the Dunes, which has been working to restore the dunes' ecology and topography. Our project focuses on the monitoring and maintenance of a 2021 restoration project within a 17,794 …

Vegetation Management Plan And Wildlife Enhancement Project For The Woodley Island Wildlife Area, Sophie Belle Shapero, Brandon Carl Bauch, Moises Ortiz, Angelina Espinoza

Environmental Science & Management Senior Capstones

The Woodley Island Vegetation Management Plan and Wildlife Enhancement Project was created by Cal Poly Humboldt students in partnership with the Humboldt Bay Harbor Recreation and Conservation District as a capstone senior project. This project provides a basic management plan for invasive species and offers suggestions for how to enhance the wildlife areas on the island. The goals of the project are to provide maps of the habitat types within the wildlife reserves, map areas of concern to be targeted for invasive species removal, and provide guidelines for how to increase habitat diversity, wildlife forage, and decrease invasive species presence. …

Woody Ryno Farms Native Prairie Plant Restoration & Invasive Management Conservation Management Report, Jezebel Kenny, Rowan Haeger, Ian Mendonca, Annika Weihl

Environmental Science & Management Senior Capstones

Coastal prairies are biodiverse and sensitive landscapes that support a variety of grassland species in mediterranean climates. Hedgerows may act as a natural barrier for coastal prairies or agricultural regions, aiding in natural protection and support for biodiversity. Humboldt County, California, hosts a number of agricultural regions, including Woody Ryno Farms, a small-scale agricultural operation, on the edge of Dows Prairie near the coast of Mckinleyville. This project aims to continue restoration activity being conducted on the site, helping to increase native biodiversity and agricultural productivity. Project objectives include conducting a plant assessment, applying treatments to individual plants where necessary, …

Hydrodynamic Tipping Points In The Mississippi Sound In Response To Bonnet Carré Spillway Openings, Mustafa Kemal Cambazoglu, Brandy N. Armstrong, Mohsena Lopa, Marc Diard, Jerry D. Wiggert

Faculty Publications

This project examines the current operational strategy of the Bonnet Carré Spillway (BCS), a Mississippi River flood-control structure located about 21 miles northwest of New Orleans, Louisiana, and managed by the U.S. Army Corps of Engineers. The BCS is part of the larger Mississippi River and Tributaries Project, a network of levees and control structures designed to minimize flooding from the American plains to southern Louisiana. The spillway is opened when river discharge at New Orleans is forecasted to exceed 1,250,000 cubic feet per second, diverting significant volumes of Mississippi River water into Lake Pontchartrain which subsequently flows into Mississippi …

A Comprehensive Survey On Facial Expression Generation: From Gans To Llm-Guided Multimodal Models, Murad Hasan, Rabab Abdelfattah, Kareem Abdelfatah, Mostafa Fouda, Ahmed Sherif

Faculty Publications

Facial expression generation (FEG) has emerged as a vital area in human–computer interaction, virtual avatars, and affective computing, aiming to synthesize natural and expressive facial behaviors across diverse interaction contexts. This survey presents a comprehensive analysis of recent advances in FEG, organized into six key paradigms: speech-driven expression generation, facial reaction generation, face video generation, facial animation, avatar-based generation, and text-driven expression generation. We review a wide range of model architectures, including VQ-VAEs, Generative Adversarial Networks (GANs), 3D Morphable Models (3DMMs), Transformers, and diffusion-based approaches, and compare their performance using commonly adopted evaluation metrics such as Fréchet Distance (FD), Peak …

Body Mass Index Has No Impact On Complications And Mortality For Patients With Stage Iv Pancreatic Ductal Adenocarcinoma, Ren Bryant, Hannah Darnell, Megan Hall, Kelsey N. Karnik, Kristen J. Mcquerry, Ruben R. Plentz

Biostatistics Faculty Publications

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of United States (USA) cancer death. Overweight and obesity developing into a growing global medical and socio-economic problem, affecting approximately 42% of adults in the USA population. The aim of our analysis was to evaluate the influence of overweight and obesity on complications and clinical outcome in patients with stage IV PDAC.

Methods: We retrospectively reviewed electronic health records of patients diagnosed with stage IV PDAC (n=162) who followed with the University of Kentucky from January 2017–October 2024. Comparisons were based on the body mass index (BMI): low BMI …

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 …

Machine Learning System For Cereal Yield Prediction In South America., Victor Gabriel Oliveira, Kelvin Henrique Ferreira Dumas

ICT

This project developed a machine learning solution to predict cereal yield in South America using FAOSTAT agricultural data. The objective was to estimate yield in kg/ha for rice, maize, and wheat based on variables such as country, crop type, year, harvested area, producer price, pesticide use, and nutrient indicators.

The project followed the CRISP-DM methodology, covering business understanding, data understanding, data preparation, modelling, evaluation, and deployment. Several regression models were tested, including Random Forest, Gradient Boosting, AdaBoost, XGBoost, KNN, and SVR.

The models were evaluated using MAE, MSE, RMSE, and R² score. The results showed that tree-based ensemble models performed …

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 …

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 …

Design, Syntheses And Biological Applications Of Fluorescent Probes For Nad(P)H, Hsa And Cellular Microenvironment Sensing, Adenike M. Olowolagba

Dissertations, Master's Theses and Master's Reports

Fluorescent probes have emerged as powerful tools for investigating complex biological processes due to their high sensitivity, selectivity, and capability for real-time, non-invasive imaging. In particular, the detection of key biomolecules such as NAD(P)H, viscosity-related parameters, human serum albumin (HSA), and pH is essential for understanding cellular metabolism, redox homeostasis, and disease progression. In Chapter 2, three coumarin-based fluorescent probes (A–C) were designed, synthesized, and characterized for monitoring NAD(P)H levels in living cells. Probes A and B feature coumarin–cyanine hybrid structures linked via vinyl and thiophene bridges to 3-quinolinium acceptors, respectively, while probe C incorporates a dicyano moiety to replace …

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, …

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 …

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 …

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 …

Bayesian Analysis Of Nominal Outcomes With Missing Values Using Multinomial And Multivariate Multinomial Probit Models, Suwash Silwal

Dissertations, Master's Theses and Master's Reports

Nominal outcomes frequently arise in health sciences, transportation, economics, market research, and related fields. These data often contain missing values, while longitudinal and panel studies generate multiple correlated nominal responses. Bayesian estimation of multinomial probit (MNP) and multivariate multinomial probit (MMNP) models provides a flexible framework for analyzing such data but remains computationally challenging due to high-dimensional likelihood integration, restrictive covariance identification constraints, and poor mixing of Markov chain Monte Carlo (MCMC) algorithms, particularly in the presence of missing data. This dissertation develops parameter-expanded data augmentation (PX-DA) methods for MNP and MMNP models with missing nominal outcomes by incorporating parameter …

Applied Forest Restoration And Conservation In Michigan: Advancing American Beech Restoration And Documenting A Historical Insect Collection, Thomas E. Panella

Dissertations, Master's Theses and Master's Reports

Forest restoration and conservation require both practical management approaches and the preservation of biological resources that support future research. This dissertation presents applied research conducted in Michigan that advances American beech (Fagus grandifolia) restoration in response to beech bark disease (BBD) while documenting an important historical entomological resource. Chapters 1–3 focus on overcoming practical barriers to American beech restoration. Restoration protocols were developed and refined for identifying BBD-resistant trees, graft propagation, rootstock collection, container production, and the field establishment of grafted BBD-resistant trees. A greenhouse experiment evaluated the influence of growing media and fertilization on container stock quality, …

Novel Chiral Interstellar Molecules: Quantum Anharmonic Ir And Vcd Predictions, Meredith Paik

Dissertations, Master's Theses and Master's Reports

The 2016 discovery of the chiral molecule propylene oxide (C₃H₆O) in the interstellar medium (ISM) has opened new avenues into explaining the origin of biomolecular homochirality on Earth. Thus, studies of chiral molecules in the ISM may be able to reveal more about the mechanism behind homochirality. However, while the search for chiral molecules in space has become an active field of study, astrochemical researchers have yet to detect other chiral molecules in the ISM. For this purpose, numerous characteristics for detectability have been outlined that may facilitate the discovery of other interstellar chiral molecules. With …

First-Principles Investigation Of Quasi-One-Dimensional Van Der Waals Magnets For Advancing Low-Dimensional Spintronics, Alyssa Horne

Dissertations, Master's Theses and Master's Reports

Van der Waals (vdW) magnets have been of great interest for advancing low- dimensional spintronics. A notable example is the quasi-one-dimensional (Q1D) vdW CrSbSe₃, as it is composed of individual one-dimensional units that are held together by the vdW forces. Finding other Q1D vdW magnets that exhibit non-metallic behavior together with long range ferromagnetic ordering is critical in developing next generation spintronics. Here in, using first-principles density functional theory (DFT), we investigate the compositional effects on electronic and magnetic behavior of Cr_1–xMn_xSbSe₃ (x = 0, 0.5, 1). When 50% of Cr is replaced …

Using Automotive Lidar To Reduce The Energy Consumption Of An Ego Autonomous Vehicle, Logan P. Schexnaydre

Dissertations, Master's Theses and Master's Reports

There is significant potential to reduce the energy consumption of the transportation sector through autonomous vehicles. Prior work on autonomous vehicle energy efficiency focuses on the whole system or the control subsystem. Yet, the sensing and processing components, which have direct and indirect effects on net energy use, are less explored. This dissertation fills this gap by modeling and evaluating these effects for lidar sensors, which provide high-resolution spatial data at the cost of high power and processing demands. I apply lidar to the energy-saving tasks of automated vehicle following and road surface profiling. For automated vehicle following, I model …

Slip Kinematics And Structural Analysis Of The Keweenaw Fault System From Lake Linden To Hancock, Michigan, Katherine M. Langfield

Dissertations, Master's Theses and Master's Reports

The Keweenaw fault is a crustal-scale fault spatially associated with Mesoproterozoic rocks of the Midcontinent Rift System. Along the fault, older Portage Lake Volcanics have been thrust southeastward over younger Jacobsville sandstone. Ideas on the fault’s origin, from oldest to most recent, include that it is: (1) a reverse fault, (2) a normal fault formed during midcontinent rifting that was reactivated and inverted to a reverse fault during the Grenville orogeny, and (3) a detached thrust fault system initiated during the Grenville orogeny.

This thesis is based on a U.S. Geological Survey EdMap grant to remap a portion of the …

Real Time Waste Classification Using Deep Learning: Comparing Mobilenetv2 And Resnet 18 With Transfer Learning And Fine Tuning, Abdul Moaiz

ICT

Waste contamination is a major problem all over the world. The Environmental Protection Agency reports that over two thirds of waste found in general household and commercial bins could have been placed in the recycling or organic waste bins instead in Ireland, with food waste and plastics being the most common misplaced items. This project presents a deep learning solution to classify nine categories of waste from camera images in real time with the goal of helping users sort waste correctly at the source. Following the CRISP DM framework, two convolutional neural network architectures were trained and compared, MobilenetV2 and …

Bitcoin Prediction System Usind Machine Learning Techniques, Carolina Azevedo De Castro

ICT

This project investigated the use of machine learning techniques to predict short-term Bitcoin price direction using historical market data obtained from Yahoo Finance. Following the CRISP-DM methodology, the dataset was analysed, prepared, and transformed through feature engineering techniques including moving averages, volatility indicators, return measures and price position metrics. Multiple classification algorithms were evaluated, including Bayesian Classification, K-Nearest Neighbour, Decision Tree, Random Forest, Logistic Regression, Support Vector Machine and XGBoost. Several optimisation strategies were also tested, including feature selection, hyperparameter tuning, feature scaling and class weight balancing. Results showed that predicting short-term Bitcoin price movements remains challenging, with most models …