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Program And Proceedings: Nebraska Academy Of Sciences 1880–2025, 145th Anniversary Year, One Hundred-Thirty-Fifth Annual Meeting

Nebraska Academy of Sciences: Programs and Proceedings

Program

Aeronautics and Space Science

Biological and Medical Sciences

Biology

Chemistry

Earth Sciences

Science Education

Anthropology

Applied Science and Technology

Physics and Engineering

Forensic Sciences

Ecology, Sustainability, and Environmental Science

Maiben Lecture: Mary Ann Vinton, "State of the Academy"

Friends of Science Awards: David Crouse and Daniel Sitzman

Manila As Sophie’S World: Mapping Sites Of Wonder In 19th C. Intramuros And Environs, Jovino G. Miroy

Philosophy Department Faculty Publications

This paper draws a philosophical heritage map of Intramuros

and environs. The map includes several religious sites in

Intramuros and two monuments of heroes outside the walls. The

paper explains the reason for drawing such a map by, firstly,

discussing the theory of place-based learning. Secondly, it will

discuss the philosophical walk as a dialogue with the

environment (nature and the city). A philosophical heritage

map is also understood not only as a tour of sites associated with

philosophers and the history of ideas but a movement along sites

of wonder, rumination, contemplation, and critical thinking. As

such, walking as …

Multi-Objective Deep Reinforcement Learning For Dynamic Algorithm Selection In Open Ran, Saish Urumkar, Byrav Ramamurthy, Sachin Sharma

Conference papers

Open Radio Access Networks (Open RAN) provide flexible, modular multi-vendor interoperability. Growing mobile data demand requires balancing network performance with power efficiency. Mobile operators need intelligent resource management to achieve Key Performance Indicator (KPI) targets while maintaining operational efficiency. This paper proposes a solution using a multi-objective deep reinforcement learning (MODRL) model deployed on the Open RAN Intelligent Controller (RIC). Three customizable operator profiles (Power Saving, Balanced, and Performance) are used which define specific priority ratios between performance and power saving objectives.

To evaluate, individual algorithms (CPU scheduling and UE connection state switching) are implemented in Open RAN, achieving 5–20%CPU …

Predicting Lung Cancer Severity Using Machine Learning Algorithms: Enhanced By Statistical Analysis, Esin Bilgin

Theses, Dissertations and Culminating Projects

Cancer is a serious and severe cause seen in every region of the world and severely affects the quality of life and life span. Among the various types of cancer, lung cancer is one of the most critical, having a fatal impact on life. While medical imaging techniques, laboratory results, and biomarkers play a significant role in diagnosis and prognosis, clinical studies are also crucial in monitoring the progression of cancer and identifying diagnostic and prognostic factors. The findings demonstrate satisfactory accuracy, and the analysis incorporates statistical data with machine learning techniques. These findings play a pivotal role in supporting …

Quantifying The Vulnerability Of Smooth Dogfish And Winter Skate To Electromagnetic Fields From Offshore Wind Transmission Cables In The Mid-Atlantic Shelf, Rachel A. Sechrist

Theses, Dissertations and Culminating Projects

The development of offshore wind structures in the northeastern U.S. will contribute to renewable energy goals, but will overlap with many marine species as well as economically important fisheries. High voltage transmission cables from offshore wind farms emit electromagnetic fields (EMFs) that elasmobranchs may detect using sensory organs known as ampullae of Lorenzini. However, sensitivity to EMFs varies by species due to differences in the number and arrangement of pores, the length of subdermal canals within the ampullae of Lorenzini sensory network, and the habitats they reside and regions they forage. For species who reside or forage in benthic regions, …

Neuroimmune Toll-Like Receptor Signaling Modulates C. Elegans Feeding Behavior In A Hunger State-Dependent Manner, Retwika Ganguly

Theses, Dissertations and Culminating Projects

Animals face the risk of encountering pathogenic microbes while searching for nutritive resources. Assessing the interplay between the benefits of nutrition vs. the risk of infection might influence the behavioral regulation of immune processes. In Caenorhabditis elegans (C. elegans), a nematode roundworm, behavioral immunity is partially regulated by the innate immune molecule TOL-1: a homolog of vertebrate Toll-like Receptor (TLR) proteins. TOL-1 influences C. elegans pathogen avoidance behaviors by promoting the development of CO2-detecting chemosensory neurons, which detect a signal linked to the presence of harmful pathogens. While TOL-1′s role in pathogen avoidance is well established, its impact in an …

Interpretable And Data-Driven Machine Learning Models For Analyzing High-Dimensional Biological Data, Junchen Yang

Yale Graduate School of Arts and Sciences Dissertations

High-dimensional biological data, including single-cell omics, are prevalent in modern biology for characterizing a wide range of biological processes and phenomena. Although our capacity to generate such data has expanded at an unprecedented pace, significant challenges remain in extracting the informative features and the underlying biological signals. In particular, these datasets not only exhibit high dimensionality but also suffer from issues including inherent noise, low signal-to-noise ratio, and intrinsic heterogeneity. While machine learning models hold great promise for analyzing such data, their complexity and sensitivity to data characteristics often impede biological interpretability. To address these interconnected challenges, this thesis develops …

Brain Tumor Classification Using Deep Learning: Custom Cnn Vs. Resnet50, Rayen Bentemessek

ICT

The project presents a deep learning solution to classify brain tumors through MRI images. Following the CRISP-DM framework, two Convolutional Neural Network (CNN) models were developed and evaluated, a custom CNN designed from scratch and a pretrained ResNet50 that was transfer learned and fine-tuned. Both models were assessed using standard performance metrics such as accuracy, precision, recall and F1-score. Despite the higher test accuracy achieved by the custom CNN, further interpretability indicated inconsistent attention to the actual tumor regions also known as shortcut learning. On the other hand, ResNet50 showed more reliable and clinically relevant focus which supported its selection …

Player Transfer Market In European Football Using Machine Learning To Analyse The Evolution Of The European Football., Pablo Lopes De Souza Oliveira

ICT

The study uses machine learning to analyse the European football transfer market from 2015 to 2025, revealing patterns in transfer fees and market values influenced by player attributes, highlighting the potential of data-driven insights.

Predicting Early Customer Inactivity In The Banking Sector Using Machine Learning: A Churn Prevention, Ivana Mc Fadden

ICT

Customer churn, when customers stop using a company’s services, is a challenge for the banking sector (Singh et al., 2023). High churn rates often signal poor customer experiences, resulting in revenue losses and increased costs to obtain new clients. Goyal and Srivastava (2015) stress that fostering loyalty through exceptional service and understanding customer needs is important for long-term retention.

This project aims to predict early customer inactivity, an indicator of churn, by using machine learning. Early identification of at-risk customers will allow banks to apply targeted interventions, reduce acquisition costs, and improve customer satisfaction (Singh et al., 2023). By analysing …

Using Machine Learning To Predict Credit Card Fraud, Pedro Henrique Das Chagas Morais

ICT

Credit card fraud poses a significant challenge to financial institutions, leading to substantial financial losses and declining customer trust. This project develops and evaluates machine learning models to detect fraudulent credit card transactions using a large, realistic synthetic dataset. Following data preprocessing, exploratory analysis, and class-balancing using SMOTE, four supervised models—Logistic Regression, Decision Tree, Random Forest, and XGBoost—were trained and compared. Performance was assessed using metrics suited to imbalanced classification, including AUC, Recall, Precision, F1-score, and Average Precision. Results show that XGBoost, particularly after hyperparameter optimisation, delivered the strongest performance (AUC 0.99, Recall 0.83, AP 0.70), outperforming other models and …

Fake News Detection Using Machine Learning Models., Anne Higgins

ICT

This project investigates the use of machine learning to detect fake news, addressing the societal and political challenges posed by the widespread dissemination of false and misleading information. Using automated classification techniques, the project analyses news content to predict the likelihood of intentional deception. The methodology follows the CRISP-DM framework, encompassing data preparation, model development, and evaluation. By leveraging machine learning, the study aims to support organisations, governments, and digital platforms in mitigating misinformation, while also considering ethical, interpretability, and strategic implications. The findings provide actionable insights for enhancing content moderation and reducing the influence of disinformation in digital media …

Data Analysis For Maintenance Reliability., Romulo Menezes Santos

ICT

This project explores the application of predictive maintenance (PdM) in modern manufacturing, highlighting its strategic role in reducing downtime, optimising resources, and improving operational efficiency. Traditional reactive and preventive maintenance approaches are insufficient for Industry 4.0 environments, where machine failures can cause significant financial and operational losses. By leveraging sensor data, historical performance records, and machine learning techniques, PdM enables early detection of potential equipment failures, allowing maintenance teams to act proactively. The approach not only enhances operational reliability and safety but also supports strategic decision-making, cost control, and competitive advantage in globalised manufacturing contexts.

Comparative Evaluation Of Ai-Generated Synthetic Data And Real-World Data Performance In Predictive Analytics., Corey Louise Hughes

ICT

There are growing restraints when it comes to Real World Data (RWD), these include topics such as privacy regulations, ethical concerns, and the cost of collecting the data, and they have drove an interest in AI-generated synthetic data as a potential alternative in predictive analytics. This project examines the possibilities of synthetic data and if it can act as a reliable substitute for RWD in predictive modelling. This project uses Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to generate synthetic reproductive health data and evaluates its predictive performance against RWD using linear regression and key metrics, including Mean Absolute …

Efficient Evaluation Of Nala In Convlstm For High-Dimensional Time-Series Traffic-Flow Forecasting ., Sergej Sisov

ICT

Accurate traffic congestion prediction is essential for effective urban mobility and energy-efficient transport planning. This study evaluates the impact of optimizers on ConvLSTM models for short-term traffic forecasting using the METR-LA dataset, incorporating weather and event data as exogenous features. Two optimizers—Adam and Nesterov-accelerated Lookahead (NALA)—are compared under a frozen-T evaluation to prevent temporal leakage. Data preprocessing included five-minute alignment of weather data and event integration based on sensor proximity. Results show that the best-performing model (Variant C-2), integrating traffic, weather, and event features with reduced dropout and NALA optimization, achieved an MAE of 5.22 and RMSE of 8.79, outperforming …

Identifying And Forecasting Key Drivers Of Greenhouse Gas Emissions In Ireland's Residential Sector Multivariate Time Series Analysis., Sallam Noor Aldeen Salman

ICT

This study evaluates advanced time-series forecasting models to predict greenhouse gas emissions (GHGE) in Ireland's residential sector. LSTM, XGBoost, and ARIMA models were tested alongside feature selection methods including PCA, XGBoost-based importance, and Granger causality. Urban population growth and electricity consumption emerged as the most significant predictors. While LSTM struggled due to limited data, XGBoost showed strong predictive performance (MAPE ~9%), and ARIMA with key features achieved the highest accuracy (MAPE 3.78%). Forecasts indicate a declining GHGE trend, offering actionable insights for environmental planning and policy in the residential sector.

Identifying Skills Gaps And Labour Market Trends Using Machine Learning: A Data Analysis Of Tendencies Within The Irish Online Job Market., Stephen Ray

ICT

The Irish labour market is a dynamic system characterised by overlapping and distinct demands. The skills required to maintain competitiveness within today's labour market are constantly evolving in response to the needs of corporations, non-governmental organisations, and capital. Labour market participants, fulfilling specific roles, contribute to a skill-based relationship where supply and demand operates in new and innovative ways. With the advent of the internet, the labour market has shifted towards a digital environment, leading to the rise of online job advertisement platforms that cater to geographically defined regions. These platforms attract vast numbers of users and prospective employees daily …

Forecasting Hourly Police Call For Service Volumes: A Comparative Analysis Of Statistical, Machine Learning And Neural Network Models For Operational Planning., Patrick Duggan

ICT

Accurate demand forecasting is critical in operational settings where resource allocation and planning decisions depend on anticipated service volumes. Transactional systems that capture timestamped records provide valuable data sources for developing demand forecasts. This study examines hourly call volume forecasting using New Orleans police calls for service data, comparing the performance of statistical models, tree-based methods, and recurrent neural networks.

The research evaluates four primary modelling approaches: ARIMA models representing traditional statistical methods, XGBoost and Random Forest as a tree-based ensemble technique, and Gated Recurrent Units (GRUs) as deep learning alternatives. A naive seasonal model serves as the baseline benchmark. …

Predictive Analysis Of Atlantic Meridional Overturning Circulation Amoc And Its Climatic Implications On Ireland Using Advanced Machine Learning Techniques., Corey Whyte

ICT

The Atlantic Meridional Overturning Circulation (AMOC) plays a key role in regulating climate across the North Atlantic, including Ireland. This study analyses AMOC variability using RAPID, ODYSSEA, and MÉRA datasets to examine relationships between subsurface ocean conditions, sea surface temperatures, and air temperature. ARIMA/SARIMA models identify trends and correlations, while a Long Short-Term Memory (LSTM) neural network forecasts future climate changes. Results show rising sea surface temperatures, moderate links between salinity and temperature, and seasonal air temperature patterns with a slight upward trend. The LSTM model outperforms traditional statistical approaches in capturing complex patterns, highlighting the value of machine learning …

Tour Demand Forecasting In Ireland: Development And Evaluation Of Classical, Deep Learning, And Hybrid Models, Ruben Elias Charleston Montfort

ICT

Tourism plays a significant role in global economies by supporting employment, infrastructure, and national development. As international travel continues to grow, accurate tourism demand forecasting has become increasingly important for effective planning and decision-making. In Ireland, tourism is a key economic sector attracting millions of visitors annually. For tour operators such as Irish Day Tours, reliable demand forecasting is essential for optimizing logistics, resource allocation, marketing strategies, and customer satisfaction. Advances in machine learning and deep learning techniques offer new opportunities to improve forecasting accuracy and support data-driven decision-making within the tourism industry.

Sonic Photovoice And Critical Literacy: Listening To And Sounding What Is Me, What Is Not Me, And What Is Between, Alexander Chadwell

Theses and Dissertations--Curriculum and Instruction

This study examined sonic photovoice, a sound-based arts research method, as a tool for developing critical literacy in middle and high school students. Participants found and recorded sounds related to three categories, “what is me,” “what is not me,” and “what is between,” composed a sound collage using the sounds, and reflected on the experience. Framed by 4EA cognition, participatory sensemaking, and relational and everyday aesthetics, the study explored how listening and sounding shaped students’ conceptions of self and reality. Data from recordings, journals, and interviews suggest that the sonic engagement can foster relational, emergent understandings of self and …

Full Ride To College, But At What Cost?: Colonial Subjects At Imperial Educational Institutions In Binti And The Fifth Season, Martha Zornow

English Faculty Publications

A marginalized young person, often of a different physiognomy from those wielding power, sees the 'ivory tower' in her universe as a path to the learning, respect, power, and agency not available to her in her home environs. At their educational institutions, while their Blackness is not the basis for their outsider status, the Othering they experience mirrors that experienced by Black students at predominantly white universities in the real world. Theorizing the Colonial Role of Educational Institutions Following Thomas Macaulay's notorious' Minute on Education'(1835), educational systems have played a vital role in reaffirming the balance of power between the …

Welcoming Historically Under-Represented Groups In Higher Education Through Awareness Of Standard English Ideology, John Hellermann, Lynn Santelmann, Jennifer Mittelstaedt, Janet Cowal, Steven L. Thorne

Applied Linguistics Faculty Publications and Presentations

In the context of changing demographics at regional universities (including our own), we highlight an ongoing project at our university that addresses the last area of acceptable bias in English-medium higher education: bias against speakers of other languages and non-standard dialects of English. We discuss the hegemonic aspect of the Standard Academic English used by default at most US institutions of higher education and its role in potential discrimination against users of languages other than English and dialects other than the Standard. Data from over 2000 surveys, 55 follow up interviews, and three focus groups from faculty, staff and students …

Trees, Schools, & Stakeholders: Building Partnerships For Schoolyard Greening With Portland Urban Forestry Learning Landscapes, Claire Brase

Environmental Science and Management Professional Master's Project Reports

Portland Urban Forestry Learning Landscapes is a city program offering free tree planting and establishment services to schools, community groups, and multi-residential zones in the city of Portland, Oregon. This project report, completed on behalf of Learning Landscapes, uses qualitative evidence from interviews with school district administrators, teachers, volunteers, grounds staff, city employees, and outreach experts to gain a deeper understanding of the factors leading to successful collaboration between local school districts and their external community partners. These conversations with school and outreach personnel across the city suggest that district staff are interested in collaborative greening organizations like Learning Landscapes …

High-Resolution Geospatial Database: National Criteria-Air-Pollutant Concentrations In The Contiguous U.S., 2016–2020, Tianjun Lu, Sun-Young Kim, Julian D. Marshall

Epidemiology and Environmental Health Faculty Publications

Concentration estimates for ambient air pollution are used widely in fields such as environmental epidemiology, health impact assessment, urban planning, environmental equity and sustainability. This study builds on previous efforts by developing an updated high-resolution geospatial database of population-weighted annual-average concentrations for six criteria air pollutants (PM_2.5, PM₁₀, CO, NO₂, SO₂, O₃) across the contiguous U.S. during a five-year period (2016–2020). We developed Land Use Regression (LUR) models within a partial-least-squares–universal kriging framework by incorporating several land use, geospatial and satellite–based predictor variables. The LUR models were validated using conventional …

Zinc Or Swim: Investigating Zinc Pollution From Restoration Materials, Megan R. Jensik

Honors Undergraduate Theses

Zinc is an essential nutrient, but can be toxic at high concentrations, negatively affecting various biological functions. Gabions are galvanized metals coated in zinc that are used for many human purposes, including coastal restoration efforts attempting to avoid using plastic materials. However, gabions can degrade in marine environments, potentially leaching metal into the water and soil. There is a knowledge gap regarding the occurrence and impacts of zinc pollution in the areas where these gabions are deployed, including the Indian River Lagoon (IRL). To address this, sediment samples were collected from the IRL and analyzed for their physical and chemical …

Performance Evaluation Of Managed Switch Configurations For Secure And Efficient Plc-Based Industrial Automation Networks, Ahmed Salama

All Graduate Theses, Dissertations, and Other Capstone Projects

This thesis explores how managed switches can improve network performance and security in PLC-based industrial systems. Using simulations in GNS3 and Cisco Packet Tracer, and packet analysis via Wireshark, the study compares unmanaged and managed switch configurations. Redundancy protocols, particularly STP and RSTP, are evaluated under failure scenarios. Results show that RSTP offers faster recovery times, making it more suitable for time-sensitive environments. Additionally, managed switch features like VLANs, port security, and MAC filtering significantly reduce vulnerabilities and improve network segmentation. The findings highlight the importance of incorporating both cybersecurity and redundancy in industrial network design as systems move toward …

Teaching Cases: How Legal Writing Textbooks Approach The Rule Support Section, Alissa Bauer

Faculty Scholarship

No abstract provided.

Dynamical Mechanisms Of Neural Computation, Frederick Michael Berl

Yale Graduate School of Arts and Sciences Dissertations

The brain is one of the most complex dynamical systems in the universe and despite our everyday familiarity with it, the intricacies of how it functions remain a mystery to us. It is enormously powerful, continuously learning, and able to perform computations flexibly and adapt to novel situations. Although science presently has an understanding of how individual neurons work and a general idea of what the functions of each region in the brain are, how the computations of each neuron are modulated by context and how the various regions work together to produce behavior are unclear. Here, we explore the …

Adaptation Of Learning Dynamics And Feature Representations Via The Neural Kernel, Lianghui Peng

Yale Graduate School of Arts and Sciences Dissertations

The ability to learn from experience is essential for both biological and artificial agents. To learn efficiently in complex, high-dimensional environments, agents must generalize strategically from limited data by focusing on features relevant for the task. The generalization strategy, known as inductive bias, shapes the dynamics of learning. Previous works have established the theory of neural tangent kernels that describes the learning dynamics of artificial neural networks. However, a comparable framework for understanding and quantifying inductive bias in biological systems performing behavioral tasks remains underdeveloped. Here, we introduce a neural kernel framework to characterize inductive biases of humans and artificial …