Physical Sciences and Mathematics Commons^™

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.

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 …

‘Waves Of Imagination’ Unconditional Spectogram Diffusion Using Diffusion Architecture., Rahul Vanukuri

Computer Science and Engineering Theses - Archive

The swift evolution of wireless communication technologies,particularly in the field of rf signals or in CBRS bands,demands increasingly sophisticated signal processing techniques to ensure efficient transmission, reception, and spectrum management.Traditional approaches to signal generation and reconstruction, although effective in controlled environments, often struggle to cope with the challenges presented by real-world noisy conditions, hardware constraints, and limited access to large-scale datasets. In response to these limitations, this thesis explores the application of diffusion models—a class of generative models known for their ability to produce high-fidelity samples—to the domain of spectrogram generation for communication signals.

Different from conventional strategies to simulate …

The Relevance Of Dnajb1 Protein Dimerization Against The Human Neurodegenerative Diseases, Erina Kotreli, Trang Le, Szymon Ciesielski

Dean's Leadership Council Library Research Prize

All biological functions in living organisms depend on the activity of proteins, long molecules that must twist and fold into specific shapes to become functional. A subset of proteins known as molecular chaperones is essential for maintaining cellular health by assisting other proteins in folding correctly and refolding those damaged by stress. When proteins misfold and clump together, they can form toxic aggregates linked to many neurodegenerative diseases, including Alzheimer’s disease (AD), one of the leading causes of death in the United States. In AD, these protein aggregates form organized, elongated structures referred to as amyloid-like fibrils, which damage neurons …

Building Cyber Resilience: Educational Programs In K-12 Education, Mildred Jones, Vukica Jovanovic, Petros Katsioloudis

Engineering Technology Faculty Publications

The article discusses the challenges of teaching cybersecurity in K-12 education. Topics mentioned include the lack of access to resources and appropriate professional development, the career and technical education cybersecurity pathways, the fundamental pathways for cybersecurity and information technology and the results of program evaluation in several local community high schools from 2021 and 2022.

Draft Final Butte Priority Soils Operable Unit (Bpsou) Unreclaimed Sites: Ur-03 Site Remedial Action Work Plan (Rawp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Berkeley Pit And Discharge Pilot Project Quarterly Pilot Project Report Third Quarter 2024, Atlantic Richfield Company, Mark Thompson

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Final Horseshoe Bend Effluent Line (Hbel) Discharge Structure Surface Enhancements Work Plan, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Securing Secrets: Exploring The Aes Encryption And Key Security Capabilities Of Chatgpt, Kayla Taylor

Student Works

The development and increasing accessibility of generative artificial intelligence (AI) tools and large language models (LLMs) have allowed cryptographers to explore a variety of cryptanalysis problems in dynamic and interactive ways. Prompt engineering, the process by which input text is tested and refined to elicit a desired response from LLMs, is a nascent area of research that remains largely unexplored in many contexts, including cryptography. This study will explore the potential applications and limitations of prompt engineering in the context of Advanced Encryption Standard (AES) encryption and key security with OpenAI’s ChatGPT (GPT-4o) through two main objectives: First, given a …

Permutations Minimizing The Number Of Collinear Triples, Joshua Cooper, Jack Hyatt

Faculty Publications

We characterize the permutations of Fq whose graph minimizes the number of collinear triples and describe the lexicographically-least one, confirming a conjecture of Cooper-Solymosi. This question is connected to Dudeney’s No-3-in-a-Line problem, the Heilbronn triangle problem, and the structure of finite plane Kakeya sets. We discuss a connection with complete sets of mutually orthogonal latin squares and state a few open problems primarily about general finite affine planes.

William Albert Noyes Jr. And A Systemic Study Of The Near Ultraviolet Photochemical Decomposition Of Acetone, Zildjan Bonita

Natural Sciences Student Research Presentations

Prior Knowledge

• Chemists observed that acetone vapor decomposes under UV light producing CO and hydrocarbons. The problem with their data was that it was inconsistent due to uncontrolled experimental variables (Herr and Noyes 2052–2053).
• Quantum yield data varied widely, pressure, temperature, and light intensity had never been systematically analyzed (Herr and Noyes 2052–2053).
• Noyes and Herr wanted to create a quantitative, controlled measurements to clarify the mechanisms of acetone photolysis (Herr and Noyes 2052–2053).

Importance of Acetone IV

• Serves as a fundamental model for photochemical bond cleavage (Journal of the American Chemical Society).
• Tests core photochemical laws (absorption → excitation …

Stephen J. Lippard: Chemistry Meets Medicine, Radwa Elrouby

Natural Sciences Student Research Presentations

Key Study

In "DNA Sequence Context Modulates the Impact of a Cisplatin 1,2-GpG Intrastrand Cross-Link", Pilch, Todd, and Lippard found that the DNA sequence around the binding site influences how tightly cisplatin binds (Pilch, Todd, and Lippard).

Biophysical techniques like melting temperature and circular dichroism showed that some sequences stabilize DNA more strongly after drug binding (Pilch, Todd, and Lippard).

This explains why cisplatin is more effective in some tumor genomes (Todd and Lippard).

Understanding The Challenges And Satisfaction Among The Medical Professional Preceptors, Cornelia Ko

West Chester University Doctoral Projects

This study evaluates the Penn Medicine Family Medicine Clerkship program through a mixed-methods approach, exploring the challenges and factors influencing the satisfaction of medical professional preceptors when precepting students. A quantitative survey with 54 preceptor participants reveals the top challenges preceptors face: workload, conflicts with patient care, and limited clinical space. Despite this, there is strong support for the program, with 89% of preceptors enjoying the role and 85% expressing willingness to continue to precept for the next three years. Finally, data analysis shows no statistical correlation between preceptor motivation with gender, ethnicity, age group, years of serving, or total …

A Personal Interview With William Patry: His Thoughts On Music, Ai, And Copyright

IP Theory

No abstract provided.

Noaa Oceanic And Atmospheric Research Ocean Carbon Observing Science Plan Fy25 To Fy35, United States National Oceanographic And Atmospheric Administration

National Oceanographic and Atmospheric Administration: Technical Reports and Related Materials

United States National Oceanographic and Atmospheric Administration oceanic and atmospheric research ocean carbon observing science plan for fiscal years 2025 through 2035.

Experiment 1--Density, Measurement And Significant Figures, Kevin Mark, Phil Mercier

Open Educational Resources

Lab objectives:

• To identify an unknown metal by measuring and calculating its density
• To determine the density of an unknown liquid by graphing mass and volume data
• To compare the accuracy and precision of water mass using measurements from a beaker and burette

Csc36000 - Modern Distributed Computing Assignment 2, Saptarashmi Bandyopadhyay

Open Educational Resources

This assignment is designed to help the student identify and mitigate common errors in Distributed Computing such as race conditions and reaching consensus, as well as reflecting on how Distributed Computing concepts apply to their class project.

Nebraska Water Center Annual Report 2025

Nebraska Water Center: Administrative Materials

Training Nebraska's future water professionals, research publications, water sciences laboratory impact and know your well curriculums and community impact

Igc Oral Presentation Summary: Xxv Igc Congress, S. Ray Smith, Echo Elizabeth Gotsick, J. R. Weinert-Nelson, Alayna Jacobs, Christopher D. Teutsch, Will Fleming, D. R. Woodfield, James L. Klotz, Jeff Lehmkuhler, Greg S. Halich, Brittany Davis, Brittany Hendrix, Krista Lea

IGC Proceedings (1977-2023)

This paper will give an overview of the XXV International Grassland Congress thematic, keynote, and plenary sessions. The theme of the XXV IGC Congress meeting was “Grasslands for Soil, Animal, and Human Health.” We had many people attending, presenting, and participating in all these sessions. Within a month after the conclusion of the Congress, the proceeding’s papers presented at the meeting will be available on the IGC app. By the end of 2023 all papers will be available for download through any search engine at internationalgrasslands.org.

Spatiotemporal Economic Impact Analysis Of The Taal Volcano Eruption Using Electricity Consumption And Nighttime Light Data, Ma Flordeliza P. Del Castillo, Toshio Fujimi, Hirokazu Tatano

SOSE Affiliate: Manila Observatory

Analyzing the spatiotemporal dimension of the economic impacts of disasters is critical for providing timely and proportionate support. However, traditional economic impact measures often lack spatiotemporal details. Hence, we estimated the daily electricity consumption (EC) loss using high-frequency EC data and analyzed temporal dimensions of the immediate impacts of Taal Volcano’s eruption on January 12, 2020. Subsequently, we computed the nighttime light (NTL) change using high-resolution NTL data to analyze the spatial distribution of these impacts. The temporal analysis revealed two EC loss peaks. The first peak coincided with the power outage and decreased energy demand, followed by a brief …

A Sequential Quadratic Programming Approach To Coupling‐Bounded Non‐Inertial Earthquake Cycle Kinematics With Distance‐Weighted Eigenmodes, Brendan J. Meade, John P. Loveless

Astronomy: Faculty Publications

Geologic and geodetic observations provide constraints on tectonic and earthquake cycle kinematics. Block models offer one approach to integrating the effects of plate rotations, elastic strain accumulation, applied basal displacements, internal block strain, and idealized pressure sources. Here, we describe the construction of block models where spatially variable slip rates are parameterized by distance‐weighted eigenmodes operating over meshes of triangular dislocation elements. This dimensionally reduced model is recast as a quadratic programming problem with upper and lower bounds on both geologic fault slip rates and spatially variable slip deficit rates. We propose iterating over successive quadratic programming estimates with evolving …

Towards Traceability Of Git Issues And Requirements: A Four Project Extension Of Pure, Karenna Kung, Alicia M. Grubb

Computer Science: Faculty Publications

Data Description: We present a dataset of four projects, each consisting of requirements, issues, and links between issues and related requirements. Requirements were extracted from the documents for the Blit, CCTNS, Space Fractions, and WARC projects from the PURE dataset (https://zenodo.org/records/7118517), and analysts developed realistic issues and matched those issues to requirements.   This repository also contains supplemental information for the thesis: "Cluster Adjuster: Towards Traceability of Git Issues and Requirements" by Karenna Kung. Note that additional supplemental material for the thesis was previously made available at doi.org/10.35482/csc.002.2025.

A Survey Of Multilingual Large Language Models, Libo Qin, Qiguang Chen, Yuhang Zhou, Zhi Chen, Yinghui Li, Lizi Liao, Min Li, Wanxiang Che, Philip S. Yu

Research Collection School Of Computing and Information Systems

Multilingual large language models (MLLMs) leverage advanced large language models to process and respond to queries across multiple languages, achieving significant success in polyglot tasks. Despite these breakthroughs, a comprehensive survey summarizing existing approaches and recent developments remains absent. To this end, this paper presents a unified and thorough review of the field, highlighting recent progress and emerging trends in MLLM research. The contributions of this paper are as follows. (1) Extensive survey: to our knowledge, this is the pioneering thorough review of multilingual alignment in MLLMs. (2) Unified taxonomy: we provide a unified framework to summarize the current progress …