Physical Sciences and Mathematics Commons^™

Multi-Fidelity Machine Learning Modeling For Aerodynamic Response Prediction Of Aerospace Vehicles, Ethan S. Jackman

Theses and Dissertations

Hypersonic vehicle design requires understanding complex aerodynamic phenomena across the full flight regime. This study presents a novel MF surrogate modeling methodology that enables the prediction the full field response across a vehicle’s surface. A Space-Filling Curve (SFC) is used to convert unstructured data into 1D vectors. The a Convolutional Autoencoder is used with transfer learning to reduce the dimensionality of the data. An Emulator-Embedded Neural Network (E2NN) combines multi-fidelity data for fast, accurate predictions. A benchmark analytical example and hypersonic application are used to evaluate the methodology. Using various numbers of samples and sampling strategies it is found that …

Impacts Of Climate Disruption On Mobility Aircraft Performance In The Pacaf Region, Hannah M. Dauterman

Theses and Dissertations

This thesis investigates the projected impacts of climate disruption on the performance and fuel management of the C-17 Globemaster III, a critical mobility aircraft in the Pacific Air Forces (PACAF) region. As rising global temperatures reduce air density, the performance of aircraft is compromised, resulting in increased fuel consumption, as well as the potential for extended runway requirements and diminished cargo capacity. Using climate projection data from Coupled Model Intercomparison Project Phase 6 (CMIP6), this research analyzes future air temperature trends and their implications for C-17 fuel consumption. Results suggest that by 2049, the U.S. Air Force may incur an …

Strong External Difference Families And Classification Of Α-Valuations, Donald L. Kreher, Maura B. Paterson, Douglas R. Stinson

Michigan Tech Publications

One method of constructing (Formula presented.) -SEDFs (i.e., strong external difference families) in (Formula presented.) makes use of (Formula presented.) -valuations of complete bipartite graphs (Formula presented.). We explore this approach and we provide a classification theorem which shows that all such (Formula presented.) -valuations can be constructed recursively via a sequence of “blow-up” operations. We also enumerate all (Formula presented.) -SEDFs in (Formula presented.) for (Formula presented.) and we show that all these SEDFs are equivalent to (Formula presented.) -valuations via affine transformations. Whether this holds for all (Formula presented.) as well is an interesting open problem. We also …

Ab Initio Studies On Mechanical Properties Of Freestanding Ferroic Monolayers, John Malcolm Davis

Graduate Theses and Dissertations

This thesis explores the role of mechanical properties and elastic couplings in freestanding paradigmatic ferroic monolayers within the framework of density functional theory. This dissertation is comprised of three interconnected published studies: (i) establishing the elastic behavior across a temperature-driven phase transition in SnSe monolayers; (ii) thermally-driven phase transitions in graphene-like silicene, germanene, and stanene, examining the structural stability and band gap occlusion; and (iii) magnetoelastic coupling in an Ising-like CrSiTe3 monolayer, focused on assessing magnetic contributions to the material stiffness by establishing a proper non-magnetic reference.

A Dynamic Hierarchical Attention Framework For Multimodal Malware Detection, Tamanna Nazmin

Graduate Theses and Dissertations

The increasing use of Android in the worldwide mobile ecosystem has come along with a significant increase in advanced malware, highlighting the critical necessity for efficient, scalable, and adaptable detection systems. Despite recent advancements in machine learning improving malware detection, the majority of current solutions are limited to one, two, or three data modalities, hence neglecting the comprehensive behavioral spectrum of contemporary multi-vector threats. This thesis presents the first comprehensive multimodal framework for Android malware detection, which combines textual, time-series (temporal), graph-based (structural), and visual information using an innovative hierarchical attention mechanism and Dynamic Fusion Controller(DFC). Our methodology consistently classifies …

Script-Based Inferences In An Image Schema Story Understander, Jamie C. Macbeth, Boming Zhang, Sharmin Badhan

Computer Science: Faculty Publications

Recent studies of large language models (LLMs) have revealed that they lack human-like cognitive models of reasoning and understanding. An important thread of research merges image schemas into symbolic artificial intelligence systems where their use as conceptual building blocks and primitives shows promise for the study of human cognition through intelligent systems that perform neurosymbolically. The work presented in this paper demonstrates image schema primitives being used in structures of a representation system called conceptual dependency (CD) and in broader commonsense knowledge structures called scripts. We present a story under- standing system that uses image schemas as primitives in scripts …

Applying Artificial Intelligence And Chatbots To Enhance Collection Development In Health Sciences Libraries, Ivan Portillo, David Carson

Library Presentations, Posters, and Audiovisual Materials

The continuous advancement of artificial intelligence (AI) and large language models (LLMs) has presented several opportunities for librarians to reduce their workload and become more efficient. This session will explore the potential of generative AI chatbots in assisting health sciences librarians with collection development. Two methods that will be discussed include the potential of AI to help discover new titles and how AI can evaluate your library collection for any potential gaps based on a college program’s curriculum.

Deep Learning For Land Use Classification: A Systematic Review Of Hs-Lidar Imagery, Muhammad Zia Ur Rehman, Syed Mohammed Shamsul Islam, David Blake, Anwaar Ulhaq, Naeem Janjua

Research outputs 2022 to 2026

Remote sensing (RS) technologies have significantly advanced Earth observation capabilities, enhancing the characterization and identification of surface materials through both spaceborne and airborne systems. These advancements are crucial for improving environmental monitoring and urban planning. As RS datasets have become more accessible, their increased complexity has necessitated a shift from traditional machine learning techniques to more robust deep learning approaches, particularly convolutional neural networks (CNNs) and transformer-based models known for their superior feature extraction capabilities. This systematic review focuses on the application of these deep learning techniques in land use classification, emphasizing the fusion of hyperspectral (HS) and LiDAR data. …

Patterns Of Ecological Quality In Portuguese Transitional Waters Over The Last Five Decades: A Framework Based On Benthic Foraminifera For The Sado Estuary, J. Moreno, F. Moreno, F. Fatela, E. Leorri, M. Leira, P. Masqué, M. C. Freitas

Research outputs 2022 to 2026

This study aimed to examine the validity of benthic foraminifera-based indices – Foraminiferal Stress Index (FSI), diversity index Exp(H'bc), and Ecological Quality Ratio (EQR) – for evaluating estuarine environmental conditions and supporting palaeoecological reconstructions, focusing on the Sado estuary (southwest Portugal). Located near heavily industrialized urban areas and designated as a Nature Reserve, this estuary faces human pressures and high natural values, leading to management conflicts. Foraminiferal assemblages were analysed in surface sediment samples, with known concentrations of several metal(loid)s, and in dated core samples. Correlations were found between metal(loid)s, organic matter, grain size, and salinity (as a proxy for …

Resource Assessment Report No. 4: North Coast Demersal Scalefish Resource - Kimberley 2025 Assessment, Fabian Trinnie, Rubie Evans-Powell, Ainslie Denham, Sybrand A. Hesp, Corey Wakefield, Craig L. Skepper, Brett Crisafulli, Stephen J. Newman

Resource Assessment Reports

The North Coast Demersal Scalefish Resource (NCDSR) comprises ecological suites of tropical demersal fish species that occur predominantly in inshore waters (20–250 m deep) and offshore waters ( > 250 m deep) of the North Coast Bioregion (NCB). More than 60 demersal species are landed by fisheries operating in the Kimberley and Pilbara regions of the NCB each year, including high-value snappers (Lutjanidae), groupers (Epinephelidae), and emperors (Lethrinidae). As outlined in the NCDSR Harvest Strategy (DPIRD, 2017), the Kimberley resource is currently monitored through annual reviews of total removals and catch rate trends of indicator species, as well as periodic (every …

Iowa Waste Reduction Center Newsletter, September 2025, University Of Northern Iowa. Iowa Waste Reduction Center.

Iowa Waste Reduction Center Newsletter

Contents:

--- Meet our Energy Efficiency Extraordinaire
--- Where’re having a party! Will you be there?
--- Textile Brewing Company joins the Platinum Green Brewery Certification Family
--- Iowa DNR composting rules now open for public comment
--- Free Assistance Available
--- Industry News

(Si15-068) Advanced Numerical Methods For The Solution Of Nonlinear Fisher Equation, Vikash Vimal, Richa Kumari, Ashish Awasthi

Applications and Applied Mathematics: An International Journal (AAM)

This paper examines the use of advanced numerical techniques to approximate solutions of the Fisher equation with higher-order accuracy. This technique integrates the method of lines with a strong stability-preserving Runge–Kutta scheme of orders four and five stages (SSPRK-54) for the numerical formulation. This scheme is then tested on two examples and the results show that it is more efficient than existing methods and requires less computing power. These equations are widely used across scientific and engineering disciplines, with particular relevance in biomedical studies, such as estimating the boundary size of tumors. The difficulties arising from their nonlinear nature are …

Exploring Parameter-Efficient Fine-Tuning Techniques For Code Generation With Large Language Models, Martin Weyssow, Xin Zhou, Kisub Kim, David Lo, Houari A. Sahraoui

Research Collection School Of Computing and Information Systems

Large language models (LLMs) demonstrate impressive capabilities to generate accurate code snippets given natural language intents in a zero-shot manner, i.e., without the need for specific fine-tuning. While prior studies have highlighted the advantages of fine-tuning LLMs, this process incurs high computational costs, making it impractical in resource-scarce environments, particularly for models with billions of parameters. To address these challenges, previous research explored in-context learning (ICL) and retrieval-augmented generation (RAG) as strategies to guide the LLM generative process with task-specific prompt examples. However, ICL and RAG introduce inconveniences, such as the need for designing contextually relevant prompts and the absence …

Evaluating Early Successional Habitat Selection By Herpetofauna And Pygmy Rattlesnakes In Tennessee, Carlin Frost

Tennessee State University Alumni Theses and Dissertations

Coupled with climate change, global biodiversity declines are exacerbated by increasing anthropogenic pressures and ecosystem destruction. Amphibians and reptiles are particularly vulnerable due to their specific and narrow habitat requirements. In Tennessee, the Western Pygmy Rattlesnake (Sistrurus miliarius streckeri) is classified as Threatened and is primarily associated with floodplains adjacent to early successional habitats (ESH) in the western Interior Plateau ecoregion. These ESH support unique flora and fauna but have declined in recent decades, largely due to urban encroachment and disturbance suppression. Studying snakes is challenging because of their limited activity periods, small home ranges, and low detectability from cryptic …

Studying Satd In Drone Systems With Human-Ai Collaboration, Leevi Rantala, Lwin Khin Shar, Mäntylä Mika V., Wei Minn, Naing Tun Yan

Research Collection School Of Computing and Information Systems

Background: Self-Admitted Technical Debt (SATD) refers to sub-optimal solutions that developers acknowledge within the source code. SATD research originated on Java projects but is expanding to other domains. We focus on SATD in drones, which are used for various critical tasks.Aims: The primary objective is to investigate SATD in drone systems. The second aim is to explore the integration of AI and human collaboration for SATD labelling and classification.Method: We conducted a sample study of SATD comments in drone systems (14 open source, 4 SDKs) to analyse the quantity and types of SATD comments present. Our study incorporates collaboration between …

Dgl: Dynamic Global-Local Information Aggregation For Scalable Vrp Generalization With Self-Improvement Learning, Yubin Xiao, Yuesong Wu, Rui Cao, Di Wang, Zhiguang Cao, Xuan Wu, Peng Zhao, Yuanshu Li, You Zhou, Yuan Jiang

Research Collection School Of Computing and Information Systems

The Vehicle Routing Problem (VRP) is a critical combinatorial optimization problem with wide-reaching real-world applications, particularly in logistics, transportation. While neural network-based VRP solvers have shown impressive results on test instances similar to training data, their performance often degrades when faced with varying scales and unseen distributions, limiting their practical applicability. To overcome these limitations, we introduce DGL (Dynamic Global-Local Information Aggregation), a novel model that combines global and local information to effectively solve VRPs. DGL dynamically adjusts local node selections within a localized range, capturing local invariance across problems of different scales and distributions, thereby enhancing generalization. At the …

The Floodnet Community Engagement Guide, Véronëque Ignace, Sofia Mariyamis, Kendra Krueger, Polly Pierone, Hayley Elszasz, Hannah Eisler Burnett

The Science and Resilience Institute at Jamaica Bay, SRIJB

"At the Intersection of Science, Policy, and Community: The FloodNet NYC Community Engagement Strategy” is a public-facing community engagement guide that documents the strategies, tools, and lessons developed through FloodNet NYC, a cross-sector partnership among researchers at NYU and CUNY and New York City agencies. Designed as a practical resource, this guide shares our approach to community engagement and dissemination so that researchers, practitioners, community organizations, and public agencies can adapt these methods to their own urban climate science projects and other community-centered efforts that address climate challenges.

Grounded in community-based participatory research (CBPR) principles, the guide presents community engagement …

Stable And Fair Cost Allocation In Platform-Enabled Lcl Consolidation, Pang Jin Tan, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

Many logistics platforms enable collaboration between agents to reduce costs, but determining fair pricing remains challenging when agents have pre-existing partnerships. This paper introduces a cooperative game theory framework to model platform-mediated collaboration, modeling the platform as an additional player. We present a novel characteristic function that distinguishes between partial collaborations (existing relationships) and full collaborations (platform-enabled). Using Shapley value, we derive fair cost allocations and platform charges that reflect each participant's contribution. We address stability concerns through an optimization model that minimizes platform subsidies while preventing profitable deviations. The framework is demonstrated through an application in freight forwarding for …

Multi-Modal Depth Estimation Using Camera And Mmwave Sensors, Alston D. Devero-Belfon

Student Theses

For accurately estimating the depth of environments with varying lighting conditions, reliable methods are limited. By utilizing wireless sensor technology in conjunction with cameras, a wide range of environments can be visualized, and objects within these environments can be tracked and monitored. Such methods offer cost-effective alternatives and provide a more secure, data-at-rest option for individuals with low vision, while also enhancing machine perception. In this work, we develop such a prototype that utilizes wireless sensors and cameras, which act in sync, enabling us to estimate the depth of objects within varying lighting environments to a level that is recognizable …

Uncovering Environmental Justice Disparities: Tree Canopy Inequality In Low-Income Marginalized Communities, Emmanuel Wallace

Tennessee State University Alumni Theses and Dissertations

Tree canopy inequality in low-income marginalized communities is a direct result of decades of environmental injustice within urban communities in the United States, specifically the Home Owner’s Loan Corporation. This phenomenon underscores the disproportionate distribution of tree cover, green spaces, and their myriad benefits in neighborhoods marked by poverty and social vulnerability. Rooted in a history of discriminatory urban planning practices and systemic disparities, tree canopy inequality has profound consequences on residents' health and well-being. It leads to urban heat island effects, reduced air quality, and additional socio-economic disparities. On average, urban communities of color have 33% less tree canopy …

Exploring The Cooling Effects Of Urban Forests In Summer, Xiaoyue Li

Tennessee State University Alumni Theses and Dissertations

As a nature-based solution to combat urban heat islands, urban forests can provide cooling and other co-benefits. However, little is known about the cooling benefits of urban forests in parking lots and under different land covers, especially with field observations. To address this gap, we measured air temperature (AT) and relative humidity (RH) and calculated heat index (HI) in three urban settings in Nashville, TN, in summer 2024. The research sites included: 1) Two different parking lots: parking lots with and without trees; 2) Three different distances from trees within the parking lot with trees: under trees, near trees, and …

Pd-Catalyzed Microwave Irradiated Cross-Coupling Of Carboxylic Acids And Amino Alcohols., Md Shahin Reza

Tennessee State University Alumni Theses and Dissertations

Amino esters are valuable pharmaceutical intermediates with anesthetic properties and applications as prodrugs. In this study, we have developed a new microwave-assisted method for the cross-coupling of aromatic carboxylic acids with amino alcohol using a palladium catalyst. This approach has enabled an efficient synthesis of amino esters, overcoming challenges such as side reactions and product separation.

Effects Of Tillage On Wheat Canopy, Stephanie Adanna Anyanwu

Tennessee State University Alumni Theses and Dissertations

Abstract Wheat canopy cover and greenness are useful crop performance indicators that provide significant insight into the performance of wheat crops. Understanding the effects of no-till and conventional tillage practices on winter wheat canopy cover and greenness will help inform agronomic decision making in wheat production. Therefore, the aim of this study was to assess the effects of no-till and conventional tillage practices on wheat canopy cover and greenness using drones. Wheat canopy cover mapping was carried out using deep learning supervised classification. The normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI) and optimized soil adjusted vegetation …

Distribution And Occurrence Of Cyanohabs, Nutrients, And Water-Quality Data In The Tennessee State University Wetland, Kristi Hill

Tennessee State University Alumni Theses and Dissertations

Toxic algal blooms are an expanding issue for aquatic ecosystems because they are hazardous to human and environmental health. Algal blooms naturally occur from the combination of warm temperatures, sunlight, and abundant nutrients in water, but what triggers them to become harmful algal blooms (HABs) and begin synthesizing toxin is not well understood, and their threat to freshwater sources was not heavily researched until recently. Microcystin (MC), a freshwater algal toxin, is a HABs toxin which targets the liver and causes sickness and death in humans and animals (pets, livestock, and wildlife). Urban freshwater wetlands are among the most HAB …

Deep Graph Anomaly Detection: A Survey And New Perspectives, Hezhe Qiao, Hanghang Tong, Nanyang Technological University, Irwin King, Charu Aggarwal, Guansong Pang

Research Collection School Of Computing and Information Systems

Graph anomaly detection (GAD), which aims to identify unusual graph instances (e.g., nodes, edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its significance in a wide range of applications. Deep learning approaches, graph neural networks (GNNs) in particular, have been emerging as a promising paradigm for GAD, owing to its strong capability in capturing complex structure and/or node attributes in graph data. Considering the large number of methods proposed for GNN-based GAD, it is of paramount importance to summarize the methodologies and findings in the existing GAD studies, so that we can pinpoint effective model …

An Efficient Security-Enhanced Accountable Access Control For Named Data Networking, Jianfei Sun, Yuxian Li, Xuehuan Yang, Guomin Yang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Named Data Networking (NDN) is embraced as the crucial implementation of Information-Centric Networking (ICN), enhancing content distribution and caching efficiency through edge routers. However, existing NDN architectures face significant security and privacy challenges, including: (a) a lack of secure and efficient access control; (b) inadequate support for flexible and selective content management by content publishers; (c) insufficient implementation of accountability and privilege revocation mechanisms. To handle these challenges, we propose ESAS, the first-ever Efficient Security-enhanced Accountable Access Control Scheme for NDN. Specifically, our ESAS incorporates anonymous authentication using group signatures at network routers to prevent unauthorized access, employs key-aggregation-based access …

Static Analysis As A Feedback Loop: Enhancing Llm-Generated Code Beyond Correctness, Scott Blyth, Sherlock Licorish, Christoph Treude, Markus Wagner

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have demonstrated impressive capabilities in code generation, achieving high scores on benchmarks such as HumanEval and MBPP. However, these benchmarks primarily assess functional correctness and neglect broader dimensions of code quality, including security, reliability, readability, and maintainability. In this work, we systematically evaluate the ability of LLMs to generate high-quality code across multiple dimensions using the PythonSecurityEval benchmark. We introduce an iterative static analysis-driven prompting algorithm that leverages Bandit and Pylint to identify and resolve code quality issues. Our experiments with GPT-4o show substantial improvements: security issues reduced from >40% to 13%, readability violations from >80% …

Educator Perceptions Of Devops Teaching Recommendations And Their Alignment With Common Challenges, Marcelo Romulo Fernandes, Pablo Paiva, Samuel Lucas De Moura Ferino, Roberta Coelho, Christoph Treude, Eduardo Aranha, Uirá Kulesza

Research Collection School Of Computing and Information Systems

DevOps education presents unique pedagogical challenges due to the diversity of tools, rapid technological change, and the multidisciplinary nature of the field. Although previous work has proposed recommendations to address these challenges, it is unclear how educators perceive these recommendations and whether they align with the challenges encountered in practice. In this paper, we present a quantitative and qualitative methods study involving 11 DevOps educators who interacted with Improve, a tool that presents a curated set of educational challenges and recommendations derived from previous literature. Educators indicated which recommendations they already use, which they intend to use, and which challenges …

A Difficult Act To Maintain, J.G. Allen

Research Collection Yong Pung How School Of Law

In "Where is Singapore's AI regulation headed?" (Issues, Summer 2025), Manoj Harjani starts by recounting its delicate balancing act in governing artificial intelligence. The city-state is able to maintain credibility on the global stage while remaining pragmatically grounded in technical experimentation, maintaining policy realism without overcommitting to rigid legal frameworks. The choice to adopt a regulatory “light touch” is often framed as a way to maintain flexibility, avoid overregulation, and enable innovation. But this obscures the extent to which this posture is itself a political and economic settlement—one that reinforces Singapore’s position in the global digital economy by facilitating capital …

Bridging The Great Wall: China’S Evolving Cross-Border Data Flow Policies And Implications For Global Data Governance, Sheng Zhang, Henry S. Gao

Research Collection Yong Pung How School Of Law

Despite the rapid expansion of the digital economy, the global regulatory framework for data flows remains fragmented, with countries adopting divergent approaches shaped by their own regulatory priorities. As a key player in the Internet economy, China’s approach to cross-border data flows (CBDF) not only defines its domestic digital landscape but also influences emerging global norms. This paper takes a comprehensive view of the evolution of China’s CBDF regime, examining its development through both domestic and international lenses. Domestically, China’s regulation of CBDF has evolved from a security-first approach to one that seeks to balance security with economic development. This …