Physical Sciences and Mathematics Commons^™

A Model Intercomparison Study To Investigate Mixing Characteristics In Non-Precipitating Stratocumulus Clouds, Fan Yang, Kyoung Ock Choi, Kamal Kant Chandrakar, Fabian Hoffmann, Pei Hou, Steve Krueger, Chunsong Lu, Mikhail Ovchinnikov, Yangze Ren, Shin Ichiro Shima, Peng Wu, Chongzhi Yin, Zeen Zhu, Seong Soo Yum

Michigan Tech Publications

Recent aircraft observations of marine stratocumulus clouds consistently showed that cloud microphysical relationships vary with altitude, indicating inhomogeneous mixing characteristics near cloud top and homogeneous mixing characteristics in mid-levels of clouds. Here, we conduct model intercomparison of an idealized, non-precipitating stratocumulus cloud to evaluate model consistency and examine whether simulations can reproduce the observed mixing characteristics. The results show that eleven large-eddy simulations with various dynamics and microphysics schemes show good agreement on the thermodynamical, microphysical, and dynamical properties of the stratocumulus-topped boundary layer in a steady state. The inter-model spread in steady-state liquid water path is significantly reduced compared …

Intelligent Deep Learning-Based Sign Language Translation System, Nada Rasem Shahin

Dissertations

The Deaf and Hard of Hearing (DHH) community uses sign language as a primary means of communication. However, the shortage of sign language interpreters and the existence of hundreds of sign languages limit accessibility and inclusion. Sign Language Machine Translation (SLMT) systems present a promising solution for bridging the communication gap between the DHH and the hearing individuals, supporting inclusive societies. In smart cities, such systems play an essential role in improving the quality of life on a community level. In particular, as the population’s well-being is critical, developing intelligent assistive technologies, such as SLMT systems, is necessary to provide …

Fully Decentralized Hierarchical Federated Learning At The Edge With Post-Quantum Secure Communication, Tariq Qayyum

Dissertations

Federated learning (FL) enables collaborative model training without centralizing raw data, but deploying FL at scale in real edge environments remains challenging because iterative training and aggregation must operate over heterogeneous, resource-constrained, and often mobile devices with time-varying connectivity. Conventional hierarchical federated learning (HFL) partially mitigates communication cost by introducing fog/edge aggregation, yet many designs retain cloud-based global aggregation and cloud-centric coordination. This places wide-area network latency on the critical path of every training round, creates a single point of failure, and limits responsiveness as model sizes and federation scale grow. Moreover, moving coordination and aggregation closer to the edge …

A Comparative Study On Performance Of Iot-Driven Ml-Enabled Forecasting Models For Efficient Air Quality Monitoring, Bara Ksiksi

Theses

Air pollution is one of the most critical environmental challenges affecting public health globally, responsible for approximately 4.2 million premature deaths annually according to the World Health Organisation. This thesis presents a comparative study of IoT-driven machine learning forecasting models for air quality monitoring in Abu Dhabi, UAE, introducing a zonal approach combined with satellite-based spatial validation. The primary objective is to evaluate forecasting performance across three distinct activity zones using ground station data from the Environment Agency Abu Dhabi (EAD), and to incorporate a spatial validation component using satellite imagery to assess the consistency of ground-based predictions at a …

Ideals And Lattices In Number Fields, Sarah Ali Alyammahi

Theses

This thesis investigates algebraic number fields and their rings of integers, which

generalize the ring of integers ℤ in ℚ. The study focuses on ideals, units, and ideal class

groups, which describe the arithmetic structure of number fields and the failure of unique factorization. Key invariants such as the norm, trace, and discriminant are developed and applied, with particular emphasis on quadratic number fields and classical examples such as the Gaussian and Eisenstein integers. Some explicit computations of ideal class groups are carried out. The thesis also explores connections with lattice theory by interpreting rings of integers as lattices and …

Llm-Driven Mission Control And Autonomous Planning For Search-And-Rescue Uavs: A Simulation-Based Evaluation, Naser Bader Alsaedi

Theses

Unmanned aerial vehicles (UAVs) are increasingly used in search‑and‑rescue (SAR) missions, yet many systems still rely on fragmented software where mission design, perception, and flight control are configured separately. This thesis examines whether a unified AI‑driven framework can reduce configuration effort and operator workload in UAV‑based SAR operations. The proposed system integrates natural‑language mission specification using a large language model (LLM) (LLaMA 3.1), autonomous coverage planning, YOLOv8‑based victim detection, and PX4/MAVSDK control within a single architecture. Operators describe missions through free‑form text or a graphical interface; the model converts these descriptions into structured mission parameters that are automatically planned and …

Transport Of Quantum Walks In Electric Fields, Yousef Mohammad Yousef Salah

Theses

This thesis presents an analysis of transport in one-dimensional discrete-time quantum walks (DTQWs) on the Hilbert space ℓ²(ℤ) ⊗ ℂ². Quantum walks serve as fundamental models of coherent quantum transport and exhibit ballistic spreading driven by superposition and interference. The primary focus of this work is the review and derivation of sharp maximal velocity bounds for several classes of quantum walk step operators, including the shift-coin walk, the split-step walk, and models with constant as well as position-dependent coin operators. We establish general a priori bounds that remain valid beyond the translation-invariant regime. For homogeneous models, Fourier and spectral analysis …

Ai Models As Cultural Beings: Investigating Ai Cultural Biases And The Impact Of Cultural Alignment On Human-Ai Creative Collaboration, Choon Ngee Tan, Meng Han, Roy Y. J. Chua, Chi-Ying Cheng

Research Collection Lee Kong Chian School Of Business

Existing research on AI cultural biases predominantly focuses on Western models, overlooking critical gaps in non-Western models. We conduct a comparative analysis of AI models – ChatGPT (U.S. developed) and ErnieBot (China developed) – from different cultures to investigate how corresponding cultural biases manifest in their outputs. Additionally, we examine how cultural alignment between human users and AI models impacts their collaborative creative performance and the underlying psychological mechanisms. In Study 1, multi-choice prompt with zero-shot technique was used to evaluate cultural biases in four widely used AI models – ChatGPT-3.5/4, ErnieBot-3.5/4 – comparing their responses to established cultural psychometric …

Application Of The Tridiagonal Representation Approach And The J- Matrix Method Of Scattering In Theoretical Physics, Tunde Joseph Osunmusanmi

Dissertations

This dissertation is about the application of the Tridiagonal Representation Approach (TRA) in handling linear phenomenons, and for the first time, the J-matrix method of scattering in handling nonlinear phenomenons. The TRA is an algebraic method for solving linear ordinary differential equations of the second order. The advantage of the method in being algebraic is reinforced by the analytic power of orthogonal polynomials and special functions. On the computational side, it is favored as being reliant on powerful numerical techniques that deal with tridiagonal matrices such as Gauss quadrature and continued fraction. In the method, the solution of the differential …

Design And Synthesis Of Coke-Resistant Nickel-Based Catalysts For Efficient Methane Reforming And Hydrogen Production, Abdulmuizz Adamson

Dissertations

Ni-based catalysts are widely employed in methane reforming and cracking processes, however, their industrial application remains constrained by rapid deactivation caused by carbon deposition (coking). This thesis investigates the role of iron on the catalytic performance and coking resistance of the catalysts with focus on the mode of incorporation, oxidation state, reaction environment, and concentration of iron present as dopant.

The results show that doping the γ-Al₂O₃ support of Ni catalysts with well-dispersed iron ions in the support matrix significantly enhances catalyst stability during methane partial oxidation (POM) and combined dry reforming and partial oxidation (DR-POM). At 700°C, catalysts containing …

2026 - Tennessee Spring Climate Summary, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Seasonal and Annual Climate Summaries

No abstract provided.

Rethinking Artifact Evaluation For Software Engineering In The Age Of Generative Ai, Christoph Treude, Christopher M. Poskitt, Rashina Hoda

Research Collection School Of Computing and Information Systems

Peer review in software engineering research operates under tight time constraints, while generative AI has substantially reduced the human effort required to produce polished research narratives. Reviewer attention is often spent on aspects of submissions such as writing quality or literature positioning that have become relatively less effort-intensive to address, rather than on evaluating the scientific substance of a paper. At the same time, assessing whether methods are implemented correctly, analyses are sound, and claims are supported by evidence remains effort-intensive and dependent on human expertise. In software engineering research, this substance is frequently embodied in artifacts, including code, data, …

Ac Magnetic Measurements With A Self-Oscillating Lc Circuit And Its Application To University Education, Harshit Agarwal, Oleksandra Uralska, Jasmin Billingsley, Maxim Yamilov, Hyunsoo Kim

Physics Faculty Research & Creative Works

Editor's Note: This paper is a welcome tutorial on a technique for AC magnetometry, allowing the measurement of a sample's magnetic susceptibility with considerable sensitivity. The authors present a low-cost setup based on an LC self-oscillator circuit, which has an operating frequency that is rather sensitive to the susceptibility of a sample introduced within its inductor. After characterizing the performance of the circuit, data taken on a high-temperature superconducting sample are presented, demonstrating how the samples superconducting transition can be detected by this noncontact magnetic method, independent of the usual resistivity-based detection. This project will be of interest to instructors …

Correlated Many-Body Quantum Dynamics Of The Peregrine Soliton, D. Diplaris, G. A. Bougas, P. G. Kevrekidis, C. L. Hung, P. Schmelcher, S. I. Mistakidis

Physics Faculty Research & Creative Works

We explore the correlated dynamics underlying the formation of the quantum Peregrine soliton, a prototypical rogue-wave excitation, utilizing interaction quenches from repulsive to attractive couplings in an ultracold bosonic gas confined in a one-dimensional box trap. The latter emulates the so-called semiclassical initial conditions and the associated gradient catastrophe scenario facilitating the emergence of a high-density, doubly localized waveform. The ensuing multiorbital variant of the Peregrine soliton features notable deviations from its mean-field sibling, including a reduced peak amplitude, wider core, absence of the side density dips, and earlier formation times. Moreover, Peregrine soliton generation yields coherence losses, while experiencing …

Federated Retrieval-Augmented Generation For Cybersecurity In Resource-Constrained Iot And Edge Environments: A Deployment-Oriented Scoping Review, Hangyu He, Yuan, Kai Wu, Wei Ni

Research outputs 2022 to 2026

Cybersecurity operations in IoT and edge environments require fast, evidence-grounded decisions under strict resource and trust constraints. While large language models can support triage and incident analysis, their parametric knowledge may be outdated and prone to hallucination. Retrieval-augmented generation (RAG) improves grounding by conditioning responses on retrieved evidence, but also introduces new risks such as knowledge-base poisoning, indirect prompt injection, and embedding leakage. Federated learning enables collaborative adaptation without centralizing sensitive data, motivating federated RAG (FedRAG) architectures for distributed cybersecurity deployments. This study presents a deployment-oriented scoping review of FedRAG for cybersecurity. The review follows PRISMA-ScR reporting guidance and synthesizes …

Traveling Waves In Reaction-Diffusion Equations With Delay In The Diffusion Term, William Barker, Van Minh Nguyen

Faculty Scholarship

We investigate the existence of traveling wave solutions for the reaction–diffusion equation ∂u(x,t)∂t=Δu(x,t−τ1)+f(u(x,t),u(x,t−τ2)), where τ1, τ2 > 0. This model is motivated by ecological applications in which migration rates incorporate historical effects and reproduction/death processes are subject to time delays at a given location. To address such systems under standard monotonicity assumptions, we extend the classical monotone iteration method. A key step involves a thorough investigation of the Green function associated with the functional equation x″(t)−ax′(t+r)−bx(t+r)=f(t), where a ≠ 0 and b > 0. Building on the resulting framework, we construct quasi-upper and lower solutions for the Belousov–Zhabotinski equations, thereby demonstrating the …

Trustgen: A Platform Of Dynamic Benchmarking On The Trustworthiness Of Generative Foundation Models, Yue Huang, Et. Al

Research Collection School Of Computing and Information Systems

Generative foundation models (GenFMs), such as large language models and text-to-image systems, have demonstrated remarkable capabilities in various downstream applications. As they are increasingly deployed in high-stakes applications, assessing their trustworthiness has become both a critical necessity and a substantial challenge. Existing evaluation efforts are fragmented, rapidly outdated, and often lack extensibility across modalities. This raises a fundamental question: how can we systematically, reliably, and continuously assess the trustworthiness of rapidly advancing GenFMs across diverse modalities and use cases? To address these gaps, we introduce TrustGen, a dynamic and modular benchmarking system designed to systematically evaluate the trustworthiness of GenFMs …

Draft Final 2025 Bpsou Subdrain Data Summary Report (Dsr), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Learning Techniques In Prediction Of Functional Epigenomic Events, Mohammad Shiri

Computer Science Theses & Dissertations

Accurately predicting functional epigenomic events from DNA sequences is critical to understanding gene regulation and the functional impact of non-coding variants. Despite considerable progress, critical challenges hamper the effectiveness and efficiency of existing deep learning approaches. These challenges include negative transfer in multi-task learning (MTL), suboptimal network architectures, and pervasive label noise, particularly the positive-unlabeled problem arising from data sparsity in single-cell assays. This dissertation presents a cohesive framework of novel learning techniques to effectively address these challenges. First, a highly scalable task grouping framework is presented to mitigate negative transfer in deep MTL. This method clusters tasks based on …

Pushing High-Performance Private Inference Towards Resource-Constrained Edge Clients, Xiangrui Xu

Computer Science Theses & Dissertations

The widespread adoption of Machine Learning as a Service (MLaaS) has enabled resource constrained edge clients, such as mobile and IoT devices, to leverage powerful deep learning mod els hosted on the cloud. However, this paradigm introduces critical privacy challenges regarding the client’s sensitive input data and the server’s proprietary model parameters. While cryptographic techniques like Homomorphic Encryption (HE) and Multi-Party Computation (MPC) enable Private Inference (PI), existing frameworks impose prohibitive computational and communication overheads that render them impractical for edge deployment. This dissertation introduces three novel frameworks—SPOT, LUTless, and PrivShap—to systematically address the efficiency bottlenecks of PI in edge …

Radicals Transforming Organic Matter: Implications For Non-Pyrogenic Black Carbon And Nitrogen Formation, Molecular Dynamics In Tropical Soils, And Lignin Valorization, João Vitor Dos Santos

Chemistry & Biochemistry Theses & Dissertations

Radicals drive the transformation of organic matter in natural and engineered systems, altering its structure, reactivity, and stability. Condensed aromatic carbon (ConAC) and condensed aromatic nitrogen (ConAN) are among the most persistent forms of organic matter in soils and are traditionally interpreted as products of biomass burning. This dissertation challenges the long-standing assumption that these materials are exclusively pyrogenic, showing that iron-mediated radical chemistry can generate condensed aromatic structures under ambient conditions.

Chapters II and III show that Fenton-like radical oxidation of lignin-rich biomass produces condensed aromatic structures compositionally similar to pyrogenic black carbon and black nitrogen when assessed with …

Draft Final Silver Bow Creek Conservation Area Materials Management Plan Revision 1, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Final Silver Bow Creek Conservation Area (Sbcca) Construction Quality Assurance Plan (Cqap), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Advancing Paleontologic Mapping: A Gis And Drone Photogrammetry Approach To The Lance Formation Beds Of Glenrock, Wyoming, William H. Steelman, Monika Angner, Norman S. Levine, W S. Persons

The Compass: Earth Science Journal of Sigma Gamma Epsilon

Small museums and volunteer-driven field programs often lack the resources required for detailed geological mapping, limiting the stratigraphic data associated with their fossil collections. To address this challenge, an affordable and replicable mapping workflow was developed that combined drone-based photogrammetry, GIS analysis, and targeted structural measurements using the Glenrock Exposure of the Lance Formation as a study site. The resulting 3D outcrop models, bedding-plane reconstructions, and 750-m-thick stratigraphic column provide the first comprehensive spatial framework for 35 fossil quarries curated by the Glenrock Paleon Museum. The results reveal tightly clustered fossil-bearing horizons, a long interval lacking preservation, and a notable …

The Compass - Volume 95 Issue 1 - Complete Issue, Scott R. Beason

The Compass: Earth Science Journal of Sigma Gamma Epsilon

No abstract provided.

Who Said Cve? How Vulnerability Identifiers Are Mentioned By Humans, Bots, And Agents In Pull Requests, Pien Rooijendijk, Christoph Treude, Mairieli Wessel

Research Collection School Of Computing and Information Systems

Vulnerability identifiers such as CVE, CWE, and GHSA are standardised references to known software security issues, yet their use in practice is not well understood. This paper compares vulnerability ID use in GitHub pull requests authored by autonomous agents, bots, and human developers. Using the AIDev pop dataset and an augmented set of pull requests from the same repositories, we analyse who mentions vulnerability identifiers and where they appear. Bots account for around 69.1% of all mentions, usually adding few identifiers in pull request descriptions, while human and agent mentions are rarer but span more locations. Qualitative analysis shows that …

Iron Deficiency And Depressive Symptoms: Effects Of Racial And Ethnic Disparity In Two Reproductive-Aged Us Women, Alexis Monkhouse

USF Tampa Graduate Theses and Dissertations

Problem: Reproductive- aged women are one of the populations most at risk for iron deficiency and depression in the United States. While many studies have looked at the relationship between these two illnesses, none have explored it from a biocultural framework, examining how race/ethnicity affects this relationship. Using data from NHANES, this thesis examined the relationship between iron status (total body iron) and depressive symptoms (PHQ-9) among reproductive-aged women in the U.S. and how race/ethnicity affects it. Methods. NHANES survey data from 2005-2010 were pulled. Demographic and laboratory data were used to determine eligibility, total body iron, and depressive scores. …

Draft Final Butte Priority Soils Operable Unit (Bpsou) Insufficiently Reclaimed Sites Sister Dump (Bres No. 38) Remedial Action Work Plan (Rawp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

High-Resolution Monitoring Of Intra-Seasonal Agricultural Drought Using Sentinel-2 And Machine Learning Across Bimodal Growing Seasons In Kenya, S. Mohammad Mirmazloumi, Harison Kipkulei, Rose Waswa, Tobias Landmann, Tom Dienya, Maximilian Schwarz, Fabrizio Ramoino, Clément Albergel, Gohar Ghazaryan

All Peer-Reviewed Publications

Drought presents significant challenges to agriculture, threatening food security and livelihoods, across many regions. In Kenya, recurrent droughts across diverse agro-ecological zones emphasize the urgent need for reliable and scalable drought assessment methods. Although drought assessment with various datasets has been carried out for this region, many of them often use course or moderate resolution data. This study uses high-resolution Sentinel-2 observations and machine learning to monitor intra-seasonal crop conditions and assess drought impacts across bimodal growing seasons. Using pixel-based supervised random forest models trained with multiple vegetation indices as input, we classify croplands into drought-affected and unaffected areas. The …

Plasma N6 Polyunsaturated Fatty Acid Levels And Risk For Dementia: A Prospective Observational Study From The United Kingdom Biobank, Aleix Sala-Vila, Nathan Tintle, Jason Westra, Evan Dejong, Tom J. Clark, Paige Miller, Martha A. Belury, William S. Harris

Faculty Work Comprehensive List

Background

Some recent observational studies have linked higher blood linoleic acid (LA) concentration with lower risk of cardiometabolic diseases. However, these associations have not been observed for other n-6 polyunsaturated fatty acids (PUFAs), which, in some settings, have been linked to higher disease risk, and thus the controversy over the health effects of n-6 PUFAs remains. Dementia is a paradigmatic case of it.

Objectives

To investigate the association of LA and non-LA n-6 PUFAs with the risk of incident dementia.

Methods

We included 273,795 participants from the UK Biobank free of dementia at baseline for whom plasma n-6 PUFAs data …