Physical Sciences and Mathematics Commons^™

Macroscopic Classical And Quantum Models Of Inverse Compton Scattering, Emerson Penn Rogers

Physics Theses & Dissertations

Inverse Compton sources — in which a relativistic electron beam scatters a laser pulse to produce tunable, collimated, high-energy radiation—have emerged as among the most promising compact radiation sources, with applications ranging from nuclear photonics to medical and nanoscale imaging and metrology. The most viable current tabletop configuration couples laser-wakefield acceleration with inverse Compton scattering, producing GeV-scale electron beams over millimeter distances. As laser intensities increase and electron energies grow, the interaction enters the radiation reaction regime, where the energy radiated by the electron becomes a significant fraction of its kinetic energy. Predicting the scattered electron energy spectrum — the …

A Comparison Of Ahdrift And Pitfall Traps For Surveying Small Vertebrates, Gabrielle Risko

All-Inclusive List of Electronic Theses and Dissertations

Understanding species occupancy is necessary to inform wildlife management and conservation decisions. To minimize the invasiveness of occupancy studies, some wildlife researchers have deployed game cameras to detect the presence of small vertebrates instead of utilizing stressful traditional live-capture methods. The Adapted Hunt Drift Fence Technique (AHDriFT) is an increasingly popular method for modifying and deploying camera traps to detect small vertebrates; however, no studies have directly compared AHDriFT to traditional pitfall traps operating at the exact same time, even though both are deployed along drift fences to capture small vertebrates. I compared these two methods (pitfall trapping and AHDriFT …

Reconstruction Of Copepod Populations In Sierra Nevada Lakes Using The Diatom Fragilaria Cyclopum, Rorie Reisner

All-Inclusive List of Electronic Theses and Dissertations

Copepods are one of the most abundant types of zooplankton, and their populations are often a vital part of the food chain, but their past populations cannot be estimated from lake sediments because their exoskeleton usually does not fossilize. Gull Lake is a glacial lake located in the subalpine region of the Sierra Nevada, California, which stands out due to the presence of the diatom Fragilaria cyclopum, which is rarer in diatom assemblages. Fragilaria cyclopum exclusively grows on copepods in lakes. Diatoms have an opaline silica skeleton that often fossilizes in lake sediments, so we can use this relationship with …

Reconstructing Stand-Age Structure In A Wet Miombo Woodland Using Dendrochronology, Zambia, Clara Tiyembekeze Zulu

All-Inclusive List of Electronic Theses and Dissertations

Dendrochronological research in tropical Africa has remained limited, leaving critical gaps in stand-age structure and climate-growth relationships in seasonally dry tropical woodlands. This study reconstructed stand-age structure and assessed climate sensitivity in the wet Miombo woodland of African Explosives Limited (AEL) forest, Copperbelt Province, Zambia. Field sampling was conducted during four African Dendrochronology Fieldschools (2021-2024) using a Modified Variable Area Transect (MVAT) design across nine plots. The series intercorrelation was 0.351; the average mean sensitivity was 0.423. The oldest tree was 170 years old, and more than 70% of the sampled species showed potential for dendrochronology. Markhamia obtusifolia was the …

Performance Of The Denitrification-Decomposition Model In Simulating Agronomic Properties And N2o Emissions Under Smallholder Climate-Smart Farming Systems, Esphorn Kibet, Collins M. Musafiri, Milka Kiboi, Onesmus K. Ng'etich, David K. Kosgei, Abdirahman Zeila, Franklin Mairura, Felix K. Ngetich

All Peer-Reviewed Publications

The DeNitrification-DeComposition (DNDC) model is a crucial tool for estimating soil greenhouse gas fluxes and understanding soil-plant interactions. This study evaluates the performance of the DNDC model in simulating soil temperature, moisture, crop yield, and nitrous oxide (N2O) fluxes across different land utilization types in Western Kenya. The land utilization types included: i) agroforestry M (agroforestry with Markhamia lutea, ii) sole sorghum, iii) agroforestry L (agroforestry with Leucaena leucocephala), iv) sole maize, and v) grazing Land, each replicated thrice. Fertilizer and manure were applied as part of the management practices, with manure applied at 2t ha⁻¹. Soil greenhouse …

Multi-Particle Reactions In Lattice Field Theories: From Euclidean To Quantum Computations, Marco Antonio Carrillo Bernal

Physics Theses & Dissertations

Amplitudes of multi-particle processes provide a probe of the dynamics described by relativistic quantum field theories. In the context of quantum chromodynamics, these amplitudes encode the way in which quarks and gluons are bound together by the strong nuclear force to form hadrons and govern their interactions. The non-perturbative nature of the strong interactions complicates the study of hadronic amplitudes from first principles. The most effective way of studying hadronic reactions non-perturbatively is the numerical framework known as lattice quantum chromodynamics. This method relies on theoretical formalisms that establish relations between numerical observables, such as finite-volume energy levels and matrix …

Understanding The Correlation Between Prediction Performance And Trust In Ai Through Scenario-Based Tasks, Likhitha Kammara

Theses and Dissertations

Trust in artificial intelligence is commonly assessed through self-reported scales or behavioral reliance, yet behavioral reliance is retrospective and can only be observed after a decision has already been made. This thesis examines whether prediction accuracy — a user's ability to predict what an AI system will recommend before its output is revealed — can serve as a prospective correlate of trust in the same empirical sense as behavioral reliance. The study was conducted in two phases using scenario-based AI decision tasks across disaster response, healthcare, and infrastructure restoration contexts, employing a between-group design in which participants either predicted AI …

Magnetic Properties And Ultrafast Spin Dynamics In Quantum Materials, Trung Kien Mac

All Graduate Theses and Dissertations, Fall 2023 to Present

Modern electronics mostly work by moving electric charge, which generates heat and hence wastes energy, especially as devices become smaller and faster. An alternative is to use the spin of electrons (a tiny magnetic property) and related “valley” states in certain atomically thin materials to store and process information more efficiently. This thesis explores how magnetism and spin behavior can be created and measured in a family of ultra-thin materials known as van der Waals quantum materials. Three kinds of materials are studied. First, atomically thin semiconductors are shaped into narrow nanoribbons. Because of their narrow size, their edges can …

Lessons Learned From The Adrenalin Load Disaggregation Challenge, András Balázs Tolnai, Zheng Ma, Igor Sartori, Clayton Miller, Stephen White, Matt Amos, Gustaf Bengtsson, Akram Hameed, Nørregaard Bo Jørgensen

Research Collection College of Integrative Studies

Crowdsourced data science competitions have emerged as a powerful mechanism for advancing research in energy informatics, offering scalable pathways for developing machine learning solutions that enhance energy efficiency and smart building operations. The ADRENALIN Load Disaggregation Challenge addressed a central problem in energy analytics—non-intrusive load monitoring (NILM) of heating and cooling loads in commercial buildings—while emphasizing the importance of model generalization across different buildings. This paper presents a comprehensive reflection on the lessons learned from organizing and executing the ADRENALIN competition, including technical insights, organizational challenges, and recommendations for future energy data challenges. In addition to the ADRENALIN case, a …

Too Hot To Work? Exploring The Average And Seasonal Effects Of Temperature Variability In The Philippine Labour Market, Paolo R. Magnata, Maire Carroline Magante

Economics Department Faculty Publications

Using temperature and employment data covering 16 regions from Q3 2003 to Q1 2022, this study examined the average and seasonal effects of temperature on year-on-year employment growth in the Philippines. Average effects are insignificant while seasonal effects are heterogeneous. During the dry and cool season (Q1), higher temperatures are associated with a temporary 0.0397% increase in employment growth. In contrast, higher temperatures in the dry and hot season (Q2) are associated with a persistent decrease of 0.1361% while higher temperatures during the wet and hot season (Q3) are associated with a persistent increase of 0.1450%, which is primarily driven …

Differential Effects Of Generative Artificial Intelligence On Formative And Summative Assessment Performance: A Quasi-Experimental Longitudinal Study, Md Istiak Morsalin

Master's Theses

Generative artificial intelligence has unsettled a core assumption of course assessment: that scores on unsupervised work reflect what students can do unassisted. This study tests whether the widespread availability of ChatGPT altered student performance differently on formative versus summative assessments in Business Analytics and Foundations, a required undergraduate quantitative-methods course taught by one instructor across nine cohorts. In a quasi-experimental longitudinal design, the Fall~2022 cohort ($n = 90$), the last to finish before ChatGPT's public release, serves as a control against eight post-ChatGPT cohorts spanning Spring~2023 through Summer~2025 ($N = 646$). Outcomes were drawn from McGraw-Hill Connect records using matched …

Examples And Evaluation Of Sustainability Research With Tribes, Aaron M. Canter, Sharon Hausam

Faculty Publications

This is a working paper produced as part of National Science Foundation project #2115169, “Transforming Rural-Urban Systems: Trajectories for Sustainability in the Intermountain West,” known as the “Intermountain West Transformation Network” (TN), led by the University of New Mexico with institutional partners at three additional universities in New Mexico, two in Arizona, and one each in Utah, Colorado, and Washington. The TN’s Indigenous and Tribal engagement was supported through a contract with Dr. Sharon Hausam, additional work by Aaron M. Canter, coordination with Dr. Lani Tsinnajinnie, and an Advisory Committee on Indigenous Information Needs primarily comprised of Indigenous representatives in …

An Analysis Of Mating Strategies In Rotifers Inhabiting Temporary Habitats, Ariel Bollinger

Open Access Theses & Dissertations

Reproduction is critical for organisms inhabiting ephemeral aquatic habitats where desiccation risk favors rapid and successful mating. Monogonont rotifers rely on sexual reproduction to produce diapausing eggs that withstand harsh conditions. I investigated how mating behaviors vary among populations of five rotifer species based on the hydroperiod of their habitat: Epiphanes brachionus and E. chihuahuaensis were sampled from habitats with short (SH) and long (LH) hydroperiods; Euchlanis dilatata was collected from a short (SH) and a permanent habitat; and Plationus patulus and Sinantherina socialis were collected from a long (LH) and a permanent (Perm) habitat. I hypothesized that SH populations …

Efficient And Universal Watermarking For Llm-Generated Code Detection, Boquan Li, Zirui Fu, Mengdi Zhang, Peixin Zhang, Jun Sun, Xingmei Wang

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have significantly enhanced the usability of AI-generated code, providing effective assistance to programmers. This advancement also raises ethical and legal concerns, such as academic dishonesty and the generation of malicious code. For accountability, it is imperative to detect whether a piece of code is AI-generated. Watermarking is broadly considered a promising solution and has been successfully applied to identify LLM-generated text. However, existing efforts on code are far from ideal, suffering from limited universality and excessive time and memory consumption. In this work, we propose a plugand- play watermarking approach for AI-generated code detection, named ACW …

Dynamic Spectral Denoising With Global-Context Attention For Multi-Behavior Recommendation, Miaomiao Cai, Yunshan Ma, Fangqi Zhu, Junfeng Fang, Zhijie Zhang, Zhiyong Cheng, Xiang Wang, See-Kiong Ng

Research Collection School Of Computing and Information Systems

Multi-behavior recommendation improves target-behavior predic-tion by exploiting heterogeneous auxiliary feedback (e.g., view,collect, and cart), yet its robustness is often undermined by behavior-dependent noise and inconsistency. We argue that the key bottle-neck is not merely noisy behaviors, but a representation-level failurecaused by two coupled heterogeneities. First, intra-behavior rep-resentation entanglement arises when multi-hop propagationblends incidental signals with true preferences in the embeddingspace. This entanglement renders coarse spatial denoising inef-fective, since it cannot suppress noise without sacrificing weak-but-informative niche signals. Second, inter-behavior reliabilityheterogeneity complicates cross-behavior fusion, as the predic-tive value of auxiliary behaviors varies substantially across usersand contexts. Without reliability calibration, aggregation can …

Hvi-Cidnet+: Beyond Extreme Darkness For Low-Light Image Enhancement, Kangbiao Shi, Xiaowen Ma, Yixu Feng, Tao Hu, Peng Wu, Guansong Pang, Qingsen Yan

Research Collection School Of Computing and Information Systems

Low-Light Image Enhancement (LLIE) aims to recover visually pleasing content and details from degraded low-light images. However, existing RGB-based methods often suffer from color bias and brightness artifacts due to inherent high color sensitivity. Although the HSV color space can decouple brightness and color, it introduces noticeable red and black noise artifacts. To address these challenges, we adopt the Horizontal/Vertical-Intensity (HVI) color space for LLIE, which is defined by the HV color map and learnable intensity. The former enforces small distances for red coordinates to alleviate red noise artifacts, while the latter adaptively compresses low-light regions to suppress black noise …

Audeter: A Large-Scale Dataset For Deepfake Audio Detection In Open Worlds, Qizhou Wang, Hanxun Huang, Guansong Pang, Sarah Erfani, Christopher Leckie

Research Collection School Of Computing and Information Systems

Speech synthesis systems can now produce highly realistic vocalisations that pose significant authenticity challenges. Despite substantial progress in deepfake detection models, their real-world effectiveness is often undermined by evolving distribution shifts between training and test data, driven by the complexity of human speech and the rapid evolution of synthesis systems. Existing datasets suffer from limited real speech diversity, insufficient coverage of recent synthesis systems, and heterogeneous mixtures of deepfake sources, which hinder systematic evaluation and open-world model training. To address these issues, we introduce AUDETER (AUdio DEepfake TEst Range), a large-scale and highly diverse deepfake audio dataset comprising over 4,500 …

Task-Aligned Haze Removal With Semantic-Aware Fusion And Contrast Self-Correction, Jinbin Wang, Aiping Yang, Guosong Jiang, Wenlong Yu, Dongwei Ren, Qinghua Hu

Research Collection School Of Computing and Information Systems

Adverse haze conditions introduce complex degradations that obscure scene details and distort structural cues critical for object detection, posing persistent challenges for vision‐based sensing systems. Although existing haze removal methods have achieved notable improvements in visual clarity, their optimisation objectives are often misaligned with downstream detection requirements, leading to limited detection performance in real‐world scenarios. To address this issue, this work proposes a task‐aligned weakly supervised haze removal framework, termed Dehaze4Detection, which explicitly aligns low‐level restoration with high‐level detection objectives. The framework incorporates a Semantic‐Aware Multi‐Scale Fusion Module (SMFM) that embeds pixel‐level semantic knowledge into the dehazing process, enabling selective …

Parameterized Approximation Schemes For Fair Clustering In Doubling Metrics, Xiaoliang Wu, Ting Liang, Zhize Li, Qilong Feng

Research Collection School Of Computing and Information Systems

Fair clustering has garnered considerable attention, with various fairness notions proposed to ensure equitable representation across demographic groups. In this paper, we focus on the k-center problem in bounded doubling metrics under two popular fairness requirements: group fairness and data summarization fairness, referred to Group Fair k-Center (Gf-k-Cen) and Data Summarization Fair k-Center (Dsf-k-Cen), respectively. Both fairness notions extend classical clustering formulation by associating each data point with a demographic label. Motivated by recent advances in parameterized approximation results for fair clustering, we investigate whether these problems admit Fixed-Parameter Tractable (FPT) approximation schemes in bounded doubling metrics. The previous algorithms …

Corruption Is Futile: Hardware-Assisted Multi-User Searchable Encryption, Feng Li, Chengyan Ma, Xiaoguo Li, Pengfei Wu, Lisha Yao, Jianting Ning, Guomin Yang, Jianfeng Ma, Deng, Robert H.

Research Collection School Of Computing and Information Systems

Searchable encryption enables writers to upload encrypted data to an untrusted cloud server, while allowing authorized readers to perform keyword searches over the encrypted content without compromising data privacy. The multi writer/multi-reader (M/M) setting better reflects real-world de ployment requirements because it aligns with the collaborative nature of many modern applications, but introduces substantial challenges in both efficiency and security. The state-of-the art scheme, delegatable searchable encryption (DSE), achieves optimal search time and forward privacy in the M/M setting. Nevertheless, DSE provides only limited security guarantees: search tokens generated by readers reveal the update count of queried keywords, and the …

Medical Firms’ Online Q&A Interactions: Effect Of Language Style On Investor Engagement, Runyu Wang, Keng Siau, Zili Zhang

Research Collection School Of Computing and Information Systems

PurposeThis study aims to investigate how language styles used by medical firms on digital question and answer (Q&A) platforms influence investor engagement. It examines the linguistic characteristics of managerial responses and compares their effects across industries, with a focus on the distinctive demands of medical communication.Design/methodology/approachWe analyze a panel dataset of 1,223 firm-month observations, constructed from 13,549 individual responses provided by 70 medical firms on a major Chinese web-based firm-investor Q&A platform over a 24-month period (2020–2021). Content analysis is used to assess linguistic features, such as first-person usage, uncertainty, understandability and concreteness. A two-way fixed effects model with clustered …

Nanostructured Negative Electron Affinity Gallium Arsenide Photocathodes With Enhanced Quantum Efficiency And Extended Charge Lifetime For Advanced Photoinjectors, Md Aziz Ar Rahman

Physics Theses & Dissertations

This dissertation presents a comprehensive experimental and computational investigation of nanostructured negative electron affinity (NEA) gallium arsenide (GaAs) photocathodes. This work was undertaken to achieve higher quantum efficiency (QE) and longer charge lifetime than conventional flat NEA GaAs electron sources commonly used in photoinjectors. Finite-Difference Time-Domain and charge-transport simulations of three types of nanostructure geometries reveal substantial QE enhancement over flat GaAs via Mie resonance mode excitation. Consequently, the truncated nanocone array (TNCA) photocathodes with heights of 400, 700, and 1000 nm were fabricated and NEA activated via Cs-NF₃ deposition. In a low-voltage ultra-high vacuum chamber with a base pressure …

Experimental Investigation Of The Discharge Modes Of Nanosecond Pulsed Plasmas At Atmospheric Pressure, Md Ziaur Rahman

Electrical & Computer Engineering Theses & Dissertations

The generation of repeatable and stable nanosecond pulsed atmospheric pressure plasmas is important to non-thermal plasma applications in various fields including medicine, material processing, food processing, and plasma ignition for combustion. This dissertation investigates the atmospheric pressure plasma initiation and formation under 10 – 200 ns pulsed power for electrode configurations applicable for transient plasma ignition (TPI) for combustion. The impacts of pulsed power parameters, gas condition, and electrode geometry on the discharge initiation and modes (i.e., streamer, transient spark and spark) are systematically evaluated for applying TPI for combustion. We evaluated the discharge modes driven by both longer and …

Interpretable Sparse Modeling Of Longitudinal Signals Via Critical-Range Rectification And Anytime Rule Compression, Jason Orender

Computer Science Theses & Dissertations

High-dimensional longitudinal data arise in clinical monitoring, industrial control systems, and other sensor-driven domains where outcomes are often governed by threshold-and-lag behavior. Traditional longitudinal workflows frequently depend on expert guessing to nominate candidate variables, lag windows, and threshold hypotheses, followed by repeated hypothesis testing over a limited set of manually specified relationships. While such approaches can be useful in narrow settings, they are often less robust in high-dimensional regimes because important interactions may be missed, multicollinearity can destabilize inference, and the resulting process can be labor-intensive and difficult to scale. This dissertation develops an end-to-end framework for interpretable sparse longitudinal …

Convergence Theory For Deep And Multi-Grade Neural Architectures, Lei Huang

Mathematics & Statistics Theses & Dissertations

This dissertation studies two complementary notions of convergence arising in modern neural network models: the convergence of recursively constructed neural network architectures and the convergence of optimization algorithms used for training neural-network-based image restoration models. The first part develops a convergence theory for deep neural networks (DNNs) viewed as recursively generated sequences of functions. Within an Lp framework motivated by statistical learning, sufficient conditions are established under which increasing-depth neural network sequences converge to well-defined limiting functions. The analysis covers both bounded-width and unbounded-width architectures, establishes explicit convergence rates, and motivates a network initialization strategy derived from the convergence conditions. …

Tagged J/Ψ Photoproduction Near Threshold, Mariana Tenorio Pita

Physics Theses & Dissertations

J/ψ production (heavy quarkonium in general) is sensitive to the gluonic content of the nucleon. Recent theoretical studies have suggested that near-threshold J/ψ production is sensitive to the gravitational form factors (GFFs) of the nucleon, which parametrize the matrix elements of the QCD energy-momentum tensor (EMT). The energy-momentum tensor of the nucleon encodes its mechanical properties through the gravitational form factors, which give access to quantities such as the mass radius and pressure distribution of the proton. Previous J/ψ electroproduction measurements were either performed near-threshold at very low Q² (in the quasi-real photoproduction regime) or far above threshold at …

Dynamics, Characteristics, And Sinking Velocities Of Small, Gel-Like Ocean Particles (24–1000 Μm): Insights From Shadowgraph Imaging And Turbulence Modeling, Huanqing Huang

OES Theses and Dissertations

Particle concentration and sinking velocity control the export flux and transfer efficiency of the biological carbon pump, and are therefore key parameters in ocean biogeochemical models. Gel-like particles form an important part of the particulate organic carbon pool, but their contribution is difficult to quantify because they are fragile and nearly transparent to conventional optical instruments. Here, a focused shadowgraph imaging system (FoSI) was applied to marine gel research to examine the ecological significance, distribution, and transport of gel-like particles along a transect from the Azores to the upwelling region off northwest Africa.

A new image processing protocol was developed …

Sem-Pdpl: Semantic Exposure Graphs For Privacy-Law-Informed Risk Assessment Of Public Social-Media Data, Heba Ismail

All Works

Public social-media content often contains self-disclosed personal attributes that appear low-risk in isolation but become privacy-relevant when linked across posts, platform accounts, or user-level traces. Existing research has advanced privacy-sensitive content detection, de-anonymization analysis, social-media research ethics, and privacy-compliance workflows; however, limited work operationalizes how personal-data disclosures combine structurally and how these structures can be translated into auditable governance actions. This paper proposes SEM-PDPL, a computational, privacy-law-informed risk-assessment framework for modeling public social-media exposure as semantic exposure graphs and mapping graph patterns to controls aligned with the United Arab Emirates Personal Data Protection Law (PDPL) and compatible with GDPR principles. …

Bayesian Variable Selection In High-Dimensional Ordinal Quantile Regression Models, Mai Dao, Md. Sakhawat Hossain, Zhuanzhuan Ma

School of Mathematical & Statistical Sciences Faculty Publications

Quantile regression (QR) provides a flexible statistical framework for modeling the entire conditional distribution of the response variable, making it useful for analysis in various fields. Despite its advantages, existing methods for QR often encounter numerical challenges in high-dimensional settings, especially for those with ordinal responses. In this paper, we use a latent-response framework to construct a Bayesian hierarchical model to conduct parameter estimation and variable selection for ordinal QR. Using the asymmetric Laplace working likelihood and the horseshoe prior for the regression coefficients, we obtain the posterior samples to be screened by the sequential two-means clustering process to identify …

The Role Of Non-Symmetric Weights In Hermite–Hadamard Inequalities For Coordinated Ga-Convex And Ga-Quasi-Convex Functions, Muhammad Amer Latif, Ayesha Shabbir

Publications and Research

This paper establishes new Fejér and Hermite–Hadamard-type inequalities for functions of two variables whose mixed second-order partial derivatives satisfy coordinated GA-convexity or coordinated GA-quasi-convexity on a rectangle in the positive quadrant. Our main results are formulated for non-negative continuous weight functions that are not necessarily symmetric with respect to the geometric means of the interval endpoints, thereby extending the classical framework to genuinely asymmetric weights. However, to obtain explicit and sharp integral bounds in certain cases, we also employ a technical lemma that assumes a special symmetric setting where the weight function is symmetric on each coordinate with respect to …