Physical Sciences and Mathematics Commons^™

Observational Constraints On The Structure-Induced Dark Energy Model, Ali̇ Kazim Çamlibel

Turkish Journal of Physics

A new phenomenological dark energy model, originally associated with large-scale structure formation and proposed as a solution to the fine-tuning and coincidence problems related to the cosmological constant, was analyzed within the framework of General Relativity in a Friedmann-Robertson-Walker spacetime and its model parameters were estimated using cosmic chronometers and recent DESI data. It turns out that the proposed model can serve as an alternative evolving dark energy model with a novel equation of state function. Given the form of this phenomenological energy density ansatz, which rises with the nonlinear structure growth in the universe and declines as cosmic voids …

On Cosmological Equation Of State From The First Law Of Thermodynamics, Ch'ng Han Siong

Turkish Journal of Physics

We derive several equations of state for different types of energy from the first law of thermodynamics. We derive the equation of state of a Machian universe from the well-known energy formulaE=mc2. This equation of state is consistent with previously reported results. We then propose hypothetical forms of energy that lead to the equations of state of the Chaplygin gas, generalized Chaplygin gas, and modified Chaplygin gas. We also obtain the energy density of the Chaplygin gas by dividing the hypothetical energy by the volume of the universe, and find that it is consistent with previously reported results. Finally, we …

Design And Fabrication Of Petri Dish Optimized For High-Frequency Acoustic Microscopy Using 3d Printing, Tuna Pesen, Beyza Ceren Eren, Arda İnanç, Bora Akgün

Turkish Journal of Physics

High-frequency imaging in acoustic microscopy requires the use of Petri dishes with thin and acoustically transparent bottoms to minimize signal attenuation and distortion. Commercially available options suitable for this purpose are often expensive or limited in compatibility with custom setups. Here, we present a cost-effective and easily customizable alternative developed in our laboratory, consisting of a 3D-printed frame combined with commercially available stretch film. The stretch film serves as an ultrathin (8 µm thickness) base that supports efficient high-frequency acoustic transmission, enabling clear and reliable imaging. Beyond acoustic microscopy, the adaptable design is also compatible with other modalities such as …

Ai And The Music Industry: Its Current Status And A Speculative Projection Of Its Evolutionary Trajectory, Rhett D. Morris, Clayton Rosati, Stefan Fritsch

Honors Projects

Music serves as one of society's biggest cultural outlets, allowing millions to share in what used to be a uniquely human form of expression. The commodification of music has built a huge industry full of companies and platforms that have used technology and property laws to shape music's relationship with the public. This study aims to look into the future to see how AI and its implementation could affect the structure of the music industry. To look into the future, this piece establishes two of the most pressing kinds of AI technology for the music industry and looks to contextualize …

Changes In Water Quality And Microbiota Over Two Years In Florida Hard Clam (Mercenaria Mercenaria) Hatcheries, Bonnie Getter

Electronic Theses and Dissertations 2020 - Present

This thesis characterized the water quality and bacterial communities in nine hard clam hatcheries over two production years through weekly or biweekly water quality and chemistry monitoring, monthly bacterial plating in the second year of production, and select 16S microbiome sampling at select hatcheries to address production bottlenecks and support the reliable production of high-quality seed for Florida’s growing hard clam aquaculture industry. Logistic regression models showed that water source was distinguished by pH, salinity, alkalinity, and nitrite, whereas geographic location was distinguished by temperature, salinity, nitrate, and calcium; temperature, dissolved oxygen, salinity, and alkalinity distinguished seasons. Vibrio bacteria differed …

From Homogeneous To Heterogeneous Adaptive Bounded-Confidence Opinion Dynamics On Networks, Sally Hafez, Fatma R. Farag, Amira S. N. Tawadros

Northeast Journal of Complex Systems (NEJCS)

Adaptive bounded-confidence models (ABCMs) elucidate the coevolution of agent states and network structure via local interactions and rewiring mechanisms. Traditional formulations assume uniform interaction parameters, leading to distinct regime shifts encompassing fragmentation, polarization, and consensus. A symmetric heterogeneous extension of the adaptive bounded-confidence model is introduced, in which interaction parameters vary according to whether agents belong to the same or different groups. The model retains the original update and rewiring protocols but integrates within-group and between-group confidence bounds alongside tolerance thresholds. Initially, the classic homogeneous model is replicated to establish a reference point. Subsequently, the heterogeneous extension is assessed under …

Wavelet-Based Multiscale Analysis Of Cave Co₂ Concentration In Response To Short-Term High-Intensity Tourism Activities: Spatiotemporal Heterogeneity And Lag Characteristics, Mingda Cao, Wenwen Song, Yan Zhang, Jie Zhang, Zhiqiang Yao

International Journal of Speleology

High-intensity tourism activities can cause a significant increase in cave air CO₂ concentration, thereby affecting the cave micro-environment and secondary carbonate deposition. During the 2023 National Day Golden Week, high-frequency continuous monitoring of cave air CO₂ partial pressure (PCO_2(A)) and visitor numbers was conducted in Dawang Cave, Anhui Province. Wavelet transform and cross-correlation analyses were used to reveal the multi-scale response characteristics of CO₂ concentration to tourism activities. The results show that: (1) PCO_2(A) exhibited clear diurnal variations (higher during the day, lower at night) and decreased spatially with enhanced ventilation, controlled jointly by …

Community Detection In Bipartite Networks Using Bipartite Stochastic Block Models With Node-Level Covariates, Geraldine Elaine Percival

Dissertations

In this age, monumental webs of data demands for perpetual cultivation of ways to untangle these webs of information. One of the many curiosities is how to systematically group entities. When clustering, one avenue to take is ascertaining the interconnectedness between the data points, and gauging their influence to each other. This perspective is programmed to model the relationships between the data presented as a network. Many of the methods being used today are algorithm-based, which may pose limitations in understanding and explaining the uncertainty revolving around the data. Hence, it is proposed to steer towards a model-based approach that …

Improving Tensorsketch Using Complex Random Variables, Amit Sharma, Mohammad Azhar Khan, Rameshwar Pratap, Keegan Kang

Faculty Conference Papers and Presentations

TensorSketch by Pham and Pagh [2013], Kar and Karnick [2012] provides efficient sketching algorithms for highdimensional polynomial kernels $\vec{x}^{\otimes p} \in \R^{d^p}$. Kar and Karnick [2012] uses dense Johnson-Lindenstrauss (JL)-type projections with computational cost $O(pDd)$, whereas Pham and Pagh [2013] extends the sparse CountSketch [Charikar et al., 2024] algorithm, yielding a faster algorithm for high-dimensional sparse inputs with running time $O\left(p(\nnz{\vec{x}} + D \log D)\right)$. However, the variance of both estimators grows exponentially with the polynomial degree $p$, scaling as $3^{p}/D$, where $D$ denotes the sketch dimension. Recent work by Wacker et al. [2023] showed that using complex-valued distribution reduces …

Dbssnet: Dual-Branch Spectral-Spatial Network With Data-Driven And Knowledge-Guided Band Selection For Uav Hyperspectral Wheat Rust Detection, Subin Kim

All Graduate Theses and Dissertations, Fall 2023 to Present

Wheat rust is a serious plant disease that can reduce crop yield and quality. In practice, the disease is often noticed only after visible symptoms appear, when some damage may already be difficult to reverse. This thesis studies whether drone-based imaging can help detect wheat rust earlier and more reliably in field environments. 

Unlike an ordinary color photograph, a hyperspectral image records reflected light at many narrow wavelengths. These measurements can reveal useful information about plant condition, but they are also high dimensional, noisy, and difficult to analyze when only a limited number of labeled field samples are available. To …

Mining Time Series Shapelets And Association Rules For Solar Flare Prediction, Drew Watson

All Graduate Theses and Dissertations, Fall 2023 to Present

Solar flares are the largest explosions in the solar system; they are caused by changes in the Sun’s magnetic field. Strong solar flares can disrupt power systems, damage satellites, and interfere with radio communication, so improving flare prediction is important. This thesis develops a way to predict severe solar flares while also helping researchers understand why those predictions are made. The approach looks for short patterns in solar magnetic field data that are linked to future flare activity. It then studies how these patterns appear together and in what order they happen over time. By doing this, the research not …

Assessing The Health Impacts Of Pm2.5 And Ozone Exposure: A Study Of Hospitalizations In El Paso, Alexia Breanna Reyes

Open Access Theses & Dissertations

Air pollution has been widely associated with adverse cardiovascular health outcomes; however, most research in arid regions has focused on high-intensity dust events rather than routine, non-dust conditions. This study aimed to evaluate the association between non-dust air pollution and cardiovascular hospitalizations in El Paso County, Texas. A time series analysis was conducted using hospitalization data from 2010 to 2014. Daily counts on non-dust event days of cardiovascular hospitalizations, including hypertensive diseases, ischemic heart disease, cerebrovascular disease, and diseases of pulmonary circulation, were examined in relation to ambient concentrations of fine particulate matter (PM2.5) and ozone. Quasi-Poisson regression models with …

Sentinel: Evaluating Occlusion-Centered Next-Best-View Selection Using Rgb-Derived Pseudo-Geometry, Paul Nassar

Master's Theses

Three-dimensional cameras provide direct geometric measurements, but their cost, weight, power requirements, and calibration constraints can limit their use in various lightweight or large-scale sensing systems. A potential alternative is to use conventional two-dimensional RGB cameras together with geometric reconstruction models that infer a partial three-dimensional representation from images. This thesis evaluates that possibility for next-best-view (NBV) selection through Sentinel, an occlusion-centered system for static, object-centric scenes with known camera poses and intrinsics. Sentinel converts source RGB observations into pseudo-geometry using monocular depth or point-map predictions, combines those predictions with camera-ray evidence, identifies occluded unknown regions, and selects a candidate …

Laplace Factor Models In High-Dimensional Data, Siqi Liu, Xuerong Meggie Wen, Akim Adekpedjou, Guangbao Guo

Mathematics and Statistics Faculty Research & Creative Works

Laplace factor models (LFMs) provide a heavy-tailed alternative to Gaussian factor models by representing high-dimensional observations through a low-rank common component and Laplace-distributed idiosyncratic errors. This paper develops an assumption-consistent finite-sample analysis of matrix concentration, covariance estimation, and Monte Carlo integration under this model. We first formulate the model with explicit dimensional, independence, covariance, and identifiability conditions. Standard matrix Laplace-transform and matrix Bernstein inequalities are then recalled with their precise applicability conditions. Because untruncated Laplace variables are neither almost surely bounded nor strongly log-concave, these standard results cannot be applied directly in the forms commonly used for bounded or Gaussian-like …

Modeling Mean And Variability Of Anxiety In Ecological Momentary Assessment Data Using Mixed-Effects Location–Scale Models, Trenzy Odero

Electronic Theses and Dissertations

Ecological Momentary Assessment is a method of collecting repeated measures of people in real time within natural environments. This results in hierarchical data that has a significant amount of variation at the person level. The traditional linear mixedeffects models assume that the residual variance is constant, which might not be true when the residual variance varies among individuals as well as in time. This thesis uses mixed-effects location-scale (MELS) models to model the mean and variance of an EMA outcome together. By introducing the possibility of variability in residual variance within and across individuals and with covariates, the MELS framework …

Enabling Multi-Task Neural Network Inference On Heterogeneous Edge Devices, Redwanul Islam Arif

Master's Theses

Deep neural networks are increasingly required to run on the devices that generate the data. If such a device must perform more than one task, the standard practice is deploying one model per task, which makes memory grow linearly with task count, which is unacceptable when the entire budget is kilobytes. This thesis asks one question in three settings: how much capability can a network acquire without incurring deployment cost?

The first study takes an ImageNet-pretrained ResNet-18, sweeps the branch point across every residual stage and the classification-head depth across one, ten, and twenty layers, and deploys the resulting multi-head …

Federated Learning For Early Medical Diagnosis: Enhanced Diabetic Retinopathy Detection In Smart Healthcare, Mohammad Nasajpour Esfahani

Master's Theses

This thesis investigates the role of federated learning as a privacy-preserving solution for modern healthcare challenges. In traditional machine learning, sensitive medical data must be centralized for model training, raising concerns about privacy, security, and regulatory compliance. Federated learning offers an alternative by allowing hospitals, clinics, and personal health devices to collaboratively train shared models without exchanging raw patient data. The study first explores how federated learning is being used across various healthcare domains, including cancer detection, medical imaging, and disease prediction— highlighting its potential to support secure collaboration across institutions. It addresses key benefits such as data privacy, scalability, …

Mono Gold: An Informal Account Of The Geology, Production, And History Of Mono County Gold And Silver Mining Districts, D. D. Trent

Eastern Sierra History Journal

Mono Gold explores the mining production, technology, and history of the Eastern Sierra, with a special focus on Mono County. Author Dee Trent, a geologist, spent decades visiting, researching, and photographing each of the mining operations discussed in this book-length monograph.

Positive Curvature And Discrete Abelian Symmetry, Lee Kennard, Elahe Khalili Samani, Catherine Searle

Mathematics

By replacing the torus with an elementary abelian two-group, we generalize Grove and Searle’s maximal symmetry result and Wilking’s half-maximal symmetry result for positively curved manifolds with an isometric torus action. © The Author(s) 2026.

Crystal Structure Of New Polar Rare Earth Borates Na2.62Ln2.12(Bo3)3 (Ln = Pr, Nd, Sm) Containing Isolated Bo3 Groups, Alevtina A. Maksimova, Mark D. Smith, Hans Conrad Zur Loye

Faculty Publications

Three new rare earth borates, Na2.62Ln2.12(BO3)3 (Ln = Pr, Nd, Sm), were synthesized via a high-temperature solution reaction using a BaCO3–H3BO3–NaF flux and structurally characterized by single-crystal X-ray diffraction analysis. The compounds crystallize in the orthorhombic space group Amm2 with lattice parameters a = 5.0985(10) Å, b = 11.180(2) Å, c = 7.1483(14) Å, and a unit cell volume of 407.48(14) ų (Z = 2) for Na2.62Pr2.12(BO3)3, a = 5.0983(10) Å, b = 11.181(2) Å, c = 7.1491(14) Å, and a unit cell volume of 407.51(14) ų (Z = 2) for Na2.62Nd2.12(BO3)3, and a = 5.08630(10) Å, b …

A Stabilized Weighted Interior Penalty Method For Thermal Convection Model In Heterogeneous Porous Media, Yuanyuan Hou, Qianqian Ding, Xiaoming He, Yanping Lin

Mathematics and Statistics Faculty Research & Creative Works

In this article, we propose and analyze a stabilized weighted interior penalty method for solving the thermal convection problems in heterogeneous porous media. We first transform the thermal convection model into the pressure primal form with homogeneous Neumann boundary condition and develop a symmetric weighted interior penalty method to handle the discontinuous Darcy number and automatically adjust the penalty coefficient corresponding to the varying permeability. Then we recover the velocity by a stabilized method and incorporate it into the energy equation to obtain the temperature. The stability and convergence rates of the numerical solutions are rigorously proved and verified by …

Single Crystal Growth Of The Orthoborate Nabalu(Bo3)2 And Optical Investigation Of Eu3+ And Tb3+ Doped Nabalu(Bo3)2, Alevtina A. Maksimova, Mark D. Smith, Lakshani W. Masachchi, Hans Conrad Zur Loye

Faculty Publications

Sodium barium lutetium borate NaBaLu(BO₃)₂ crystals were synthesized using a high-temperature solution method. The crystal structure of this layered orthoborate is described and corresponds to that of an extensive orthoborates family, NaBaR(BO₃)₂ (R = Sc, Y, Yb, Tb – Lu). NaBaLu(BO₃)₂ was doped by Eu³⁺ and Tb³⁺ to explore its performance as a host material and to investigate the luminescent properties of the Eu³⁺ and Tb³⁺ doped materials. The emission spectra of the NaBaLu(BO₃)₂:Ln (Ln = Eu, Tb) crystals are presented. The most …

Temperature And Salinity Effects On The Physiology Of Bullfrog Tadpoles (Lithobates Catesbeianus) From A Suburban Temperate Wetland, Angela Brown

Theses, Dissertations and Culminating Projects

One of the most robust amphibians is the American Bullfrog, Lithobates catesbeianus, which is a widespread species that is invasive worldwide, and is broadly tolerant of a range of environmental conditions. Despite this tolerance, survival and performance of adult L. catesbeianus can be negatively affected by increasing temperatures and salinity resulting from anthropogenic impacts, such as climate change and road salts. This study focuses on the effects of acute salinity exposure (mimicking road salt influx into a pond) on L. catesbeianus tadpoles at different larval stages. Increased salinity was predicted to negatively affect two physiological performance measures. Critical maximum temperature …

A Comprehensive Study And Performance Optimization Of Shared Virtual Memory, Bennett Cooper

All Dissertations

High performance computing (HPC) is dominated by heterogeneous systems that mainly derive performance from GPU accelerators. A majority of HPC applications have gravitated towards GPUs, which require explicit programming. Historically, programming explicitly to take advantage of GPUs has proven a significant barrier in fully utilizing GPU acceleration. Unified Memory (UM) is a technology designed to lower the barrier of entry to GPU programming by merging all memory domains of a system. While UM provides easier access to the capabilities of heterogeneous systems, the performance cost of UM greatly detracts from the benefit. Additionally, UM is implemented on a vendor-by-vendor basis …

Alaska Earthquake Center Quarterly Review October-December 2025, Heather Mcfarland, Beth Grassi, Austin Holland, Nate Murphy, Elisabeth Nadin, Carolyn Parcheta, Michael West

Alaska Earthquake Center Reports

This series of technical quarterly reports from the Alaska Earthquake Center (AEC) includes detailed summaries and updates on Alaska seismicity, the AEC seismic network and stations, fieldwork, our online presence, public outreach, and lists publications and presentations by AEC staff. Multiple AEC staff members contributed to this report.

Llm-As-A-Judge For Software Engineering: Literature Review, Vision, And The Road Ahead, Junda He, Jieke Shi, Terry Yue Zhuo, Christoph Treude, Jiamou Sun, Zhenchang Xing, Xiaoning Du, David Lo

Research Collection School Of Computing and Information Systems

The rapid integration of Large Language Models (LLMs) into software engineering (SE) has revolutionized tasks from code generation to program repair, producing a massive volume of software artifacts. This surge in automated creation has exposed a critical bottleneck: the lack of scalable and reliable methods to evaluate the quality of these outputs. Human evaluation, while effective, is very costly and time-consuming. Traditional automated metrics like BLEU rely on high-quality references and struggle to capture nuanced aspects of software quality, such as readability and usefulness. In response, the LLM-as-a-Judge paradigm, which employs LLMs for automated evaluation, has emerged. This approach leverages …

Efficient Test-Time Retrieval Augmented Generation, Hailong Yin, Bin Zhu, Jingjing Chen, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Although Large Language Models (LLMs) demonstrate significant capabilities, their reliance on parametric knowledge often leads to inaccuracies. Retrieval Augmented Generation (RAG) mitigates this by incorporating external knowledge, but these methods may introduce irrelevant retrieved documents, leading to inaccurate responses. While the integration methods filter out incorrect answers from multiple responses, but lack external knowledge like RAG methods, and their high costs require balancing overhead with performance gains. To address these issues, we propose an Efficient Test-Time Retrieval-Augmented Generation Framework named ET2RAG to improve the performance of LLMs while maintaining efficiency. Specifically, ET2RAG is a training-free method, that first retrieves the …

Continuous Query For Top-K Maximal Sum Intervals Over Streaming Data, Zhongshuai Zhang, Xiaochun Yang, Baihua Zheng, Rui Zhu, Haomin Li, Bin Wang

Research Collection School Of Computing and Information Systems

The continuous identification of top-k maximal sum intervals using a sliding window over a data stream is a critical operation for applications in IoT and beyond. A maximal sum interval is a non-overlapping, contiguous subsequence with the maximal sum in a sequence of signed values. Existing algorithms are ill-suited for streaming contexts: they either exhaustively enumerate all intervals even for small k values, or depend on indexes that require frequent and costly restructuring. We propose a novel partition-based strategy. Our core insight is a partitioning scheme that guarantees that any maximal sum interval is fully contained within a single partition, …

Success Of New Ideas In Online Platforms: An Idea Network Perspective, Yimei Zhou, Qian Tang, Vincent Z.W. Mack Mack, Shao Yi Liaw

Research Collection School Of Computing and Information Systems

On online platforms, new ideas often emerge by recombining existing ones within idea networks. Unlike traditional knowledge networks, idea networks represent curated, meaning-based associations among ideas, offering a distinct lens on recombination. Drawing upon a hypergraph perspective, we investigate how new idea success depends on their structural and content attributes, and how collaborative participation shapes these attributes. Using data from an ideation platform, we find that both structural embeddedness and bridging benefit new idea success. Content diversity has no direct effect, but it amplifies the benefits of bridging while constraining those of embeddedness. Both crowd contributions and ideator expertise strengthen …

Approximation And Learning-Based Algorithms For Influence Maximization In Multilayer Social Networks, Xueqin Chang, Ruize Liu, Qing Liu, Baihua Zheng, Yunjun Gao

Research Collection School Of Computing and Information Systems

Motivated by the observation that users in the real world often engage across multiple social networks simultaneously, we study the problem of influence maximization in multilayer social networks (Mlim), aiming to select a small set of nodes that maximizes the total influence spread across all layers. To this end, we introduce a hybrid propagation model that jointly captures layer-specific diffusion dynamics and probabilistic cross-layer propagation. Based on this model, we formally define the Mlim problem and establish its NP-hardness, monotonicity, and submodularity. To address the Mlim problem, we first propose a greedy baseline Mlim-Greedy, which achieves a (1-1/e) approximation. Since …