Digital Commons Network^™

Enhanced No₂ Gas Sensing Using Silver-Doped Cadmium Telluride Nanocrystalline Thin Films, Tunis Balasim Hassan

Karbala International Journal of Modern Science

Nitrogen dioxide (NO₂) is a toxic pollutant that necessitates sensitive and reliable monitoring systems. Conventional gas sensors often lack adequate responsiveness and fast recovery under changing conditions and therefore create a need for semiconductors with enhanced performance, especially at high industrial temperatures (around 250 °C). The study therefore aims to synthesis and evaluate silver-doped cadmium telluride (Ag:CdTe) thin films as NO₂ gas sensors. Pure CdTe and Ag:CdTe with silver concentrations of 5, 10, and 15 wt% were prepared by a co-precipitation process. XRD verified cubic symmetry with a progressive fall in crystallite size (6.67 nm to 5.46 nm at 15 …

Design Considerations For Hypertension Chronotherapy Trials: Insights From Experience And Modelling, Olivia Walch, Amy Rogers, Yitong P. Huang, Marc D. Ruben, Kenneth A. Dyar, Robert W. V. Flynn, Isla S. Mackenzie, Roberto Manfredini, Francesco P. Cappuccio, Filippo Pigazzani

Mathematics Sciences: Faculty Publications

Chronotherapy aims to maximise treatment efficacy while minimising side effects by scheduling treatment according to personal biological rhythms. In recent years, randomised clinical trials (RCTs) have been conducted to evaluate whether scheduled blood pressure interventions can improve patient outcomes. However, reports of time-of-day effects have attracted rebuttals and engendered methodological debate. A perfectly controlled chronotherapy trial (i.e., a trial that assesses the effect of assigning time of intervention) will never be feasible in the real world; yet some factors may be more critical to consider and control for than others. To advance the conversation about how best to evaluate the …

Exploring Resource-Efficient Deep Learning For Medical Image Segmentation, Pallabi Dutta

Doctoral Theses

Automated medical image segmentation improves diagnostic accuracy by au tomating the precise delineation of target anatomical structures in the input images. Artificial Intelligence (AI), and specifically, Deep Learning (DL), has emerged as a state-of-the-art approach for this task. However, the significant computational demands of DL approaches often hinders their deployment. Ad vanced models, including Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), require substantial processing power and a large memory footprint, limiting their use in resource-constrained settings. This thesis aims to address this challenge by developing a series of novel, resource-efficient DL models that achieve high segmentation accuracy with reduced …

Parameter Estimation In Ode Models Using Least-Squares Regression, Ulrich A. Hoensch

CODEE Journal

We present a method of estimating model parameters for non-linear ODEs using least-squares regression. The coefficient of determination can be used as a measure of model fit. The method is demonstrated using US population data to fit a logistic growth model. Also, a competing species model is used to describe the interaction of two different species of yeast.

Robert, Powers; Unl Chemistry; Nmr-Assisted Drug Discovery, Mark Griep, Robert Powers

Department of Chemistry: Faculty Interviews

Dr. Robert Powers became a chemistry professor at the University of Nebraska-Lincoln in 2003 and is about to retire after 23 years. Prior to UNL, Bob was a drug discovery researcher for 11 years at American Cyanamid, which eventually became Wyeth and is now part of Pfizer. Bob was born in Jersey City, New Jersey. Something in his youth must have sparked an interest in chemistry because he earned a bachelor's in that subject from Rutgers University in New Brunswick. Then he traveled 1200 miles west to Purdue University in Indiana where he earned his doctorate. Next, he did postdoctoral …

Continuous Polygenic Trait Evolution Under Brownian Motion With Gaussian Mixture Models, Mary S. Hopkins

Mathematics & Statistics ETDs

Gaussian mixed-models (GMMs) show promise as a tool for modeling polygenic trait evolution for multiple taxa with established phylogenetic comparative methods (PCMs). When phenotypic traits are influenced by more than one gene, neither a gene tree nor a species tree may be completely adequate to model specific cross-taxa dependencies. In such cases common solutions include using trees inferred from concatenated DNA sequences [35, 95] and consensus gene trees [35]. The GMM-based model, first proposed by Jiang in 2017 [55] allows traits to evolve on more than one tree with distinct topologies. This approach provides a framework for trait evolutionary modeling …

Microtubules In Breast Cancer: Exploring The Α/Β-Tubulin Toggle Switch And Its Implications In Human Breast Cancer, Annemarie Ianos

Student Theses and Dissertations

Microtubules, composed of a/b-tubulin heterodimers, play a central role in breast cancer tumor growth by polymerizing, leading to metastasis and depolymerizing, contributing to proliferation. Human enzymes protein kinase Ca (PKC-a) and cyclin-dependent kinase 1 (Cdk-1) mediate phosphorylation at sites a:Ser165 and b:Ser172, respectively, influencing the growth of microtubules. It is possible that alternating phosphorylation at these sites contribute to an a/b-tubulin “toggle switch” that mediates microtubule instability and tumor growth.

The project investigates the influence of the toggle switch model on microtubule stability by determining the impact of mutants (a:S165D, a:S165N, a:S165S_P, b:S172S_P and a:S165S_P/b:S172S …

Shaping Emergent Competitive And Cooperative Behaviors In Multi-Agent General-Sum Games, Ethan F. Erickson

Honors Projects

Reinforcement learning (RL) algorithms can train agents to solve problems in environments using complex behaviors that are not explicitly programmed, known as emergent behaviors. The goal of our research is to investigate how different RL reward values influence the emergence of competitive and cooperative behaviors in games with teams of multiple agents. Specifically, we focus on general-sum games, in which the sum of gains and losses of each team may be non-zero, allowing situations for agents to mutually benefit or mutually fail. Using Unity’s ML-Agents Toolkit to train agents with RL self-play in bounded 2D environments, we identify high-level behaviors …

Ab Initio Method Development For Electronic Structure Response And Symmetry Quantification, Duc Anh Lai

Chemistry Theses and Dissertations

Electronic structure provides a fundamental framework for understanding molecular properties and reactivity, as it encodes the spatial distribution of electrons and their response to external and internal perturbations. This dissertation develops theoretical and computational frameworks to characterize and manipulate electronic structure through two complementary directions: the response to oriented external electric fields and the quantification of symmetry regulation in electron density.

First, a rigorous theoretical and computational framework is established for treating electric fields with arbitrary orientations relative to molecular structure. The concept of the rotational potential energy surface is introduced to characterize the dependence of molecular energy on field …

Nanoscale Structure And Spontaneous Self-Assembly Of Hydrothermal Organic Products, Glorianne P. Dorce

Chemistry and Chemical Biology ETDs

Carbon nanomaterials derived from citric acid and urea exhibit behaviors that challenge conventional structure–property models based on static bulk descriptions. This study examines how precursor pairing and reaction duration, post‑synthetic thermal history, and time‑dependent aging govern nanoscale organization and optical response. Through controlled synthesis and processing, distinct nanostructures with tunable structural and spectroscopic profiles are generated.

A multiscale framework integrating nano‑FTIR, atomic force microscopy, and thermal analysis reveals chemical heterogeneity and continuous structural reorganization across length scales. By correlating local chemical environments with optical behavior, we show that fluorescence efficiency and photostability depend on specific nanoscale architectures rather than average …

Mapping Homogeneous Configuration States For Learning Based Motion Planners, Yazied Hasan

Computer Science ETDs

Reinforcement learning (RL) excels at solving complex tasks, but training times can become prohibitively large for challenging motion-planning problems. Methods that address this cost often require additional training or tuning, counteracting the goal of reducing training time. A more effective approach is to exploit inherent task equivalences: many elements of the state space, dynamics, or structure are functionally interchangeable, enabling simplification or knowledge reuse. We present learning solutions that leverage these equivalences to enhance the RL process. First, we leverage the symmetry of homogeneous multi-agent teams to simplify the task to a single strategy. Second, we map correspondences between distinct …

Computational Design Of Peptides And Proteins Through Machine Learning Approaches, Emily J. Hendrix

Chemistry and Chemical Biology ETDs

Advancements in machine learning have emerged as a pivotal tool in computational biochemistry, offering new advancements to address challenges in protein structure and function. However, current machine-learning approaches offer limited insight in understanding protein dynamics. The purpose of this work is to combine traditional physics-based computational tools, such as molecular dynamics and coarse-grained simulations, with recently developed AI-driven computational tools to bridge gaps and advance the understanding of proteins in both structural and dynamic aspects. I investigated several approaches such as (i) traditional physics-based methods to study protein conformation and ensembles; (ii) identifying a peptide inhibitor for the PICK1 PDZ …

Heavy Metal And Metalloid Accumulation In The Gallinas River Following The Hermit's Peak / Calf Canyon Wildfire, Olivia A. Kelly

Geography ETDs

This study evaluates the persistence and bioavailability of heavy metals and metalloids in the Gallinas River three years after the Hermit’s Peak/Calf Canyon Wildfire of 2022 using a multicompartment sampling framework that includes water, sediment, and benthic macroinvertebrate tissue analysis via inductively coupled plasma optical emission spectrometry (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS). Results indicate that the Hermit’s Peak/Calf Canyon Fire continues to influence the hydrogeochemical condition of the Gallinas River. Sediments contain elevated concentrations of several metals and metalloids, and these same elements are detectable in macroinvertebrate tissues, linking sediment contamination to biological uptake. Zinc (Zn), silicon …

The Extraction And Chemical Characterization Of The Avian Pigments Turacin And Turacoverdin, Sarah R. Bekkali

Honors Scholar Theses

Bird coloration is a trait that extends beyond mere aesthetics as it has an extensive range of biological significance. Plumage patterns and hues can influence camouflage, mate choice, social dominance, and physiological performance. Bird fitness, their ability to survive and reproduce, is greatly dependent on color. Melanins, carotenoids, and pterins are well-studied pigment systems that are commonly found across many avian species’. Alternatively, porphyrin-based pigments are rare and less-studied as they only found in turacos a sub-Saharan African bird belonging to the family Musophagidae. This thesis focuses on two pigments of interest: turacin, the deep crimson-red pigment found in …

Saving The Great Basin: Creating Places For The Birds, Bees And Beyond, Carlos Gomez

Hospitality Design Graduate Student Capstones

This project looks at how vacant and underused parcels along the Truckee River in Reno, Nevada, can be rethought as part of a larger ecological system. Rather than treating these parcels as empty leftover spaces, the project sees them as opportunities to create small habitat patches that can support native species, improve stormwater function, and strengthen the river corridor over time. The work focuses on three sites along the Truckee River: California Avenue, Island Avenue, and Commercial Row. Each site responds to a different condition along the urban transect, from a sloped residential river edge to a tighter urban parcel …

Semi-Rational Strategies For Antifungal Peptide Design, Akilah I. Mateen

Seton Hall University Dissertations and Theses (ETDs)

A recently emerged opportunistic fungi, Candida auris, has been subject to increased scrutiny due to its virulence and rapid geographical spread. Due to the indiscriminate use of antimicrobials as treatments for infectious diseases and as pesticides, the ubiquitous threat of multidrug resistance (MDR) looms large. The lack of progress in antifungal development is of high concern in the treatment of infectious diseases and a rise in fungal resistance highlight the need for updated treatment strategies. This work describes three strategies used to address these concerns:

•  The synthesis of a photosensitizer-membrane-active peptide (PS-MAP) conjugate, Ir-HKII15, that combines the ability of …

Accuracy Of Parameter Estimation For A Simple Gene Regulatory Network Model Is Sensitive To Network Motif, Number Of Parameters Estimated, And Magnitude And Direction Of Regulatory Relationships, Nikki C. Chun, Kam Dahlquist

Honors Thesis

A gene regulatory network (GRN) is a set of transcription factors that regulate the expression of genes encoding other transcription factors. The dynamics of a GRN explain how gene expression changes over time. GRNmap is a MATLAB software package that uses ordinary differential equations to model dynamics of small-scale GRNs. We used the program to estimate production rates, expression thresholds, and regulatory weights for each transcription factor in three related literature-derived GRNs based on yeast cold shock microarray data previously collected in the Dahlquist Lab. We noticed large differences in estimated weight values when 1-2% of the expression values were …

Phase Identification Of (La, Sr)Coo3 Solid Oxide Cell Electrode Films Using Dft Based Exafs, Musab A. Siddiqui

Seton Hall University Dissertations and Theses (ETDs)

Perovskite structured mixed ionic electronic conductor (MIEC) materials formed as films by metal-organic precursor deposition have excellent electrochemical performance in solid oxide cell (SOC) air electrode applications due to the large surface area provided by the manufacturing approach. MIEC films created by metal organic precursor deposition are often multi-phased due to low heat treatment temperatures and locally generated low oxygen partial pressures caused by the release of carbonaceous gases during the drying step of the fabrication process. In this work, we use extended x-ray absorption fine structure spectroscopy (EXAFS) to examine the phase contents of La0.8Sr0.2CoO3 (LSC82) and La0.6Sr0.4CoO3 (LSC64) …

Cold Plasma Treatment Of Hydroponically Grown Basil: Effects On Essential Oil Composition For Sustainable Applications (Ocimum Basilicum), Judith Serwaa Marfo

Seton Hall University Dissertations and Theses (ETDs)

Abstract Essential oils are known to have medicinal benefits and pharmaceutical applications. This study investigates the impact of cold plasma treatment on hydroponically cultivated basil (Ocimum basilicum), focusing on physical growth traits, and essential oil composition. Preliminary trials validated our solvent extraction protocol using IPA, hexanes, and methanol without heat on store-bought basil. Rotary evaporation and GC-FID analysis successfully identified key compounds; eugenol, estragole, eucalyptol, and linalool. Plasma-treated hydroponic plants exhibited enhanced physical characteristics, including larger leaves and intensified green pigmentation, compared to untreated controls under identical conditions. The plasma treatment didn't just increase how much oil was extracted, but …

A Descriptive Analysis Of Plant Leaf Disease Detection Using Machine Learning And Deep Learning Models: A Systematic Review, Arzoo Chamoli, Anuj Kumar

Turkish Journal of Electrical Engineering and Computer Sciences

Plant leaf disease detection (PLDD) is a growing active research area with burgeoning practical applications across various sectors such as agricultural monitoring, food security, and environmental conservation. Accurate segmentation and classification of plant leaf diseases remains a key challenge in the field of plant leaf disease prediction. The challenge demands automated methods for the plant disease identification because it needs to develop better crop management systems, which will boost agricultural production. In this article, we provide a systematic review of various machine learning (ML) and deep learning (DL) methods extensively used for PLDD. The review strategy follows a formal protocol, …

Complex-Valued Convolutional Neural Network With Time-Frequency Representation For Electrocardiogram-Based Arrhythmia Detection, Kajeeth Kumar Gurusamy, Muthurajkumar Sannasy

Turkish Journal of Electrical Engineering and Computer Sciences

This research proposes an end-to-end procedure for arrhythmia detection based on electrocardiogram (ECG) signals using complex-valued convolutional neural network (CVCNN) incorporated with time-frequency representation. The proposed model leverages complex numbers to capture amplitude and phase information that enhances the ability of the model for detecting time-frequency variation in cardiac signals. First, signal preprocessing techniques---including normalization, wavelet denoising, and R-peak detection---are applied. Subsequently, the model extracts complex features from raw ECG data by employing the Hilbert transform to derive the analytic signal and the short-time Fourier transform (STFT) to generate a time–frequency representation. The proposed CVCNN framework effectively learns spatial-temporal features …

Thermal Physiological Ecology Of The Relict Leopard Frog At Hot And Cold Springs, Robert P. Pelletier Iii

UNLV Theses, Dissertations, Professional Papers, and Capstones

The relict leopard frog (Rana onca) once ranged across drainages in southern Nevada, northwestern Arizona, and southwestern Utah. Following a decline, the species only persisted in a few geothermally influenced hot springs, which led to the perspective that hot springs were high-quality habitat. Rana onca has been under intensive, multiagency management and the species has been translocated to establish additional populations, including at cold-water sites. Three research studies are presented into the thermal physiological ecology of R. onca with the aim of informing conservation strategy. The research was focused at a thermally influenced hot spring and a cold-water spring to …

Accurate Diagnosis Of Diseases By A Novel Ai Pipeline Based On Feature Extraction, Feature Ranking, And Feature Selection From Medical Images, Tuğba Nur Bozkurt, Mehmet Emi̇n Yüksel

Turkish Journal of Electrical Engineering and Computer Sciences

The rapid growth of the global population has led to a substantial increase in the number of patients, while the availability of healthcare professionals has not expanded at a comparable rate. This imbalance highlights the urgent need for efficient and reliable computer-aided decision support systems that can reduce clinical workload while maintaining high diagnostic accuracy. In this study, a novel and systematically integrated artificial intelligence-based pipeline is proposed for medical image classification, combining statistical significance-driven feature ranking with evolutionary feature selection in a unified framework. The proposed pipeline consists of four sequential stages: feature extraction, ranking, selection, and classification. Features …

Accelerating Defensive Cyber Operations Via Unsupervised Log Clustering And Automated Regex Template Synthesis, Charles Matthew Jones

Theses and Dissertations

Modern Security Operations Centers (SOCs) ingest millions of log entries per day, but manual parsing does not scale to the volume, heterogeneity, and rapid evolution of log formats. This dissertation investigates whether unsupervised clustering can automate the generation of candidate field-extraction templates while remaining deployable in production and feasible under realistic runtime and memory constraints. The central research question asks whether a machine-learning-assisted pipeline that proposes extraction templates for security engineer review—relative to reproducible human-authored baselines such as hand-written regular expressions—can measurably improve rule-set deployability and corpus-scale extraction quality. Improvement is quantified using four applicability metrics: Coverage Rate (CR), Exclusive …

Assessment Of Local Ecological Knowledge, Oyster Predation, And Habitat Function To Improve Management Of Nearshore Habitats, Cynthia Evelyn Marie Lupton

Theses and Dissertations

Coastal ecosystems along the northern Gulf of Mexico are ecologically and economically vital yet increasingly threatened by environmental change, habitat degradation, and knowledge gaps that complicate restoration and management efforts. This dissertation integrates social and ecological approaches to improve understanding of predator dynamics, stakeholder perceptions, and habitat function related to nearshore habitats. Thereby, providing information that can inform adaptive management and restoration in the region through addressing three overarching objectives: 1) evaluate stakeholder perceptions of oyster (Crassostrea virginica) predation and management practices; 2) quantify environmental and habitat factors influencing oyster drill (Stramonita haemastoma complex) abundance; and, 3) assess the influence …

Unknowing Sacrifices: Public Health And Radiation Illness In New Mexico And Nevada, 1940s-1960s, Beatriz Avila-Marquez

UNLV Theses, Dissertations, Professional Papers, and Capstones

The detonation of the first atomic bomb in the early morning of July 16, 1945, in New Mexico welcomed the atomic age that forever changed the world. After that day, thousands of lives were lost due to nuclear weapons, and hundreds of thousands more continued to suffer from the effects of atomic testing. The Atomic Energy Commission, fueled by the arms race of the Cold War, chose Nevada to continue the United States’ nuclear weapons testing. Knowledge of the dangers of radiation exposure, the effects of radiation, and techniques to prevent exposure are now available in part due to the …

Interpretable Deep Learning Models For Trustworthy Prediction Of Enzyme Functions, Louis Dumontet

UNLV Theses, Dissertations, Professional Papers, and Capstones

Trustworthy prediction of enzyme function from protein sequences remains a central challenge in computational biology, particularly when annotated data are limited, imbalanced, or incomplete. This dissertation develops interpretable deep learning methods for enzyme discovery and enzyme function prediction from amino acid sequences. First, it introduces PEPIC, an interpretable convolutional neural network for substrate-level prediction of hydrolytic plastic-degrading enzymes. Using curated and expanded sequence datasets, PEPIC improved predictive performance over benchmark methods, identified sequence regions aligned with catalytic and substrate-binding residues, and supported the discovery and experimental validation of a previously uncharacterized PET-degrading enzyme. Second, this dissertation investigates the integration of …

Establishing A Comprehensive Framework For Sfrt Lattice Treatments: Optimization, Planning, And Clinical Evaluation, Gregory M. Gill

UNLV Theses, Dissertations, Professional Papers, and Capstones

Spatially fractionated radiation therapy (SFRT) using lattice radiotherapy (LRT) has emerged as a promising treatment technique for bulky, nonresectable tumors by delivering spatially heterogeneous dose distributions consisting of high-dose vertices embedded within lower-dose regions. Although early clinical experiences have demonstrated potential therapeutic benefit, widespread clinical implementation of LRT remains limited due to the absence of standardized treatment planning workflows, consistent optimization strategies, and clearly defined evaluation metrics for heterogeneous dose distributions. The objective of this study is to develop and evaluate a structured framework to support efficient, reproducible, and safe clinical implementation of LRT.

To address these challenges, a comprehensive …

Methylation-Dependent Regulatory Pathway That Governs The Stability Of The Sox Family Proteins And Related Developmental Regulators, Keshari Gayathri Rajawasam

UNLV Theses, Dissertations, Professional Papers, and Capstones

The SRY (Sex-determining Region Y) protein is a transcription factor encoded on the Y chromosome and is the key regulator responsible for initiating male sex determination in mammals. During early embryonic development, SRY activates the genetic program that leads to testis formation by promoting the expression of downstream genes involved in male gonadal differentiation. Mutations or dysregulation of SRY can lead to disorders of sex development such as male-to-female sex conversion and hermaphroditism, highlighting its critical role in sex determination.

SRY belongs to the SOX (SRY-related HMG-box) family of transcription factors, which includes the proteins SOX1, SOX2 and SOX3. These …

Visual Interpretability Of Multimodal Tissue Perfusion Classification Using Grad-Cam And Saliency Maps, Metehan Zorluoglu

UNLV Theses, Dissertations, Professional Papers, and Capstones

Accurate identification of the tissue perfusion phase from hand images can aid doctors in decision-making with non-invasive techniques. The present study proposes a multimodal deep learning model for classifying the tissue perfusion phase using infrared, thermal, and visible spectrum images of the human hand. The proposed model consists of various preprocessing techniques such as manipulation, homography alignments, and masking. The significant contribution of this thesis is the interpretability analysis of deep learning models, achieved through the analysis of saliency maps and the Gradient-weighted Class Activation Mapping (Grad-CAM) methods. The purpose of this method is to find out how the convolutional …