Physical Sciences and Mathematics Commons^™

Novel, Ultra-Fast Electrochemical Sensors For The Enhanced Detection Of Neurotransmitters And Toxic Heavy Metals In Aqueous Solutions, Noel Manring

Theses and Dissertations

Neurodegenerative diseases (NDDs) are a growing global health concern, affecting millions of people worldwide. NDDs arise from the progressive loss of neuronal cell function, ultimately leading to cell death. Abnormal levels of neurotransmitters, the chemical messengers in the brain, have also been linked to NDDs. Despite various treatments and medications aimed at alleviating physical and mental symptoms, there is currently no cure or means to halt disease progression. While the exact pathogenesis of NDDs remain poorly understood, cognitive decline is largely attributed to brain volume reduction, particularly in the hippocampus. Clinical studies suggest that environmental factors, such as exposure to …

Advancing Biofabrication Methodologies To Develop Biomimetic Tissue Scaffolds For Musculoskeletal Applications, Nashaita Yezdi Patrawalla

Theses and Dissertations

Musculoskeletal injuries impose a significant global burden, affecting millions and generating an economic toll exceeding $14 billion. Tissue engineering has gained traction as a promising strategy offering potential for functional restoration of damaged musculoskeletal tissues. This research aims to advance biofabrication techniques for generation of biomimetic tissue scaffolds, by optimizing scaffold design and modulating fabrication process parameters to generate scaffolds for musculoskeletal regeneration with application-specific tailored properties. In this realm, collagen-based biomaterials have been extensively investigated for tendon and ligamentous applications, and in combination with bioceramics incorporated into the collagen framework to produce composite scaffolds that better recapitulate the native …

Decoding Neural Networks: An Information-Theoretic Guide To Interpretability, Error Analysis And Efficiency, Mackenzie J. Meni

Theses and Dissertations

This dissertation addresses critical challenges in neural network design by leveraging entropy-based techniques to improve model efficiency, interpretability, and bias reduction. Focusing on the unique demands of computer vision applications, particularly object detection and classification for real-time systems, this work introduces a series of innovative methods centered on information theory. At the core of these methods is the Probabilistic Explanations of Entropic Knowledge (PEEK) framework, a tool developed to analyze and visualize entropy distributions across feature maps. PEEK offers insights into information flow within neural networks, making it possible to pinpoint layers that contribute meaningfully to decision-making or identify those …

Neural Network Architecture Search Enabled Wide-Deep Learning (Nas-Wd) For Spatially Heterogenous Property Awared Chicken Woody Breast Classification And Hardness Regression, Chaitanya Pallerla, Yihong Feng, Casey M. Owens, Ramesh Bahadur Bist, Siavash Mahmoudi, Pouya Sohrabipour, Amirreza Davar, Dongyi Wang

Poultry Science Faculty Publications and Presentations

Due to intensive genetic selection for rapid growth rates and high broiler yields in recent years, the global poultry industry has faced a challenging problem in the form of woody breast (WB) conditions. This condition has caused significant economic losses as high as $200 million annually, and the root cause of WB has yet to be identified. Human palpation is the most common method of distinguishing a WB from others. However, this method is time-consuming and subjective. Hyperspectral imaging (HSI) combined with machine learning algorithms can evaluate the WB conditions of fillets in a non-invasive, objective, and high-throughput manner. In …

An Examination Of Hilbert Spaces, Fnu Sumaiya

Theses and Dissertations

This independent study focuses on the exploration of Hilbert spaces, a cornerstone of modern analysis and a fundamental construct in mathematics. It starts by introducing vector space, metric and metric spaces and then Hilbert spaces and its properties.

This study includes good amount of mathematical proofs, theorems and examples. Through extensive research and analysis, this independent study aims to offer a comprehensive examination of Hilbert spaces, detailing their properties and their pivotal role in modern analysis.

Anomaly Detection Using Unsupervised Machine Learning Algorithms: A Simulation Study, Edmund F. Agyemang

School of Mathematical & Statistical Sciences Faculty Publications

This study presents a comprehensive evaluation of five prominent unsupervised machine learning anomaly detection algorithms: One-Class Support Vector Machine (One-Class SVM), One-Class SVM with Stochastic Gradient Descent (SGD), Isolation Forest (iForest), Local Outlier Factor (LOF), and Robust Covariance (Elliptic Envelope). Through systematic analysis on a synthetically simulated dataset, the study assessed each algorithm’s predictive performance using accuracy, precision, recall, and F1 score specifically for outlier detection. The evaluation reveals that One-Class SVM, Isolation Forest, and Robust Covariance are more effective in identifying outliers in the synthetic simulated dataset, with Isolation Forest slightly outperforming the other algorithms in terms of balancing …

Nature-Based Solutions For Flood Risk Mitigation: Mapping And Hydrological Modeling In An Under-Resourced Urban Area On The Mississippi Gulf Coast, Jennifer Makenzie Holifield

Master's Theses

Flooding is the most frequent and costly disaster in the United States, affecting people disproportionately based on environmental, societal, and economic limitations. Through stakeholder engagement, open-access research, and nature-based solutions (NBS), ecology and hydrology can be bridged with local-scale politics to raise awareness of local issues and form strong interdisciplinary bonds. I approached nature-based equity from a ground front, in which I engaged with local stakeholders and city officials to gain insight and calibration on flooding experiences and flooding presence within Moss Point, MS. This engagement helped calibrate GIS models, from which, I found that the flooding hazards of Moss …

Modeling Hourly Storm Surges Using Deep Learning Techniques, Md Abu Zafor

Master's Theses

Storm surges cause coastal flooding, one of the most devastating coastal hazards. Accurate modeling of storm surges is essential for predicting and mitigating these impacts. Traditional approaches model surges at individual tide gauges and often focus on daily time scales, leading to data redundancy and limiting their ability to capture sub-daily variability. This study addresses these limitations using deep learning (DL) algorithms to model hourly surges simultaneously at multiple tide gauges along the U.S. East Coast and Gulf of Mexico. Three algorithms, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a hybrid ConvLSTM are employed to model surges using …

Array Of Graphene Solar Cells On 100 Mm Silicon Wafers For Power Systems, Syed M. Rahman, Md R. Kabir, Tamzeed B. Amin, James M. Mangum, Ashaduzzaman, Paul M. Thibado

Physics Faculty Publications and Presentations

High electrical conductivity and optical transparency make graphene a suitable candidate for photovoltaic-based power systems. In this study, we present the design and fabrication of an array of graphene-based Schottky junction solar cells. Using mainstream semiconductor manufacturing methods, we produced 96 solar cells from a single 100 mm diameter silicon wafer that was precoated with an oxide layer. The fabrication process involves removing the oxide layer over a select region, depositing metal contacts on both the oxide and bare silicon regions, and transferring large-area graphene onto the exposed silicon to create the photovoltaic interface. A single solar cell can provide …

An Empirical Framework Characterizing The Metallicity And Star-Formation History Dependence Of X-Ray Binary Population Formation And Emission In Galaxies, Bret D. Lehmer, Erik B. Monson, Rafael T. Eufrasio, Amirnezam Amiri, Keith Doore, Antara Basu-Zych, Kristen Garofali, Lidia Oskinova, Jeff J. Andrews, Vallia Antoniou, Robel Geda, Jenny E. Greene, Konstantinos Kovlakas, Margaret Lazzarini, Chris T. Richardson

Physics Faculty Publications and Presentations

We present a new empirical framework modeling the metallicity and star formation history (SFH) dependence of X-ray luminous (L ≳ 10³⁶ erg s⁻¹) point-source population X-ray luminosity functions (XLFs) in normal galaxies. We expect that the X-ray point-source populations are dominated by X-ray binaries (XRBs), with contributions from supernova remnants near the low luminosity end of our observations. Our framework is calibrated using the collective statistical power of 3731 X-ray detected point sources within 88 Chandra-observed galaxies at D ≲ 40 Mpc that span broad ranges of metallicity (Z ≈ 0.03–2 Z_⊙), SFH, …

Microplastic Degradation Compared Among Dated Salton Sea Strandlines, Maria Alejandra Roman Medrano

Loma Linda University Electronic Theses, Dissertations & Projects

This thesis investigates the environmental degradation of plastic materials, with a focus on polyethylene terephthalate (PET), through a multi-phase study conducted at the Salton Sea, a closed-basin lake in a semi-arid region of California. The research addresses three core objectives: (1) developing a microplastic (MP) extraction method tailored for clay-rich sediments, (2) characterizing surface degradation of various plastic types, including PET, BoPET, HDPE, LDPE, and EPS, using advanced microscopy and spectroscopy, and (3) examining the long-term weathering of PET through samples collected from dated lake strandlines.

The extraction protocol successfully isolates MPs without oxidation, using ZnCl₂ and extended soaking to …

A Nursery Study Of The Interplay Between Soil Texture, Rooting Space, And Nurse Plants On Seedling Growth And Root Development In Woody Species Of Tamaulipan Thornscrub, Lance I. Santos

Theses and Dissertations

Reforestation attempts in semi-arid regions are plagued with seedling mortality. Blanket greenhouse practices may not be optimal, in addition, commensalism interactions between nurse plants are considered. To better understand the effects of soil texture and nurse plant interactions on growth and drought tolerance, two experiments were developed. For soil texture, Border palo verde seedlings were grown in different-sized plant bands with three soil types. To observe the effects of nurse plants, an experiment using guayacan, was developed using Texas Ebony, as a leguminous nurse plant. Plants were also exposed to a drought experiment. The results suggest that some soil textures …

Teachers’ Perceptions On The Impact And Effectiveness Of Certification Programs In Preparing And Retaining Educators, Teresa De Jesus Padilla

Theses and Dissertations

Mathematics is integral to STEM fields, making math critically important for the stability and development of the nation. As a result, mathematics teachers have a crucial role in our society. A role whose importance needs the necessary support to accomplish its numerous responsibilities. However, research indicates that certification routes—traditional and alternative—often fail to adequately prepare math teachers for the challenges they face, leading to high turnover rates. This study explores the impact of these certification routes on teachers’ abilities to support student achievement, address diverse learning needs, and manage additional duties. Surveying secondary math teachers in the Rio Grande Valley, …

Real-Time Motion Augmentation And Synthesis For Animating The Hands And Eyes Of Virtual Humans And Avatars, Ryan Canales

All Dissertations

Virtual Reality (VR) enables users to interact within virtual worlds via an embodied virtual representation of themselves called an “avatar”. Because avatars are essential for immersive experiences, it is important to consider how altering or augmenting avatar motion affects virtual experiences. This dissertation aims to improve virtual experiences by addressing some of the many challenges in animating avatars and virtual humans.

In our first study, we addressed the lack of tactile feedback during virtual grasping by using visual feedback techniques. We augmented the avatar’s hand motion to remain outside virtual objects (“outer hand”) even when the user’s hand penetrated them. …

Quantifying Greenhouse Gas Exchange And Ecosystem Services In Ecosystems Managed For Nature-Based Solutions Using Eddy Covariance, Remote Sensing, And Models, Lucas Clay

All Dissertations

As global temperatures rise due to climate change, nature-based solutions are increasingly being used to restore nature and to remove carbon from the atmosphere. Nature-based solutions draw on methodologies such as ecological forestry and improved forest management to reduce emissions and maximize resilience. These protocols are still in their infancy and additional measurements and case studies are important to optimizing these protocols. Herein this dissertation we describe multiple measurement methodologies for both greenhouse gas (GHG) emissions and for ecosystem services. In coastal wetland ecosystems, the need for GHG measurements is paramount to understanding the ability of these ecosystems to reduce …

Sufex-Enabled Stereospecific Deoxygenative Diversification Of Alcohols, Amaechi Odoh

All Dissertations

Alcohols represent an important class of functional group in organic synthesis and medicinal chemistry, given that they are structurally diverse and prevalent in many natural products, biomolecules and renewable feedstocks. As such, they are highly attractive precursors for building-block synthesis, late-stage functionalization, and numerous synthetic transformations. However, existing strategies for the deoxygenative diversification of alcohols operate under constrained conditions, often leading to competing pathways (such as elimination), poor stereospecificity, low efficiencies, and limited substrate compatibility. To address these challenges, we have developed a conceptually novel use of sulfonyl fluorides as bifunctional reagents for the one-step deoxygenative diversification of complex alcohol …

Cross-Interactions Of Amyloid Proteins: Implications For Aggregation Dynamics And Disease Pathology, Zhiyuan Song

All Dissertations

Amyloid aggregation is a pervasive form of protein misfolding implicated in the pathology of several prominent human diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), and type 2 diabetes (T2D). These diseases are marked by the accumulation of amyloid fibrils and toxic oligomers, which are highly dynamic and heterogeneous, posing challenges for understanding their roles in disease progression. Despite significant advances in the diagnosis and management of these conditions, no definitive cure is available. We investigate amyloid aggregation and cross-seeding, with a focus on amyloid-beta (Aβ), tau, alpha-synuclein (αS), and human islet amyloid polypeptide (IAPP) through discrete molecular dynamics (DMD) …

Agricultural Water Use In South Carolina - A Series Of Investigations To Support Long-Term Planning, Charles Pellett

All Dissertations

Agricultural water use in South Carolina has been the subject of some public debate. While there are several sources of information available, the accuracy of the relevant data has been called into doubt. Self-reported estimates may be biased; survey sampling and extrapolation methods yield wide ranges of uncertainty; and reporting compliance or response rate can change over time.

Agricultural water users self-report that their estimates of water use volumes are less accurate than the other major sectors of water use. Center pivot irrigation is the largest and fastest growing kind of agricultural water use in SC. Since the year 2000, …

Artificial Intelligence And Machine Learning In Cancer Pain: A Systematic Review, Vivian Salama, Brandon Godinich, Yimin Geng, Laia Humbert-Vidan, Laura Maule, Mohamed A Naser, Abdallah S R Mohamed, Clifton D Fuller, Amy C Moreno

Faculty, Staff and Student Publications

Background/objectives: Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer.

Methods: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer," "Pain," "Pain Management," "Analgesics," "Artificial Intelligence," "Machine Learning," and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines.

Results: Forty four studies from 2006 to 2023 were included. Nineteen studies used …

A Shared Mechanism For Tnp-Atp Recognition By Members Of The P2x Receptor Family, Jin Wang, Motoyuki Hattori, Chang-Zhu Li, Ye Yu, Chang-Run Guo

Faculty, Staff and Student Publications

P2X receptors (P2X1-7) are non-selective cation channels involved in many physiological activities such as synaptic transmission, immunological modulation, and cardiovascular function. These receptors share a conserved mechanism to sense extracellular ATP. TNP-ATP is an ATP derivative acting as a nonselective competitive P2X antagonist. Understanding how it occupies the orthosteric site in the absence of agonism may help reveal the key allostery during P2X gating. However, TNP-ATP/P2X complexes (TNP-ATP/human P2X3 (hP2X3) and TNP-ATP/chicken P2X7 (ckP2X7)) with distinct conformations and different mechanisms of action have been proposed. Whether these represent species and subtype variations or experimental differences remains unclear. Here, we show …

A Near-Wall Methodology For Large-Eddy Simulation Based On Dynamic Hybrid Rans-Les, Michael Tullis, D. Keith Walters

Mechanical Engineering Faculty Publications and Presentations

Attempts to mitigate the computational cost of fully resolved large-eddy simulation (LES) in the near-wall region include both the hybrid Reynolds-averaged Navier–Stokes/LES (HRL) and wall-modeled LES (WMLES) approaches. This paper presents an LES wall treatment method that combines key attributes of the two, in which the boundary layer mesh is sized in the streamwise and spanwise directions comparable to WMLES, and the wall-normal mesh is comparable to a RANS simulation without wall functions. A mixing length model is used to prescribe an eddy viscosity in the near-wall region, with the mixing length scale limited based on local mesh size. The …

Debtor Eligibility Prediction Using Deep Learning With Chatbot-Based Testing, Reski Noviania, Enny Itje Sela, Luther Alexander Latumakulita, Steven R. Sentinuwo

Knowledge Engineering and Data Science

Predicting debtor eligibility is essential for effective risk management and minimizing lousy credit risks. However, financial institutions face challenges such as imbalanced data, inefficient feature selection, and limited user accessibility. This study combines Recursive Feature Elimination (RFE) and Deep Learning (DL) to improve prediction accuracy. It integrates a chatbot interface for user-friendly testing. RFE effectively identifies critical features, while the DL model achieves a validation accuracy of 97.62%, surpassing previous studies with less comprehensive methodologies. The chatbot's novel design not only ensures accessibility but also enhances user engagement through flexible input options, such as approximate values, enabling non experts to …

Manifold Learning And Undersampling Approaches For Imbalanced Class Sentiment Classification, L.M. Risman Dwi Jumansyah, Agus Mohamad Soleh, Utami Dyah Syafitri

Knowledge Engineering and Data Science

Movie reviews are crucial in determining a film's success by influencing audience decisions. Automating sentiment classification is essential for efficient public opinion analysis. However, it faces challenges such as high-dimensional data and imbalanced class distributions. This study addresses these issues by applying manifold learning techniques, Principal Component Analysis (PCA) and Laplacian Eigenmaps (LE) to reduce data complexity and undersampling strategies (Random Undersampling (RUS) and EasyEnsemble) to balance data and improve predictions for both sentiment classes. On reviews of The Raid 2: Berandal, EasyEnsemble achieved the highest average G-Mean of 0.694 using Term Frequency-Inverse Document Frequency (TF IDF) features with a …

Constructing Qur’An Recitation Classification Using Alexnet Algorithm, Harits Ar Rosyid, Dzulkifli Abdullah, Mohammed S. Alqahtani

Knowledge Engineering and Data Science

The growing demands for accurate and efficient methods in the Qur'an recitation classification highlight the limitations of existing models, particularly in assisting the memorization process. This study aims to address these challenges by implementing the AlexNet Convolutional Neural Network architecture, widely recognized for its effectiveness in image classification, to classify the Qur'an recitations using the Mel Frequency Cepstral Coefficient (MFCC) as the feature extraction method. The research involves several stages, including data collection, preprocessing (audio segmentation by verse), data augmentation, feature extraction, and classification using the AlexNet architecture, followed by performance evaluation. Key results demonstrate that the combination of MFCC …

Deep Learning Approach For Dental Anomalies X-Ray Imaging Using Yolov8, Amelia Ritahani Ismail, Md Salim Sadman Taseen

Knowledge Engineering and Data Science

Dental X-ray imaging is a critical diagnostic tool for identifying various dental anomalies. However, manual interpretation is time-consuming, prone to human error, and requires specialized expertise. Deep learning models, particularly object detection frameworks like YOLO, have demonstrated promising results in automating medical image analysis. This study aims to develop and evaluate a YOLOv8-based deep learning model for automated detection and classification of 14 dental anomaly categories, including Caries, Crowns, Fillings, Implants, and Periapical lesions. The proposed approach addresses limitations in previous YOLO versions by leveraging anchor-free detection and enhanced feature extraction for improved accuracy. The model was trained on a …

Optimal Strategy For Handling Unbalanced Medical Datasets: Performance Evaluation Of K-Nn Algorithm Using Sampling Techniques, Yulita Salim, Aulia Putri Utami, Abdul Rachman Manga, Huzain Azis, Fadhila Tangguh Admojo

Knowledge Engineering and Data Science

This study addresses the critical role of medical image classification in enhancing healthcare effectiveness and tackling the challenges of imbalanced medical datasets. It focuses on optimizing classification performance by integrating Canny edge detection for segmentation and Hu-moment feature extraction and applying oversampling and undersampling techniques. Five diverse medical datasets were utilized, covering Alzheimer’s and Parkinson’s diseases, COVID-19, brain tumours, and lung cancer. The K-Nearest Neighbors (K-NN) algorithm was implemented to enhance classification accuracy, aiming to develop a more robust framework for medical image analysis. The evaluation, conducted using cross-validation, demonstrated notable improvements in key metrics. Specifically, oversampling significantly enhanced lung …

A Hierarchical Density-Based Spatial Clustering Of Applications With Noise (Hdbscan) Approach For Identifying Potential Villages In Buleleng Regency, Dina Nur Amalina, Achmad Fauzan

Knowledge Engineering and Data Science

Buleleng Regency, located in Bali Province, possesses diverse village potential, including agricultural production and tourist attractions. However, this potential has not been fully optimized. Therefore, it is important to enhance village potential by clustering villages based on their specific characteristics to identify and prioritize those requiring special attention. This approach aims to promote equitable village development and reduce poverty levels. This study clusters villages in Buleleng Regency based on their potential using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) method. The data utilized in this study comprises village potential data obtained from the Buleleng Regency Statistics Office …

Classification Of Anxiety Levels Entering The World Of Work In Final Year Students Using The Neighbor Weighted K-Nearest Neighbor Method, Awang Hendrianto Pratomo, Muhammad Fahmi Adam, Dessyanto Boedi Prasetyo

Knowledge Engineering and Data Science

This study evaluates the accuracy of the Neighbor Weighted K-Nearest Neighbor (NWKNN) method in classifying the anxiety levels of final-year students as they prepare to enter the workforce, particularly in cases of unbalanced data distribution. The system was developed using the prototype method, and NWKNN was applied to classify anxiety levels into low, medium, and high categories. Testing using the Confusion Matrix demonstrated strong performance, achieving an accuracy of 94% based on a dataset of 1009 students, with a 90:10 ratio of training to test data. The results indicate that NWKNN effectively provides classification input values, making it a reliable …

Comparative Analysis Of Bpnn And Lvq For Sundanese Character Recognition, Haviluddin Haviluddin, Herman Santoso Pakpahan, Dinda Izmya Nurpadillah, Hario Jati Setyadi, Medi Taruk, Rayner Alfred

Knowledge Engineering and Data Science

The Sundanese script (Aksara Sunda), an essential part of Sundanese cultural heritage, has been used since the 14th century AD. However, recognizing handwritten Sundanese characters remains challenging due to variations in individual writing styles. This study compares the performance of Backpropagation Neural Network (BPNN) and Learning Vector Quantization (LVQ) for recognizing handwritten Sundanese vowel (Swara) characters. A dataset was collected from 15 individuals, each writing seven Sundanese vowel characters, which were then used for training and testing the recognition models. Experimental results show that BPNN outperforms LVQ, achieving a higher classification accuracy (95.23%), lower Mean Squared Error (MSE), and faster …

From A Timeline Contact Graph To Close Contact Tracing And Infection Diffusion Intervention, Yipeng Zhang, Zhifeng Bao, Yuchen Li, Baihua Zheng, Xiaoli Wang

Research Collection School Of Computing and Information Systems

This paper proposes a novel graph structure to address the problems of information spreading in a real-world, frequently updating graph, with two main contributions at hand: accurately tracing infection diffusion according to fine-grained user movements and finding vulnerable vertices under the virus immunization scenario to mitigate infection diffusion. Unlike previous work that primarily predicts the long-term epidemic trend at the census level, this study aims to intervene in the short-term at the individual level. Therefore, two downstream tasks are formulated to illustrate practicalities: Epidemic Mitigating in Public Area problem (EMA) and Epidemic Maximized Spread in Public Area problem (ESA), where …