Physical Sciences and Mathematics Commons^™

Development Of Novel Hpge Detector For Rare Event Physics, Mathbar Singh Raut

Dissertations and Theses

This dissertation focuses on the enhancement of crystal quality in High Purity Germanium (HPGe) detectors, which play a crucial role in rare-event physics investigations such as dark matter detection, neutrinoless double-beta decay, geo-neutrinos, and solar neutrinos. The primary objective of this research is to improve crystal quality by controlling impurity concentration, dislocation density, and growth environment. Additionally, the development of advanced Ge detectors for various physics applications is explored. One significant aspect examined in this dissertation is the investigation of systematic errors associated with the Hall effect system. The goal is to identify a reliable technique that minimizes the systematic …

Mentoring Deep Learning Models For Mass Screening With Limited Data, Suprim Nakarmi

Dissertations and Theses

Deep Learning (DL) has an extensively rich state-of-the-art literature in medical imaging analysis. However, it requires large amount of data to begin training. This limits its usage in tackling future epidemics, as one might need to wait for months and even years to collect fully annotated data, raising a fundamental question: is it possible to deploy AI-driven tool earlier in epidemics to mass screen the infected cases? For such a context, human/Expert in the loop Machine Learning (ML), or Active Learning (AL), becomes imperative enabling machines to commence learning from the first day with minimum available labeled dataset. In an …

Drug Delivering Properties Of Novel Materials For Dentinal Desensitization, Abu Ahammadullah

Dissertations and Theses

This study focuses on the development of non-toxic dental materials that can adhere to dentin, occlude dentinal tubules, and release a drug with or without biological triggers. The properties of those materials are examined by SEM, UV-vis absorption spectroscopy, and a fluoride-selective electrode. The study also investigates the surface modification of calcium carbonate surface with casein, which stabilizes its micro-and nanoparticles in aqueous suspensions, making them suitable for use in dentifrices. The results indicate that the morphology and composition of the particles affect their ability to hold and release eugenol, with the casein-coated calcium carbonate microspheres being the most acid-sensitive …

Analyzing Pulmonary Abnormality With Superpixel Based Graph Neural Networks In Chest X-Ray, Ronaj Pradhan

Dissertations and Theses

In recent years, the utilization of graph-based deep learning has gained prominence, yet its potential in the realm of medical diagnosis remains relatively unexplored. Convolutional Neural Network (CNN) has achieved state-of-the-art performance in areas such as computer vision, particularly for grid-like data such as images. However, they require a huge dataset to achieve top level of performance and challenge arises when learning from the inherent irregular/unordered nature of physiological data. In this thesis, the research primarily focuses on abnormality screening: classification of Chest X-Ray (CXR) as Tuberculosis positive or negative, using Graph Neural Networks (GNN) that uses Region Adjacency Graphs …

Multimodal Learning: Generating Precise Chest X-Ray Report On Thorax Abnormality, Gaurab Subedi

Dissertations and Theses

Chronic respiratory diseases, ranking as the third leading cause of death worldwide according to the 2017 World Health Organization (WHO) report, affect a staggering 544.9 million individuals. Compounding this public health challenge is the fact that over 80% of health systems grapple with shortages in their radiology departments, highlighting an urgent need for accessible and efficient diagnostic solutions. While various image classification models for analyzing thorax abnormalities have been developed, relying solely on one type of dataset (image data, for example) for thorax abnormality analysis is insufficient. Integrating texts with image data could provide more accuracy as well as analysis. …

Background Discrimination Of A Neutrino Detector With Dense Neural Networks, Perry Siehien

Dissertations and Theses

Neutrinos are subatomic particles that weakly interact with matter due to their neutral charge and small cross section. Detectors that search for neutrinos require sensitive instrumentation, which makes them susceptible to various background sources such as gamma rays. Additionally, coherent elastic neutrino-nucleus scattering events, or CEvNS, are the weakest neutrino interactions at 1-25 keV, making them exceptionally difficult to observe. To understand the physics of CEvNS events within the detector material, the recoil signatures of relevant interactions must be determined. Traditional analysis methods are effective, but cannot be applied to energies below 50 keV, due to the overlap of discrimination …

Hart, Robert W., Papers, 1924-1966, Special Collections, Leonard H. Axe Library

Finding Aids

Personal documents, correspondence, and financial records of Robert W. Hart, former professor at Pittsburg State University.

Robert William Hart was born in 1896 to William and Sarah Hart and grew up in Crawford County, Kansas. In 1916 he graduated from the State Manual Training Normal School (now Pittsburg State University), and found early work as an engineer. Robert served as an ensign in the navy during World War I. He taught in the public schools in Fort Scott and Pittsburg from 1916-1918; 1921-1923. In 1920 he married Retta Statler and they had two children. In 1923 he was hired as …

Model-Based Imputation Of Below Detection Limit Missing Data And Group Selection In Bayesian Group Index Regression, Matthew Carli

Theses and Dissertations

Investigations into the association between chemical exposure and health outcomes are increasingly focused on the role of chemical mixtures, as opposed to individual chemicals. The analysis of chemical mixture data required the development of novel statistical methods, one of these being Bayesian group index regression. A statistical challenge common to all chemical mixture analyses is the ubiquitous presence of below detection limit (BDL) data. We propose an extension of Bayesian group index regression that treats both regression effects and missing BDL observations as parameters in a model estimated through a Markov Chain Monte Carlo algorithm that we refer to as …

Enhancing Neuromorphic Computing With Advanced Spiking Neural Network Architectures, Paolo Gabriel Alejandro Cachi Delgado

Theses and Dissertations

This dissertation proposes ways to address current limitations of neuromorphic computing to create energy-efficient and adaptable systems for AI applications. It does so by designing novel spiking neural networks architectures that improve their performance. Specifically, the two proposed architectures address the issues of training complexity, hyperparameter selection, computational flexibility, and scarcity of neuromorphic training data. The first architecture uses auxiliary learning to improve training performance and data usage, while the second architecture leverages neuromodulation capability of spiking neurons to improve multitasking classification performance. The proposed architectures are tested on Intel's Loihi2 neuromorphic chip using several neuromorphic datasets, such as NMIST, …

Effects Of Slip On Highly Viscous Thin-Film Flows Inside Vertical Tubes (Constant Radius, Constricted And Flexible), Mark S. Schwitzerlett

Theses and Dissertations

Viscous liquid film flows in a tube arise in numerous industrial and biological applications, including the transport of mucus in human airways. Previous modeling studies have typically used no-slip boundary conditions, but in some applications the effects of slip at the boundary may not be negligible. We derive a long-wave model based on lubrication theory which allows for slippage along the boundary. Linear stability analysis verifies the impact of slip-length on the speed, growth rate, and wavelength of the most unstable mode. Nonlinear simulations demonstrate the impact of slip-length on plug formation and wave dynamics. These simulations are conducted for …

A Literary Analysis Of The Oort Cloud: Summarising Its History And Proposing A Mission To Image Oort Cloud Objects, Avital Keeley

Senior Honors Theses and Projects

The Oort Cloud was hypothesised in 1950 but has lost popularity as a research topic. This literary research summarises the most notable articles and simulations on the Oort Cloud with an analysis on the outdated simulations and assumptions. Finally, this research conflates the hypothesised values of Oort Cloud parameters to calculate the required specifications of instrumentation needed to image an Oort Cloud Object, and a rough budget is proposed of $5 million to launch a 35 km interferometer.

A Secure And Robust Knowledge Transfer Framework Via Stratified-Causality Distribution Adjustment In Intelligent Collaborative Services, Ju Jia, Siqi Ma, Lina Wang, Yang Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

The rapid development of device-edge-cloud collaborative computing techniques has actively contributed to the popularization and application of intelligent service models. The intensity of knowledge transfer plays a vital role in enhancing the performance of intelligent services. However, the existing knowledge transfer methods are mainly implemented through data fine-tuning and model distillation, which may cause the leakage of data privacy or model copyright in intelligent collaborative systems. To address this issue, we propose a secure and robust knowledge transfer framework through stratified-causality distribution adjustment (SCDA) for device-edge-cloud collaborative services. Specifically, a simple yet effective density-based estimation is first employed to obtain …

Ranked Keyword Search Over Encrypted Cloud Data Through Machine Learning Method, Yinbin Miao, Wei Zheng, Xiaohua Jia, Ximeng Liu, Kim-Kwang Raymond Choo, Robert H. Deng

Research Collection School Of Computing and Information Systems

Ranked keyword search over encrypted data has been extensively studied in cloud computing as it enables data users to find the most relevant results quickly. However, existing ranked multi-keyword search solutions cannot achieve efficient ciphertext search and dynamic updates with forward security simultaneously. To solve the above problems, we first present a basic Machine Learning-based Ranked Keyword Search (ML-RKS) scheme in the static setting by using the k-means clustering algorithm and a balanced binary tree. ML-RKS reduces the search complexity without sacrificing the search accuracy, but is still vulnerable to forward security threats when applied in the dynamic setting. Then, …

Building The Soil And The Community At Hollands Track Farm, Newdegate, Western Australia, Jamie Bowyer, Nick Kelly

Natural resources published reports

The Kelly family have been farming at Hollands Track Farm, about 18 km south-west of Newdegate, since 1965. They currently produce grains, beef, eggs and sheep. Long-term annual average rainfall is 350 mm. The Kellys were early adopters – in the 1990s – of the no-till farming system, which is now widely used in Western Australia. Around 2002 they sold the last of their sheep and moved to a continuous cropping program with increased fertiliser and chemical use. They were very good at this system, and it served them well. They really enjoy the challenge of growing good crops. Over …

Biological Oscillator Synchronization With The Cellular Potts Model, Rose Una

WWU Honors College Senior Projects

Similar to how neurons synchronize their firing in the brain, individual cells of certain single-celled species can synchronize their internal oscillatory molecular clocks to those of their neighboring cells. This study develops and analyzes an abstract, discrete agent-based computational model to investigate the movement and synchronization of internal oscillators in biological cells. We adapt a Cellular Potts Model to explore this oscillator synchronization process with two-dimensional cells on a square lattice. Model assumptions are motivated by behavior in single-celled species of slime mold (Dictyostelium discoideum) and slime bacteria (myxobacteria). The effects of the spatial attraction parameter and the neighboring clock …

Just-In-Time Obsolete Comment Detection And Update, Zhongxin Liu, Xin Xia, David Lo, Meng Yan, Shangping Li

Research Collection School Of Computing and Information Systems

Comments are valuable resources for the development, comprehension and maintenance of software. However, while changing code, developers sometimes neglect the evolution of the corresponding comments, resulting in obsolete comments. Such obsolete comments can mislead developers and introduce bugs in the future, and are therefore detrimental. We notice that by detecting and updating obsolete comments in time with code changes, obsolete comments can be effectively reduced and even avoided. We refer to this task as Just-In-Time (JIT) Obsolete Comment Detection and Update. In this work, we propose a two-stage framework named CUP2 (Two-stage Comment UPdater) to automate this task. CUP2 consists …

Removal Of Amoxicillin Via Chromatographic Monolithic Columns: Comparison Between Batch And Continuous Fixed Bed, Mustafa Deni̇z Ağlamaz, Koray Şarkaya, Deni̇z Türkmen, Mustafa Uçar, Adi̇l Deni̇zli̇

Turkish Journal of Chemistry

This study presented a hydrophobic interaction-based poly(HEMA-MATrp) monolithic chromatographic column (MCC) to remove amoxicillin from aqueous solutions. In addition to their porous structure, monolithic-filled columns offer superior properties without loss of performance, which is one of the points that make them unique. The specific surface area of the monolithic column synthesized by the bulk polymerization of 2-hydroxyethyl methacrylate and N-Methacryloyl-L-tryptophan. Also, poly(HEMAMATrp) MCC has been characterized via FTIR, SEM, and elemental analysis. According to BET analysis, the specific surface area of the poly(HEMA-MATrp) monolithic chromatographic column (MCC) is 14.2 mg/g. The adsorption and desorption of amoxicillin in an aqueous solution …

Enzymatic Removal Of Sulfa Drugs From Synthetic Wastewater By Soybean Peroxidase, Maryam Sharifzadeh

Electronic Theses and Dissertations

Sulfa drugs are a broad family of antibiotics that are widely used in the treatment of a wide range of infections, and they have been found in surface and groundwater, as well as present in sewage and effluent (treated sewage and sludge)municipal or industrial wastewater treatment plants (WWTPs). in concentrations of ng/L to >g/L. The continued presence of these so-called emerging pollutants (ECs) and their metabolites can cause adverse ecological effects, including bacterial resistance, even at very low concentrations. In this study, the first aim was to explore the feasibility of oxidation processes catalyzed by soybean peroxidase as an eco-friendly …

Eps 202-102: Society Techn & Environmnt, Sweeta Chauhan

Chemistry, Environmental and Forensic Science Syllabi

No abstract provided.

Reducing Disposable Plastic Waste From Protein Quantitative Assays, Justin Buiter

All Capstone Projects

Protein concentration quantification is important for many areas of biology and biochemistry. This quantification happens in large quantities in laboratories and needs to be done accurately and quickly. The Bradford assay is one of the most common ways of accomplishing this task (1). This assay relies on the binding of the protein and the Coomassie Blue G250 dye. The anionic blue form binds to the protein and has a maximum at 595 nm. This allows for accurate quantification by reading how much of the sample absorbs at 595 nm (2).

In 2014, it was estimated that 5.5 million tons of …

Cure Unity, Ssisandesh Devireddy

All Capstone Projects

No abstract provided.

Eye Lens Β-Crystallins Are Predicted By Native Ion Mobility-Mass Spectrometry And Computations To Form Compact Higher-Ordered Heterooligomers, Amber D. Rolland, Takumi Takata, Micah T. Donor, Kirsten J. Lampi, James S. Prell

Faculty Publications - Department of Biological & Molecular Science

Eye lens crystallin proteins maintain the refractive properties of the lens but are not replaced after denucleation. Rolland et al. use native ion mobility-mass spectrometry, kinetics experiments, and computations to reveal that b-crystallins form heterodimers. These likely assemble into compact heterooligomers that enable the very high protein concentrations found in lens tissue.

Homopurine Guanine-Rich Sequences In Complex With N-Methyl Mesoporphyrin Ix Form Parallel G-Quadruplex Dimers And Display A Unique Symmetry Tetrad, Ming Ye, Erin V. Chen, Shawn H. Pfeil, Kailey N. Martin, Tamanaa Atrafi, Sara Yun, Zahara Martinez, Liliya A. Yatsunyk

Physics & Engineering Faculty Publications

DNA can fold into G-quadruplexes (GQs), non-canonical secondary structures formed by pi-pi stacking of G-tet-rads. GQs are important in many biological processes, which makes them promising therapeutic targets. We identified a 42-nucleotide long, purine-only G-rich sequence from human genome, which contains eight G- stretches connected by A and AAAA loops. We divided this sequence into five unique segments, four guanine stretches each, named GA1-5. In order to investigate the role of adenines in GQ structure formation, we per-formed biophysical and X-ray crystallographic studies of GA1-5 and their complexes with a highly selective GQ ligand, N-methyl mesoporphyrin IX (NMM). Our data …

Deep Learning Models For Ct Image Standardization, Md Selim

Theses and Dissertations--Computer Science

Multicentric CT imaging studies often encounter images acquired with scanners from different vendors or using different reconstruction algorithms. This leads to inconsistencies in noise level, sharpness, and edge enhancement, resulting in a lack of homogeneity in radiomic characteristics. These inconsistencies create significant variations in radiomic features and ambiguity in data sharing across different institutions. Therefore, normalizing CT images acquired using non-standardized protocols is vital for decision-making in cross-center large-scale data sharing and radiomics studies. To address this issue, we present four end-to-end deep-learning-based models for CT image standardization and normalization. The first two models require paired training data and can …

Competition And Herbivory Influence The Survival, Growth, And Physiology Of Native Tree Seedlings In The Kentucky Inner Bluegrass Savanna-Woodland, James D. Shaffer

Theses and Dissertations--Biology

Terrestrial plant communities are shaped by competition for resources, herbivory, and abiotic processes. Savanna systems represent a dynamic coexistence of contrasting life forms (grasses and trees) shaped by competition and disturbance. The Kentucky Inner Bluegrass Savanna-Woodland (KIBSW) is described as an open woodland of shade intolerant species; however, climatic, and edaphic conditions can support closed-canopy forest. After European pioneer settlement (c1750-1800), over 99% of “savanna-woodlands” have been lost. KIBSW remnants are experiencing a recruitment failure, leading to a dominance shift in tree communities. I researched how tree-grass competition and mammalian herbivory influence KIBSW regeneration and maintenance. The KIBSW does not …

Structured Attention For Image Analysis, Xin Xing

Theses and Dissertations--Computer Science

Attention mechanism, an approach to maintain the local and global features over the input, is the crucial element of the Transformer. This dissertation explores structured attention for image analysis, proposing attention-based methods for multi-label learning and Alzheimer’s Disease (AD) diagnosis.
For the multi-label learning task, I present two works under the Vision Transformer (ViT) framework. The first work focuses on supervised learning of multi-label classification. I address the problems of the multi-label classification and propose a model named AssocFormer, which adopts the association module to access the objects’ association relationship to improve the model performance. The second work addresses the …

Spatiotemporal Retrievals Of Soil Moisture And Geomorphologic Data For Landslide Sites In Eastern Kentucky, Lindsey Sebastian Bryson, Daniel M. Francis

Civil Engineering Research Data

These data are the soil texture, land information system-based soil moisture estimates from assimilation of NASA SMAP satellite-based observations and NOAH 3.6 Land Surface Model estimates, artificial neural network machine learning code, and in-situ soil moisture measurements.

Expanding Current Definitions Of Environmental Stewardship Through Organizational Mission Statement Analysis, Alyssa S. Thomas, Michele Romolini

Center for Urban Resilience Scholarship

In recent years, environmental stewardship has been emphasized as one solution to social-ecological sustainability concerns, especially at the local scale. The Stewardship Mapping and Assessment Project (STEW-MAP) is a national research program developed by the USDA Forest Service that has been implemented at numerous locations in the United States and internationally. This study compared the mission statements of environmental stewardship groups in the Los Angeles River Watershed to previously proposed definitions and frameworks of organizational environmental stewardship to see how well they were reflected. A thematic analysis of the mission statements was also carried out to identify locally important themes …

Assessment Of An Evapotranspiration Algorithm Accounting For Land Cover Types And Photosynthetic Perspectives Using Remote Sensing Images, C. Sur, W. H. Nam, X. Zhang, Tsegaye Tadesse, Brian D. Wardlow

School of Natural Resources: Faculty Publications

No abstract provided.

Anticipation, Autonomic Regulation, And Aces, Sadie K. Schiermeyer, Ella Bursky, Timothy Oakley, Emily Pyle

Science University Research Symposium (SURS)

Stress and anticipation are some of the most commonly talked about topics in the current age of self-help and wellness. Anticipatory stress has been found to aid in autonomic regulation and stress relief (Nasso et al., 2018). What’s more, adverse childhood experiences (ACEs) have been previously associated with a blunted response to stress, but the mechanisms have not been investigated. The primary aim of this study was to identify if the anticipation of a stressor significantly influenced an individual’s autonomic response. Additionally, it sought to identify any correlation between childhood trauma and individuals’ stress response. Participants were comprised of undergraduate …