Physical Sciences and Mathematics Commons^™

Test Event Example 12/14/23, Metzalli Demolastname

Annual Research Symposium

No abstract provided.

Rapid Cryogenic Electrical Characterization Of Materials And Devices Using Gifford-Mcmahon Cryocoolers, Margaret Marte, Bernardo Langa Jr., Patrick Johnson, Deepak Sapkota, Kathryn Evancho, Bernadeta Srijanto, Dale Hensley, Kasra Sardashti

Journal of the South Carolina Academy of Science

Thin-film heterostructures are necessary building blocks for superconducting and phononic quantum computing devices. Many new generations of quantum hardware demand extensive materials research to optimize performances at cryogenic temperatures (below 10 K). Here, we demonstrate compact cryogenic measurement systems capable of reaching sub-10K temperatures in less than three hours with the ability to measure AC/DC resistance and dielectric properties of thin-film materials. Our platform utilizes Gifford-McMahon (GM) cryocoolers as effective tools for providing high throughput cooling-warming cycles. We successfully used the GM-based measurement systems to measure 1) the superconducting transition temperature for Nb thin films (Tc ~7.8 K), and 2) …

A Small Forest Owner's Engagement With A Carbon Sequestration Effort In Northeastern U.S., Frederick Pond

University Libraries Faculty and Staff Publications

In 2023, a small forest landowner in central Vermont enrolled 140 acres in the Family Forest Carbon Program[FFCP], engaging his local forestland in combating global warming.

FFCP is a collaboration of The Nature Conservancy and American Forest Foundation, developed to offer small landowners the opportunity to engage their asset in carbon sequestration locally.

This poster presents the experience of a small forest owner's process in entering a twenty year contract to manage a small woodlot under the direction of FFCP while enrolled with the state UVA program, also known as Current Use.

Challenges to the process, advantages/downsides, future perspectives are …

A Randomised Non-Descent Method For Global Optimisation, Dmitry A. Pasechnyuk, Alexander Gornov

Machine Learning Faculty Publications

This paper proposes novel algorithm for non-convex multimodal constrained optimisation problems. It is based on sequential solving restrictions of problem to sections of feasible set by random subspaces (in general, manifolds) of low dimensionality. This approach varies in a way to draw subspaces, dimensionality of subspaces, and method to solve restricted problems. We provide empirical study of algorithm on convex, unimodal and multimodal optimisation problems and compare it with efficient algorithms intended for each class of problems.

2023 December 14 - Tennessee Weekly Drought Summary, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Weekly Drought Summaries

No abstract provided.

Utilizing Multitask Transfer Learning For Sonographic Rheumatoid Arthritis Synovitis Grading, Jordan Marie Claire Sanders

Doctoral Dissertations and Master's Theses

Classifying the four sonographic Rheumatoid Arthritis (RA) synovitis grades (Grade 0, Grade 1, Grade 2, and Grade 3) is a difficult problem due to the complexity of the relevant markers. Therefore, the current research proposes a Multitask Transfer Learning (MTL) framework for sonographic RA synovitis grading of Ultrasound (US) images in Brightness mode (B-Mode) and Power Doppler mode.

In the medical community, the lack of reliability of scoring these images has been an issue and reason for concern for doctors and other medical practitioners. The human/machine variability across the acquisition procedure of these US images creates an additional challenge that …

Improved Image Recognition Via Synthetic Plants Using 3d Modelling With Stochastic Variations, Chris C. Napier, David M. Cook, Leisa Armstrong, Dean Diepeveen

Research outputs 2022 to 2026

This research extends previous plant modelling using L-systems by means of a novel arrangement comprising synthetic plants and a refined global wheat dataset in combination with a synthetic inference application. The study demonstrates an application with direct recognition of real plant stereotypes, and augmentation via a plant-wide stochastic growth variation structure. The study showed that the automatic annotation and counting of wheat heads using the Global Wheat dataset images provides a time and cost saving over traditional manual approaches and neural networks. This study introduces a novel synthetic inference application using a plant-wide stochastic variation system, resulting in improved structural …

Race: An Efficient Redundancy-Aware Accelerator For Dynamic Graph Neural Network, Hui Yu, Yu Zhang, Jin Zhao, Yujian Liao, Zhiying Huang, Donghao He, Lin Gu, Hai Jin, Xiaofei Liao, Haikun Liu, Bingsheng He, Jianhui Yue

Michigan Tech Publications, Part 2

Dynamic Graph Neural Network (DGNN) has recently attracted a significant amount of research attention from various domains, because most real-world graphs are inherently dynamic. Despite many research efforts, for DGNN, existing hardware/software solutions still suffer significantly from redundant computation and memory access overhead, because they need to irregularly access and recompute all graph data of each graph snapshot. To address these issues, we propose an efficient redundancy-aware accelerator, RACE, which enables energy-efficient execution of DGNN models. Specifically, we propose a redundancy-aware incremental execution approach into the accelerator design for DGNN to instantly achieve the output features of the latest graph …

Learning Mortality Risk For Covid-19 Using Machine Learning And Statistical Methods, Shaoshi Zhang

Electronic Thesis and Dissertation Repository

This research investigates the mortality risk of COVID-19 patients across different variant waves, using the data from Centers for Disease Control and Prevention (CDC) websites. By analyzing the available data, including patient medical records, vaccination rates, and hospital capacities, we aim to discern patterns and factors associated with COVID-19-related deaths.

To explore features linked to COVID-19 mortality, we employ different techniques such as Filter, Wrapper, and Embedded methods for feature selection. Furthermore, we apply various machine learning methods, including support vector machines, decision trees, random forests, logistic regression, K-nearest neighbours, na¨ıve Bayes methods, and artificial neural networks, to uncover underlying …

Movie Recommendation System Using Content Based Filtering, Sribhashyam Rakesh

Al-Bahir Journal for Engineering and Pure Sciences

The movie recommendation system plays a crucial role in assisting movie enthusiasts in finding movies that match their interests, saving them from the overwhelming task of sifting through countless options. In this paper, we present a content-grounded movie recommendation system that leverages an attribute-based approach to offer personalized movie suggestions to users. The proposed method focuses on attributes such as cast, keywords, crew, and genres of movies to predict users' preferences accurately. Through extensive evaluation, our content-grounded recommendation system demonstrated significant improvements in performance compared to conventional methods. The precision and recall scores increased by an average of 20% and …

Ssl Everywhere: Leveraging Hsms For Enhanced Intra-Domain Security, Yazan Aref

Electronic Thesis and Dissertation Repository

In a world where digitalization is rapidly advancing, the security and privacy of intra-domain communication within organizations are of critical concern. The imperative to secure communication channels among physical systems has led to the deployment of various security approaches aimed at fortifying networking protocols. However, these approaches have typically been designed to secure protocols individually, lacking a holistic perspective on the broader challenge of intra-domain communication security. This omission raises fundamental concerns about the safety and integrity of intra-domain environments, where all communication occurs within a single domain. As a result, this thesis introduces SSL Everywhere, a comprehensive solution designed …

Tikaram And Chandrakala Dhananjaya: A Collaborative Couple In Mathematics From Nepal, Deepak Basyal, Brigitte Stenhouse

Mathematics and Statistics

Within the history of mathematics and mathematics education in Nepal, Tikaram and Chandrakala Dhananjaya are relatively well-known figures for their two books Śiśubodha Taraṅgiṇī and Līlāvatī. This is despite there being almost no archival or manuscript materials offering a window into their lives: we have no letters, notebooks, diaries, or school records. Rather than focusing on either individual in isolation, in this article we present an argument for considering the Dhananjayas as an analytically indivisible collaborative couple in mathematics. Of the two aforementioned books, one is attributed to Chandrakala and the other to Tikaram; but in fact, both are translations …

Celestial Bodies, Rebecca L. Rand, Mark Popinchalk

Capstones

Most of us will never come close to touching space. But space touches us every day. On Celestial Bodies, journalist Rebecca Rand and astronomer Mark Popinchalk explore the ways outer space interacts with life on earth.

In Episode 1, hosts Rebecca Rand and Mark Popinchalk explore how, for millions of years, trees have been recording celestial events in space. Within the rings of their trunks, trees store radiation from solar flares, supernovae, and changes in the earth’s magnetic field. The hosts talk to Dr. Ben Pope to learn more about what we can discover by looking at radioactive molecules …

Mutational Analysis Of The Nitrogenase Carbon Monoxide Protective Protein Cown Reveals That A Conserved C‑Terminal Glutamic Acid Residue Is Necessary For Its Activity, Dustin L. Willard, Joshuah J. Arellano, Mitch Underdahl, Terrence M. Lee, Avinash S. Ramaswamy, Gabriella Fumes, Agatha Kliman, Emily Y. Wong, Cedric P. Owens

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Nitrogenase is the only enzyme that catalyzes the reduction of nitrogen gas into ammonia. Nitrogenase is tightly inhibited by the environmental gas carbon monoxide (CO). Many nitrogen fixing bacteria protect nitrogenase from CO inhibition using the protective protein CowN. This work demonstrates that a conserved glutamic acid residue near the C-terminus of Gluconacetobacter diazotrophicus CowN is necessary for its function. Mutation of the glutamic acid residue abolishes both CowN’s protection against CO inhibition and the ability of CowN to bind to nitrogenase. In contrast, a conserved C-terminal cysteine residue is not important for CO protection by CowN. Overall, this work …

Probing And Enhancing The Reliance Of Transformer Models On Poetic Information, Almas Abdibayev

Dartmouth College Ph.D Dissertations

Transformer models have achieved remarkable success in the widest variety of domains, spanning not just a multitude of tasks within natural language processing, but also those in computer vision, speech, and reinforcement learning. The key to this success is largely attributed to the self-attention mechanism, particularly its ability to scale in performance as it grows in the number of parameters. Extensive effort has been underway to study the major linguistic properties learned by these models during the course of their pretraining. However, the role of certain finer linguistic phenomena present in language and their utilization by Transformers has not been …

Deep Learning Uncertainty Quantification For Clinical Text Classification, Alina Peluso, Ioana Danciu, Hong-Jun Yoon, Jamaludin Mohd Yusof, Tanmoy Bhattacharya, Adam Spannaus, Noah Schaefferkoetter, Eric B. Durbin, Xiao-Cheng Wu, Antoinette Stroup, Jennifer Doherty, Stephen Schwartz, Charles Wiggins, Linda Coyle, Lynne Penberthy, Georgia D. Tourassi, Shang Gao

School of Public Health Faculty Publications

INTRODUCTION: Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the state-of-the-art models to address real-world classification. Although the strength of activation in DNNs is often correlated with the network's confidence, in-depth analyses are needed to establish whether they are well calibrated. METHOD: In this paper, we demonstrate the use of DNN-based classification tools to benefit cancer registries by automating information extraction of disease at diagnosis and at surgery from electronic text pathology reports from the US National …

The Tor Algebra Of Trimmings Of Gorenstein Ideals, Luigi Ferraro, Alexis Hardesty

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

Let (R,\mathfrak m,\Bbbk ) be a regular local ring of dimension 3. Let I be a Gorenstein ideal of R of grade 3. Buchsbaum and Eisenbud proved that there is a skew-symmetric matrix of odd size such that I is generated by the sub-maximal pfaffians of this matrix. Let J be the ideal obtained by multiplying some of the pfaffian generators of I by \mathfrak m; we say that J is a trimming of I. Building on a recent paper of Vandebogert, we construct an explicit free resolution of R/J and compute a partial DG algebra structure on this …

E-Governance: The Implication Of Next Social Generation Welfare Information System, Yaya Mulyana Abdul Aziz, Andre Ariesmansyah

Smart City

Accelerating bureaucracy can be performed by e-governance present in order to improve the quality of government administration in the world. E-governance draft is closely related to the development of information and communication technology (ICT) globally. One form of embodiment of e-governance is the implementation of a smart city. smart cities are expected to be able to become a liaison between the demands of the community in appropriate, effective, and efficient services from the city government, by utilizing ICT. There are various related definitions of smart city in this world. One of them is as explained by Nam and Pardo who …

Ohio Recovery Housing: Resident Risk And Outcomes Assessment, Elyjiah Potter, Bivin Sadler

SMU Data Science Review

Addiction and substance abuse disorder is a significant problem in the United States. Over the past two decades, the United States has faced a boom in substance abuse, which has resulted in an increase in death and disruption of families across the nation. The State of Ohio has been particularly hard hit by the crisis, with overdose rates nearly doubling the national average. Established in the mid 1970’s Sober Living Housing is an alcohol and substance use recovery model emphasizing personal responsibility, sober living, and community support. This model has been adopted by the Ohio Recovery Housing organization, which seeks …

Deep Learning Image Analysis To Isolate And Characterize Different Stages Of S-Phase In Human Cells, Kevin A. Boyd, Rudranil Mitra, John Santerre, Christopher L. Sansam

SMU Data Science Review

Abstract. This research used deep learning for image analysis by isolating and characterizing distinct DNA replication patterns in human cells. By leveraging high-resolution microscopy images of multiple cells stained with 5-Ethynyl-2′-deoxyuridine (EdU), a replication marker, this analysis utilized Convolutional Neural Networks (CNNs) to perform image segmentation and to provide robust and reliable classification results. First multiple cells in a field of focus were identified using a pretrained CNN called Cellpose. After identifying the location of each cell in the image a python script was created to crop out each cell into individual .tif files. After careful annotation, a CNN was …

Investigation Into A Practical Application Of Reinforcement Learning For The Stock Market, Philip Traxler, Sadik Aman, Will Rogers, Allyn Okun

SMU Data Science Review

A major problem of the financial industry is the ability to adapt their trading strategies at the same rate the market evolves. This paper proposes a solution using existing Reinforcement Learning libraries to help find new strategies at a practical scale. Using a wide domain of ticker symbols, an algorithm is trained in an environment that better represents reality. The supplied decision-making algorithm is tested using recorded data from the U.S stock market from 2000 through 2022. The results of this research show that existing techniques are statistically better than making decisions at random. With this result, this research shows …

Differentiation Of Human, Dog, And Cat Hair Fibers Using Dart Tofms And Machine Learning, Laura Ahumada, Erin R. Mcclure-Price, Chad Kwong, Edgard O. Espinoza, John Santerre

SMU Data Science Review

Hair is found in over 90% of crime scenes and has long been analyzed as trace evidence. However, recent reviews of traditional hair fiber analysis techniques, primarily morphological examination, have cast doubt on its reliability. To address these concerns, this study employed machine learning algorithms, specifically Linear Discriminant Analysis (LDA) and Random Forest, on Direct Analysis in Real Time time-of-flight mass spectra collected from human, cat, and dog hair samples. The objective was to develop a chemistry- and statistics-based classification method for unbiased taxonomic identification of hair. The results of the study showed that LDA and Random Forest were highly …

Impact Of Covid-19 On Recruitment Of High School Athletes To Di Track And Field, Christopher Haub, Jon Paugh, Alonso Salcido, Monnie Mcgee

SMU Data Science Review

Due to COVID-19, in the spring of 2020, the NCAA gave scholarship athletes an extra year of eligibility but did not increase the number of scholarships a school could issue. This potentially led to increased competition for scholarships as coaches could choose between retaining athletes or recruiting new ones. Furthermore, the Spring 2020 track and field season for high school seniors ended early – limiting high school athletes’ chance to get their best scores, and interrupting student to college interaction. This research looks specifically at the impact of COVID-19, and the resulting NCAA policy changes, on the recruitment to DI …

A Prompt Engineering Approach To Creating Automated Commentary For Microsoft Self-Help Documentation Metric Reports Using Chatgpt, Ryan Herrin, Luke Stodgel, Brian Raffety

SMU Data Science Review

Microsoft collects an immense amount of data from the users of their product-self-help documentation. Employees use this data to identify these self-help articles' performance trends and measure their impact on business Key Performance Indicators (KPIs). Microsoft uses various tools like Power BI and Python to analyze this data. The problem is that their analysis and findings are summarized manually. Therefore, this research will improve upon their current analysis methods by applying the latest prompt engineering practices and the power of ChatGPT's large language models (LLMs). Using VBA code, Microsoft Excel, and the ChatGPT API as an Excel add-in, this research …

The Impact Of The Covid-19 Pandemic On Faculty Productivity And Gender Inequalities In Stem Disciplines, Monnie Mcgee, Raag Patel, Roslyn Smith, Satvik Ajmera

SMU Data Science Review

Women and minorities within STEM disciplines historically encounter obstacles in academic advancement, a situation compounded by the COVID-19 pandemic due to the imposition of additional responsibilities like caregiving. This study meticulously probes into the pandemic's influence on traditional academic productivity metrics – specifically publication and submission frequency, citation volume, and leadership in scholarly entities, by employing Natural Language Processing to extract and analyze data from key journals within various scientific domains. A critical revelation from the research indicates a notable downturn in publication activity during 2021, potentially attributed to pandemic-induced disruptions, with a compensatory surge observed in 2022. Although a …

Predicting Land Reclamation Of Bond Released Surface Mines, Kendall Scott, Austin Webb, Tadd Backus, Robert Slater

SMU Data Science Review

Accurately measuring the recovery of released surface mines in the UnitedStates poses crucial challenges. This study aims to develop a prediction of land classification, that considers various environmental and coal mine variables. By utilizing this prediction, the researchers and environmentalists (specifically Appalachian Voices, the group heading this research) can better understand the relevant factors for successful reclamation. Efficient management of mine recovery is essential for environmental sustainability, regulatory compliance, and resource utilization. This study focuses on the Appalachian Forest area, which risks becoming a net carbon source (a place that emits more carbon than it absorbs) due to mine recovery. …

Utilizing Computer Vision For Automated Cellular Microscopy, Ahmed Awadallah, Ryan Bass, James Burke, Robert Price, John Santerre

SMU Data Science Review

Abstract. Post-acquisition data analysis of microscopy images is a vital yet time-consuming process for researchers. Quantitative fields such as biology and microbiology often require using images as primary data sources. Finding methods to automate this process would increase the throughput, quality, and reproducibility. This research aims to provide a novel end-to-end pipeline that reduces the workload on researchers in identifying cell cytoplasm and nuclei while creating a process that can scale to the researcher's needs. The proposed methodology utilizes various image-processing techniques to rapidly identify the boundaries of cells and nuclei, including filtering, thresholding, and deep learning. The results …

12.04.2023 Orsp Newsletter, Liz Williamson

ORSP Newsletter

Week of December 4, 2023

First Order Approximation On The Basilica Julia Set, Xintan Xia, Taryn Flock

Mathematics, Statistics, and Computer Science Honors Projects

We consider the basilica Julia set of the quadratic polynomial P (z) = z^2 - 1, with its successive graph approximations defined in terms of the external ray parametrization of the set. Following the model of Kigami and later Strichartz, we exploit these graph approximations to define derivatives of functions defined on the fractal, an endeavor complicated by asymmetric neighborhood behaviors at approximated vertex points across levels, and by the topology of these vertices. We hence differentiate even and odd levels of approximations of the Julia set and construct, accordingly, normal derivatives corresponding to each level category at the vertices, …

In-Depth Geochemical Analysis Of Turbidite-Associated Shales Of The Pindos Basin, Greece, Jonathon Michael Sevy

Theses and Dissertations

Detailed geochemical analysis of the turbidite-associated shales of the Cretaceous Katafito Formation, Greece, reveals important details regarding the paleoenvironment, paleoproductivity, and regional tectonics of the Pindos Basin. The Katafito Formation was deposited along an active margin at the early onset of closure of the Tethys Sea in the Pindos sub-basin. While careful studies of the coarse clastic component of turbidites are common, this study consisted of a detailed geochemical characterization of the fine-grained portions, which helped reveal paleoenvironmental information about the basin. This study combined organic and inorganic geochemistry utilizing elemental, mineralogical, and organic chemical signatures from fine-grained turbidite-associated sediments …