Physical Sciences and Mathematics Commons^™

Targeting Macrophytes: Increased Water Quality Through Optimized Vegetation Considerations For Constructed Wetlands, Austin Mcbrady

Masters Theses (Archived)

This study of constructed wetland design investigated relationships between macrophyte species selection and planting density for water quality improvement. A lab-scale wetland was compared against a pilot-scale wetland in San Antonio, Texas at Mitchell Lake to measure differences in effluent water quality improvement using three native macrophyte species. Using a novel, two-phase method, a targeting macrophyte was identified from among other species based on its marked capability for improving water quality factors, then was planted in varied majority densities to compare differences in treatment effectiveness. The results of this study showed that this complimentary approach to wetland design displayed significant …

Interactive Effects Of Sublethal Concentrations Of Fracking Biocides And Abandoned Mine Drainage On Amphipod Behavior, Kelly Lenhart

Electronic Theses and Dissertations

This thesis examined the sublethal effects of three pollutants, namely glutaraldehyde, 4,4-dimethyloxazolidine (DMO), and abandoned mine drainage (AMD), on amphipods. The primary objective was to investigate their combined effects on amphipods. The three pollutants, despite having the potential to combine in the environment, have not been studied to determine their potential for detrimental interactive effects which could result in unexpected environmental damage.

The research employed a series of experimental setups involving controlled exposure of amphipods to varying, putatively sublethal, concentrations of the chemicals of interest. Subsequently, effects were assessed via both behavioral and feeding assessments. To facilitate this analysis, novel …

Ascot App, Milla Penelope Markovic

Honors Thesis

The Ascot App is a research tool for acquiring and analyzing data. The app comprises of both mobile and web platforms, each serving a unique purpose. The mobile side allows users to input data through the app’s form, which is uploaded to a database for further processing and analysis. The web app, which is still under development as of April of 2024, allows users to manage their research project and download data in the form of a parsed CSV. These components ensure a seamless process for research teams to record data with persistence and security while allowing for analysis.

Ascot …

Confined Feni Alloy Nanoparticles In Carbon Nanotubes For Photothermal Oxidative Dehydrogenation Of Ethane By Carbon Dioxide, Jinqiang Zhang, Meng Li, Xiaojie Tan, Lei Shi, Kun Xie, Xiaoli Zhao, Shuaijun Wang, Shiyong Zhao, Huayang Zhang, Xiaoguang Duan, Haijun Chen, Yuezhao Zhu, Mingbo Wu, Hongqi Sun, Shaobin Wang

Research outputs 2022 to 2026

Oxidative dehydrogenation of ethane with CO2 (ODEC) is an attractive reaction for reduction of carbon footprints and ethene production. In this work, we present photothermal catalysis on confined bimetal catalysts for ODEC. Carbon nanotubes confined non-noble bimetal alloy (i.e., CoNi@CNTs and FeNi@CNTs) catalysts were prepared and FeNi@CNTs showed effective performance in photothermal catalytic ODEC to ethene. Experiments and simulations reveal that UV and visible lights (420 – 490 nm) are responsible for ODEC and non-oxidative dehydrogenation of ethane, respectively, to ethene. Additionally, ODEC to ethene is preferred to C-C cracking to methane on FeNi@CNTs in light ( > 490 nm)-induced thermocatalysis. …

Observed Impacts Of Large Wind Farms On Grassland Carbon Cycling, Donghai Wu, Steven M. Grodsky, Wenfang Xu, Naijing Liu, Rafael M. Almeida, Liming Zhou, Lee M. Miller, Somnath Baidya Roy, Geng Xia, Anurag A. Agrawal

School of Earth, Environmental, & Marine Sciences Faculty Publications

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Toughness Of Recursively Partitionable Graphs, Calum Buchanan, Brandon Du Preez, K. E. Perry, Puck Rombach

Theory & Applications of Graphs

A simple graph G = (V,E) on n vertices is said to be recursively partitionable (RP) if G ≃ K1, or if G is connected and satisfies the following recursive property: for every integer partition a₁, a₂, . . . , a_k of n, there is a partition {A₁,A₂, . . . ,A_k} of V such that each |A_i| = a_i, and each induced subgraph G[A_i] is RP (1 ≤ i ≤ k). We show that if S is a …

Simulation-Based Adaptive Interface For Personalized Learning Of Ai Fundamentals In Secondary School, Sara Guerreiro-Santalla, Dalila Duraes, Helen Crompton, Paulo Novais, Francisco Bellas

STEMPS Faculty Publications

This paper presents the first results on the validation of a new Adaptive E-learning System, focused on providing personalized learning to secondary school students in the field of education about AI by means of an adaptive interface based on a 3D robotic simulator. The prototype tool presented here has been tested at schools in USA, Spain, and Portugal, obtaining very valuable insights regarding the high engagement level of students in programming tasks when dealing with the simulated interface. In addition, it has been shown the system reliability in terms of adjusting the students’ learning paths according to their skills and …

Solar-Powered Microgrids In Northern California: An Opportunity For Resilience, Marina Riddle

Master's Projects and Capstones

Planned and unplanned power outages have been increasing in frequency and duration, negatively impacting all public sectors, and threatening public safety. These outages are deadly to those who rely on medical devices. As climate change-fueled extreme weather events (wildfires, earthquakes, storms, etc.) also increase in frequency, our electrical grid must be prepared to bounce back. Microgrids provide necessary redundancy and reliability. Through a novel GIS suitability analysis, based on solar radiation, land use type, local energy demand, distance to transmission lines, distance to roads, and slope, optimal locations for solar-powered microgrids throughout Northern California were determined. The counties of Fresno, …

Introduction To Physics, Kalani Hetti

Open Educational Resources

Introduction to physics, class code PHY 114 at college of Staten Island covers general physics concepts by using very simple algebraic calculations. Topics may include scientific measurements, significant figures, estimation, units, linear and rotational motion, vectors, forces, energy, momentum, collision, impulse, projectile motion, circular motion, thermodynamics, oscillating waves, electricity and magnetism, properties of lights, reflection, refraction, atomic nuclei, and radioactivity. This course is designed to teach general concepts and laws of physics to everyday life enforcing student’s critical thinking, logical patterns, organization, and everyday life applications. In this document, all the class materials including lectures, worksheets, homework assignments, quizzes, and …

Tipping The Mesoscales: Advances In Multipeak Bragg Coherent Diffraction Imaging, J. Nicholas Porter

Theses and Dissertations

Material failure begins with strain between atoms and cascades upward into macroscopic damage such as cracks. Therefore, our ability to predict (and therefore prevent) material failure is largely limited by our understanding of this process. This understanding, however, has been impeded by the difficulty of directly observing such phenomena. In this thesis, I discuss recent advances in Bragg coherent diffraction imaging (BCDI) which produce three-dimensional, mesoscopic images of interior strain in microcrystals. In particular, I present a novel algorithm, based on the concept of cyclic-constrained optimization (CCO), for the rapid, coupled reconstruction of a microcrystal from multiple Bragg diffraction patterns. …

An Empirical Study Of Machine Learning Techniques For Accurate Stock Price Forecasting, Daniel Paliulis, Hari Patchigolla

Honors Scholar Theses

This paper presents a comprehensive approach to predicting future stock prices of companies using machine learning and time series analysis. The research problem is centered around addressing the complexity and emotion-driven nature of stock investment decisions. To create an objective determinant in stock decisions, we propose a machine learning model utilizing time series data from major companies, including Amazon, Apple, Google, Nvidia, Meta, Tesla, Salesforce, Intel, and Microsoft. We explore the use of Long Short-Term Memory (LSTM) neural networks, to capture the temporal dynamics of stock prices. These models are designed to process sequential data, maintaining short term and long …

Optimization Of Transition-Metal Inclusive Carbon Aerogels For Electrochemical Energy Applications, Allen Dalton Davis

Electronic Theses & Dissertations

The ever-growing need for energy alongside rising concerns for climate change demands the development of renewable energy technologies. Hydrogen fuel cells are a promising technology that can serve to either supplement energy generation or act as a lone power source. Yet for these devices to be truly green, the hydrogen that serves as fuel must be procured from a renewable resource. Electrolytic water splitting is a process that allows for the dissociation of water into H₂ and O₂. For this process to be practical, the electrolyzer needs to demonstrate high efficiency and stability, as well as a …

Microplate-Like Metal Pyrophosphate Engineered On Ni-Foam Towards Multifunctional Electrode Material For Energy Conversion And Storage, Rishabh Srivastava

Electronic Theses & Dissertations

High clean energy demand, dire need for sustainable development, and low carbon footprints are the few intuitive challenges, leading researchers to aim for research and development for high-performance energy devices. The development of materials used in energy devices is currently focused on enhancing the performance, electronic properties, and durability of devices. Tunning the attributes of transition metals using pyrophosphate (P₂O₇) ligand moieties can be a promising approach to meet the requirements of energy devices such as water electrolyzers and supercapacitors, although such a material’s configuration is rarely exposed for this purpose of study.

Herein, we grow …

Exploring Soybean Oil-Based Polyol And Effect Of Non-Halogenated Flame Retardants In Rigid Polyurethane Foam, Sahithi Kondaveeti

Electronic Theses & Dissertations

To address the increasing demand for sustainable biomaterials due to the depletion of fossil fuel resources and growing environmental concerns, a new type of biodegradable and environmentally friendly rigid polyurethane foam (RPUF) has been synthesized. These foams are derived from chemically modified soybean oil-based polyol obtained from soybean oil by epoxidation followed by a ring-opening reaction. Polyurethane foam is generally used in construction, furniture, and automobile industries but is highly flammable and releases toxic gases and smoke during combustion. In this study, a highly efficient synergistic effect halogen-free flame-retardant (FR) melamine salt, 2-carboxyethyl(phenyl)phosphinic acid melamine salt (CMA) was synthesized from …

Enhanced Mechanical Strength Of Soybean Oil-Based Non-Isocyanate Polyurethane Adhesive For Wood Application By Introducing Nanofillers, Vatsal Chaudhari

Electronic Theses & Dissertations

Polyurethane (PU) is a versatile material that finds extensive use in various industries including bedding, construction, automotive, and packaging. Historically, this particular polymer relied significantly on petrochemical resources, a practice that was considered to have negative environmental impacts. The conventional method for preparing PU involves the use of isocyanate, which is a disadvantage due to its negative impact on the environment and human health. The resolution of this problem entails identifying an appropriate substitute for petroleum-derived products that minimize their impact on both the environment and human health. The researchers earlier utilized soybean oil, for the formulation of PUs in …

Castor Oil Polyol-Based Adhesives With Additional Crosslinker For Improvement In Mechanical And Thermal Properties., Yash Desai

Electronic Theses & Dissertations

Polyurethanes (PU) have been promising polymeric materials with many applications, including adhesives. Projecting an estimated revenue from 42.8 billion $ in 2021 to 61.5 billion $ by 2026. However, a large number of PU adhesives are sourced from petroleum products. Therefore, to lower the dependence on non-renewable resources and provide sustainable and affordable alternatives. In this work will synthesize bio-based adhesives of polyurethane from modified castor oil-based polyol. Generally, polyurethane reaction depends on the properties of polyol and isocyanates. The most important aspect of these reactions is the OH number of the polyol, which is responsible for the crosslinking and …

Nuclear Power: Extremely Dangerous, Perfectly Safe, Or Somewhere In Between?, Jackson Still

Quest

Multiple Genre Argument

Research in progress for ENGL 1301: Composition I

Faculty Mentor: W. Scott Cheney, Ph.D.

Standard research papers and five-paragraph essays can train students to blend quotations and organize paragraphs, but advanced writing in the disciplines and the workplace requires much more robust and nuanced thinking. To this end, the Multiple Genre Argument (MGA) pushes students into new writing situations where they create fictional genres to supplement traditional research—a challenging and often confusing task. Learning new skills requires becoming more comfortable with encountering this kind of difficulty and uncertainty. In their book Writing Analytically, David Rosenwasser and Jill …

Test Event Example 12/14/23, Metzalli Demolastname

Annual Research Symposium

No abstract provided.

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.

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) …

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

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 …

Movie Recommendation System Using Content Based Filtering, Sribhashyam Rakesh

Al-Bahir

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 …

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 …

Ai Empire: Unraveling The Interlocking Systems Of Oppression In Generative Ai's Global Order, Jasmina Tacheva, Srividya Ramasubramanian

Media Studies - All Scholarship

As artificial intelligence (AI) continues to captivate the collective imagination through the latest generation of generative AI models such as DALL-E and ChatGPT, the dehumanizing and harmful features of the technology industry that have plagued it since its inception only seem to deepen and intensify. Far from a “glitch” or unintentional error, these endemic issues are a function of the interlocking systems of oppression upon which AI is built. Using the analytical framework of “Empire,” this paper demonstrates that we live not simply in the “age of AI” but in the age of AI Empire. Specifically, we show that …

Stability Of Metal Fluoride Films When Irradiated With Electron Beam, Devin M. Lewis, Waseem Ashraf

Student Works

LiF is an essential mirror coating for the upcoming LUVOIR space telescope. In order to improve the technology readiness level of this material a study was to be conducted, observing the surface changes of LiF thin films as exposed to humidity for extended periods of time. However, initial attempts to characterize the film with SEM showed that LiF is unstable under all electron beam parameters experimented with. This creates the concern that LiF is also sensitive to other forms of radiation damage. Further investigation may suggest the need to transition to another mirror coating that is more resilient to radiation …

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 …

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 …