Physical Sciences and Mathematics Commons^™

Conservation Genomics Of Endangered Turtles : The Big-Headed Turtle And The Beale's Eyed Turtle, Wing Him Lee

Lingnan Theses (MPhil & PhD)

Turtles are one of the most threatened animal groups in the world, with 51.3% of the turtle species categorized as globally threatened (critically endangered, endangered and vulnerable) on the IUCN Red List. Overexploitation for the food and pet trade is the main threat, especially for turtles in Asia. In Hong Kong, all five native freshwater turtles are listed as endangered species locally. This study will focus on two species, the Big-headed Turtle (Platysternon megacephalum) and Beale’s Eyed Turtle (Sacalia bealei). Some of the world’s remaining wild populations of P. megacephalum and S. bealei are in Hong …

Environmental Dna (Edna) Survey Of Introduced Freshwater Species In Reservoirs And Streams In Hong Kong, Sze Man Yau

Lingnan Theses (MPhil & PhD)

Different organisms have been introduced worldwide both intentionally and unintentionally by humans. Some of the introduced species can establish populations outside their native geographic range and invade the environment, causing negative ecological impacts to the ecosystem. Freshwater ecosystems are prone to species invasion due to the aquatic linkage between freshwater habitats, thus facilitating the dispersal of introduced species. To minimise the potential negative impacts of introduced species, early detection and rapid response is vital. Compared to traditional survey methods (e.g., hand nets and cast nets), environmental DNA (eDNA) survey is a sensitive method for species detection by extracting the species’ …

Improving Implicit Communication In Remote Collaboration Through Augmented Reality And Digital Twins, Nicholas Levergne

LSU Master's Theses

Large scale digital twinning projects are beginning to emerge across the tech industry. Within these projects is a desire to integrate augmented reality capabilities into industrial workflows. However, research on augmented reality technology for remote collaboration lacks ecologically valid studies of real world scenarios. Additionally, prior remote collaboration literature is focused on white-collar applications instead of blue-collar field work. Prior AR collaboration software is similarly limited, with most software allowing mixed camera views and annotation that requires participants to be stationary. This thesis introduces SpectAR, an augmented reality and desktop remote collaboration software suite developed in Unreal Engine 5.1.1. SpectAR …

Modeling Metaverse Perceptions For Bolstering Traffic Safety Using Novel Trss-Based Owcm-Ram Mcdm Techniques: Purposes And Strategies, Mona Mohamed, Asmaa Elsayed, Marwa Sharawi

Neutrosophic Systems with Applications

The Metaverse has the potential to revolutionize various aspects of human life, including transportation systems. The integration of the Metaverse into intelligent transportation systems has the potential to significantly improve traffic safety in smart cities. By creating a virtual replica of the physical world, the Metaverse can provide a platform for testing new traffic management systems, road designs, and vehicle technologies in a controlled and safe environment before implementing them in the real world. One way to integrate the Metaverse into intelligent transportation systems (ITS) is by enhancing traffic safety. This can be achieved by developing an evaluation model that …

United Congregational Church (Crayon Academy Preschool), Environmental Resource Management (Erm)

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Butte Priority Soils Operable Unit (Bpsou) Draft Final Unreclaimed Sites Field Sampling Plan (Fsp) Package #10: Ur-44, Ur-45, Ur-46, Ur-47, And Ur-48, Mike Mcanulty

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Residential Metals Abatement Program Investigation Summary Report (Non-Residential Parcels – Indoor Dust) United Congregational Church (Crayon Academy Preschool), Environmental Resource Management (Erm)

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

How Generative Ai Models Such As Chatgpt Can Be (Mis)Used In Spc Practice, Education, And Research? An Exploratory Study, Fadel M. Megahed, Ying-Ju (Tessa) Chen, Joshua A. Ferris, Sven Knoth, L. Allison Jones-Farmer

Mathematics Faculty Publications

Generative Artificial Intelligence (AI) models such as OpenAI's ChatGPT have the potential to revolutionize Statistical Process Control (SPC) practice, learning, and research. However, these tools are in the early stages of development and can be easily misused or misunderstood. In this paper, we give an overview of the development of Generative AI. Specifically, we explore ChatGPT's ability to provide code, explain basic concepts, and create knowledge related to SPC practice, learning, and research. By investigating responses to structured prompts, we highlight the benefits and limitations of the results. Our study indicates that the current version of ChatGPT performs well for …

Ai Is A Viable Alternative To High Throughput Screening: A 318-Target Study, Izhar Wallach, Denzil Bernard, Kong Nguyen, Gregory Ho, Adrian Morrison, Adrian Stecula, Andreana Rosnik, Ann Marie O’Sullivan, Aram Davtyan, Ben Samudio, Bill Thomas, Brad Worley, Brittany Butler, Christian Laggner, Desiree Thayer, Ehsan Moharreri, Greg Friedland, Ha Truong, Henry Van Den Bedem, Ho Leung Ng, Kate Stafford, Krishna Sarangapani, Kyle Giesler, Lien Ngo, Michael Mysinger, Mostafa Ahmed, Nicholas J. Anthis, Niel Henriksen, Arthur L. Haas, Et Al

School of Medicine Faculty Publications

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, …

Uncertainty Analysis In Machine Learning Models, Ayorinde E. Olatunde, Weiqi Yue, Pawan K. Tripathi, Roger H. French, Anirban Mondal

Faculty Scholarship

No abstract provided.

Chiral Recognition Of Chiral (Hetero)Cyclic Derivatives Probed By Tetraaza Macrocyclic Chiral Solvating Agents Via ¹H Nmr Spectroscopy, Yu Wang, Hongmei Zhao, Chunxia Yang, Lixia Fang, Li Zheng, Hehua Lv, Pericles Stavropoulos, Lin Ai, Jiaxin Zhang

Chemistry Faculty Research & Creative Works

In the Field of Chiral Recognition, Chiral Cyclic Organic Compounds, Especially Heterocyclic Organic Compounds, Have Attracted Little Attention and Have Been Rarely Studied as Chiral Substrates by Means of 1H NMR Spectroscopy. in This Paper, Enantiomers of Thiohydantoin Derivatives, Representing Typical Five-Membered N,N-Heterocycles, Have Been Synthesized and Utilized for Assignment of Absolute Configuration and Analysis of Enantiomeric Excess. All Enantiomers Have Been Successfully Differentiated with the Assistance of Novel Tetraaza Macrocyclic Chiral Solvating Agents (TAMCSAs) by 1H NMR Spectroscopy. Surprisingly, Unprecedented Nonequivalent Chemical Shift Values (Up to 2.052 Ppm) of the NH Proton of Substrates Have Been Observed, a New …

Advancing Single-Cell Proteomics Through Innovations In Liquid Chromatography And Mass Spectrometry, Kei Grant Isaac Webber

Theses and Dissertations

Traditional proteomics studies can measure many protein biomarkers simultaneously from a single patient-derived sample, promising the possibility of syndromic diagnoses of multiple diseases sharing common symptoms. However, precious cellular-level information is lost in conventional bulk-scale studies that measure tissues comprising many types of cells. As single cells are the building blocks of organisms and are easier to biopsy than traditional bulk samples, performing proteomics on a single-cell level would benefit clinicians and patients. Single-cell proteomics, combined with mass spectrometry imaging, can be used to analyze cells in their microenvironment, preserving spatial information. We have previously used laser-capture microdissection to isolate …

Modeling Metaverse Perceptions For Bolstering Traffic Safety Using Novel Trss-Based Owcm-Ram Mcdm Techniques: Purposes And Strategies, Mona Mohamed, Asmaa Elsayed, Marwa Sharawi

Neutrosophic Systems with Applications

The Metaverse has the potential to revolutionize various aspects of human life, including transportation systems. The integration of the Metaverse into intelligent transportation systems has the potential to significantly improve traffic safety in smart cities. By creating a virtual replica of the physical world, the Metaverse can provide a platform for testing new traffic management systems, road designs, and vehicle technologies in a controlled and safe environment before implementing them in the real world. One way to integrate the Metaverse into intelligent transportation systems (ITS) is by enhancing traffic safety. This can be achieved by developing an evaluation model that …

Advancing Text Summarization And Classification: Deep Insights From Transformer-Based Statistical Learning, Kun Bu

USF Tampa Graduate Theses and Dissertations

Artificial Intelligence (AI) is a part of human's daily life nowadays. Machine Learning (ML) as one aspect from AI has been rapidly developing during the past two decades, especially from the statistical learning approaches, which emphasized the use of probability and statistics to model data, such as Support Vector Machines (SVMs) for classification and regression tasks to the ensemble learning techniques, such as Random Forest, Gradient Boosting Machine (GBM), and stacking. Ensemble learning has evolved into a pivotal concept in contemporary machine learning, empowering practitioners to amalgamate multiple models to enhance generalization, accuracy, and robustness. As the field of machine …

Cardiogpt: An Ecg Interpretation Generation Model, Guohua Fu, Jianwei Zheng, Islam Abudayyeh, Chizobam Ani, Cyril Rakovski, Louis Ehwerhemuepha, Hongxia Lu, Yongjuan Guo, Shenglin Liu, Huimin Chu, Bing Yang

Mathematics, Physics, and Computer Science Faculty Articles and Research

Numerous supervised learning models aimed at classifying 12-lead electrocardiograms into different groups have shown impressive performance by utilizing deep learning algorithms. However, few studies are dedicated to applying the Generative Pre-trained Transformer (GPT) model in interpreting electrocardiogram (ECG) using natural language. Thus, we are pioneering the exploration of this uncharted territory by employing the CardioGPT model to tackle this challenge. We used a dataset of ECGs (standard 10s, 12-channel format) from adult patients, with 60 distinct rhythms or conduction abnormalities annotated by board-certified, actively practicing cardiologists. The ECGs were collected from The First Affiliated Hospital of Ningbo University and Shanghai …

Nowcasting Heavy Rainfall With Convolutional Long Short-Term Memory Networks: A Pixelwise Modeling Approach, Yi Victor Wang, Seung Hee Kim, Geunsu Lyu, Choeng-Lyong Lee, Soorok Ryu, Gyuwon Lee, Ki-Hong Min, Menas C. Kafatos

Institute for ECHO Articles and Research

The recent decades have seen an increasing academic interest in leveraging machine learning approaches to nowcast, or forecast in a highly short-term manner, precipitation at a high resolution, given the limitations of the traditional numerical weather prediction models on this task. To capture the spatiotemporal associations of data on input variables, a deep learning (DL) architecture with the combination of a convolutional neural network and a recurrent neural network can be an ideal design for nowcasting rainfall. In this study, a long short-term memory (LSTM) modeling structure is proposed with convolutional operations on input variables. To resolve the issue of …

Associations Between Sleep Duration, Sleep Disturbance And Cardiovascular Disease Biomarkers Among Adults In The United States, Prince Nii Ossah Addo, Paddington T. Mundagowa, Longgang Zhao, Mufaro Kanyangarara, Monique J. Brown Ph.D., Mph, Jihong Liu Sc.D.

Faculty Publications

Background

Sleep problems are associated with abnormal cardiovascular biomarkers and an increased risk of cardiovascular diseases (CVDs). However, studies investigating associations between sleep problems and CVD biomarkers have reported conflicting findings. This study examined the associations between sleep problems and CVD biomarkers in the United States.

Methods

Data were from the National Health and Nutrition Examination Survey (NHANES) (2007–2018) and analyses were restricted to adults ≥ 20 years (n = 23,749). CVD biomarkers [C-reactive Protein (CRP), low-density lipoproteins, high-density lipoproteins (HDL), triglycerides, insulin, glycosylated hemoglobin (HbA1c), and fasting blood glucose] were categorized as abnormal or normal using standardized cut-off …

Estimation Of Useful-Stage Energy Returns On Investment For Fossil Fuels And Implications For Renewable Energy Systems, Emmanuel Aramendia, Paul E. Brockway, Peter G. Taylor, Jonathan B. Norman, Matthew Kuperus Heun, Zeke Marshall

University Faculty Publications and Creative Works

The net energy implications of the energy transition have so far been analysed at best at the final energy stage. Here we argue that expanding the analysis to the useful stage is crucial. We estimate fossil fuelsʼ useful-stage energy returns on investment (EROIs) over the period 1971–2020, globally and nationally, and disaggregate EROIs by end use. We find that fossil fuelsʼ useful-stage EROIs (~3.5:1) are considerably lower than at the final stage (~8.5:1), due to low final-to-useful efficiencies. Further, we estimate the final-stage EROI for which electricity-yielding renewable energy would deliver the same net useful energy as fossil fuels (EROI …

Downstream Leakage Of Enhanced Rock Weathering, Shuang Zhang

Sustainability Seminar Series

ERW has garnered increasing interest as a promising technique for durable carbon dioxide removal, offering a range of potential co-benefits including increased soil pH and nutrient release. However, the impacts of ERW on river chemistry and the potential loss of initially captured CO2 during river transport remain poorly constrained. The current lack of tools for robustly predicting the effect of riverine degassing on the ERW life cycle undermines the use of this practice as a carbon mitigation strategy. I will present results from a dynamic river network model designed to quantify the downstream carbon leakage of ERW in the North …

A Systematic Review: Mirror Neurons & Schizophrenia, Yashesvi Sharma, Surajit Dey

Annual Research Symposium

This research project establishes a link between Mirror Neuron System (MNS) activity and this information's implications in treating and understanding schizophrenia, specifically, schizophrenic patients with negative symptoms.

Update From Aristotle To Newton, From Sets To Fuzzy Sets, And From Sigmoid To Relu: What Do All These Transitions Have In Common?, Christian Servin, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In this paper, we show that there is a -- somewhat unexpected -- common trend behind several seemingly unrelated historic transitions: from Aristotelian physics to modern (Newton's) approach, from crisp sets (such as intervals) to fuzzy sets, and from traditional neural networks, with close-to-step-function sigmoid activation functions to modern successful deep neural networks that use a completely different ReLU activation function. In all these cases, the main idea of the corresponding transition can be explained, in mathematical terms, as going from the first order to second order differential equations.

Nowcasting Solar Euv Irradiance With Photospheric Magnetic Fields And The Mg Ii Index, Kara L. Kniezewski, Samuel J. Schonfeld, Carl J. Henney

Student Publications

A new method to nowcast spectral irradiance in extreme ultraviolet (EUV) and far ultraviolet (FUV) bands is presented here, utilizing only solar photospheric magnetograms and the Mg II index (i.e., the core-to-wing ratio). The EUV and FUV modeling outlined here is a direct extension of the SIFT (Solar Indices Forecasting Tool) model, based on Henney et al. (2015, https://doi.org/10.1002/2014sw001118). SIFT estimates solar activity indices using the earth-side solar photospheric magnetic field sums from global magnetic maps generated by the ADAPT (Air Force Data Assimilative Photospheric Flux Transport) model. Utilizing strong and weak magnetic field sums from ADAPT maps, Henney …

Natural Resource Policy Analysis: Controversy Over Forest Management At Sparta Mountain Wildlife Management Area, James Bates

Environmental Science & Sustainable Development Student Projects

New Jersey contains the densest human population in the United States. As New York City to the east has grown, urban sprawl has reached much of the Garden State. For the natural environment, the impact of this sprawl is significant. The eastern deciduous forest that once covered the northern half of the state has been reduced to select areas where development is restricted. The remaining areas of protected forest are valuable to the public as natural gems amidst a world of modern infrastructure. Most of these protected forests are owned and managed by the state of New Jersey, by the …

Navigating The Maze: The Role Of Pre-Enrollment Socio-Cultural And Institutional Factors In Higher Education In The Age Of Ai, Emily Barnes, James Hutson

Faculty Scholarship

This article explores the complex interplay between pre-enrollment socio-cultural and institutional factors and their impact on the higher education landscape. It challenges traditional metrics of academic achievement, presenting a nuanced perspective on student success that emphasizes the importance of socio-economic backgrounds, cultural capital, and K-12 education quality. The analysis extends to the significant role of institutional attributes in shaping student readiness and decision-making processes. The study advocates for the integration of artificial intelligence (AI)-driven assessments by higher education institutions to cater to the diverse needs of the student body, promoting an inclusive and supportive learning environment. Anchored in an extensive …

Rethinking Plagiarism In The Era Of Generative Ai, James Hutson

Faculty Scholarship

The emergence of generative artificial intelligence (AI) technologies, such as large language models (LLMs) like ChatGPT, has precipitated a paradigm shift in the realms of academic writing, plagiarism, and intellectual property. This article explores the evolving landscape of English composition courses, traditionally designed to develop critical thinking through writing. As AI becomes increasingly integrated into the academic sphere, it necessitates a reevaluation of originality in writing, the purpose of learning research and writing, and the frameworks governing intellectual property (IP) and plagiarism. The paper commences with a statistical analysis contrasting the actual use of LLMs in academic dishonesty with educator …

Sustainable Living At Imsa, Vignesh Tiruvannamalai, Zuyu Liu

Goal 4: Intrapersonal Intelligence Narrative

Students will collect any recyclable materials they have discarded to create wing decorations in conjunction with everyone in the wing and learn about sustainability, including statistics and lifestyle guidance.

Phys 211h

Department of Physics and Astronomy: Syllabi

No abstract provided.

Phys 918

Department of Physics and Astronomy: Syllabi

No abstract provided.

Phys 926

Department of Physics and Astronomy: Syllabi

No abstract provided.

Monarch Science Observer, Volume 18, College Of Sciences, Old Dominion University

College of Sciences Newsletter

Spring 2024, issue of Monarch Science Observer, ODU Colleges of Sciences Newsletter.