Physical Sciences and Mathematics Commons^™

Modeling The Synergistic Interplay Between Malaria Dynamics And Economic Growth, Ruijun Zhao, Hope Enright, Calistus Ngonghala, Olivia Prsoper

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Seshaiyer: Understanding Non-Linear Dynamics Of Interacting Subpopulations And Implicit Human Behavior Using Physics-Informed Neural Networks, Naima Aubry-Romero, Alonso Ogueda-Oliva, Padmanabhan Seshaiyer

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Harvesting Modulation In Three Species Food Chains Using A Robust Control Approach, Hector Puebla, Mariana Rodriguez-Jara, Priti Kumar Roy

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Identifiability And Convergence Behavior For Markov Chain Monte Carlo Using Multivariate Probit Models, Xiao Zhang

Michigan Tech Publications

Multivariate probit models have been popularly utilized to analysis multivariate ordinal data. However, the identifiable multivariate probit models entail the covariance matrix for the underlying multivariate normal variables to be a correlation matrix, which brings a rigorous task to conduct efficient statistical analysis. Parameter expansion to make the identifiable model to be non-identifiable has been inevitably explored. However, the effect of the expanded parameters on the convergence of Markov chain Monte Carlo (MCMC) is seldomly investigated; in addition, the comparison of MCMC developed based on the identifiable model and that based on the non-identifiable model is hardly ever explored, especially …

Predicting And Monitoring Immune Checkpoint Inhibitor Therapy Using Artificial Intelligence In Pancreatic Cancer, Guangbo Yu, Zigeng Zhang, Aydin Eresen, Qiaoming Hou, Farideh Amirrad, Sha Webster, Surya M. Nauli, Vahid Yaghmai, Zhuoli Zhang

Pharmacy Faculty Articles and Research

Pancreatic cancer remains one of the most lethal cancers, primarily due to its late diagnosis and limited treatment options. This review examines the challenges and potential of using immunotherapy to treat pancreatic cancer, highlighting the role of artificial intelligence (AI) as a promising tool to enhance early detection and monitor the effectiveness of these therapies. By synthesizing recent advancements and identifying gaps in the current research, this review aims to provide a comprehensive overview of how AI and immunotherapy can be integrated to develop more personalized and effective treatment strategies. The insights from this review may guide future research efforts …

Holistic & Socially Just Admissions Procedures In An Artificially Intelligent (Ai) World, Lucinda Bratini, Ali Cunningham Abbott, Tracy N. Baker

Faculty and Staff Publications & Presentations

In our ever-evolving social context, the counseling profession continues to center social justice and decolonial praxis in training programs. Simultaneously, we remain attuned to shifts in machine learning and artificial intelligence (AI) which require us to adjust and grow. This roundtable discussion shares the development of a holistic admissions review (HAR) pilot process, which incorporates relational, diversity and social justice values alongside AI innovations and current CACREP considerations.

Comparison Of Efficient Deep Learning Architectures For Lactobacillus Species Identification, Dea Aisyah Rusmawati, Ishak Ariawan, Afrinal Firmanda

Karbala International Journal of Modern Science

Identifying microorganism species, such as Lactobacillus, is essential in ensuring the food products' quality and safety. Traditional laboratory practice requires expert knowledge and experience, but the method is expensive and time-consuming due to complex sample preparation. Faster, more accurate, and cheaper computational methods, such as transfer learning technology, are needed for the Lactobacillus species classification. The technique has been effective in a variety of image recognition contexts. Deep learning architecture can also be applied as an innovative strategy for digital image-based identification. Therefore, this research aims to compare several deep-learning architectures in classifying bacterial strains of Lactobacillus. The four architectures …

Mathematically Modeling How Trapping Specific Crayfish Life Stages Impacts Removal Efficacy, Relena Pattison, Courtney L. Davis

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Improving Infectious Disease Predictions Through The Use Of Metapopulation Sir Modeling And Graph Convolutional Neural Networks, Petr Kisselev, Padmanabhan Seshaiyer

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Modeling Insurance Impact On Opioid Addiction, Ihlara K. Williamson, Eli E. Goldwyn

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Statistically Operationalizing Structural Racism: What Is The Impact On Health Inequities In The United States?, Loni Tabb Phd

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Ethical Responsibility In The Design Of Artificial Intelligence (Ai) Systems, David K. Mcgraw

International Journal on Responsibility

This article aims to provide an overview of the ethical questions surrounding the responsibilities of designers of artificial intelligence (AI) systems. First, the author delves into the philosophical underpinnings of this responsibility, examining various ethical theories to grasp the moral obligations individuals have towards others and society. The author contends that designers of technology bear the responsibility of considering the broader societal implications of their creations. Subsequently, the author scrutinizes the fundamental question of whether AI systems present unique ethical concerns compared to conventional technologies, pinpointing factors such as complexity, opacity, autonomy, unpredictability, uncertainty, and the potential for significant social …

Modeling Of Radio Array Distribution, Yahya Emran Abdelhadi

Thesis/ Dissertation Defenses

In the quest to understand the mysteries of the universe through radio astronomy, selecting the right tools and methods is crucial. Just as radio astronomy provides a unique lens through which to study the universe, the adoption of open-source software, such as Python, similarly offers a distinct advantage in scientific research. Today most research is done on open-source code which reduces the limitation to have access to the science and research resources to improve the quality and the contributed researcher. For some people, access to software and tools is very expensive and limited. This work focuses on converting the IDL …

Developing A Framework For Digital Twin Data Quality And Security Controls, Ahmad Abdelbaset Hassan

Thesis/ Dissertation Defenses

This thesis is concerned with the data quality and security of the digital twin and how it is going to impact its adoption, trustworthiness, and potential for real-world applications. By addressing the potential vulnerabilities and ensuring the integrity of data, this research aims to contribute to the development of robust and trustworthy digital twin standards and policies that is going to complement the existing international standards across different domains. Moreover, it underscores the important need to establish robust standards to ensure the successful and secure deployment of digital twins across industries. Previous research, while valuable, may not have fully addressed …

Hybridizing Reinforcement Learning With Metaheuristics For Improved Traffic Signal Control And Optimization In Urban Transportation Networks, Jiyana Nikhil Jaisinghani

Thesis/ Dissertation Defenses

Managing road traffic in metropolitan cities is a crucial aspect of Intelligent Transportation Systems (ITS). The rapid growth of population and vehicles has led to increasing traffic congestion, which negatively affects travel times, fuel consumption, and air quality in urban areas. Intersections and their traffic lights are key contributors to this congestion, making efficient and adaptable Traffic Signal Control (TSC) and Traffic Signal Scheduling (TSS) essential. TSC manages traffic flow at intersections, while TSS optimizes the timing and sequencing of traffic signals. The techniques, Reinforcement Learning (RL) and Metaheuristic Optimization (MO), have shown promising results in addressing traffic control challenges …

Hell You Talmbout: Mixtapes As Method For Online Environmental Justice Pedagogy, Elspeth Iralu, Caitlin Grann

Dialogue: The Interdisciplinary Journal of Popular Culture and Pedagogy

This paper takes on the mixtape as a pedagogical method for approaching urgent and critical topics within the undergraduate online classroom. Drawing on two case studies from different sections of an introductory course on environmental and social justice taught in an American studies department, we demonstrate how mixtape-inspired assignments offer a method for theorizing and enacting the connections between popular culture and critical scholarship around injustice in the humanities and social sciences while also altering the space of the classroom to promote deeper student engagement, comprehension, and reflection. We argue that introducing popular culture as both content and method within …

Simplify Workflows: Ai As A Coding Companion, Tiffany Garrett

Library Scholarship

Artificial Intelligence is on everyone’s minds and has been the topic of the past two Matheson Lectures. But, what is the role of academic health sciences libraries? Moving the theoretical into practical, seven of our colleagues will present real-life case studies. What worked - what didn’t - what would they do differently?

This entry is from one of those case presentations on how a librarian at Roseman University of Health Sciences used AI to complete simple computer programming projects that optimized a few library workflows.

Competitive Conquest: Charting The Climb To Pokémon Supremacy, Robert Dilworth

BCoE Publications

This manuscript presents a comprehensive exploration of optimizing Pokémon gameplay through data-driven methodologies, aimed at enhancing competitive performance in high-stakes environments. In the first section, we introduce a robust Pokémon teambuilding algorithm that leverages statistical analysis of championship-winning compositions. By employing multiple linear regression techniques, we predict team performance based on critical factors such as Base Stat Totals (BSTs) and various coverage types. This integration of data science principles into Pokémon strategy underscores the importance of offensive capabilities over defensive considerations, ultimately contributing to advancements in teambuilding strategies. Our proficiency in R programming facilitated the development of an efficient codebase …

Uvcandels: Catalogs Of Photometric Redshifts And Galaxy Physical Properties, Vihang Mehta, Marc Rafelski, Ben Sunnquist, Harry I. Teplitz, Claudia Scarlata, Xin Wang, Adriano Fontana, Nimish P. Hathi, Kartheik G. Iyer, Anahita Alavi, James Colbert, Norman Grogin, Anton Koekemoer, Kalina V. Nedkova, Matthew Hayes, Laura Prichard, Brian Siana, Brent M. Smith, Rogier Windhorst, Teresa Ashcraft, Micaela Bagley

Pacific Faculty Work

The UltraViolet imaging of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey Fields (UVCANDELS) program provides deep Hubble Space Telescope (HST) F275W and F435W imaging over four CANDELS fields (GOODS-N, GOODS-S, COSMOS, and Extended Groth Strip). We combine this newly acquired UV imaging with existing HST imaging from CANDELS as well as existing ancillary data to obtain robust photometric redshifts and reliable estimates for galaxy physical properties for over 150,000 galaxies in the ∼430 arcmin² UVCANDELS area. Here, we leverage the power of the new UV photometry to not only improve the photometric redshift measurements in these fields, but …

2024 November 08 - Tennessee Weekly Drought Summary, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Weekly Drought Summaries

No abstract provided.

Re: Approval Letter For The Butte Priority Soils Operable Unit (Bpsou), Draft 2024 Residential Metals Abatement Program Investigation Summary Report (Non-Residential – Interior Dust) After The Bell Child Care Center (Dated April 25, 2024), Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Re: Approval Letter For The Butte Priority Soils Operable Unit (Bpsou) Draft Residential Metals Abatement Program (Rmap) – Interior School Dust – Investigation Summary Report (Isr) – Kennedy Elementary School Post-Remedial Action Surface Dust Sampling (Dated June 12, 2024), Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Re: Approval Letter With Comments For The Butte Priority Soils Operable Unit (Bpsou) Revised Draft Final Insufficiently Reclaimed Sites Field Sampling Plan (Fsp) For Bres No. 181 – Mountain Con-3 Dump (Dated October 4, 2024), Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Comment Letter For The Butte Priority Soils Operable Unit (Bpsou) Draft Reclaimed Areas Weed Management Plan (Dated April 11, 2024), Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Telu Activation Function For Fast And Stable Deep Learning, Alfredo Fernandez

USF Tampa Graduate Theses and Dissertations

We propose the Hyperbolic Tangent Exponential Linear Unit (TeLU), a neural network hidden activation function defined as $TeLU(x)=x \cdot tanh(e^x)$. TeLU’s design is grounded in the core principles of key activation functions, achieving strong convergence by closely approximating the identity function in its active region while effectively mitigating the vanishing gradient problem in its saturating region. Its simple formulation enhances computational efficiency, leading to improvements in scalability and convergence speed. Unlike many modern activation functions, TeLU seamlessly combines the simplicity and effectiveness of ReLU with the smoothness and analytic properties essential for learning stability in deep neural networks. TeLU’s ability …

Exploring Llm Integration And Its Influence On Agent Behavior And Productivity In Insurance Call Centers, Gerardo L. Wibmer Gonzalez

USF Tampa Graduate Theses and Dissertations

This study investigates the theoretical impacts of supportive AI tools, specifically Large Language Models (LLMs), on agent behavior and communication dynamics in call centers. While technological advancements have streamlined operations, limited research addresses the indirect ways these tools influence agent behavior. Using frameworks like context switching—the cognitive shift required when external stimuli prompt attention shifts—and the Hawthorne effect, where perceived observation modifies behavior, we examine how LLMs shape communication patterns. In call centers, this context switching occurs indirectly as agents adapt to AI note-taking features, whereas in industries like Architecture, Engineering, and Construction (AEC), automation tools prompt more immediate changes …

Re: Comment Letter For The Butte Priority Soils Operable Unit (Bpsou) Draft Final Insufficiently Reclaimed Sites Field Sampling Plan (Fsp) For Bres No. 120 – Bonanza Dump (Dated July 22, 2024), Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Enhancing Data Standards To Advance Translation In Spinal Cord Injury, Vanessa K. Noonan, Suzanne Humphreys, Fin Biering-Sørensen, Susan Charlifue, Yuying Chen, James D. Guest, Linda A. T. Jones, Jennifer French, Eva Widerström-Noga, Vance P. Lemmon, Allen W. Heinemann, Jan M. Schwab, Aaron A. Phillips, Marzieh M. Rizi, John L. K. Kramer, Catherine R. Jutzeler, Abel Torres-Espin

Department of Physical Therapy Faculty Papers

Data standards are available for spinal cord injury (SCI). The International SCI Data Sets were created in 2002 and there are currently 27 freely available. In 2014 the National Institute of Neurological Disorders and Stroke developed clinical common data elements to promote clinical data sharing in SCI. The objective of this paper is to provide an overview of SCI data standards, describe learnings from the traumatic brain injury (TBI) field using data to enhance research and care, and discuss future opportunities in SCI. Given the complexity of SCI, frameworks such as a systems medicine approach and Big Data perspective have …

Re: Comment Letter For Butte Priority Soils Operable Unit (Bpsou) Residential Metals Abatement Program (Rmap) Individual Site Work Plan (Iswp) Attic Remediation – 3 Pintlar Peaks Dr. (Dated October 24, 2024), Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Results From A Multi-Laboratory Ocean Metaproteomic Intercomparison: Effects Of Lc-Ms Acquisition And Data Analysis Procedures, Mak A. Saito, Jaclyn K. Saunders, Matthew R. Mcilvin, Erin M. Bertrand, John A. Breier, Margaret Mars Brisbin

School of Earth, Environmental, & Marine Sciences Faculty Publications

Metaproteomics is an increasingly popular methodology that provides information regarding the metabolic functions of specific microbial taxa and has potential for contributing to ocean ecology and biogeochemical studies. A blinded multi-laboratory intercomparison was conducted to assess comparability and reproducibility of taxonomic and functional results and their sensitivity to methodological variables. Euphotic zone samples from the Bermuda Atlantic Time-series Study (BATS) in the North Atlantic Ocean collected by in situ pumps and the autonomous underwater vehicle (AUV) Clio were distributed with a paired metagenome, and one-dimensional (1D) liquid chromatographic data-dependent acquisition mass spectrometry analysis was stipulated. Analysis of mass spectra from …