Digital Commons Network^™

The Shorthorn: May 03, 1932

The Shorthorn Newspaper Collection: 1930s

No abstract provided.

The Shorthorn: April 19, 1932

The Shorthorn Newspaper Collection: 1930s

No abstract provided.

The Shorthorn: April 05, 1932

The Shorthorn Newspaper Collection: 1930s

No abstract provided.

The Shorthorn: March 01, 1932

The Shorthorn Newspaper Collection: 1930s

No abstract provided.

The Shorthorn: February 09, 1932

The Shorthorn Newspaper Collection: 1930s

No abstract provided.

The Shorthorn: January 12, 1932

The Shorthorn Newspaper Collection: 1930s

No abstract provided.

The Shorthorn: May 12, 1931

The Shorthorn Newspaper Collection: 1930s

No abstract provided.

The Shorthorn: April 21, 1931

The Shorthorn Newspaper Collection: 1930s

No abstract provided.

The Shorthorn: February 10, 1931

The Shorthorn Newspaper Collection: 1930s

No abstract provided.

The Shorthorn: December 09, 1930

The Shorthorn Newspaper Collection: 1930s

No abstract provided.

The Shorthorn: November 25, 1930

The Shorthorn Newspaper Collection: 1930s

No abstract provided.

The Shorthorn: December 02, 1930

The Shorthorn Newspaper Collection: 1930s

No abstract provided.

The Shorthorn: October 28, 1930

The Shorthorn Newspaper Collection: 1930s

No abstract provided.

The Shorthorn: January 28, 1930

The Shorthorn Newspaper Collection: 1930s

No abstract provided.

The Shorthorn: January 07, 1930

The Shorthorn Newspaper Collection: 1930s

No abstract provided.

War Strategy Algorithm- Based Hybrid Optimization For Accurate And Rapid Speech Recognition, Shahad Thamear Abd Al-Latief, Salman Yussof, Azhana Ahmad, Saif Mohanad Khadim, Ahmed Alkhayyat

Iraqi Journal for Computer Science and Mathematics

Speech recognition-based applications increased and developed as a result of artificial intelligence's rapid growth, particularly Machine Learning, which play a crucial role in many aspects of daily life, such as applications related to human-computer interaction, and natural language processing. The complexity and diversity of speech signals provides challenges in maximizing the rate of accuracy and efficiency of speech recognition systems. Hyperparameter tuning is a crucial step in machine learning that has a significant role in optimizing the performance and generalization by determining the optimal values for the model's hyperparameters. This paper employed the recently developed WAR Strategy optimization algorithm for …

Hybrid Methods For Detecting Face Morphing Attacks, Essa M. Namis, Khalid Shaker, Sufyan Al-Janabi

Iraqi Journal for Computer Science and Mathematics

The face morphing process blends two or more facial images to produce a singular morphed facial image that shows the vulnerabilities of Face Recognition Systems (FRS). The widespread use of facial recognition algorithms, especially in Automatic Border Control (ABC) systems, has elicited concerns about potential attacks, as modified passports pose a significant risk to national security. This research presents a hybrid approach for feature extraction from facial images. The suggested approach involves three stages: The initial phase involves preprocessing the image through resizing and face identification, using the Viola-Jones algorithm to detect and locate the human face in the image, …

Finding General Mathematical Formulas For Extraction The Minimal Path Sets Of Complex Parallel-Series Networks, Mariem Hassan Lafta, Zahir Abdul Haddi Hassan

Iraqi Journal for Computer Science and Mathematics

Most real-world technological systems are highly complex, making it challenging to examine their reliability. Many systems can be represented as Complex Parallel-Series Networks (CPSN). The large number of components and subnetworks, along with their intricate connection, complicates the identification, evaluation, and potential failure of the CPSN. A minimal path set is a minimal set of components whose proper functioning (success) guarantees the success (operability) of the system. The set is minimal in the sense that removing any component from it means it no longer guarantees system success. The primary research problem is to identify these minimal path sets, both for …

The Indiana Statesman, March 27, 2025, Indiana State University

Student Newspaper

Campus accessibility check; Educator of the Week: Russell Lodge; Son of Holocaust survivors speaks

Cost Modeling For State Longitudinal Data Systems, Benjamin Boer

Practitioner Toolkits and Resources

A tool to support the development and scaling of longitudinal data supports in state policymaking.

Teaching Legal Analysis Through The Lens Of Second Language Pedagogy: A Consciousness-Raising Approach, Marta Baffy

Journal of Legal Education

No abstract provided.

The Asset Management Optimization Engine: An Ai And Machine Learning Model Approach To Pavement Asset Management, Matt Versdahl

USF Tampa Graduate Theses and Dissertations

While state Departments of Transportation (DOT) face major funding challenges, the need to find optimal ways to preserve and maintain pavement assets remains. Asset management employs a lowest cost lifecycle method to analyze asset costs and determine the best investment strategies to preserve it throughout its lifecycle. As new technology emerges, so do opportunities to leverage it. DOTs collect a significant amount of performance data on pavement and use it to decide how to keep it in a state of good repair. The literature in this area focuses on engineering techniques applied to treatment strategies. This dissertation research focuses on …

Evaluating A Large Language Model’S Accuracy In Chest X-Ray Interpretation For Acute Thoracic Conditions, Adam M. Ostrovsky

SKMC Student Presentations and Publications

BACKGROUND: The rapid advancement of artificial intelligence (AI) has great ability to impact healthcare. Chest X-rays are essential for diagnosing acute thoracic conditions in the emergency department (ED), but interpretation delays due to radiologist availability can impact clinical decision-making. AI models, including deep learning algorithms, have been explored for diagnostic support, but the potential of large language models (LLMs) in emergency radiology remains largely unexamined.

METHODS: This study assessed ChatGPT's feasibility in interpreting chest X-rays for acute thoracic conditions commonly encountered in the ED. A subset of 1400 images from the NIH Chest X-ray dataset was analyzed, representing seven pathology …

Computational Methods And Optimization For Naturalistic Functional Brain Mapping Using Diffuse Optical Tomography, Morgan Fogarty

McKelvey School of Engineering Graduate Student Theses & Dissertations

Naturalistic neuroimaging has significantly advanced our understanding of the brain in everyday situations. Stimulus designs that previously targeted individual brain regions are now being replaced by activities such as watching movies, engaging in conversations, and interacting with virtual reality environments. However, traditional functional neuroimaging techniques like functional magnetic resonance imaging (fMRI) are inherently unnaturalistic, as participants must lay down and remain still within the magnet's bore. Consequently, these naturalistic paradigms require an equally naturalistic neuroimaging modality. High-density diffuse optical tomography (HD-DOT) offers a wearable alternative to fMRI by utilizing overlapping optical measurements to densely sample cortical brain activity. HD-DOT has …

New Petrographic And Geochemical Constraints On Pre-Eruptive Storage Conditions And Their Control On Eruptive Styles In Martinique’S Recent Volcanic Systems, Abigail Martens

USF Tampa Graduate Theses and Dissertations

This dissertation investigates the volcanic systems of Martinique Island, focusing on magma storage conditions, magmatic evolution, and eruption dynamics. Chapter 3 presents a petrographic analysis of volcanic rocks from the island’s old, intermediate, and recent arcs, examining the evolution of plagioclase textures and the increasing abundance of other mineral phases, such as pyroxene, amphibole, olivine, and quartz, which provide insights into changing magmatic conditions. The main research is divided into two projects. Chapter 4 addresses the pre-eruptive storage conditions (pressure and temperature) of magmas beneath recent (<5 Ma) volcanic complexes (Morne Jacob, Piton du Carbet, Trois Îlets, Mt. Conil, and Mt. Pelée), exploring magma evolution, crystallization, and the role of lower crustal injections and flank collapses in triggering eruptions. Results reveal variable magma storage depths (<6 km), with significant temperature and pressure variations, indicating complex magmatic systems. Chapter 5 focuses on the Roches-Genty ignimbrite in southwestern Martinique, investigating its stratigraphy, geochemistry, and age using field mapping, grain size analysis, and 40Ar/39Ar dating. The study identifies two distinct sub-Plinian eruptive phases associated with the Dizac ignimbrite, with ages of 3.37 ± 0.14 Ma and 2.10 ± 0.26 Ma, providing insights into past explosive volcanic activity. Geochemical analysis confirms an andesitic composition, similar to regional volcanic systems. This dissertation enhances understanding of Martinique’s volcanic history, providing valuable insights into magmatic processes and eruption dynamics, with implications for volcanic hazard assessments in the Lesser Antilles.

Relationship Between Motivation, Requesting Accommodations, Rapport, And Adequate Progress In College Students With Disabilities During The Covid-19 Pandemic, Harold Holmes

USF Tampa Graduate Theses and Dissertations

College students with disabilities often face challenges including difficulty accessing accommodations and developing satisfactory rapport with instructors when navigating their college experience. These challenges can negatively impact their academic progress and their ability to complete their programs of study. The first purpose of this study was to describe the levels of self-efficacy, perceived control, willingness to request accommodations, instructor- student rapport, and adequate progress towards a degree among college students with disabilities. The second purpose was to determine any relationships between these variables. Undergraduate students with disabilities at a public university in the southeast region of the United States completed …

Artificially Intelligent Maxwell's Demon For Optimal Control Of Open Quantum Systems, Paolo A. Erdman, Robert Czupryniak, Bibek Bhandari, Andrew N. Jordan, Frank Noé, Jens Eisert, Giacomo Guarnieri

Mathematics, Physics, and Computer Science Faculty Articles and Research

Feedback control of open quantum systems is of fundamental importance for practical applications in various contexts, ranging from quantum computation to quantum error correction and quantum metrology. Its use in the context of thermodynamics further enables the study of the interplay between information and energy. However, deriving optimal feedback control strategies is highly challenging, as it involves the optimal control of open quantum systems, the stochastic nature of quantum measurement, and the inclusion of policies that maximize a long-term time- and trajectory-averaged goal. In this work, we employ a reinforcement learning approach to automate and capture the role of a …

Re: Comment Letter For Butte Priority Soils Operable Unit Berkeley Pit Geochemical And Limnological Evaluation Pre-Design Investigation (Pdi) Work Plan (Dated December 17, 2024), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Lung Segmentation With Lightweight Convolutional Attention Residual U-Net, Meftahul Jannat, Shaikh Afnan Birahim, Mohammad Asif Hasan, Tonmoy Roy, Lubna Sultana, Hasan Sarker, Samia Fairuz, Hanaa A. Abdallah

Instructional Technology and Learning Sciences Faculty Publications

Background: Examining chest radiograph images (CXR) is an intricate and time-consuming process, sometimes requiring the identification of many anomalies at the same time. Lung segmentation is key to overcoming this challenge through different deep learning (DL) techniques. Many researchers are working to improve the performance and efficiency of lung segmentation models. This article presents a DL-based approach to accurately identify the lung mask region in CXR images to assist radiologists in recognizing early signs of high-risk lung diseases. Methods: This paper proposes a novel technique, Lightweight Residual U-Net, combining the strengths of the convolutional block attention module (CBAM), the Atrous …

On Regularity And Convergence Of Solutions To The Boltzmann-Enskog Equations, Christian Ennis

LSU Doctoral Dissertations

The Boltzmann equation describes the time evolution of the density function in position-velocity space for a classical particle subjected to possible collisions by other particles in a diluted gas that expands in vacuum for a given initial distribution. While many authors have studied the probabilistic interpretation of the spatially homogeneous Boltzmann equation, there is a dearth of articles on the stochastic framework of the full (that is, spatially inhomogeneous) Boltzmann equation. In this thesis, we examine a stochastic process, developed by S. Albevario, B. Ruediger, and P. Sundar, whose law is a weak solution to a mollified Boltzmann equation. This …