Digital Commons Network^™

Concentration And Composition Of Organic Contaminants In Blackfin Tuna, Yellowfin Tuna, And The Common Clubhook Squid In The Western Gulf Of Mexico, Lisa Rose-Mann

USF Tampa Graduate Theses and Dissertations

Cephalopods are an important component of pelagic food webs. They play ecological roles not only as predators but also as prey. However, their role as prey in the trophic transfer of organic contaminants to apex predators such as tunas in the Gulf of Mexico (GoM) is not well known. To investigate the importance of cephalopods in tuna diets, stomach samples were collected in February 2020 from tunas caught by rod and reel aboard the F/V Gulf Eagle near two large offshore floating oil/gas platforms in the northwestern GoM. Cephalopod samples were obtained from gut content analysis of 41 tunas caught …

Comparative Analysis Of Developed Expanding Urbanization And Floods Hit Erbil City Following Heavy Rainfall, Bahar M. Ahmad, Shawnm M. Saleh, Shuokr Qarani Aziz, Sarwah Othman Ismael, Sarkawt H. Muhammad

Polytechnic Journal

Climate change, urbanization, land use and land cover changes, and political and economic problems, caused increasing impervious layers and floods in Erbil Province in recent years. Due to economic growth and immigration from the middle and south of Iraq, the demand for residential areas has increased leading to more urban and impervious surface areas. The research aims to study the factors behind the increasing impervious areas and floods in Erbil City. Data were collected through several site visits and from related governmental sectors. Results illustrated that after 2017, urbanization noticeably increased. From 2017 to 2021, the construction zones and shrub …

A Transcendental Phenomenology: Exploring The College Choice Process Of Black Undergraduate Students Who Transfer From Predominantly White Institutions To Historically Black Colleges And Universities, Heather Oliver Freeman

LSU Doctoral Dissertations

Researchers have studied college students and the college choice process extensively in the higher education realm of academia. Often, college choice is explored from a lens of the traditional high school student approaching the initial college choice decision. Furthermore, higher education is an expansive topic that not only includes public and private institutions, two- and four-year institutions, but also predominantly White institutions (PWIs) and historically Black colleges and universities (HBCUs). The purpose of this transcendental phenomenological study was to explore the college choice process and transfer experiences of undergraduate Black students who transfer from a PWI to an HBCU. This …

Hemp Cultivation Under Off-Season High Tunnel Growth Chamber Environments In South Louisiana, Gabriel Lococo

LSU Master's Theses

Industrial hemp (Cannabis sativa) production in Louisiana began in 2020 with 932 acres cultivated for fiber and cannabidiol (CBD) extraction. By 2024, production had declined to just 58.4 acres (Louisiana Department of Agriculture and Forestry, personal communication). During the 2020–2021 season, over 40% of field-grown hemp was lost due to tropical storms, insect pressure, and diseases associated with warm, wet conditions. To mitigate these challenges, hemp was planted and harvested between November and January, a cooler period with reduced environmental stress.

‘Sour Citron’, a day-neutral cultivar, was selected for its insensitivity to photoperiod, enabling flowering during shorter days. …

Intracranial Directed Connectivity Links Subregions Of The Prefrontal Cortex To Major Depression, John Myers, Jiayang Xiao, Raissa K Mathura, Ben Shofty, Victoria Gates, Joshua Adkinson, Anusha B Allawala, Adrish Anand, Ron Gadot, Ricardo Najera, Hernan G Rey, Sanjay J Mathew, Kelly Bijanki, Garrett Banks, Andrew Watrous, Eleonora Bartoli, Sarah R Heilbronner, Nicole Provenza, Wayne K Goodman, Nader Pouratian, Benjamin Y Hayden, Sameer A Sheth

Staff and Researcher Publications

Research on the neural basis of major depressive disorder suggests that it is fundamentally a disease of cortical disinhibition, where breakdowns of inhibitory neuronal systems lead to diminished emotion regulation and intrusive rumination. Subregions of the prefrontal cortex are thought to be sources of this disinhibition. However, due to limited opportunities for intracranial recordings from humans with major depression, this hypothesis has not been directly tested. Here, we use intracranial recordings from the dorsolateral prefrontal, orbitofrontal, and anterior cingulate cortices from patients with major depression to measure daily fluctuations in self-reported depression symptom severity. Results indicate that directed connectivity within …

Migration Delays At Head-Of-Tide Weirs Are A Function Of Tidal Cycles And River Flows In Anadromous Twaite Shad Alosa Fallax, Mark I.A. Yeldham, J. Robert Britton, Charles Crundwell, Peter Davies, Jamie R. Dodd, Chris Grzesiok, Andrew D. Nunn, Randolph Velterop, Jonathan D. Bolland

School of Biological and Marine Sciences

Riverine barriers can have significant negative impacts on the spawning migrations of anadromous fishes, with barriers of low passage efficiency limiting access to upstream spawning areas. Even fish that pass a barrier can be negatively affected via migration delays and energetic costs. The spawning migrations of 74 twaite shad Alosa fallax were tracked at two head-of-tide weirs on a bifurcated channel in the River Severn’s upper estuary, western England, to test how barriers in tidal areas can affect river entry and incur migration delays. Although the weirs had a combined passage efficiency of 98.9%, median passage time was 19.8 h, …

Genome Report: Whole-Genome Assembly Of The Relapsing Fever Tick Ornithodoros Turicata Dugès (Acari: Argasidae), Perot Saelao, Sheina B Sim, Scott M Geib, Brian E Scheffler, Anna K Childers, Alexander R Kneubehl, Pete D Teel, Job E Lopez

Faculty, Staff and Students Publications

The soft tick family Argasidae contains vectors of medical and veterinary importance, but few molecular resources are available compared to hard ticks (Ixodidae). One example is Ornithodoros turicata, a recognized vector of Borrelia turicatae, causal agent of human relapsing fever, and a putative vector of African swine fever virus. To address the current lack of molecular resources for the Argasidae, we generated a chromosome-level genome assembly for O. turicata using PacBio sequencing in conjunction with an Illumina Hi-C library. The resulting reference genome has a total of 1.1 Gb in length and was assembled into 10 chromosomes and 368 unplaced …

A Single High-Zinc Activation Enhancer Can Control Two Genes Oriented Head-To-Head In Caenorhabditis Elegans, Hanwenheng Liu, Brian Earley, Adelita Mendoza, Patrick Hunt, Sean Teng, Daniel Luke Schneider, Kerry Kornfeld

2020-Current year OA Pubs

Enhancers play critical roles in gene expression, but a full understanding of their complex functions has yet to be defined. The cellular response to excess zinc levels in Caenorhabditis elegans requires the HIZR-1 transcription factor, which binds the high-zinc activation (HZA) enhancer in the promoters of multiple target genes. Cadmium hijacks the excess zinc response by binding and activating HIZR-1. By analyzing the genome-wide transcriptional response to excess zinc and cadmium, we identified two positions in the genome where head-to-head oriented genes are both induced by metals. In both examples, a single predicted HZA enhancer is positioned between the two …

Engineers Love Trees, Too!, Thomas T. Burke Jr

Stormwater Drainage Conference

No abstract provided.

Cluster College International Anthem, Callison College

Callison College and School of International Studies

No abstract provided.

Callison Promotional Brochure, Callison College

Callison College and School of International Studies

No abstract provided.

Callison Mail Advertisment, Callison College

Callison College and School of International Studies

No abstract provided.

Stormwater Treatment Practices - Guidance For Maintenance And Targeting Pollutants, Peter T. Weiss

Stormwater Drainage Conference

Stormwater Drainage Conference

Exploring And Improving Initialization For Deep Graph Neural Networks: A Signal Propagation Perspective

Defensive Publications Series

Graph Neural Networks (GNNs) often suffer from performance degradation as the network depth increases. This paper addresses this issue by introducing initialization methods that enhance signal propagation (SP) within GNNs. We propose three key metrics for effective SP in GNNs: forward propagation, backward propagation, and graph embedding variation (GEV). While the first two metrics derive from classical SP theory, the third is specifically designed for GNNs. We theoretically demonstrate that a broad range of commonly used initialization methods for GNNs, which exhibit performance degradation with increasing depth, fail to control these three metrics simultaneously. To deal with this limitation, a …

Intelligent Test Orchestrator For Dynamic Test Sequencing, Na

Defensive Publications Series

In hyperscale data centers, the sheer volume of installed devices necessitates a substantial daily volume of diagnostic testing. Running all tests can be time-consuming, leading to longer repair times (MTTR) and impacting data center efficiency. This disclosure describes an intelligent test orchestrator (ITO) that leverages machine learning to dynamically sequence tests to rapidly detect failure without executing entire test suites. The ITO dynamically sequences tests based on component lists for each device, diagnostic test coverage information, component failure rates, repair records, test coverage, etc. The described intelligent test orchestrator reduces the time needed to restore repaired devices to the production …

Scalability Matters: Overcoming Challenges In Instructglm With Similarity-Degree-Based Sampling

Defensive Publications Series

Large Language Models (LLMs) have demonstrated strong capabilities in various natural language processing tasks; however, their application to graph-related problems remains limited, primarily due to scalability constraints and the absence of dedicated mechanisms for processing graph structures. Existing approaches predominantly integrate LLMs with Graph Neural Networks (GNNs), using GNNs as feature encoders or auxiliary components. However, directly encoding graph structures within LLMs has been underexplored, particularly in the context of large-scale graphs where token limitations hinder effective representation. To address these challenges, we propose SDM-InstructGLM, a novel instruction-tuned Graph Language Model (InstructGLM) framework that enhances scalability and efficiency without relying …

Llm-Based Navigation Guidance Revisions Based On Conversational Context, Florian Hartmann, Matthew Sharifi

Defensive Publications Series

When a vehicle has multiple occupants engaged in a conversation, audio instructions from a digital map/ navigation application can be disruptive and/or unnecessary. This disclosure describes techniques, implemented with specific user permission to access and analyze data related to the presence of multiple users in a vehicle and the conversational context. With user permission, the ongoing conversation in the vehicle is analyzed locally using a large language model and the analysis is used to enhance the audio delivery of dynamic navigational guidance. A transcript of the conversation related to the ongoing drive is provided to an LLM along with a …

Uncertainty Estimation For Heterophilic Graphs Through The Lens Of Information Theory

Defensive Publications Series

While uncertainty estimation for graphs recently gained traction, most methods rely on homophily and deteriorate in heterophilic settings. We address this by analyzing message passing neural networks from an information-theoretic perspec tive and developing a suitable analog to data processing inequality to quantify information throughout the model’s layers. In contrast to non-graph domains, information about the node level prediction target can increase with model depth if a node’s features are semantically differ ent from its neighbors. Therefore, on heterophilic graphs, the latent embeddings of an MPNN each provide different information about the data distri bution – different from homophilic settings. …

Wide & Deep Learning For Node Classification

Defensive Publications Series

Wide & Deep, a simple yet effective learning architecture for recommendation systems developed by Google, has had a significant impact in both academia and industry due to its com bination of the memorization ability of general ized linear models and the generalization ability of deep models. Graph convolutional net works (GCNs) remain dominant in node classification tasks; however, recent studies have high lighted issues such as heterophily and expressive ness, which focus on graph structure while seemingly neglecting the potential role of node features. In this paper, we propose a flexible frame work GCNIII, which leverages the Wide & Deep …

Alphafold Database Debiasing For Robust Inverse Folding

Defensive Publications Series

The AlphaFold Protein Structure Database (AFDB) offers unparalleled structural coverage at near-experimental accuracy, positioning it as a valuable resource for data-driven protein design. However, its direct use in training deep models that are sensitive to fine-grained atomic geometry—such as inverse folding—exposes a critical limitation. Comparative analysis of structural feature distributions reveals that AFDB structures exhibit distinct statistical regularities, reflecting a systematic geometric bias that deviates from the conformational diversity found in experimentally determined structures from the Protein Data Bank (PDB). While AFDB structures are cleaner and more idealized, PDB structures capture the intrinsic variability and physical realism essential for generalization …

Implicit Graph Neural Networks With Flexible Propagation Operators

Defensive Publications Series

Due to the capability to capture high-order information of nodes and reduce memory consumption, implicit graph neural networks have become an explored hotspot in recent years. However, these implicit graph neural networks are limited by the static topology, which makes it difficult to handle heterophilic graph-structured data. Furthermore, the existing methods inspired by optimization objectives are limited by the explicit structure of graph neural networks, which makes it difficult to set an appropriate number of network layers to solve optimization problems. To address these issues, we propose an implicit graph neural network with flexible propagation operators in this paper. From …

Learning Resilient Graph Structures In Heterophilic Settings

Defensive Publications Series

Graphs provide a fundamental way to model relationships between entities and are central to numerous machine learning tasks. Standard graph-based methods often assume the provided graph structure is both accurate and complete. However, real-world graphs frequently suffer from noise and sparsity, negatively impacting downstream tasks like node classification and clustering. While graph representation learning has advanced significantly, many methods implicitly assume graph homophily (connections predominantly between nodes of the same class), struggling when faced with heterophily (connections predominantly between different classes). This paper introduces a novel method, Resilient Graph Learning for Heterophily (RGLH), designed to learn high-quality graph structures directly …

Halo: Heterophily-Aware Label-Free Ordering For Unsupervised Graph Fraud Detection

Defensive Publications Series

Graph fraud detection (GFD) has rapidly advanced in protecting online services by identifying malicious fraudsters. Recent supervised GFD research highlights that heterophilic connections between fraudsters and users can greatly impact detection performance, since fraudsters tend to camouflage themselves by building more connections to benign users. Despite the promising performance of supervised GFD methods, the reliance on labels limits their applications to unsupervised scenarios; Additionally, accurately capturing complex and diverse heterophily patterns without labels poses a further challenge. To fill the gap, we propose a Heterophily-guided Unsupervised Graph fraud dEtection approach (HUGE) for unsupervised GFD, which contains two essential components: a …

Biochar-Based Filtration And Barrier Systems For Urban Stormwater Treatment, Krishna R. Reddy

Stormwater Drainage Conference

Conference that deals a wide range of issues dealing with stormwater drainage. Professor Krishna Reddy of University of Illinois Chicago presented an invited talk titled "Biochar-Based Filtration and Barrier Systems for Urban Stormwater Treatment"

Tax Trends, E. S. Linett

Tax Adviser

No abstract provided.

Design Of A Simplified Biomimetic Autonomous Entertainment Robot, Jessica E. Morris, Shanny May Ruiz, Morgan Snyder, Hannah Guild, Gabriel Cardona-Tous

Graduate Scholarship and Creative Works

The advancement of technology has paved the way for robots with the purpose of entertainment to become more popular. However, the exploration of biomimetic actions as a factor of entertainment is majorly limited to the mimicry of young domesticated animals such as puppies and kittens, with some notable exceptions. However, there is a wide range of possibilities for robotics that mimic animals marketed as pets, outside of the common examples. This work seeks to explore the opportunity for autonomous systems with entertainment driven and expressive actions in the family of Testudines (turtles and tortoises), an undomesticated species, but still often …

A Neuromorphic Spike-Based System For Real-Time Behavioral Anomaly Detection In Saas And Self-Hosted Environments, Marcel Witt

Defensive Publications Series

This publication introduces NeuraSentry™, a neuromorphic anomaly detection framework inspired by biological spike-based learning and synaptic plasticity. It combines competitive vector detectors, temporal spike traces, and retroactive learning signals to detect evolving behavioral fraud and insider threats in both SaaS and on-premise systems. Key innovations include sparse activation using cosine similarity, HDBSCAN-based meta-neuron clustering, and delayed reward modulation modeled after dopamine and serotonin systems. NeuraSentry delivers adaptive, explainable, and scalable behavioral threat detection, without relying on labeled training data or fixed rules. This work establishes prior art to prevent overly broad patents in the field of neuromorphic security analytics.

Key Management Server Driven Adaptive Client-Side Caching Of Cryptographic Keys, Himanshu Kishna Srivastava Mr.

Defensive Publications Series

In conventional cryptographic systems, encryption keys are typically retrieved from a centralized key management server (KMS). To reduce latency and improve performance, clients often cache these keys locally. While this caching mechanism enhances efficiency. However, this approach also introduces several security challenges e.g. cached keys may become outdated or exposed to unauthorized access, and clients generally lack a reliable mechanism to detect when a key has been updated or revoked by the KMS. Moreover, because each client may implement its own caching logic—governing how long keys are stored, when they are refreshed, or how they are secured—this results in inconsistent …

Tax Clinic, William T. Diss

Tax Adviser

No abstract provided.

Going Concern Value Reexamined, Lawrence H. Danzig, Robert A. Robison

Tax Adviser

No abstract provided.