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Tim Seibles: 48th Annual ODU Literary Festival
No full textTim Seibles, the former Poet Laureate of Virginia, was born and raised in Philadelphia, Pennsylvania. He is the author of several books of poetry including Hurdy-Gurdy, Hammerlock, and Buffalo Head Solos. His first collection, Body Moves, (1988) was re-released by the Carnegie Mellon University Press as part of their Contemporary Classics series. Fast Animal was one of five poetry finalists for the 2012 National Book Award. In 2013 he received the Pen Oakland Josephine Miles Award for poetry. In 2014 Tim received an honorary Doctorate of Humane Letters from Misericordia University for his literary accomplishments. During that same year, he won the Theodore Roethke Memorial Poetry Award for Fast Animal, a prize given triennially for a collection of poems. In 2015, he chaired the panel of judges that decided the winner of the National Book Award in poetry. One Turn Around the Sun was published in 2017. His most recent collection, Voodoo Libretto: New & Selected Poems, was released in 2022. He has been a workshop leader for Cave Canem, a writer’s retreat for African American poets, and for the Hurston/Wright Foundation, another organization dedicated to developing black writers. Tim Seibles lives in Norfolk and is now an Emeritus Professor of English at ODU
Faithful Narratives from Complex Conceptual Models: Should Modelers or Large Language Models Simplify Causal Maps
No full text(1) Background: Comprehensive conceptual models can result in complex artifacts, consisting of many concepts that interact through multiple mechanisms. This complexity can be acceptable and even expected when generating rich models, for instance to support ensuing analyses that find central concepts or decompose models into parts that can be managed by different actors. However, complexity can become a barrier when the conceptual model is used directly by individuals. A ‘transparent’ model can support learning among stakeholders (e.g., in group model building) and it can motivate the adoption of specific interventions (i.e., using a model as evidence base). Although advances in graph-to-text generation with Large Language Models (LLMs) have made it possible to transform conceptual models into textual reports consisting of coherent and faithful paragraphs, turning a large conceptual model into a very lengthy report would only displace the challenge. (2) Methods: We experimentally examine the implications of two possible approaches: asking the text generator to simplify the model, either via abstractive (LLMs) or extractive summarization, or simplifying the model through graph algorithms and then generating the complete text. (3) Results: We find that the two approaches have similar scores on text-based evaluation metrics including readability and overlap scores (ROUGE, BLEU, Meteor), but faithfulness can be lower when the text generator decides on what is an interesting fact and is tasked with creating a story. These automated metrics capture textual properties, but they do not assess actual user comprehension, which would require an experimental study with human readers. (4) Conclusions: Our results suggest that graph algorithms may be preferable to support modelers in scientific translations from models to text while minimizing hallucinations
Enhancing IoT Security Using Lightweight Machine Learning Algorithms: A Comprehensive Approach Using Ensemble Learning, Feature Selection, and Federated Transfer Learning
No full textThe rapid expansion of the Internet of Things (IoT) has introduced significant security vulnerabilities due to the resource-constrained nature of IoT devices and their exposure to cyber threats. Traditional security solutions are often infeasible due to the high computational and storage demands they impose. This dissertation presents a lightweight, AI-driven security framework that enhances IoT network resilience by integrating feature selection, ensemble learning, and federated transfer learning while maintaining data privacy and minimizing computational overhead.
The proposed framework consists of three primary components: Feature Selection for Intrusion Detection, which optimizes performance by reducing redundant data and improving detection accuracy with minimal resource consumption; Ensemble Learning with Adaptive Model Selection, designed to enhance threat detection while conserving energy through efficient machine learning models; Federated Transfer Learning for IoT Security which enables collaborative model training across distributed devices without requiring raw data transfer, ensuring privacy preservation and real-time adaptability.
Experimental evaluations using benchmark IoT security datasets demonstrate that the proposed framework achieves up to 99.97% accuracy while significantly reducing computational costs compared to conventional security mechanisms. Furthermore, the federated learning approach mitigates privacy risks by preventing direct data exchanges among IoT nodes. The findings highlight the feasibility of scalable, privacy-preserving, and resource- efficient intrusion detection for IoT networks.
This research contributes to the advancement of AI-driven cybersecurity solutions, providing a robust and adaptable approach to safeguarding IoT environments from evolving threats. By addressing key challenges in IoT security, this work paves the way for future developments in smart, efficient, and self-adaptive security mechanisms for large-scale deployments.
Development of Highly Sensitive & Selective Sensor
No full textThis work presents the development of innovative electrochemical sensors for selective and sensitive dopamine (DA) detection using flexible laser-induced graphene (LIG) electrodes modified with advanced nanocomposites. An LIG electrode synthesized from pyralux film was functionalized with Nb4C3Tx MXene and silver nanoparticles (AgNPs), significantly enhancing its electrochemical performance. The modified electrode exhibited a peak anodic current increase from 150 μA to 330.4 μA and demonstrated a wide linear detection range (100 nM to 10 μM) with a low detection limit of 1 nM and high sensitivity (160.96 μA/nM cm -2). It also displayed excellent selectivity against common interfering compounds, ensuring accurate DA detection. To further improve the electrocatalytic activity of the fabricated sensor, the flexible LIG electrode was integrated with Nb₄C₃Tx MXene, polypyrrole (PPy), and iron nanoparticles (FeNPs). The modification increased the peak anodic current from 43 μA to 104 μA, extending the linear detection range to 1 nM – 1 mM with an ultralow detection limit of 70 pM and a sensitivity of 0.283 μA/nM cm-2. The sensor effectively detected DA in biological samples with high recovery and selectivity, distinguishing it from uric acid, ascorbic acid, glucose, and sodium chloride. The improved LIG-based sensor platform demonstrated exceptional stability, reproducibility, and suitability for real-time DA detection in complex biological matrices. Its flexibility and high performance highlight its potential for advanced biosensing applications, particularly in point-of-care diagnostics
The Association Between Food Benefit Online Ordering and Redemptions: Evidence from the Special Supplemental Nutrition Program for Women, Infants, and Children
No full textObjective:
To examine how the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) online food benefit ordering could influence WIC benefit redemptions.
Design:
A cross-sectional study. We compare the average redemption rates between online ordering early adopters and non-adopters among WIC customers before and after implementing WIC online ordering. A propensity score-weighted difference-in-difference model was used to estimate the coefficients.
Setting:
The Oklahoma WIC programme and a grocery store chain in Oklahoma.
Participants:
12743 Oklahoma WIC households that had redeemed their food benefits at the grocery store chain in 2020.
Results:
WIC online ordering significantly positively affected redemption rates for eight of the fifteen food categories. For example, the difference-in-difference coefficients (P–values) of these food categories were cheese or tofu (0·077, \u3c 0·01), yogurt (0·092, \u3c 0·01), whole milk (0·082, 0·022), low-fat milk (0·060, \u3c 0·01), eggs (0·049, 0·033), breakfast cereal (0·085, \u3c 0·01) and infant formula (0·073, 0·039). Two food categories with significantly negative difference-in-difference coefficients had relatively lower redemption rates overall: canned fish (Coefficient = –0·209, P \u3c 0·01) and infant cereal (Coefficient = –0·138, P = 0·015). There were no significant changes in the redemption of fruits and vegetables (Coefficient = 0·031, P = 0·121).
Conclusion:
Adopting WIC online ordering was positively associated with benefit redemption rates among most food benefit categories. Our findings provide preliminary but important evidence regarding online food benefit redemption among low-income consumers
Two Essays on the Role of Anthropomorphism in Consumer Behavior
No full textThe first essay of this dissertation investigates the contradictory findings on the relationship between anthropomorphism and self-control by focusing on how everyday anthropomorphized objects, rather than the products being used for consumption, influence consumer behavior. While previous studies have shown that using anthropomorphized products may decrease consumers’ self-control, this paper examines how unrelated [to consumption] anthropomorphized objects in a consumer’s environment—such as a water bottle with a humanlike face—can evoke a sense of social presence, increasing consumers’ perceptions that they are being negatively evaluated. Drawing on mind perception theory, I propose that this fear of negative evaluation can enhance self-control. Furthermore, the paper explores how the facial expressions of anthropomorphized objects, particularly smiling (vs. sad) faces signaling approval (vs. disapproval), may reduce this effect by alleviating consumers’ feelings of being negatively evaluated. Through five studies, this research offers a novel perspective on the relationship between anthropomorphism and self-control, contributing to the literature on social influence and consumer well-being. These findings suggest that anthropomorphized objects can be strategically designed to serve as an effective tool for enhancing self-control, offering practical insights for marketers and policymakers aiming to encourage positive consumer behaviors.
The second essay examines how gendered brand anthropomorphism influences consumers’ perceptions of product size. While brand anthropomorphism—imbuing brands with human-like characteristics—has been widely studied in consumer behavior, the impact of gender attribution within this context remains underexplored. Drawing on gender differences theory and the literature on consumer decision-making, the study proposes that when brands are anthropomorphized with explicit gender cues, consumers rely on gender-related stereotypes to form size judgments, perceiving brands anthropomorphized as female as smaller than brands anthropomorphized as male. The research further examines how this effect influences consumer evaluations, showing that alignment between product size and consumer usage goals enhances product favorability. Through five experimental studies and one analysis of secondary data (IRI dataset), this research contributes to the literature by demonstrating that gendered brand anthropomorphism shapes spatial judgments, expanding our understanding of factors that influence consumers’ size perceptions and decision-making. The findings also offer practical implications, suggesting that brands can leverage gendered anthropomorphism to influence consumer perceptions, particularly in contexts where size plays a crucial role in purchase decisions
The Inside Scoop: Elucidating the Three-Way Relationship Between Schistosoma Mansoni, the Gut, and Vaccines
No full textSchistosomiasis is a neglected tropical disease that affects over 250 million people worldwide. This blood fluke infection burdens communities and areas with limited to no access to clean fresh water. When a host is exposed to cercariae, the infectious agent, in a body of water, it matures in the host’s circulation into adult female and male worms. Copulated adult worms migrate to the mesentery, in the case of Schistosoma mansoni, and produce eggs that can cross the intestinal barrier and get excreted in the feces to continue the lifecycle. The effects of S. mansoni infection on the host are several. Schistosomiasis affects the host’s immune responses by causing a bias towards an anti-inflammatory response and a regulatory response chronically. The host’s fecal microbiome composition and diversity are also affected by schistosomiasis via the former’s dysbiosis towards an inflammatory composition. In addition, recent research has shown that helminths, including schistosomes, impair third-party vaccine responses in the infected hosts. Even though schistosomiasis can be treated chemotherapeutically, recurrent infections are a common problem in schistosome-endemic countries. In addition, the distribution of the medication can be limited in endemic areas where other infectious diseases are also prevalent such as HIV. The research presented here focuses on the longer term of enhancing vaccine responses in individuals immunocompromised because of helminth infections. To do so, this project has two main aims, one being to evaluate fecal microbiome and S. mansoni interactions to identify the presence or absence of global changes. The second aim confirms the effect of fecal microbiota transplants and antibiotics on mice infected with S. mansoni (2A). The second part of aim 2 records vaccine responses in infected mice subjected to fecal microbiota transplants to observe the role of microbiome on Schistosoma-caused vaccine failure
Not Right Now: Factors Affecting Interruption Decisions in a Healthcare Paradigm
No full textInterruptions are common in the workplace, but when they happen during high-stakes critical tasks, they can have serious consequences. In the field of healthcare, interruptions can lead to serious adverse events or medication errors. Research on interruption management strategies addresses the potentially harmful outcomes of interruptions but often fails to account for the initial decision to accept or reject the interrupting task. Two experiments were performed to examine the decision-making outcomes for interruptions.
In experiment I, the participants monitored two EKG displays while simultaneously entering medication information into a patient chart. The decision to accept or reject an interruption was investigated using three moderators: priority, cost of the interruption, and method of the interruption. Priority results are in support of expectancy-valence theory such that high priority interruptions were accepted more than low priority. Low-cost interruptions were accepted more than high-cost which shows that tasks that were close by were performed more than tasks located further away, The results were consistent with expectations and showed a significant difference for decision-making in which high priority, low-cost, and face-to-face tasks, were accepted more frequently.
In experiment II, the joint effects among the three moderators were examined with the addition of mental workload as a moderator. According to the memory for goals model, more interrupting tasks were expected to be accepted in the low workload condition. The results demonstrated that high priority and low-cost tasks strongly influenced the decision to accept interruptions. Low workload tasks and alarm interruptions were also more likely to be accepted but with smaller effect sizes. Both experiments provide evidence that the priority and cost moderators have a strong effect on interruption decisions, but the effect for workload was weaker and there were mixed effects for the interruption method. This research is a steppingstone to understanding how various moderators impact the decision to accept or reject an interruption. Knowledge of how these moderators influence decision-making outcomes may help create work environments where important interruptions are accepted, and nuisance interruptions are more likely to be rejected
Predictors of Healthcare Providers\u27 Readiness for Health System Transformation in Saudi Arabia
No full textThe healthcare system in Saudi Arabia is undergoing significant health reform to warrant sustainable welfare provision. Privatization or public-private partnerships (PPPs) were introduced in 2021 and outlined the vision for the health system for 2023. The planned initiatives aim to be comprehensive and effectively reach individuals and society, including citizens, noncitizens, and visitors. This dissertation seeks to study and explore privatization, the role of healthcare providers, and their capacity to adapt to the transformation of the healthcare system. To achieve this ambitious goal, several interrelated projects have been undertaken. The first project was a systematic review aimed at studying the transformation of the health system in Saudi Arabia since the launch of Health Vision 2030 and identifying the issues and steps the government has taken toward privatizing healthcare. The second project investigated the validity and reliability of the Arabic version of the readiness to change constructs among healthcare providers. The third project explored the predictor variables and the ability to forecast the readiness level for health system transformation in Saudi Arabia.
The systematic review found that the government made significant progress in facilitating and implementing the legislation\u27s roadmap to implement the reform and achieve the health vision of 2023; however, health clusters and the Ministry of Health need to practice causation, as this fundamental Arabic version of the readiness to change framework was valid and reliable for examining the ability of healthcare providers to change in healthcare settings. The third the project found that constructs of the ROC framework significantly predicted the readiness level among healthcare providers
Building Heat-Resilient Caribbean Reefs: Integrating Thermal Thresholds and Coral Colonies Selection in Restoration
No full textCaribbean reefs face increasingly frequent and intense bleaching events, adding to the numerous other threats impacting these ecosystems. Addressing these challenges requires global action to reduce climate drivers, along with local efforts like reef restoration. Active restoration using thermotolerant coral colonies offers a potential strategy to alleviate these impacts; however, gaps remain in identifying context-specific temperature thresholds to guide colony selection and standardize thermotolerance assessment methods. This study addressed these gaps in two phases. First, by determining practical thresholds to differentiate species responses to heat stress; and second, by developing a framework to identify and prioritize resilient colonies for restoration. In the first phase, 70 colonies of Acropora cervicornis, Diploria labyrinthiformis, Montastraea cavernosa, Orbicella annularis, O. faveolata, Porites astreoides, and P. porites were sampled from reefs in the southeastern Dominican Republic. Heat stress responses were assessed through 3-hour heat pulse assays above the local maximum monthly mean (MMM) temperature, combining visual bleaching ranks, pixel intensity as a proxy for chlorophyll loss, and pulse amplitude modulated (PAM) fluorometry. Species-specific T₅₀ thresholds were identified as the temperatures where 50% of colonies showed signs of stress. In the second phase, intraspecific thermotolerance was further examined for D. labyrinthiformis, M. cavernosa, O. annularis, O. faveolata, and P. astreoides using 99 colonies from known parent sources. Heat pulse assays at control (MMM) and T₅₀ temperatures were repeated four times to assign colony-specific thermal performance scores. This study integrates inter- and intraspecific thermotolerance data into a practical selection framework, offering valuable insights to guide restoration under climate change