Digital Commons Network^™

Consumer Preferences For Low-Methane Beef: The Impact Of Pre-Purchase Information, Point-Of-Purchase Labels, And Increasing Prices, Kelly A. Davidson, Brandon R. Mcfadden, Sarah Meyer, John C. Bernard

Agricultural Economics and Agribusiness Faculty Publications and Presentations

Cattle production is estimated to be the largest methane (CH₄) emitter associated with consumer demand in the United States of America (U.S.). With a national commitment to reducing methane emissions by 30% before 2030, methane-reducing additives (MRAs) in livestock feed are being explored as a viable solution. This study investigated consumer preferences for low-methane ground beef produced using one of three MRAs with varying levels of methane mitigation: Aspagopsis taxiformis (seaweed), the organic compound 3-nitroxypropanol (3NOP), or a blend of essential oils (e.g. garlic extract and citrus). In a nationally representative survey, 3,009 respondents completed a labeled discrete …

Editorial: To Know Or Not To Know: Causes And Evolution Of Lack Of Awareness Of Cognitive Decline In Neurodegenerative Diseases, Volume Ii, Federica Cacciamani, Kristy A. Nielson, Bernard J. Hanseeuw, Geoffroy P. Gagliardi, Patrizia Vannini

Psychology Faculty Research and Publications

Anosognosia, or the lack of awareness of cognitive, behavioral, or functional deficits, represents a critical challenge in the clinical management of neurodegenerative diseases such as Alzheimer's disease (AD) and frontotemporal dementia. This condition not only impacts patients, caregivers, and healthcare systems (D'Souza et al., 2011; Turró-Garriga et al., 2013) but also raises important questions about its underlying mechanisms and evolution (Hanseeuw et al., 2020; Starkstein, 2014; Vannini et al., 2017). Despite its critical importance, significant gaps remain in our understanding of cognitive self-awareness in neurodegenerative diseases. Building on the momentum of the first edition of To Know or Not to …

Alexithymia: Toward An Experimental, Processual Affective Science With Effective Interventions, Olivier Luminet, Kristy A. Nielson

Psychology Faculty Research and Publications

Alexithymia is a multi-dimensional personality trait involving difficulty identifying feelings, difficulty describing feelings, and an externally oriented thinking style. Poor fantasy life is debated as another facet. For over 50 years, the alexithymia literature has examined how alexithymia-related disturbances in perceiving and expressing feelings contribute to mental and physical disorders. We review the current understanding of alexithymia—including its definition, etiology, measurement, and vulnerabilities for both mental and physical illness—and its treatment. We emphasize the importance of further experimental and processual affective science research that (a) emphasizes facet-level analysis toward an understanding of the nuanced bases of alexithymia effects on neural, …

Editorial: Midlife Brain Health: Understanding Brain Aging In Middle-Age And Effects Of Interventions To Prevent Neurodegeneration In Late Life, Junyeon Won, Marissa A. Gogniat, Takuya Kurazumi, Kristy A. Nielson

Psychology Faculty Research and Publications

With the rapid increase in the aging population, the prevalence of age-related neurodegenerative diseases such as Alzheimer's disease (AD) has risen significantly, affecting over 55 million people worldwide in 2023, with projections suggesting this number will exceed 78 million by 2030 (Better, 2023). While much research has been focused on understanding and treating AD in older adults, there is growing emphasis on early interventions to prevent its onset (Crous-Bou et al., 2017; Dohm-Hansen et al., 2024). In this regard, middle-age has gained recognition as a critical period for the development and prevention of AD (Ritchie et al., 2017). For example, …

Rethinking Behavioral Reassignment In School Systems: A Restorative Model For Holistic Intervention, Gabriel M. Velez, Thomas Durkin, Kristin Haglund, John H. Grych, Julie Novak, Sherri Walker, Emily Goldstein Nolan, Amanda Krzykowski, Janessa Doucette, Drew Deluito, Bridget Schock

Psychology Faculty Research and Publications

Growing evidence over the last few decades has highlighted that punitive approaches in schools perpetuate violence, furthering inequities, deepening issues of safety, and negatively impacting young people’s development. These impacts are particularly felt in under-resourced urban schools situated in contexts of community violence. Partially, in response, an increasing number of schools and school districts are engaging in restorative practices. This trauma-informed, strengths-based framework focuses on relationships, healing, and accountability. This article contributes to the growing literature on the potential of restorative justice to promote equity in urban schools and educational systems by outlining the development and model of a restorative …

A Whole-Brain Voxel-Based Analysis Of Structural Abnormalities In Ptsd: An Enigma-Pgc Study, Jacklynn M. Fitzgerald

Psychology Faculty Research and Publications

Background

Patients with posttraumatic stress disorder (PTSD) exhibit smaller regional brain volumes in commonly reported regions including the amygdala and hippocampus, regions associated with fear and memory processing. In the current study, we have conducted a voxel-based morphometry (VBM) meta-analysis using whole-brain statistical maps with neuroimaging data from the ENIGMA-PGC PTSD working group.

Methods

T1-weighted structural neuroimaging scans from 36 cohorts (PTSD n = 1309; controls n = 2198) were processed using a standardized VBM pipeline (ENIGMA-VBM tool). We meta-analyzed the resulting statistical maps for voxel-wise differences in gray matter (GM) and white matter (WM) volumes between PTSD patients and …

An Iterative Shifting Disaggregation Algorithm For Multi-Source, Irregularly Sampled, And Overlapped Time Series, Colin O. Quinn, Ronald H. Brown, George F. Corliss, Richard J. Povinelli

Electrical and Computer Engineering Faculty Research and Publications

Accurate time series forecasting often requires higher temporal resolution than that provided by available data, such as when daily forecasts are needed from monthly data. Existing temporal disaggregation techniques, which typically handle only single, uniformly sampled time series, have limited applicability in real-world, multi-source scenarios. This paper introduces the Iterative Shifting Disaggregation (ISD) algorithm, designed to process and disaggregate time series derived from sensor-sourced low-frequency measurements, transforming multiple, nonuniformly sampled sensor data streams into a single, coherent high-frequency signal. ISD operates in an iterative, two-phase process: a prediction phase that uses multiple linear regression to generate high-frequency series from low-frequency …

Computer Vision-Based Framework For Data Extraction From Heterogeneous Financial Tables: A Comprehensive Approach To Unlocking Financial Insights, Iftakhar Ali Khandokar, Priya Deshpande

Electrical and Computer Engineering Faculty Research and Publications

Information extraction from financial document images is crucial in computer vision and NLP, as financial data often exists in image or PDF format, enabling organizations to analyze and make informed business decisions using OCR advancements. The table contents of financial document images are one of the prominent structures to confine important portions of data of the document and many Deep learning-based methods have been proposed to detect Table regions inside document images. The shortcomings of the current approach are that it is bounded within the detection of the table region and struggles in cases such as handling different layouts and …

Correlate Of Savings Attitude And Generational Poverty As Mediated By Financial Literacy: Evidence From Ghana, Adu Bismark Owusu-Sekyere, Williams Kwasi Peprah [email protected]

Faculty Publications

This study examines the mediating role of financial literacy between savings attitudes and generational poverty in Ghana. Generational poverty is a significant issue in Ghana, with a multidimensional poverty rate of 45.6% and a consumer expenditure poverty rate of 23.4%. The problem persists due to low financial literacy and poor savings attitudes, which hinder economic growth and development. The research employed a quantitative methodology, utilizing a series of regression analyses to test the mediation effects of financial literacy. A correlational design was adopted to assess the relationships between the variables. The study’s population included 400 individuals from the five regions …

Predation Of A Sperm Whale (Physeter Macrocephalus) By Killer Whales (Orcinus Orca) In Guanaja, Honduras, Gaby M. Ochoa, Eric Angel Ramos, Daniel Gonzalez-Socoloske

Faculty Publications

No abstract provided.

Book Review: We're All Traditionalists Now (Most Of The Time), Richard H. Fallon, Jr., The Changing Constitution: Constitutional Law In The Trump-Era Supreme Court, Michael Gentithes

Akron Law Faculty Publications

This review highlights the scope and importance of Richard Fallon’s book, The Changing Constitution: Constitutional Law in the Trump-Era Supreme Court. Fallon, a giant of constitutional work, provides a comprehensive, accessible, and vitally important catalogue of the Court’s methodological changes in his posthumously published book. The book argues that while textualism and originalism “have achieved unparalleled prominence,” the Court is also “not consistently originalist” and often relies on a version of traditionalism, similar to but importantly distinct from originalism, that marks an important and undertheorized change. And in yet other areas, the Court eschews either originalism or traditionalism for …

Strategic Leadership In Cybersecurity Risk Management: Elevating The Role Of Executive Managers, Lordt Becklines, Omar F. El-Gayar

Research & Publications

As the frequency and sophistication of cyber threats continue to escalate, cybersecurity has evolved from a technical concern to a central pillar of strategic enterprise governance. This TREO Talk presents the findings of a rigorous systematic literature review (SLR) encompassing 69 scholarly publications selected using the PRISMA 2020 framework. The study investigates how executive managers (EMs) influence the success of cybersecurity risk management (CRM) programs and frames their participation as both a requirement and a competitive advantage. Despite global investment in security infrastructure, many organizations remain vulnerable to breaches, largely due to the absence of sustained executive leadership in CRM. …

Integrating Generative Ai Into Knowledge Management Systems For Context-Aware Decision Support, Thad J. Dymkowski, Omar F. El-Gayar

Research & Publications

In complex organizations, traditional Knowledge Management Systems (KMS) and decision support systems (DSS) often struggle with three persistent challenges: capturing and converting tacit knowledge, like employee expertise and contextual insights, into explicit shareable formats; dynamically adapting recommendations and knowledge delivery to rapidly changing organizational contexts, user roles, and project needs; and enabling fast, relevant knowledge retrieval for timely decision-making. These challenges result in knowledge silos, loss of critical expertise, and decision delays. Integrating Generative AI (GenAI) with KMS, and context-aware DSS (CADSS) directly addresses these challenges by automating the collection, extraction, and structuring of tacit knowledge; personalizing insights based on …

Human Cognitive Bias Mitigation Approaches To Fairness Within The Machine Learning Value Chain: A Review And Research Agenda, Stephen Surles, Cherie Noteboom, Omar F. El-Gayar

Research & Publications

This systematic review examines the influence of human cognitive biases on machine learning (ML) systems across the 9 phases of the ML algorithmic value chain. Following the PRISMA guidelines, it synthesizes 19 studies on bias integration and management within ML, highlighting techniques to reduce bias and increase fairness. The review identifies key gaps: the unclear translation of human cognitive biases to ML biases, absence of metrics to measure biases, re-introduction of biases during debiasing, and the critical need for human intervention. These findings prompt several research themes spanning human cognition and algorithmic bias. The theoretical implications are three-fold: extending bias …

Ai-Powered Operations: Navigating Ethics, Automation, And Strategic Innovation In The Digital Era, Lordt Becklines, Omar F. El-Gayar

Research & Publications

Artificial Intelligence (AI) is redefining Operations Management (OM) by transforming how organizations plan, execute, and optimize their core processes. This TREO Talk presents a comprehensive synthesis of AI’s evolution and impact in OM, underscoring its role as a strategic enabler of agility, resilience, and innovation in digitally driven enterprises. Leveraging a Systematic Literature Review (SLR) guided by PRISMA methodology across nine scholarly databases, this research constructs a validated framework that captures the multidimensional role of AI in transforming operational strategies, capabilities, and outcomes across industries. AI's influence spans predictive analytics, robotic process automation, supply chain orchestration, logistics, and real-time quality …

Non-Functional Requirementsand Key Propositionselicitation For An Hdss:A Grounded Theory Approach, Mohammad Tafiqur Rahman, Omar F. El-Gayar

Research & Publications

Deciding humanitarian actions during relief distribution is a crucial and challengingtask.Humanitarian decision-makers (HDM) make manycritical decisions during disaster responses,but they sufferfrom incomplete, irrelevant, and sometimes excessive humanitarian operations-related disaster datain many situations.Hence, for constructive and meaningful support in decision-making, HDMs oftenappreciate computer-basedinformation systems(IS) (i.e. humanitarian decision support systems) that require non-functional requirements (NFRs) for their development. To identify those necessary NFRs, we applied the grounded theory approach to analyze 61 literature-and field-based decision-making parameters reported in the primary author's previous research (Rahman and Majchrzak (2020)). Our analysis resulted in 13 essentialNFRs and four key propositions to guide relief distribution decision-making …

Evaluating Topic Models With Openai Embeddings: A Comparative Analysis On Variable-Length Texts Using Two Datasets, Abdullah Wahbeh, Mohammad Al-Ramahi, Omar F. El-Gayar, Ahmed Elnoshokaty, Tareq Nasralah

Research & Publications

Topic modeling is a crucial unsupervised machine learning technique for identifying themes within unstructured text. This study compares traditional topic modeling methods, like Latent Dirichlet Allocation (LDA), against advanced embedding-based models, specifically BERTopic-OpenAI. The analysis utilizes two distinct datasets: user reviews from the mental health app Replika and the 20newsgroup dataset. For the Replika dataset, both methods identified common themes, but BERTopic-OpenAI uncovered additional nuanced topics, demonstrating its enhanced semantic capabilities. Quantitative evaluation of the 20newsgroup dataset further highlighted BERTopic-OpenAI's advantage through achieving higher topic coherence and diversity than the best-performing LDA model. These results suggest that embedding-based models provide …

A Review Of Reasoning In Artificial Agents Using Large Language Models, Nagraj Naidu, Omar F. El-Gayar

Research & Publications

The increasing sophistication and the use of large language models (LLMs) in artificial agents highlights the need to investigate their reasoning capabilities and limitations. Understanding these aspects is crucial, given the integral role of reasoning in decision-making processes, which are central to a software or embodied agent. This research paper presents a systematic review of the topic. We review the literature by selecting and analyzing highly cited papers using both PRISMA and snowballing. The gathered literature is categorized using a detailed framework of facets and categories. In the results section, we elaborate on our findings and illustrate the mapping through …

Exploring The Interoperability For Information Exchange Between Acute And Post-Acute Care Settings, Madhu Gottumukkala, Cherie Noteboom, Omar F. El-Gayar

Research & Publications

The seamless transfer and assimilation of healthcare data are foundational to delivering holistic, timely, and effective patient care across the healthcare spectrum. However, disparities in Electronic Health Record (EHR) system adoption, especially in long-term and post-acute care (LTPAC) settings, consistently obstruct interoperability. This study explores the factors that impede and facilitate health information exchange in LTPAC environments, focusing on technical and organizational facets. Grounded theory guided our qualitative case study research, involving 35 stakeholder interviews. Key technical findings highlight the need for integrated, reliable data structures, robust infrastructure, and standardized practices. Organizational insights reveal a shift towards integrative, patient-centric strategies …

Latent Subtypes Of Comorbidities In Multiple Sclerosis Patients: Insights From Social Media, Laxmi Manasa Gorugantu, Nevine Nawar, Omar F. El-Gayar

Research & Publications

Multiple Sclerosis is a chronic neurological disease associated with various physical and cognitive impairments. Individuals with MS might also experience medical and psychiatric comorbidities that can exacerbate the severity of the disease and lower their overall well-being. Therefore, it becomes essential to identify these co-occurring health conditions at the early stages to optimize treatment and improve patient outcomes. Consequently, the objective of this study is to explore the commonly occurring comorbidities among MS patients around the globe from social media discourse. Furthermore, it aims to unveil public perceptions, providing insights that might not be captured via clinical research methods. The …

Highlighting Competency Gaps In Health Informatics Education Using Advanced Text-Embedding Models, Omar F. El-Gayar, Abdullah Wahbeh, Mohammad Al-Ramahi, Ahmed Elnoshokaty

Research & Publications

The healthcare sector anticipates substantial growth, with 125,000 job openings by 2026, including 69,000 middle-skilled positions. Despite this growth, data use and integration inefficiencies cost the sector $750 billion annually. Health informatics, an interdisciplinary field blending healthcare, computer, information, and cognitive sciences, can address these challenges through enhanced healthcare management via information technology. However, there is a critical disparity between competencies taught in educational programs and those demanded by the job market. This study examines competencies from job postings on Indeed.com and accredited health informatics programs, comparing them with the Health Information Technology Competencies (HITComp) database. Utilizing advanced text-embedding models, …

Towards Adaptive Learning: A Review Of Machine Learning On Lms Data, Cindy Zhiling Tu, Gary Yu Zhao, Omar F. El-Gayar

Research & Publications

This study presents a literature survey on the application of machine learning (ML) in learning management system (LMS) data analytics, aiming to provide insights into adaptive learning development and propose an agenda for future research. The literature survey is based on a proposed adaptive learning framework and critically analyzes the results within this context. The results reveal that machine learning methods can be used to evaluate the effectiveness of instructional interventions and combining online behaviors with textual data can improve the outcome of performance prediction. Key findings also highlight several open issues, including using small datasets and the need for …

Generative Ai And Academic Integrity In Higher Education: A Systematic Review And Research Agenda, Kyle Bittle, Omar F. El-Gayar

Research & Publications

This systematic literature review rigorously evaluates the impact of Generative AI (GenAI) on academic integrity within higher education settings. The primary objective is to synthesize how GenAI technologies influence student behavior and academic honesty, assessing the benefits and risks associated with their integration. We defined clear inclusion and exclusion criteria, focusing on studies explicitly discussing GenAI’s role in higher education from January 2021 to December 2024. Databases included ABI/INFORM, ACM Digital Library, IEEE Xplore, and JSTOR, with the last search conducted in May 2024. A total of 41 studies met our precise inclusion criteria. Our synthesis methods involved qualitative analysis …

A Systematic Literature Review Of Ai, Education, And Change In Radiology Practice, Cherie Noteboom, Vahini Atluri, Sai Mounika Chintalapudi

Research & Publications

Radiology is undergoing a role change as Artificial Intelligence (AI) becomes increasingly embedded in clinical workflows, reshaping the roles, responsibilities, and educational needs of radiologists. As professionals known for their autonomy and expertise, radiologists face significant transitions that require effective change management and educational support. The systematic literature review is guided by the PRISMA methodology and synthesizes findings from peer-reviewed research between 2019 and 2024 to explore how AI integration influences radiology practice and training. Anchored in the GrandFusion Framework—a novel theoretical synthesis of Dewey’s Experiential Learning, Simon’s Bounded Rationality, Lewin’s Change Management, Davis’s Technology Acceptance Model, and Rogers’s Diffusion …

From Imaging To Insights: Ai’S Role In Radiology Transformation Through 2pdt And 4aim, Cherie Noteboom, Sai Mounika Chintalapudi, Vahini Atluri

Research & Publications

Artificial intelligence (AI) is progressively integrated into radiologists' processes, improving diagnostic precision, decision-making, and operational efficiency. This systematic literature review (SLR) is a Meta- analysis utilizing the PRISMA framework that investigates the transformative impact of AI in radiology by analyzing studies published from January 2019 to December 2024 across the academic databases of ACM Digital Library, IEEE/IET, Elsevier ScienceDirect, ProQuest, and PubMed. The research employs the People, Process, Data, and Technology (2PDT) framework to classify AI technologies and assess their effects on patient outcomes, radiologists' experiences, and healthcare system performance. The findings indicate the substantial contributions of AI, including enhanced …

A Systematic Literature Review On Ai Chatbots In Automating Customer Support For E-Commerce, Sai Mounika Chintalapudi, Omar F. El-Gayar, Cherie Noteboom

Research & Publications

The rapid advancement of artificial intelligence (AI) has significantly transformed the e-commerce sector, particularly through the integration of AI-powered chatbots. This study conducts a systematic literature review to examine how chatbots align with the eight foundational features of e-commerce technology: ubiquity, global reach, universal standards, interactivity, information richness, information density, personalization/customization, and social technology. Guided by the PRISMA framework, the review addresses two primary research questions: (1) How do AI-driven chatbots support the unique features of e-commerce technology? and (2) What technological advancements enhance their functionality in this context? Relevant literature was retrieved from five major academic databases: ACM Digital …

Mapping The Intersection Of Blockchain And Project Management: A Bibliometric Review, Cherie Noteboom, David Zeng, Sai Neelima Seru, Sai Mounika Chintalapudi

Research & Publications

This study presents a bibliometric analysis of 93 peer-reviewed articles examining the intersection of blockchain technology and project management. Utilizing PRISMA and analytical tools such as VOSviewer, bibliometrix R, and python, it identifies publication trends, keyword co-occurrence, and three thematic clusters: blockchain infrastructure, project management, and industry application domains. Results reveal rapid growth since 2018 and conceptual tensions between centralized project methods and decentralized technologies. This analysis contributes a structured overview of how blockchain is reshaping project environments, offering insights for both researchers and practitioners seeking to align emerging technologies with project governance and execution models.

The Impact Of Knowledge Management In The Banking Industry: A Systematic Literature Review, Sai Anurag Illendula, Omar El-Gayar

Research & Publications

The banking industry is experiencing unprecedented transformation due to rising employee turnover, increasing regulatory pressures, and intense competition from fintech companies offering personalized digital services. In this knowledge-intensive and low-margin environment, effective Knowledge Management (KM) is critical for maintaining a competitive edge. Despite its importance, the relationships between KM processes, performance outcomes, and mediating variables remain underexplored. This study conducts a Systematic Literature Review (SLR) of peer-reviewed journal articles to investigate how KM influences both organizational performance (OP) and employee performance within the banking sector. The review reveals that effective KM processes enhance efficiency, foster innovation, mitigate risks, and empower …

Enhancing Climate Change Mitigation: An Ai-Based System For Real-Time Monitoring Of Environmental Changes Using Satellite Data, Yesu Vara Prasad Kollipara, Omar El-Gayar

Research & Publications

This study presents a real-time AI system for environmental monitoring using satellite imagery. The framework integrates EfficientNet-B0 with attention for spatial classification, LSTM for temporal forecasting, and transfer learning for cross-satellite generalization. Trained on EuroSAT and validated on Landsat-8, the system achieved 77.7% classification accuracy with inference times under 0.15 seconds per image. A cloud-based dashboard enables visualization of deforestation, carbon trends, and land cover changes for policymakers. Aligned with SDG 13 and CORSIA, the approach demonstrates scalability, efficiency, and policy relevance, offering a practical AI-based tool for climate change mitigation.

Systematic Review Of Lattice-Based Cryptography Algorithms For Securing Blockchain Networks In The Post-Quantum Era, Salim Arfaoui, Omar El-Gayar

Research & Publications

The rise of quantum computing threatens blockchain security as traditional cryptographic schemes become vulnerable. Lattice-based cryptography has emerged as a promising quantum-resistant solution. This systematic literature review uniquely provides an in-depth, blockchain-specific analysis of lattice-based cryptographic integration challenges. Following PRISMA guidelines, we analyzed 48 studies published between 2017-2025 from major academic databases. Unlike previous reviews that broadly examine post-quantum cryptography, we identify critical blockchain-specific implementation barriers, performance bottlenecks, and standardization gaps. Key challenges include computational overhead, protocol compatibility, and the need for lightweight cryptographic standards. Despite these challenges, lattice-based cryptography demonstrates significant potential for enhancing blockchain security in the quantum …