Digital Commons Network^™

Utilizing The Invasive Fruitwood Pyrus Calleryana As Substrate For Mushroom Cultivation, An Experimental Study In Practical Sustainability, Matthew D. Helton

Masters Theses and Doctoral Dissertations

This study evaluated the feasibility of using woodchips from the invasive Pyrus calleryana (Callery pear) as substrate for mushroom cultivation. Pleurotus ostreatus, Hericium erinaceus, and Ganoderma lucidum were grown on a pear wood substrate and on a conventional oak substrate to compare, yield, sensory attributes, and metabolite production. P. ostreatus showed no significant differences in yield or biological efficiency (p > 0.05), only sweetness (p = 0.022) differed significantly. H. erinaceus yield and efficiency were similar (p > 0.05), with greater second-flush yields in oak controls (p = 0.034) but no sensory differences (p > 0.05). G. lucidum performance was comparable across treatments …

Evaluation Of A No-Code Ai Model For Detecting Periapical Radiolucencies: Impact Of Anatomical Region On Diagnostic Performance, Manal Hamdan, Kevin W. Yu, Marguerite Miller, Stephanie J. Sidow, Zaid Badr, Sergio E. Uribe

School of Dentistry Faculty Research and Publications

Background

To develop a no-code artificial intelligence (AI) model for the detection of apical radiolucent lesions and to assess how lesion location influences the model’s diagnostic performance.

Methods

312 periapical radiographs were retrospectively collected, each accompanied by a corresponding cone-beam computed tomography (CBCT) scan obtained within six months and a radiology report authored by board-certified oral and maxillofacial radiologists. These reports served as the reference standard, and all findings were cross-verified by the primary investigator through CBCT review. The dataset included 181 images with at least one apical radiolucent lesion and 131 lesion-free controls. Using the no-code AI platform LandingLens …

Understanding The Out-Of-Distribution Generalization Of Chain-Of-Thought Reasoning In Llms, Nuojing Liang, Xiaotong Yuan, Pan Zhou

Research Collection School Of Computing and Information Systems

Chain-of-Thought (CoT) reasoning substantially improves Large Language Models (LLMs), yet its out-of-distribution (OOD) generalization mechanism remains theoretically underexplored. We study CoT as compositional OOD generalization: the target task lacks complete reasoning trajectories or direct input-output pairs, while training provides only in-distribution (ID) atomic step data. We show that CoT generalizes by repeatedly applying a shared single-step predictor to compose reusable ID transitions. Theoretically, the OOD task risk is controlled by the sum of ID subtask risks, scaled by a depth-independent hard-decoding coefficient Bℓ b = O(k) that is independent of the reasoning depth T. A Rademachercomplexity analysis further gives an …

Reduced Mediators Released By Cyanobacteria During Exoelectrogenesis Detected Using Differential Pulse Voltammetry, Laura T. Wey, Monica Brachi, Yagut Allahverdiyeva, Shelley D. Minteer

Chemistry Faculty Research & Creative Works

Cyanobacteria generate electrical current through exoelectrogenesis (extracellular electron transfer) downstream of photosynthesis, yet the identity of the endogenous redox mediator(s) responsible remains unresolved. Here, we apply differential pulse voltammetry (DPV) to detect and characterise redox-active species released by Synechocystis sp. PCC 6803 under illumination. To enable sensitive detection, we developed an electrolyte intermediate between BG11 (Blue-Green-11) growth medium and MOPS (3-(N-morpholino)propanesulfonic acid) buffer that minimises background electrochemical interference while maintaining short-term cellular functionality. DPV revealed multiple light-enhanced oxidation peaks (0.1–0.65 V vs. saturated calomel electrode (SCE)) that were not resolvable using cyclic voltammetry, providing evidence that cyanobacteria release reduced compounds …

Team-On-Team Negotiation: How Internal Structure Shapes What Teams Can Do At The Table, Leyi Holly Shi, Tianyu He, Robert B. Lount Jr., Michael Schaerer, Roderick I. Swaab

Research Collection Lee Kong Chian School Of Business

Team negotiation research presents a paradox: teams sometimes achieve more integrative outcomes than solo negotiators, yet in other cases they escalate competitiveness and harm relational outcomes. Existing theory has not resolved this inconsistency, in part because it treats negotiating teams as if they could change positions as easily as individual negotiators. We argue this assumption overlooks a central constraint of team-on-team negotiation: before any adaptive external exchange can occur, teams must internally authorize revisions to their positions as new information emerges. Drawing on evidence from research on team negotiation, multiparty negotiation, and team design, we identify this internal capacity as …

Generative Ai Adoption And Solvers' Popularity On Supply-Driven Crowdsourcing Platforms: The Dual Role Of Price Signals, Zimeng Zhu, Carol Hsu, Fiona Fui-Hoon Nah, Na Liu

Research Collection School Of Computing and Information Systems

Purpose – We investigate the effect of solvers’ adoption of Generative AI (GenAI) on their popularity in a supply-driven crowdsourcing platform. We also examine the impact of price signals as well as their heterogeneous impact based on the solvers’ membership duration on the platform. Design/methodology/approach – Our analysis focuses on solvers who adopt GenAI for design-related gigs on the supply-driven crowdsourcing platform. By combining propensity score matching (PSM) with multi-period difference-in-differences (DID), we examine how GenAI adoption impacts solvers’ popularity and how price signals affect this main effect. Findings – Our findings reveal that solvers who adopt GenAI tend to …

A Deep Learning Ensemble Framework For Multi-Subtype Renal Tumor Classification Using Contrast-Enhanced Ct, Hisham Abdeltawab, Ahmed Alksas, Mohamed Ghazal, Ashraf Khalil, Norah Saleh Alghamdi, Rasha T. Abouelkheir, Ahmed Elmahdy, Mohamed Abou El-Ghar, Sohail Contractor, Ayman El–Baz

All Works

Renal cell carcinoma (RCC) is considered the most aggressive and common form of renal cancer. Therefore, early detection is crucial to ensure appropriate and effective treatment planning. In our study, we propose a novel computer-aided diagnostic (CAD) approach which incorporates a deep learning ensemble to differentiate between five renal tumor subtypes, utilising the modality of contrast-enhanced computed tomography (CE-CT). The addressed renal lesions are malignant tumors (chromophobe RCC (chRCC), papillary RCC (pRCC), and clear cell RCC (ccRCC)) and benign tumors (renal oncocytoma (RO) and angiomyolipoma (AML)). Our study includes 280 patients who underwent renal biopsy, 112 patients were diagnosed with …

Integrating Machine Learning And Explainable Ai For Employee Attrition Prediction In Hr Analytics, Maytha Al-Ali, Majed Alwateer, Shatha Abed Alsaedi, Hossam Magdy Balaha, Mahmoud Badawy, Mostafa A. Elhosseini

All Works

Employee attrition poses significant challenges to organizations, impacting productivity, morale, and financial stability. Predicting attrition and understanding its underlying drivers are critical for implementing effective retention strategies. In this study, we propose a comprehensive framework that utilizes advanced machine learning techniques to predict employee attrition and job change likelihood. The framework integrates robust preprocessing pipelines, state-of-the-art predictive models, and explainability tools such as SHAP (SHapley Additive exPlanations) to ensure transparency and fairness in HR analytics. By addressing key challenges such as class imbalance, feature selection, and model interpretability, our approach provides actionable insights for proactive talent management. We evaluate the …

Artificial Intelligence (Ai) For Social Innovation In Health Education: Promoting Health Literacy Through Personalized Ai-Driven Learning Tools – A Systematic Review, Dina Mansour Tbaishat, Maha Waleed Elfadel

All Works

Background: Artificial Intelligence (AI) is transforming health education by enabling personalized, adaptive, and scalable approaches that may enhance aspects of health literacy. Despite rapid adoption, comprehensive synthesis of AI tools’ impact on health literacy as social innovation is limited. Understanding these effects guides educators, developers, and policymakers in designing potentially effective, inclusive, and ethical AI interventions. This review examines generative AI models, chatbots, and adaptive learning systems in supporting health literacy globally. Methods: A systematic review was conducted following PRISMA guidelines. Literature was identified primarily through PubMed/Medline, Scopus, and ScienceDirect. Connectedpapers.com was used exclusively as a citation chasing tool, performing …

Radio Frequency Tagging–Enabled Patient Monitoring: Integrating Mobility Tracking With Early Warning Systems For Enhanced Safety, Ahed Abugabah, Prashant Kumar Shukla, Piyush Kumar Shukla, Abhishek Dwivedi

All Works

Ensuring patient safety in healthcare environments requires continuous monitoring systems capable of identifying early warning signs of clinical risk. Traditional surveillance methods often fail to capture meaningful patterns in patient movement, limiting their ability to prevent incidents such as falls, prolonged immobility, or unnoticed health deterioration. Radio Frequency Tagging technology has been increasingly adopted for real-time patient tracking; however, existing systems are generally limited to location detection and lack predictive insights into patient behaviour. To overcome these limitations, this study presents a Radio Frequency Tagging-based patient monitoring framework that integrates mobility tracking with an early warning mechanism to enable proactive …

The Association Between Cyber Behaviors And Hedonic And Eudaimonic Well-Being: The Moderating Role Of Personality Traits, Areej Elsayary, Juan Calmaestra, Mercedes Gómez-López

All Works

The increasing integration of digital technologies into everyday life has intensified engagement in various cyber behaviors, raising important questions about their relationship with individuals' well-being. This study examined the associations between online moral disengagement, problematic internet use (compulsive internet use), benign and toxic disinhibition, cyberaggression, and cybervictimization with both hedonic and eudaimonic well-being among emerging adults in the United Arab Emirates. Furthermore, it explores the moderating role of personality traits in these associations. Data were collected from 671 emerging adults (46.8% women) aged 18–29 years (M = 22.56; SD = 3.04). Results showed that cybervictimization and cyberaggression exhibited the strongest …

Understanding Chatbot-Assisted Collaborative Learning Among Female Undergraduate Students, Mohammad Amin Kuhail, Ahmed Shuhaiber, Sinan Salman, Nazik Alturki

All Works

Computer programming can be daunting for beginners due to complex concepts and syntax. Traditional teaching methods, while engaging through gamification and active learning, often lack personalized approaches. Recent advancements in artificial intelligence (AI), particularly large language models (LLMs), present new possibilities for personalized and interactive learning environments. This study introduces a chatbot-assisted collaborative learning environment (CCLE) that leverages an LLM (GPT-4) to enhance collaborative programming education. The CCLE enables real-time guidance and collaboration through natural language interactions, allowing students to work together on programming tasks, edit code collaboratively, and engage with both peers and the educational chatbot. We conducted an …

A Robust Approach For Olive Leaf Disease Detection In Uncontrolled Environments, Rima Grati, Khouloud Boukadi, Emna Ben Abdallah, Ahmed Seffah

All Works

Detecting diseases in olive leaves is crucial for maintaining tree health and ensuring stable olive production. Early signs of infection often appear on the leaves, making them a key indicator for timely disease detection and intervention. Traditionally, farmers rely on visual inspection or laboratory tests to diagnose plant diseases. However, recent advancements in deep learning (DL) have significantly improved the accuracy and efficiency of olive leaf disease diagnosis. Numerous studies in the literature have explored this task using CNN-based architectures and, more recently, Vision Transformers. While these models have shown promising performance on benchmark datasets, they are often trained and …

Engineering Sulfur Concrete Mixes: A Data-Driven Approach To Aggregate Packing And Binder Efficiency, Vasilia Al Khaldi, Abdel Hamid I. Mourad, Maisa El Gamal

All Works

Processing window (mass loss < 3%). A first-order, order-of-magnitude carbon-footprint estimate—presented with explicit caveats rather than as a full life-cycle assessment—indicates that replacing Portland cement with by-product sulfur is the dominant contributor to a substantial reduction in embodied CO₂ per cubic metre relative to equivalent-strength Portland cement concrete. The valorization of steelmaking by-products aligns with circular economy principles, enabling high-performance, recyclable, and low-carbon construction materials without chemical binder modification.

An Advanced Healthcare System With An Automated Vit Model For Dermoscopic Skin Cancer Identification, Ahed Abugabah, Prashant Kumar Shukla, Suchi Mishra, Abhishek Dwivedi

All Works

Early and reliable diagnosis of skin cancer from dermoscopic images remains challenging due to class imbalance, subtle inter-class variations, lesion boundary ambiguity, and illumination inconsistency, which can degrade the robustness of conventional convolutional neural networks (CNNs). To address these limitations, this study proposes an automated smart healthcare framework for dermoscopic skin cancer diagnosis using an Enhanced Vision Transformer (E-ViT) that improves global-context modeling through self-attention while strengthening fine-grained lesion representation learning. Unlike standard ViT configurations, the proposed architecture integrates multi-scale patch embedding and attention refinement to better capture border irregularities and color–texture heterogeneity that are critical for melanoma discrimination. Furthermore, …

Fair And Explainable Educational Recommendations With A Hybrid Graph-Gru Framework, Edmund Evangelista, Syed M.Salman Bukhari

All Works

Artificial Intelligence (AI) recommender systems are increasingly used in education to personalize learning and help students navigate large collections of digital learning resources. However, many existing approaches emphasize predictive accuracy over fairness, robustness, diversity, and transparency. This creates an important educational challenge. The students with limited participation histories may receive less reliable support, while highly popular resources may dominate recommendation lists and limit access to other useful learning materials. To address this challenge, this study aims to develop and evaluate a responsible educational recommender framework that supports personalized learning resource navigation while making recommendation behavior more fair, stable, diverse, and …

Large Language Models In Nlp: Evolution, Architectural Trends, And Open Challenges, Haseeb Javed, Babar Shah, Farman Ali, Daehan Kwak

All Works

The rise of Large Language Models (LLMs) has transformed how Natural Language Processing (NLP) and its subdomains are approached. Recent technological advancements have driven this transformation. This study offers researchers a detailed overview of LLMs, comparing them with traditional rule-based systems, statistical techniques, machine learning, neural networks, and the rise of transformer-based architectures. From a wider perspective, language models such as GPT, BERT, T5, PaLM, and LLaMA have facilitated the transformation of entire sectors, including healthcare and business, due to their highly scalable nature. Despite their wide range of applications, LLMs face numerous challenges, such as output biases, limited interpretability, …

Anticipatory Governance And Leadership For Ai Implementation In Higher Education: A Scoping Review, Sandra Baroudi

All Works

As the education sector attempts to address rapid changes caused by Artificial Intelligence (AI), it becomes crucial to examine approaches to leadership at the organizational and system levels. This scoping review explores how anticipatory models of governance are conceptualised and operationalised globally within higher education settings in the context of AI-related transformations. Using a scoping review design, academic and grey literature published between 2020 and 2025 was searched across Scopus, EBSCOhost, ERIC, Google Scholar, and ProQuest. Nineteen sources were selected based on clear inclusion and exclusion criteria and analysed thematically. Findings revealed that while AI offers promising opportunities to transform …

Integrating Student-Mined Data In Youth Computer Science Learning: An Investigation Of Learning Outcomes And Educational Strategies, Alex Acquah

Open Access Theses & Dissertations

As data increasingly shapes participation in society, there is growing emphasis on preparing youth to engage with data through computer science education. Existing K-12 computing and data science curricula have expanded opportunities for learners to analyze and interpret data; however, many learning experiences continue to rely on datasets and inquiry structures designed by others, limiting authentic engagement, personal relevance, and learner agency. This dissertation investigates the integration of student-mined data in youth computer science learning and examines its influence on computational thinking, epistemic agency, and learning experiences among high school students. Using a qualitative approach, data collected from youths of …

Enhancing Teachers’ Intercultural Literacy: Examining The Impact Of A Fulbright-Hays Group Projects Abroad Program To Indonesia, Paul R. Wallace

Journal of Global Education and Research

The Fulbright-Hays Group Projects Abroad (GPA) Program offers institutional grants to support overseas experiences and curriculum development for educators. The study reported here focused on changes in the intercultural literacy of participants involved in a 4-week Fulbright-Hays GPA Program to Indonesia. Participants in this study included K-12 teachers from public schools and teacher education majors. Before and after traveling to Indonesia, eight participants completed a self-assessment of intercultural literacy, which measured competencies, understandings, attitudes, language proficiencies, participation, and identities deemed necessary for successful living and working in a cross-cultural environment. This article presents the results of the assessment survey, along …

Changing Expectations In Higher Education Post-Pandemic, Gregory R. Mackinnon, Tyler Maclean, Mohamad El Maouch, William Yuan, Zhao Kaibin, Bi Dandan, Marwa Saab

Journal of Global Education and Research

A sample of higher education students in Canada and China was surveyed and interviewed to ascertain any changes in their educational expectations given their experiences with emergency remote teaching during the pandemic. In addition, the instructors of these students were interviewed to corroborate student feedback. The research suggested that students in Canada and China expressed expectations of increased flexibility and improved pedagogy. Whereas Canadian students were overtly demanding of the instructor and the educational system, Chinese students were comparatively subtle in their expectation that the post-pandemic level of support be maintained. The primary concern of the Chinese cohort was a …

Advancing Blended Education: Caribbean Lecturers’ Reflective Narratives Of Caution, Creativity, And Change, Mia A. Jules, Donna-Maria B. Maynard, Grace A. Fayombo, Jason E. Marshall, Tanya Newton, Kamilah Hutson, Amanda Kellman, Cherise Bynoe, Mikaila Collymore, Jo-Ann Prosper-Chase, Laura Lee Foster, Adicia Clarke

Journal of Global Education and Research

Blended teaching requires lecturers to constantly self-reflect on their pedagogical practice to enhance student learning. However, there is yet to be a significant corpus of literature that highlights the cognitive resources that lecturers in higher education should possess to effectively use blended learning strategies. It is important to understand the intellectual resources required for blended pedagogy so that such capabilities can be fostered during faculty-training programs; ultimately resulting in innovative strategies to ensure quality learning outcomes and the advancement of university-level blended teaching mandates. This qualitative case study explored how twelve Caribbean lecturers experienced and navigated the teaching process in …

Gender Gap In Returns To Publications, Piotr Śpiewanowski, Ivan Stetsyuk, Oleksandr Talavera

All Works

Gender equity in science depends not only on who participates in research, but also on how their work is rewarded. We examine gender gaps in monetary returns to research productivity, pay, and credit attribution using a novel dataset linking salary and publication records for professors at U.S. public universities. While our main contribution shows that there is no robust evidence of a significant gender gap in monetary returns to publications, we uncover substantial heterogeneity across disciplines and authorship structures. Women in medical and health sciences experience a negative gap in returns of 10.7% relative to men for equivalent research output. …

How Leaders Build Employee Trust In Artificial Intelligence: Voice Opportunities, Humility, And Trust Transfer, Jack Mcguire, David De Cremer, Devesh Narayanan

Research Collection Lee Kong Chian School Of Business

Artificial intelligence is increasingly central to organizational work, yet employee trust in AI remains fragile. Although prior research has primarily explained trust in AI through technological characteristics such as transparency, reliability, and accuracy, we argue that trust in AI is also shaped by the social context in which employees encounter these systems. Drawing on affect-as-information theory and social information processing theory, we develop and test a model in which leader-provided voice opportunities reduce employees’ negative affect about AI-related work experiences, thereby enhancing perceptions of leader trustworthiness and, in turn, trust in AI. We further propose that this indirect effect depends …

Ambulatory Blood Pressure Variability In Prediction Of Target Organ Injury: The Ship Ahoy Study, Coral D Hanevold, Jang Dong Seo, Stephen R Daniels, Bonita E Falkner, Michael A Ferguson, Joseph T Flynn, Julie R Ingelfinger, Philip R Khoury, Marc B Lande, Kevin E Meyers, Mark Mitsnefes, Joshua Samuels, Elaine M Urbina

Faculty, Staff and Student Publications

Background: Ambulatory hypertension is associated with elevated left ventricular mass index (LVMI), cardiac dysfunction, and increased arterial stiffness in adolescents. Whether the addition of measures of BP variability improves the prediction of subclinical cardiovascular target organ damage (TOD) over mean BP measures is not known.

Methods: We assessed clinic and ambulatory BP (ABP), anthropometrics, and TOD in 397 adolescents. ABP means standard deviation (SD), BP, and heart rate (HR) dipping were calculated; coefficients of variability (CV) were calculated (SD/mean) to assess ABP variabilities. Measures of TOD included LVMI, left ventricular hypertrophy (LVH), LV systolic shortening, LV diastolic function (e'/a'), and …

Robust Hybrid Tree-Based Machine Learning-Assisted Optimization Of Well Parameters To Reduce Asphaltene Precipitation Risk In Oil Fields, Malek Jalilian, Madani, Alireza Keshavarz, Stefan Iglauer, Abbas Khaksar Manshad, Amir H. Mohammadi

Research outputs 2022 to 2026

Asphaltene precipitation is a persistent flow-assurance issue in carbonate oil wells, leading to increased intervention frequency and production losses. This study utilizes multi-decade surveillance and operational data from a mature carbonate field (1983–2023) to train and optimize five tree-based models: Decision Tree, Random Forest, Extra Trees, Gradient-Boosting Decision Tree, and CatBoost, employing a Tree-Structured Parzen Estimator. These models, constrained by operational parameters, were integrated to identify optimal settings that can minimize the frequency of asphaltene precipitation and the subsequent cleanups. The novelty of this research lies in the direct integration of interpretable tree ensembles with an optimizer that adheres to …

The Effect Of School-Based Educational Session On Knowledge, Attitudes, And Beliefs Of Cervical Cancer Among Adolescents In Mixed Day High Schools In Embu County: A Quasi-Experimental Study, Lucy W. Kivuti-Bitok, Mugo, Dorothy Mituki, Christine Marete, Irene Ngune, Jessica Wells

Research outputs 2022 to 2026

Background: School-based interventions have proven effective in enhancing knowledge and altering attitudes toward cervical cancer among adolescents. This study aimed to evaluate the effect of a school-based educational intervention on the attitudes, beliefs, and knowledge of cervical cancer among adolescents in mixed-day high schools in Embu County. Methods: This study used a single-group, repeated cross-sectional design to assess the effect of a structured health education session on cervical cancer among adolescent boys and girls in mixed-gender secondary schools. A self-administered anonymous questionnaire was administered before and immediately after the post-intervention. The education session lasted 30–45 min, and the study population …

Effects Of Resisted Movement Training On Change-Of-Direction Speed In Recreationally Active And Trained Individuals: A Systematic Review With Network Meta-Analysis And Meta-Regression, Mingyang Zhang, Urs Granacher, Ruixiang Yan, Hallvard Nygaard Falch, Rodrigo Ramirez-Campillo, Kai Xu, Xing Zhang, Jinming Li, Shubo Xu, Yulin Luo, Duanying Li, Jian Sun

Research outputs 2022 to 2026

Background: The ability to change directions quickly is critical for performance and injury prevention in multidirectional sports, yet the influence of resisted movement training (R-MT) on change-of-direction (COD) speed remains uncertain. Therefore, we aimed to examine the effectiveness of various R-MT methods on COD speed in individuals ranging from recreational to international level and to determine the relationship between test angle and COD adaptations. Methods: We systematically searched PubMed/MEDLINE, Embase, Web of Science, SPORTDiscus, CINAHL, Scopus, and Google Scholar from inception to August 2025. Random-effects pairwise meta-analyses and a Bayesian network meta-analysis (NMA) synthesized effects of vertically resisted plyometric and …

Sarcomerogenesis: Evidence, Context And Key Stimuli—Response To The Commentary By Power Et Al., Anthony J. Blazevich, Walter Herzog, João Pedro Nunes

Research outputs 2022 to 2026

No abstract provided.

Discriminative Performance Of Externally Validated Dementia Risk Prediction Models: A Systematic Review And Meta-Analysis, Blossom C.M. Stephan, Jacob Brain, Kaarin J. Anstey, Tanya Buchanan, Claire V. Burley, Elissa Burton, Jennifer Dunne, Linda Errington, Matthew Gorringe, Zhongyang Guan, Bronwyn Myers, Serena Sabatini, Marc Sim, William Stephan, Eugene Yee Hing Tang, Narelle Warren, Mario Siervo

Research outputs 2022 to 2026

Background: Data on the external validation of current dementia risk prediction models has not yet been systematically synthesised. This systematic review and meta-analysis collated results from three previous reviews to evaluate the predictive discriminative performance of dementia risk models when validated in population-based settings. Methods: Embase (via Ovid), Medline (via Ovid), Scopus, and Web of Science were searched from inception to June 2022 with an updated search conducted up to November 2024. Included studies (1) had a population-based cohort design; (2) assessed incident late-life (i.e. ≥ 60 years) dementia; and (3) reported predictive performance of at least one dementia risk …