Digital Commons Network^™

Providing Effective Student Feedback: Effects Of Sociotropy, Student-Teacher Connectedness, And Expressed Disappointment And Praise On Students' Self-Esteem And Motivation, Emily Davied

All Graduate Theses and Dissertations, Fall 2023 to Present

This study examined how different types of teacher feedback (i.e., expressed pride, expressed disappointment, and near-neutral feedback) impact students’ motivation to learn and self-esteem. It also explored whether such effects vary depending on students' personality and how connected they feel to their teacher. Undergraduate students (N = 221) imagined receiving feedback from a teacher and completed surveys assessing their motivation, self-esteem, sociotropy, and connectedness with the teacher. The results showed that feedback expressing disappointment lowered students' self esteem compared to expressing pride and lowered students' motivation compared to near neutral feedback. Although sociotropy (i.e., dependence on others' approval) and teacher …

A Performance-Optimized V2v Task Offloading Framework For Real-Time Vehicular Communication, Tariq Qayyum, Asadullah Tariq, Ikbal Taleb, Mohamed Adel Serhani, Zouheir Trabelsi

All Works

As vehicular applications become increasingly complex, their computational demands often exceed the capabilities of individual vehicles. Vehicular Edge Computing (VEC) alleviates this limitation by enabling task delegation to nearby edge resources; however, high mobility, dynamic topology, and fluctuating vehicle density make real-time offloading decisions challenging. To address these issues, we propose a performance-optimized Vehicle-to-Vehicle (V2V) task offloading framework for dense and dynamic Vehicular Ad-hoc Networks (VANETs). The framework follows a two-stage design: (i) context-aware edge-node selection based on live topology capture via periodic beaconing, and (ii) cumulative score-based dynamic priority queuing at the selected edge node. The priority score jointly …

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, …

Fig-Gan: Fundus Image Generation Via Deep Learning Based Generative Adversarial Network For Amd Disease Diagnosis, Kailasa Thrishul, Ahed Abugabah, Amina Salhi, Manel Ayadi, Mohamed M. Sithik, D. Jayaprakash, A. Ahilan

All Works

Globally, age-related macular degeneration (AMD) remains a main cause of irreversible vision loss. Recently, deep learning models have primarily focused on classifying fundus images for early detection of AMD progression. However, existing models rarely address the generation of future progression-aware fundus images, particularly when complete real longitudinal follow-up scans are unavailable. This limitation makes it difficult to track retinal changes over time and highlights the need for generative models capable of producing realistic drusen-level structural variations. To address these issues, a novel deep learning-based FIG-GAN model is to generate synthetic future fundus images from baseline inputs. Multi-Attention U-Net (MAU-Net) is …

The Role Of Corporate Sustainability Goals In Shaping Organizational Intentions And Adoption Of Green Technologies In Small- And Medium-Sized Enterprises, Syed Zamberi Ahmad, Abdul Rahim Abu Bakar, Imane Belyamani, Manar Fawzi Bani Mfarrej

All Works

This study investigates green technology adoption (GTA) among small and medium-sized enterprises (SMEs) in the United Arab Emirates (UAE), focusing on the influence of corporate sustainability goals (CSG) and sustainability motivation (SM). Utilizing institutional theory, the theory of planned behavior (TPB), and resource-based view (RBV), the research highlights how SMEs integrate environmental, social, governance (ESG) and economic considerations into their CSG to enhance GTA. Addressing a gap in prior research that has largely emphasized external drivers of adoption while underexploring internal organizational mechanisms, the study conceptualizes CSG as strategic intent and models SM as a second-order construct . Based on …

The Impact Of Generative Ai Training On Teachers’ Curriculum Adaptation Using Reflective Practices, Areej Elsayary

All Works

Generative AI tools offer teachers opportunities to adapt curricula to meet diverse student needs. However, many educators lack the structured training and reflective frameworks necessary for effective and critical use of these tools. Numerous studies have examined the appropriate use of generative AI (GenAI) tools in facilitating differentiation, feedback, and personalized learning. However, teachers need to be trained on the “what” and “how” of integrating GenAI into curriculum adaptation to ensure a positive impact on student learning. This study evaluates the impact of training in-service teachers to use GenAI tools for curriculum adaptation. Specifically, the study investigates three research questions: …

Reimagining Sustainable Sulfur Concrete: A Systematic Review Of Sulfur, Waste Integration, And Durability, Vasilia Al Khaldi, Abdel Hamid I. Mourad, Maisa Elgamal

All Works

Sulfur concrete (SC) - a thermoplastic, waterless composite in which molten sulfur-based binder replaces Portland cement - is attracting growing research attention as a sustainable construction alternative capable of superior performance in chemically aggressive environments. This systematic literature review synthesises evidence from 127 sources (2000–2025), examining three interlinked dimensions: (i) the role of waste and industrial by-product materials as aggregate and filler substitutes; (ii) the influence of aggregate type, morphology, and grading on mechanical and microstructural performance; and (iii) durability under acid, saline, and freeze-thaw exposures. A transparent, PRISMA-aligned screening and classification protocol - here used as a structured review …

The Willing And The Compelled: How Power And Place Shape Human-Bison Coexistence In Poland And The United States, Patrick Orville Kelly

All Graduate Theses and Dissertations, Fall 2023 to Present

Wildlife recovery programs depend not just on animals, but on how people feel about living with them. The degree to which people accept the disruptions wildlife brings to their lives, known as social tolerance, can determine whether a recovery program succeeds or fails, yet it remains understudied and underapplied in management plans. This dissertation studies how people in Poland and the western United States feel about living with the two living species of bison, the largest land animals in Europe and North America. In Poland, we surveyed 242 residents living at different distances from European bison habitat. Surprisingly, the most …

Flight System Development For A Bio-Inspired Rotating Empennage Experimental Unmanned Aerial Vehicle, Ashton Gilbert

All Graduate Theses and Dissertations, Fall 2023 to Present

Unmanned aerial systems, commonly known as drones, are increasingly used for research, environmental monitoring, and testing new aircraft concepts. Many commercially available drone flight controllers work well for standard aircraft designs, but they can be difficult to adapt for experimental vehicles that use unconventional control methods or require detailed monitoring during testing. This creates challenges for researchers who need flexible and reliable tools to safely evaluate new flight technologies. This work presents the development of a custom drone flight control system designed specifically for experimental research. The system integrates custom electronics and software that allow researchers to easily modify control …

A Critical Review Of Pharmaceutical Pollutants In Soil And Air: Ecotoxicological Impacts On Animal, Plant And Microbial Communities - Health Hazards And Waste Management, Md Faisal Amin, Md. Saydur Rahman

School of Integrative Biological & Chemical Sciences Faculty Publications

Pharmaceutical contamination in soil and air has become a critical environmental concern due to its widespread sources, complex behavior, and long-lasting ecological impacts. This review comprehensively explores the presence, fate, and effects of pharmaceutical compounds in soil, highlighting their entry routes, environmental persistence, and biological consequences. Pharmaceuticals infiltrate terrestrial and aquatic environments through various pathways, including agricultural application, pharmaceutical manufacturing waste, sewage sludge, hospital and household discharges, irrigation with contaminated water, and atmospheric deposition. A wide range of drug classes, such as antibiotics, analgesics, non-steroidal anti-inflammatory drugs, antidepressants, anticancer drugs, and hormones, have been identified in both soil and air …

Bayesian Linear, Heteroskedastic, Multilevel, And Dynamic Models For Assessing The Effects Of Policy Uncertainty, Green Finance, Innovation, And Environmental Stringency On Sustainable Economic Growth In Oecd Economies, Md Qamruzzaman, Abdulrahman Alomair, Abdulaziz S. Al Naim, Sylvia Kor

Student Publications [Scholarly]

This study analyzes how policy uncertainty, green finance, green innovation, and the stringency of environmental policy affect sustainable economic growth in OECD member countries from 1996 to 2023. Given the growing evidence that advanced economies are facing structural lock-ins, a lack of coherent policy credibility, and insufficient financial and innovative capabilities, the current study integrates these factors into a coherent empirical framework that continues to prevent disintegration in the current literature. Using environmentally adjusted multifactor productivity as a proxy for green economic growth, the analysis employs a panel of Bayesian econometric models that include linear, heteroskedastic, multilevel, random-effects, and panel …

Assessing Future Drought Scenarios In Rangpur, Bangladesh Using Cmip6 Climate Projections And Drought Indices, Mijanur Rahman, Hossain Al Mahbub, Mst Sanjida Alam, Janifer Alam, Nusrut Sharmin, Md Naim Molla

Student Publications [Scholarly]

Climate-induced drought has become a growing concern across South Asia as rising temperatures and monsoon variability intensify hydroclimatic stress. This study evaluates historical (1979–2014) and future (2015–2100) hydroclimatic conditions in Rangpur using observed data and CanESM5 projections for three CMIP6 scenarios (SSP1-1.9, SSP2-2.6, SSP5-8.5). Multi-scale Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were applied to detect droughts, while Tmax and Tmin Z-scores quantified heat anomalies and compound drought–temperature events. Historical records show strong rainfall variability with no significant long-term trend, but significant warming particularly in Tmin indicating increasing evaporative demand. Future projections reveal moderate precipitation increases (+ …

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 …

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 …

The Economics Of Modern Basketball Fandom, Holden Velasco

Capstones

In an increasingly money-sucking economy, many Americans are feeling the aching of their wallets. Basketball, once serving as an escape from the outside world, has now turned into a money-making machine where fans are paying to keep it running. It’s inescapable, whether it’s the rising costs to watch the professionals, forking thousands of dollars for children to play or sports gambling being plastered everywhere. Basketball no longer feels like basketball.

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 …

Green Waste Biochar And Plant Growth-Promoting Bacteria Enhance Tomato Growth Under Combined Nutrient Deficiency And Salinity Stress, Soumaya Tounsi-Hammami, Munawwar Ali Khan, Mahra Alqemzi, Salama Ali Almehairi, Aneesa Rasheed Anwar

All Works

This study characterized a green-waste-derived biochar from date palms and ghaf trees and investigated its potential as a soil amendment with halotolerant Bacillus spp. to improve tomato seedling quality under dual stress of salinity and nutrient deficiency. Biochar was produced through pyrolysis at 450 °C and then characterized for yield, pH, electrical conductivity, proximate analysis, surface morphology, energy-dispersive X-ray spectroscopy, and heavy-metal content. Its effectiveness was tested both alone and in combination with a Bacillus sp. mix, using a completely randomized design with varying NPK fertilizer levels and saline irrigation. Tomato seedlings were evaluated 45 days after planting for various …

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 …

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 …

Towards Promoting Innovation In Inclusive Education: Behavioural Intention Of Teachers Towards Adopting Ai To Teach Students With Learning Disabilities In The Uae, Fatima Zraydi, Maxwell Peprah Opoku, Bernadette M. Guirguis, Laurent Gabriel Ndijuye, Ebenezer Mensah Gyimah

All Works

The integration of artificial intelligence (AI) in education offers significant potential for identifying and supporting all students, including those with learning difficulties. Although discussions on the potential of AI to advance the learning of students are ongoing, AI usage among teachers to leverage it in the teaching of students with learning disabilities in nonwestern contexts, such as the United Arab Emirates, is unresearched. The study was guided by a unified theory of acceptance and use of technology to examine teachers’ intentions toward adopting AI tools to enhance educational outcomes for students with learning disabilities in the UAE. Using a quantitative …

Image And Metadata-Driven Personality Inference For Career Recommendation: A Social Media-Based Ai Framework For Adolescents, Heba Ismail, Maryam Alhefeiti, Ashraf Khalil

All Works

This study presents a novel AI-based framework that leverages Instagram image and metadata analysis to infer Big Five personality traits and deliver personalized career recommendations for high school students in the UAE. Addressing the limitations of traditional recommender systems that rely on self-reported questionnaires or text, the proposed approach uses multimodal visual features—including profile metrics, HSV color patterns, semantic image labels, and texture analysis—to enable a non-intrusive, scalable personalization method. A pilot study involving data from 30 student accounts served as a proof of concept. Correlation analysis identified profile and HSV features as the most predictive, and four machine learning …

A Data-Driven Koopman Framework For Station-Keeping On Near Rectilinear Halo Orbit, Anusha Sharma Malladi

Theses and Dissertations

Cislunar missions have gained significant attention in recent decades, motivating the need for efficient modeling and reliable control. In this work a Koopman operator based framework is developed for approximating the error dynamics around a reference Near Rectilinear Halo Orbit (NRHO) in the Earth-Moon Circular Restricted Three-Body Problem (CR3BP). A decoder free neural network is used to learn a lifted linear representation of the nonlinear CR3BP dynamics and a residual based approach is used to identify the corresponding control input matrix. The model is then implemented in a receding-horizon target point controller and compared with uncontrolled propagation and a State …

Extending Geometric Acoustic Ray Tracing To Multi-Room Environments: A Case Study On Gunshot Sound Transmission Between Adjacent Rooms, Tyler Ton

Theses and Dissertations

Accurate localization of gunshots in multi-room building environments remains a challenging problem in acoustic forensics and public safety applications. Existing approaches model sound propagation within a single room, neglecting the transmission of acoustic energy through walls and other building materials. This thesis presents a study on modeling multi-room gunshot acoustic transmission, combining geometric ray tracing with structural acoustic transmission-loss modeling to generate impulse responses for two horizontally adjacent rooms separated by a shared wall, providing a foundation for future inter-room gunshot localization work. The proposed system uses GSound-SIR, a geometric acoustics engine, to simulate sound propagation in both of the …

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 …

Acute Management Of Hydropneumothorax Secondary To Esophageal Perforation After Laparoscopic Fundoplication, Chenxi Shi, Yu Wei Chang, Leah Joyner

Student Publications

Background: Laparoscopic fundoplication is commonly performed for refractory gastroesophageal reflux disease and symptomatic hiatal hernia, but rare postoperative complications such as esophageal perforation may result in significant morbidity and mortality if not promptly recognized.

Case report: We present a case of esophageal perforation complicated by massive hydropneumothorax in a woman who presented to the emergency department with progressive chest pain, abdominal pain, and respiratory distress two weeks after hiatal hernia repair with Nissen fundoplication. Emergent tube thoracostomy performed in the emergency department drained a large volume of purulent fluid and resulted in immediate respiratory improvement. Computed tomography with oral contrast …