Digital Commons Network^™

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 …

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 …

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 …

Smart Medical System Integrating Clinical Workflows For Robust Skin Cancer Detection Across Heterogeneous Pathologies, Ahed Abugabah, Prashant Kumar Shukla, Suchi Mishra, Abhishek Dwivedi

All Works

Skin cancer is among the most prevalent and life-threatening dermatological diseases worldwide, with melanoma responsible for a substantial proportion of skin cancer–related deaths due to delayed and unreliable diagnosis. Conventional clinical screening based on visual inspection and expert interpretation is inherently subjective and often affected by inter-observer variability, lesion heterogeneity, and imaging artifacts, highlighting the need for accurate and generalizable automated diagnostic systems. This study proposes a novel hybrid deep learning architecture for skin cancer classification that integrates an attention-guided autoencoder with a transformer-inspired global context modeling module, forming a unified and robust representation learning framework. The encoder–decoder structure is …

Temporally Rigorous And Traceable Predictive Maintenance Via Joint Labeler-Model Optimization, Maytha Al-Ali, Ahmad Alharbi

All Works

Predictive maintenance (PdM) is a critical enabler of intelligent asset management in Industry 4.0, yet many existing frameworks remain difficult to operationalize due to methodological fragmentation. Common limitations include sacrificing temporal realism and class granularity for computational expediency, decoupling labeling strategy design from model hyperparameter optimization, and insufficient support for reproducibility and deployment traceability; particularly in rare-failure regimes. To address these challenges, we propose a unified, end-to-end, and fully traceable PdM framework that jointly optimizes labeling and model parameters while enforcing strict temporal fidelity. The proposed pipeline co-optimizes the failure lookahead window () and LightGBM hyperparameters within a single Bayesian …

Kindergarten Teachers’ Perceptions Of Instructional Approaches For Teaching Sequencing Concepts: Worksheet, Hands-On, And Beebot-Integrated Lessons, Savannah P. Partin

Electronic Theses and Dissertations

The purpose of this qualitative multiple-case study was to explore kindergarten teachers’ perceptions of three instructional approaches for teaching sequencing concepts: worksheet-based instruction, hands-on manipulative instruction, and BeeBot-integrated instruction. Sequencing is a foundational early literacy skill that supports reading comprehension, story retelling, and understanding the order of events. Although sequencing is commonly taught through traditional instructional methods, limited research has examined how kindergarten teachers perceive the use of educational robotics as a tool for literacy instruction. The study was guided by constructivist theory, sociocultural theory, and developmental systems theory. Participants included six kindergarten teachers from a public elementary school in …

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 …

Drag As Evidence: Motion-Grounded Latent Recomposition For Drag-Based Editing, Xinyu Pu, Hongsong Wang, Jie Gui, Pan Zhou

Research Collection School Of Computing and Information Systems

Modern image editors excel at semantic manipulation and visual synthesis, yet remain limited in precise spatial control, motivating the development of drag-based editing. However, existing drag-based methods often struggle to balance drag accuracy with natural, plausible, and intent-aligned generation. We propose MoReDrag, a motion-grounded drag-based editing method. Our key insight is to treat pixel-space warping as coarse motion evidence, and to inject this evidence into the generative sampling trajectory. Specifically, MoRe-Drag performs region-aware latent recomposition over refinement, inpainting, and anchor regions, coupled with stage-adaptive conditioning that progressively shifts from motion-grounded structure formation to semantic refinement. We further support an instruction-free …

Llm-Enabled Applications Require System-Level Threat Monitoring, Yedi Zhang, Haoyu Wang, Xianglin Yang, Jin Song Dong, Jun Sun

Research Collection School Of Computing and Information Systems

LLM-enabled applications are rapidly reshaping the software ecosystem by using large language models as core reasoning components for complex task execution. This paradigm shift, however, introduces fundamentally new reliability challenges and significantly expands the security attack surface, due to the non-deterministic, learning-driven, and difficult-to-verify nature of LLM behavior. In light of these emerging and unavoidable safety challenges, we argue that such risks should be treated as expected operational conditions rather than exceptional events, necessitating a dedicated incident-response perspective. Consequently, the primary barrier to trustworthy deployment is not further improving model capability but establishing system-level threat monitoring mechanisms that can detect …

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 …

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 …

Ai-Driven Collision Risk Prediction And Debris Optimization In Low Earth Orbit, Emad Alkhazraji, Mohamed E. Yahia, R. E. Tolba, N. S. Alharthi

All Works

Debris congestion and collision in low Earth orbit have become a major point of concern due to the rapid expansion of activities in that region. In this work, an AI-driven framework is developed and deployed to predict, analyze, and optimize collision risk across the altitude inclination space. Local debris object population, mass density distribution, and conjunction derived risk are computed. Then, an AI surrogate model, viz., gradient-boosted decision trees, was trained to predict collision conjunction relative probabilities and risk. Thereafter, data analysis and explain ability techniques are utilized to uncover underlying risk drivers and reveal how altitude, inclination, mass loading, …

More Than Just Play: The Impact Of Campus Recreation On Retention In Modern Higher Education, Corbin D. Hedges

Masters Theses and Doctoral Dissertations

Student retention remains a central concern in higher education, particularly as the demographic composition of college populations shifts toward a growing number of contemporary students. While extensive research has examined retention strategies for traditional students, there is a notable gap in understanding how institutional resources, such as Campus Recreation, support contemporary student success. This study explores the impact of Campus Recreation participation on student retention, with a specific focus on contemporary students at the University of Tennessee at Chattanooga. Using a convergent mixed-methods design, this research investigates participation trends, perceived benefits, and barriers to engagement in Campus Recreation programs. Quantitative …

Arithmetic And Necessity: A Quasi-Concrete Modal Structuralist Account, Benjamin Faltesek

Mathematics, Philosophy and Physics (D.Sc.) Dissertations

This dissertation addresses the necessity of arithmetic by distinguishing two tasks that are often run together: the derivation task, which seeks to prove that mathematical truths are necessary, and the characterization task, which asks what kind of necessity arithmetic has and what grounds it. Setting the former aside for now, I focus on the latter, arguing that an adequate explanation of arithmetical necessity requires two sources: logical necessity and the necessary possibility that the right kind of structure exists. The second source makes mathematical modality distinctively mathematical, and the dissertation’s positive contribution emerges chiefly in that area. I survey and …

Stimuli-Responsive Proton Transfer: Photoacid Design, Co2 Sorbent Regeneration, And Polymer Applications, Melyse Ava Laud

Theses and Dissertations

Control over proton transfer enables the regulation of many pH-dependent chemical and material processes. Stimuli-responsive functional materials provide a means of achieving this control. Functional materials such as Metastable-state photoacids (mPAHs) reversibly release large amounts of protons when stimulated by light from a weakly acidic site within their structure. However, the dark acidities of previously reported mPAHs limit their use under basic conditions. To address this limitation, a benzimidazole-type mPAH was developed with an improved ground-state pKa of 11.9 and a metastable-state pKa of 9.8, enabling reversible proton transfer under mildly basic conditions. Additional mPAHs were examined to determine how …

Historical Piccolo Concerti And Works By Women Composers, Repertoire Catalogue, And A New Concerto For Piccolo And Orchestra, Mikaela Storm

Doctoral Scholarly Projects & Capstones

The following document is an overview of historical piccolo concerti with an emphasis on works by women, a repertoire catalogue of the piccolo concerti, and an analysis/performance guide for my own Concerto for Piccolo and Orchestra: The Dreamer. The primary goal of this document and subsequent score is to provide a resource for future composers, music theorists, conductors, and performers. The piccolo has limited repertoire in comparison to many of the standard instruments in our modern Western musical canon, and the number of piccolo concertos (particularly by women) is even smaller. The concerto that I have written, subtitled “The Dreamer,” …

The Hidden Heatwave: Thermal Hazards In The New York City Subway System, Abdou Rachid Bah, Maimuna Muntaha, Lenny L. Metlitsky, Cesar Pascal Vasquez, Tyler Ayala, Isaac Morel Lopez, Shaquan A. Larose, Kingston Ditsch, Kip Nielsen, Shaunak Sharma, Reginald U. Avila-Ortega Blake, Hamidreza I. Norouzi

Publications and Research

Underground transit systems (Subway) are susceptible to frequent heat waves, which pose serious health and safety risks to commuters and transit workers. Yet, the patterns and extent of temperature changes within the subway system remain insufficiently understood. To address this gap, we conducted a study of thermal variability across 112 subway stations in New York City. Temperature and relative humidity were measured at both platform and street levels. Results show that during the summer, 70% of subway stations had platform heat index higher than street level heat index. We also developed a machine learning forecasting model to predict platform-level temperatures. …

Climate Change Impacts On Fish-Related Ecosystem Services: Synthesis, Network Analysis, And Strategic Pathways For Coordinated Adaptation, Ebenezer Afrifa-Yamoah, Awuah-Mensah, Stephen Bradshaw, Ute Mueller

Research outputs 2022 to 2026

Climate change is rapidly disrupting fish-related ecosystem services that underpin global food security, economic livelihoods, and cultural identity across both marine and freshwater systems. While impacts are increasingly recognised, disciplinary fragmentation has hindered integrated understanding of how climate-driven changes in fish populations affect ecosystem service delivery. This review makes two integrated contributions: a hierarchical synthesis tracing causal pathways from physical climate drivers through biological and ecological responses to socioeconomic outcomes, and a qualitative network analysis that maps the interconnections, implementation readiness, and strategic sequencing of seven major adaptation strategies. Ocean warming, acidification, deoxygenation, and their increasingly frequent co-occurrence as compound …

Efficient Removal Of Methyl Orange Dye Via A Mnfe₂O₄/Go Nanocomposite With A Ctab Dual-Layer Surfactant Coating, Taher Karami, Soleiman Bahar, Reza Mostafazadeh

Research outputs 2022 to 2026

This study investigated the adsorption of methyl orange (MO) from aqueous solutions using a novel MnFe₂O₄/GO nanocomposite coated with cetyltrimethylammonium bromide (CTAB). The dual-layer surfactant modification facilitates both electrostatic and lipophilic interactions. This enhancement significantly improves dye removal efficiency. The adsorption process was monitored using spectrophotometry at 464 nm, with various characterization techniques confirming the structural and magnetic properties of the nanocomposite. The optimized parameters for maximum adsorption include a 2-minute ultrasonic dispersion, pH 6.8, and a surfactant-to-adsorbent ratio of 1, achieving a maximum adsorption capacity of 285.7 mg/g. The kinetic data followed a pseudo-second-order model, whereas the adsorption isotherm …

Closing The Interactivity Gap: Towards Efficient In Situ Analysis, Bidirectional Steering, And Gaussian-Based Visualization For Large-Scale Scientific Computing, Andres Sewell

All Graduate Theses and Dissertations, Fall 2023 to Present

Scientists use supercomputers to simulate weather, blood flow, plasma, and other systems. The largest machines produce more data than can be saved to disk, so scientists are only able to analyze a fraction of what gets generated. Visualizing the data during the run slows the simulation, because the rendering shares the simulation’s graphics processing units (GPUs). Most simulation codes offer no way to interact with a run in progress. The results are often too large to visualize on a local workstation, and exploring them on the supercomputer means waiting in the queue and spending allocation time. We call these three …

Prioritizing Fuel Treatments To Reduce Post-Fire Debris Flow Risk In Utah, Kipling B. Klimas

All Graduate Theses and Dissertations, Fall 2023 to Present

Wildfire is a natural part of many forest ecosystems, but in the western United States, wildfires have become larger, more frequent, and more severe in recent decades. There are serious problems after severe wildfire, including erosion, flooding, and debris flows that threaten water quality, roads, and communities. Because of this, land managers need better ways to decide where fuel treatments, such as prescribed burning or thinning, can most effectively reduce wildfire damage and post-fire risks.

In this dissertation, I studied how fuel treatments might reduce wildfire severity and lower the risk of post-fire debris flows in Utah. First, I built …

Intersectional Realities And Adaptive Leadership: A Phenomenological Study Of Black Women Administrators In Higher Education, Jocelyn Vernet

Electronic Theses and Dissertations

Existing literature consistently documents the acute underrepresentation of Black women in administrative leadership. Furthermore, Black women administrators that are represented and visible in higher education often oversee programs and task forces that heighten their experiences to high-risk situations. Emerging narratives suggest Black women employ effective coping strategies and embrace opportunities to strengthen and cultivate support networks while in their leadership roles. However, there is a significant lack in understanding how these strategies are structured and sustained to overcome the underrepresentation of Black women administrators in higher education. Additionally, basing their experiences solely through a survival lens limits a deeper understanding …

Representing The Brand: A Study To Examine The Best Practices For Structuring And Operating Communications Teams In Higher Education Institutions, Margaret L. Reisser

Masters Theses and Doctoral Dissertations

As higher education institutions increasingly compete for students, faculty, funding, and recognition, academic leaders are recognizing the essential role communications and public relations professionals play in maintaining and promoting the value of their institutional brands. Much has been written about the value of communications professionals in brand management in the corporate arena. However, it appears less attention has been paid to their role in higher education. This mixed-methods study was undertaken to examine the best practices for structuring and operating communications and public relations organizations for optimal success in promoting the higher education institutions they represent. Communications and public relations …

Effect Of Preheating Palm Oil Biodiesel Fuel On The Performance And Emissions Of Diesel Engine Common Rail Direct Injection, Nanang Romandoni, Achmad Aminudin, Hari Setiapraja, Bima Randi Wardiasaputra, Kelvin Dwi Wardana, Ahmad Yusril Aminullah, Deni Nur Fauzi, Kholis Nur Faizin, Nur Ihda Farikhatin Nisa

Journal of Mechanical Engineering Science and Technology (JMEST)

Fossil fuel consumption continues to increase while its reserves continue to decrease and produce high emissions that trigger global warming. Therefore, more environmentally friendly alternative energy is needed. Biodiesel is a sustainable choice because it is biodegradable, low in sulphur, and can replace fossil fuels. This study investigates the influence of biodiesel fuel preheating temperature on engine performance, fuel consumption, and emissions in a Common Rail Direct Injection (CRDI) diesel engine. The experimental method was carried out by heating B40 and B50 fuels at temperatures of 40°C to 60°C at intervals of 10 °C. Engine performance was evaluated using a …

Structural And Electrochemical Characterization Of Na2co3/ Γ- Al2o3 Electrodes For Supercapacitor, Wiwin Dwiana, Jeniva Dinta Nanda Fahreza, Anissa Chairani Alfin Nadhira, Fatimatuz Zahra, Erma Surya Yuliana, Muhammad Safwan Abd Aziz, Nandang Mufti

Journal of Mechanical Engineering Science and Technology (JMEST)

The development of electrode materials with suitable structural and surface properties is crucial for improving electrochemical energy storage systems. Gamma alumina (𝛾-Al₂O₃) has attracted considerable interest in electrochemical research due to its unique structural and surface properties. However, its charge-storage capacity is still limited, highlighting the need for structural and compositional modifications to improve its electrochemical performance. In this study, Na₂CO₃ was introduced into 𝛾-Al₂O₃ as a sodium precursor to induce phase modification and enhance the electrochemical activity. The systematic incorporation of Na₂CO₃ also provides an opportunity …

A Systematic Review Of Alkali-Treated Polylactic Acid Biocomposites Relevant To Pineapple Leaf And Water Hyacinth Hybrid Design, Muhammad Rizky Adrianto Subandi, Tri Hartutuk Ningsih, Hanna Zakiyya

Journal of Mechanical Engineering Science and Technology (JMEST)

The increasing demand for sustainable materials has accelerated the development of biodegradable polymer composites. Polylactic acid (PLA)-based natural fiber composites have attracted significant attention due to their renewable characteristics; however, their performance remains limited by weak fiber–matrix compatibility, moisture sensitivity, and inconsistent interfacial behavior. Alkali treatment has been widely applied to modify lignocellulosic fibers by removing non-cellulosic components and improving surface characteristics, although its effectiveness remains dependent on treatment conditions and fiber properties. This systematic review evaluates the influence of alkali treatment on PLA-based natural fiber composites and assesses the available evidence supporting future pineapple leaf fiber (PALF) and water …

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Thin Sections

No abstract provided.

The Benefits And Barriers To Expanding Crna Programs In Michigan, Grace Van Zweden

Honors Theses

The demand for Certified Registered Nurse Anesthetists (CRNAs) and their delivery of safe, high-quality anesthesia care has increased significantly due to nationwide anesthesia provider shortages and the growing reliance on CRNAs to meet healthcare system needs. This demand underscores the importance of expanding nurse anesthesia programs in Michigan and across the United States to support nurses pursuing advanced education and to improve patient access to essential care within their communities. The CRNA profession has evolved over time to meet the changing needs of the healthcare system, and current CRNA programs require applicants to satisfy rigorous and highly specific admission criteria. …

Mgrre_Thinsections_Mgrre_11_25, Mgrre

Thin Sections

No abstract provided.

When The Bridge Is Not At Sea : Investigating Transfer Of Learning From Full-Mission Bridge Simulator To Navigation Training Onboard, Thammawan Phanphichit

World Maritime University Ph.D. Dissertations

This dissertation explores, through a mixed-methods approach, how fullmission bridge simulators support transfer of learning to navigation training onboard, and under what conditions transfer can be trusted. Qualitative and quantitative components were integrated across the four articles. The first study explores how key onboard training stakeholders perceive its challenges, limitations, and the possibility of reducing required sea-time. Findings show broad agreement that OBT remains important, though openness to simulator use was conditional on credible evidence that simulator-developed competence can transfer onboard. The second and third studies examine this through transfer of learning theory. A systematic review identifies elements influencing transfer …