Digital Commons Network^™

Dblp: Noise Bridge Consistency Distillation For Efficient And Reliable Adversarial Purification, Chihan Huang, Belal Alsinglawi, Islam Al-Qudah

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Recent advances in deep neural networks (DNNs) have led to remarkable success across a wide range of tasks. However, their susceptibility to adversarial perturbations remains a critical vulnerability. Existing diffusion-based adversarial purification methods often require intensive iterative denoising, severely limiting their practical deployment. In this paper, we propose Diffusion Bridge Distillation for Purification (DBLP), a novel and efficient diffusion-based framework for adversarial purification. Central to our approach is a new objective, noise bridge distillation, which constructs a principled alignment between the adversarial noise distribution and the clean data distribution within a latent consistency model (LCM). To further enhance semantic fidelity, …

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

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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

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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

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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

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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 …

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

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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, …

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

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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, …

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

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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 …

Does Self-Efficacy Of Teachers Toward Inclusive Education Differ Between Primary And Secondary School? A Cross-Cultural Study Of Ghana And The United Arab Emirates, Ahmed Mohamed, Maxwell Peprah Opoku, Bernadette M. Guirguis, Ebenezer Mensah Gyimah, Maya Al Yafi, Shouq Alqahtani, Safa Alharthi, Mahra Aldhanhani, Al Zahra Aljaberi

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There are ongoing debates on the level of education that students with special educational needs could participate in and enjoy as part of their fundamental right to education. There is a notion that students with special educational needs could be more easily included in primary schools than in secondary schools. However, teachers were excluded from such discussions. The current study aims to invigorate such discussion by exploring primary and secondary school teachers' self-efficacy across Ghana and the United Arab Emirates (UAE). a total of 897 teachers were recruited from Ghana and the UAE to rate their self-efficacy via the Teacher …

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

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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 …

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

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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: …

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

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

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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 …

Primitive Accumulation In The Late Twentieth Century: Mexico, The United States, And Nafta, Mark Huffman

Scholars Week

Between 1971 and 1994, American capital succeeded in redirecting state action toward the interests of the largest transnational firms, facilitating extreme concentrations of wealth and capital alongside a profound defeat of organized labor and the dismantling of social programs. The post-Keynesian political economy produced the North American Free Trade Agreement, an event which demonstrated both the ideological capture of the American state by neoliberalism and the purpose of the ideology as a social force (as opposed to a strictly economic one). This article investigates the neoliberal turn generally and NAFTA specifically as engines of social dislocation in North America and …

Dataset For Age-Related Lens Membrane Composition And N101d Deamidation Modulate Αa–Crystallin Membrane Interactions Relevant To Cataract, Preston Hazen, Jordyn Anderson, Navdeep Kalkat, Binod Aryal, Nawal K. Khadka, Laxman Mainali

Biophysics Research Data

Purpose: αA-crystallin (αAc), the predominant subunit comprising α-crystallin (αABc), becomes increasingly deamidated and membrane bound with cataract formation. Furthermore, the lens lipids and cholesterol (Chol) composition significantly change with age and cataract formation. Despite this, the relationship between αAc deamidation and lens membrane composition in cataractogenesis remains unclear. To address this, we analyzed the role of N101 deamidation and membrane composition in the membrane interactions of αAc.

Methods: Experimentation involved using electron paramagnetic resonance (EPR) to analyze wild-type (WT) and N101D-αAc membrane binding and its physical effects on the membrane properties of 60-year-old (yo) and 20-yo model human lens lipid …

Ai Tutors, Worksheets, And Task Design: Matching Ai Tools To Learning Structure, Brock Casselman

AI in the Classroom: A Virtual Summit

Artificial intelligence is increasingly used by students as a learning resource in STEM courses. However, classroom observations and survey data suggest that unstructured AI use may correlate with reduced student achievement. Two concerns are especially relevant: AI systems can produce incorrect responses in tasks requiring precise validation, and students may use AI in ways that reduce active engagement with course material.

This session presents two instructional approaches designed to address these limitations: structured AI-generated worksheets and in-chat AI tutors. AI worksheets generate controlled sets of practice problems within instructor-defined course constraints. In-chat tutors provide interactive practice through problem generation, Socratic …

Beetle Panic!, Ross Lowrance

Game Design

Beetle Panic! is a 3D game designed in GDevelop that combines exploration, collectible-based scoring, time management, and enemy avoidance in a compact, interactive level. Players navigate the environment to collect four coins, each worth 25 points, while managing a 60-second timer and avoiding a pursuing enemy known as the Beetle Bot. Environmental objects, including movable blocks, provide opportunities for tactical interaction by obstructing the enemy's movement. Contact with the enemy resets the scene, adding risk to the player's efforts to complete the objective before time expires.

The game incorporates a scoring system, countdown timer, audio feedback, and end-state messaging to …

Marbs & Carbs, Kevin M. Mcghee

Game Design

Marbs & Carbs is a 3D rolling game set in an oversized kitchen. Guide Marb across countertops, past stove burners, and through sink-side shortcuts while collecting pasta and donuts that make the marble grow. Larger sizes open new routes, including a breakable sink drain. Collect at least 25 carbs to enter the mouse hole and finish the level. Special pasta can turn Marb into a tumbling cube or pyramid, changing how it moves and adding a score bonus. A results screen tallies your pickups and time before the next run.

This game is inspired by the classic Atari arcade game …

Fairytale Bounce, Caroline Sexton

Game Design

This project is a 3D fairy-themed platformer created in Unity. Players control a fairy as they travel through a colorful fantasy environment filled with floating platforms, magical mushrooms, collectibles, and enemies. The goal is to navigate through the level, collect fairy dust crystals, avoid enemies, and reach the end. The game includes a health system, checkpoints, background music, and sound effects.

This game was created as my final project for a 3D game development course. I focused on creating a playable level with different platforming challenges while learning more about Unity, 3D level design, scripting, and gameplay mechanics.

Leaflet Stress, Joseph Chibuike Chibuike Nwokeafor, Joshua D. Hofmeister, Brendan B. Yeats, Lakshmi Prasad Dasi, Charanjit S. Rihal, Juan A. Crestanello, Leigh Griffiths, Mohamad A. Alkhouli, Hoda Hatoum

BILEAFLET TRANSCATHETER MITRAL VALVE (TMV) FINITE-ELEMENT DATASET

This dataset contains numerical results associated with the open-access research article “Comparative Analysis of Leaflet Materials, Stent Materials, and Stent Cell Density for Bileaflet Transcatheter Mitral Valve Design,” published in Annals of Biomedical Engineering (2026).

The study employed finite element simulations to investigate the effects of leaflet material properties, stent materials, and stent cell density on the biomechanical performance of bileaflet transcatheter mitral valves.

The deposited data support the results reported in the publication.

Data Availability and Reuse: The datasets in this collection are publicly available for research and reuse under the Creative Commons Attribution 4.0 International (CC BY 4.0) …

Stent Geometry, Joseph Chibuike Chibuike Nwokeafor, Joshua D. Hofmeister, Brendan B. Yeats, Lakshmi Prasad Dasi, Charanjit S. Rihal, Juan A. Crestanello, Leigh Griffiths, Mohamad A. Alkhouli, Hoda Hatoum

BILEAFLET TRANSCATHETER MITRAL VALVE (TMV) FINITE-ELEMENT DATASET

This dataset contains numerical results associated with the open-access research article “Comparative Analysis of Leaflet Materials, Stent Materials, and Stent Cell Density for Bileaflet Transcatheter Mitral Valve Design,” published in Annals of Biomedical Engineering (2026).

The study employed finite element simulations to investigate the effects of leaflet material properties, stent materials, and stent cell density on the biomechanical performance of bileaflet transcatheter mitral valves.

The deposited data support the results reported in the publication.

Stent Stress, Joseph Chibuike Chibuike Nwokeafor, Joshua D. Hofmeister, Brendan B. Yeats, Lakshmi Prasad Dasi, Charanjit S. Rihal, Juan A. Crestanello, Leigh Griffiths, Mohamad A. Alkhouli, Hoda Hatoum

BILEAFLET TRANSCATHETER MITRAL VALVE (TMV) FINITE-ELEMENT DATASET

This dataset contains numerical results associated with the open-access research article “Comparative Analysis of Leaflet Materials, Stent Materials, and Stent Cell Density for Bileaflet Transcatheter Mitral Valve Design,” published in Annals of Biomedical Engineering (2026).

The study employed finite element simulations to investigate the effects of leaflet material properties, stent materials, and stent cell density on the biomechanical performance of bileaflet transcatheter mitral valves.

The deposited data support the results reported in the publication.

Opening Area, Joseph Chibuike Nwokeafor, Joshua D. Hofmeister, Brendan B. Yeats, Lakshmi Prasad Dasi, Charanjit D. Rihal, Juan A. Crestanello, Leigh Griffiths, Mohamad A. Alkhouli, Hoda Hatoum

BILEAFLET TRANSCATHETER MITRAL VALVE (TMV) FINITE-ELEMENT DATASET

This dataset contains numerical results associated with the open-access research article “Comparative Analysis of Leaflet Materials, Stent Materials, and Stent Cell Density for Bileaflet Transcatheter Mitral Valve Design,” published in Annals of Biomedical Engineering (2026).

The study employed finite element simulations to investigate the effects of leaflet material properties, stent materials, and stent cell density on the biomechanical performance of bileaflet transcatheter mitral valves.

The deposited data support the results reported in the publication.

Board Gender Diversity, Cultural Logics, And Corporate Tax Behaviour, Atia Hussain, Ammad Ahmed, Bao Thi Ngoc Nguyen

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Corporate tax aggressiveness tests corporate accountability, yet less is known about when board gender diversity translates into more restrained tax behaviour across institutional settings. Using 27,275 firm-year observations from 29 countries across Europe, Asia, and Australasia during 2013–2023, we examine whether board gender diversity is associated with lower tax aggressiveness and whether national culture conditions this association. Board gender diversity is negatively associated with tax aggressiveness: greater female board representation is associated with higher cash effective tax rates and more restrained tax behaviour. This association is stronger in high masculinity, uncertainty avoidance, and long-term orientation settings, but weaker in high …

Life Cycle Assessment Of Emerging Battery Technologies- A Comparative Review Of Sodium-Ion, Zinc-Air, And Aluminum-Ion Systems, Abdulmonem Fetyan, Zishan Shaikh, Syed Nasir Shah, Mohamed Adel Allam, Hemant Mittal, Ahmer A.B. Baloch

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The accelerating global demand for sustainable energy storage solutions has heightened interest in alternative battery chemistries beyond lithium-ion. This review presents a comprehensive comparative analysis of life cycle assessment (LCA) studies on three emerging battery technologies: sodium-ion, zinc-air, and aluminum-ion. By systematically examining peer-reviewed literature published between 2010 and 2025, we evaluate the environmental performance of these chemistries across key life-cycle stages, from raw material extraction to end-of-life treatment. The review employs bibliometric mapping to identify thematic trends and methodological patterns, including system boundaries, functional units, and impact assessment approaches. Sodium-ion batteries show promise due to material abundance and moderate …

Data From: Physics-Data-Hybrid Modeling And Control For Vtol Aircraft, Samantha Dawn Burton

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The skeleton code corresponding to many of the results from the dissertation "Physics-Data-Hybrid Modeling and Control for VTOL Aircraft" By Samantha Dawn Burton.

Sensors And Smart Food Packaging For Supply Chains: Bridging Expiration Dates And Actual Perishability, Muhammad Umar Azam, Jolina Rodrigues, Mohamed Hamid Salim, Sarath Haridas Kaniyamparambil, Khalid Askar, Imane Belyamani, Blaise L. Tardy, Nouha Alcheikh

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The food supply chain is undergoing critical changes to minimize environmental hazards, such as those associated with packaging, and reduce food waste and loss. One of the critical components of the latter is associated with the high variance in the perishability of individual foodstuffs. Herein, we review the state of the art in smart sensor systems and emphasize the critical role they could play in addressing the mismatch between “batch” based expiry dates and individual food products’ time-dependent responses to spoilage. Following a brief overview of food shelf life and associated legislation, a subsequent section summarizes the development of sensors …

The Paleoceanography Of The Jurassic Western Interior Seaway, Richard Reynolds, Rhodri Jerrett, Marc Davies, Uwe Balthasar, Gregory Price

Faculty of Science and Engineering Datasets

Supplimentry material for the paper: The Paleoceanography of the Jurassic Western Interior Seaway

Lee, Jay, Jay Lee

Queer Mississippi (Complete Collection)

Jimmie “Jay” Lee, a 2022 graduate of the University of Mississippi, disappeared from his off-campus apartment on July 8, 2022. Well-known in the LGBTQ+ community in Oxford, his friends created a social media campaign, "Justice for Jay Lee", to keep his name in the news and advocate for Queer and Black victims of crime until his remains were found in February 2025 in Carroll County.

His case was profiled by Dateline NBC on January 29, 2026. (Trailer: "Bringing Jay Home")

Florida Breeding Bird Atlas Ii Distribution Change Data, Pivot Tables, Usf Digital Initiatives, Florida Ornithological Society

Breeding Bird Atlas II Supplemental Files and Data

These pivot tables represent the change in Florida breeding bird distribution between the first Florida breeding bird atlas (BBA I) and the second breeding bird atlas (BBA II). Data collection for BBA I occurred from 1986 to 1991, and BBA II data collection occurred between 2011 and 2016.