Digital Commons Network^™

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, …

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 …

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 …

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, …

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 …

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 …

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: …

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 …

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 …

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 …

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 …

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

Inland Bird Banding Association 2025 Board Of Directors Meeting Minutes, The Ibba Board

North American Bird Bander

No abstract provided.

Invitation To 2026 Ibba Annual Meeting: Oklahoma Biological Station, Oklahoma, 13 - 15 Nov 2026, Paula Cimprich

North American Bird Bander

No abstract provided.

Open Call For Banding (Or Birding) Photos To Be Featured In North American Bird Bander, Kellie Hayden

North American Bird Bander

No abstract provided.

Literature Reviews: Summer 2026, Claire Stuyck

North American Bird Bander

No abstract provided.

Species Snapshot: Great-Tailed Grackle (Quiscalus Mexicanus), Steven Gabrey

North American Bird Bander

No abstract provided.

Inland Flyway Review: Fall 2024 Migration Report, Vernon Kleen

North American Bird Bander

No abstract provided.

Two Juvenile Gray Catbirds Undergo Likely Incomplete Molt In New Jersey Meadowlands, Cailin O’Connor, Michael Turso

North American Bird Bander

No abstract provided.

Encounters Of Banded Golden Eagles With Non-Functional Satellite Transmitters, Dale W. Stahlecker, Robert K. Murphy, Kenneth V. Jacobson

North American Bird Bander

No abstract provided.

Do Environmental Factors During Nesting Influence The Band Size Of Hatching Year Birds?, Audrey J. Hicks, Mark H. Conway, Timothy Brush

North American Bird Bander

We analyzed the relationship between the band size given to three species of Hatching Year (HY) birds and the average temperature and total rainfall received between May and July of the year they hatched. We selected 3 species that breed locally and each of which are commonly given different band sizes: Long‑billed Thrashers (Toxostoma longirostre), Green Jays (Cyanocorax yncas), and White‑eyed Vireos (Vireo griseus). Data were collected across 18 locations in the Lower Rio Grande Valley of Texas between 1999 and 2024. There was a significant relationship between weather and band size given to HY Long‑billed Thrashers. The more rainfall …

An Instance Of Supernumerary Rectrices In A Carolina Chickadee In Arkansas, Kevin J. Krajcir, Maureen R. Mcclung

North American Bird Bander

No abstract provided.

Seasonal Common Yellowthroat Populations At Chippewa Run, Leelanau County, Michigan (2011‑2024), Logan B. Clark, William C. Scharf, Alice Van Zoeren

North American Bird Bander

We summarized 13.5 years (2011- 2024) of Common Yellowthroat (Geothlypis trichas) captures: 758 banded and 109 recaptured (14.4%), with spring - fall tick screening from 2011 to 2020 at the Chippewa Run banding station, Empire, Michigan. Age and sex composition was consistent between years, and capture phenology followed a repeatable sequence (After Hatching Year [AHY] males most common in spring, AHY females most common through summer, and Hatching Year [HY] individuals most common in late summer to fall). AHY males were largest, exhibiting bimodal distributions in both mass and wing chord, with fall individuals showing longer wings than those captured …

Front Matter, North American Bird Bander

North American Bird Bander

No abstract provided.

Back Matter, North American Bird Bander

North American Bird Bander

No abstract provided.

Sla Elastic Resin Curing Test Data, Richard Amesimenu, Johnson Nwogu, Haijun Gong

Faculty Datasets

3D printing via stereolithography (SLA) enables high-resolution polymer components, but as-printed parts may exhibit incomplete polymerization and reduced mechanical performance. This study investigates the effects of UV post-curing time on SLA-printed elastic photopolymer resin. Specimens were evaluated using Differential Scanning Calorimetry (DSC), uniaxial tensile testing (ASTM D412 Type C), and Durometer Type M hardness testing. Results show that increased post-curing enhances crosslink density, tensile strength, elastic modulus, and hardness, while extended curing may reduce ductility. An optimal post-curing window balancing stiffness and flexibility was identified. Testing datasets include DSC (dsc/), tensile testing (tensile/), and hardness testing (hardness/) results for uncured …

Humboldt Wetland Restoration & Mitigation Geodatabase, Sylvia Van Royen

Spatial Data

This geodatabase brings together restoration and mitigation project data for wetlands and eelgrass in and around Humboldt Bay, alongside referential layers, such as historic saltmarsh extent, to support future restoration planning. The scope of the database is limited to projects that have restored tidal influence, created or restored tidal slough channels or wetland channels, restored wetland contours, or created off-channel ponds. Projects were excluded that only performed invasive plant removal, vegetation management, or fish passage barrier removal.

Project data was sourced from Coastal Development Permits and a collection of restoration project documents called the Low Tide Archive. All of the …

Ox Creek Nutrients, Geochemistry And Streamflow Raw Data, Md Helal Ahmmed, Tyson L. Jeannotte, Taufique H. Mahmood

Datasets

This dataset contains hydrologic, nutrient, and hydrogeochemical observations collected from the Ox Creek Watershed, a cold-region agricultural watershed in North Dakota, from 2024 to 2026. The dataset includes laboratory-measured nutrient concentrations and major hydrogeochemical constituents from collected water samples and in situ water-level measurements. The observations span contrasting hydrologic conditions, including spring snowmelt and summer rainfall-runoff events. The dataset supports investigation of how changes in runoff regimes, hydrologic pathways, and hydrogeochemical conditions influence nutrient mobilization and transport in cold-region agricultural watersheds.