Digital Commons Network^™

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 …

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 …

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 …

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 …

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, …

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 …

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 …

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, …

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 …

Nepal Brick Dust And Silica Exposure 2023-2025 Combined Data, Brinly Openshaw, Willow A. Call, Parker D. Willis, Claire Owens, Gregory L. Snow, Seshananda Sanjel, James D. Lecheminant, Neil E. Peterson, Andrew J. South, Clifton B. Farnsworth, Jayson Gifford, Scott C. Collingwood, James D. Johnston

ScholarsArchive Data

A quantitative, cross-sectional study was conducted over an approximately three-year period with sample collection events occurring March 13-21, 2023, March 17 – 23, 2024, and February 18 – 26, 2025. Samples were taken across 4 brick kilns in Bhaktapur, Nepal, including Kiln 1 in 2023, Kiln 2 in 2024, and Kilns 3 and 4 in 2025. Participants (N = 248) were recruited by convenience sampling from kiln 1 (n = 49), kiln 2 (n = 70), kiln 3 (n = 69), and kiln 4 (n = 60).

Dataset For Meteorological Data From Four Weather Stations In Colorado During The 2025-2026 Winter, Kelly Elder, Banning Starr, Coleman Kane, Jihan Dahanayaka, Hans-Peter Marshall

CryoGARS Snow Data

This dataset contains observations from four Colorado meteorological stations used as part of a US Army Cold Regions Research and Engineering Lab (CRREL) snow strength project.  The purpose of these data is to measure the meteorological variables that impact the trafficability and supportability of a seasonal snowpack and will be used as forcing data to model snow strength across time and space.  Measured data include snow depth, air temperature, relative humidity, wind speed, wind direction, soil temperature, and radiation.

Dataset For Meteorological Data From Two Idaho Weather Stations During The 2025-2026 Winter, Jihan Dahanayaka, Coleman Kane, Thomas Van Der Weide, Hans-Peter Marshall

CryoGARS Snow Data

This dataset contains observations from three Idaho meteorological stations used as part of a US Army Cold Regions Research and Engineering Lab (CRREL) snow strength project.  The purpose of these data is to measure the meteorological variables that impact the trafficability and supportability of a seasonal snowpack and will be used as forcing data to model snow strength across time and space.  Measured data include snow depth, snow water equivalent (SWE), air temperature, relative humidity, wind speed, wind direction, snow temperature, soil temperature, and radiation.

Dataset For Snow Pit And Snow Strength Data From Colorado, Idaho, Alaska, And Wyoming During The 2025–2026 Winter, Coleman Kane, Kelly Elder, Katherine Gura, Stine Pedersen, H.P. Marshall, Jihan Dahanayaka

CryoGARS Snow Data

This dataset contains snowpack and snow strength observations from three study plots in Idaho, five study plots in Colorado,  three study plots in Alaska, and four study plots in Wyoming.  These data were collected in an effort to model the trafficability and supportability of a seasonal snowpack, and will be assimilated in SnowModel to model strength across time and space.  Data collected include stratigraphy, density, permittivity, temperature, specific surface area (SSA), snow depth transects, ram penetrometer profiles, Snow Scope profiles, SnowMicroPen (SMP) profiles.

Dataset For The 100-Mile Zone: Mapping Environmental Protection And Interiorizing Border Enforcement, Carolyn Koehn, Lisa Meierotto

Public Policy and Administration Research Data

U.S. border enforcement has increasingly extended beyond the geopolitical boundary, producing a militarized landscape in which immigration enforcement, national security, and environmental protection intersect. This article examines the environmental implications of the U.S. “100-Mile Zone,” the area extending inward from the nation’s international borders where federal immigration authorities exercise expanded enforcement powers. Situating the analysis within scholarship on militarized landscapes and the “spatial stretching” of borders, the article considers how the expansion of Department of Homeland Security (DHS) authority has transformed both the geography of immigration enforcement and the governance of protected lands. Using original spatial analysis, this study maps …

The Application Of Machine Learning And Deep Learning On Demand Forecasting Across Time-Critical Industries: A Systematic Review, Asmaa Seyam, Sujith Samuel Mathew, May El Barachi, Cheng Zhang, Jun Shen

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The applications of machine learning and deep learning in demand forecasting have attracted increasing attention, as they offer remarkable predictive capabilities that help automate forecasting processes and achieve higher accuracy. While numerous review studies have examined solutions within specific industries, there is a lack of comprehensive literature review investigating these solutions across different sectors. Therefore, this study overviews machine learning and deep learning applications in demand forecasting across time-critical industries, including power, tourism, water, transportation, and food. A two-tier classification framework is proposed to categorize demand forecasting studies by both application industry and methodological architecture. In addition, the most popular …

Uncovering Customer Sentiment And Brand Perception By Leveraging Large Language Models: A Case Study In The Automotive Industry, Sujith Samuel Mathew, Kadhim Hayawi, Faseela Abdullakutty, Neethu Venugopal, May El Barachi

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Analyzing customer perceptions has become increasingly important in the automotive industry as it provides actionable insights into consumer satisfaction, preferences, and areas requiring improvement. This study proposes a novel perception analysis framework for automotive reviews using advanced Large Language Models (LLMs), including BERT, FLAN-XXL, and Mistral 7B, leveraging zero-shot learning to categorize reviews without task-specific training data. The framework follows a two-stage evaluation process, beginning with zero-shot perception classification and followed by a detailed topic-wise perception analysis. Model performance was evaluated using accuracy, precision, recall, and F1-score across reviews from five major automotive brands—Toyota, Kia, Honda, Nissan, and Hyundai. While …

Deployment-Oriented Evaluation Of Temporal And Spatial Forecasting Approaches For Electric Vehicle Charging Demand In Energy Systems, Maher Alaraj, Eyob Solomon Getachew

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Accurate short-term electric vehicle (EV) charging demand forecasting is important for charging infrastructure operation, grid management, and energy-system planning. This study presents a deployment-oriented and reproducible evaluation of temporal, spatial, and unified spatio-temporal forecasting approaches for day-ahead EV charging demand prediction. Using publicly available charging-session data aggregated at hourly resolution across ZIP-code regions, we compare persistence and ARIMA baselines, XGBoost, Long Short-Term Memory (LSTM) networks, Graph Convolutional Networks (GCNs), and a unified GCN+LSTM architecture under a consistent preprocessing pipeline, leakage-free validation protocol, and rolling-origin evaluation framework. For the Boulder ZIP-code dataset considered in this study, temporal information provided the dominant …

Dustmambanet: A Hybrid Inceptionv3–State-Space Network For Robust Solar Panel Dust Detection, Kadhim Hayawi, Sakib Shahriar

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To develop a robust, scalable vision-based model for automatic detection and quantification of dust accumulation on solar photovoltaic panels, overcoming limitations of existing convolutional and attention-based methods and supporting proactive maintenance. We propose DustMambaNet, a hybrid model that consists of a pretrained InceptionV3 convolutional neural network as a feature extractor and two selective state space sequence modules. The state space modules use gated depthwise convolutions to represent long-range spatial dependencies that are of linear complexity, after rearranging spatial features to form sequences. The network provides a binary classification of dust with a severity index (DSI) and a continuous one. All …

Tolkien Fanzine Dataset, 1967-2017 [Bulk 1969-1979], Robinson Ensz, Gabriel Marotto, Aidan Oakey, Ivy Stanich, Ethan Thomas

Tolkien Resources

This dataset reveals the heretofore hidden contents of fan magazines (fanzines) related to the works of J.R.R. Tolkien that were created from 1969 through the publication of Tolkien’s The Silmarillion in 1977. It supplements the earlier Tolkien Fandom Review by S. Gary Hunnewell that discussed fanzines created through 1968. The dataset provides an item level index of the fanzines’ content and is intended to aid researchers in Fan Studies. It is accompanied by an inventory document that lists the fanzine titles and provides brief notes about some of them. Data was drawn from Marquette’s large holding of Tolkien fanzines, including …

Toward Equitable Energy Futures: The Influence Of Digital Government And Financial Development On Clean Energy Justice, Martin Spraggon, Muhammad Hafeez, Sana Ullah, Chanyanan Somthawinpongsai, Zurul Aisya Osman, Sidra Sohail

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Fossil fuel energy is the largest source of carbon emissions and the main driver of climate change and global warming. Therefore, the world is switching towards clean and green energy sources as an alternative to fossil fuel-based energy sources. Nevertheless, the equitable distribution of clean energy resources in urban and rural areas among every segment of society has become a concern for policymakers. Thus, urban-rural clean energy justice is an investigation topic that needs our attention. In this analysis, our main focus is on investigating the influence of financial development and digital government on clean energy justice using an advanced …

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 …

The Impact Of Partner Support On Vaginismus Treatment Outcomes: A Mixed Methods Study, Anna R. Ames, Alisha H. Redelfs, Nana Mensah

ScholarsArchive Data

A mixed-methods design was used to gain a nuanced understanding of how partner support affects the experiences of people with vaginismus.

Study aim: To guide partners of people with vaginismus in understanding how they can best help their partners with their vaginismus experience. 

• Quantitative Data: cross-sectional survey to reveal correlations in the relationship between vaginismus treatment outcomes and partner support [Available]
• Qualitative Data: Individual interviews to explore specific ways that people with vaginismus receive, or want to receive, support from their partners and the significance that they place on partner support  [Not Available]

Survey Data
Survey participants were recruited in …

Dataset For Effects Of Demineralization Protocols On Hydrogen And Oxygen Isotope Ratios In Fossil Bone Collagen, Linda M. Reynard

Geosciences Research Data

This dataset accompanies a peer-reviewed publication. The effect of three commonly used preparation protocols on bone collagen d2H and d18O values was investigated, using six generally well-preserved faunal bones (cattle, bison, horse) ranging in age from the Holocene to Late Pleistocene. The treatments tested were ethylene diamine tetraacetic acid demineralization without gelatinization and hydrochloric acid demineralization with and without gelatinization. There were small d2H changes noted by method; however, given the expected water isotopic variation among labs this should be an insignificant source of inter-lab variation.

Theological Truths That Divide Us And Engaging In Disagreement Well, David M. Westfall

Faculty Work Comprehensive List

""Only when we locate our differences within the shared context of God’s will to unite all things in his Son can we “get with the program” and start to relate in love, allowing our objective identity in him to exert continual pressure on our relationship.""

Posting about ­­­­­­­­how pursuing unity is part of Christian transformation from In All Things, an online hub that offers insight into maintaining and faithful and orthodox Reformed Christian worldview while fearlessly engaging in every aspect of contemporary life – until all is made new.

Dataset For 'Variability Of Howitzer Artillery Infrasound Recorded In Little Cottonwood Canyon, Utah', Owen A. Walsh, Jeffrey B. Johnson

Boise State University Infrasound Data Repository

These data are a supplement to the MS thesis, “Variability of Howitzer Artillery Infrasound Recorded in Little Cottonwood Canyon, Utah”, by Department of Geosciences student, Owen Walsh. Data provided are for howitzer artillery infrasound signals recorded by six infrasound microphones (infraBSU v2) from March 22 through April 5, 2023 in Little Cottonwood Canyon, Utah, USA. This dataset consists of 142, 20-second signals sampled at 100 Hz. Data provided have been filtered using a 0.5 Hz (2-second) highpass, acausal, 2-pole butterworth filter.

Ground To Roof Snow Load Ratio (Gr) Data Release, Brennan Bean, Cooper Nelson, Jesse Wheeler, Scout Jarman, Salam Adil Al-Rubaye, Marc Maguire

Browse all Datasets

This data release provides historical ground-to-roof snow load ratio (GR) datasets used for snow load research and model development. The release includes original referenced datasets, cleaned country specific datasets, and a master dataset that combines Canadian and United States datasets into a standardized format for research and engineering applications.