Digital Commons Network^™

Toward Mapping Multiphase Multicomponent Mixtures With Neural Networks, Kristen L. Hallas, Melissa De Jesus, Christine J. Wu, Jianzhi Li, Jason Bernstein, Philip C. Myint

School of Mathematical & Statistical Sciences Faculty Publications

Equation of state (EOS) tables are commonly used in hydrodynamic simulations of high-pressure, high-temperature phenomena in fields like planetary science, astrophysics, and high-energy-density science. However, generating and storing EOS tables for multiphase, multicomponent mixtures over a wide range of pressures and temperatures is computationally infeasible due to their memory-intensive nature. To address this issue, we have developed a neural network-based machine learning model to predict new EOS tables for binary mixtures. In particular, a deep feedforward neural network trained on a set of ten EOS tables at particular mixture compositions is able to predict nine new (hold-out) EOS tables at …

Robust Statistical Methods For Microbiome Abundance Data, Yiming Shi

WUSM Theses and Dissertations – All Programs

Differential abundance analysis in microbiome studies aims to identify taxa whose abundance differs across biological or clinical conditions. The observed data are typically taxon-specific sequencing read counts, representing reads assigned to different taxa within each sample. These counts are indirect measurements of the underlying microbial abundance profile and are constrained by sample-specific library sizes. Microbiome count data are also typically sparse, overdispersed, and heteroscedastic. Together, these characteristics create substantial challenges for differential abundance analysis and make the results highly sensitive to normalization procedures, model specification, and the statistical methods used for inference.

Normalization defines the scale on which samples are …

Port And Vessel Communication Traffic Intrusion Detection: A Variational Autoencoder‑Enhanced Multilayer Perceptron Approach, Chien-Lin Chiang, Hsien-Cheng Chou, Ming-Yuan Peng, Yi-Yuan Chiang, Yu-Shun Liu

Journal of Marine Science and Technology–Taiwan

Port and vessel networks increasingly operate on IP/Ethernet backbones with high‑noise, high‑dimensional traffic. We present a lightweight hybrid intrusion‑detection model that couples a variational autoencoder (VAE) with a multilayer perceptron (MLP) and augments training with a boundary‑oriented latent‑space mixup strategy. The VAE models the distribution of normal traffic and identifies anomalies through reconstruction errors. Subsequently, it generates robust latent vectors, enabling the MLP to perform highly accurate supervised classification. On the UNSW‑NB15 dataset, the proposed pipeline attains ≥97% accuracy and an outstanding recall of 99.56% in binary intrusion detection, and visualization of the latent space (PCA) together with reconstruction‑error analyses …

Sar Ship Detection Based On Shallow Feature Guidance, Chenxu Xia, Peng Chen, Ya Zhang, Ying Li

Journal of Marine Science and Technology–Taiwan

Maritime ship detection is of great significance for both military security and civilian applications. Synthetic Aperture Radar (SAR), with its all-weather and all-day imaging capability, plays a vital role in maritime surveillance. Nevertheless, SAR ship targets typically appear small in scale, embedded in complex backgrounds, blurred at boundaries, and easily confused with near-shore features, which pose substantial challenges for accurate detection. To address these issues, we propose a SAR ship detection network that integrates dual enhancements of small-object representation and edge information. The network introduces two key components: the Small Target Refine Pyramid (STRP) to strengthen shallow feature representation for …

Enhancing Shipboard Safety Management Under The Ism Code: An Innovative Risk Assessment Framework With A Stern Tube Case Study, Pi-Yen Lin

Journal of Marine Science and Technology–Taiwan

The shipboard safety management system (SMS) is designed to enhance safe operations, risk management, and emergency response to improve overall ship safety and efficiency. This paper demonstrates the use of an engine room simulator (ERS) for collecting failure modes and applies it to a comprehensive failure analysis of the stern tube lubricating oil system. A new risk closeness coefficient method was developed, integrating expert background knowledge and weighted risk assessments. The analysis, based on multiple expert evaluations, covered five subsystems, eight main components, 23 failure modes, and 112 failure causes. This study presents 26 recommendations for maritime practitioners and onboard …

Interpreting The Trispectrum As The Cross-Spectrum Of The Wigner-Ville Distribution, Aviva Abosch

All Publications

The fourth-order time-invariant spectrum, or trispectrum, has a simple derivation as the cross-spectrum among frequency bands in the Wigner-Ville distribution (WVD). Viewed this way, the trispectrum gains intuitive meaning as a measure of the linear dependence of power across frequencies, which yields some insight into its structure and interpretation. We highlight, in particular, a two-dimensional subdomain as useful for identifying modulated oscillations when the modulating envelope is non-negative or lowpass. Spectral characteristics of the carrier and modulating signals are revealed along separate axes of a two-dimensional representation of this domain. The application of this framework, combined with a previously described …

Long Range Battery-Free Wireless Power Transfer Testbed For Underground Mines Iot And Lpwan Devices, Anabi Hilary Kelechi, Samuel Frimpong, Sanjay Madria

Mining Engineering Faculty Research & Creative Works

Underground mines are susceptible to occasional roof falls and cave-ins, temporarily destroying the existing wireless communications and telemetry infrastructure. During this temporary outage, intermittent provision of electrical energy wirelessly to the already deployed low-power wireless area networks (LPWAN) and Internet of Things (IoT) devices assumes a fundamental requirement. In this article, we propose and design a long-range far-field radio frequency (RF) wireless power transfer (WPT) testbed to power LPWAN and IoT devices at 35 m in an underground mines facility. Class AB external power amplifier (PA) was introduced to achieve a long-distance RF WPT, in the 880 MHz band. Thus, …

Revisiting Ulam Stability For Boundary Value Problems, Martin Bohner, Snezhana Hristova, Agnieszka B. Malinowska, Ewa Girejko

Mathematics and Statistics Faculty Research & Creative Works

The main goal of this paper is to apply Ulam stability theory to boundary value problems for dynamic equations, while addressing several common misconceptions found in the existing literature. We identify the key issues that arise when applying Ulam stability to such problems and propose three distinct approaches to overcome them. To enhance clarity and accessibility, we begin with nonlinear ordinary differential equations and subsequently extend the analysis to nonlinear dynamic equations on time scales. Since a time scale is defined as any nonempty closed subset of the real numbers, our results are applicable to dynamic equations on continuous, discrete, …

Soil-Science Expertise In An Ai-Exposed World: Competencies, Curricula, And Careers For Soil Security, Gian Franco Capra, Thiago Assis Rodrigues Nogueira, Arun Dilipkumar Jani

Biology, Agriculture and Chemistry Faculty Publications and Presentations

Generative artificial intelligence (AI) is reshaping the world of knowledge-based work, with highly qualified professions appearing more exposed to substitution than routine manual ones. Soil science occupies a hybrid position, being a field that requires extensive knowledge and at the same time a strong dependency on field and laboratory activities, time-consuming and intrinsically “embodied.” This review examines what AI entails for soil science, asking which competencies are more or less exposed, how curricula should respond, and what actions institutions should undertake in the current AI-dominant academic environment. The analysis rests on two axes, namely the AI exposure of a task …

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 …

Harnessing Digital Twin (Dt) Technology For Food Security And Climate Resilience In Sub-Saharan Africa (Ssa), Henri E.Z. Tonnang, Francis Chianu, Siyabusa Mkuhlani, Francis Muthoni, John Michael Humphries Choptiany, Franck B.N. Tonle, Bonoukpoe M. Sokame, Mercy Lung’Aho

All Peer-Reviewed Publications

Sub-Saharan Africa (SSA) faces chronic food insecurity despite possessing approximately 60% of the world’s uncultivated arable land. Field trials generate evidence but are costly and insufficiently scaled to address accelerating climate and demographic pressures. Digital twin (DT) technology, defined as the continuous, bidirectional virtual replication of physical systems using real-time data, supports monitoring, modelling, and optimisation of agrifood systems. To our knowledge, however, no published synthesis has examined DT agriculture research through the lens of SSA food systems or smallholder farming realities. A PRISMA-compliant systematic review was conducted across bibliographic databases using a pre-defined Boolean search and adapted PICOS eligibility …

Optimizing Inventory Management, William Hawkins

Agricultural Economics and Agribusiness Undergraduate Honors Theses

Inventory management is extremely important for businesses to maximize their profitability. Many factors go into the decision-making process for how much inventory to hold and when to restock this inventory. Through the process of an internship, I determined that some of the most important factors were the amount of a part that needed to be ordered at a time and what order quantities would allow for high levels of customer satisfaction. In this research, I compared these factors between the different models I studied, and the data I received from Riggs CAT to determine whether or not maximum efficiency and …

Pieces Solving A Puzzle: Emerging Roles And Cross-Field Analogies During A Distributed Epistemic Game, Jenna Matthews

All Graduate Theses and Dissertations, Fall 2023 to Present

In an effort to describe why some problems can’t be solved, a character compared them to a cubic meter of platinum - a block so dense that all the people who could squeeze around it still couldn’t lift it off the ground. In the same way, these problems simply can’t have enough people gathered around and communicating about them - and so they remain unsolved.

This research looks at the first Polymath Project (2009) as an example of a distributed epistemic game to examine how groups engage with complex problems. and without a locally omniscient individual. In this setting of …

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 …

Quantifying Customer Sentiment For Automobile Brand Perception Analysis Using Machine Learning On Twitter, Sujith Samuel Mathew, Kadhim Hayawi, Neethu Venugopal, May El Barachi

All Works

Social networking sites provide a platform for individuals to express their opinions publicly. Brand managers actively use these platforms to gain insights into brand perceptions, as users often share their views on products and services. In this study, we use sentiment analysis to assess customer sentiment towards five leading automobile brands, analyzing text content shared on Twitter(or X). The research models the ’Brand Polarity Score’, which indicates whether customers perceive the brand positively or negatively. This score is further weighted based on the tweet’s influence, characterized by the engagement metrics of the tweet and the author’s follower count. We also …

Power At Sea Develop Phase, Jake Lauer, Isabella Heinemann, Bailey Gargasz, Zachery Boyer

Mechanical Engineering

This project develops a renewable, wave-powered charging system designed to extend the mission duration of Autonomous Underwater Vehicles by eliminating the need for frequent manual battery replacement. Building upon a prior oscillating water column prototype from the CONCEPT phase, the team redesigned and tested an improved system capable of converting wave-induced air motion into electrical power using a Wells turbine. Key enhancements include doubling the column diameter to increase displaced air volume, integrating a flared inlet and bi-directional nozzle to improve airflow, and selecting corrosion-resistant materials suitable for long-term deployment in marine environments. Multiple prototypes were constructed and evaluated through …

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 …

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

All Works

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

All Works

Ensuring patient safety in healthcare environments requires continuous monitoring systems capable of identifying early warning signs of clinical risk. Traditional surveillance methods often fail to capture meaningful patterns in patient movement, limiting their ability to prevent incidents such as falls, prolonged immobility, or unnoticed health deterioration. Radio Frequency Tagging technology has been increasingly adopted for real-time patient tracking; however, existing systems are generally limited to location detection and lack predictive insights into patient behaviour. To overcome these limitations, this study presents a Radio Frequency Tagging-based patient monitoring framework that integrates mobility tracking with an early warning mechanism to enable proactive …

A Systematic Review Of Audio Deepfake Detection Techniques For Digital Investigation, Mahra Alnaqbi, Richard Adeyemi Ikuesan

All Works

Deepfake technology has been driven by advanced machine learning and revolutionized multimedia creation by synthesizing hyper-realistic content. It includes images, videos, and audio. While its creative applications in entertainment and accessibility are significant, the technology also poses critical risks, especially in fraud, disinformation, and identity theft. Audio deepfakes are a subset of this phenomenon that replicate human voices with enhanced precision, mimicking tone, accent, and subtle vocal nuances. This has raised concerns in security-sensitive domains like voice authentication and forensic investigations. This systematic literature review (SLR) adopts PRISMA guidelines to explore the state-of-the-art in audio deepfake detection. It examines existing …

Understanding Chatbot-Assisted Collaborative Learning Among Female Undergraduate Students, Mohammad Amin Kuhail, Ahmed Shuhaiber, Sinan Salman, Nazik Alturki

All Works

Computer programming can be daunting for beginners due to complex concepts and syntax. Traditional teaching methods, while engaging through gamification and active learning, often lack personalized approaches. Recent advancements in artificial intelligence (AI), particularly large language models (LLMs), present new possibilities for personalized and interactive learning environments. This study introduces a chatbot-assisted collaborative learning environment (CCLE) that leverages an LLM (GPT-4) to enhance collaborative programming education. The CCLE enables real-time guidance and collaboration through natural language interactions, allowing students to work together on programming tasks, edit code collaboratively, and engage with both peers and the educational chatbot. We conducted an …

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

All Works

Detecting diseases in olive leaves is crucial for maintaining tree health and ensuring stable olive production. Early signs of infection often appear on the leaves, making them a key indicator for timely disease detection and intervention. Traditionally, farmers rely on visual inspection or laboratory tests to diagnose plant diseases. However, recent advancements in deep learning (DL) have significantly improved the accuracy and efficiency of olive leaf disease diagnosis. Numerous studies in the literature have explored this task using CNN-based architectures and, more recently, Vision Transformers. While these models have shown promising performance on benchmark datasets, they are often trained and …

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 …

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 …

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 …

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

All Works

Early and reliable diagnosis of skin cancer from dermoscopic images remains challenging due to class imbalance, subtle inter-class variations, lesion boundary ambiguity, and illumination inconsistency, which can degrade the robustness of conventional convolutional neural networks (CNNs). To address these limitations, this study proposes an automated smart healthcare framework for dermoscopic skin cancer diagnosis using an Enhanced Vision Transformer (E-ViT) that improves global-context modeling through self-attention while strengthening fine-grained lesion representation learning. Unlike standard ViT configurations, the proposed architecture integrates multi-scale patch embedding and attention refinement to better capture border irregularities and color–texture heterogeneity that are critical for melanoma discrimination. Furthermore, …

Fair And Explainable Educational Recommendations With A Hybrid Graph-Gru Framework, Edmund Evangelista, Syed M.Salman Bukhari

All Works

Artificial Intelligence (AI) recommender systems are increasingly used in education to personalize learning and help students navigate large collections of digital learning resources. However, many existing approaches emphasize predictive accuracy over fairness, robustness, diversity, and transparency. This creates an important educational challenge. The students with limited participation histories may receive less reliable support, while highly popular resources may dominate recommendation lists and limit access to other useful learning materials. To address this challenge, this study aims to develop and evaluate a responsible educational recommender framework that supports personalized learning resource navigation while making recommendation behavior more fair, stable, diverse, and …

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

All Works

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

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 …

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

All Works

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, …