Digital Commons Network^™

Vitamin D Status During Pregnancy Modulates The Effect Of Pre-Pregnancy Obesity On Gestational Diabetes Mellitus Risk: A Birth Cohort Study, Ali H. Ziyab, Abdullah Al-Taiar, Reem Al-Sabah, Majeda S. Hammoud, Saeed Akhtar

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Objective:

To determine whether gestational vitamin D status modulates the effect of pre-pregnancy obesity on gestational diabetes mellitus (GDM) risk while stratifying by maternal age.

Design:

Birth cohort.

Setting:

A major maternity hospital in Kuwait.

Participants:

Pregnant women in their second/third trimester of gestation were enrolled. Pre-pregnancy body mass index (BMI, kg/m²) was categorized as under/normal weight (< 25.0), overweight (25.0 to < 30.0), and obesity (≥30.0). Gestational 25-hydroxyvitamin D concentrations were categorized as deficiency (< 50 nmol/L) or insufficiency/sufficiency (≥50 nmol/L). GDM status was ascertained according to international guidelines. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) were estimated using logistic regression.

Results:

Data from 957 pregnant women were analyzed, with GDM affecting 166 (17.4%) pregnancies. Pre-pregnancy obesity and gestational vitamin D deficiency was ascertained in 275 (28.7%) and 533 (55.7%) pregnant women, respectively. The association between pre-pregnancy obesity and GDM risk differed according …

The Crucial Role Of Machine Learning Models In Predicting Current Childhood Asthma: Model Comparison, Calibration, And Shap-Based Interpretation, Aditya Chakraborty, A. K.M. Raquibul Bashar

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Background: Asthma is one of the most prominent chronic diseases in children and one of the most challenging ailments to diagnose in infants and preschoolers in the United States. Predictive models can be instrumental in improving early diagnosis, personalized treatment strategies, and disease progression. By utilizing nationalized data, this study focuses on building and comparing high-performing analytical predictive models based on the relevant risk factors and identifying the most influential predictors.

Methods: We analyzed cross-sectional BRFSS Asthma Call-Back Survey data (2011-2020; N = 9,813) and randomly split participants into training and testing sets. An XGBoost model (hyperparameters tuned via grid …

Imperfections And Scars: The Aesthetics Of Curated Decay In Urban Conservation: The Aesthetics Of Curated Decay In Urban Conservation, Ioana Moldovan, Connell Vaughan, Michael O’Hara, Silivan Moldovan, Ioana Cecălășan

Research Outputs: 2025-Present

This paper explores the value of imperfections and curated decay in the conservation of architecture and public art as vehicles of cultural memory. While conventional heritage practice treats physical degradation as a threat, newer conservation ethics argue for embracing material impermanence within an aesthetics of care. We examine how acknowledging patina, weathering, and even structural decline can become an act of care, maintaining the “spirit” and authenticity of a place. The theoretical framework integrated the aesthetics of imperfection, including concepts like the Japanese wabi-sabi, which finds beauty in the incomplete and impermanent, critical heritage theory (which questions whose memories and …

Fleet: A Federated Learning Emulation And Evaluation Testbed For Holistic Research, Osama Abu Hamdan, Hao Che, Engin Arslan, Md Arifuzzaman

Computer Science Faculty Research & Creative Works

Federated Learning (FL) presents a robust paradigm for privacy-preserving, decentralized machine learning. However, a significant gap persists between the theoretical design of FL algorithms and their practical performance, largely because existing evaluation tools often fail to model realistic operational conditions. Many testbeds oversimplify the critical dynamics among algorithmic efficiency, client-level heterogeneity, and continuously evolving network infrastructure. To address this challenge, we introduce the Federated Learning Emulation and Evaluation Testbed (FLEET). This comprehensive platform provides a scalable and configurable environment by integrating a versatile, framework-agnostic learning component with a high-fidelity network emulator. FLEET supports diverse machine learning frameworks, customizable real-world network …

Dcmm-Transformer: Degree-Corrected Mixed-Membership Attention For Medical Imaging, Huimin Cheng, Xiaowei Yu, Shushan Wu, Luyang Fang, Chao Cao, Jing Zhang, Tianming Liu, Dajiang Zhu, Wenxuan Zhong, Ping Ma

Computer Science Faculty Research & Creative Works

Medical images exhibit latent anatomical groupings, such as organs, tissues, and pathological regions, that standard Vision Transformers (ViTs) fail to exploit. While recent work like SBM-Transformer attempts to incorporate such structures through stochastic binary masking, they suffer from non-differentiability, training instability, and the inability to model complex community structure. We present DCMM-Transformer, a novel ViT architecture for medical image analysis that incorporates a Degree-Corrected Mixed-Membership (DCMM) model as an additive bias in self-attention. Unlike prior approaches that rely on multiplicative masking and binary sampling, our method introduces community structure and degree heterogeneity in a fully differentiable and interpretable manner. Comprehensive …

Explaining The Unseen: Multimodal Vision-Language Reasoning For Situational Awareness In Underground Mining Disasters, Mizanur Rahman Jewel, Mohamed Elmahallawy, Sanjay Kumar Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Underground mining disasters produce pervasive darkness, dust, and collapses that obscure vision and make situational awareness difficult for humans and conventional systems. To address this, we propose MDSE, Multimodal Disaster Situation Explainer, a novel vision-language framework that automatically generates detailed textual explanations of post-disaster underground scenes. MDSE has three-fold innovations: (i) Context-Aware Cross-Attention for robust alignment of visual and textual features even under severe degradation; (ii) Segmentation-aware dual pathway visual encoding that fuses global and region-specific embeddings; and (iii) Resource-Efficient Transformer-Based Language Model for expressive caption generation with minimal compute cost. To support this task, we present the Underground Mine …

Let’S Talk About The Biological Reality Of Sex, Baby, John Parsi

FIU Law Review

Sex is at the very core of individual identity. It affects the conception of the self and the way others view the individual. Every day, one’s sex may open and close doors, access to communities, and opportunities. Laws that define sex may affirm scientific knowledge and protect people, or threaten to unravel the facts and the reality of sex. A deep unmooring of sex is underway, and this Article aims to halt it. On day one of the current presidential term, January 20, 2025, President Trump signed an Executive Order on “Defending Women from Gender Ideology Extremism and Restoring Biological …

Cruel And Unusual Punishments And The Constitutional Status Of The Idea Of Progress, R. George Wright

FIU Law Review

To this point, we have developed some idea of what progress might have meant to the constitutional Framers, ratifiers, and later judges. We need not attempt the fruitless task of doing any precise head count of the variations of the concrete meaning, perceived likelihood, or value of progress among those parties. Doubtless some persons were more optimistic than others.178 But we need only assume that enough constitutional Framers, ratifiers, and judicial interpreters sufficiently shared any mainstream understanding of progress. Any such constitutional legitimacy of the idea, or the multiple ideas, of progress licenses the language of progress embraced in Trop.

Race, Reparative Justice, And Climate Change-Related Migration, Monica Visalam Iyer

FIU Law Review

The question of how to appropriately respond to migration linked to climate change is increasingly being debated in academia, in government and policy circles, and, crucially, in international legal and climate policy forums. These debates often center on data and on understanding the true numbers of people who might migrate in the context of climate change, and how much of this migration can accurately be linked to climate change, or on the security and logistical concerns associated with responding to this “challenge,” or on the appropriate legal box into which people migrating in this context can be shoved. Too often, …

When Convenience Compromises Rights: How The European Union And Washington State Confront Facial Recognition Risks, Debora Rodriguez Lugo

FIU Law Review

As facial recognition technology (FRT) becomes increasingly embedded in policing, concerns and controversies over the lack of consent, misidentification, and mass surveillance grow. This Comment compares how the European Union (EU) and Washington State (Washington) regulate law enforcement’s use of FRT to mitigate risks to individual rights. The EU governs FRT use through a robust legal framework, including the General Data Protection Regulation (GDPR), the Law Enforcement Directive (LED), and the Artificial Intelligence Act (AI Act), which offer protections against these risks. In contrast, Washington’s pioneering statute, Wash. Rev. Code section 43.386, promotes transparency and limits the use of FRT. …

The Protection Of Geographical Indications Under Comparative Lens: Whether Law Artificially Creates Scarcity Of Goods, Domenico Di Micco

FIU Law Review

The economic principles of supply and demand dictate that market prices are contingent on the conditions under which supply and demand converge. Within this theoretical framework, the concepts of scarcity and abundance assume paramount significance. When goods are scarce, prices tend to escalate, while in cases of abundant supply, prices tend to decline. But what factors determine scarcity and abundance? In many cases, scarcity is inherent to the nature of the commodity (for example, diamonds are rarer than common stones). In other cases, such as geographical indications, scarcity is constructed through the legal denomination associated with the commodity, thereby creating …

Virtual Harm, Real Consequences: Constitutional Justifications For Criminalizing Ai-Generated Child Pornography, Adam Abdin

FIU Law Review

Advances in artificial intelligence (AI) have enabled the creation of hyper-realistic child sexual abuse images without any real child involved. These AI-generated depictions pose a unique challenge: they replicate the most pernicious form of pornography—child pornography—while potentially being claimed as “speech” immune from regulation. This paper argues that it is both constitutionally permissible and morally imperative to criminalize AI-generated child pornography. This article presents two doctrinal pathways to justify a ban. First, such content can be treated as unprotected speech under existing First Amendment exceptions. Courts have long held that child pornography and obscenity lie outside First Amendment protection, and …

The Anchored: Issue 2, Winter 2026, Writer's Club Of The Hope-Western Prison Education Program

The Anchored

The Anchored is a quarterly publication of the Writer’s Club of the Hope-Western Prison Education Program.

Multiple Changepoint Detection For Non-Gaussian Time Series, Robert Lund, Thomas J. Fisher, Norou Diawara, Michael Wehner

Mathematics & Statistics Faculty Publications

This article combines methods from existing techniques to identify multiple changepoints in non‐Gaussian autocorrelated time series. A transformation is used to convert a Gaussian series into a non‐Gaussian series, enabling penalized likelihood methods to handle non‐Gaussian scenarios. When the marginal distribution of the data is continuous, the methods essentially reduce to the change of variables formula for probability densities. When the marginal distribution is count‐oriented, Hermite expansions and particle filtering techniques are used to quantify the scenario. Simulations demonstrating the efficacy of the methods are given and two data sets are analyzed: 1) the proportion of home runs hit by …

Adaptive Multi-Grade Deep Learning For Highly Oscillatory Fredholm Integral Equations Of The Second Kind, Jie Jiang, Yuesheng Xu

Mathematics & Statistics Faculty Publications

This paper studies the use of Multi-Grade Deep Learning (MGDL) for solving highly oscillatory Fredholm integral equations of the second kind. We provide rigorous error analyses of continuous and discrete MGDL models, showing that the discrete model retains the convergence and stability of its continuous counterpart under sufficiently small quadrature error. We identify the DNN training error as the primary source of approximation error, motivating a novel adaptive MGDL algorithm that selects the network grade based on training performance. Numerical experiments with highly oscillatory (including wavenumber 500) and singular solutions confirm the accuracy, effectiveness and robustness of the proposed approach.

Changepoint Analyses Confirms Global Tropical Cyclone Frequency Decline, Michael Wehner, Thomas Fisher, Norou Diawara, Robert Lund

Mathematics & Statistics Faculty Publications

Changes in tropical cyclone frequencies as the climate warms is a topic of significant current debate [1, 2]. There is no accepted theory of how tropical cyclogenesis might respond to a warmer ocean-atmosphere system as multiple controlling factors exist [3–7]. Anthropogenic warming of surface ocean temperatures due to increased greenhouse gas concentrations [8] increases the potential for tropical cyclogenesis [9–11]; however, realized cyclogenesis also requires an initial local disturbance [12–16] to develop. Most multi-decadal tropical cyclone permitting climate models (i.e. resolutions of 15-50km) exhibit frequency decreases in warmer climates, despite the increase in tropical cyclogenesis potential [17–25]. In this paper, …

From Screens To Stress: Public Health Implications Of Cancer Worry In A Digitally Connected World, Shreya Mathur, Ethan Burns, Michael Pokojovy, Tzu-Liang Bill Tseng, Sunil Mathur

Mathematics & Statistics Faculty Publications

Aims:

This study examines how digital information environments, genetic testing experiences, health behaviors, and psychological distress influence cancer-related worry among adults in the United States. It further considers the public health implications of elevated or reduced cancer worry for prevention and risk communication.

Methods:

Data were drawn from a nationally representative survey of 6,252 U.S. adults. Measures included reliance on social media for health decision-making, smoking status, history of genetic testing, and psychological distress. Multivariable analyses assessed associations between these factors and levels of cancer worry.

Results/Findings:

Reliance on social media for health decisions was associated with greater cancer worry, …

A Composite Narxnn Approach To Photovoltaic Power Forecasting With Integrated Weather Inputs And Uncertainty Quantification, Denisse Urenda Castañeda, Sharmin Abdullah, Jackson Morgan, Honglun Xu, Michael Pokojovy, Tzu-Liang Tseng

Mathematics & Statistics Faculty Publications

Solar photovoltaics (PV) are a major source of sustainable energy. Yet, their power output is highly sensitive to environmental variability, particularly solar irradiance, cloud cover, wind, and temperature. Accurate forecasting of PV power is essential for efficient grid integration and energy planning, especially in applications requiring reliable longer-term forecasting rather than one-step-ahead predictions. This study presents a PV power forecasting approach using Nonlinear Autoregressive models with Exogenous Inputs (NARX), integrating large-scale numerical weather historical data as exogenous variables. Although NARX models effectively capture temporal dependencies, they can become overly dependent on historical power values, reducing responsiveness to real-time weather changes. …

A New Parallel-In-Time Direct Inverse Method For Nonlinear Differential Equations, Nail K. Yamaleev, Subhash Paudel

Mathematics & Statistics Faculty Publications

We propose a new method for parallelization of the first-order backward difference discretization (BDF1) of the first-order time derivative in nonlinear partial differential equations, such as conservation law equations. The time derivative term is discretized by using the method of lines based on the implicit BDF1 scheme, while the inviscid and viscous terms are approximated by conventional 2nd-order central discretizations of the 1st- and 2nd-order derivatives in each spatial direction. The global system of nonlinear discrete equations in the space-time domain is solved by the Newton method for all time levels simultaneously. For the BDF1 discretization, this all-at-once system at …

The Adaptive Solution Of High-Frequency Helmholtz Equations Via Multi-Grade Deep Learning, Peiyao Zhao, Rui Wang, Tingting Wu, Yuesheng Xu

Mathematics & Statistics Faculty Publications

The Helmholtz equation is fundamental for modeling wave propagation in acoustics, electromagnetics, and geophysics; however, high-frequency regimes remain notoriously difficult due to the severe numerical “pollution effect.” We propose FD-MGDL, an adaptive framework that synergizes finite difference discretizations with Multi-Grade Deep Learning (MGDL) to efficiently resolve high-frequency wavefields. Unlike standard physics-informed neural networks (PINNs), which frequently suffer from spectral bias and heavy automatic differentiation overhead, FD-MGDL employs a progressive, grade-wise training strategy that incrementally incorporates shallow sub-networks to refine residual errors. By leveraging ReLU activations in refinement grades, the framework reformulates the highly non-convex global optimization problem into a sequence …

Environmental Load In Technology-Rich Classrooms: A Practitioner Reflection On Student Attention, Cognitive Fatigue, And Instructional Design, Christopher Morales

Essays in Education

This practitioner essay examines student disengagement in technology-rich classrooms through the lens of cognitive load and environmental demand. Rather than interpreting all off-task behavior as laziness, avoidance, or lack of motivation, the essay argues that some students may be responding to cumulative overload created by screens, transitions, digital platforms, visual clutter, noise, lighting, and fragmented instructional routines. Drawing from classroom observation and cognitive load theory, the essay introduces environmental load as a practical concept for educators seeking to preserve student attention without lowering academic expectations. The essay offers reflective questions and instructional adjustments that can help teachers and school leaders …

Echoes Of The Nation: Janáček, Bartók, And The Articulation Of Central European Identity In Opera, Elyse Morris

Dissertations and Theses @ UNI

This thesis examines how opera functioned as a medium for articulating and negotiating national identity in the final decades of the Austro-Hungarian Empire. Focusing on Leoš Janáček’s Jenůfa (1904) and Béla Bartók’s Bluebeard’s Castle (1911), it explores how composers employed dramatic structure, linguistic choice, and musical language to construct culturally specific forms of expression within a rapidly shifting political and social landscape. Drawing on approaches from musicology and cultural history, the study considers how operatic works not only reflect national identity but actively participate in its formation.

Through analysis of libretto, language, and musical style, this thesis demonstrates that both …

Low-Energy Co2 Sequestration Via Palm Waste Biochar Activated With Carbide Lime For Mineral Carbonation Of Industrial Wastes, Maisa El Gamal, Ameera F. Mohammad, Basim Abu-Jdayil, Imen Ben Salem, Suhaib Hameedi

All Works

A sustainable method for enhancing CO₂ capture and sequestration was developed by accelerating the carbonation of alkaline industrial wastes using carbide lime-activated biochar (BC(CLW)), an alkaline by-product of acetylene production. The biochar was produced from palm tree waste through slow pyrolysis at 400 °C, followed by activation with carbide lime slurry to enhance its alkalinity, surface basic sites, and reactivity toward CO₂. Three industrial residues, namely ladle furnace slag (LF), baghouse dust (BH), and cyclone silo dust (CD), were blended with 0, 5, 7, and 10 wt. % BC(CLW) and carbonated under ambient conditions. Characterization by thermogravimetric analysis (TGA), Fourier-transform …

Mapping Multiclass-Targeted Hate Speech In Online Discourse: An Open Dataset, Sanaa Kaddoura, Sumaia Al-Kohlani

All Works

Online social networks have become central spaces for public discourse, where hostile and discriminatory language toward social groups can cause psychological and social consequences for marginalized communities. Although multiple public hate speech datasets are available, many rely on binary categorization practices that obscure linguistic, cultural, and contextual variation across targeted groups. As a result, minority and less visible forms of hate speech remain insufficiently documented and analyzed. This discussion paper examines methodological limitations in existing hate speech annotation schemes and presents a re-annotation framework applied to the HatEval2019 dataset. The proposed framework introduces target-specific multiclass labels that distinguish subcategories of …

Urban Trees In The Urban Hydrology Cycle, Jorge R. Vera

Theses, Dissertations and Capstones

In urban forestry, trees provide numerous economic, ecological, and aesthetic benefits to the surrounding community. One of their key roles is intercepting stormwater runoff, reducing flooding risk, and improving water quality. Chapter 1 of this thesis characterized the ecological and economic contributions of urban trees on the Marshall University campus and provides guidance on their management and conservation in the future. Chapter 2 examined how urban tree species modify the urban hydrology cycle by altering the volume and timing of precipitation delivery. In Chapter 1, we inventoried 114 acres of Marshall University's main campus in Huntington, WV, from June 28 …

Clinical And Dermoscopic Nail Changes In Patients With Chronic Kidney Disease: A Cross-Sectional Study, Ghada Nagy Mansour Borham, Hassan Abdel-Raheim Fayed, Eman Nagy Ahmed Abdelrazik, Amany Awad Mohamed

Mansoura Medical Journal

Background: Nail abnormalities are commonly observed among patients with chronic kidney disease (CKD) and may reflect underlying systemic and metabolic alterations. Dermoscopy (Onychoscopy) has emerged as a valuable noninvasive tool for detecting subtle nail changes.

Aim:  To describe the patterns and dermoscopic features of nail changes in patients with chronic kidney disease (CKD) and to explore their association with disease severity and renal function parameters.

Methods: This cross-sectional descriptive study included 127 patients with different stages of CKD. All participants underwent a comprehensive clinical and dermoscopic evaluation of the nail apparatus. Dermoscopic findings included longitudinal ridging, white …

A Comparative Study Of Topical 5% 5-Fluorouracil With Needling Versus 100% Trichloroacetic Acid With Needling Versus Needling Alone In Treatment Of Multiple Warts, Nesma Abd-Elmonem Saad, Hassan A. Fayed, Manar Ali

Mansoura Medical Journal

Aim: This study aimed to compare the efficacy and safety of topical 5% 5-Fluorouracil (5-FU) with Needling versus 100% Trichloroacetic acid (TCA) with Needling versus Needling Alone in treatment of multiple warts.

Methods: This study included a total of 60 patients clinically diagnosed with multiple warts, who were randomly allocated (computer-generated simple randomisation, 1:1:1, concealed in sequentially numbered opaque sealed envelopes) into three equal groups (n=20 per group). The first group received 100% TCA with Needling; the second group received topical 5% 5-FU combined with Needling; the third group underwent Needling alone. The study was single-blinded at the level …

Validation Of Anorectal Manometry Based On The Continence Clinical Scoring Systems For Evaluation Of Fecal Continence In Patients With Anorectal Malformations, Momen Abdelglil, Mohamed El-Sherbiny, Mahmoud Abdelwahab, Adham Elsaeid

Mansoura Medical Journal

Background/purpose: Anorectal malformations (ARM) are complex anomalies associated with long-term problems such as fecal incontinence. Clinical scoring systems are commonly used to assess bowel function but lack proper validation. Anorectal manometry is an objective tool for evaluating anorectal function. This study aimed to correlate anorectal manometric parameters and clinical continence scoring systems to enhance the postoperative evaluation of children with ARMs.

Patients and methods:

We conducted a descriptive cross-sectional study including 47 children who had undergone definitive surgical repair for ARMs between January 2010 and June 2021. Fecal continence was assessed using the Kelly and Templeton & Ditesheim scoring systems. …

Solubility And Dissolution Mechanism Of Novel Multi-Ester Headgroup Surfactants In Supercritical Co2, Ning Xu, Yan Ling Wang, Baojun Bai, Shi Zhang Cui, Yu Zhang, Wen Jing Shi, Zhao Nian Zhang, Wen Hui Ding, Pei Xu Ma, Zan Gao

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

To address the limited solubility and applicability of conventional hydrocarbon surfactants in supercritical CO₂, a series of multi-ester headgroup surfactants were designed and synthesized by leveraging the CO₂-philic properties of ester groups. The molecular structures were characterized using Fourier transform infrared (FT-IR) spectroscopy and ¹H NMR. A custom-designed laser-based apparatus was developed to quantify surfactant solubility and systematically investigate phase behavior in CO2. Molecular dynamics (MD) simulations were employed to elucidate structure–solubility relationships across multiple scales, including solubility parameters, interaction energies, radial distribution functions (RDFs), and free volume fractions. Results indicate that, at 323.15 K, …

Lorawan-Based Asset Tracking System Development And Performance Analysis Within Stem-Pbl Framework: A Mixed-Method Study, Yousef Alfarraj, Ghada Abdelhady, Abdelrahman Ahmed, Mahmoud Elaasar, Jair J. Aguilar

Teaching and Learning Faculty Publications

This study examines the integration of project-based learning (PBL) within the integrative STEM framework to advance practical problem-solving skills and investigate students' perceptions of the STEM-PBL approach through developing an IoT-enabled asset tracking system using Long-Range Wide Area Network (LoRaWAN) technology to tackle real-world logistical challenges. Adopting a mixed-methods approach, the study combines an experimental approach utilizing quantitative system performance analysis with qualitative insights gathered through participant interviews with ten graduate students. Results reveal that the LoRaWAN system demonstrated high reliability with only 0.437% downtime, validating the technical feasibility of the educational project. Furthermore, participants perceived the STEM-PBL approach positively, …