Digital Commons Network^™

Prepregnancy Overweight And Obesity In Kuwait Birth Cohort Study, Abdullah Al-Taiar, Ali H. Ziyab, Eelaf A. Husain, Maryam M.Y. Mohammad, Maryem A. Shamsah, Noor Salah Alali, Reem Sharaf-Alddin, Majeda S. Hammoud

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Objectives: This study aimed to estimate the prevalence of prepregnancy obesity and overweight, and to identify a set of sociodemographic factors that could guide weight management interventions for women prior to conception.

Design, Sample, and Measurements: Pregnant women (N = 989) were recruited in the Kuwait Birth Cohort Study during the period June 2017 to February 2020. Prepregnancy weight and height were self-reported, while current weight and height were measured in a standardized procedure. Data on sociodemographic factors were collected through face-to-face interviews conducted by a trained data collector. Multinomial logistic regression was used to investigate the association between sociodemographic …

The Impact Of Substance Abuse Problems And Serious Mental Illness/Serious Emotional Distress On Post-Discharge Residential Status Among Clients With Behavioral And Cognitive Disorders: Evidence From Samhsa Mh-Cld Data, Eden Moges, Norma Rochez, William Peycha, Aditya Chakraborty

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

BACKGROUND: Behavioral and cognitive disorders can undermine housing stability, a key determinant of mental health recovery, with persistent disparities across demographic and socioeconomic groups. This study examined the associations of substance abuse problems (SAP) and serious mental illness/serious emotional distress (SMISED) with residential status at discharge from mental health facilities.

METHODS: This cross-sectional study used the utilized data from the Substance Abuse and Mental Health Services Administration (SAMHSA). Descriptive statistics were used to summarize demographic characteristics, whereas univariate and multivariable logistic regression models were employed to assess associations between the covariates and the residential outcome, adjusting for a variety of …

Paneugenesis: A Regenerative Systems Hypothesis For Advancing Health Promotion, Craig M. Becker, Leslie Hoglund, Beth Chaney, Joseph G. L. Lee, Michael Stellefson, Alex Davis

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Despite major advances in health promotion science, dominant approaches remain largely prevention-and risk-reduction-oriented. The prevailing orientation creates a substantial opportunity to advance generative, system-design strategies that intentionally produce well-being rather than merely prevent disease. This article proposes paneugenesis as a regenerative systems framework and a testable theoretical model for the intentional creation of net-positive outcomes, thereby extending health promotion beyond its traditional pathogenic emphasis. Paneugenesis integrates systems science, salutogenesis, behavioral science, complexity theory, and quality management principles into a unified four-function process: operationalizing idealized outcomes, identifying key precursors, optimizing processes, and continually plotting progress through feedback mechanisms. The central hypothesis …

The Impact Of True Crime Podcasts On Cold Case Investigations And Victims' Advocacy, Kyle Gamache, Karlie Rice, Ashley Allen, Willem Pontbriand

Arts & Sciences Faculty Publications

True crime has long captivated public interest, evolving from sensationalized accounts in the Middle Ages to the widespread popularity of modern podcasts. This qualitative study explores the ethical complexities of true crime media by examining how it intersects with criminal investigations, public perception, and victim advocacy. Through in-depth interviews with law enforcement officers, victim advocates, and true crime content creators, the study investigates differing attitudes toward the role of podcasts in active and cold cases. Using Colaizzi’s (1978) phenomenological method, data were analyzed to uncover shared and divergent themes across professional roles. Law enforcement participants voiced concerns over web- sleuthing …

Meta-Analysis Of The Health Effects Of Gas Cooking In Homes, P. Jacob Bueno De Mesquita, Núria Casquero-Modrego, Erik Pontes

Arts & Sciences Faculty Publications

Background: Given existing evidence linking indoor gas combustion with potential health effects, we aimed to quantify from epidemiologic field studies the health impacts associated with residential gas cooking and unvented heating.

Methods: We evaluated and synthesized results from peer-reviewed epidemiologic studies that isolated associations between exposure to residential gas cooking and/or unvented heating and an unrestricted range of health outcomes. We focused on studies conducted since 2000 that cited at least one of five, well-regarded studies. We evaluated relevant studies and computed pooled effect estimates, where at least three studies contributed data, using random effect meta-analysis models with inverse-variance weighting. …

Mapping Functional Connectivity In The Pigeon Brain With Wide-Field Optical Imaging, Kathryn C Chenard, Annie R Bice, Seana H Bice, Joseph P Culver, Paula Gerliz, Xavier Helluy, Onur Güntürkün, Jason W Trobaugh, Mehdi Behroozi, Carlos A Botero

2020-Current year OA Pubs

SIGNIFICANCE: Adapting optical imaging technology to avian models can overcome many limitations imposed by functional magnetic resonance imaging (fMRI), which currently restricts the number of species used to study functional connectivity. Developing advanced technology to expand the diversity of species that can be effectively imaged is crucial for addressing significant questions that are currently unreachable, such as understanding the evolution of cognition from a comparative perspective.

AIM: We assessed the potential of optical imaging technology to measure functional connectivity in birds, utilizing pigeons as an avian model. We evaluated whether we could partition the dorsal surface of the pigeon brain …

Uncertainty-Guided Test-Time Optimization For Personalizing Segmentation Models In Longitudinal Medical Imaging, Jaehee Chun, Austin Castelo, Mckell Woodland, Caleb O'Connor, Mais Al Taie, Mohamed Eltaher, Aashish Gupta, Bilel Daoud, Shanli Ding, Jeddy Bennett, Anirban Maitra, Matthew A Firpo, Kimberly Kirkwood, Eugene J Koay, Kristy K Brock

Faculty, Staff and Student Publications

Background: Accurate and consistent image segmentation across longitudinal scans is essential in many clinical applications, including surveillance, treatment monitoring, and adaptive interventions. While personalized model adaptation using patient-specific prior scans has shown promise, current approaches typically rely on fixed training durations and lack mechanisms to determine optimal stopping points on a per-patient basis, particularly in the absence of validation labels.

Purpose: We propose an uncertainty-guided test-time optimization (TTO) framework that dynamically adjusts the personalization duration for each patient using a validation-free stopping criterion based on predictive uncertainty.

Methods: Our framework personalizes a generalized segmentation model using patient-specific prior imaging and …

Advancing Cybersecurity Through Userland Memory Forensics: From Runtime Analysis To Security Applications, Hala Ali

Theses and Dissertations

Memory forensics has become a crucial component of digital investigations, particularly for detecting malware operating solely in system memory. As operating system vendors implemented kernel access restrictions, malware authors shifted to userland malware. However, existing memory forensics techniques have largely focused on kernel-level analysis, leaving userland runtimes insufficiently covered. This dissertation addresses this gap by expanding memory analysis capabilities across two distinct paradigms: interpreted and compiled runtimes. The first phase targets the Python runtime, developing automated recovery techniques that enable several security applications. For malware detection, these techniques extract critical forensic artifacts such as encryption keys and command-and-control configurations. For …

Theoretical Study Of Long-Range Molecular Interactions, Adrian Luis Batista-Planas

Doctoral Dissertations

Describing intermolecular forces is fundamental to modeling and predicting the behavior of molecular systems. In particular, long-range molecular interactions—with electrostatic, induction, and dispersion as main components—play a critical role, especially for low-temperature and low-density regimes. Long-range interactions are often described through perturbation theory, representing the electronic charge distribution via multipolar series of the moments and polarizability tensors corresponding to each molecule. However, while the theory is well-established, obtaining the resulting analytical expressions (and their practical implementation) constitutes a highly complex and system-dependent task. To address this challenge, we developed Long-Range-Fit (LRF), an interactive and user-friendly software package designed to automate …

Security And Energy-Efficiency In Federated Learning, Priyesh Ranjan

Doctoral Dissertations

Federated Learning (FL), which facilitates collaborative model training and protects users' privacy, has drawn great interest from the research community. With FL, the participants train their models on local data and submit the corresponding updates for aggregation to a server. While concealing the participants' identities, FL may attract adversaries aiming to hamper the underlying model. These adversaries aim to submit malicious weight updates that corrupt the performance of the server model. Further, these models when communicated to the participating clients extend the behavior which is undesirable. Additionally, FL suffers from increased energy consumption at the edge device level due to …

Adaptive Machine Learning Framework For Microstructural Optimization And Mechanical Performance Prediction In Steels, Henry Adekola Haffner

Doctoral Dissertations

Evolution of microstructures, such as polygonal ferrite, acicular ferrite, bainite, and martensite, plays a pivotal role in determining the final microstructural and mechanical properties of steel products. Given the established inter-relationship between processing parameters, microstructure, properties, and performance, precise control of phase transformation is essential to achieve pre-determined properties. To understand transformation routes in different steel grades, time-temperature-transformation (TTT) and continuous-cooling-transformation (CCT) diagrams are necessary and can be described using the Johnson-Mehl-Avrami-Kolmogorov equation and Scheil’s additivity rule. This study presents a comprehensive computational framework for predicting and optimizing microstructure and mechanical properties in advanced high-strength steels (AHSS) using adaptive machine …

Exploring New Poly-Anion Based Materials For Lithium – Ion Battery Cathodes, Sutapa Bhattacharya

Doctoral Dissertations

The escalating global demand for sustainable energy storage solutions has driven intensive research into novel electrode materials. Polyanion-based cathode materials have emerged as promising candidates due to their structural stability, safety, and voltage tunability enabled by the inductive effect of polyanionic groups. This research explores the synthesis, crystal structure, and electrochemical performance of new polyanion-based cathode materials featuring vanadium, molybdenum, and iron within phosphate and selenite frameworks.

Novel selenite-based materials, such as LiFe(SeO₃)₂ and Li₀.₂₅V₂O₃(SeO₃)₂, were synthesized and characterized, revealing stable electrochemical cycling associated with Fe2+/Fe3+ and V⁴⁺/V⁵⁺ transitions. Additionally, a systematic investigation was conducted on molybdenum phosphate compounds, including …

Essays On Accelerated Failure Time Models For Recurrent Event Data, Emmanuel Masavo Djegou

Doctoral Dissertations

Recurrent event data arise in many fields such as medicine, reliability, insurance, and economics, where the same event may occur repeatedly for a subject. Accelerated Failure Time (AFT) models provide an intuitive framework for relating covariates to event times and offer a useful alternative to proportional hazards models, allowing direct prediction of event timing under right censoring. However, existing AFT extensions for recurrent events, such as accelerated gap time (AGT) models, often fail to account for interventions between events and may not capture complex temporal patterns.

In this work, we first propose a class of semiparametric AGT models incorporating an …

Zirconium Carbide Based Materials For Extreme Aerospace Environments, Nathaniel Hyman Blatt

Doctoral Dissertations

This research focuses on the processing and properties of zirconium carbide-based materials to promote their use in extreme environment aerospace applications, including nuclear thermal propulsion and hyper sonics. Several carbide systems including ZrC, ZrC-Mo cermets, (Zr, Nb)C, and a high entropy carbide were developed. The ZrC-Mo cermet was studied extensively to understand the effect of starting carbide grain size on the final microstructure, composition, elastic moduli, hardness, fracture toughness, room and elevated temperature flexural strength, thermal diffusivity, electrical resistivity, thermal expansion coefficient, and thermal conductivity. It was shown that heat transport in the cermets was dominated by the ZrC phase …

Incremental Cluster Validity Indices And Their Role In Interpreting Lifelong Learning Systems, Niklas Max Melton

Doctoral Dissertations

Clustering and supervised learning are often treated as distinct paradigms, yet both rely on structure in feature space. This dissertation investigates the relationship between cluster validity indices (CVIs) and supervised learning in real-time and lifelong learning settings where data arrive incrementally and cannot be revisited. Across four studies, it develops methods for online cluster validation, uses supervised learning to improve their interpretability, and applies these ideas to evaluating performance degradation in continual learning.

The first study extends incremental cluster validity indices (iCVIs), enabling widely used validation metrics to operate in streaming environments. Experiments on synthetic and real-world datasets show systematic …

Probability Of Detection For Corrosion-Induced Mass Loss And Yield Strain In Metal Structures Using Fe-C Coated Long Period Fiber Gratings Sensors And Electroresistive Strain Gauges, Ying Zhuo

Doctoral Dissertations

"Graphene-based, Fe-C coated long-period fiber grating (LPFG) sensors individually reveal a causal relation between LPFG wavelength change and Fe-C mass loss. Their correlation parameters vary significantly between the sensors mainly because hydrophobic multilayer graphene and graphene oxide make sensor fabrication inconsistent. This study explores MXene-based, Fe-C coated LPFG sensors due to MXene’s inherent hydrophilic property in the presence of surface functional groups such as hydroxyl and oxygen and the probability of detection (POD) for mass loss from Fe-C coated LPFG sensors when deployed at a fixed location. A novel fabrication process incorporating Piranha solution pretreatment is proposed to ensure uniform …

Bond Graph And Extended Generalized Average Method With Applications In Cyber-Physical Systems And Power Electronics, Arnold Anthony Fernandes

Doctoral Dissertations

"This research examines two applications of control theory. The first application considers the bond graph (BG) modeling technique, which is used to develop the MATLAB structural analysis toolbox (MATSAT), an open-source toolbox for sensor placement and qualitative system analysis that considers the observability and fault-detection capabilities of multi-domain cyberphysical systems. The toolbox provides information on redundant sensors, guiding the system designer in cost and security trade-offs. The toolbox uses traditional BG causality assignment procedures. Additionally, MATSAT provides optimal causality assignment methods that perform significantly better at assigning causality to BGs with increased junctions, sources, and simple meshes, without encountering causality …

Quantifying, Forecasting, And Mitigating Construction Labor And Material Challenges In A Dynamic Global Economy Using Econometrics And Deep Learning, Ahmed Gamal Elsayed Mohamed Shiha

Doctoral Dissertations

"The construction industry contributes to the global economy, yet its cost management practices remain constrained by labor shortages and material price volatility. These challenges are intensified by economic disruptions, geopolitical tensions, and trade policy shifts. Despite a growing body of literature, five knowledge gaps remain unaddressed: (1) the absence of dynamic, and localized measures of construction labor shortages; (2) limited empirical investigation of macroeconomic leading indicators of labor shortages; (3) underutilization of deep learning (DL) algorithms in forecasting local construction labor earnings; (4) the limited treatment of structural breaks in existing construction material price forecasting models; and (5) the lack …

An Energy-Stable Mixed Cg-Dg Scheme For A Full Shliomis Phase Field Model Of Two-Phase Ferrofluid Flows, Mahdi Gharehbaygloo

Doctoral Dissertations

"Ferrofluids are magnetic nanoparticle suspensions whose motion couples surface tension, flow field, magnetostatics, and magnetization dynamics. This dissertation develops, analyzes, and validates an energy-stable finite element method for a two-phase ferrofluid model that couples the Cahn-Hilliard equations with the full Shliomis model of single-phase ferrofluids, retaining its damping torque term, magnetic torque term, and magnetic stress term.

The spatial discretization is a mixed continuous Galerkin (CG) and discontinuous Galerkin (DG) formulation. It uses continuous ��2 elements for the phase field, chemical potential, velocity, and magnetostatic potential, discontinuous ��2 elements for the magnetization, and discontinuous ��1 elements for the pressure. The …

Innovative Methods In Intact Rock Deformation Detection, Ali Abdullah M Alzahrani

Doctoral Dissertations

"An accurate assessment of intact rock deformation is imperative in engineering activities carried out either in or above rock masses. However, instruments used for post-peak deformation are easily debonded and cannot record the whole process of post-peak deformation required in the failure modeling of rocks. This dissertation focuses on the investigation of the performance of two non-contact measuring systems (Laser Displacement Sensor (LDS) and Inductive Proximity Sensor (IPS)) compared to the contact type instrument (conventional Strain Gauge System (SGS)) in monitoring intact rock deformations. Subsequently, an IPS was installed in a high-pressure triaxial testing system before investigating its performance in …

Harnessing Coherent-Wave Control For Sensing Applications In Scattering Media, Pablo Xavier Jara Palacios

Doctoral Dissertations

"Diffuse electromagnetic waves are widely used for non-invasive sensing and imaging in complex scattering media such as biological tissues. A fundamental limitation of such techniques is the scarcity of detected photons: as the source-detector separation increases to access deeper regions of the medium, the signal strength decays rapidly, leading to poor signal-to-noise ratio and limited sensitivity. This dissertation addresses this challenge through a combination of theory and computation.

We show that coherent control of the incident optical wavefront can compensate for the scarcity of detected photons that limits conventional diffuse optical imaging, typically performed in the near-infrared spectral region. By …

Language Acquisition And Emergent Literacy In The 21st Century, Albert H. White Jr.

Open Touro Created

2026

Grounded in contemporary scholarship and pedagogical research, this work employs an integrated, multidisciplinary approach to 21st-century literacy instruction while prioritizing diversity, equity, and inclusion. Through its multimodal, universally accessible design—featuring interactive components, multimedia elements, and curated links to the New York State Department of Education Standards and Instruction Website—the resource facilitates critical engagement with content and alignment with state-mandated learning standards. Supplementary materials for pre-service teacher preparation and professional development are embedded throughout, enabling educators to customize and repurpose content for their specific contexts. Developed in partnership with Touro University and Colleges, this OER supports the continuous professional development …

Location, Location, Reaction: How Mandatory Ip Disclosure Silences Critics And Sparks Backlash, Sirui Li, Ping Xu, Yue Guo

Political Science Faculty Publications

Previous research has identified two competing outcomes of regulatory disclosure for negative online sentiment: chilling effects, which suppress negative online sentiment, and reactance effects, which intensify it. In this study, we apply these theories to examine the impact of mandatory IP location disclosure on citizens' negative online sentiment expressed on Sina Weibo, a leading Chinese social media platform. We argue that the mandatory IP location disclosure can have opposite effects on negative online sentiment depending on the type of social media context. To test this, we use a quasi-natural experiment and analyze more than 160,000 comments posted before and after …

When The Internet Attacks, Craig Cowie

Cardozo Law Review

Courts have struggled with applying personal jurisdiction in cases involving intentional torts where the defendants act outside the forum, and the problem is particularly apparent and acute when the defendants use the internet to commit the tort. For example, is there jurisdiction when a defendant doxxes someone and calls for violence? What if they leave a bad Yelp review? Or tweet a defamatory statement? Courts have used many tests for determining whether personal jurisdiction is appropriate in these situations, but there has been relatively little recent scholarship on whether these tests are appropriate for analyzing personal jurisdiction in these contexts. …

Much Ado About Misjoinder: An Alternative To Fraudulent Misjoinder To Preserve Defendants’ Right To Removal In Pharmaceutical And Medical Device Products Liability Cases, Alexander Flaum

Cardozo Law Review

Unresolved questions surrounding the contours of the fraudulent misjoinder doctrine have understandably led to reluctance by courts to adopt it, despite its utility in protecting defendants’ access to federal court. This is particularly troubling in the context of pharmaceutical and medical device products liability cases. It is common in these actions for plaintiffs, whose only connection is having consumed a particular pharmaceutical product at different points in time and for different durations, to strategically join in one action to defeat complete diversity and prevent removal to federal court. However, federal courts are not powerless to prevent such procedural gamesmanship. By …

Browser-Based Phishing Detection System Using Modern Web Technologies, Muhammad Arshad, Beena Sherin Kuriakose, Choo Wou Onn, Farhan Ahmad Siddiqui, Mohammad Shahid Kamal

Research Outputs: 2025-Present

Phishing remains one of the most persistent cybersecurity threats, exploiting human trust to steal sensitive information through deceptive websites. Traditional detection methods, reliant on blacklists and reactive reporting, offer limited protection against rapidly evolving zero-day attacks. To address these challenges, this study proposes and evaluates a hybrid browser-resident phishing detection framework that combines three complementary detection mechanisms: a locally executed Random Forest model using URL lexical features, lightweight real-time DOM structure analysis, and VirusTotal’s multi-engine reputation service. Communication between the client and server is optimised using gRPC over the QUIC protocol, ensuring secure, high-performance data exchange with built-in retry mechanisms …

Empirical-Based Model Of Spatio-Temporal Errors, Godwin Naaba Ndaa

Masters Theses

This research develops an empirical model to characterize spatial-temporal InSAR errors and improve the accuracy of deformation time-series analysis. Using standardized Sentinel-1 HyP3 products and MintPy, the study quantifies how correlated noise affects velocity precision and validates the results against continuous GNSS velocities.

Residual velocities are near-Gaussian, with σ ~0.92–2.04 cm/yr and ~1 cm/yr on average. Variograms show power-law spatial structure with a non-zero nugget implicating troposphere and decorrelation while errors drop exponentially with more acquisitions; spatial uncertainty is strongly affected by unwrapping errors, coherence, and tropospheric noise, not simply troposphere.

A comparative assessment of on-demand cloud processing with other …

Assessment Of Simulation Software Used For Cubesat Gnc Verification And Validation By University Research Teams, Alexander Taiyo Newett

Masters Theses

As the growth in university satellite teams continues, along with the greater trend in the small satellite market, the need for a guidance, navigation, and control verification and validation pipeline suitable for these young and inexperienced teams becomes evident. Much of the mathematical theory and software implementation of GNC concepts are large hurdles for teams largely composed of undergraduate students.

Many software packages exist that can help these teams achieve GNC verification and validation. If the learning curves of these software packages can be overcome, new satellite teams have the opportunity to better build and test GNC algorithms that are …

Assessment And Comparison Of Selected Carbon Ablation Models In Hypersonic Free-Flight And Arc-Jet Conditions, Andrew Steven Heider

Masters Theses

Accurate prediction of ablative thermal protection system (TPS) performance is critical for hypersonic vehicle design. However, numerical prediction of ablation remains challenging because results are influenced by complex physics and the choice of surface chemistry model and assumptions made in material response calculations. The objective of this work is to evaluate several carbon ablation models and their implementation within modern computational fluid dynamics (CFD) codes. Numerical simulations were performed using NASA’s LAURA flow solver and the commercial CFD code ANSYS Fluent, which coupled Navier-Stokes with surface chemistry models describing carbon oxidation, nitridation, and sublimation reactions. Several reaction sets were considered, …

Autonomous Navigation Development And On-Ground Validation For Satellite Rendezvous And Proximity Operations, Logan Banker

Masters Theses

Spacecraft rendezvous and docking are critical mission phases for various applications of spaceflight, including active debris removal (ADR) and in-orbit servicing, assembly, and manufacturing (ISAM). While previous missions utilized humans to perform rendezvous and docking, this style of mission greatly increases safety risks and cannot be implemented on a large scale. Autonomous servicing satellites provide a path towards scalable rendezvous and proximity operations (RPO) because these autonomous agents do not require human intervention. This work presents a lightweight convolutional neural network (CNN) for the navigation system of an agent which analyzes monocular images and predicts the target's position and orientation …