Digital Commons Network^™

A Global Application Programming Interface-Enabled Earthquake Ground Motion Relational Database For Engineering Applications, Tristan E. Buckreis, Chukwuebuka C. Nweke, Pengfei Wang, Scott J. Brandenberg, Maria E. Ramos-Sepúlveda, Rashid Shams, Shako Mohammed, Renmin Pretell, Silvia Mazzoni, Paolo Zimmaro, Jonathan P. Steward

Civil & Environmental Engineering Faculty Publications

We present a application programming interface (API)-enabled relational database of global earthquake ground motion intensity measures, associated metadata, and processed time-series data. Raw ground motion records were processed by the authors using either manual or semi-automated processing procedures, and every processed record has passed a quality review by a trained analyst. Computed intensity measures include peak acceleration and velocity, pseudo-spectral acceleration response spectra, cumulative absolute velocity, Arias Intensity, and Fourier amplitude spectra. The processed time-series data, associated metadata, and ground motion intensity measures were organized into a web-served relational database consisting of 32 tables connected by primary/foreign key pairs. Ground …

The Bioremediation Of Nutrients And Heavy Metals In Watersheds: The Role Of Floating Treatment Wetlands, Nirmal Kumar, Bhupinder Singh, Yuanze Chen, Abishek Kafle, Ram L. Ray, Sandeep Kumar, Xiaonan Shan, Venkatesh Balan

Civil & Environmental Engineering Faculty Publications

Floating treatment wetlands (FTWs) are engineered systems that utilize floating platforms planted with aquatic vegetation to treat polluted water such as stormwater, agricultural runoff, and wastewater. FTWs have emerged as promising and environmentally sustainable solutions for water purification. This review synthesizes the current knowledge on FTW design, plant selection, and performance evaluation. It highlights key factors influencing nutrient and heavy metal removal, including the hydraulic retention time, mat thickness, and types of plant species. Recent findings on the roles of root architecture, microbial interactions, and seasonal variability in treatment efficiency are also discussed. Additionally, the review explores advanced analytical methods …

Laboratory Investigation Of Shape And Initial Orientation Effects On Surf Zone Object Migration, Temitope E. Idowu, Jack A. Puleo

Civil & Environmental Engineering Faculty Publications

Discarded objects like munitions in marine environments pose public safety risks. The behavior of various density objects deployed at four cross-shore positions in the surf zone of a large-scale 120 m x 5 m x 5 m wave flume were observed under different forcing conditions. Net migration was predominantly directed offshore, with approximately 70 % offshore migration observed near the outer surf zone. Density, shape, and initial orientation were identified as important to object behavior, with density acting as the dominant driver in 67 % of the object pairing scenarios. The influence of shape and initial orientation on net migration …

Improving The Psychosocial Work Environment To Prevent Sickness Absence And Turnover In Nurses: A Systematic Review, Laurent Corthésy-Blondin, Alessia Negrini, Samantha Vila Masse, Christine Genest

Revues de littérature, synthèses de connaissances

In nurses, the high rates of sickness absence (SA) and turnover generate staggering costs. Their prevalence could be reduced by acting on the work-related psychosocial factors (WRPFs) that can impact the mental health of nurses and lead to their absences and departures. However, there is a lack of consensus regarding the effectiveness of interventions that target WRPFs from a SA and turnover prevention perspective. This systematic review aims to identify interventions that target WRPFs to reduce SA and turnover in nurses, describe the methods used to evaluate their effectiveness, and report on their effectiveness at improving SA, turnover rates, and …

Developing A Guideline And Feasibility Tool For Highway Infrastructure Readiness For Autonomous Vehicles, Soujanya Pillala

Master's Theses and Doctoral Dissertations

Autonomous Vehicles (AVs) are one of the advanced technologies that is being developed, and infrastructure modifications supporting AVs will be the next major research that is needed. A systematic literature review methodology is used to explore key parameters needed for implementation of AVs. The key parameters are analyzed against economic implications, costs and projected benefits of the integration of Level 4 and Level 5 AVs into the highway system, offering a nuanced understanding of deployment of AV technologies. The research aims at developing a guideline and framework for highway infrastructure readiness for AVs, a toolbox for policy makers and government …

Finishing My Sentence: A Critical Autobiography Of A Black Woman Who Has Been The Principal And The Prisoner, K.C. Wilbourn Snapp

Master's Theses and Doctoral Dissertations

This critical autobiography explores how my experiences as a Black woman born and raised inDetroit profoundly shaped my worldview, leadership style, and professional decisions as a school principal. Anchored in the theoretical framework of critical race feminism, this study examines the intersections of race, gender, and place, revealing how Detroit’s complex social, historical, and cultural dynamics informed my development and the paradigms that guided my thinking as both an educator and a leader. Through self-reflection and scholarly inquiry, I analyze how these lived experiences influenced my approach to problem-solving, community engagement, and the pursuit of equitable learning environments. This work …

Flexural Post-Cracking Performance Of Macro Synthetic Fiber Reinforced Super Workable Concrete Influenced By Shrinkage-Reducing Admixture, Jingjie Wei, Nima Farzadnia, Kamal H. Khayat

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Most literature has focused on the effect of shrinkage-reducing admixture (SRA) in shrinkage mitigation resistance of concrete. This study aims to examine the impact of SRA on the efficiency of macro synthetic fibers (MSF) in enhancing the flexural post-cracking behavior of fiber-reinforced super workable concrete (FR-SWC). A comparative analysis of the influence of fiber combinations on the flexural post-cracking behavior of beams is also included. Results showed that a higher dosage of SRA, particularly at 5 % by mass of binder, had a noticeable negative impact on flexural post-cracking performance of beams while exhibiting positive effect on workability and shrinkage …

Active Phytoextraction Of Toluene Shifts The Microbiome And Enhances Degradation Capacity In Hybrid Poplar, Michael Benisrael, Dasiel Obregon, Philipp Wanner, Jeremy Fernandes, Joel G. Burken, Ramon Aravena, Beth L. Parker, Elizabeth A. Haack, David T. Tsao, Kari E. Dunfield

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Hybrid poplars are widely recognized for their effectiveness in remediating subsurface aromatic hydrocarbon contaminants, including benzene, toluene, ethylbenzene, and xylene isomers (BTEX). While BTEX compounds are frequently found in the transpiration streams of poplars at contaminated sites, the microbial dynamics within these trees, particularly in response to hydrocarbon exposure, remain underexplored. This study utilized high-throughput amplicon sequencing to investigate the trunk microbiome in hybrid poplars at a field-scale toluene phytoremediation site. Across the plant growth season (spring to late summer), we observed a significant seasonal increase in bacterial diversity and richness, particularly in trees located in areas with the highest …

Performance And Economic Evaluation Of Asphalt-Based High Friction Surface Treatment (Hfst) Applications, Alireza Roshan, Magdy Abdelrahman

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

High Friction Surface Treatments (HFSTs) are recognized for enhancing friction between tires and road surfaces, with reduced road accidents being a key benefit. Epoxy-based HFSTs, though widely used, come with challenges like compatibility issues with existing pavements, higher installation and removal costs, and reduced durability tied to substrate quality. Recently, state agencies have increasingly focused on developing asphalt-based alternative binders for HFST applications as highlighted in the National Cooperative Highway Research Program (NCHRP) RFP #NCHRP 10-145. This study introduces asphalt-based HFSTs as an alternative to traditional epoxy-based treatments. Various aggregate types were examined for friction performance and the effect of …

Bridging Law And Machine Learning: A Cybersecure Model For Classifying Digital Real Estate Contracts In The Metaverse, Faris Kamil Mihna, Hazim Akram Sallal, Lobna Abdalhusen Al-Seedi, Hasan Ali Al- Tameemi, Mustafa Abdulfattah Habeeb, Yahya Layth Khaleel, Dheyaa A. Mohammed

Mesopotamian Journal of Big Data

The metaverse indicates an ever-evolving digital ecosystem where virtual real estate has now become an asset class. These properties, subject to smart contracts on the blockchain and represent as non-fungible tokens (NFTs), gives rise to new legal and cyber issues due to the decentralized and dematerialized nature of these digital assets .This paper proposes a machine learning approach to classify the digital real estate contracts into Ownership and Lease contracts. The study utilizes a dataset of one thousand digital real estate contracts collected from platforms such as Decentraland and The Sandbox. The dataset also included attributes such as plot size, …

Uir: Implementing Deep Neural Networks In Addition To Conventional Algorithms For Ultra-Image Recovery, Ali Salim Rasheed, Alaa Hamza Omran, Mohammad Aljanabi, Mohammed Almaayah, Rami Shehab, Mansour Obeidat, Theyazn H. H. Aldhyani, Mosleh Hmoud Al-Adhaileh

Mesopotamian Journal of Big Data

Many of the images that can be accessed through web search engines or social media networking sites are rare and not high quality because they are endangered or disappear. There must be a way to increase the quality of these images and conduct experiments to reduce noise, remove blur, and make them sharper to reach high-quality surfaces. Approaches that seek to achieve better results compete to increase the efficiency of those low-resolution images and generate images with the same color (RGB) characteristics but with higher quality. Deep learning algorithms, especially the use of convolutional neural networks (CNNs), have achieved advanced …

Ai-Driven Smart Contract Vulnerability Detection: A Systematic Review Of Methods, Challenges, And Future Prospects, Saad Al Azzam, Raenu Al Kolandaisamy, Ghassan Al Dharhani

Mesopotamian Journal of Big Data

Smart contracts (SCs) have become an essential component in the world of decentralized applications, automating transactions across blockchain networks without the need for intermediaries, and with this rise in adoption, the technology has also brought forth growing concern due to security vulnerabilities, which have led to serious financial damage, and the problem is far from being solved. Traditional auditing methods often struggle to capture the more intricate vulnerabilities hidden within smart contract logic, particularly owing to the irreversible nature of blockchain transactions. Given these challenges, researchers have been actively exploring more advanced detection techniques. Despite progress, many existing studies tend …

A Hybrid Machine Learning Approach For Enhanced Diabetes Prediction: Integrating Image And Numerical Data, Ahmad Shaker Abdalrada, Ali Fahem Neamah, Huda Lafta Majeed, Ahmed Raad Al-Sudani

Mesopotamian Journal of Big Data

Diabetes mellitus (DM) continues to escalate as a worldwide health emergency issue, with approximately 537 million adults currently diagnosed and forecasts estimating a further increase to 643 million by 2030. Early and precise foretelling of DM remains a decisive factor for timely intervention, thereby mitigating severe downstream sequelae such as cardiovascular disease, peripheral neuropathy, and diabetic retinopathy (DR). Conventional prognostic frameworks typically depend on exclusively either structured tabular measurements or visual medical imagery, which constrains comprehensive diagnostic capacity. This contribution confronts such limitation by advancing a hybrid machine learning (ML) methodology that synergistically combines deep learning—specifically, convolutional neural networks (CNNs) …

Psoa-Crl: A Hybrid Multi-Objective Routing Mechanism Using Particle Swarm Optimization And Actor-Critic Reinforcement Learning For Vanets, Mustafa Maad Hamdi, Baraa Saad Abdulhakeem, Ahmed Adil Nafea

Mesopotamian Journal of Big Data

Vehicular ad hoc networks (VANETs) serve vehicles and infrastructure systems to communicate in real time for critical safety functions and traffic control. The highly mobile nature of VANETs with rapid topology changes, high mobility, and frequent disconnections is a very challenging situation for routing protocols. Most of the current approaches are static and tend to focus on a single metric rather than being flexible in practical environments. This paper introduces a hybrid routing method that is capable of maintaining a high packet delivery rate, low delay, and stable connectivity in VANETs with dynamic traffic situations. To address these problems, in …

A Robust Model For Android Malware Detection Via Ml And Dl Classifiers, Omar Almomani, Areen Arabiat, Muneera Al Tayeb, Mohammed Amin Almaiah, Mansour Obeidat, Theyazn H. H. Aldhyani, Rami Shehab, Mahmood Rowad

Mesopotamian Journal of Big Data

The rapid growth of sophisticated Android malware (AM) threats is significant, as Android devices often store private and sensitive personal and financial information. These threats allow stealing of data, interference with device functioning, and network compromise. One of the greatest difficulties in efficient interception systems is ensuring a high level of detection accuracy for distinguishable AM variants. This study focuses on developing a robust Android malware detection model via machine learning (ML) and deep learning (DL) techniques. The model combines ML classifiers, which consist of logistic regression (LR) and decision trees (DTs), and a DL classifier, an artificial neural network …

Gai Research - A Case Study On The Implications Of Generative Artificial Intelligence (Gai) On Teaching Within An Irish Technological University., Moya Cronin

Theses

The rapid emergence of Generative Artificial Intelligence (GAI) has introduced a transformative age of technology, gaining prominent attention in higher education. This mixed-methods case study examined how GAI was understood, applied, and managed within an Irish Technological University at a time of turbulence when no official GAI policies were available as a point of reference. Lecturing and research staff were invited to participate in an online survey (𝑛 = 145) and semi-structured interviews (𝑛 = 8) to share their experiences with GAI technology within the case study university. Lack of guidance was seen to affect staff’s confidence and adoption, but …

A Relational Approach To Public Private Contracting For Innovation: The Example Of Arpa-H, Liza Vertinsky

Case Western Reserve Law Review

No abstract provided.

Strengthening Critical Infrastructure For Equity And Resilience: Findings From The Environmental Justice Data Project, Nakeina E. Douglas-Glenn Phd, Luisa Nazareno Phd, Emily Schreer, Amidu Kalokoh, Curtis Brown Mpa, Melvin Brown, Yali Pang Phd, Morgan Woodle Phd, Research Institute For Social Equity Rise

Research Institute for Social Equity

In 2023, the Research Institute for Social Equity, an institute of the Virginia Commonwealth University’s L. Douglas Wilder School of Government and Public Affairs, partnered with Resilient Virginia to explore the impact of climate change and degradation due to disasters and environmental hazards on the critical infrastructure in and around historically marginalized and underserved communities in the Richmond and Southside Virginia areas. During the study period, 2023-2025, the research team conducted both primary and secondary research in three Virginia localities: Charles City County, the City of Emporia, and a portion of the City of Richmond. Secondary research included a thorough …

Interplay Between Computational Techniques And Quantum Theory: Advancing Quantum Chaos And Quantum Error Correction, Refaat Ismail

Theses and Dissertations--Physics and Astronomy

This dissertation investigates computational techniques addressing critical problems arising within quantum chaos and quantum error correction (QEC). First, we investigate quantum dynamics via the Lanczos algorithm, establishing how its computational features — branching patterns, convergence, and complexity saturation — relate to physical properties like spectral statistics and operator matrix elements. We derive a formula connecting Lanczos coefficients to spectral densities for two-branched Lanczos sequences, revealing how branching patterns encode level repulsion—a hallmark of quantum chaos. Furthermore, we develop an analytic framework predicting Krylov complexity saturation from static system properties alone. Specifically, we decompose the saturation value into a spectral density-dependent …

Shining Light On Policy: The Case For Solar Panel Mandates On New Construction Projects, Emily Glazier

Cardozo Journal of Equal Rights & Social Justice

The note argues that solar panel mandates on new construction projects are a necessary and sensible approach to reducing greenhouse gas emissions and achieving climate goals, but their implementation must include provisions to protect vulnerable communities and address environmental justice concerns. While such mandates face legal and political challenges, the benefits of decreased emissions and energy independence outweigh the costs, particularly when paired with measures like community solar systems, incentives, and exemptions.

Recognition Of Alzheimer’S Disease Stages Via Inceptionv3 And Resnet50, Iman Aljubouri, Mostafa Ragheb, Mohamad Hamady

Mesopotamian Journal of Big Data

Early and precise detection of Alzheimer’s disease (AD) is essential for successful treatment. This research presents a system that autonomously detects and categorizes the phases of Alzheimer’s disease via brain scans and sophisticated deep learning techniques, including InceptionV3 and ResNet50. These models started with pretrained weights and were augmented by including bespoke classification layers, which consisted of dropout, batch normalization, and dense layers to increase performance and mitigate overfitting. The preprocessing processes included scaling the picture to 224 by 224 pixels, using average filtering for denoising, and converting the color space to guarantee compatibility with the models. Evaluations of the …

Intraspecific Variation In Plant–Fungal Interactions Across Tidal Elevation In A Salt Marsh, Torrie Hanley, Catherine Gehring, Ron Deckert, Behzad Mortazavi, Christina Richards, A. Randall Hughes

Biology Faculty Publications

Interspecific interactions vary depending on environmental and genetic factors, with intraspecific variation potentially altering these relationships. Specifically, intraspecific variation within host plants and their endosymbionts may affect above- and belowground interactions depending on environmental conditions. We examined how intraspecific variation within the salt marsh foundation species Spartina alterniflora and a common fungal root endosymbiont (Lulworthia sp.) affected their interactions. We conducted a glasshouse experiment comparing interactions between Spartina growth forms (tall-form from low marsh and short-form from high marsh) and Lulworthia isolates from different marsh locations, and a laboratory experiment exposing Lulworthia isolates from both endpoints of a tidal elevation …

Attention's Loop: A Sculptor's Reverie On The Coexistence Of Substance And Spirit, Elizabeth King, Katherine Wetzel

Attention's Loop

This is a book about attention and memory. I am a sculptor, so it is also a book about size, dirt, artifice, work, and eye. The text is a set of stories and interruptions that pile up to make a play of overlapping loops. My organizing principle is the image of the round-trip, so one may open the book and step into it at any point. Most of Katherine Wetzel’s photographs are of a single sculpture. It is a self-portrait, a particular kind of round-trip, and it is small: one-half life-size. Called Pupil, it is jointed and movable, and …

Department Of Medicinal Chemistry, Virginia Commonwealth University: Historical And Personal Reflections, Richard A. Glennon

VCU University History Books

This book presents a historical account of Virginia Commonwealth University's Department of Medicinal Chemistry, compiled and recollected in 2025 by Professor Emeritus Richard A. Glennon. Included are personal reflections alongside a historical overview of the department's origins and faculty, as well as newly developed programs and initiatives.

The Tyranny Of Rockefeller Center: Architecture And Infrastructure In Greater New York, Joesph M. Watson

Funded Scholarship

Rockefeller Center has long been described as a city in miniature. The project’s aspirations to completeness have been discussed in a variety of terms, but most critics and historians focus on the contrast between the project’s cohesive visual language and the more varied urban landscape surrounding it. This preoccupation has led to blind spots in received understanding of this well-known project. This article focuses on little-known infrastructural and housing projects—some built, others not—that carry the Center’s history from the heart of Manhattan to the margins of the metropolis. To ensure financial viability, these projects mostly sought to pull wealthy, white …

Forgoing Nuclear: Nuclear Power Plant Closures And Carbon Emissions In The United States, Luke Petach

Funded Scholarship

This paper examines the effect of nuclear power plant decommissioning on electricity generation and carbon emissions in the United States. Using data on nuclear reactor closures in the United States between 1993 and 2022 and data on state-level carbon emissions and electricity generation from the Energy Information Administration (EIA), this paper adopts a difference-in differences (DiD) approach to estimate the effect of nuclear decommissioning. Two-way fixed-effects (TWFE) DiD estimates suggest a nuclear plant closure increases annual state-level per capita carbon emissions between 6% and 8%. The increase in state-level carbon emissions is driven by a substitution toward fossil fuel electricity—particularly …

Precision Measurement Of The Muon’ S Anomalous Precession Frequency Using Energy Integrated Analysis In The Fermilab Muon G- 2 Experiment, Joseph Peck

Theses and Dissertations--Physics and Astronomy

This dissertation investigates the anomalous magnetic moment of the muon, aµ, through the Fermilab Muon g -2 Experiment, which aims to measure aµ to a precision of about 140 parts-per-billion.

Muons were injected into a magnetic storage ring where their decay positrons were detected by calorimeters. The changing spin and momentum orientation leads to an oscillation in decay positron counts, known as the anomalous precession frequency ! a. Using the magnetic field and !a measurements, we can determine the value of a µ. This study focuses on the Q-Method determination of !a, which utilizes an energyintegrated reconstruction rather than high-energy …

Spectral Divergence Prioritizes Key Classes, Genes, And Pathways Shared Between Substance Use Disorders And Cardiovascular Disease, Everest Castaneda, Elissa Chesler, Erich J. Baker

Funded Scholarship

Substance use disorders (SUDs) are heterogeneous diseases with 

overlapping biological mechanisms and often present with co-occurring disease, 

such as cardiovascular disease (CVD). Gene networks associated with SUDs 

also implicate additional biological pathways and may be used to stratify 

disease subtypes. Node and edge arrangements within gene networks impact 

comparisons between classes of disease, and connectivity metrics, such as 

those focused on degrees, betweenness, and centrality, do not yield sufficient 

discernment of disease network classification. Comparatively, the graph 

spectrum’s use of comprehensive information facilitates hypothesis testing 

and inter-disease clustering by using a larger range of graph characteristics. 

By adding a connectivity-based …

Influence Of Virtual Reality On Lower Extremity Joint Kinematics During Overground Walking, Hunter Derby, Nathan Conner, Adam C. Knight, Harish Chander

Funded Scholarship

The inability to regain balance following a trip-induced event is one of the leading causes of falls and fall-related injuries in older adults. Virtual reality (VR) has the potential to expose individuals to realistic environments with minimal injury risk compared to real-world exposures. The purpose of this study was to compare lower extremity joint kinematics during overground walking when exposed to real and virtually generated trip obstacles. A total of 14 healthy participants [7 male, 7 female; age: 23.46 ± 3.31 years; height: 173.85 ± 8.46 cm; mass: 82.19 ± 11.41 kg; shoe size (men’s): 9.03 ± 2.71 s] were …

New Jersey's Mitigation And Adaptation To Climate Change: An Economic Analysis Of The State's Societal Benefits Charge And Community Solar Energy Program, Nicole S. Provost

Theses, Dissertations and Culminating Projects

Understanding the current trajectory of New Jersey’s emissions and the existing legislation and programs implemented to help reduce emissions, it is evident that the State requires further actions to spur heightened emission reductions to reach its goals. One known impactful policy that could significantly accelerate this process is a carbon tax, which applies to emission-intensive goods. Although this policy does not currently exist in New Jersey, the State has a Societal Benefits Charge (SBC) that applies an approximate 3% charge to electricity and gas consumption on residential and commercial utility customers’ monthly energy bills, where revenues fund programs that offer …