Digital Commons Network^™

Great Salt Lake Basin Integrated Plan – Longterm Actions, Jake M. Serago

Spring Runoff Conference

GSL’s water level is in long-term decline. Water users face shortages and challenges. Increased temperatures, greater precipitation extremes and population changes are stressing water supplies in the basin. 84% of Utah residents think the Government is accountable for resolving issues facing GSL.

Photoplethysmography For Measuring Cognitive Load In Xr Environments: A Systematic Review, Alya Alshehhi, Qammer H. Abbasi, Rami Ghannam

School of Engineering Technology Faculty Publications

Photoplethysmography (PPG) is a low-cost, low-power biosensing technology with growing applications in education, particularly for monitoring cognitive load in eXtended Reality (XR) learning environments. Measuring cognitive load is critical for preventing overload and optimising immersive learning, yet existing approaches such as self-reports or Electroencephalography (EEG) are often intrusive, costly or impractical for real-time use. This systematic review is the first to synthesize a decade of research (2015–2025) on the use of PPG for cognitive load measurement in XR. Twenty-three studies were identified and analysed according to PRISMA guidelines, with attention to sensor placement, integration with other modalities, research design, and …

Arid: Agglomerative Regionalization Via Information Divergence, A Novel Clustering Algorithm For Geo-Spatial Data, Joshua David Sills

Dissertations and Theses

Regionalization is a clustering problem that seeks to partition geospatial data into geographically contiguous regions while remaining internally homogeneous in their attributes. It has been successfully applied towards the development of urban planning, natural resource discovery, and ecological analysis. Existing popular approaches optimize homogeneity using Euclidean or variance-based criteria, which ignore distributional differences such as variance shifts, multimodality, and higher-order dependence. This thesis introduces ARID (Agglomerative Regionalization via Information Divergence), a spatially constrained agglomerative clustering framework that replaces distance-based merging with an information-theoretic, Ward-like criterion that utilizes the Kullback-Leibler (KL) divergence. ARID constructs a neighborhood graph from coordinate space and …

Critical Heavy Metal Contamination In The Tigris River, Baghdad: A Comprehensive Assessment Using The Metal Index (Mi), Talib Kamil Abed, Abass J. Kadhem, Muwafaq Hussein Al-Lami

AUIQ Technical Engineering Science

One of the most important rivers in Iraq is the Tigris River, which serves as the primary source of drinking water and irrigation for agricultural purposes. In recent events, researchers have identified growing concerns about heavy metal contamination in the river. To decide the status of the Tigris River, the metal index (MI) was used in this study. To conduct this study, three locations were chosen along the river, and eight likely harmful heavy metals were examined: iron (Fe), lead (Pb), nickel (Ni), zinc (Zn), chromium (Cr), cadmium (Cd), manganese (Mn), and copper (Cu). The results were worrying. The metal …

Equity Evaluation Of Residents’ Transit+Walk Accessibility To Sacramento Healthcare Facilities, Masoud Ghodrat Abadi, Ahoura Zandiatashbar, Aaron Pickett, Stephanie Nemet, Soheil Sharifi

Mineta Transportation Institute

Access to healthcare is a key component of public health equity, yet many U.S. communities remain dependent on private vehicles for medical travel. This study introduces an integrated framework, the Transit+Walk Score (TWS), to evaluate how effectively residents of Sacramento, California, can reach healthcare facilities via public transit and how safely, comfortably, and conveniently they can walk from transit stops to those facilities. The research aims to inform planners, transit agencies, and policy makers in designing equitable and multimodal transportation systems. The research team combined geospatial network modeling and field-based walkability audits to assess 123 healthcare facilities across Sacramento, with …

Information Manager (Bim) Role Profiles, Davitt Lamon, Robert Moore, Stephen Lynam

Reports

This document, Information Manager (BIM) Role Profiles, has been prepared in support of the Irish Government’s Building Information Modelling (BIM) Mandate. It is the result of a collaborative effort between Build Digital, the Department of Public Expenditure, Infrastructure, Public Service Reform and Digitalisation (DPER), the Infrastructure Division, and the National Standards Authority of Ireland (NSAI). Its primary purpose is to define the competencies, responsibilities, and experience required for Information Management (BIM) roles to facilitate the successful digital delivery of public works projects. It is important to recognise that under the ISO 19650 series of standards, Information Management is defined as …

Identification Of Thruster Faults In Underwater Vehicles By Using Custom Encodings In Spiking Neural Networks, Donovan Gegg

LSU Master's Theses

Autonomous Underwater Vehicles (AUVs) are untethered robotic platforms used for tasks such as seafloor mapping, infrastructure inspection, and environmental monitoring. Recent technological advances have produced smaller, more affordable platforms, broadening access to research teams and small companies alike. This miniaturization comes at the cost of them handling drawbacks associated with a more compact machine such as reduced battery capacity as well as limited processing and sensing capabilities. These constraints make small-sized marine vehicle’s reliability critical as they can cause malfunctions, making the loss of a vehicle more likely. Actuator faults are particularly consequential as unintended and unstable control in an …

Multi-Objective Optimization Of Waste Incineration For Minimal Carbon Monoxide And Sulfur Dioxide Emission And Rate Maximization: A Case Study Of Mbezi, Mkuranga-Pwani, Tanzania, Grangay M. Nyanghura, Enock W. Nshama

Tanzania Journal of Science

Incineration is widely employed for hazardous waste disposal, but results in harmful flue gas emissions. This study optimizes a double-chamber incineration process to reduce sulfur dioxide (SO2) and carbon monoxide (CO) emissions while maximizing the incineration rate. The effects of waste mass, primary chamber temperature (PT), and secondary chamber temperature (ST) were analyzed using a full factorial design of 27 experiments. ANOVA revealed that mass had the greatest impact on emissions and incineration time, ST had a moderate effect, and PT had little influence. Regression analysis provided models for incineration time, CO, and SO2 emissions. Single-objective optimization using sequential quadratic …

A Novel Optuna-Vmd-Ml Framework For Enhanced Settlement Prediction Of Buildings Around Foundation Pits, Jing Zhang, Zhenlin Wang, Shiyu Sheng, Sifan Shen, Chuang He, Huafeng Shan, Haijie He, Li Ai

Civil Engineering Faculty Publications

The prediction of building settlement around foundation pit is of vital importance to ensure the safety and stability of urban construction projects. However, current predictions of buildings surrounding foundation pit face numerous challenges. Therefore, this study proposes a framework that integrates Optuna-based hyperparameter optimization, variational mode decomposition (VMD), and machine learning (ML) for accurate and timely settlement prediction in practical engineering scenarios, referred to as the Optuna–VMD–ML framework. Optuna is employed to tune the hyperparameters of both VMD and the ML models; the Optuna-tuned VMD decomposes the settlement time series into mode components, which are then used to train the …

Documenting For Success: A Case Study Of A Graduate Writing Retreat, Elliott Rose, Denise Wetzel, Sara C. Kern, Katie O'Hara-Krebs, Laurin Davis, Salah Seoudi, Carmen Cole

Transforming Libraries for Graduate Students

Graduate students often find themselves balancing the demands of research and writing while managing complex academic and personal needs. Writing retreats provide an intentional space for graduate students to focus on their academic goals while supporting community-building, institutional belonging, and a welcoming environment. This proposal outlines a model for academic librarians and staff to plan and implement an effective graduate writing retreat designed to support graduate student academic activities through intentional collaboration and documentation.

Penn State University’s (PSU) Graduate Student Writing Retreat started as an event housed within the University Library’s Science, Technology, Engineering, and Math (STEM) department. The retreat …

Let’S Walk And Talk: Building Trust (And Better Projects) Using Community Walk Audits, Ashlyn Devine, Taylor Firestine

Purdue Road School

This session introduces participants to the benefits gained from engaging in community-centered walking audits as practical tools for advancing quality active transportation projects and building trust with the public. Drawing from Health by Design’s Indiana Road to Zero Academy, participants will join a facilitated walking audit near the Purdue campus to discuss common concerns and questions that these exercises address while demonstrating how collaborative notetaking can translate into better planning, engineering, and policy decisions that reflect the unique needs of the community.

Sustainable Guiding Framework Of The Egyptian Rural Development Initiative (Hayah Karima), Eman Badawy Ahmed

Mansoura Engineering Journal

The Hayah Karima initiative launches to improve the standard of living for the most needy community groups across the Egyptian countryside, and it also contributes to improving the quality of services provided to the neediest villages. The research aims to propose a guiding framework for sustainability in villages of the Hayah Karima Initiative to achieve Egypt’s Sustainable Vision 2030. To achieve the goal, the research will study the goals, strategies, and principles of the Hayah Karima Initiative, the Egypt Vision 2030, COP 27, the sustainability evaluation criteria, and the Green Villages Initiative. Consequentially, in addition to A questionnaire to measure …

Safe-Calibrated Tcn–Transformer Transfer Learning For Reliable Battery Soh Estimation Under Lab-To-Field Domain Shift, Ehab Bayoumi

Mechanical Engineering

Battery state-of-health (SoH) estimation is central to transportation electrification because it conditions safety limits, warranty accounting, power capability management, and long-horizon fleet optimization. Although deep temporal architectures can achieve high laboratory accuracy, field deployment is frequently limited by laboratory (Lab)-to-field (L2F) domain shift that alters input statistics, feature definitions, and noise regimes. Under such a shift, predictors may remain strongly monotonic, preserving degradation ordering and become operationally unreliable due to systematic output distortion (e.g., compression/warping of the SoH scale). A deployment-complete L2F transfer learning pipeline is presented, built around a gated Temporal Convolutional Network (TCN)–Transformer fusion backbone, domain-specific adapters and …

Development And Preliminary Pressure Control Validation Of Woundwatch: Npwt System With Remote Monitoring Capabilities, Swetha Senthil Nathan

The Cardinal Edge

Chronic wounds, including diabetic foot ulcers, pressure sores, and venous leg ulcers, affect over 40 million individuals worldwide and are often complicated by delayed healing due to limited access to continuous monitoring. Negative Pressure Wound Therapy (NPWT) is an established treatment that promotes wound healing through controlled sub-atmospheric pressure. However, conventional NPWT systems lack real-time physiological feedback and require frequent in-person follow-ups, limiting their effectiveness in remote or low-resource settings. This study presents WoundWatch, a portable, low-cost NPWT device that integrates real-time temperature, humidity, and pressure sensing with wireless data transmission via an IoT platform. The system includes a vacuum …

The Developing Role Of Ai In Modern Engineering Research, Rianna Pais

The Cardinal Edge

No abstract provided.

Targeting Glioblastoma: Mechanistic And Clinical Perspectives From Uofl Research, Swetha Senthil Nathan

The Cardinal Edge

Glioblastoma (GBM) remains one of the most aggressive and treatment-resistant brain tumors, with median survival lingering at 12–15 months despite surgery, radiation, and chemotherapy. This article explores cutting-edge research and clinical efforts at the University of Louisville aimed at improving GBM treatment outcomes. Dr. Joseph Chen investigates the tumor microenvironment, revealing how physical properties such as stiffness and porosity drive GBM cell proliferation, migration, and resistance to apoptosis. His lab’s findings suggest that porosity, rather than stiffness alone, may be a more accurate predictor of tumor invasion and progression-free survival. Additionally, his research highlights the role of hyaluronic acid and …

Experimental Study Of Dimpled Serpentine Reactors For Hydrogen Production Via Methanol Steam Reforming, Mohamed I. Zeid, Mahmoud A. Shouman, Osama Abdelrehim, Ahmed M. Hamed

Mansoura Engineering Journal

Hydrogen is widely regarded as a clean energy carrier, with methanol steam reforming (MSR) emerging as a promising route for portable and on-board applications due to its favorable operating conditions and high hydrogen yield. Reactor geometry plays a decisive role in hydrogen productivity, pressure drop, and energy efficiency, yet the balance among these factors remains insufficiently addressed. In this study, three serpentine aluminum microreactors: a plain serpentine reactor (PSR), a rhombusdimple reactor (RDR), and a hemispherical-dimple reactor (HDR) were fabricated and coated with a copper (II) oxide, zinc oxide, aluminum oxide (CuO/ZnO/Al₂O₃) catalyst to experimentally investigate the effect of dimple …

Impact Of Urban Roadway Work Zones On Road Users And Other Stakeholders, Lyndsey Renee Harris

Lyles School of Civil Engineering Graduate Student Reports

As urban populations continue to rise at an unprecedented rate, the need for highly maintained infrastructure in cities -- buildings, utilities, and transportation networks -- has become increasingly critical. An urban work zone refers to a designated area within a city where roadway construction, maintenance, or rehabilitation activities are taking place. These zones are typically characterized by high traffic volumes, complex roadway networks, and proximity to residential, institutional, and commercial land uses. Urban work zones often involve lane closures, detours, reduced speed limits, and temporary traffic control measures to ensure safety for road workers and road users. Due to the …

Engineering 50 Years From Today: A Purdue Engineering Community Perspective, Arvind Raman

Purdue University Press Books

Since opening its doors in 1874, Purdue University’s College of Engineering and its graduates have been at the forefront of technological breakthroughs and are now poised to lead the next generation of engineers in the United States and around the globe. Featuring more than twenty-five essays from former Boilermakers, Engineering 50 Years From Today both celebrates the program’s 150th anniversary and looks ahead, exploring what the field of engineering might look like half a century from now. Leading luminaries from Purdue’s engineering community, including prominent university professors as well as innovators working in the private sector, reveal how technologies related …

Enhancing Aviation Safety: A Framework For Predictive Maintenance Using Generative Data Augmentation And Uncertainty Quantification, Muhammad Najjar

Student Research Symposium (SRS)

Predictive maintenance is a critical component of aviation safety, yet the development of accurate data-driven models is often hindered by the severe class imbalance inherent in real-world flight data; healthy flights are abundant, while flights preceding a failure are rare. This imbalance biases machine learning models, leading to poor detection of critical maintenance needs. This project addresses this challenge by leveraging the NGAFID aviation maintenance dataset to develop and validate an integrated predictive framework. We propose training a Time-series Generative Adversarial Network (TimeGAN) to synthesize high-fidelity, realistic "pre-maintenance" flight data. This synthetic data is used to create a balanced training …

Building Services Engineering January/February 2026

Building Services Engineering

No abstract provided.

Ionic Liquid Pilocarpine Serves As Therapeutic Cosolvent And Permeation Enhancer For Glaucoma Medication, Ashish Trital, Lei Xu, Burhan Ates, Tzu Chen Wang, Vimalin Mani, Hu Yang

Chemical and Biochemical Engineering Faculty Research & Creative Works

The efficacy of hydrophobic ocular drugs is significantly hindered by their low bioavailability, for which poor water solubility is a major contributing factor. In this work, we synthesized pilocarpine-derived ionic liquid [Pilo-OEG] Cl (PO) and evaluated its cosolvent properties for the codelivery of the hydrophobic antiglaucoma drug brimonidine (BM) for glaucoma therapy. Pilocarpine was quaternized with 2-[2-(2-chloroethoxy) ethoxy] ethanol in a one-pot reaction to yield PO, and its structure was confirmed using ¹H NMR and FT-IR spectroscopy methods and further characterized for its rheological property and thermal stability. The HET-CAM assay and cell viability study showed that PO was …

An Ensembled Two-Phase Deep Learning Approach For A Psychiatric Disorder Detection, Prajna Paramita Debata, Midhun Chakkaravarthy, Brojo Kishore Mishra

Turkish Journal of Electrical Engineering and Computer Sciences

Computational Psychiatry represents a burgeoning realm within scientific inquiry, delving into the intricate interplay of neurobiology within the brain. The escalating prevalence of mental illness underscores the urgency to confront this challenge. Among the prevalent disorders, Schizophrenia and Bipolar Disorder loom large, affecting a significant portion of the population at some point in their lives. However, pinpointing psychiatric disorders poses a formidable challenge. Genetic predispositions significantly influence the development of mental illnesses, with intriguing overlaps observed among certain disorders. This convergence complicates accurate diagnosis. Here, a deep learning approach is considered for significant gene biomarker identification and classification of Schizophrenia …

A Joint Optimization-Based Novel Attack For Genomic Beacon Reconstruction, Kousar Saleem, Si̇nem Sav

Turkish Journal of Electrical Engineering and Computer Sciences

Genomic data sharing has become an essential component of biomedical research, enabling large-scale collaborations and accelerating discoveries in human genetics. To balance the need for accessibility with privacy concerns, several controlled-access mechanisms have been proposed, including genomic beacons. Genomic beacons answer simple presence/absence queries about specific genetic variants. However, prior work has demonstrated that beacons remain vulnerable to genome reconstruction attacks, where an adversary can recover large portions of participants’ genomes using summary statistics. Building on insights from prior reconstruction attacks, we introduce an approach that unifies SNP correlation and allele frequency alignment objectives within a single-stage joint optimization framework. …

Roadmap: Integrating Artificial Intelligence In Structural Health Monitoring Systems, Simon Laflamme, Erik Blasch, Flippo Ubertini, Zheng Liu, John Wertz, Christine Knott, Matthew Cherry, Eric Lindgren, Fu-Kuo Chang, Amrita Kumar, Jack Poole, Keith Worden, Austin Downey, Jie Wei, Patrick F. Musgrave, Adrian S. Wong, Guiseppe Quaranta, Marco Martino Rosso, Giuseppe Carlo Marano, Yu Chen, Et. Al.

Faculty Publications

Advances in computing and machine learning (ML) methods have led to a rapid rise in artificial intelligence (AI) research and applications in many fields. AI research benefitted from advances in computation hardware, collection and distribution of large data sets, and proliferation of software techniques. AI techniques include ML for provable results, deep learning for data exploration, reinforcement learning for control, and active learning for adaptive systems. Likewise, AI algorithms can handle large amounts of data, construct unknown representations, and provide a direct link between data and classification for decision making. These unmatched capabilities have been seen as a path to …

Quantifying And Integrating Community Hardship Into Two-Stage Stochastic Grid Reliability Optimization With Battery Storage, Fredrica Arthur

LSU Master's Theses

Traditional reliability planning for conventional distribution systems is largely utility-oriented, with a focus on collective system performance metrics like Expected Energy Not Supplied (EENS), where implicitly all unserved energy is considered of equal weight in terms of post-outage economic hardship. Yet, it is well understood that extended outage durations cause an uneven level of hardship to socioeconomically disadvantaged communities. This thesis proposes a community-informed reliability planning framework where the hardship caused by outages is explicitly considered in the battery energy storage system (BESS) location and sizing problem. First, a hardship-weighted Energy Not Supplied (WENS) measure is proposed, where income, education, …

Reducing Range Anxiety Through Predictive Modeling Of Ev Battery Degradation, Caleb Thornsbury, Christian Castro, Bivin Sadler

SMU Data Science Review

Electric Vehicles (EV) range anxiety remains one of the top barriers for broader adoption. Range anxiety can be attributed to battery pack age and degradation over time. This paper plans to explore how to address this issue by creating a machine learning model that can predict degradation based on usage, temperature, battery chemistry, charging habits and exploring whether other factors tie into range degradation. This research will be using real world charging data along with lab tested chemistry data to build a model that can be chemistry specific for degradation. This paper will help perspective used-EV buyers learn about battery …

A Transcendental Phenomenological Study Of Cognitive Overload In Manufacturing Engineers During Online Training In An Industrial Environment, Sarah J. May

Doctoral Dissertations and Projects

The purpose of this transcendental phenomenological study was to explore experiences of cognitive overload among engineers in manufacturing during digital training in industrial environments. Engineers in manufacturing manage substantial physical and cognitive demands in order to maintain safe and efficient industrial environments. In addition to these responsibilities, engineers are often expected to engage in digital training within active work settings, frequently without physical or cognitive separation from job-related tasks. A qualitative research design was employed, with data collected from 10 participants and analyzed through triangulation and thematic coding. Data collection methods included a qualitative questionnaire, individual interviews, and a letter-writing …

Redesigning Online Graduate Orientation To Foster Academic Resilience And Prevent Underperformance, Stella Michael-Makri, David E. Rodriguez

Journal of Academic Underperformance

Graduate students in fully online programs often begin their academic journey without adequate preparation for the emotional, structural, and cultural challenges of graduate-level work. For students who are first-generation, racially marginalized, international, or returning to education after time away, this lack of scaffolding can lead to early disengagement, underperformance, or attrition. Orientation, often treated as a checklist of logistical tasks, represents a missed opportunity for meaningful academic intervention. This manuscript proposes a five-module conceptual model for online graduate orientation designed to proactively support online graduate students in the domains of emotional regulation and academic identity, time management and executive functioning, …

Three-Dimensional Printing Of The Epineurium For Peripheral Nerve Repair: A Comprehensive Review Of Novel Scaffolds For Nerve Conduits, Alynah J. Adams, Iulianna C. Taritsa, Kaavian Shariati, Aaron Dadzie, Jose A. Foppiani, Maria Jose Escobar-Domingo, Daniela Lee, Angelica Hernandez-Alvarez, Kirsten Schuster, Helen Xun

School of Medicine Publications

Background: Nerve conduits are used to bridge peripheral nerve defects caused by trauma, iatrogenic injury, or oncologic disruption. Three-dimensional (3D) biomimetic scaffolds for peripheral nerve regeneration have advanced significantly in recent years, driven by improvements in printing technology and neuronal seeding techniques. We report on published designer conduits that can recreate the epineurium, a critical yet challenging-to-manufacture feature of nerve tissue.

Methods: A medical librarian conducted a literature search for our systematic review on EMBASE, Web of Science, and PUBMED, following PRISMA guidelines, for articles from January 2010 to January 2026 for the systematic review. Descriptive statistical analysis was performed …