Digital Commons Network^™

Asphalt Pavement Rejuvenation Investigated By Nuclear Magnetic Resonance Relaxometry, Catherine Skaggs

Miners Solving for Tomorrow Research Conference

Asphalt is a relatively inexpensive material commonly used for road pavements; however, asphalt ages from exposure to oxygen and UV radiation, leading to loss of elasticity, which leads to cracks and potholes. To slow or reverse asphalt aging, pavement preservation treatments are in use. It’s unclear whether these treatments truly rejuvenate the pavement structure or merely offer a short-term benefit. Nuclear Magnetic Resonance (NMR) relaxometry is an analytical technique that uses the recovery of excited nuclear magnetization as a parameter to gain insights into molecular environments. Recovery-time distributions gathered from asphalt materials show differences between regular and rejuvenated asphalt samples, …

Harnessing Street-View Imagery: A Success Story From Northwest Washington State, Danny Hagen, Falon Hoven

GIS/ValuationTechnologies Conference 2026

This session highlights how county assessor offices in Washington State are using street-view imagery to improve valuation accuracy, streamline appeals, and enhance public trust. Building on Skagit County’s initial implementation, we will share a new year of results, expanded examples, and cross-county collaboration with Whatcom County. Topics will include: Overview of assessment processes in Washington State How street-view imagery integrates with existing data sources Successes in valuation, training, and disaster response Lessons learned on public perception, cost-sharing, and implementation challenges Future plans for regional collaboration and expanded applications Participants will come away with practical insights on the benefits, limitations, and …

Outlier Season Is Open, Bring Your Chainsaw, Luke Jorgensen

GIS/ValuationTechnologies Conference 2026

A common struggle in mass appraisal is dealing with data that contains outliers and incorrect information. When trying to remove outliers we wish to do so in a way that makes the data truly more accurately represent the population, while doing this it can be hard to truly determine what is an outlier and what is not without adding bias. Isolation Forests offer the ability for us to use an unsupervised machine learning technique to identify outliers in our data. In this presentation we will discuss how Isolation Forests work, how they can be applied to Mass Appraisal and some …

Spatial Evaluation Of Assessment Price Performance Through 3d Visualization And Geospatial Analytics, Su Yeon Jung Ph.D. (Economics)

GIS/ValuationTechnologies Conference 2026

Accurate evaluation of assessment prices is essential for fair and transparent property taxation. This presentation introduces a geospatial framework that visualizes and measures assessment ratio performance across local areas using 3D mapping techniques. Based on ratio-study principles, the approach employs GIS tools and 3D visualization to show spatial characteristics of areas where assessment levels are relatively high or low. A case study of Korean housing markets demonstrates how geospatial visualization reveals local inequities in assessment levels, helping assessors and policymakers better understand regional discrepancies and improve uniformity. This presentation highlights how 3D mapping and spatial analytics enhance interpretation of assessment …

Succession Success In Seven Steps, Damian Lara Esq.

GIS/ValuationTechnologies Conference 2026

Session will provide the tools and teach the best practices to develop and implement a successful succession plan. Presentation materials will include templets, questioners and guidelines to implement a customizable out of the box succession plan.

Quantifying The Impact Of Mobile Distractions On College Students' Attention Performance, Sonipriya Paul, Jessica L. Bolton, Ashwin Ashok

Artificial Super Intelligence (ASI) Conference

This research identifies that it is important to understand how humans sustain and recover attention to their primary activity amid mobile device interruptions. In this paper, we present our key findings and insights from an user-study based measurement of attention performance of graduate students. Ten participants (graduate students) independently completed attention tasks in an office environment (with no background noise) while receiving periodic messages (distraction), with continuous data recording on a wearable electroencephalography (EEG) headset. The attention tasks were designed in line with the standard GO/NO-GO test from psychology domain. Our experimentation included three variations of the GO/NO-GO test and …

Qbiasnet: Quantum-Enhanced Variational Classifiers For Ethical Bias Detection In Multimodal Ai Models, Nikunj Doshi, Kavach Shah, Shikhar Patel

Artificial Super Intelligence (ASI) Conference

Bias in multimodal AI systems that jointly process image and text inputs creates measurable risks in sensitive deployment contexts including public health, financial services, and automated hiring. Classical detection approaches face a fundamental architectural limitation in that they cannot efficiently model intersectional bias. Intersectional bias emerges from the nonlinear interaction of multiple protected attributes simultaneously across visual and linguistic modalities. This paper introduces QBiasNet, a hybrid quantum-classical system that encodes cross-modal CLIP embeddings into an 8-qubit Variational Quantum Circuit (VQC) implemented in PennyLane. By exploiting quantum entanglement, the VQC learns high-order feature correlations that linear probes and shallow neural networks …

Meduncertainvlm: Multi-Modal Uncertainty Quantification In Vision-Language Models For Clinical Documentation, Shikhar Patel, Rushabh Darji

Artificial Super Intelligence (ASI) Conference

Vision-language models are increasingly deployed for clinical documentation tasks in radiology. A common application involves extracting diagnostic labels from chest radiographs with reports. These models must communicate calibrated uncertainty to avoid influencing patient care incorrectly. Overconfident predictions from poorly calibrated models pose a documented patient safety risk. Existing medical vision-language models produce only point estimates without any confidence bounds. Standard post-hoc calibration methods such as temperature scaling omit cross-modal signals entirely. They cannot detect cases where image and text branches make genuinely inconsistent predictions. This paper presents MedUncertainVLM, a framework that addresses this gap directly and systematically. The system uses …

Ballistic Trajectories From Triangular Libration Points To Moon, Collin Gentry

Miners Solving for Tomorrow Research Conference

As stable points in the Earth-Moon system, the triangular libration points, L4 and L5, have many advantageous properties for space exploration. Ballistic trajectories at varying delta-Vs and impulse angles are computed and propagated from the libration points, and trajectories that arrive at the lunar surface are investigated. Preliminary conclusions are drawn about the accessibility of the lunar surface from the triangular libration points, and the implications for mission design are discussed. These trajectories present an alternative means of accessing the Moon, expanding the viability of the triangular points for missions and offering additional options for the use of cisular space.

Validating Endosomal Antibodies For Use In Immunofluorescence Assays In A Cell-Based Model Of Prader Willi Syndrome, Hayden O'Dell

Miners Solving for Tomorrow Research Conference

Prader-Willi syndrome (PWS) is a rare progressive metabolic disease characterized by a wide range of behavioral and cognitive defects, most notably severe hyperphagia. The disorder is caused by the complete loss of some paternally inherited genes on chromosome 15q11-q13, resulting in endocrine dysfunction and a loss of control over feeding behaviors. Among the deleted genes within this region is MAGEL2. Our lab recently characterized a paternally deficient MAGEL2 rat model of PWS, discovering a defect in pituitary secretory capacity that is consistent with the impaired vesicular trafficking and secretion observed in PWS patients. To further characterize the defects in vesicular …

Ai Adoption Tensions For Organ Procurement Organizations, Joely Grace Hall

Miners Solving for Tomorrow Research Conference

Artificial intelligence (AI) has the potential to improve efficiency in healthcare, yet its adoption remains limited, with only 22% of healthcare organizations having implemented domain-specific AI tools. Adoption may be especially complex in specialized domains such as organ transplantation, where ethical, legal, and operational challenges are dominant. This study examined factors influencing AI acceptance within Organ Procurement Organizations (OPOs), focusing on technological, organizational, and environmental contexts.

Semi-structured interviews with 16 OPO executives from 10 OPOs revealed key tensions shaping AI adoption. We identified five tensions that are holding back OPO leaders from AI adoption, (1) misconceptions, (2) training approach, (3) …

Community Dynamics In Urban Ponds Could Be Influenced By Artificial Habitats, Keagan Miller, Uma Adrianna Misra

Miners Solving for Tomorrow Research Conference

Urban ponds are subject to nutrient influxes from stormwater runoff, and blooms of harmful algae can develop in response. One management strategy includes deployment of Floating Treatment Wetlands (FTWs) or floating mats of aquatic plants designed to maximize uptake of nutrients to plant biomass. An understudied component of these artificial systems is their potential to provide microhabitat for aquatic biota. This study focused on the intersection of aquatic insect communities and fish presence between natural and artificial habitat types. Insect communities were found to be weakly dissimilar between habitat types (ANOSIM: R=0.254, p=0.048). Specifically, Anisoptera and Zygoptera were absent or …

2nd Annual Miners Solving For Tomorrow Research Conference - Undergraduate Schedule, Blake Bowman, Carter Lawson, Celia Freed, Adam Merchiori, Allie Dingfield, Catherine Skaggs, Drake O'Leary, Quinten Bachman, Elizabeth Bone, Joely Grace Hall, William Wisnasky, James Meyer, Sindhujaa Jaiganesh, Sophia Nicolette Petilla Militante, Brantley Carter, Adrianna Sasser, Alec Gamache, Micah Gargrave, Keagen Miller, Uma Adrianna Misra, Collin Gentry, Christy Johnson, Brady Carlson, Logan Williamson, Jivanji Sumariwalla, Hayden O'Dell, Elijah Brakensiek, Katharine Gray, Lukas Farthing, Jacob Harl, Landon Meyer, Nehemiah Milton, Lana Herkenhoff, Jenna Mueller, Lucas Ethington, Maya Southard, Aster Davidson, Ethan Beane, Jacob Penn, Connor Jordan, Amanda Hodges, Ethan Keuhn, Milan Jebaraj, Ariel Pilger, Sydney Clark, Maris Reinkemeyer, Manav Raja Vinotha, Punit Sesha Sai Turlapati, Benjamin Sullins, Benjamin Biehl, Jamie Koester, Aidan Sengupta

Miners Solving for Tomorrow Research Conference

No abstract provided.

2nd Annual Miners Solving For Tomorrow Research Conference, Blake Bowman, Carter Lawson, Celia Freed, Adam Merchiori, Allie Dingfield, Catherine Skaggs, Drake O'Leary, Quinten Bachman, Elizabeth Bone, Joely Grace Hall, William Wisnasky, James Meyer, Sindhujaa Jaiganesh, Sophia Nicolette Petilla Militante, Brantley Carter, Adrianna Sasser, Alec Gamache, Keagan Miller, Uma Adrianna Misra, Collin Gentry, Christy Johnson, Brady Carlson, Logan Williamson, Jivanji Sumariwalla, Hayden O'Dell, Elijah Brakensiek, Katharine Gray, Lukas Farthing, Jacob Harl, Landon Meyer, Nehemiah Milton, Lana Herkenhoff, Jenna Mueller, Lucas Ethington, Maya Southard, Aster Davidson, Ethan Beane, Jacob Penn, Connor Jordan, Amanda Hodges, Ethan Keuhn, Milan Jebaraj, Ariel Pilger, Sydney Clark, Maris Reinkemeyer, Manav Raja Vinotha, Punit Sesha Sai Turlapati, Benjamin Sullins, Benjamin Biehl, Jamie Koester, Aidan Sengupta, Antai Dong, Connor Bell, Dennis Dadzie, Joshua Gary, Adebayo Olayinka Oke, Deeshani Mitra, Yejun Kim, Josiah Mcdermott, Samiksha Aryal, Nicholas Brenner, Timothy Ennis, Maryam Sharifi Paroushi, Sujan Maharjan, Md Saiduzzaman, Hari Dhital, Manuela Isabel Arenas Alvarez, Eyuel A Getahun, Lucas Greiner, Amaneh Babaee, Shane Cairns, Nazish Khalid, Joseph Walton, Jie Shi, Ehsan Asheghianamiri, Mariam Elazhary, Makuach James Makeny Panther Athach, Rasman Mubtasaim Swargo, Shiva Kumar Goud Kasani, Pablo Jara, Sara Mccauley, Hartzell Weston, Al Mojahid Afridi, Matik Heskin, Nima Mahmoudzadeh, Effat Eskandari, Amitav Sen, Rabin Pandey, Amirhossein Habibi, Nuzaer Omar, Mizanur Rahman Jewel, Nathan Tibbetts, Manoj Twarakavi, Fahara Bristy, Akhrorbek Narmatov, Mahnaz Asgari Sooran, Tafang Hei, Nathan Roberts, Khosro Ghorbani Zadeh

Miners Solving for Tomorrow Research Conference

No abstract provided.

Cross Correlation-Based Physiological Synchronization For Multi-Modal Wearable Cognitive Stress Monitoring System, Fariha Alam, Nora Tin, Razvan Voicu

Artificial Super Intelligence (ASI) Conference

Cognitive stress arises from sustained mental demand, information overload, or task complexity, and requires continuous monitoring for timely detection and intervention in real-world settings. It produces subtle physiological changes, including shifts in heart rate and autonomic activity that require continuous monitoring to detect reliably. Wearable sensors such as photoplethysmography (PPG) and electrodermal activity (EDA) enable non-invasive tracking of these responses. However, multi-modal sensor integration remains challenging, as hardware or software-level synchronization does not consider the physiological response delays. To address this, the study collected PPG and EDA sensor data from ten participants under two cognitive stress-inducing conditions. After preprocessing, heart …

Vibroacoustic Characterization Of Manipulator Robot Via Kinematic Discretization, Gershom Richards Pe, David A. Guerra-Zubiaga

Artificial Super Intelligence (ASI) Conference

Using vibroacoustic signals to characterize the health and status of industrial equipment is well-used. Many recent studies leverage a black box approach for developing a vibroacoustic machine learning (ML) architecture capable of identifying anomalous behaviors in the robot operation. While useful for routine automation systems, it is not suitable for dynamic robot systems with flexible or behavioral programming, or robots that will experience consistently changing environmental factors. To address these shortcomings, a discretized approach is explored. The proposed Long Short-Term Memory (LSTM)-Transformer Hybrid ML architecture segments its training data by manipulator joints to incorporate the impact of each subsystem on …

Agripestdatabase-V1.0: A Structured Insect Dataset For Training Agricultural Large Language Model, Yagizhan Bilal Durak, Ashley Morgan-Olvera, Ahsan Ul Islam, Iftekhar Ibne Basith, Shahidul Islam, Syed Hasib Akhter Faruqui

Artificial Super Intelligence (ASI) Conference

Agricultural pest management increasingly relies on timely and accurate access to expert knowledge, yet high quality labeled data and continuous expert support remain limited, particularly for farmers operating in rural regions with unstable or no internet connectivity. At the same time, the rapid growth of artificial intelligence (AI) and large language models (LLMs) has created new opportunities to deliver practical decision support tools directly to end users in agriculture through compact and deployable systems. This work addresses (i) generating a structured insect information dataset to be used for LLM training, and (ii) adapting a lightweight LLM model (<= 7B) by fine tuning it for possible future edge device uses in agricultural pest management. The textual data collection was done by reviewing and collecting information from available pest databases and published manuscripts on nine selected pest species. These structured reports were then reviewed and validated by a domain expert. From these reports, we constructed question-answer (Q/A) pairs to support model training and evaluation. A LoRA-based fine-tuning approach was applied to multiple lightweight LLMs and evaluated. Initial evaluation shows that Mistral 7B achieves an 88.9% pass rate on the domain-specific Q/A task, substantially outperforming Qwen 2.5 7B (63.9%), and LLaMA 3.1 8B (58.7%). Notably, Mistral demonstrates higher semantic alignment (embedding similarity: 0.865) despite lower lexical overlap (BLEU: 0.097), indicating that semantic understanding and robust reasoning are more predictive of task success than surface-level conformity to reference text in specialized domains. By combining expert organized data, well structured Q/A pairs, semantic quality control, and efficient model adaptation, this work contributes towards providing support for farmer facing agricultural decision support tools and demonstrates the feasibility of deploying compact, high-performing language models for practical field-level pest management guidance.

A Method To The Madness: Using Gis To Streamline Personal Property Assessment And Collections, Katie Williamson Pps, Preston Ellison

GIS/ValuationTechnologies Conference 2026

How our personal property department streamlined processes and efficiency with the assistance and guidance from our GIS department.

Improving Assessment Equity - A Multi-Disciplinary Case Study, Kevin Keene, Colin Weidig Ph.D.

GIS/ValuationTechnologies Conference 2026

The mayor of Philadelphia commissioned a task force to study assessment and property tax equity and make recommendations for improving equity. A group with expertise in many aspects of housing and property taxes was convened in response. Members of the task force will present the process that was followed and the recommendations that were made in a panel discussion that will engage attendees.

Assessing Impact: Ratio Studies Using Demographic Data, Josh Myers

GIS/ValuationTechnologies Conference 2026

Ratio Studies usually focus on measuring equity using data known directly by a valuation agency during the valuation process, such as location, quality, and building size. However, in assessing the human impact of the valuation process, it can also be helpful to measure equity using demographic data that was not directly included in the valuation process. This presentation does exactly that using data from Mecklenburg County, NC and GIS data derived from the US Census Bureau’s 2020 American Community Survey.

Machine Learning For Ratio Studies, Paul Bidanset, Joe Wehrli

GIS/ValuationTechnologies Conference 2026

Ratio studies are an essential tool for evaluating the fairness and uniformity of assessments, but performing them thoroughly can be time consuming and limited by traditional spreadsheet methods. This session explores how machine learning can be applied to ratio study data to identify inequities and patterns that may not be visible through simple statistics. Participants will see how machine learning such as SHAP and recursive partitioning methods can detect areas or submarkets where ratios diverge, suggesting potential issues with valuation or data quality. The focus is on plain language interpretation and practical understanding rather than complex coding, showing how assessors …

Overcoming Pediatric Vulnerabilities In Global Health: The Translational Potential Of Nanomedicine, Ahmed Khairy, Mohamed Fawzi Hemida, Abdelrahman Aly Mohamed, Mina Nageh, Mahdi Ahmed, Alaa Eldeeb, Lindsey Dixon, Marium Aisha, Yossef Alnasser

GW Research Showcase 2021-2026

No abstract provided.

Evaluation Of A Telecommuting Nurse Staff Model In Ambulatory Care, Movlet Lewis, Karen J. Whitt, Cynthia L. Allen

GW Research Showcase 2021-2026

No abstract provided.

Full Practice Authority For Advanced Practice Registered Nurses In Texas: A Policy Analysis, Carly Duncan, Mary Jean Schumann, Lori Bachelor, Jane Champion

GW Research Showcase 2021-2026

No abstract provided.

Exploring Perceptions Of Plant-Based Proteins In Dining Among University Students And Food Services Providers, Daria Faulkner, Khadidiatou Ndiaye, William Dietz, Michael Long

GW Research Showcase 2021-2026

No abstract provided.

A Qualitative Analysis Of Multi-Level Stakeholder Perspectives On Medicaid Reentry Policy Implementation For Justice-Involved Youth In New Mexico, Elizabeth Laferriere, Fran Buntman, Frances Laufer, Maria Keneally, J. Zoë Beckerman

GW Research Showcase 2021-2026

No abstract provided.

Association Between Caffeine Consumption And Academic Performance In Gwu Undergraduate Students, Elden Badua, Leslie Davidson

GW Research Showcase 2021-2026

No abstract provided.

Association Between Cardiovascular Fitness And Strength Fitness Status Across Distinct Phenotypes In Exercising Women, Gianna Filippou, Jordyn Walters, Andrea Osorio, Felipe Cabello, Allie Chetta, Clara De Torres Ensenat, Karla Cordero-Ortega, Shery Kamel, Carmen P. Ortega-Santos

GW Research Showcase 2021-2026

No abstract provided.

Integrated Mortality Surveillance In The United States, Colombia, & Zambia: Facilitators, Barriers, & Lessons, Chinwe Lucia Ochu, Erin Nichols, Abdisalan Noor, Karen A. Mcdonnell

GW Research Showcase 2021-2026

No abstract provided.

Assessing Differences In Physician Assistant Program Admissions: Observing The Influence Of Demographic Characteristics On Likelihood Of Acceptance, Nicole Wen, Mary L. Warner

GW Research Showcase 2021-2026

No abstract provided.