Digital Commons Network^™

Innovator - Winter 2025

Innovator

06 - Message from the President

08 - Time Machine

10 - News

14 - New Leader for a New Century

18 - Fashion’s Fairy Godmother

22 - Diploma School of Nursing

26 - Question & Innovate

28 - Alumni Impact

30 - Ram Roundup

32 - From the Washington Ballet to Wimbledon

36 - Homecoming 2024 and Alumni Awards

42 - JCLS Alumni Day

44 - Class Notes

50 - Love Train

52 - In Memory

54 - Trivia

Innovator - Summer 2024

Innovator

06 - A Message from the President

10 - Time Machine

16 - The Nexus

32 - Our History

38 - Weaving a Legacy of Excellence

40 - Bridging the Gaps

44 - Harnessing the Qualities That Create Healthcare Heroes

48 - Question & Innovate

52 - Alumni Impact

54 - Ram Roundup

58 - Be Kind

62 - Class Notes

68 - In Memory

70 - Trivia

Innovator - Fall 2023

Innovator

06 - Get to Know Jefferson's Interim President

08 - Leadership Announcements

10 - Time Machine

14 - The Nexus

22 - Climate Change on Your Block

30 - Humana and Jefferson

34 - Question & Innovate

38 - Caffeinated Creativity

42 - Alumni Impact

46 - Ram RoundUp

50 - Brotherhood's Enduring Legacy

56 - Class Notes

60 - In Memory

62 - Trivia

Nihilism: A Philosophical Essay, Stanley Rosen

Hannah Arendt Marginalia - All

New Haven : Yale University Press, 1969.

Call # : B828.3 .R6

Contains marginal lining  PDF Information : 7.7 MB, 24 Pages

Advancing Equitable Faculty Evaluations: Refining Behavior-Based Rubrics For Systemic Change, Celia Freed

Miners Solving for Tomorrow Research Conference

Bias in faculty evaluations continues to influence decisions about promotion, tenure, and resource allocation, disproportionately affecting women and underrepresented faculty. To address these inequities, this study develops and implements behavior- and-outcome based evaluation rubrics designed to improve fairness, clarity, and consistency. Using a case study approach, calibration data, participant feedback, and refinement are used to guide rubric development and standardization. Over one academic year, this project progressed through data analysis, rubric revision, faculty feedback, and pilot implementation, followed by evaluation of its effectiveness. The study advances research on structured evaluation systems and examines how behavior-based criteria can potentially enhance inclusivity …

Biosensors For Biomedicine, Drake O'Leary

Miners Solving for Tomorrow Research Conference

This project investigated the use of engineered bacterial reporter systems to detect cellular stress responses associated with antibiotic activity. While constructs were successfully introduced and tested against known antibiotics, the reporter consistently produced a blue signal across conditions, limiting the ability to distinguish specific stress responses. This suggests issues such as leaky expression, insufficient regulatory control, or lack of specificity in the reporter design. Although results were inconclusive, this work highlights key challenges in developing reliable biosensors and provides a foundation for future optimization. Improving signal specificity and reducing background expression will be critical for enabling accurate characterization of antibiotic …

Optimizing Duckweeds Harvesting Strategies To Maximize Nutrient Recovery And Its Role As A Phytosensor In Different Water Sources While Maximizing Sustainable Biomass Growth, Basma Ghannam

Thesis/ Dissertation Defenses

This thesis is concerned with determining the optimal water treatment and harvesting strategy for Lemna minor duckweed plants. The main objective of this thesis is to examine how duckweed plants grow in terms of biomass, photosynthetic activity, leaf morphology, chlorophyll and carotenoid content, and their efficiency in nutrient removal from different water sources. The methodology of the study included assessing duckweed in three water samples including tap water, saline water, and greywater and three harvesting strategies, including low, medium, and high harvesting intensities to determine their growth and nutrient removal efficiency. The results of the study showed that L.minor grown …

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 …

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.

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 …

Multi-Frequency Temporal Sampling For Training Video Object Counting Models, Ivor Rendulic, Filip Pavetić

Defensive Publications Series

Machine learning models may face challenges in counting distinct objects in videos, particularly when presented with variations in frame rates or object speeds. Disclosed systems and techniques can address these challenges with a data augmentation methodology for generating training data. For example, an approach can involve creating multiple training examples from a single source video by sampling its frames at various temporal frequencies. A dynamic labeling protocol may be used to assign an object count label to a new sequence that reflects the number of objects discernible in that specific, potentially sparser, sequence. Training a model on these varied representations …

System And Method For Predictive Network Equipment Decommissioning Management, N/A

Defensive Publications Series

The technology described in this paper relates to a predictive network equipment decommissioning management system. The described technology automates the creation and management of decommissioning requests to improve accuracy and reduce cycle times. By utilizing multi-signal context heuristics, the system determines the specific decommissioning scenario for various assets. A hybrid inference engine is employed to predict the presence of unmonitored passive components. An ambiguity resolution engine identifies vague or untagged parts using computer vision and topological exclusion techniques. A dynamic granularity transformation engine re-aggregates discrete components into top-level assemblies, accommodating for configuration drift over time. Furthermore, a retrofit analysis engine …

Automated Security Policy Validation From Natural Language Documentation Using Large Language Models, Leonid Kuligin, Aleksandr Ostapenko

Defensive Publications Series

Determining security permissions from natural language documentation, for example, runbooks, can be a time-consuming and error-prone process that may lead to runtime failures or security vulnerabilities. A system can use large language models (LLMs) to analyze documentation, infer a candidate permission policy, and generate a corresponding test script. The script can then be executed in a sandboxed environment using the candidate policy. An iterative refinement loop may diagnose permission-related failures and use an LLM to propose policy modifications to address those failures. This automated process can assist in generating a functional permission policy derived from operational instructions, which may reduce …

The Vehicle Based Comfortable And Relaxing Seating Experience For The Passenger In A Compact Space., Punarjeewa Abeysekera

Defensive Publications Series

This paper describes a vehicle based comfortable and relaxing seating experience for the passenger in a compact space. The vehicle based comfortable and relaxing seating experience for the passenger in a compact space, will involve positioning an L shaped comfortable and relaxing seating configuration within inside the vehicle. The L shaped comfortable and relaxing seating configuration that will be positioned within the compact space, will provide the passenger or passengers in the vehicle with a comfortable and relaxing experience during the vehicle navigation.

Session Management Architecture For Server-Sent Events Transport In Persistent Connection Delivery Systems, Aaron Burton

Defensive Publications Series

A method for operating a session management process over an SSE transport, where the process maintains an in-memory map of session identifiers to open HTTP response streams, receives personalization payloads from an upstream enrichment pipeline, and writes those payloads as formatted SSE event frames to all registered EventSource connections matching the target session identifier. The process stores HTTP response stream references rather than WebSocket handles, delivers payloads by writing SSE-formatted text to each open stream, and relies on HTTP keep-alive semantics and the EventSource reconnection protocol to manage connection lifecycle without application-layer ping/pong. Client-side reconnection is handled natively by the …

Origai Ecosystem - Integrated Ai-Powered Entrepreneurship Platform, Clinton Orah

Defensive Publications Series

This publication discloses the complete technical architecture of the OrigAI Ecosystem: an integrated suite of AI-powered products guiding entrepreneurs through a six-phase startup lifecycle — Think, Plan, Build, Launch, Scale, and ongoing operations. The primary product, BCAI (Business Creation AI), implements a phase-embedded plan builder architecture in which AI tool capabilities surface as contextual engines within business plan sections rather than as a standalone toolkit, with outputs written permanently to a structured business plan. The ecosystem includes a 20+ tool suite spanning competitive intelligence, offer design, viral marketing, pitch coaching, business registration, and scale-phase analytics, all operating on a shared …

Unified Oam Interworking Across Evpn And L2vpn Domains, Anonymous

Defensive Publications Series

This disclosure describes a control-plane assisted framework for unified Operations, Administration, and Maintenance (OAM) across heterogeneous Layer-2 VPN domains, including L2VPN and EVPN interconnected via an interworking Provider Edge (PE). The interworking PE advertises cross-domain service extension and unified OAM capability using new signaling constructs, including an EVPN Extended Community and an LDP OAM Interworking TLV with an associated Status Code, enabling end PEs to recognize multi-domain services without requiring protocol convergence. The framework supports dual OAM modes, including end-to-end service validation and domain-specific diagnostics. For full-service OAM, the interworking PE performs probe translation, fan-out to multiple remote endpoints, and …

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 …

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 …

The Lost World Of Thomas Jefferson, Daniel Boorstin

Hannah Arendt Marginalia - All

Boston : Beacon Press, 1960.

Call # : B878 .B6 1960

Contains marginalia, underlining, marginal lining and endpaper notes

PDF Information : 10.2 MB, 28 Pages

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.

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.