Digital Commons Network^™

The Human Right To Due Process In U.S. Immigration Courts, Sarah Dávila A., Alejandra Palacios, Emily Mccabe

Faculty White Papers

No abstract provided.

J Mich Dent Assoc Nov. 2026

The Journal of the Michigan Dental Association

The November 2026 issue of the Journal of the Michigan Dental Association provides comprehensive coverage of professional, legal, and operational developments impacting Michigan dentists. Key topics include an analysis of dental emergency visits at Federally Qualified Health Centers (FQHCs), leadership principles within organized dentistry, advocacy efforts surrounding Senate Bill 105 regarding virtual credit card reimbursement fees, practice transition, embezzlement prevention, employee documentation compliance, and member services. This issue highlights community, equity, and legislative engagement aimed at supporting dental practices and improving statewide public oral health.

Nov. 2026 Dentistry And The Law - Practice Break-Ups/Ownership Of Patient Records, Daniel Schulte Jd

The Journal of the Michigan Dental Association

This legal column addresses complex issues surrounding dental practice break-ups and the ownership of patient records. Author Daniel J. Schulte, JD, provides guidance on establishing clear employment, shareholder, and buy-sell agreements from the inception of a practice partnership. Proactive legal structuring ensures continuity of patient care, clarifies record ownership rights, and mitigates the risk of costly litigation during practice transitions.

Managing Collaboration Under Conservation Crisis: Trust, Risk, And Control In The Gulf Of Maine Fishery Management Network, Evelyn Roozee, Owen Temby, Dongkyu Kim, Antonia Sohns, Anthony Rocha Lima, Jasper R. De Vries, Gordon M. Hickey

School of Earth, Environmental, & Marine Sciences Faculty Publications

Transboundary fishery management and the intertwined conservation of critically endangered species represent significant governance challenges that require ongoing inter-organizational communication, collaboration, and collective action to achieve shared objectives. Previous research suggests that inter-organizational collaborative performance depends heavily on different dimensions of trust and complementary control mechanisms to mitigate the perceived risks of collaborating and enable the reciprocal sharing of resources and the coordination of activities. However, it is unclear how proposed relationships between trust, risk, and control are shaped by disruptions to established transboundary management networks. This study presents the results of survey research conducted in the Gulf of Maine …

Effects Of Demineralization Protocols On Hydrogen And Oxygen Isotope Ratios In Fossil Bone Collagen, Linda M. Reynard

Boise State University Publications and Presentations

The effect of three commonly used preparation protocols on bone collagen δ2H and δ18O values was investigated, using six generally well-preserved faunal bones (cattle, bison, horse) ranging in age from the Holocene to Late Pleistocene. The treatments tested were ethylene diamine tetraacetic acid demineralization without gelatinization and hydrochloric acid demineralization with and without gelatinization. There are significant shifts in δ18O values among preparation methods. No systematic shifts in bone collagen δ2H values were noted among the three treatments, and most offsets were low (< 2–3 ‰) and smaller than typical measurement uncertainties. A few sample/treatment combinations yielded slightly higher inter-treatment δ2H differences (∼ 5–7 ‰). Lower mass fraction of hydrogen in …

Reliability And Validity Of Upper Extremity Neurocognitive Reactive Tests In Collegiate Athletes, Matthew L. Gauthier, Marisa Pontillo, George J. Davies

Physical Therapy Faculty Publications

Objectives: Neurocognitive function is vital in sports, but evidence supporting neurocognitive reaction testing in upper extremity return-to-sport testing is limited. This study's purpose was to quantify measurement properties of upper extremity reactive testing in collegiate athletes, hypothesizing acceptable measurement properties for clinical use.

Design: Cross-Sectional Study.

Setting: Collegiate athletics.

Participants: Sixty-three healthy athletes (25 females, age = 20.8 ± 1.6 years, BMI = 25.6 ± 2.6 kg/m2; 38 males, age = 20.8 ± 1.6, BMI = 25.7 ± 2.6 kg/m2).

Main outcome measure(s): Successful touches and average reaction time on the Upper Extremity Reactive Closed Kinetic Chain Test (UERCKCT) and …

The Influence Of Childhood Experiential History On Elon Musk’S Personality, Amelia Brower, Aubrey Immelman

Psychology Faculty Publications

This paper presents the results of a developmental causal analysis of the adult personality of innovative entrepreneur and business leader Elon Musk, from the conceptual perspective of personologist Theodore Millon.

Psychodiagnostically relevant data relating to Musk’s personality were collected from biographical sources and media reports and synthesized into a personality profile using the Millon Inventory of Diagnostic Criteria (MIDC), which yields 34 normal and maladaptive personality classifications congruent with DSM-III-R, DSM-IV, and DSM-5-TR.

The findings of the personality assessment were previously reported in a separate paper. Musk’s primary personality patterns were found to be Ambitious/self-serving and Dominant/controlling, …

November 2026, Swosu Bulldog Wellness Committee

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Organic Solvent-Assisted Pore Structure Modulation In Porous Carbons For Superior Energy Storage Performance In Supercapacitors, Jibi Kunjumon, Gurwinder Singh, Ajanya M. Ruban, Harleen Kaur, Alan Larson, Ranjit Pati, Et Al.

Michigan Tech Publications

The pore structure is a significant feature of porous carbons that accounts for their excellent performance in supercapacitors; however, the literature on pore modulation and cleansing is scarce. Here, a novel approach based on organic solvent-assisted pore modulation is reported to tune the pore hierarchy and local carbon-layer arrangement in porous carbons derived from waste biomass. The strategy involves adsorbing n-heptane into porous carbon at room temperature, thereby facilitating realignment of the carbon layers, improving pore hierarchy, and generating short-range graphitic domains. The solvent-assisted pore modulation, together with the high surface area and graphitization, led to a significant increase of …

Nonhuman Speakers And The Constitution, Helen Norton

Fordham Law Review

Long-standing free speech law sometimes does—and sometimes does not—turn on speakers’ human character. This Essay explores how these doctrinal threads might shape the constitutional doctrine applied to generative artificial intelligence’s (AI) speech outputs if we assume that a human speaker cannot be identified as the creator of those words and images—and that those words and images are nevertheless considered “speech” for First Amendment purposes.

For example, First Amendment doctrine sometimes requires proof of a speaker’s culpable mental state before that speaker can be held liable for what would otherwise be unprotected speech. These requirements seek to prevent the chilling of …

Digital Gaze And Constitutional Democracy At 250, Catherine Powell

Fordham Law Review

The panopticon of digital surveillance undermines identity formation and realization of our authentic selves. This Essay offers a new insight by conceptualizing alienation from genuine selfhood as a form of “double consciousness.” W.E.B. Du Bois formulated the notion of double consciousness—the dual awareness inherent in negotiating between a self-defined identity and an externally imposed one. Just as Du Bois speaks of “double consciousness” for African Americans, the ever-present digital gaze to which we are now all subject—regardless of our race—creates a similar duality. I call this duality “online double consciousness,” which is compounded for people of color and other historically …

Clickwrap Accountability, Alicia Solow-Niederman

Fordham Law Review

Picture a public‑facing generative artificial intelligence (AI) chatbot on a government website that can answer questions about benefits eligibility. Systems like these expose a sociotechnical-legal divide: They route around the standard due process framework because there has been no formal government determination, yet they induce reliance from users, above and beyond past rounds of automated legal guidance. Moreover, if there is an error, current doctrine on government errors and equitable estoppel makes relief unlikely.

This Essay focuses on government deployment of public‑facing generative AI chatbots and critically assesses the front‑end power dynamics that determine who is filtered out of government …

Algorithmic Models And The Freedom Of Speech, Alexander Tsesis

Fordham Law Review

Intentionality and reason are intrinsic features of the human mind that large language models (LLMs) entirely lack. Although LLMs generate natural-language sentences through algorithmic processes, autonomously produced machine speech lacks the consciousness of human speakers. This intrinsic distinction between natural persons and artificial machines is consequential to whether LLM-generated outputs are covered by the First Amendment. Scholars who equate the constitutional value of human speech with algorithmic outputs tend to emphasize the informational value of those outputs to audiences while giving insufficient attention to the absence of machine consciousness. The conscious underpinnings of thought and their relation to constitutional protections …

Climate Change Under Customary International Law: Must The United States Protect The Climate System?, Daniel Rees

Fordham Law Review

On July 23, 2025, the International Court of Justice (ICJ) issued its “Advisory Opinion on Obligations of States in Respect of Climate Change,” concluding that “the full gamut of international law applies to climate change.” Beyond treaty-based duties, the ICJ recognized that customary international law imposes obligations on all states to protect the climate. The advisory opinion emerged from unprecedented participation by states and international organizations, reflecting a growing consensus that addressing climate change is a legal obligation. This Note explores the legal implications of recognizing a customary international law duty to protect the climate system. It posits that these …

Differences In Infection Prevention And Control Staffing Between Free-Standing And System-Affiliated Hospitals: A National Survey., Lucas Philip, Vittorio Maio, Pamela De Cordova, Elizabeth Monsees, Raegan Davis, Pat Moeller, Andrew Dick, Scott W Keith, Monika Pogorzelska-Maziarz

Manuscripts, Articles, Book Chapters and Other Papers

BACKGROUND: Infection prevention and control (IPC) staffing varies across US hospitals, and traditional bed-based benchmarks may no longer reflect contemporary workloads or organizational structures. With most hospitals now system-affiliated, understanding how affiliation influences infection preventionist (IP) staffing is critical for modernizing guidance.

METHODS: A national survey was distributed to US acute care hospitals participating in the National Healthcare Safety Network (NHSN) from August to December 2023, assessing IPC staffing, resource availability, and organizational structure. Hospitals were categorized as free-standing or system-affiliated; differences were assessed with Wilcoxon rank-sum and chi-square tests.

RESULTS: Of 741 responding hospitals, 550 (74%) were system-affiliated and …

Lrq-Solver: A Transformer-Based Neural Operator For Fast And Accurate Solving Of Large-Scale 3d Pdes, Peijian Zeng, Guan Wang, Haohao Gu, Xiaoguang Hu, Tiezhu Gao, Zhuowei Wang, Aimin Yang, Xiaoyu Song

Electrical and Computer Engineering Faculty Publications and Presentations

Solving large-scale PDEs on complex three-dimensional geometries remains a central challenge in scientific and engineering computing, often due to expensive pre-processing stages and high computational overhead. We present Low-Rank Query-based PDE Solver (LRQ-Solver), a physics-integrated deep learning framework for efficient CAE simulations of complex three-dimensional geometries in CAD-driven design analysis. Built upon the Parameter-Conditioned Lagrangian Modeling (PCLM) that embeds physical consistency into the learning process and the Low-Rank Query Attention (LR-QA) module that reduces attention complexity from O(N2) to O(NC2+C3) via covariance decomposition, LRQ-Solver supports multi-configuration analysis within iterative design workflows. On two benchmark datasets, it achieves a 28.6% error …

Mind The Hazard: Modeling And Interpreting Comfort With Personalized Sensing, Yufei Zhang, Matteo Favero, Patrick Chwalek, Sailin Zhong, Denis Lalanne, A. Joseph Paradiso, Clayton Miller, Andrew Sonta

Research Collection College of Integrative Studies

Recent advances in personalized sensing and comfort feedback have spurred the development of data-driven comfort models tailored to individual needs. However, because current models treat sequential comfort feedback independently, they are subject to unstable predictions and limited interpretability, hindering their deployment in building management. This study introduces a dynamic modeling framework that utilizes a Neural Ordinary Differential Equations-based Continuous-time Markov Chain to model the transitions in comfort states over time. Our modeling approach, developed through a field study utilizing smart glasses and mobile app feedback, tracks occupants' comfort transitions across daily activities and contexts. The results demonstrate that this model …

Emotional Urgency And Lack Of Perseverance Are Linked To Anxiety Sensitivity And Improve During Early Recovery From Opioid Use Disorder., Breanna A. Mcnaughton-Long, Abigail J. Pleiman, Carmen Buchfink, Chrysantha B. Davis, Rayus Kuplicki, Martin P. Paulus, Hung-Wen Yeh, Maëlle C M Gueguen, Jennifer L. Stewart

Manuscripts, Articles, Book Chapters and Other Papers

Few studies have identified how facets of impulsivity relate to recovery in opioid use disorder (OUD). Treatment-enrolled individuals with OUD (n = 167) and healthy controls (CTL; n = 46) completed the Urgency, Premeditation, Perseverance, Sensation Seeking, Positive Urgency (UPPS-P) Impulsive Behavior Scale along with demographic and clinical measures at visit 1. Over the next three months, abstinence and return to use were documented for participants with OUD (via self-report, alcohol breathalyzer, urine drug screen, and secondary contact communication). Participants with OUD who returned to use (n = 59) did not complete further study visits, whereas those who remained abstinent …

Climate Transition Stress Testing For Corporate Credit Risk: A Replicable Data-Mining Pipeline Using Esg Risk Scores, Huynh Long Hung Nguyen, Alan @ Ali Madjelisi Megargel, Rajesh Krishna Balan, Paul Robert Griffin

Research Collection School Of Computing and Information Systems

We present a replicable data-mining pipeline for illustrative, scenario-conditioned corporate distress analysis under feature decoupling. The pipeline links Compustat financial statements with Sustainalytics ESG Risk Rating records for 2,144 U.S. non-financial public firms, constructs a standardized Transition Risk Index (TRI), estimates a contemporaneous accounting-distress classifier, and applies transparent transition-cost sensitivities. Distress is defined as annual operating income insufficient to cover annual interest expense; interest coverage itself is excluded from the predictors. The resulting model is not a forward default forecast or a regulatory probability-of-default model. On a held-out cross-sectional test set, the logistic specification obtains AUC 0.909, PR-AUC 0.822, and …

Tiny Large Language Models For Iot Networks: Potentials And Challenges, Muhammed Golec, Suhib Bani Melhem, Yaser Khamayseh, Abdulmalik Alwarafy, Naofal Al-Dhahir

All Works

Large Language Models (LLM), which have gained great momentum in recent years, have revolutionized the field of Artificial Intelligence (AI); while their applicability for hardware-constrained Internet of Things (IoT) environments has begun to be questioned. This has led to the emergence of compact architecture and resource-efficient Tiny LLM models. This survey paper systematically examines Tiny LLMs for IoT networks and classifies existing approaches in five basic dimensions: model architectures, optimization strategies, transfer learning methods, deployment paradigms, and explainability-security integration. By applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, 139 related studies published between 2020 and 2025 …

Pattern Formation In Quantum Hierarchical Cellular Neural Networks, Wilson A. Zuniga-Galindo, B. A. Zambrano-Luna, Chayapuntika Indoung

School of Mathematical & Statistical Sciences Faculty Publications

We present a new class of quantum neural networks (QNNs) whose states are solutions of p -adic Schrödinger equations with a non-local potential that controls the interaction between the neurons. These equations are obtained as Wick rotations of the state equations of p -adic cellular neural networks (CNNs). The p -adic CNNs arise as continuous limits of large discrete hierarchical neural networks (NNs). The CNNs are bio-inspired by the Wilson–Cowan model, which describes the macroscopic dynamics of large populations of neurons. We provide a detailed study of the discretization of the new p -adic Schrödinger equations, which allows the construction …

Life Participation In Parents Of Children With Ckd., Anastasia Hughes, Andrea Matus Gonzalez, Anita Van Zwieten, Chandana Guha, Simon Carter, Germaine Wong, Dale Coghlan, Amanda Dominello, Jonathan C. Craig, Martin Howell, Siah Kim, Karine Manera, Colm O'Reilly, Javier Recabarren Silva, Angela Rejuso, Benedicte Sautenet, Nicole Scholes-Robertson, Amanda Sluiter, Rebecca Wu, Mignon Mcculloch, Natalia Armijo, Ximena Atilano-Carsi, Patricia De La Cerda, Gabriel Alexis Gallardo Letelier, Carla Galvez, Nicole Gonzalez-Agüero, Maria Maldonado, Viola Pinto, Lilian Reyes, Lena Rodriguez, Angelica Rojo, Alejandro Sepúlveda, Luca G. Torrisi, Andrea K. Viecelli, Bashir Admani, Bethany J. Foster, Alexander J. Hamilton, Augustina Jankauskiene, Rebecca J. Johnson, Jasmijn Kerklaan, Isaac Liu, Stephen D. Marks, Cozumel S. Pruette, Franz Schaefer, Sebastian Wolfenden, Allison Jaure, Song-Kids Life Participation Workshop Investigators

Manuscripts, Articles, Book Chapters and Other Papers

BACKGROUND: The caregiver burden among parents of children with chronic kidney disease (CKD) is high and can impair their life participation, which may in turn have negative consequences on their children's care and outcomes. We aim to describe effects on life participation in parents of children with CKD, from the perspectives of their children, health professionals, and the parents themselves.

METHODS: We thematically analyzed data from the Standardized Outcomes in Nephrology-Children and Adolescents Initiative (SONG-Kids) life participation workshops involving children with childhood CKD, parents or caregivers, and health professionals, for content related to perspectives on life participation among parents of …

Machine Learning Local Potentials For Accelerated Electron-Phonon Interactions Calculations Within The Projector Augmented-Wave Framework, Yi Xia

Mechanical and Materials Engineering Faculty Publications and Presentations

Electron–phonon interactions govern carrier dynamics, transport, and many optical and quantum phenomena in solids, but finite-displacement calculations within the projector augmented-wave (PAW) framework require up to 6N self-consistent supercell calculations for an N-atom system, making them costly for large, low-symmetry, and disordered materials. We introduce MLLocP, a machine learning (ML) strategy that learns the self-consistent local potential (LocP) on real-space grids and supplies its displacement derivatives to the evaluation of all-electron PAW electron–phonon matrix elements. Analysis of the PAW decomposition identifies the local-potential contribution as a natural target for machine learning, while the remaining PAW quantities are retained within the …

Behind The Peacock Throne: Rethinking Imperial Ethnicity In The Mughal Empire, Torre Nordgren

Armstrong Undergraduate Journal of History

The Mughal Empire was one of the most ethnically diverse early modern states to have ever ruled, containing an array of states that are currently at the center of a geopolitical tug of war today. This brings forth the two main questions of how the Mughal Empire effectively administered their domains, and to what extent are signs of an early ethnic conflict present in the Empire? Utilizing an imperial framework with Anatoly Iseanko’s model to observe translated primary sources, a swath of rich secondary sources, and Mughal art; the early stages of an ethnic conflict reveals a rift between Hindu …

German Jewish Integration And The Illusion Of Permanence, 1781–1933, Matthew M. Larian

Armstrong Undergraduate Journal of History

This paper argues that German Jewish integration from the late eighteenth century through the Weimar Republic was a genuinely successful project, and it was that success itself, not failure of foresight, that left the community unprepared for the Nazi rupture. The paper reframes the standard question: instead of asking why German Jews failed to predict Nazism, it asks how the success of integration itself narrowed the imagination of what was possible.

Interpreting The Mystery Of The Edmund Fitzgerald: A Reexamination Of Maritime Documentation, Phillip C. Brady

Armstrong Undergraduate Journal of History

The sinking of the SS Edmund Fitzgerald on November 10^th, 1975 remains one of the most mysterious and highly debated shipwrecks in the history of the Great Lakes. The most probable of the three reasons, hatch failure, compromising seas, or shoaling, can be concluded through a thorough analysis of prominent sources paired with new research gathered from the receptivity of each conclusion to the average person.

A Finite Element Model For Thermomechanical Stress-Strain Fields In Transversely Isotropic Strain-Limiting Materials, Saugata Ghosh, Dambaru Bhatta, S. M. Mallikarjunaiah

School of Mathematical & Statistical Sciences Faculty Publications

This paper presents a comprehensive computational framework for investigating thermo-elastic fracture in transversely isotropic materials, where classical linear elasticity fails to predict physically realistic behavior near stress concentrations. We address the challenge of unphysical strain singularities at crack tips by employing a strain-limiting theory of elasticity. This theory is characterized by an algebraically nonlinear constitutive relationship between stress and strain, which intrinsically enforces a limit on the norm of the strain tensor. This approach allows the development of very large stresses, as expected near a crack tip, while ensuring that the corresponding strains remain physically bounded. A loosely coupled system …

The Role Of Prior Experience And Confidence Levels In The Effectiveness Of Automated Feedback For Programming Education, Mary Benjamin, Leo Ureel, Laura Brown, Jon Sticklen, Kedmon Hungwe, Michelle Jarvie-Eggart

Michigan Tech Publications

Automated feedback is increasingly used to support programming instruction, yet little is known about how such tools influence students' self-efficacy development. This study examines how prior programming experience and initial confidence shape self-efficacy gains among first-year engineering students using an automated code critiquer for formative feedback. Pre- and post-intervention surveys were collected from 380 students enrolled in an introductory MATLAB programming course, employing two validated instruments: the Computer Programming Self-Efficacy Scale (CPSES) and the Longitudinal Assessment of Engineering Self-Efficacy (LAESE). Students with prior programming experience demonstrated significantly greater gains in both CPSES and LAESE than novices, although distinctions among basic, …

Semiconductor Supply Chain Risk Monitoring And Forecasting: A Hybrid Big Data Architecture And Empirical Validation, Ming Gong, Sangwhan Cha

Harrisburg University Other Works

Global semiconductor manufacturing networks are characterized by deep geographical concentration, high capital intensity, and acute vulnerability to regional supply disruptions. While public merchandise trade statistics provide transparent, reproducible empirical records, transforming these raw macroeconomic streams into actionable supply chain risk intelligence requires both rigorous analytical framing and scalable computational architecture. In this paper, we propose an enterprise-grade cloud data architecture designed for semiconductor risk monitoring, combining scheduled distributed batch processing on Google Cloud (Dataproc/PySpark, BigQuery) with an event-driven streaming ingestion lane (Pub/Sub, Dataflow/Apache Beam). To rigorously evaluate the underlying scoring and forecasting logic without premature distributed deployment costs, we implement …

Smart Building Iot Analytics: An Integrated Cloud Pipeline And The Consequences Of Cumulative-Meter Data Semantics For Forecasting And Anomaly Detection, Likhitha S. Gudalwar, Naga Bhavya Kore, Zeyu Wang, Sangwhan Cha

Harrisburg University Other Works

Commercial and institutional buildings account for roughly one-third of global energy demand and nearly 37% of energy-related greenhouse gas emissions. While modern smart facilities deploy dense Internet of Things (IoT) sensor meshes to monitor power draw and environmental conditions, transforming continuous, heterogeneous streams into reliable analytical intelligence requires rigorous data engineering and precise semantic modeling. In this paper, we formulate, implement, and critically audit an end-to-end cloud pipeline deployed on Google Cloud Platform (GCP). The architecture adapts the Lambda design pattern, separating bulk historical ingestion from an asynchronous real-time streaming lane (Pub/Sub, Eventarc, 2nd-generation Cloud Functions) with an explicit dead-letter …