Digital Commons Network^™

Ai-Thropology: A Study Of Artificial Intelligence Use In Anthropology, Carly R. Junod

Honors Undergraduate Theses

This study examines how undergraduate anthropology students utilize Artificial Intelligence tools in their coursework and the impact on perceptions of learning. As the use of AI becomes more commonplace, I investigated whether it has been integrated into the praxis of undergraduate anthropology students. Using a Likert-scale survey distributed to 26 anthropology students across multiple subfields, I investigated AI usage patterns, perceptions of academic integrity, and correlations with demographic factors. The results reveal that anthropology students exhibit conservative AI usage patterns, with the majority rarely using AI for core academic tasks such as drafting papers. ChatGPT and Grammarly emerged as the …

Discomfort In Silence: Investigating Workplace Discrimination In Relation To Employee Disclosure Of Menopausal Health Issues, Alysia Furbish

Honors Undergraduate Theses

Menopause is a universal developmental transition for women that often coincides with peak career years, yet it remains underexamined in organizational research and is frequently treated as a private or taboo workplace topic. This lack of visibility shapes whether women feel able to disclose menopause-related challenges, seek accommodations, and access organizational support. Drawing on literature related to pregnancy and motherhood discrimination, menopause stereotypes, and disclosure literature, this study examines how symptom burden, ideal worker norms, and stigma-related identity management contribute to anticipated workplace discrimination and disclosure decisions. Data were collected via an online survey of 106 working women recruited through …

The Effects Of Bicultural Identity On Risk-Taking And Moral Decision-Making In South Asian American College Students, Meera Puthukkudy

Honors Undergraduate Theses

The intent of this thesis was to explore how biculturalism in South Asian American college students impacts their behaviors, specifically moral decision-making and risk-taking behaviors. Bicultural individuals must navigate the complexities of their identity by integrating norms, values, and expectations from both their heritage culture and their dominant Western culture, which inherently creates a unique cognitive and psychological framework for evaluating choices. Participants in this study completed a survey online and were asked to complete three measures. First, the extent to which participants feel cultural harmony or conflict between their respective cultural identities was measured using the Bicultural Identity Integration …

To Print A Remake: An Analysis Of Hollywood Remakes And Their Cultural Value, Connor F. Seaton

Honors Undergraduate Theses

In recent years, audiences and movie critics have expressed concern that Hollywood’s growing reliance on remakes, sequels, franchises, and similar adaptations has led to a broader worry that originality is fading from modern cinema and that the industry is instead focused on using adaptations to maximize profits. Although adaptation is often seen as a commercially driven framework for reproducing existing intellectual property in a new media format, this thesis argues that it should be recognized as an autonomous cultural category with its own artistic, historical, and social significance and merit. By analyzing adaptation scholarship and reviewing its complex historical development, …

How Religious Priming Affects Morality In Sports, Daniel Prias

Honors Undergraduate Theses

This research focuses on filling the hole in existing literature regarding religious priming and how it affects morality in sports. This study examined whether different types of moral priming influenced moral reasoning in undergraduate soccer athletes. Participants were randomly assigned to one of four conditions: an external religious prime, an internal religious prime, a secular prime, or a no-prime control group. Moral reasoning was assessed using the Moral Reasoning in Sports Scenarios Questionnaire (MRSSQ), strength of religious faith was measured using the Santa Clara Strength of Religious Faith Questionnaire (SCSRFQ), and personality traits were assessed using the Mini International Personality …

Symbolic Racism And Punitive Policy Preferences In The First Quarter Of The Twenty-First Century, Anthony T. Fulinello

Honors Undergraduate Theses

Despite the decay in the acceptability of explicit racism brought about by the passage of the Civil Rights Act of 1964, implicit racism may continue to inform individual decision making even into the first quarter of the twenty-first century. Utilizing data from 25 years of the General Social Survey, this study examines how indicators of symbolic racism, a form of implicit racism, impact punitive policy preferences among the general United States population using separate logistic regression models isolating each indicator, plus a full model, for each outcome. Punitive policy preferences were operationalized as support for increased spending on crime prevention …

Breaking The Resistance: A Narrative Review Of The Evolution From Traditional Drugs To Precision Therapies In Epilepsy, Tirath Patel, Fathimathul Henna, Ashfaq Ahmed, Ayesha Ahmad, Noor Ul Huda, Syed Aaraiz Ul Hassan, Aiman Muhammad, Amna Javed, Maryem Filal, Nikhilesh Anand

School of Medicine Publications

Background: Epilepsy is a chronic neurological disorder characterized by recurrent seizures, affecting approximately 50 million individuals globally. While conventional antiseizure medications (ASMs) control seizures in 70-80% of patients, about 30% experience drug resistance or intolerable side effects, necessitating alternative approaches.

Objective: To critically compare the efficacy, safety, and accessibility of conventional and emerging therapies for epilepsy, particularly in the context of treatment-resistant cases and global disparities in care.

Methods: This narrative review synthesized evidence from 120 peer-reviewed articles published between 2015 and 2025. Literature was retrieved from PubMed, Embase, Cochrane Library, and Scopus using predefined search terms related to epilepsy …

Reduced Threat-Related Neural Efficiency: A Possible Biomarker For Pediatric Anxiety Disorders, Julia O. Linke, Reut Naim, Simona P. Haller, Parmis Khosravi, Beck Scheinberg, Meghan E. Byrne, Anita Harrewijn, Ellen Leibenluft, Melissa A. Brotman, Anderson M. Winkler, Daniel Samuel Pine

School of Medicine Publications

Objective: Pediatric anxiety disorders are common and predict adult psychopathology, yet current treatments, such as cognitive-behavioral therapy (CBT), produce lasting remission in less than 50% of affected youths. To support the search for improved, mechanistically grounded interventions, this study evaluated neural efficiency, defined as similarity in functional connectivity between a threat task and rest, as a potential biomarker. The study evaluated neural efficiency in relation to anxiety diagnosis and treatment response.

Methods: The authors compared 103 youths with an anxiety disorder diagnosis (mean age, 12.5 years [SD=2.91], 62% female) to 103 youths with no psychiatric diagnosis (mean age, 13.4 years …

Coffin–Lowry Syndrome: A Systematic Review Of Rps6ka3 Confirmed Cases And Implications For Diagnosis And Counseling, Sabyasachi Maity, Miranda Montion, Danielle Boothe, Mona Attarpour, Ivy Mageto, Simran Agarwal, Rujul Patel, Chloe Lark, Bharathi Gadad, Nikhilesh Anand

School of Medicine Publications

Background: Coffin–Lowry syndrome (CLS) is a rare X-linked disorder caused by pathogenic variants in RPS6KA3, presenting with intellectual disability, distinctive facial and skeletal features, and variable systemic involvement. Advances in genomic technologies have expanded the mutation spectrum, yet genotype phenotype correlations remain incompletely understood.

Methods: We conducted a systematic review of published cases (n = 72) following PRISMA guidelines. Demographic, phenotypic, and genotypic data were extracted, standardized, and summarized using descriptive statistics. Associations between mutation type and key clinical features were assessed with Chi-square or Fisher’s exact tests. Diagnostic approaches and global distribution were also analyzed.

Results: The cohort …

Oregon’S History Of Using The Law To “Stack The Deck” In Favor Of White Males, Janet Steverson

Lewis & Clark Law Review

The United States has a sordid, racist history that permeates society still today. The State of Oregon has its own racist history, systematically excluding persons of Black African ancestry in its early years. This Essay explains this history and how seemingly historical practices continue to provide advantages to some, while detrimentally impacting others. This history is put in context by providing explanations of particular terms and this Essay further explains why examining these historical laws are critical in moving forward.

From Chainsaws To Courtrooms: Corporate Climate Litigation And Deforestation In Brazil, Maria Antonia Tigre, Mariana Barbosa Cirne

Lewis & Clark Law Review

Corporate climate litigation has emerged as one of the most dynamic and high-profile tools for addressing the private sector’s role in the climate crisis. While much of the scholarship and public debate has centered on cases in the United States and Europe, litigation targeting corporations in the Global South—particularly Brazil—has grown rapidly, both in volume and significance. Brazil now ranks among the world’s top jurisdictions for corporate climate damage claims. These suits have pioneered methods for quantifying climate damages, often from illegal deforestation, and have tested the reach of the civil liability regime in novel ways. This Article offers the …

Temperature Compensation In Loop And Patch Fss Strain Sensors: Analysis And Experimental Validation, Swathi Muthyala Ramesh, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Frequency selective surfaces (FSSs) are arrays of conductive elements or apertures that exhibit frequency-dependent reflection and transmission properties. Their electromagnetic response is influenced by geometry and environmental conditions, making them attractive for wireless strain-sensing applications. However, temperature variations can produce frequency shifts similar to those caused by strain, reducing measurement accuracy. This work investigates the effects of intrinsic temperature compensation on two common FSS unit cell geometries—loop and patch—through comprehensive simulation analysis. The results show that loop-based cells offer superior thermal stability, while patch-based cells provide greater strain sensitivity, illustrating the trade-off between thermal robustness and mechanical responsiveness. A patch-type …

Mitigating Hysteresis In Metal-Coated Fibers Via Optimized Thermal Treatment For Advanced Distributed High-Temperature Sensing Applications, Koustav Dey, Rony Kumer Saha, Bohong Zhang, S. Narasimman, Farhan Mumtaz, Jeffrey D. Smith, Rex E. Gerald, Ronald J. O'Malley, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Metal-coated optical fibers are widely employed in sensing applications owing to their superior mechanical strength and corrosion resistance. However, their calibration at elevated temperatures is hindered by hysteresis, manifested as discrepancies between heating and cooling cycles, primarily caused by residual strain from mismatched thermal expansion coefficients (TECs) between the metal coating and silica cladding. This research introduces an optimal heat treatment procedure aimed at minimizing the impact of the mismatch in TECs between the cladding and the coating materials that causes the residual strain in gold (Au) and copper (Cu) coated fibers for achieving reliable distributed high temperature sensing up …

Large Language Models For Neurology: A Mini Review, Donald C. Wunsch, Daniel B. Hier

Electrical and Computer Engineering Faculty Research & Creative Works

Large language models have the potential to transform neurology by augmenting diagnostic reasoning, streamlining documentation, and improving workflow efficiency. This Mini Review surveys emerging applications of large language models in Alzheimer's disease, Parkinson's disease, multiple sclerosis, and epilepsy, with emphasis on ambient documentation, multimodal data integration, and clinical decision support. Key barriers to adoption include bias, privacy, reliability, and regulatory alignment. Looking ahead, neurology-focused language models may develop greater fluency in biomedical ontologies and FHIR standards, improving data interoperability and supporting more seamless collaboration between clinicians and AI systems. Two future developments have the potential to be particularly impactful: (1) …

Evaluation Of Multiple Generative Large Language Models On Neurology Board-Style Questions, Mohammad Almomani, Vijaya Valaparla, James Weatherhead, Xiang Fang, Alok Dabi, Chih Ying Li, Peter Mccaffrey, Dan Hier, Jorge Mario Rodríguez-Fernández

Electrical and Computer Engineering Faculty Research & Creative Works

Objective: To compare the performance of eight large language models (LLMs) with neurology residents on board-style multiple-choice questions across seven subspecialties and two cognitive levels. Methods: In a cross-sectional benchmarking study, we evaluated Bard, Claude, Gemini v1, Gemini 2.5, ChatGPT-3.5, ChatGPT-4, ChatGPT-4o, and ChatGPT-5 using 107 text-only items spanning movement disorders, vascular neurology, neuroanatomy, neuroimmunology, epilepsy, neuromuscular disease, and neuro-infectious disease. Items were labeled as lower- or higher-order per Bloom's taxonomy by two neurologists. Models answered each item in a fresh session and reported confidence and Bloom classification. Residents completed the same set under exam-like conditions. Outcomes included overall and …

Safe Optimal Control Framework For Cooperative Manipulation Of Objects In Human–Robot Teams, Irfan Ganie, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article introduces a distributed deep neural network (NN)-based adaptive control framework for cooperative object manipulation in human–robot teams with unknown agent dynamics by using three distinct multilayer NN observers (MNNOs). The first observer, termed the reference point estimator, enables each robotic agent to estimate the object's reference center using consensus-based learning, even without direct access to global reference trajectories. The second observer, referred to as the human force-to-trajectory estimator, uses human-applied forces to infer the intended position, velocity, and acceleration of the object, enabling real-time estimation of human intent. Together, these two observers allow distributed estimation of human-intended motion. …

Self-Calibrating Uav Navigation: Reinforcement Learning Approaches For Horizontal Trajectory Estimation, Shirin Nasr-Esfahani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Accurate unmanned aerial vehicle (UAV) trajectory estimation is essential for autonomous navigation, particularly in GPS-denied environments. Visualodometry and simultaneous localization and mapping (SLAM) approaches require precise camera intrinsic parameters, which are typically obtained through predefined or offline calibration. Instead, in this work, we propose a reinforcement learning (RL)-based self-calibration framework that estimates camera intrinsic parameters directly from monocular video sequences, without requiring prior knowledge of the camera, environment, or calibration targets. This intrinsic parameter estimation is then leveraged to achieve robust UAV trajectory estimation using only video data. We formulate the problem as a sequential decision-making task, where an RL …

Integrating Optical And Radiofrequency Interferometry For Enhanced Phase Sensing, Ruimin Jie, Zhaopeng Zhang, Chen Zhu, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Interferometry is a crucial investigative technique used across diverse fields to achieve high-precision measurements. It works by analyzing the phase difference between two interfering waves, which results from variations in optical path lengths within an interferometer. We introduce a novel method for directly measuring changes in the phase difference within an optical interferometer, importantly, with the added advantage of a controllable enhancement factor. This approach is achieved through a two-step process: first, the optical phase difference is encoded into a sub-GHz radiofrequency (RF) signal using microwave-photonic manipulation; then, RF interferometry-assisted phase amplification is implemented at the destructive interference point. In …

Enhancing Measurement Accuracy: The Impact Of Missing Data On Parameter Estimation In Mass-Spring-Damper Systems, Michkath Omanda Bouraima, Steven Thompson, Maciej J. Zawodniok

Electrical and Computer Engineering Faculty Research & Creative Works

In sensor-driven dynamic systems, missing data can severely degrade parameter estimation accuracy. This article investigates the impact of missing data on phase estimation in a mass-spring-damper system using an information-theoretic framework based on the Cramér-Rao Lower Bound (CRLB). Closed-form CRLB expressions are derived for four scenarios: complete data, missing completely at random (MCAR) deletion, MCAR-based imputation, and missing at random (MAR) missingness via a selection-weighted formulation. These bounds are used as theoretical benchmarks to evaluate classical imputation methods (last observation carried forward (LOCF), linear interpolation) and advanced approaches (Kalman filtering, Rauch-Tung-Striebel (RTS) smoothing, Bayesian inference, and transformer-based imputation) through Monte …

Rf-Attennet: A Hybrid Attention-Enhanced Network For Mixed Signal Classification In Uav Swarm Detection, Prajoy Podder, Mohammad Atikur Rahman, Maciej Zawodniok, Sanjay Madria

Electrical and Computer Engineering Faculty Research & Creative Works

The continuous increase of UAVs, particularly in swarms, creates significant challenges for security and airspace regulation. Traditional RF fingerprinting methods struggle to detect and classify UAV swarms due to overlapping signals and interference. This study introduces RF-AttenNet, a hybrid deep learning model designed to classify mixed UAV signals by analyzing composite RF spectrograms. RF-AttenNet uses dual attention mechanisms, channel and spatial attention to focus on critical spectral features, enabling the model to effectively separate and identify overlapping UAV signals. We have developed custom composite UAV datasets that simulate real-world swarm interference, incorporating both single and mixed UAV classes. RF-AttenNet achieves …

Transformer-Customer Relationship Identification Based On Load-Switching Fluctuation Characteristics Considering Same-Feeder-Adjacent-Transformer Condition, Yanan Zhang, Gan Zhou, Yuyuan Liu, Wei Gu, Yanjun Feng, Yujue Wang, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Accurately identifying the connectivity between transformers and downstream three-phase customers in low-voltage distribution networks is challenging, because voltage curves of different phases and nearby nodes can be weakly distinguishable, especially when adjacent transformers on the same feeder serve geographically close customers with highly similar voltage curves. This paper proposes a novel method based on load-switching fluctuation characteristics recorded by smart meters. By extracting localized current and voltage fluctuations and establishing correlation matching, the method overcomes the limited discriminability using steady-state measurements. The method operates in two stages: first, switching-induced fluctuation characteristics are extracted and matched to cluster customers by the …

Corrections To: Enhancing Measurement Accuracy: The Impact Of Missing Data On Parameter Estimation In Mass-Spring-Damper Systems (Ieee Transactions On Instrumentation And Measurement (2026) 75 (1–12) Doi: 10.1109/Tim.2026.3676091), Michkath Omanda Bouraima, Steven Thompson, Maciej J. Zawodniok

Electrical and Computer Engineering Faculty Research & Creative Works

In the above article [1], a wording ambiguity appears in Proposition 4 regarding the description of the missing at random (MAR) mechanism. The published sentence states that the probability of observing the kth sample depends on the realized measurement value. This wording may be interpreted as dependence on the current unobserved value y[tk], which could suggest a missing not at random (MNAR) mechanism. The intended MAR mechanism is that the observation probability for the kth sample depends only on previously observed measurement information, such as y[tk-1], and not on the current unobserved value y[tk]. Therefore, the corrected wording clarifies that …

Shedding Light On Cellular Glycolysis Pathway Kinetics: Combining Kinetic, Mechanistic Modelling Approaches With Label Free Microspectroscopic Imaging. (Spectralomics), Nitin Patil

Doctoral

Cellular metabolism and its intricate biochemical mechanisms which allow the conversion of nutrients into energy to sustain life are well understood, leading to numerous breakthroughs in science and technology, human health and wellbeing. From an industrial perspective the pathway kinetics are significant for monitoring cellular bioprocesses, screening desired metabolite phenotypes in drug discovery processes, etc.

An initial review of glycolysis pathway kinetics highlighted the lack of sophisticated approaches to monitor the metabolic pathway kinetics as a function of time in a non-destructive, non-invasive, label-free manner. Fluxomics, a metabolomics based approach providing high-throughput insights, can be considered a gold standard approach, …

Nh 4-H Livestock Quiz Bowl Packet, Unh Cooperative Extension

UNH Cooperative Extension

No abstract provided.

Nh 4-H Horse Quiz Bowl Packet, Unh Cooperative Extension

UNH Cooperative Extension

No abstract provided.

Ai Bots As Law Teachers: What Is Left For Human Professors To Do?, Harold I. Abramson

Scholarly Works

This article considers what bots can do as AI law teachers, how students are already using bots for learning, how to integrate the use of AI into law courses, and what faculty can do to stay ahead of the bots in their classrooms. During this discussion, the article confronts the existential question of what is left for human law professors to do in an AI world. It concludes that there is a future for humans for now, and one that might make us better teachers.

A Survivor's Tale: How The Northern And Eastern Districts Of Oklahoma Survived The Tidal Wave Collision Of Mcgirt V. Oklahoma, Darbi E. Robertson

American Indian Law Review

No abstract provided.

The Bad Man’S Hourglass: Extending The Clock On Justice, Liv G. Cummins

American Indian Law Review

No abstract provided.

With Religious Freedom Came Religious Authority: Apache Stronghold V. United States And The Exclusion Of Native American Religion Within The Legal Understanding Of Religious Freedom, Cassidy Beck

American Indian Law Review

No abstract provided.

“Discovered!”: Plaintiff’S Brief To The United States Supreme Court In Johnson & Graham’S Lessee V. M‘Intosh (1823), Mark Savage

American Indian Law Review

No abstract provided.