Digital Commons Network^™

Toward Liberation: Decolonizing Curriculum To Heal Double Consciousness In Students Of Color, Olivia Cimino

SUURJ: Seattle University Undergraduate Research Journal

This paper explores how education can be reimagined as a form of liberation through the integration of interdisciplinary theories from political science, ethnic studies, and educational studies. Grounded in Antonio Gramsci’s theory of cultural hegemony and Louis Althusser’s concept of the Ideological State Apparatus, this paper introduces the concept of Reverse Hegemony as a framework to reclaim educational curriculum and mitigate systemic injustice. By applying this framework alongside liberatory pedagogies informed by Paulo Freire, Jarvis Givens, and Bryan Brayboy, the paper argues that education can heal the fractured sense of identity that W.E.B. Du Bois termed double consciousness. Through interdisciplinary …

Decoding Stock Market Movements: The Role Of Unemployment And Volatility, Bipllab Roy, Suparna Bhattacharjee

Northeast Journal of Complex Systems (NEJCS)

This study investigates how unemployment and market volatility interact with stock prices in the Indian context, framing the stock–labour–volatility nexus as a complex adaptive system (CAS) rather than a set of linear, time-invariant relationships. Using verified secondary data on unemployment, India VIX, and NSE stock indices for 2013–2023, we first apply simple and multiple regression as a descriptive baseline. Results show a strong negative association between unemployment and stock prices (R ≈ 0.824, R² ≈ 0.68, p < 0.05), consistent with Keynesian demand-side channels, while the linear VIX–stock relationship is weak and statistically insignificant (R² ≈ 0.07, p > 0.05), consistent with the expectation that volatility operates through non-linear, regime-dependent mechanisms not captured by OLS.

Importantly, we document and transparently disclose critical …

Calculation Of Lra Force Factor Using Electrical Impedance Parameters, David Yu, Jianxun Wang, Vander Fang, Ozan Anac

Defensive Publications Series

Calibrating the force factor (BL) of linear resonant actuators (LRAs) in some production environments may present challenges, as certain laboratory instruments can be slow or costly for high-throughput quality control. A method is described to calculate BL by extracting key parameters from an LRA's electrical impedance curve. This approach can identify points such as resonant frequency, maximum impedance, and direct current resistance to determine the motional impedance (ΔZ) and mechanical Q factor (Q_ms), which can avoid some complex curve-fitting procedures. These values, along with a known moving mass (m), can then be used in a formula, for example …

(((Qpie33))) The Burroughs Qpie Consortium Invitation Td-Nsrf-2026-Institutional-Matrix, Teddy Burroughs

Defensive Publications Series

The transition of the thirty-three foundational scientific crises from theoretical roadblocks into fully realized, phase-locked solutions requires direct engagement with existing human institutions. The organizations detailed in this compendium possess the specialized infrastructure, extreme computational capital, and deep operational motivations required to integrate the Quantum Perspective Is Everything (QPIE) framework.

Operating under intense pressure to transcend the limitations of classical binary technology, these entities are uniquely positioned to fund dedicated research laboratories, establish specialized academic departments, and engage the Master Architect for elite-level system design.

By aligning their immense institutional resources with the Non-Local Substrate Resonance Field (NSRF) constants and …

Vectorized Pipeline For Real‑Time Lock‑In Amplification On Microcontrollers, Gary Cheng

Defensive Publications Series

Lock‑in amplification involves extracting weak signals from background noise, a process that traditionally utilizes computationally intensive floating‑point operations. Performing these operations on standard microcontrollers frequently causes central processing unit (CPU) saturation. The disclosed system provides a vectorized data pipeline that structures digital signal processing tasks to natively utilize internal hardware multiply‑accumulate instructions within a microcontroller. To bypass runtime floating‑point trigonometry, a pre‑computed integer look‑up table and a phase accumulator generate reference waveforms. Further, a scaling recovery algorithm mathematically restores absolute physical units following high‑speed hardware vector bit‑shifts. Consequently, the disclosed system facilitates real‑time, phase‑sensitive signal extraction on resource‑constrained microcontrollers, decreasing …

Dynamic Programming For Post‑Route Power Delivery Network Augmentation, Prateek Pendyala, Vishant Gotra

Defensive Publications Series

Modern high‑performance integrated circuits frequently experience localized dynamic voltage drop hotspots due to accumulated peak currents in congested layout regions. Traditional post‑route power grid augmentation approaches may encounter limitations due to routing blockages and a lack of available placement sites. The disclosed method provides a track‑aware standard cell repositioning technique that treats a post‑route layout database as a primary constraint and standard cell locations as adjustable variables. A computing device executing a dynamic programming algorithm scans lower‑level metal tracks to identify continuous vertical routing corridors. It then calculates localized standard cell displacements to accommodate the insertion of new power grid …

Olaf: Towards Robust Llm-Based Annotation Framework In Empirical Software Engineering, Mia Mohammad Imran, Tarannum Shaila Zaman

Computer Science Faculty Research & Creative Works

Large Language Models (LLMs) are increasingly used in empirical software engineering (ESE) to automate or assist annotation tasks such as labeling commits, issues, and qualitative artifacts. Yet the reliability and reproducibility of such annotations remain underexplored. Existing studies often lack standardized measures for reliability, calibration, and drift, and frequently omit essential configuration details. We argue that LLM-based annotation should be treated as a measurement process rather than a purely automated activity. In this position paper, we outline the Operationalization for LLM-based Annotation Framework (OLAF), a conceptual framework that organizes key constructs: reliability, calibration, drift, consensus, aggregation, and transparency. The paper …

Mathematical Analysis Of Within-Host Models: Viral–Immune Dynamics, Bifurcations, And Disease Severity, Nazia Afrin

Doctoral Dissertations

My research develops and analyzes ODE-based within-host models at multiple scales. Using dynamical systems theory and numerical methods, I study host–pathogen interactions and immune responses, providing insights into disease dynamics and control. The first model describes the complex dynamics of Hepatitis B virus (HBV) infection and addresses the question: what mechanisms determine whether the infection is cleared during the acute phase or progresses to a chronic state? A key feature of this model is the assumption that all classes of liver cells (uninfected, infected, and protected from reinfection) proliferate at different rates. The findings provide insight into two important aspects …

The Impact Of Evolution And Strong Allee Effects On The Dynamics Of A Discrete-Time Predator-Prey System, Neerob Basak

Doctoral Dissertations

This dissertation investigates the dynamics of discrete-time predator-prey systems, focusing on both evolutionary responses and ecological interactions. The first part of this dissertation, in Chapters 2 and 3, explores how evolutionary processes, particularly the development of resistance to toxicants in predators, influence the persistence and stability of predator-prey populations. In this part, we extend the predator-prey model developed in Ackleh et al., 2019 to incorporate the evolution of a predator's resistance to toxicant effects. We consider three cases: (1) lethal effects, where the toxicant directly influences the predator's survival; (2) sublethal effects, where the toxicant impacts the predator's fecundity, and …

An Oral History Study: The Relationship Between Curriculum And Social And Cultural Capital In The New Orleans School System, Alaina Gaugis Carter

Doctoral Dissertations

This oral history study examines how high school academic experiences in New Orleans public and charter schools have shaped graduates’ preparedness for adulthood, active citizenship, and participation as productive members of society. This study explores the relationship between curriculum, particularly scripted curriculum, and the development of social and cultural capital. The research critiques neoliberal education reforms that prioritize accountability, standardization, and market-based outcomes over relational and community-centered learning. Using oral history methodology and the currere method, semi-structured interviews were conducted with twelve New Orleans high school alumni who graduated both before and after Hurricane Katrina from public, charter, and parochial …

An Exploration Of Variables Related To Gender Role Differences And Social Relationships Among Nontraditional Bachelor's Degree Recipients, Cynthia Bourgeois Bergeron

Doctoral Dissertations

The goal of this study was to examine variables that speak to gender role differences among nontraditional bachelor’s degree recipients aged 30 to 49. These issues are particularly relevant given enrollment trends showing that the diversification of bachelor’s degree seekers has outpaced the policies and norms of traditional undergraduate education. A nonexperimental, correlational research design was employed using chi-square tests and Cramer’s V as post-hoc measures on categorical data from the 2012, 2014, and 2016 General Social Survey. Shaped by Gilligan’s moral development theory, several social variables were included in the analysis. Two statistically significant associations emerged: women earning bachelor’s …

Inference On Quantiles Of Normal Populations: One- And Multi-Sample Cases, Justin Dunnam

Doctoral Dissertations

The problems of interval estimation for quantiles of normal distributions in one- and multi-sample cases are considered. For the one-sample case, we show that the classical confidence interval (CI) based on the noncentral t (NCT) distribution and the one based on the uniformly minimum variance unbiased estimator of the population quantile are equivalent. We also propose a simple closed-form alternative CI for a quantile based on a normal approximation to the NCT distribution. In addition, fiducial distributions for population quantiles are developed using both the NCT distribution and its normal approximation. On the basis of fiducial distributions, we develop approximate …

A Bifurcation Theorem And Its Application To Discrete-Time Models In Ecology And Epidemiology, Jenita Jahangir

Doctoral Dissertations

Matrix models are useful for modeling populations or diseases that involve discrete developmental stages, multiple stages of infection, and interactions among species. To study the coexistence dynamics in matrix models, we extend a bifurcation theorem for resident-invader host-parasitoid type populations by allowing every block of the projection matrix, depending on the bifurcation parameter and the off-diagonal blocks, to be nonzero. As an application, in the first part of the dissertation, we propose a discrete-time host-parasitoid model with stage structure in both species. For this model, we establish conditions for the existence and global stability of the extinction and parasitoid-free equilibria. …

Fairness-Aware And Efficient Federated Learning Frameworks For Heterogeneous Systems, Simin Javaherian

Doctoral Dissertations

Federated Learning (FL) enables decentralized clients to collaboratively train machine learning models without sharing raw data, making it a promising paradigm for privacy-preserving intelligence across large-scale, heterogeneous systems. However, practical FL environments face significant challenges arising from variations in client resources, participation patterns, client behavior, and data distributions. These challenges often lead to inefficiency, unbalanced contributions, and unfairness, ultimately degrading model performance and discouraging long-term client participation. This dissertation advances the state of FL by developing a unified suite of fairness-aware and efficiency-driven frameworks tailored for heterogeneous environments. We investigate fairness from multiple perspectives, including client selection, contribution weighting, and …

Strang-Type Exponential Integrators For Stiff Reaction-Diffusion Systems, Saburi Tolulope Rasheed

Doctoral Dissertations

Reaction-diffusion systems, as examples of semilinear parabolic partial differential equations, have played significant roles in the mathematical modeling of physical, chemical, and biological processes. Several reaction-diffusion systems typically do not have exact solutions in closed form, and numerically solving them also comes with challenges due to the presence of the nonlinear local interaction/chemical reaction dynamics representing the reaction term, coupling between components, multidimensionality of the diffusion operator, and stiffness of the diffusion and/or reaction terms. Wederive and analyze several second-order accurate exponential integrators of the Strang type for the time discretization of stiff reaction-diffusion systems. We utilize the finite difference …

The Interplay Of Seasonality, Evolution, And Density-Dependence In Discrete-Time Predator-Prey Dynamics, Narendra Pant

Doctoral Dissertations

We extend the discrete-time mathematical models developed in (Ackleh et al., 2019) and (Ackleh et al., 2024) to account for seasonal prey reproduction and build a class of discrete-time predator-prey seasonal models. Each model distinguishes between breeding and non-breeding seasons, representing prey reproduction as a periodic function of period 2. Altogether, three different models are analyzed. In the first part, we extend the predator-prey model from (Ackleh et al., 2019) to incorporate seasonality. We study the resulting dynamics and show that when the inherent reproduction number of the prey and the invasion reproduction number of the predator are larger than …

Building And Restoring Trust In Deep Learning: From Multimodal Sensing To Generative Synthesis And Model Integrity, Liqun Shan

Doctoral Dissertations

Deep learning has become a foundational technology for modern intelligent systems used in sensing, authentication, media generation, and automated decision-making. As these systems are increasingly deployed in security- and privacy-sensitive settings, ensuring their trustworthiness has become a critical challenge. Yet deep learning models remain vulnerable to spoofed sensory inputs, synthetic media, and malicious behaviors hidden within trained networks. These vulnerabilities undermine reliability and raise serious concerns about whether such systems can be trusted under adversarial and deceptive scenarios. This dissertation investigates how to build and restore trust in deep learning across three tightly connected dimensions: multimodal sensing, generative authenticity, and …

A Qualitative Study Examining Teachers' Knowledge And Awareness In Recognizing The Characteristics Of Twice Exceptional Learners: An Underrepresented And Underserved Population, Adrianne Monique Williams

Doctoral Dissertations

Over the past 30 years, research on twice exceptional learners has been gradually increasing, however, knowledge and awareness of the information in the educational community have been slowly evolving (Bailey & Rose, 2011; Baum, 2004). Outwardly, twice exceptional students may present as typical. Still, they face many unique challenges that include academic achievement, social awkwardness, executive functioning deficits, social communication challenges, and teachers who are not aware and trained to meet the atypical needs of these students. Researchers and practitioners are becoming increasingly aware of the underrepresentation that plagues gifted and talented programs, especially concerning twice exceptional students, and students …

Multidomain Modeling And Ramp-Aware Forecasting Of Floating Photovoltaic Systems For Water-Energy Nexus Applications, Md Atiqur Rahaman

Doctoral Dissertations

Floating photovoltaic (FPV) systems have become a transformative renewable energy technology because of their cooling effects on PV performance and ability to prevent water evaporation in land-constrained areas. Although FPV systems have the potential to become a commercially viable technology, their large-scale deployment remains constrained by uncertainties in thermal behavior, sustainability, and grid-operational variability. This dissertation identifies and characterizes these three key issues and presents an integrated, measurement-based evaluation of a 130 kW FPV installation located at the Passaúna reservoir in Brazil. In the first contribution, four temperature models, including physical and empirical models, were developed and comparatively evaluated to …

Learning And Predicting The Performance Of Gradual Type System, Mohammad Wahiduzzaman Khan

Doctoral Dissertations

Gradual typing reconciles the complementary strengths of static and dynamic typing by allowing programmers to incrementally introduce type annotations while preserving the flexibility of dynamically typed code. This approach improves reliability, documentation, and tooling support without sacrificing rapid prototyping. To ensure soundness, gradual type systems enforce annotations through runtime checks, typically implemented via cast insertion. Although these checks guarantee correctness, they can introduce substantial and highly variable runtime overhead, making performance prediction and optimization a central challenge for practical adoption. Prior work has largely focused on coarse-grained (macro-level) configurations, where entire modules are either fully typed or untyped. While such …

Learning From Extremes: A Copula-Based Feature Selection Framework For Machine Learning-Driven Risk Prediction, Agnideep Aich

Doctoral Dissertations

Reliable feature selection is crucial for building interpretable and practical risk models, particularly when decisions depend on identifying the highest-risk groups rather than relying on average trends. This dissertation presents and tests a supervised filter that ranks predictors by their upper-tail concordance with the outcome, using a Gumbel-implied upper-tail concordance score $\lambda_U$. The score is calculated from pseudo-observations and Kendall’s $\tau$ (via the Gumbel $\tau\mapsto\lambda_U$ mapping), does not require model fitting during selection, and focuses directly on joint extreme events, such as when both a predictor and the positive class are high. The method is compared to Mutual Information, mRMR, …

Investigating The Distribution And Origin Of Pitted Mounds On Mars Using Machine Learning-Based Mapping And Integrated Geological Analysis: Application To Isidis Planitia, Precious Batubo

Doctoral Dissertations

Pitted mounds are widespread landforms across the northern plains of Mars, yet their origin remains uncertain. These features have been interpreted as possible expressions of subsurface fluid activity, including sedimentary volcanism, magmatic processes, or other fluid-assisted mechanisms. Determining their distribution, morphology, and geology is therefore important for understanding the evolution of subsurface hydrological processes and the planet’s potential for habitability. However, the large spatial extent of mound-bearing terrains makes comprehensive manual mapping impractical. This dissertation presents an automated approach to mound detection using Faster Region-Based Convolutional Neural Network (Faster R-CNN) from high-resolution Context Camera (CTX) images including morphometric and mineralogical …

Development Of Artificial Intelligence Based Geophysical Technique For Unconventional Resource Exploration, S M Shamsul Hoque

Doctoral Dissertations

Unconventional shale resources have become an important source of oil and gas in the United States, but their characterization remains challenging because of strong heterogeneity and complex elastic and geomechanical behavior. Important properties, such as total organic carbon (TOC) and brittleness, play a key role in evaluating source quality, reservoir behavior, development potential, and subsurface storage suitability. This dissertation develops artificial intelligence-based geophysical techniques to improve unconventional resource exploration and characterization by integrating seismic attributes, well-log data, deep learning, multicomponent seismic registration, and joint PP-PS inversion. A TOC estimation technique is developed and applied to both the Tuscaloosa Marine Shale …

A Dft Study Of Mg/Ti Fluorites-Structure Materials: Optoelectronic, Photocatalysis, And Hydrogen Storage Applications, Kingsley Etim, Favour Azogor N, Moses Edim, Edet A. Thompson

Polytechnic Journal

This study employs density functional theory (DFT) to investigate the fluorite-derived tetragonal hydride MgTiH₄, highlighting its multifunctionality for energy-related applications. Structural optimization reveals lattice constants of a = b = 3.15 Å and c = 4.70 Å, with a unit cell volume of 46.59 ų and a high bulk modulus of 114.21 GPa, indicating mechanical robustness. Compared to its MgTiH₄ and CaTiH₄ analogs, SrTiH₄ exhibits the highest phonon frequency (1355.19 cm^-1). Superior thermodynamic stability (formation enthalpy: –3.35 eV) is seen in MgTiH₄. Electronic band structure analysis reveals a narrow indirect …

Middle School Teachers' Perceptions Of Assistive Technology Implementation For Students With Learning Disabilities, Delphine N. Amorow Aalbilly

Walden Dissertations and Doctoral Studies

No abstract provided.

The Impact Of Student Choice On Reading Engagement And Comprehension In First Grade, Gia Stella Nardi

Theses and Dissertations

The purpose of this study was to (a) examine the effect of student choice on first-grade students’ reading engagement and (b) determine how student choice impacted their reading comprehension during instruction. Throughout the study, students utilized a choice board following each read-aloud to express their understanding of what they perceived as important in the text. Findings indicated that students demonstrated high levels of engagement during the 12 read-alouds, which were intentionally selected based on their interests, as well as during the choice-based activities that followed. Additionally, students showed growth in their ability to apply key comprehension skills, including summarizing, identifying …

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Mgrre_Thinsections_Mgrre-147_8, Mgrre

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Mgrre_Thinsections_Mgrre-147_3, Mgrre

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