Digital Commons Network^™

Assessing The Effectiveness Of Tilt-Informed Assessments In Calculus I, Samuel Horelick

Inquiry: The Journal of the Virginia Community Colleges

Transparent Design in Learning and Teaching (TILT) is widely promoted as an evidence-based framework intended to clarify expectations, promote equity, and improve student learning. While prior research reports positive outcomes across many disciplines, less is known about how transparency functions in quantitative, problem-solving courses such as calculus, where students often value efficiency and autonomy. This study examines the effects of a TILT-informed assignment redesign in two sections of Calculus I at a Virginia Community College System institution. One section completed a traditional assignment, while the other completed an equivalent task redesigned to make the purpose, task, and evaluation criteria explicit. …

Intervention Levers For Stunting Reduction In Indonesia: Evidence From The 2023 Indonesian Health Survey Data, Iwan Ariawan, Hafizah Jusril, Zahra Izza Afifa, Azka Fitri, Elmarizha Sekar Utami, Mikail Hasan, Dwi Puspasari

Kesmas

Stunting remains the largest public health challenge among macro-nutrition problems in Indonesia, affecting almost a quarter of children under five in 2023. The prevalence is considered high according to the World Health Organization standard. This study analyzed 15 aggregated provincial variables from the 2023 Indonesian Health Survey using Structural Equation Modeling (SEM), focusing on determinants of stunting among children under two to identify primary intervention levers. Findings indicated that intervention urgency should focus on the first 1,000 days, particularly the steep increase in stunting prevalence observed in the 12–24-month age range. While the highest prevalence is in Eastern provinces (e.g., …

Educational Content Creation For Non-Textbook Learning Resources, Dorina Tila

Open Educational Resources

These are materials that will be supporting faculty teaching and students enrolled in ECO 1200: Macroeconomics. Some items are also helpful in other economics, finance, and business classes. The set includes:

• Customized Summaries of Modules,
• Up-to-date Case Examples and Assignments
• Economic Experiments and Game Designs and Instructions,
• Instructions that Ethically and Effective Use Generative AI

Rethinking The Canon: A Conceptual History Of The Layered Foundations Of Western Politics, Dembael Seidi, Muhammad Andi Firmansyah

Jurnal Wacana Politik

Many accounts of Western political history suggest a direct, straight line from Ancient Greece to modern democracy. This article argues that this “Great Books” narrative is a myth that ignores how political ideas actually change over time. Using Reinhart Koselleck’s Begriffsgeschichte (conceptual history) framework, we demonstrate that key terms such as “law” and “citizenship” were not merely preserved but fundamentally transformed as they moved through Greco-Roman, Islamic, and Judeo-Christian civilizations. We call this transformation “conceptual alchemy”: a series of semantic translations through which Greek rationalism was retheorized via Islamic metaphysics and later synthesized by medieval scholasticism. Consequently, rather than a …

From Process Retrieval To Understanding: A Look Into Curiosity And Metacognition In My Physics Classrooms, Nahuel Acosta Burroso

Critical and Creative Thinking Capstones Collection

In the modern high school physics classroom, the rapid proliferation of generative artificial intelligence (GenAI) has created a unique educational challenge. While these tools can solve complex problems quickly, they often lead to cognitive offloading, where students bypass the internal dialogue and deep thinking needed for true understanding. This project follows an action research journey that shifts the focus from restricting technology to cultivating curiosity and metacognition. I define curiosity as the gap identifier that appears when a student realizes they do not know something, while metacognition acts as the navigator that helps them decide what to do next. By …

Adaptive Multimodal Smart Home Control On A Raspberry Pi 5 Using Hand Gestures, Voice Cues, And User Feedback, Vaibhav Bora

Theses

A real time multimodal smart home control system deployed on a Raspberry Pi 5 is presented. The system combines hand gestures, short voice cues, and proximity aware interaction to execute household commands such as light brightness control, fan speed adjustment, and stop or kill switch actions. Lightweight gesture and keyword spotting voice classifiers were trained offline and exported to TensorFlow Lite for efficient on device inference. For more natural spoken phrases, the system additionally integrates a locally deployed pretrained offline ASR component rather than a speech recognizer trained from scratch. Using a USB camera and microphone, the system operates fully …

Robustness Of Ai-Driven Histopathology Under Real-World Adversarial Examples, Ruchik N. Yajnik

Theses

This robustness of histopathology classification models under adversarial and real-world perturbations resembling clinical artifacts is being investigated.

Using whole-slide images from the CAMELYON17 cohort, four representative architectures—ResNet-18, ResNet-50, HIPT-2MLP, and ViT-B/16 —are benchmarked across controlled pixel-level distortions and artifact-like transformations. Adversarial methods include iterative Fast Gradient Sign, Projected Gradient Descent, Salt-and-Pepper noise, and the Adversarial Watermark—Stain Shift (AWSS). Three defense strategies—Randomized Smoothing, Adversarial Training, and an Artifact Detector—are evaluated for their ability to preserve diagnostic accuracy and model reliability. Structured perturbations consistently degrade performance, with transformer-based models showing the greatest sensitivity. The benchmark developed here offers a reproducible framework for …

Sound Detection Of Memory Leaks In Llvm Ir Programs Using Accumulation Analysis, Robert Blacha

Theses

Resource leaks occur when a limited resource such as memory is allocated by a program and needlessly held past the point of use. Leaks can lead to a degradation of services which can be specifically triggered with malicious behavior, for example abusing a memory leak in a program to cause a server to slow down and crash for a denial-of-service attack.

Prior work has demonstrated that accumulation analysis provides a sound detection of resource leaks with a working implementation for programs written in Java. While useful, current implementations are limited to programs written in Java, which has a garbage collector, …

Perceptual And Geometric Advances In Crowd Simulation, Bilas Talukdar

Dissertations

Simulating realistic crowd motion remains a fundamental challenge in computer graphics and multi-agent systems, as it requires modeling both physically plausible interactions and perceptually natural behaviors. Existing crowd simulation methods typically employ simplified geometric abstractions, most commonly circular agent representations, and model navigation using either analytical interaction formulations (e.g., force, velocity, or constraint-based methods) or learned policies derived through reinforcement learning. Despite their effectiveness, these approaches often overlook detailed geometric structure and do not explicitly account for perceptual realism. This dissertation addresses these challenges by improving the realism of virtual crowd simulation through two key advancements: perceptual preference learning and …

Principles Of Privacy And Security In Artificial Intelligence And Applications, Khang Tran

Dissertations

Modern artificial intelligence (AI) systems have transformed critical domains such as healthcare, software engineering, finance, and the legal system. Despite their broad impact, concerns about trustworthiness, especially regarding privacy and security, remain major obstacles to wider adoption. Addressing these concerns requires both a systematic understanding of the privacy and security risks inherent in AI systems and the development of principled foundations for trustworthy AI that safeguard client privacy and security. This goal is particularly challenging because of the complexity of modern large-scale AI systems, the trade-offs between privacy and model utility, and the need to simultaneously ensure other important properties …

Mechanics And Physical Attributes Of Nature-Based Alterations: Rock Reinforcement And Urban Heat Island Assessment, Mary Chikondi Ngoma

Dissertations

Ground improvement is critical to geotechnical and geo-engineering systems, where modification of the properties of geomaterials (rocks and soils) is required to maintain stability and prevent failure of infrastructure installed within and around them. This need has become increasingly important with rapid urbanization and population growth, which intensify demands on surface and subsurface systems and further challenge the performance of supporting geomaterials. As a result, there is growing interest in nature-based solutions, particularly biologically mediated processes such as biocementation, which can enhance the physical, hydraulic, and mechanical properties of geomaterials while offering environmentally sustainable alternatives to conventional ground improvement techniques. …

Enabling Ml/Ai In 6g And Future Wireless Communication With Privacy Preservation, Mec Offloading And Quantum Computing, Changshi Zhou

Dissertations

The forthcoming sixth-generation (6G) and future wireless networks are envisioned to support an unprecedented range of services, delivering ultra-low latency, massive connectivity, and intelligent real-time responsiveness. These capabilities will enable emerging applications such as extended reality (XR), autonomous vehicles (AVs), industrial robotics, and the Internet of Things (IoT) to reach their full potential. Achieving this vision requires the integration of enabling technologies such as artificial intelligence and machine learning (AI/ML) and quantum computing, which are poised to play central roles in shaping the landscape of wireless communication systems.

In AI-native, data-driven, and computing-centric 6G networks, ML models will be deeply …

Differential-Geometric Methods For Neural Signed Distance Fields: Parameterized Surface Extraction And Curvature Regularization For Cad Models, Haotian Yin

Dissertations

Neural signed distance fields have emerged as a powerful framework for representing three-dimensional geometry through continuous and differentiable neural functions. Their flexibility, resolution independence, and compatibility with gradient-based optimization make them especially attractive for surface reconstruction and geometric learning. However, despite these advantages, two fundamental challenges remain for engineering-grade applications. First, higher-order geometric properties such as curvature are difficult to model reliably during training and often require computationally expensive second-order differentiation. Second, while neural signed distance fields provide implicit surface representations, they do not directly yield a globally consistent forward map or parameterization for downstream geometric processing.

This dissertation addresses …

Holistic Dram Enhancements: From Intrinsic In-Memory Operations To Robust Security Mechanisms, Ranyang Zhou

Dissertations

Dynamic Random-Access Memory (DRAM) is both the performance bottleneck and a critical security boundary of modern computing systems. Its physical properties make it an attractive substrate for near-data computation—yet those same properties expose it to disturbance-based hardware attacks. This dissertation argues that these two dimensions are not independent: the architectural choices that make DRAM efficient also reshape its threat landscape. Addressing both requires a unified approach to memory architecture and security co-design.

The first part of this dissertation attacks the memory wall through four processing-in-DRAM (PIM) frameworks. ReD-LUT and LT-PIM unify lookup-table arithmetic with charge-sharing logic, achieving up to 37.8x …

Disentangling Non-Thermal Electron Injection And Decay In Solar Flares Using Multi-Wavelength Radio Observations, Brian Eugene O’Donnell

Dissertations

The broadband microwave imaging spectroscopy capability provided by the Expanded Owens Valley Solar Array (EOVSA) allows new diagnostics of high-energy processes in solar flares, providing spatially and temporally resolved spectra rich in information about the acceleration and transport of energetic electrons.

In this work, injections and transport of energy and particles into the solar corona during flares are studied. This is accomplished through the development and use of the PIP_Decomp Fitter, an automated fitting tool made by the author to fit injection and precipitation/decay parameters using the spatially resolved radio spectra obtained by EOVSA. These tools are used to study …

A Generative Ai-Driven Computational Framework For Industry-Scale Discovery Of Novel Battery Materials, Joy Datta

Dissertations

The growing demand for sustainable, high-energy-density electrochemical storage has motivated the exploration of multivalent-ion batteries based on earth-abundant elements such as aluminum, calcium, magnesium, and zinc. While multivalent charge carriers offer higher theoretical energy density than lithium, their practical deployment is hindered by sluggish ion transport, strong ion-host interactions, and structural degradation of electrode materials. Identifying host materials that can reversibly accommodate multivalent ions while maintaining structural integrity remains a fundamental challenge. The dissertation develops a scalable, end-to-end computational framework that integrates density functional theory (DFT), machine learning (ML), and generative artificial intelligence (GenAI) to accelerate the discovery of next-generation …

Toward Learning-Based Reconstruction And Part Decomposition Of Man-Made 3d Geometry: Neural Implicit Representations And Scalable Supervision, Shen Fan

Dissertations

Digital three-dimensional (3D) models are central to engineering design, analysis, and manufacturing, but learning pipelines for man-made geometry often operate on sampled carriers that do not preserve all of the structure present in exact CAD representations. This dissertation studies learning-based reconstruction and part decomposition for structured man-made 3D geometry, from general object benchmarks to CAD-derived datasets, with a focus on neural implicit representations trained from signed-distance samples, point clouds, and tessellated meshes. The goal is to make these models more accurate, more part-aware, and more consistently supervised.

First, signed distance function (SDF) reconstruction with implicit neural representations is improved through …

Anonymity And Accountability In Secure Messaging, Erin Kenney

Dissertations

Encypted messaging has become more and more prevalent as time moves on, and its benefits in assuring privacy cannot be overstated, but it also brings along with it concerns on how to moderate platforms where all messages are hidden. Message Franking, followed by Traceback systems, addressed these concerns by allowing the sender of a message to be proven when reported, even for forwarded messages in the case of Traceback, however these systems damage the privacy guarantees that originally motivated encrypted messaging to begin with.

In practice, even without those concerns encrypted messaging alone is not enough to prevent the most …

Glass Transition Temperature Of Plga Nanoparticles And The Application In Drug Delivery, Guangliang Liu

Dissertations

The glass transition temperature (T_g) of poly(D,L-lactic-co-glycolic acid) (PLGA) nanoparticles plays a crucial role in governing molecular mobility, diffusion, and consequently, drug release kinetics. However, the interaction among residual surfactant, drug effect, nanoscale confinement, and release medium on T_g remains insufficiently characterized. This study aims to bridge this gap by correlating the thermal behavior of PLGA nanoparticles with their drug release behavior under physiologically relevant conditions.

In the present study, PLGA nanoparticles were synthesized using both nano-emulsion and surfactant-free nano-precipitation approaches. The influence of residual surfactants - poly(vinyl alcohol) (PVA) and didodecyldimethylammonium bromide (DMAB) - was systematically …

Parameter Density Estimation For Cardiac Electrophysiology Models Using Data Consistent Deep Learning, Michael Luo

Dissertations

Mathematical models of biological rhythms and excitable systems can provide insights into mechanisms underlying cardiac electrical dynamics. However, estimating the parameters of these models from experimental observations is often difficult due to noise, heterogeneity, and unobserved variables. For example, in an electrocardiogram (ECG) recording, information about the electrical properties of different regions of the heart is compressed into a single voltage trace. Additionally, variability within these signals may contain important information about population heterogeneity, regional differences in electrophysiology, and time-dependent modulation.

This dissertation develops, explores, and evaluates methods that perform feature-based distributional inference for complex nonlinear dynamical systems. The objective …

A Network-Cascade Framework For Short-Run Production Failure Under Maritime-Energy Chokepoint Disruption, Siyao Liu

Double Helix Methodology

Abrupt maritime-energy disruption can generate system-wide production losses before firms and policymakers can adjust. Existing assessments usually emphasize direct exposure or long-run equilibrium responses, which makes them less suitable for short-run risk assessment in energy-dependent production systems. We develop a threshold-cascade framework that combines dual-track dependence topology, edge-level inventories, smooth operability bands, and a separate price-validation step to identify the blockade intensity at which a localized chokepoint shock becomes systemic production loss. The framework is evaluated against the March 2021 Suez blockage and the 2022 Russia–Ukraine producer-price episode, and then applied to a 2026 Strait of Hormuz stress scenario using …

Evaluating Flood Risk Management Performance Under Climate Change Based On Nature-Based Solutions — Empirical Evidence From Taihu Lake Basin, Shaofeng Chen, Conglin Zhang

Double Helix Methodology

Identifying a flood risk management (FRM) performance evaluation method that aligns with the sustainable development goals of the natural-social complex ecosystem is essential for effective and timely responses to flood risks. Using the Taihu Lake Basin (TLB) as a case study, this research systematically examines the evolving needs of FRM under climate change and proposes a performance evaluation method based on Nature-based Solutions (NbS). The method covers multiple dimensions including flood resources, socio-economic, and environmental factors. Principal component analysis (PCA) is employed to assess changes in the FRM level in TLB from 2010 to 2020. The key findings are as …

Machine Learning-Driven Prediction And Mechanistic Insight Into Co2 Adsorption On Biomass-Derived Activated Carbons Using Explainable Ai (Xai), Dalia A. Ali Dr.

Chemical Engineering

To improve CO2 uptake in Biomass-Derived Activated Carbon (BDAC), this study develops a multiscale hybrid digital twin framework. By integrating microscopic descriptors from Density Functional Theory and Molecular Dynamics (DFT/MD) with experimental data from 63 chemically diverse biomass precursors, a Gaussian Process Regression (GPR) model was developed using the Materń 5/2 Automatic Relevance Determination (ARD) kernel. The framework achieved high internal training accuracy (R2 = 0.968) and Root Mean Square Error (RMSE = 0.2552), while providing a realistic generalization baseline across heterogeneous precursors with a 5-fold Cross Validated (CV) R2 of 0.1567 and CV RMSE of 0.283. Explainable Artificial Intelligence …

Static Data-Race Detection For Gpu Programs: Behavioral Types With Partial Completeness Guarantees, Zhen Rong Liew

Graduate Doctoral Dissertations

GPUs are essential to modern computing but notoriously difficult to program correctly. Static analysis tools can verify data-race freedom, but their over-approximations produce spurious reports of data races that do not occur, limiting their practical usefulness. This dissertation establishes when Memory Access Protocols (MAPs), a compositional abstraction modeling memory access behavior between synchronization barriers, can be simultaneously sound and complete. We first prove that MAP-based analysis is sound and complete for well-typed Jaminan programs---those without data-dependent array indexing. This result establishes the theoretical boundary: completeness is achievable when data-dependent control flow and indexing are absent. Jaminan extends MAPs with symbolic …

Small Molecule Antioxidants As Potential Therapeutics For Preeclampsia, Maxim Mastyugin

Graduate Doctoral Dissertations

The major goal of this work was to develop a therapeutic intervention against preeclampsia (PE). Preeclampsia affects up to 5-7% of all pregnancies and has no causative treatments. It is associated with oxidative stress caused by a prolonged ischemic state and increase in reactive oxygen species (ROS) in the placenta. The cardinal symptoms include high blood pressure, kidney dysfunction, and in severe cases, eclampsia. With no treatment for the underlying causes available, we offer small molecule antioxidants which may scavenge ROS in the placenta arising from the initiating event of PE, the oxidative stress. By reducing placental ROS, these antioxidants …

Simulating The Movement Of A Major League Fastball, Max M. Moss

Graduate Masters Theses

The orientation of the seams on a baseball may seem arbitrary in nature; however, they play an enormous role in the aerodynamics of the ball during its short flight to home plate. This thesis aims to model the movement of a four-seam fastball as accurately as possible using the fourth order Runge-Kutta method. The model incorporates the effect of the seams on the path of the ball by utilizing the oscillating cross-sectional area, which varies for each angle of rotation. The simulated results demonstrate a strong agreement with the observed pitch trajectories, accurately reproducing both induced vertical break and horizontal …

Role Of Technical Assistance In Pollution Prevention In Massachusetts Craft Breweries, Shubhechchhya Regmi

Graduate Masters Theses

This study aims to examine pollution from Massachusetts craft breweries, and if it can be lessened through a pollution prevention program. This study analyzed data from two assessments with the aim of analyzing the role of technical assistance in pollution prevention across eight impact categories. The first assessment was conducted on 29 MA breweries between 2022 and 2024, and 23 were assessed a second time (in summer 2024 and summer 2025). Craft breweries face several constraints in adopting pollution prevention practices the pollution prevention program known as BetterBev helps to bridge this gap by providing technical assistance, identifying inefficiency practices …

Re: Approval Letter For The Draft Final 2026 Butte Priority Soils Operable Unit (Bpsou) Type B Borrow Material Submittal #3 (Dated May 6, 2026), Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Re: Conditional Approval Letter For The Butte Priority Soils Operable Unit (Bpsou) 2026 Revised Draft Final Backfill And Cover Soil Quality Assurance Project Plan (Qapp) (Dated May 13, 2026), Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

The Effect Of State Abortion Restrictions On Female Labor Force Participation And School Enrollment, Elena T. Albano

Graduate Masters Theses

This paper examines the causal effect of state-level abortion restrictions on young women’s labor force participation and school enrollment following the Supreme Court’s 2022 decision in Dobbs v. Jackson Women’s Health Organization. Using a difference-in-differences design that exploits the cross-state variation in post-Dobbs abortion policies, I estimate that abortion bans led to a decline in combined school and labor force participation among young women by at least 1 percentage point. This effect is qualitatively robust to a triple difference specification that uses men as a within-state control group, confirming that the results do not reflect broader economic shocks specific to …