Digital Commons Network^™

Beyond Shutdown: Why Punitive Policies Fail Students With Oppositional Defiant Behaviors And What Alternative Schools Should Do Instead, Mark Barrett

Critical and Creative Thinking Capstones Collection

Punitive shutdown policies at therapeutic alternative schools often do not reduce class avoidance among students with oppositional defiant behaviors. This synthesis examines why these policies do not work and proposes evidence-based alternatives. I draw on research in exclusionary discipline, Collaborative and Proactive Solutions, Self-Determination Theory, and function-based intervention, as well as my daily experience as a behavioral teacher. I argue that punitive approaches fail because they misunderstand what drives oppositional behavior. Deterrence assumes students avoid class to escape work and will return if the consequences are harsh enough. In reality, students with oppositional defiant disorder are mainly motivated by resisting …

Narrative Roots - What We Inherit, What We Tell, What We Become: Intergenerational Storytelling, Meaning-Making, And Mental Health, Adam Sholder

Critical and Creative Thinking Capstones Collection

This capstone synthesis explores how the family stories we inherit and the stories we tell across generations shape who we become, with particular attention to their impact on identity and mental health. Drawing on narrative identity theory, reflective practice, and relational understandings of family life, it introduces the Recursive Narrative Integration Model, which positions intergenerational storytelling as both a method of inquiry and a mechanism of meaning-making. Informed by a recursive process of creative and reflective writing, this synthesis approaches storytelling not only as an object of study, but as a lived and generative practice through which experience and narrative …

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 …

Reading Isabella Hammad And Beyond: The Unsaid – The Point Of Recognizing Turning Points, Narratives (Stories), And Strangers, Rory Conces

Philosophy Faculty Publications

The essay is a reflection on Isabella Hammad’s book, Recognizing the Stranger: On Palestine and Narrative (2024). The essay’s approach is to follow the four themes that reflect the title and the subtitle of the book and to draw out what is unsaid from what is said by Hammad. The unsaid reflects my conceptual and historical baggage, so there is a wide assortment of theorists that I connect Hammad with, including Edward Said, Hannah Arendt, Amartya Sen, Didier Fassin, and Slavoj Žižek.

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 …

Significant Crash Characteristics Associated With E-Scooter And E-Bike Crashes, Aimee Jefferson

Dissertations

Micromobility devices—namely e-scooters and e-bikes—have rapidly gained popularity in the United Sates, rising from 35 million annual shared rides in 2017 to over 133 million in 2023 (NACTO 2024). But also increasing is the number of injuries associated with these devices; however, most research emphasizes injury and demographic patterns rather than crash characteristics that would inform prevention strategies. Most existing crash research also relies on small sample sizes, lacks nuance distinguishing between involved parties (motorists, pedestrians, single device), and fails to distinguish between bicycle and micromobility crash patterns despite micromobility devices often being instructed to use conventional bicycle facilities. These …

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 …

Membrane-Engineered Nanotherapeutic Platforms For Drug Delivery: Hollow Fiber Membrane Synthesis Of Lipid Nanoparticles, Biomimetic Nanocarriers, And Nanobubbles, Zhixiang Liu

Dissertations

Lipid-based nanocarriers have emerged as a cornerstone technology for RNA therapeutics, enabling effective intracellular delivery for applications ranging from vaccination to gene regulation. However, current manufacturing approaches, particularly microfluidic-based platforms, face inherent limitations in scalability, throughput, and structural tunability due to their reliance on confined channel geometries and restricted mixing architectures. Addressing these challenges requires fundamentally new strategies that decouple nanoparticle formation from traditional microscale flow constraints while maintaining precise control over physicochemical properties.

This dissertation presents a comprehensive framework for the design, engineering, and application of advanced lipid-based nanocarriers, centered on a hollow fiber membrane (HFM)—assisted nanopore-mediated assembly platform. …

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 …

Mapping The Future Of Digital Logistics: A Multi-Layered Analysis Of Technological Evolution And Integration, Jiang Yu

Double Helix Methodology

In the context of accelerating digital transformation and increasingly complex global supply chains, this study develops a multi-layered analytical framework to investigate the technological evolution and future trajectory of digital logistics. Using 107,202 patent records from the IncoPat database covering 1960–2023, the study integrates the entropy weight method, grey relational analysis, Latent Dirichlet Allocation (LDA), and a Hidden Markov Model (HMM) to identify core patents, extract technological topics, and forecast their dynamic evolution through 2028. The findings identify 27 major technological topics and reveal a significant transition from traditional operational optimization toward integrated intelligence and autonomous execution. Technologies related to …

The Cultivation Of Public Perception: Cultivated Meat Narratives And Misinformation On Tiktok, Anna M. Balkus

Graduate Masters Theses

Cultivated meat is an emerging tissue engineering technology that has the potential to address major issues threatening food systems, including climate change, rapid population growth, and the spread of disease. Among technical challenges, public perception remains a significant barrier for the widespread adoption of cultivated meat. While social media has transformed communication globally, it has simultaneously contributed to corrupt online environments infected with misinformation—unintentional inaccuracies that differ from disinformation, which is spread with the intent to mislead. The way the public receives and digests information regarding new food technologies, like cultivated meat, will be shaped by their social media environment. …

Empowering Lifelong Choice: Supporting Older Individuals With Idd To Thrive At Work, Transition To Retirement With Dignity, And Maintain Community Connections, Leslie Quarles

Critical and Creative Thinking Capstones Collection

This synthesis addresses the need to support older individuals with intellectual and developmental disabilities in their engagement in continued work. The project traces the journey from an initial focus on supporting older individuals with intellectual and developmental disabilities to work as long as they wish to an eventual shift toward a phased retirement approach. It considers issues such as a reimagined visualization of the support team structure, integration of tailored job supports and Community Life Engagement (CLE). The author's familial history sparked a sense of injustice that would not stay buried, shaping a career path in disability services and access. …

Indonesia And The Global Maritime Fulcrum: Structural Crisis Of The Four Pillars Of Maritime Power, Frisca Ayu Suciandika

Journal of Terrorism Studies

This article critically examines Indonesia’s position in realizing its vision as a Global Maritime Fulcrum through an analysis of four main pillars of maritime power: maritime science, financial strength, maritime industry, and naval defense. The study is motivated by the paradox between Indonesia’s vast geographical and historical potential as an archipelagic state and its limited structural capacity to build modern maritime power. This research employs a qualitative descriptive approach using library research and critical analysis of academic literature, international data, and Indonesian maritime policy documents. The findings show that Indonesia still faces an epistemic deficit in maritime science due to …

Digital Communication And The Evolution Of Ideological Narratives In Indonesia: Insights From Social Media Platforms, Tia Mariatul Kibtiah, Muhammad Fikri Fawazi

Journal of Terrorism Studies

This study examines how pro-caliphate and anti-democratic narratives are articulated and normalised within YouTube comment spaces in Indonesia. As the world’s largest Muslim-majority country with high levels of digital engagement, Indonesia provides an important context for analysing how political-religious discourses circulate through everyday digital interaction. The research analyses 1,354 YouTube comments related to the caliphate, governance, and democracy. Data were collected using Google Apps Script, manually filtered for relevance, and examined through qualitative content analysis informed by Social Network Theory (SNT) and the Radicalisation Factor Model (RFM). The findings show that 34.49% of comments endorsed ideological pro-caliphate narratives, while 35.97% …

The Effect Of Incompatibility Types On The Escalation Of Global Armed Conflicts, Michael Haratua Rajagukguk, Ruben Cornelius Siagian Rcs

Journal of Terrorism Studies

Global armed conflicts show variations in intensity influenced by the type of issue incompatibility. This study aims to analyze the influence of territorial, ideological, and combined incompatibilities on the probability of high-intensity conflict escalation, as well as its implications for conflict mitigation strategies. Global conflict dyadic data were analyzed using a multilevel logistic regression model with controls for time, region, and conflict type. The results show that ideology-based conflicts have a lower probability of reaching high intensity than territorial conflicts (OR ≈ 0.51, p < 0.05), while conflicts with a combination of ideology and territory tend to be at greater risk of high escalation (AM effect ≈ +26.6 points), although the effect is not yet statistically significant. Temporal analysis shows a downward trend in conflict intensity over time, especially in the post-Cold War period, while regional effects and conflict types show significant variation. These findings fill a gap in the literature on the dual interaction of ideological and territorial claims, showing that the highest escalation of violence occurs when symbolic and material dimensions are intertwined. In conclusion, conflict escalation is more influenced by a combination of issues and structural characteristics of conflict than by ideology alone. The novelty of this research lies in the identification of the strengthening effect of dual incompatibility and the downward trend in conflict intensity after the Cold War, which emphasizes the importance of a relational and temporal context-based mitigation approach in global conflict management.

Trends Of Pediatric Psychotropic Polypharmacy In Massachusetts Before And During The Covid-19 Pandemic, Gianna D'Ambrozio

Graduate Doctoral Dissertations

Youth in the U.S. and internationally are increasingly being diagnosed with depression and related psychiatric conditions (Murthy, 2021) many of whom are being subjected to non-evidence-based, or what has been termed irrational, psychotropic polypharmacy (Barnett & Concepcion Zayas, 2019). Psychotropic polypharmacy can be defined as “the use of 2 or more psychiatric medications in the same patient,” (Zito et al., 2021) though definitions can vary. Using data from the Center for Health Information Analysis’ Massachusetts All-Payer Claims Database, this study examined the sociodemographic factors associated with high rates of polypharmacy, a hypothesized increase in pediatric psychotropic polypharmacy rates in Massachusetts …

Pickleweed In The Disappearing Marsh: Population Genetics & Metal Accumulation In Salicornia Depressa, Alice Kaisla

Graduate Doctoral Dissertations

Salicornia is a genus of succulent halophytes found in salt marshes around the world. Salicornia species have a remarkably high tolerance for heavy metal pollution and have been shown to accumulate high concentrations of heavy metals in their tissue under experimental conditions. As a result, they are both a useful system for studying evolution in polluted coastlines and a potential candidate for use in phytoremediation. My research focuses on Salicornia depressa, known as pickleweed, a species commonly found in marshes along the heavily urbanized and industrialized New England coast. I investigate genetic diversity and population structure in S. depressa populations …

Should I Stay Or Should I Go?: A Phenomenological Case Study Of Teacher Turnover In An Urban School Merger, Haven Jones

Graduate Doctoral Dissertations

School mergers are becoming a more common occurrence in urban school districts within the U.S., as an attempt to address student enrollment declines and resulting budgetary concerns. Yet very little research has been conducted to understand how the context of a school merger influences known K-12 education phenomena, such as the teacher turnover decision-making process. This study utilized a phenomenological case study approach to explore the impact of a school merger on teacher turnover within a specific middle school-high school merger in a Northeastern city. Fifteen teacher participants were interviewed and three primary source documents were analyzed to determine what …

Adapting Population Genetics, Practice, And Policy For Plant Conservation In A Changing World, Cooper M. Kimball-Rhines

Graduate Doctoral Dissertations

Biodiversity conservation is an inherently interdisciplinary goal that requires effective collaboration among academics, ecosystem managers, policymakers, and other stakeholders. Codesigning research with these groups ensures experiments are useful and improves adoption of policy and practice recommendations while advancing our understanding of basic science. This dissertation explores the power of codesigned research in shaping plant conservation strategies using stakeholder engagement, causal inference, demographic modeling, and multi-omic sequencing techniques. In chapter 1 I discuss the history of New England ecosystems and the need for research-driven changes to plant conservation policy and practice in the face of global change. In chapter 2, I …

Streamlined Biomedical Image Processing Pipelines, Jiehyun Kim

Graduate Doctoral Dissertations

This dissertation focuses on advancing carotid artery analysis through a series of visualizations and deep learning tools for calcified plaque assessment and related biomedical imaging tasks. Accurate plaque evaluation is essential, but current workflows depend on slow, clinician-dependent manual review. To address these limitations, this work introduces the CACTAS framework, a set of tools and methods that enable fast and reliable plaque segmentation for clinicians.

The first study, the CACTAS-Tool, provides a web-based labeling tool that enables clinicians to label plaque directly in three dimensions through a streamlined one-click interface. This tool significantly reduces the effort required to generate high-quality …