Digital Commons Network^™

Deep Reinforcement Learning With Directed Asymmetry And Kolmogorov-Arnold Networks For Dismantling Interdependent Multiplex Networks, Soumyajit Dev

Master’s Dissertations

Identifying the minimum-cost node-removal sequence that fragments a complex network - the network dismantling problem is NP-hard and central to infrastructure resilience. In interdependent multiplex networks, this difficulty is compounded by cascading cross-layer failures. While deep reinforcement learning (DRL) agents utilizing graph neural network (GNN) encoders achieve near-optimal dismantling, current state-of-the-art architectures suffer from two critical limitations. Topologically, existing agents strictly assume undirected edges, rendering them inapplicable to directed systems - such as supply chains or gene regulatory cascades - where failure propagation is fundamentally asymmetric. To resolve this, we propose Disassembling Directed Interdependent Networks (DDIN). DDIN introduces an asymmetric …

Trophic Transfer Of Fluorescent Polyethylene Microplastics In A Simplified Artemia–Oreochromis Aquatic Food Chain, Dativa J. Shilla

Tanzania Journal of Science

Microplastics (MPs) are pervasive contaminants in aquatic ecosystems, yet experimental evidence of their trophic transfer within tropical African food webs remains scarce. This study provides experimental evidence of indirect trophic transfer of microplastics from Artemia salina to Oreochromis urolepis, a cichlid species of ecological and economic importance in African inland and coastal waters. A simplified two-level food chain was established by exposing A. salina nauplii to fluorescent polyethylene microplastics (10–20 µm; 1, 10, and 100 mg L⁻¹) and subsequently feeding them to juvenile O. urolepis under controlled laboratory conditions. These concentrations were selected to represent a range of exposure levels …

A System For The Prediction Of Election Results Using Vader And Hybridized Machine Learning Model, Abraham E. Evwiekpaefe, Khadijah Kabir, Georgina N. Obunadike

Tanzania Journal of Science

Integrating different classifiers along with sentiment lexicons like Vader, can enhance the performance of sentiment analysis systems. However, such a hybrid model remains underexplored, particularly in the context of regional elections in developing countries like Nigeria. The aim of this research is to develop a hybrid model that combines three machine learning classifiers and Vader lexicon to possibly achieve a higher accuracy. A case study of the 2023 governorship election in Kogi, Bayelsa and Imo State, Nigeria was examined. Twitter API library was utilized to extracted public and personal tweets using hashtags and keywords related to the target data from …

Direct Minimization Of Hartree-Fock-Roothaan Energy Functionals Using Derivative-Free Optimization Algorithms: A Case Study On The Use Of Noninteger Slater-Type Orbitals, Ali̇ Bağci

Turkish Journal of Physics

This study presents an evaluation of derivative-free optimization algorithms for the direct minimization of Hartree−Fock−Roothaan energy functionals involving nonlinear orbital parameters and noninteger-order quantum numbers. The analysis focuses on atomic calculations employing noninteger Slater-type orbitals. Analytic derivatives of the energy functional are not readily available for these orbitals. Four methods are investigated under identical numerical conditions: Powell’s conjugate−direction method, the Nelder−Mead simplex algorithm, coordinate-based pattern search, and a model-based algorithm utilizing radial basis functions for surrogate-model construction. Performance analysis is first carried out using the Powell singular function, a well-established test case exhibiting challenging properties, including Hessian singularity at the …

(Re)Claiming Homeplace: Dance As Embodied Resistance & Healing For Health & Spatial Justice, Preeya G. Kannan

University Honors Theses

This thesis argues that dance functions as both an upstream public health intervention and a form of radical spatial resistance; creating healing, agency, and belonging among historically marginalized communities. Drawing on bell hooks' concept of homeplace, Jasbir Puar's assemblage theory, Nancy Krieger's eco-social theory, and Katherine McKittrick's Black geographies, I position the body as both an ecological and political landscape shaped by histories of colonialism, racial capitalism, displacement, and resilience.

Through this community-based participatory research study, Homeplace, I explore dance as a form of social prescription that centers liberation rather than pathology. Movement is often reduced in public health discourse …

Extending Unibreak: Semantic Retrieval And Harmful-Intent Direction Suppression For Token-Level Llm Jailbreaking, Sanket Saha

Master’s Dissertations

Token-level adversarial perturbations remain one of the most efficient known attacks against the safety alignment of instruction-tuned large language models (LLMs). Among recent works, the UniBreak framework (You et al., 2026) stands out for unifying gradient-based optimization with an evolutionary perturbation repository. However, its repository relies solely on accumulated success frequency without utilizing query content, and its fitness function implicitly assumes that suppressing refusal tokens is sufficient to elicit harmful responses. In this dissertation, we extend UniBreak along both axes and re-evaluates the framework under stricter generalization and judgment protocols. Specifically, we introduce a semantic perturbation repository that replaces frequency-only …

Gamma Belief Functions And Fuzzy Sets And Application To Combining Predictive Models, Liping Liu

University Research

Extending classic finite frameworks to continuous settings, this paper proposes the concept of gamma belief functions and gamma fuzzy sets. It shows that both the combination of gamma belief functions and the intersection of gamma fuzzy sets remain within the gamma family, enabling their application in combining gamma probability judgments in decision making and gamma regression models in ensemble learning. Using both simulated and real datasets, and under both constant and varying dispersion assumptions, experimental results show that the combined gamma regression models closely approximate the reference models learned from the full datasets, aligning with the objectives of bootstrapping. Notably, …

Engineering Nanoelectrocatalytic Systems For Co2 And Nitrate Conversion Into Value-Added Chemicals, Abdelrahman Mohamed Abdelmohsen

Theses and Dissertations

The thesis addresses two major related environmental crises: nitrate pollution of water systems and the ever-increasing levels of atmospheric carbon dioxide. Both challenges are closely linked to anthropogenic disturbance of nitrogen and carbon cycles and require sustainable, energy efficient mitigation techniques. In this study, we investigate electrochemical conversion routes as a unified approach to convert these pollutants into value-added compounds: ammonia (NH3) via nitrate reduction (NO3-RR) and ethylene (C2H4) via carbon dioxide reduction (CO2RR). The first half of this work deals with the design and engineering of Cu-Zn alloy electrocatalysts for efficient NO3-RR. Tuning the alloy composition and surface nanostructure …

Detection Of C60 Combination Bands In The Near-Ir Spectrum Of Tc 1, Morgan M. Giese, Vincent J. Esposito, Simon Van Schuylenbergh, Jan Cami, Els Peeters, Charmi Bhatt, Dries Van De Putte, A. G. G. M. Tielens, Michael J. Barlow, Jeronimo Bernard-Salas, Alessandra Candian, Bryan Changala, Nick L. J. Cox, Harriet L. Dinerstein, D. A. García-Hernández, Marco A. Gómez-Muñoz, Kay Justtanont, Kathleen E. Kraemer, Eric Lagadec, Arturo Manchado, Ana Monreal Ibero, Raghvendra Sahai, Ameek Sidhu, G. C. Sloan, N. C. Sterling, Jeremy R. Walsh, Roger Wesson, Joshua Cole Whitman, Albert Zijlstra

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

We report the detection of a set of new near-infrared emission features between 3.5 and 5.2 μm in JWST/NIRSpec observations of Tc 1, the planetary nebula known for displaying the cleanest and most prominent mid-infrared cosmic fullerene spectrum. These broad features share the same spatial distribution as the well-known C₆₀ and C₇₀ mid-infrared emission bands, peaking in an asymmetric ring approximately 5″–6″ from the central star. Through comparison with new anharmonic quantum chemical calculations, we demonstrate that these features arise from C₆₀ combination bands, marking their first detection in an astrophysical environment. The total energy radiated …

Examining The Impact Of The Palisades And Eaton Fires On Air Quality In Los Angeles, Ryan Edward Glenn

Earth Sciences Undergraduate Senior Theses

Wildland-urban-interface (WUI) fires in the US are increasing in frequency and intensity with disproportionately large impacts on air quality and human health. In January 2025, the Palisades and Eaton wildfires swept across the Los Angeles Basin, burning residential areas and destroying vegetation. Despite their significance, WUI fires remain understudied compared to wildland fires, especially in regard to emissions composition. Here, I utilize a combination of field samples, modeling, and remotely-sensed datasets to survey the emissions profiles and variations during the 2025 Palisades and Eaton wildfires. I find that 75.1% of Los Angeles County was exposed to surface PM2.5 rated ‘unhealthy’ …

Gamification By Design: Methodological Foundations Of The Gamespro Speaking Model For Primary Education, Mohd Zulhairi Che Abd Rahman, Hasiah Mohamed, Siti Zulaiha Ahmad, Aurelio P. Vilbar

Journal of Informatics and Web Engineering

This study presents the development and validation of the GAMESPro model, a structured gamification model designed to enhance English-speaking proficiency among Malaysian primary school students. Despite national reforms and policy initiatives aimed at improving communicative competence, oral English skills remain underdeveloped, particularly in rural and resource-constrained schools. Existing gamification practices in Malaysian classrooms often rely on isolated digital tools and lack pedagogical coherence. Addressing this gap, the study employed the Design and Development Research (DDR) methodology, encompassing three phases: Needs Analysis, Design and Development, and Evaluation. A Systematic Literature Review identified key gamification principles, while validation of ten experts confirmed …

The Development Of Decentralized Ticketing System Using Web3, Yu-Xian Cheng, Kah-Ong Michael Goh, Check-Yee Law, Connie Tee, Yong-Wee Sek, Md Ismail Hossen

Journal of Informatics and Web Engineering

Traditional ticketing systems often suffer from major drawbacks such as ticket fraud, duplication, inflated resale prices, lack of transparency, and centralized control over transactions. These issues result in reduced trust and limited flexibility for both event organizers and ticket buyers, especially in unregulated secondary markets. To address these gaps, this paper presents the design and development of a Decentralized Ticketing System (DTS) using Web3 technologies. The system leverages Ethereum blockchain, smart contracts written in Solidity, and NFT-based ticket issuance to ensure security, transparency, and verifiable ownership. Features include wallet-based login via MetaMask, multi-ticket purchasing, QR-based validation, controlled resale pricing, and …

An Ai-Based Framework For Heart–Brain–Body Coherence In Wellness Monitoring: A Longitudinal Simulation Study, Asiah Lokman, Akalpita Tendulkar, Yujiao Zhang, Mantena Sireesha

Journal of Informatics and Web Engineering

Present wellness monitoring systems primarily emphasize single-modality metrics, neglecting the intricate, interdependent interactions among cardiac, neural, and behavioral regulatory systems. This paper introduces a machine learning framework that implements heart–brain–body coherence as a dynamic, longitudinally evaluated wellness metric. In contrast to previous simulation-based studies that utilized independently generated, cross-sectional data categorized by deterministic IF-THEN rules, the current study adopts a longitudinal simulation design featuring 80 virtual subjects monitored across 30 consecutive daily profiles (N=2,400 records). This design incorporates physiologically realistic inter-variable correlations and probabilistic coherence labelling derived from a continuous risk-scoring function. The subject-level train/test split (56 training subjects, 1,680 …

Multi-Agent Ai Systems For Detecting Emerging Therapeutic Targets And Intervention Patterns In Neuroplasticity Research, Raza Hasan, Salman Mahmood

Journal of Informatics and Web Engineering

The exponential growth of neuroplasticity research presents profound challenges to the manual synthesis of literature, hindering the identification of emerging therapeutic targets and intervention patterns. While multi-agent Artificial Intelligence (AI) systems have been proposed conceptually to address this, concrete implementations demonstrating end-to-end utility are scarce. This paper details the successful implementation and application of a novel four-agent AI system designed to automate the discovery, extraction, analysis, and validation of patterns in neuroplasticity literature. Our platform consists of four parts: a Literature Discovery Agent (LDA) for corpus gathering, a Concept Extraction Agent (CEA) with a multi-level NLP strategy fallback mechanism for …

Ai-Powered Continuous Health Monitoring For Chronic Disease Management In Malaysia: Real-Time Risk Prediction Using Wearable Sensors And Digital Biomarkers, Umi Najiah Ahmad Razimi, Yong Yoke Leng, Nor Hapiza Mohd Ariffin, Mohammed Hazim Alkawaz

Journal of Informatics and Web Engineering

Chronic Non-Communicable Diseases (NCDs), such as diabetes mellitus, hypertension, and cardiovascular disease, make up a significant portion of the mortality cases in Malaysia, as well as impose consistent demands for costs related to health care expenditure. According to the cost reports at the national level, major NCDs (cardiovascular disease, diabetes, and cancer) are responsible for RM 9.65 billion annually as direct healthcare cost [3]. However, the current approach to managing chronic diseases focuses heavily on periodic assessments in clinic setting at weekly or monthly intervals. Here, a predictive analysis framework based on AI technology, leveraging continuous monitoring with patient data …

Digital Bodily Autonomy: Consent Issues, Labor Displacement, And Legal Understandings Of Ai Generated Deepfake Pornography, Mariah Barrett

Undergraduate Theses, Capstones, and Recitals

In the United States, nonconsensual pornographic deepfakes are becoming an increasingly prevalent problem as AI deepfake creation software improves and becomes widely available. Despite this, patchwork legislation across the country is inconsistent and conflicting regarding this issue. In this paper, I explore the background of pornography and obscenity laws and demonstrate how these frameworks are not properly constructed to apply to the digital sphere. Then, I address major themes within deepfake literature such as consent issues, bodily autonomy, labor displacement, and verifiable identity as a commodity through the case study of OnlyFans. I explore current and proposed legislation within the …

A Novel, Embedding-Based Approach To Longitudinal Survey Data Imputation, Julia Rezvani

University Honors Theses

Longitudinal surveys are ubiquitous in the social sciences as a means of tracking changes in behavior and opinions with time and identifying potential causal mechanisms. These surveys are frequently plagued by missing data and semantic drift, both of which limit their effectiveness and scientific utility. Imputation algorithms allow researchers to fill gaps in collected survey datasets, imperfectly reconstructing lost data. Although deep learning algorithms have been used in imputation to great success, approaches which simultaneously leverage the semantic and temporal structure of longitudinal surveys have not yet been developed. We propose a novel imputation architecture which is capable of leveraging …

Ka Here Tāngata:1 Mātauranga And Te Ao Māori In Biocultural Kākāpō (Strigops Habroptilus) Conservation Approaches, Sophie Perfetto

Electronic Theses and Dissertations

This master's thesis examines conservation as a form of living cultural heritage practice through the co-management of kākāpō (Strigops habroptilus), a critically endangered parrot and taonga species in Aotearoa New Zealand. Situated within ongoing histories of colonial dispossession and Western frameworks of land and species management, contemporary kākāpō conservation is shaped through the partnership between the Department of Conservation’s Kākāpō Recovery Programme and Ngāi Tahu. Drawing on semi-structured interviews collected during fieldwork from July to August 2025, this study examines how institutional partnerships structure decision-making, authority, and responsibility in contemporary conservation governance. Findings demonstrate that Māori knowledge systems …

Quantifying Terrain Controls On Satellite-Based Snow Water Equivalent Estimation: A Spatially Explicit Machine Learning Approach, Brant Giovannetti

Geography and the Environment: Graduate Student Capstones

Terrain variables are widely incorporated into machine learning Snow Water Equivalent (SWE) models but are rarely evaluated for their independent contribution relative to spectral predictors. Using a four-tier stepwise Random Forest framework with Harmonized Landsat Sentinel-2 imagery and Airborne Snow Observatory LiDAR ground truth, this study isolates the contribution of elevation, slope, northness, and eastness across Peak and Ablation snowpack regimes in the East Taylor River Watershed, Colorado. During peak snowpack, adding terrain improved R² by 0.214, with elevation alone accounting for 42.8% of model importance. During ablation, full-dataset terrain gains were modest, increasing R² by only 0.036. However, when …

Data Governance Maturity, Ai Integration, And Equity In Colorado K-12 Public Schools, John R. Curtin

Electronic Theses and Dissertations

Colorado's 179 K-12 public school districts operate as autonomous governance units, each responsible for securing and managing student data assets that span health, financial, residential, and academic records. The accelerating integration of artificial intelligence (AI) and machine learning (ML) tools into administrative workflows, productivity software, and instructional platforms has fundamentally altered the risk landscape for student data, yet governance frameworks at the state, district, and school levels have not kept pace. This dissertation investigates whether Colorado's decentralized educational governance structure is institutionally capable of producing equitable, secure, and sustainable data governance outcomes in the AI era.

Drawing on Institutional Theory …

Uniform Stability Of Katyusha In Strongly-Convex Settings, Don Li

University Honors Theses

Acceleration of convergence and reduction of variance constitute a trade-off in the design of stochastic optimization machine learning algorithms. Katyusha was introduced to address this trade-off, synthesizing Nesterov Accelerated Gradient (NAG) and Stochastic Variance-Reduced Gradient (SVRG) into a single first-order optimizer with promising empirical performance. However, the generalization properties of Katyusha remain largely unexplored. We conjecture that, in the smooth quadratic regime (i.e., under assumptions of strong convexity and smoothness of the loss function, and boundedness of gradients), Katyusha is uniformly stable in the sense of Bousquet and Elisseeff. Instantiating our framework for NAG, we extend the use of Lyapunov …

Beyond Emergency: Charting Pathways To Sustainable Development In Fragile And Conflict-Affected Settings: A Case Study Of Yemen, Sharif Abdurabu Salem Farid Alsahbool

Theses and Dissertations

For nearly a decade, Yemen's eastern governorates of Hadramawt, Shabwah, and Al‑Mahrah have been relatively stable, yet the international aid system has kept delivering short‑term humanitarian relief like food baskets and water trucking instead of shifting to sustainable development that could rebuild people's lives, create jobs, fix infrastructure, and strengthen local government. This thesis investigates why this transition has taken so long. Based on 21 interviews with government officials, international NGO staff, and local NGO managers in these three governorates, the study develops a new way of understanding the problem. It introduces three ideas: the 'emergency mentality', which is a …

Hot Jupiter Atmospheres Through Time, Annabelle E. Niblett

Physics and Astronomy Undergraduate Senior Theses

Transmission spectroscopy of hot Jupiters enables mass and atmospheric composition measurements, providing insight into population dynamics and evolution when other methods are hindered by stellar activity. To effectively plan observations and interpret transmission spectra, we must have a robust understanding of how atmospheres evolve over time. To that end, we present a suite of hot Jupiter transmission spectra models with ages ranging from 3 Myr to 11 Gyr. We incorporate the cooling and contraction of the planet, which impacts the atmospheric profile, and the evolution of the stellar UV spectrum, which moderates photodissociation and photoionization. Using PICASO,  VULCAN, and FastChem …

Context-Sensitive Control-Flow Analysis For Non-Standard Language Semantics, Tim Whiting

Theses and Dissertations

Control-Flow Analysis (CFA) is a static technique for understanding the execution of higher-order programs without running the code. CFA aids in identifying bugs, assisting program comprehension, and exposing optimization opportunities. Constructing a CFA typically begins with an idealized evaluator--such as an abstract machine or interpreter--and proceeds by approximating components of evaluation to ensure that: (a) the analysis is sound (representing all possible behaviors), and (b) the analysis is guaranteed to terminate (finitely representable). These constraints necessitate a loss of precision, providing an approximation of the values that flow to program locations rather than exact execution traces. This imprecision causes the …

Comparative Analysis Of Leaflet Materials, Stent Materials, And Stent Cell Density For Bileaflet Transcatheter Mitral Valve Design, Joseph Chibuike Nwokeafor, Joshua D. Hofmeister, Breandan B. Yeats, Lakshmi Prasad Dasi, Charanjit S. Rihal, Juan A. Crestanello, Leigh Griffiths, Mohamad A. Alkhouli, Hoda Hatoum

Michigan Tech Publications

Background: The biomechanical performance of bileaflet transcatheter mitral valves (TMVs) depends on complex interactions between leaflet material behavior and stent design. However, the contributions of leaflet materials and constitutive models, stent materials, and stent geometry to valve function and durability remain poorly understood. Methods: A parametric finite element study was conducted using a CAD model of a bileaflet TMV subjected to physiological pressure loading. Five leaflet material models were evaluated: 3 glutaraldehyde-fixed tissues—bovine pericardium (BP; FBP1, FBP2) and porcine pericardium (PP; FPP)—and 2 unfixed tissues—bovine (UBP) and porcine pericardium (UPP). BP was modeled as a linear elastic (FBP1) and Ogden …

Investigating Broadband Plasma Waves As A Driver Of Radiation Belt Energetic Electron Loss To Earth's Atmosphere, Lauren Zanarini

Physics and Astronomy Undergraduate Senior Theses

Wave-particle interactions play a central role in transferring energy between different particle populations in space plasmas. In the radiation belts, these interactions govern the acceleration, scattering, and loss of energetic particles to Earth’s atmosphere. Understanding the drivers of these energetic particle losses is essential, as these particles can collide with satellites and contribute to ozone depletion as they enter the atmosphere. Developing a clearer picture of these processes will improve our ability to predict radiation belt variability and quantify the impacts, ultimately allowing us to mitigate the adverse effects.

Previous research has identified wave modes important for scattering electrons into …

Tracing The Seam: Machine Learning Models Of The Open–Closed Boundary From Upstream Solar Wind Drivers, Arnav Singh

Physics and Astronomy Undergraduate Senior Theses

The open–closed magnetic field line boundary (OCB) demarcates closed terrestrial field lines from those threaded into the interplanetary medium, and its latitude encodes the instantaneous balance between dayside and nightside reconnection that governs much of the high-latitude space-weather hazard. No single instrument resolves it globally in real time. In this thesis I close that gap empirically. Pairing nearly three decades (1983–2012) of Defense Meteorological Satellite Program particle-precipitation boundaries with the 5-minute OMNI solar-wind and geomagnetic-index record yields ∼9.1×10⁵ causally matched crossings, partitioned into six hemispheric/MLT sectors after a |MLat| ≥ 40° physics cut.

On this corpus I train three …

The Method Of Periodic Averaging Applied To Reduced Coupled Mode Theory Models For Fiber Laser Amplifiers, Rebecca Nicole Bryant

Dissertations and Theses

Fiber laser amplifier (FLA) models are often implemented without rigorous mathematical justification or thorough numerical validation. Without a proper theoretical basis for assumptions and approximations, or a technical analysis of model performance, there is significant uncertainty about the limitations of any given reduced model and its suitability for an application. This research aims to address the lack of comprehensive assessment of FLA models by directly comparing distinct models and recommending a mathematical alternative to replace heuristic model-reduction techniques. The work in this dissertation is divided into two projects: a comparative study that uses existing FLA models to assess the validity …

Describing Hidden Curriculum In An Undergraduate Computing Context, Joseph R. Teahen, Briana C. Bettin, Leo Ureel

Michigan Tech Publications

Hidden Curriculum (HC) is the set of essential knowledge, skills, and norms students are expected to know, but never explicitly taught. HC is disproportionately experienced across identities and communities. In computing education, most research addresses immediately identifiable HC within the researcher's context. While such work is important, without proper HC descriptive studies, we could miss more subtle HC that affects student success. This work presents results from interviews with undergraduate computing faculty, students, and peer mentors on their HC experiences. The results demonstrate several categories of HC including development tools, professional skills, institutional navigation, social well-being, and physical well-being. These …

Assessment Of The Readiness Of Architectural Design Companies To Adopt Eco-Friendly Nanotechnologies In Sustainable Building Design, Ashwaq Essa Al Khalil

Dissertations and Theses

Nanotechnology is emerging as a promising innovation in sustainable architectural design, although its adoption remains slow. Research highlights the advantages of incorporating nanotechnologies in architecture, including improved material performance, enhanced thermal efficiency, reduced energy consumption, improved durability, and progress toward the Sustainable Development Goals (SDGs). These advantages make nanomaterials a promising solution for sustainable building design, particularly in hot-climate regions where energy consumption and carbon emissions associated with conventional building design practices remain major concerns.

Despite this potential, nanotechnology adoption in architecture faces several challenges, including limited real-world applications, slow diffusion in practice, and persistent organizational, economic, and regulatory barriers. …