Physical Sciences and Mathematics Commons^™

Early Icu Physiological Subtyping From Time-Series Data: A Comparative Study Of Feature Complexity, Predictive Performance, And Model Interpretability, Rojeena Khadka

College of Graduate Studies: Theses & Dissertations

Intensive Care Unit (ICU) patients do not follow a single uniform physiological pattern. Patients admitted with the same diagnosis show different clinical trajectories over time making standardized classification and treatment approaches insufficient. The increasing availability of large-scale electronic health records in MIMIC-IV makes it possible to investigate such heterogeneity through data-driven approach that captures how physiology evolves during the early phase of ICU admission. This thesis compares two analytical pipelines designed to identify physiological subtypes from the first 48 hours of ICU time series data. This study then assesses how well these subtypes predict in-hospital mortality. The first approach, referred …

Factorial Design: A New Look Through Overlap Measures, Sarah W. Liebenow

College of Graduate Studies: Theses & Dissertations

Traditional factorial analysis often relies on ANOVA, which assumes normality and equal variances. This thesis presents a nonparametric approach for assessing main and interaction effects in a 2 × 2 factorial design using the overlap coefficient, estimated through kernel density methods. A bootstrap procedure is used to approximate its sampling distribution for hypothesis testing. Simulation studies compare the overlap-based test with the ANOVA F-test, permutation F-test, and the Kruskal–Wallis test under heteroskedasticity and non-normal conditions. Results show that the overlap measure is highly sensitive to differences in spread and shape, detecting effects that traditional methods frequently miss.

Gauge-Invariant Gravitational Wave Polarization In Metric F(R) Gravity With Cosmological Implications, Ramesh Radhakrishnan, David Mcnutt, Delaram Mirfendereski, Alejandro Pinero, Eric Davis, William Julius, Gerald Cleaver

Physics & Astronomy Faculty Publications

We develop a fully gauge-invariant analysis of gravitational-wave polarizations in metric 𝑓⁡(𝑅) gravity with a particular focus on the modified Starobinsky model 𝑓⁡(𝑅) =𝑅 +𝛼⁢𝑅2 −2⁢Λ, whose constant-curvature solution 𝑅𝑑 =4⁢Λ provides a natural de Sitter background for both early- and late-time cosmology. Linearizing the field equations around this background, we derive the Klein–Gordon equation for the curvature perturbation 𝛿⁢𝑅 and show that the scalar propagating mode acquires a mass 𝑚2𝜓 =1/(6⁢𝛼), highlighting how the same scalar degree of freedom governs inflationary dynamics at high curvature and the propagation of gravitational waves in the current accelerating Universe. Using the scalar–vector–tensor …

Geographic Variation In Proteomic Responses To Ocean Acidification In A Cold-Water Coral (Balanophyllia Elegans), Keana A. Richmond

Cal Poly Humboldt theses and projects

In the face of a rapidly changing climate, assessing organismal responses to future stressors in the context of current, natural exposure to stress could provide key insights to understanding marine ecosystem resilience. I used Balanophyllia elegans, a cold-water, solitary, azooxanthellate coral as a model to better understand how varying oceanographic conditions across its geographic range have shaped its ability to tolerate and potentially adapt to current and future ocean acidification conditions. I collected B. elegans individuals from four sites across 2,500km of their range and subjected them to two pH treatments to investigate site-specific protein expression in response to …

Early Successional Response To Stream Daylighting In Redwood National And State Parks, Orick, California, Rylee Rawson

Cal Poly Humboldt theses and projects

In-channel stream restoration projects can act as both disturbance and recovery mechanisms, disrupting existing conditions to reestablish the hydrologic and ecological processes that sustain stream ecosystems. These interventions initiate successional trajectories in which channel form and biological communities reorganize over time. In Redwood National and State Parks (California, USA), the Redwoods Rising initiative restores buried headwater channels by recontouring legacy logging roads and daylighting streams buried by Humboldt crossings. Using the National Park Service Klamath Network wadable stream monitoring protocol, I evaluated nine restored reaches spanning one to five years post-restoration, alongside old-growth reference reaches and available pre-treatment data. I …

Assessing Water Storage In Northern California Forests, Christopher Gerard Gressierer

Cal Poly Humboldt theses and projects

The climate is changing, with rising temperatures projected to increase vegetation stress and mortality. Precipitation is also expected to become more extreme, with longer dry seasons intensifying droughts, and wetter wet seasons increasing the risk of landslides or floods. Summer baseflows in northern California are declining, and a shift in water availability may alter vegetation communities, resulting in mortality and species composition changes. These trends highlight the need for increased attention and management to be focused on evaluating and maintaining watershed water storage to maintain ecosystem health.

This study estimates water storage capacity using a water balance approach, drawing in …

"What Power Do We Have?" Community Capacity And Justice In Samoa, California’S Floating Offshore Wind Terminal Development, Lauren Mccall Hart

Cal Poly Humboldt theses and projects

Floating offshore wind (OSW) is a relatively novel technology in the United States, which is positioned to play an integral role in California’s decarbonization strategy. Two federal OSW lease areas have been established off the coast of Northern California, and plans are underway to develop supporting port infrastructure, including the Humboldt Bay Offshore Wind Heavy Lift Marine Terminal (HLMT). The proposed HLMT site is adjacent to communities on the Samoa Peninsula of Humboldt Bay, which currently face socio-environmental vulnerabilities and critical infrastructure gaps. The social implications of OSW port development remain poorly understood, particularly in rural and underserved communities. This …

Depth And Soil Physical Context Govern Soil Organic Carbon, While Woody Cover Moderates Native Vegetation Relationships In California Rangelands, Dabid Zuniga Garcia

Cal Poly Humboldt theses and projects

Soil organic carbon (SOC) is central to soil function and an important component of climate mitigation in rangeland ecosystems, but the controls on SOC across California’s working rangelands remain understudied. Using data from Point Blue Conservation Science’s Rangeland Monitoring Network, this study evaluated how soil properties, vegetation structure, and recent climate relate to SOC concentration at depths of 0–10 cm and 10–40 cm across 145 ranches spanning four California regions. Linear mixed-effects models showed that SOC was consistently higher in topsoil than subsoil and highest in coastal sites relative to interior valleys. Bulk density was the strongest negative predictor of …

Gaze Transition Entropy And Automation Trust In Multitasking Workspace, Yusuke Yamani, Austin Jackson, Tetsuya Sato, Feyishola Ashimi, Michael S. Politowicz, Eric T. Chancey, Makoto Itoh

Psychology Faculty Publications

Safe flight operation requires visual scanning across multiple displays in a cockpit, which collectively represent the state of the aircraft and supporting automation. Trust is a crucial factor that drives human-automation interaction, and recent work has suggested a relationship between an operator's visual attention and automation trust. One index that captures predictability of eye movements between different areas of interest is gaze transition entropy. The current work reanalyzed data from Sato et al., which examined eye movement patterns and trust in automation associated with the system monitoring task of the Multi-Attribute Task Battery. Results showed credible positive correlations between the …

Ai Deployment Authorisation: A Global Standard For Machine-Readable Governance Of High-Risk Artificial Intelligence, Daniel Djan Saparning

Student Publications

Modern artificial intelligence (AI) governance lacks a formal, enforceable mechanism for determining whether a given AI system is legally permitted to operate in a specific domain and jurisdiction. Existing approaches-such as model cards, audits, and benchmark evaluations provide descriptive information about model behaviour and training data but do not produce binding deployment decisions with legal or financial force. This paper introduces the AI Deployment Authorisation Score (ADAS). This machine-readable, regulator-grade framework evaluates AI systems across five legally and economically grounded dimensions: Risk, Alignment, Externality, Control, and Auditability, derived from safety engineering, alignment theory, algorithmic accountability, and liability economics. ADAS produces …

Reversible Fluorescence Quenching And Recovery In Pvp-Supported Copper Nanoparticles, Sara Rayan

Senior Honors Theses and Projects

Copper nanoclusters stabilized by polyvinylpyrrolidone (PVP) exhibit unique fluorescence properties that are highly sensitive to changes in their chemical environment, making them promising candidates for sensing applications. This study investigates the reversible fluorescence quenching and recovery behavior of PVP-supported copper nanoparticles in response to iron(III) ions and evaluates the selectivity of this response across different chemical conditions. Fluorescence quenching was examined using Fe³⁺ introduced as FeCl₃, followed by recovery experiments using ascorbic acid to assess reversibility through redox-driven interactions. To determine whether the observed response is specific to iron, potassium ferricyanide was used as an alternative Fe³⁺ source. Additional comparisons …

Investigating The Effects Of Atg3 On Autophagosome Size And Number, Keeran R. Senthil Kumar

Senior Honors Theses and Projects

Macroautophagy, or autophagy, is a highly conserved catabolic process in all eukaryotes which allows for the degradation of large cytosolic contents. It occurs in two forms: bulk autophagy, which is induced by nutrient deprivation and targets nonspecific cargo, and selective autophagy, which is induced by cellular stress and sequesters specific substrates. The process of autophagosome formation is carried out by a group of Atg (autophagy-related) proteins, one of which is Atg3. Atg3 functions as an E2-like enzyme by conjugating Atg8 — a ubiquitin-like protein — to phosphatidylethanolamine (PE), a step required for autophagosome membrane formation. This lipidation process is catalyzed …

Efficient Organic Molecular Crystal Structure Prediction Using The Density Functional Tight-Binding Method, Maureen M. Kitheka, Yan Jing, Yan Yao, Puja Goyal

Faculty Publications – Chemistry

Over the years, computational crystal structure prediction (CSP) for organic molecules has thrived as an area of research, spanning various scientific disciplines and having significant applications in industries such as pharmaceuticals and agrochemicals. Within the field of batteries, redox-active organic materials (ROMs) such as quinones have received increased attention as promising electrode materials for rechargeable batteries. However, experimental determination of the crystal structure of intermediate species formed during the discharge/charge cycle can often be challenging. Incomplete X-ray diffraction patterns can also lead to difficulties in crystal structure determination for ROMs used in batteries. Use of a semiempirical electronic structure method …

Detecting And Repairing Conflicting Constraints In Co-Trained Physics-Informed Neural Networks For Composite Curing Processes, Cooper J. Evans

Dissertations, Master's Theses and Master's Reports

Composite materials have become a critical component of modern manufacturing, especially in the automotive and aerospace industries. The curing process for these composites has been modeled using a variety of partial differential equations representing the heat transfer and composite curing kinetics. Optimizing the applied temperature profile is critical for maximizing the efficiency and capacity of composite part manufacturers. Constraints must be placed on the inputs and outputs of the model, including but not limited to, the applied temperature profile, part temperature, and final degree of cure. Conflicting sets of constraints are easy to unknowingly impose due to the highly coupled …

Learning Without Training, Ryan O'Dowd

CGU Theses & Dissertations

We live in an era of big data. Whether it be algorithms designed to help corporations efficiently allocate the use of their resources, systems to block or intercept transmissions in times of war, or the helpful pocket companion known as ChatGPT, machine learning is at the heart of managing the real-world problems associated with massive data. With the success of neural networks on such large-scale problems, more research in machine learning is being conducted now than ever before. This dissertation focuses on three different projects rooted in mathematical theory for machine learning applications. Common themes throughout involve the synthesis of …

On The Influence Of Faraday Waves On Transport In Resonant Acoustic Mixing, Preston David Silverstein

CGU Theses & Dissertations

Resonant acoustic mixing (RAM) uses low frequency high acceleration oscillatory forcing to combine fluids, particles, and powders. Although progress has been in adjacent RAM fields, there are still gaps in understanding how Faraday surface instabilities affect momentum transport to the bulk fluid and particles therein. This work investigates the energy pathways that an oscillatory mixer has and connects surface deformation to bulk rotational flow and particle forcing. This study is conducted in three phases. In phase 1, a thermodynamic first- and second-law analysis couples the surface features with the scale of subsurface rotational features. Experimental surface measurements showed that for …

Front Matter, Konstantin Likharev

Essential Graduate Physics

Includes: Copyright and License; Preface; Disclaimer; Versions, Corrections, and Acknowledgments; Solution Request Templates; Notation; General Table of Contents

Gradient-Based, Post-Optimality Sensitivity Analysis With Respect To Parameters Of State Equations, Gene Hou, Jonathan Degroff

Mechanical & Aerospace Engineering Faculty Publications

Design optimization is a computational tool that can enable a designer to investigate the effectiveness of a design concept in an organized format. However, this design process requires the design variables, constraints, and objective function to be properly defined and expressed in mathematical forms. Post-optimality analysis thus becomes a necessary step to investigate different variations in the problem formulation and parameters to ensure that optimization produces a stable and trustworthy outcome. One efficient way to achieve this aim is to compute the local derivative of the optimized objective function with respect to the optimization problem parameters, such as bounds on …

Uncertainty-Aware Estimation, Planning, And Control For Tracking Multiple Drifting Patches In Flow Fields, Daniel O. Akanji, Krishnanand N. Kaipa, Cong Wei

Mechanical & Aerospace Engineering Faculty Publications

In this study, we present a replay-based framework for uncertainty-aware persistent tracking of multiple advected surface patches using an autonomous marine vehicle operating in spatiotemporal-varying currents. The method combines three components: local flow estimation, covariance-aware patch-boundary propagation with intermittent boundary fusion, and mission-level scheduling over multiple patches. Each patch is represented by a polygonal boundary, whose vertices are propagated through the estimated flow field while carrying per-vertex covariance, thereby quantifying uncertainty growth during advection. A flow-aware gain-scheduled linear quadratic regulator (LQR) was designed to shape the desired surge speed to take advantage of favorable currents. When the vehicle services a …

Mechanical-Medical Convergence In Heart Failure: Artificial Intelligence, Finite-Element Modeling, And 3d Printing For Diagnosis And Prognosis, Quazi Noor E. Sabrina, Quazi Md Zobaer Shah, Quazi Noor E. Sohela, Md Mahabub Hasan Mousum, Md. Moyeen Uddin Chisty, Quazi Md. Akbar Shah

Mechanical & Aerospace Engineering Faculty Publications

Heart failure remains a leading cause of global morbidity and mortality, yet routine clinical indices often miss the regional biomechanical disturbances that drive progression and shape treatment response. This State-of-the-Art review examines how finite-element (FE) modeling, additive manufacturing, and artificial intelligence (AI) are converging to improve the diagnosis, phenotyping, procedural planning, and prognostic assessment of heart failure (HF). Although these technologies have matured in structural heart disease and transcatheter intervention research, their greatest translational potential may lie in HF, where patient-specific ventricular remodeling, myocardial stress–strain heterogeneity, valve-ventricular coupling, and device-tissue interaction are incompletely captured by conventional clinical indices. We synthesize …

Lens: Llm-Enabled Narrative Synthesis For Mental Health By Aligning Multimodal Sensing With Language Models, Wenxuan Xu

Dartmouth College Master’s Theses

Multimodal health sensing offers rich behavioral signals for assessing mental health, yet translating these numerical time-series measurements into natural language remains challenging. Current LLMs cannot natively ingest long-duration sensor streams, and paired sensor–text datasets are scarce. To address these challenges, we introduce LENS, a framework that aligns multimodal sensing data with language models to generate clinically grounded mental-health narratives. LENS first constructs a large-scale dataset by transforming Ecological Momentary Assessment (EMA) responses related to depression and anxiety symptoms into natural-language descriptions, yielding over 100,000 sensor–text QA pairs from 258 participants. To enable native time-series integration, we train a patch-level encoder …

Evidence For A Spectral Break Or Curvature In The Spectrum Of Astrophysical Neutrinos From 5 Tev To 10 Pev, Karen Andeen

Physics Faculty Research and Publications

We report improved measurements of the all flavor astrophysical neutrino spectrum with IceCube by combining complementary neutrino samples in two independent analyses. Both analyses show evidence of a harder spectrum at energies below  ∼30  TeV compared to higher energies where the spectrum is well characterized by a power law. The spectrum is better described by a log parabola or a broken power law, the latter being the preferred model. Both, however, reject a single power law over an energy range 5 TeV–10 PeV with a significance  >4⁢𝜎, providing new constraints on properties of cosmic neutrino sources.

Radical-Based Oxidative Pretreatment Enhances Biofuel Production From Lignocellulosic Biomass Via Hydrothermal Liquefaction, João Vitor Dos Santos, Louis C. Bondurant, Patrick G. Hatcher

Chemistry & Biochemistry Faculty Publications

The sustainable production of biofuels from lignocellulosic biomass is a central goal in the transition to low-carbon energy systems. However, hydrothermal liquefaction (HTL), a promising thermochemical conversion pathway, is constrained by the high oxygen content and complex aromatic structure of lignin, which lowers bio-oil quality. Here, we used a model system of brown-rot-degraded white oak (Quercus alba) to test whether radical-based oxidative pretreatment could enhance HTL performance by converting lignin into more aliphatic intermediates. Oxidation was performed under simulated Fenton conditions using fixed Fe(II) (60 ppm) and two hydrogen peroxide concentrations (3 and 8 M), resulting in extensive lignin depolymerization …

Twenty-One Years Of Global Atmospheric Chlorine Inventories From Atmospheric Chemistry Experiment Fourier Transform Spectrometer (Ace-Fts) Measurements, N. Raymond, P. Bernath, C. Boone, M. P. Chipperfield

Chemistry & Biochemistry Faculty Publications

We present atmospheric chlorine inventories over 21 years (2004-2024) and five latitude bands (82-60°N, 60-30°N, 30°N-30°S, 30-60°S, 60-82°S) across altitudes from the surface up to 61 km. These inventories were calculated using the Atmospheric Chemistry Experiment-Fourier Transform Spectrometer (ACE-FTS) version 5.3 retrievals of the volume mixing ratios (VMRs) of 13 chlorine-containing species. Of these, five are product gases: HCl, HOCl, ClONO₂, COClF, COCl₂, and eight are source gases: CCl₄, CH₃Cl, CFC-11 (CCl₃F), CFC-12 ( CCl₂F₂), CFC-113 (CClF₂CCl₂F}), HCFC-22 (CHF₂Cl), HCFC-141b ( C₂H₃Cl₂F), and HCFC-142b (C₂H₃}Cl₂F). Where necessary, ACE-FTS data were supplemented with data from the TOMCAT 3-D chemical transport model, …

Scanning Electron Microscopy As A Potential Tool For Distinguishing Charcoal From Dark, Oxidized, Biomass, João Vitor Dos Santos, Aleksandar I. Goranov, Lais Gomes Fregolente, Joao Marcos De Lima-Faria, Diego Stefani Teodoro Martinez, Patrick G. Hatcher

Chemistry & Biochemistry Faculty Publications

Condensed aromatic carbon (ConAC) is widely used as a proxy for organic materials derived from biomass burning. However, recent evidence shows that ConAC can be also formed through nonpyrogenic oxidative processes. This study presents a proof-of-concept investigation using scanning electron microscopy (SEM) to distinguish charcoal from dark, ConAC-rich materials produced by ambient oxidation of biomass. Pine wood exposed to long-term iron-mediated oxidation was compared with unaltered wood and a controlled laboratory-generated charcoal reference. Conventional geochemical analytical methods, including benzenepoly(carboxylic acid) (BPCA) analysis, Fourier transform–ion cyclotron resonance–mass spectrometry (FT-ICR-MS), solid-state 13C nuclear magnetic resonance (NMR) spectroscopy, and elemental analysis, confirmed substantial …

Hybrid Learning And Optimization Methods For Solving Capacitated Vehicle Routing Problem, Monit Sharma, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We propose a hybrid quantum–classical framework for the Capacitated Vehicle Routing Problem (CVRP) that integrates the Augmented Lagrangian Method (ALM) with deep reinforcement learning (RL). Directly solving CVRP via Variational Quantum Eigensolver (VQE) requires a slack-based QUBO formulation, where converting inequalities to equalities greatly increases the qubit count. To circumvent this, we employ an ALM-based reformulation that enforces constraints through Lagrange terms instead of slack variables, drastically reducing quantum resource demands. An RL agent, trained with Soft Actor–Critic, adaptively tunes the Lagrange penalties to improve convergence and feasibility. Experiments show that RL-Q-ALM outperforms static-penalty and plain VQE baselines in both …

Extraction And Characterization Of Fresh And Dehydrated Cactus (Opuntia Ficus Indica) Polysaccharide For Hydrogel Preparation, Aye Thwe Thwesoe, May Myat Khine

ASEAN Journal on Science and Technology for Development

In this research, extraction of polysaccharide compounds from Cactus (Opuntia Ficus Indica) leaves in both fresh and dehydrated condition by solvent precipitation method using three types of water, acid (HCL) and NaOH. Before extraction, the physicochemical properties were examined to determine optimum yield (%) of extracted polysaccharide by optimization of Box-Behnken Design (BBD) of response surface methodology (RSM). The polysaccharide-based acrylamide hydrogel was prepared by free radical polymerization. The functional and structural characterization was done by FTIR, XRD and SEM for examination of extracted polysaccharide as raw polymer backbone in hydrogel preparation and prepared hydrogel as adsorbent for metal removal. …

Mechanical Property Evaluation Of Paper Die Materials In Explosive Forming, Higa Yoshikazu, Kadena Hisashi, Shimojima Ken, Iyama Hirofumi

ASEAN Journal on Science and Technology for Development

This study investigates the feasibility of using paper materials as substitutes for molds in explosive forming by systematically evaluating their mechanical properties. Tensile tests were conducted on three paper types differing in thickness and internal fiber structure, using a custom-designed gripping tool. Specimens were prepared in multiple orientations to account for anisotropic behavior. Results indicate that coated paper, exhibiting the highest specific tensile strength and energy absorption per unit basis weight, is the most suitable for formability. In contrast, thick paper, characterized by the lowest specific tensile energy absorption, offers the best dimensional accuracy. Anisotropy was observed in matte and …

Ai-Assisted Hybrid Ga–Pso Channel Allocation Under 3gpp Tr 38.901 Uma For Efficient 5g Radio Resource Management, Sharada Narsingrao Ohatkar

ASEAN Journal on Science and Technology for Development

The increased traffic and heterogeneity in the 5G/B5G network require efficient radio resource management (RRM). However, the existing methods, such as GA and PSO, have poor convergence speed and require proper initial conditions. Additionally, learning-based methods have high computational complexity. Hence, in this paper, a novel AI-assisted Hybrid Channel Allocation (AI–HCA) framework is proposed by using a support vector regression (SVR)-based predictive initialization method and GA-PSO optimization. Simulation results using the 3GPP UMa channel model show that the proposed method has a 28% reduction in call blocking probability (CBP), a 15-25% enhancement in spectral efficiency (SE), and a 18-25% enhancement …

Systematic Approaches To Characterizing Vulnerabilities And Enhancing Robustness Of Text And Vision-Language Models, Poojitha Thota

Computer Science and Engineering Dissertations

The proliferation of artificial intelligence (AI) across critical domains, including news summarization, privacy-policy analysis, and medical decision support, has raised growing concerns about the security and robustness of these systems against adversarial manipulation. This dissertation investigates adversarial robustness in generative AI by addressing three key research goals: (1) characterizing adversarial vulnerabilities across generative models, (2) developing systematic defenses to improve the robustness of generative models, and (3) designing deployment-time safeguards for securing LLM interactions.

Towards the first goal, we characterize adversarial vulnerabilities across text-based and multimodal systems. In abstractive text summarization, we show that inference-time perturbations can exploit lead bias …