Physical Sciences and Mathematics Commons^™

Integrating Multi-Method Monitoring To Characterize Seasonal Habitat Use Of Green Sturgeon (Acipenser Medirostris) In Humboldt County, California, Olivia S. Boeberitz

Cal Poly Humboldt theses and projects

Green sturgeon (Acipenser medirostris) are a long-lived, anadromous, iteroparous species that spawns in large West Coast North American rivers but ranges widely in the coastal ocean. For coastal California Native American Tribes, green sturgeon are a vital part of their cultural heritage and traditions, and central to their food security. Two genetically distinct population segments (DPSs) of green sturgeon are recognized in the United States, Canada, and Mexico; the southern DPS is listed as “threatened” under the Endangered Species Act and the northern DPS is considered of “conservation concern.” Although green sturgeon are known to use large estuaries …

Geochemical And Petrological Observations Of The June Lake Basalt, Eastern California, Daniel Jacob Abel

Cal Poly Humboldt theses and projects

The June Lake basalt is a monogenetic cinder cone and 11-km-long lava flow in eastern California located ~10 km NW of the Long Valley caldera. The stratigraphic position of the June Lake basalt between Tahoe and Tioga glacial deposits suggests an eruption age of only ~20-45ka. This age places the June Lake Basalt among the youngest volcanic eruptions in the Long Valley-Mono Basin region, where volcanic activity spans from the late Pliocene to the Holocene. The goal of this study is to use field observations coupled with whole-rock and crystal geochemical analyses to investigate the magma source and pre-eruption magma …

Assessing Coho Salmon (Oncorhynchus Kisutch) Distribution Using Traditional Sampling Methods In Conjunction With Edna On Trinity River Tributaries Within The Hoopa Tribal Boundaries, Ely D. Boone

Cal Poly Humboldt theses and projects

Detecting the presence of species using environmental DNA (eDNA) is a fast-growing field of study that shows much promise for population monitoring. In fisheries biology, eDNA is a potentially powerful tool compared to traditional fisheries monitoring techniques such as visual observation via snorkel surveys. When eDNA and traditional survey methods are employed in tandem, using the same spatial and temporal sampling design, eDNA often detects target species when traditional methods do not. In this study, instead of utilizing this paired study design, I implemented eDNA and snorkel surveys at different sampling scales with eDNA being collected at a single downstream …

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 …

Contribution Of Lagoon-Rearing Juvenile Chinook Salmon (Oncorhynchus Tshawytscha) To The Adult Spawning Population In The Mattole River, Emma Held

Cal Poly Humboldt theses and projects

Bar-built estuaries are rivermouth confluences characterized by periods of disconnection between the river and the ocean due to a sandbar that builds across the mouth. Sandbar timing characteristics vary between years and between systems, but when closed, a lake-like lagoon forms in the estuary footprint. The Mattole River has a bar-built estuary that forms in late spring or early summer, often overlapping with juvenile Chinook salmon outmigration. Juveniles that do not leave the river before the bar closes must remain in the lagoon until the mouth opens in fall. The purpose of this thesis was to understand if lagoon-rearing is …

Triangulating Primary Sources, Professional Judgement, And Llm-Generated Summaries: Educating Nurses In An Ai-First World, Sarah Oerther, Daniel B. Oerther

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

No abstract provided.

A Transdisciplinary Approach To Advancing Water Security And Public Health: The “Nurse + Engineer” Model For Achieving Sustainability In Wash Systems Using Community-Based Participatory Research, Daniel B. Oerther, Sarah Oerther

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Human health is linked to the health of the environment, a concept referred to as planetary health. As environmental challenges become increasingly complex, single-disciplinary approaches are insufficient to achieve sustainable solutions. This article presents a transdisciplinary framework, termed the "Nurse + Engineer" model, as a novel methodology for addressing complex problems at the nexus of environmental engineering and public health. The model integrates the technical design and systems-level thinking of environmental engineering with the community-based, patient-centered care of nursing. This work details the key lessons learned from the retrospective application of this framework to a series of multi-year, community-based participatory …

First Remi Experiments At A Cryogenic Ion Storage Ring, M. Schulz, F. Herrmann, W. Zhang, A. Dorn, M. Grieser, F. Grussie, H. Kreckel, O. Novotny, A. Wolf, T. Pfeifer, C. D. Schröter, R. Moshammer

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

We have recorded double, triple, and quadruple coincidences between neutralized projectiles, recoiling target ions, and two electrons created in collisions of slow anions with neutral atoms. The experiments were performed at the cryogenic storage ring in Heidelberg. The recoil ions and ejected electrons were momentum-analyzed using a Reaction Microscope (ReMi) spectrometer. Various processes involving electron detachment from the projectile accompanied by various transitions in the target were investigated. Detachment without any transition in the target is qualitatively well described by a quasi-free electron model in the case of an Ar target. In detachment with single target ionization, no signatures of …

Self-Calibrating Uav Navigation: Reinforcement Learning Approaches For Horizontal Trajectory Estimation, Shirin Nasr-Esfahani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Accurate unmanned aerial vehicle (UAV) trajectory estimation is essential for autonomous navigation, particularly in GPS-denied environments. Visualodometry and simultaneous localization and mapping (SLAM) approaches require precise camera intrinsic parameters, which are typically obtained through predefined or offline calibration. Instead, in this work, we propose a reinforcement learning (RL)-based self-calibration framework that estimates camera intrinsic parameters directly from monocular video sequences, without requiring prior knowledge of the camera, environment, or calibration targets. This intrinsic parameter estimation is then leveraged to achieve robust UAV trajectory estimation using only video data. We formulate the problem as a sequential decision-making task, where an RL …

New Metrics For Disambiguating Feature Overlap And Catastrophic Forgetting In Incremental Learning Contexts, Niklas M. Melton, Leonardo Enzo Brito Da Silva, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Catastrophic forgetting remains a central challenge in lifelong learning, where newly acquired knowledge interferes with previously learned tasks, degrading performance over time. Mitigation strategies such as rehearsal and regularization have been proposed, but both introduce limitations, either by retaining old data or by constraining model updates in ways that may impair learning. Complicating matters, recent findings show that feature-space overlap between tasks can produce similar performance drops even in models that memorize data, making it difficult to distinguish true forgetting from representational interference. Current accuracy-based metrics fail to disentangle these effects, undermining diagnostic clarity. In this work, we introduce the …

Mtl_Tx: A Multi-Task Transformer Model For Improved Radiation Time-Series Estimation, Hongfang Zhang, Adam Stavola, Hal Ferguson, Bence Budavari, Hongyi Wu, Chiman Kwan, Jiang Li

Electrical & Computer Engineering Faculty Publications

Controlling radiation doses at potential radioactive facilities is critical to ensuring the safety of both personnel and the public. At the Thomas Jefferson National Accelerator Facility (JLab), multiple sensors are deployed around the three experimental halls to monitor key parameters, including single-beam current, energy levels, current leakage, and radiation values during accelerator operations. In this study, we developed a Multi-task Transformer model, MTL_TX, to accurately estimate radiation doses at sensor locations based on historical data, with the aim of enhancing safety in accelerator facilities and surrounding public areas. To improve estimation accuracy, we integrated two innovative components into the proposed …

Digital Twin Technologies For Battery Systems: Advancements, Applications, And Future Directions, Seyed Saeed Madani, Yasmin Shabeer, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, François Allard

Electrical & Computer Engineering Faculty Publications

The relationships among deep learning, edge computing, artificial intelligence (AI), and the most recent advancements in digital twin (DT) technology for battery energy storage systems are discussed in this paper. The study highlights the need for improved cloud-edge coordination, AI model development, and stronger cybersecurity features by demonstrating real-world applications of digital twin technology in electric vehicles (EVs), aircraft, and grid storage. It also described DT-based structures for fault detection, real-time monitoring, and optimization through standardization and battery management system (BMS) fusion. Because DT-based solutions for distributed energy resources (DERs) offer improved energy management systems, various studies have been conducted …

Qubit Lattice Algorithm Simulations Of The Scattering Of A Bounded Two Dimensional Electromagnetic Pulse From The Infinite Planar Dielectric Interface, Min Soe, George Vahala, Linda Vahala, Efstratios Koukoutsis, Abhay K. Ram, Kyriakos Hizanidis

Electrical & Computer Engineering Faculty Publications

Qubit lattice algorithm (QLA) simulations are performed for a two-dimensional spatially bounded pulse propagating onto a plane interface between two dielectric slabs. QLA is an initial value scheme that consists of a sequence of unitary collision and streaming operators, with appropriate potential operators, that recover Maxwell equations in inhomogeneous dielectric media to the second order in the lattice discreteness. For the case of total internal reflection, there is transient energy transfer into the second medium due to the evanescent fields as the Poynting unit vector of the pulse is rotated from its incident to reflected direction. Because of the finite …

Multimodal Machine Learning For Alzheimer's Disease Classification Using Adni Biomarker Fusion, Hesameddin Mostaghimi, Hamid R. Okhravi, Bahar Niknejad, Daniel A. Cohen, Michel A. Audette, Alzheimer's Disease Neuroimaging Initiative

Electrical & Computer Engineering Faculty Publications

Despite ongoing advances, accurate diagnosis of Alzheimer’s disease (AD) remains challenging due to its multifactorial nature, comorbidities, and clinical heterogeneity. Accordingly, approaches that combine multimodal data may improve AD classification by integrating complementary information. To investigate this, we evaluated classification performance using a preprocessed ADNI-3 dataset comprising a shared set of clinical/cognitive features along with four imaging modality-based cohorts: trimodal (MRI + amyloid PET + tau PET), MRI + amyloid PET, MRI + tau PET, and MRI-only. We trained a range of supervised machine learning (ML) and deep learning (DL) classifiers using stratified five-fold cross-validation and evaluated performance using accuracy, …

An Explainable Cs-Mitigation Triangular (Ecsmt) Framework To Secure Graph Neural Networks, Sabah Ettahri, Sergio Pallas Enguita, Chung-Hao Chen, Wen-Chao Yang

Electrical & Computer Engineering Faculty Publications

This research addresses cyber risk by defending against backdoor attacks on Graph Neural Networks (GNNs). We propose the Explainable Complex System-Mitigation Triangular (ECSMT) Framework, which integrates Robust Training, Graph Regularization, and Data Sanitization into a lightweight, hardware-efficient defense layer. To evaluate structural generalizability, we conducted empirical evaluations across three distinct benchmark domains (AIDS, MUTAG, and PROTEINS) using a Graph Isomorphism Network (GIN) backbone. Under a baseline 5% backdoor subgraph trigger injection ratio, ECSMT achieves excellent utility retention, securing a Clean Accuracy (CA) of 97.33% (±0.62%) while reducing the Attack Success Rate (ASR) from 97.00% down to 69.45% on the primary …

Generalized Inverter Fault Detection Using Normalized Current Features And A Lightweight Bilstm Network, Mohammad Zamani Khaneghah, Mohamad Alzayed, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

Fault detection and diagnosis of three-phase inverter-fed motor drives is essential for ensuring system reliability, safety, and continuous operation in applications such as electric vehicles and industrial automation. This paper proposes a data-driven fault detection framework based on normalized current features and a lightweight bidirectional long short-term memory (BiLSTM) network which can be generalized to different motor power rating in the same controller system. A compact set of six time-domain features, consisting of the mean and root-mean-square (RMS) values of the phase currents, is extracted and normalized with respect to the average RMS value. This normalization effectively removes dependency on …

Affordable Course Content And Open Education Resources For Undergraduate Courses Teaching Fundamentals Of Wireless Communications And Networking, Dimitrie C. Popescu, Otilia Popescu

Electrical & Computer Engineering Faculty Publications

Wireless communication systems and networks along with the services they provide have become an essential component of the modern 21st century society, fueling job growth in the wireless industry and increasing the need for engineers specialized in wireless communication systems. As a consequence, over the past two decades, undergraduate courses teaching fundamentals of wireless communication systems and networks have become common in electrical and computer engineering and technology programs. At the same time, the number of textbooks dedicated to wireless systems and networks published by mainstream publishers has also grown, with availability in various formats and offerings and a significant …

Automated Writer And Acquisition-Condition Classification Of Digitally Captured Handwriting Using Statistical Dynamic Features And Support Vector Machines, Long-Huang Tsai, Hsiang-Ju Lai, Wen-Chao Yang, Jiajun Jiang, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

Digitally captured handwriting preserves pen trajectories and dynamic signals, but it also records hardware- and input-dependent properties that can confound forensic interpretation. This study revises a support vector machine (SVM) screening framework using 16,500 samples from 30 writers, 11 writing-content categories, and five acquisition conditions spanning three tablets and stylus or finger input. Twenty-four raw and derived time-series variables were summarized by maximum, minimum, mean, median, and standard deviation, yielding 120 features; the mode statistic was removed. Writing direction and angular velocity were recalculated with atan2-based vector formulas. Unavailable device/API channels were encoded as zero, and Z-score parameters were estimated …

Physics-Informed Temperature Prediction Of Lithium-Ion Batteries Using Decomposition-Enhanced Lstm And Bilstm Models, Seyed Saeed Madani, Yasmin Shabeer, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, François Allard

Electrical & Computer Engineering Faculty Publications

Accurately forecasting the operating temperature of lithium-ion batteries (LIBs) is essential for preventing thermal runaway, extending service life, and ensuring the safe operation of electric vehicles and stationary energy-storage systems. This work introduces a unified, physics-informed, and data-driven temperature-prediction framework that integrates mathematically governed preprocessing, electrothermal decomposition, and sequential deep learning architectures. The methodology systematically applies the governing relations to convert raw temperature measurements into trend, seasonal, and residual components, thereby isolating long-term thermal accumulation, reversible entropy-driven oscillations, and irreversible resistive heating. These physically interpretable signatures serve as structured inputs to machine learning and deep learning models trained on temporally …

Comparative Assessment Of Energy And Emission Costs For Geothermal Heat Pumps And Fossil-Fuel Heating Systems Across U.S. Climatic Zones, Md Shahin Alam, Shima Afshar, Seyed Ali Arefifar, Mohammad Haq

Electrical & Computer Engineering Faculty Publications

In response to growing concerns over global warming and energy sustainability, transitioning from fossil-fuel-based heating systems to renewable alternatives is essential. This study evaluates the economic and environmental performance of geothermal heat pumps for building heating and compares it with conventional coal-fired boilers, natural-gas boilers, and diesel furnaces. Using the heating degree-day (HDD) method, heating energy demand was analyzed for four U.S. cities—Anchorage (AK), San Francisco (CA), Salt Lake City (UT), and Las Vegas (NV)—representing diverse climatic zones. The analysis integrates thermodynamic and economic parameters, including the coefficient of performance (COP = 2–5) and annual fuel-utilization efficiency (AFUE = 80–97%), …

Regulating Galaxy Growth: Ionized Gas Conditions, Star Formation Histories, And Environmental Effects, Shweta Jain

University of Kentucky Doctoral Dissertations

Understanding galaxy formation and evolution requires tracing how baryons cycle between stars, gas, and the interstellar medium (ISM) across cosmic time. The efficiency of star formation, the buildup of metals, and the regulation of gas inflows and outflows are governed by evolving physical conditions within galaxies and their environments. My thesis investigates how the ionized ISM, star formation histories, and environmental processes have shaped galaxy evolution over the past ∼ 12 Gyr. The primary focus of this work is the evolution of the physical conditions of ionized gas in galaxies, including gas-phase metallicity and ionization state. Using new and archival …

Modeling Rank Distribution And The Relative Importance Factor Index In Discrete Power-Law Models: Application To Social Resilience Using The Scopus Database, Brian Llinas, Jose Padilla, Humberto Llinas, Erika Frydenlund, Katherine Palacio

VMASC Publications

Prior research on power-law distributions has primarily focused on modeling frequency patterns, with less attention given to rank distributions and how ranked positions reflect relative importance among elements. In discrete power-law distributions, frequency-based metrics often provide limited discrimination in the tail, where elements may exhibit similar counts but differ in relative dominance. These patterns are especially evident, for instance, in academic publishing, where keywords, affiliations, and citations commonly exhibit power-law behavior. To address this limitation, we introduce the Relative Importance Factor (RIF) Index, a statistical measure derived from the estimated discrete power-law rank distribution rather than an additional independent parameter. …

Standardization Of Neuromuscular Reflex Analysis—Role Of Fine-Tuned Vision-Language Model Consortium And Openai Gpt-Oss Reasoning Llm-Enabled Decision Support System, Eranga Bandara, Ross Gore, Sachin Shetty, Ravi Mukkamala, Christopher K. Rhea, Brittany S. Samulski, Amin Hass, Atmaram Yarlagadda, Shaifali Kaushik, Malith De Silva, Andriy Maznychenko, Inna Sokolowska, Kasun De Zoysa

VMASC Publications

Background/Objectives: Accurate assessment of neuromuscular reflexes, such as the Hoffmann reflex (H-reflex), plays a critical role in sports science, rehabilitation, and clinical neurology. Conventional interpretation of H-reflex electromyography (EMG) waveforms is subject to inter-rater variability and interpretive bias, limiting reliability and standardization. This study aims to develop an automated, interpretable, and robust agentic AI–driven framework for H-reflex waveform analysis. Methods: We propose a fine-tuned Vision–Language Model (VLM) consortium combined with a reasoning Large Language Model (LLM)–enabled decision support system for automated H-reflex interpretation. Multiple VLMs were fine-tuned on curated datasets of H-reflex EMG waveform images annotated with expert clinical observations, …

Distilling The Complexity Of Agent-Based Simulations Into Textual Explanations Via Large Language Models, Noé Y. Flandre, Philippe J. Giabbanelli

VMASC Publications

Communicating the design and results of agent-based models (ABMs) to subject matter experts is challenging, which hinders participation and limits trust in simulation-based decision support. Large language models (LLMs) can communicate ABMs as textual summaries, thus complementing traditional disclosure through statistical and visualization techniques. While prior work translated the structure of conceptual models into narratives via LLMs, our extension covers the dynamics of simulation models via an automated simulation-to-text method that extracts contextual information from NetLogo ABMs, performs repeated simulations, and generates narrative descriptions (including the model’s purpose, parameters, and simulation dynamics) using mutimodal LLMs. Furthermore, four summarization algorithms spanning …

Machine Learning Classification Of Prostate Cancer Genomic Sequences Using K-Mer And Sequence-Derived Features, Kuldeep Rawat, Hirendra Nath Banerjee, Jamie Noble, Saa Naudia Deloatch, Satyendra Banerjee, Sachin Shetty, Soumya Banerjee

VMASC Publications

Prostate cancer disproportionately impacts African American men, who experience significantly higher mortality rates and earlier disease onset than other populations. Current diagnostic approaches, including prostate-specific antigen testing and biopsy, lack sufficient specificity and sensitivity, underscoring the need for accurate, molecular-level classification tools. This paper presents a machine learning framework for binary classification of genomic DNA sequences as cancerous or healthy. A dataset of 1684 FASTA-formatted sequences obtained from the National Library of Medicine - GenBank was analyzed, with 1662 sequences retained after quality control filtering. Feature engineering yielded 67 attributes, including GC content, Shannon entropy, sequence length, and trinucleotide k-mer …

Hierarchy And Ideology Antagonism: Artificial Intelligence In Ridley Scott's Alien, Grace Anastasia Pula

Theses, Dissertations and Culminating Projects

This thesis examines the objectively threatening structure of artificial intelligence (AI) in the narrative plot of Alien and how it exerts control over the humans. Using a structuralist approach with Louis Althusser's Ideological State Apparatuses (ISAs), I will examine the character relationships and how an android, Ash, enforces a patriarchal, hierarchical system. Drawing on Mark Coeckelbergh’s AI Ethics and Jacques Ellul’s The Technological Society, I will outline broader fears that technology will surpass human intellect and serve a destructive function within an oppressive system. Analyzing two examples of AI characters, the film showcases capitalist ambitions through technological identities and their …

Dyno : Dynamic Neurosymbolic Orchestrator For Multi-Agent Systems, Ritvik Garimella, Chathurangi Shyalika, Renjith Prasad, Amit Sheth

Publications

Large Language Model (LLM)-based multi-agent systems (LaMAS) represent an emerging paradigm for tackling complex, multi-step reasoning and decision-making problems. As these systems scale, orchestration, which is the ability to coordinate, manage, and evaluate the interactions among diverse agents, becomes central to their success. While recent orchestrators such as AgentFlow have demonstrated promise in managing communication and task delegation, they remain limited in their ability to understand task semantics, coordinate heterogeneous agent types (e.g., reactive vs. cognitive), and adaptively align outputs with human-defined goals. In this position paper, we introduce the DYNO (Dynamic Neurosymbolic Orchestrator), a system developed as part of …

Using Nmr Spectroscopy And Linear Discriminant Analysis To Molecular Profile Varietal Honey, Taylor Mac

Master's Theses and Doctoral Dissertations

In recent years, varietal honey has been a massive target of adulteration through mislabeling and the addition of other sugars. Unethical companies do this to cut production costs while still charging the consumer full price. Previous studies have used nuclear magnetic resonance (NMR) to unravel possible adulteration in honey, but currently, there are no standard rapid methods to authenticate varietal honey. In our research, we collected NMR signatures (“fingerprints”) and combined them with linear discriminant analysis (LDA) to predict the varietal, country, and region of varietal honey. As a part of the project, we investigated whether an adjustment to the …

Does Fishery Management For Groupers (Teleostei: Epinephelidae) Protect Them Effectively? Context From The Iucn's Red List Of Threatened Species, Sean T. Fennessy, Christi Linardich, Kevin Rhodes, Joao P. Barreiros, David Pollard, Eloy Sosa-Cordero, Felicia Coleman, Alfonso Aguilar-Perera, Christopher R. Malinowski, Thierry Brulé, Pedro Afonso, Kayan Ma, Min Liu, Muktha Menon, Colin Wen, Stanley K. H. Shea, Sean N. Porter, Matthew Craig, Yvonne Sadovy De Mitcheson

Biological Sciences Faculty Publications

Worldwide, groupers (Epinephelidae) are commercially valued fishes, which also play key ecological roles on tropical and subtropical reefs. In 2007 and 2016, the IUCN's Groupers and Wrasses Specialist Group assessed all 160+ grouper species, with 17 of these being identified as threatened in 2016 and the major threat factor being overexploitation. Our present study aimed to identify whether management measures (MMs) for previously assessed groupers were established, whether these measures aligned with IUCN's Red List categories, and whether they effectively protect grouper populations. Experts in grouper biology and management assigned scores per grouper species based on the extent to which …

The Coral Microbiome Under Climate Change: Shifting Symbiotic Interactions, Katherine E. Parker, Kate M. Quigley

Biological Sciences Faculty Publications

Host–microbe symbioses are central to organismal health, yet these complex partnerships are often strongly shaped by the environment. Climate change is increasingly altering environmental conditions, disrupting the balance of host–microbe interactions and pushing them along a continuum from mutualism (both partners benefit) toward parasitism (one partner gains at the other’s expense). Reef-building corals provide a clear example of this vulnerability, as the coral holobiont, an ecological unit comprising diverse photosynthetic algal symbionts (Symbiodiniaceae) and other microbial partners, depends largely on these mutualistic interactions for nutrition, immunity, and stress tolerance. Increasingly, evidence suggests that under heat stress, the algal symbionts retain …