Digital Commons Network^™

Multimodal Aerial Image-Based Ground Object Detection And Classification Using Yolo, Erik Nielsen

All Theses

Object detection in unmanned aerial vehicles (UAVs) present a unique challenge due to small object sizes, varying viewpoints, and changing environmental conditions. These challenges are exacerbated when operating during daytime and nighttime scenarios where illumination differences can heavily impact detection performance. This work is motivated by military object detection applications where the ability to reliably identify small objects such as landmines or unexploded ordnance from aerial imagery presents a critical safety need and a significant technical challenge. In this paper, we investigate object detection using both visible (RGB) and infrared (IR) imagery to improve robustness and reliability across diverse operating …

Beyond The Interface: Human Perceptions Of Generative-Ai Chatbots As Conversational Partners, Browning W.E. Blair

All Theses

Generative AI (gen-AI) chatbots are becoming embedded in everyday communicative life, yet it remains unclear whether users perceive these systems as socially reciprocative conversational partners. Therefore, this study examines how young adults understand and interact with gen-AI chatbots, focusing on perceptions of conversational partnership, anthropomorphism, politeness, discomfort, and technical understanding. Guided by the CASA framework, Media Equation Theory, and the uncanny valley hypothesis, this study employed four semi-structured, online focus groups with 15 undergraduate students and recent college graduates in the United States. Findings indicate that participants did not broadly perceive gen-AI chatbots as conversational partners in the interpersonal sense. …

Energetic Eutectics: An Experimental And Computational Investigation On Explosive Deep Eutectic Solvents, Connor J. Parker

All Theses

This study reports on the incorporation an extreme gradient boosting (XGBoost) machine learning strategy tailored to identifying potential high-energy, insensitive explosive deep eutectic solvents. 1,1-diamino-2,2-dinitroethylene (FOX-7) was investigated as the explosive of interest in our eutectic modeling. The resulting predictions highlighted a wide range of binary compositions demonstrating significant depression from the FOX-7 melting temperature with >70% probabilities of formation. Subsequent computational investigation into select mixtures provided underlying stabilization energies and optimized mixture geometries, which were then assessed experimentally with near simulant compounds oxalyldihydrazide and 1-methyl-3- nitroguanidine replacing FOX-7.

Experimental results with these chosen simulants did not demonstrate classical eutectic …

Deep Learning For Predicting Impact Energy And Compression After Impact Strength Of Composite Materials Using C-Scan Images, K. T. Tan, Jason P. Mack, Faizan Mirza, Zhong-Hui Duan

University Research

Traditional assessment of post-impact performance in carbon fiber reinforced polymer (CFRP) composites often relies on simplified scalar metrics that fail to capture the complex spatial interactions driving failure. This study addresses this limitation by developing an automated, end-to-end deep learning framework that shifts from manual feature extraction to the direct interpretation of raw damage morphology from ultrasonic C-scans. Using a ResNet18-based convolutional neural network (CNN) trained on 1,428 augmented images, the model achieved coefficients of determination (R2) of 0.7948 ± 0.0847 for compression after impact (CAI) strength and 0.9436 ± 0.0098 for impact energy. Beyond prediction, this dual-purpose methodology serves …

Empowering Sustainable Household Waste Management Through Rubbin: App-Based Transactions Using Google Maps Api And Qr Code Recognition, H.A. Danang Rimbawa, Muhammad Abditya Arghanie, Muhammad Rey Renoult, Dea Dwi Ananda

Smart City

The Digitization of environmentally friendly technology must be applied to advance smart cities. Waste management carried out conventionally often causes irregularities in the classification data collection, difficulties in accessing information related to garbage collection schedules, and a lack of transaction history information, which causes a decrease in the quality of waste collection. The development of functional application features is urgently needed as a container for proper waste management. The Rubbin App has been created, which implements Google Maps API for route optimization and waste mapping, QR code recognition using Enhanced Adaptive Median Filter, and private chat for clients and collectors …

Dementia Detection In Low-Resource Languages: Evaluating Translation-Assisted Transfer Learning For Multilingual Clinical Assessment, Kylar A. Deloach

Honors Theses

Alzheimer's disease (AD) is a growing global health concern, with millions of people affected worldwide and cases expected to rise significantly in the coming decades. Early detection is critical for patient treatment and care, and recent advances in natural language processing (NLP) have shown promise in identifying linguistic markers associated with AD. However, most existing work has focused on English, leaving speakers of other languages with limited access to such tools. This study investigates how effective AD detection models trained on English data are at transferring to Greek, a low-resource language with limited dementia-related speech data available. We propose a …

Getting Your Master's, Is It Worth It?, Vy Le, Madyson Schreifels

Celebrating Scholarship and Creativity Day (2018-)

This project uses a differential equation model to evaluate whether earning a master’s degree is financially worthwhile. Using UC Berkeley’s Master of Computer Science program as a case study, we modeled student loan repayment and compared long-term earnings between bachelor’s and master’s degree holders. The model predicts monthly loan payments and estimates the break-even point where the higher salary from the master’s degree outweighs the total investment cost. Results suggest that the degree becomes financially beneficial after approximately 15 years.

Analysis Of The Flavonoid Composition Of Honey Using High-Performance Liquid Chromatography, Bryce Phillips

Honors Theses

Honey is a naturally occurring substance that has a complex composition including compounds such as flavonoids, that contribute to potential health benefits. Quantitative analysis of flavonoids in honey can be challenging due to their similar structural characteristics, matrix effects, and chromatographic peak separation issues. This study aimed to establish calibration curves for five flavonoids–kaempferol, luteolin, chrysin, myricetin, and quercetin– to estimate their concentrations in five honey samples from varying geographic origins. Liquid-liquid extraction was used to isolate the flavonoids from honey. High Performance Liquid Chromatography was utilized to establish calibration curves and analyze honey samples. The calibration curves assembled demonstrated …

An Analysis Of A Family Of Root Finding Methods, Arabella Fuzak

Celebrating Scholarship and Creativity Day (2018-)

This thesis analyzes the behavior of a family of iterative root-finding methods, the Hansen-Patrick Family, which has a parameter, alpha, to create methods such as Newton’s methods, Halley’s method, and Euler’s method. By varying the parameter alpha, with both real and complex values, this project examines how the parameter can change convergence, divergence, and stability for different functions. This is shown by basin maps and Mandelbrot-like sets to visualize this behavior and classify points based on whether they converge to a root, diverge to infinity, or remain bounded without converging.

A Comparative Study Of Traditional Training And Xr-Based Simulation In Healthcare Professional Education, Fazal Qudus Khan, Gohar Khan, Ibrar Ahmad, Owais Khan, Suleman Shah

All Works

Immersive Virtual Reality or VR/VX stands poised to revolutionize healthcare education through interactive learning modules, overcoming current deficiencies in existing methodologies for instruction. This research compared the effectiveness of VR/VX-based training to traditional CT scanner operator training using a within-subjects design, involving 30 subjects, and concluded the effectiveness of using VR/VX in enhancing knowledge retention, task accomplishment, and engagement, and proved it by showing significant enhancement in immediate knowledge acquisition scores (Δ = 8.87, t(29) = 6.71, p < .0001), relative to delayed knowledge retention scores (Δ = 11.03, t(29) = 6.85, p < .0001), procedural achievement scores (Δ = 5.40, t(29) = 4.45, p = .0001), reduced overall task completion time using VR/VX for increased speed of execution (t(29) = 10.74, p < .0001), as well as reduced task errors for lower error rates using VR/VX in comparison to existing methodologies, as testified by the results, t(29) = 8.14, p < .0001, respectively, while showing no significant difference in usability, although assessed superior in terms of engagement and relative usefulness by the involved subjects.

Re: Comment Letter For Draft Final 2026 Interim Site-Wide Groundwater Monitoring Quality Assurance Project Plan (Dated April 17, 2026), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Gauss Elimination Method For Solving The System Of Neutrosophic Linear Equations, Elsayed Badr, Shokry Nada, Saeed Ali, Aya Rabie

Neutrosophic Systems with Applications

This paper proposes a unified computational framework for solving linear systems under trapezoidal Neutrosophic uncertainty. The system is formulated as A( I )x = b( I ), where both the coefficient matrix and the right-hand side vector incorporate an indeterminacy parameter I, expressed as A( I ) = A₀ + IA₁ and b( I ) = b₀ + Ib₁. A decomposition strategy is developed to separate the model into deterministic and indeterminacy components, yielding a solution of the form x( I ) = x₀ + Ix₁. The deterministic component is obtained via …

Warming Climate Amplifies Vapor Pressure Deficit Limits On Gross Primary Productivity, Shiqin Xu, Nate G. Mcdowell, Tim R. Mcvicar, Diego G. Miralles, Stephen Sitch, Pablo Sanchez-Martinez, Joshua B. Fisher, Pierre Friedlingstein, Hylke E. Beck, Matthew F. Mccabe

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Ongoing climate warming may profoundly impact terrestrial gross primary productivity (GPP), a key component of the global carbon cycle. However, uncertainty in the relative roles of atmospheric water demand (vapor pressure deficit, VPD) and root-zone soil moisture (SM) limits predictions of drought impacts on GPP. Here, we show that growing-season GPP was more strongly constrained by VPD than SM globally, based on observation-constrained model estimates, satellite retrievals and Dynamic Global Vegetation Model simulations. The importance of VPD increased with higher temperatures and more severe and prolonged droughts. This pattern reflects VPD’s critical role in regulating stomatal conductance and plant hydraulic …

An Improved Logarithmic Ratio-Product Type Estimator For Mean Modeling And Estimation Under Neutrosophic Uncertainty, Anchal Yadav, Mukesh Kumar

Neutrosophic Systems with Applications

In classical statistics, population mean estimation generally assumes precise and determinate data along with known auxiliary information. However, in real-world situations where observations are imprecise or expressed in interval form, such as temperature variations or financial market data, classical approaches become less effective. To address this limitation, neutrosophic statistics provide a more flexible framework for handling uncertainty and indeterminacy. This study proposes a neutrosophic logarithmic ratio-product type estimator for estimating the finite population mean using auxiliary information. The bias and mean squared error (MSE) of the proposed estimator are derived using a first-order approximation. Furthermore, performance evaluation is carried out …

Ai-Driven Multispectral Drone Monitoring For Afforestation In Arid Environments, Hesham Morgan, Ali Elgendy, Brandon Tran, Tamer Ismail, Mohamed M. Moursy, Yehya Kh. Shehadeh, Ahmed Elgharib, Ahmed Abdullah Al-Dughairi, Ali El Mubarak, Khaled Allam Harhash, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Monitoring large-scale afforestation projects in arid and semi-arid environments requires accurate, high-resolution, and repeatable methods to assess tree survival and growth. In this study, we integrated unmanned aerial vehicle (UAV) multispectral imaging with an advanced object detection framework to evaluate vegetation establishment in the Shuayb Al-Budai afforestation site, part of the Imam Turki bin Abdullah Royal Natural Reserve, Kingdom of Saudi Arabia (KSA). Multispectral datasets were acquired using a MicaSense Altum-PT sensor and processed through a masked Region-based Convolutional Neural Network (RCNN) with two backbone architectures: ResNet-101 and VGG19-BN. The Mask R-CNN–ResNet-101 model achieved superior performance, with an overall accuracy …

Artificial Intelligence, Synthetic Media, And The Criminal Justice System, Jeffrey J. Jacobs

Doctoral Dissertations and Projects

Artificial intelligence (AI) technologies continue to evolve at an exponential pace, and their utilization of synthetic media in criminal activity presents both unprecedented opportunities and significant challenges for criminal justice systems. This dissertation explores the intersection of AI-driven synthetic media crime and the U.S. criminal justice system, identifying how such technologies are being utilized in criminal activity and evaluating the system’s capacity to adapt. This study uses a longitudinal, mixed-methods research design; it also collects and analyzes data from law enforcement agencies, AI developers, and cybersecurity experts to forecast future crime trends and assess the efficacy of current justice system …

A Deep Learning-Based Approach For Bot Detection In Trending Hashtags On X, Mehboob Hussain, Muhammad Rizwan Rashid Rana, Muhammad Imran, Muhammad Shoaib, Muhammad Hasaan Mujtaba

Makara Journal of Technology

The widespread presence of bots on social media platforms, such as X (formerly Twitter), poses a significant threat to the integrity of online information by facilitating the dissemination of misinformation and manipulating public discourse. This study proposes a robust deep learning-based framework, DeepBot, to detect bot participation in trending hashtags and discussions on X. The approach uses a dataset sourced from Kaggle, comprising user profile metadata, including follower count, tweet frequency, account verification status, and engagement metrics. The data were subjected to comprehensive preprocessing, including noise removal, part-of-speech (POS) tagging, and word embedding using the pre-trained GloVe model. RoBERTa is …

Cooperation And Conflict: Oswald Veblen And Abraham Flexner, Juan Carvajalino, Della Dumbaugh

Department of Math & Statistics Faculty Publications

In 1929, Caroline Bamberger Fuld and her brother, Louis Bamberger, sold their department stores, Bamberger & Co., at a particularly timely moment. They invested their sizeable fortune in a new academic initiative in America, an institute devoted to research that would go by the name of the Institute for Advanced Study. This paper introduces two colleagues, Abraham Flexner and Oswald Veblen, and explores the cooperation and conflict between them as they worked alongside one another to shape the Institute for Advanced Study and the School of Mathematics within it.

Building Human-Aware Ai: Learning From And Assisting Human Decision-Makers, Saumik Narayanan

McKelvey School of Engineering Graduate Student Theses & Dissertations

As artificial intelligence systems become more capable, they are increasingly used not only as standalone problem-solvers, but as systems that learn from and interact with humans. In these settings, success depends not only on the strength of the model in isolation, but also on how well it fits the humans it is trained on or deployed alongside. This dissertation argues that human heterogeneity is a central ingredient in the design of effective human-aware machine learning systems. Rather than treating differences between people as noise to be averaged away, I show that variation in human expertise and preferences can provide useful …

Smart Kitchen: Towards Real-Time Ai Systems For Cognitive Support In Daily Activities, Ruiqi Wang

McKelvey School of Engineering Graduate Student Theses & Dissertations

The rapid growth of the aging population and the rising prevalence of Subjective Cognitive Decline (SCD) highlight the need for continuous, unobtrusive assessment of functional cognition during everyday activities. Vision-based smart home systems offer a promising pathway for monitoring behavior and supporting independent living. However, enabling real-time cognitive assistance remains challenging. It requires not only accurately interpreting complex human behaviors to detect cognitive errors, but also supporting real-time deployment on resource-constrained edge devices and under dynamic wireless network conditions. This dissertation presents Smart Kitchen, an AI-driven system for real-time cognitive error detection through the monitoring of daily cooking activities. Under …

Revisiting Visualization Literacy: What Standardized Assessments Reveal And Conceal Across Instruments, Cultures, And Ai Systems, Saugat Pandey

McKelvey School of Engineering Graduate Student Theses & Dissertations

Data visualizations are now a primary medium for public communication, scientific reasoning, and decision-making. By transforming complex data into accessible graphical forms, visualizations are widely assumed to make information comprehensible to broad audiences. Yet the ability to accurately read, interpret, and critically evaluate a visualization, what researchers call visualization literacy, is neither uniform nor universal. It is a learned, multidimensional skill shaped by prior exposure, educational opportunity, and context. While the field has developed standardized instruments to measure visualization literacy, these tools were built under narrow assumptions: participants are typically paid, English-speaking, and recruited from Western online panels. When any …

Benefits Of Traffic Reprofiling For Delay Sensitive Networking, Jiaming Qiu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Deterministic networking systems, such as Time-Sensitive Networking (TSN) and Deterministic Networking (DetNet), require strict end-to-end delay guarantees while efficiently utilizing limited network resources. Conventional approaches typically focus on fixed traffic profiles, which can lead to suboptimal resource utilization in scheduling or admission control problems. This dissertation investigates traffic reprofiling—the proactive reshaping of traffic arrival patterns—as a complementary mechanism for improving both resource efficiency and delay performance under strict service guarantees. The dissertation consists of three parts. The first part studies bandwidth minimization under hard delay constraints for Service Curve Earliest Deadline First (SCED) schedulers. We show that traffic reprofiling can …

Intermediate-Scale Outflow Dynamics Of Eta Carinae, Edmund J. Garcia, Matthew C. Fleenor

Departmental Honors & Graduate Capstone Projects

η Carinae (η Car) is a binary system, with the larger star being an extremely massive, luminous blue variable (LBV) beyond the Eddington Limit. Surrounding the η Car system, numerous multi-wavelength imaging campaigns reveal axisymmetric structures with the expanding bipolar Homunculus Nebula (¡1 pc). In combination with the episodic eruptive history of the η Car system, our initial intermediate-scale imaging revealed further axisym- metric structures (1-5 pc). To gain a more expansive view of how the small scale structure connects to panoramic imaging of the η Car region, we constructed a deep, optical, narrowband mosaic of 189 images utilizing the …

Molecular Catalysts For Electrochemical Nitrogen Activation Toward Sustainable Ammonia Synthesis, Jin-Xiu Han, Hao Xue, Xian-Biao Fu

Journal of Electrochemistry

Homogeneous electrocatalytic nitrogen reduction reaction (NRR) provides a powerful framework to interrogate molecular nitrogen-fixation pathways under mild conditions. By tuning the metal center, ligand architecture, and reaction medium, these systems enable capturing key intermediates and delivering mechanistic insight at the molecular-level resolution. Nevertheless, advances remain constrained by highly reduced operating potentials, intense competition from the hydrogen evolution reaction (HER), limited durability in turnover, and inadequate long-term stability.

In this review, we take electron delivery to the molecular active site as the guiding principle for organizing homogeneous electrochemical N₂ activation and transformation. We classify reported systems into direct electron transfer …

Water-Driven Solid Electrolyte Interphase Governs Continuous-Flow Ammonia Electrosynthesis, Peng-Bo Liu, Sheng-Liang Zhai, Ji Huang, Zhong-Shuo Zhang, Jie Zeng, Shao-Feng Li

Journal of Electrochemistry

Flow-cell architectures have emerged as a powerful platform for continuous and stable lithium-mediated nitrogen reduction (Li-NRR), enabling ambient-condition electrochemical ammonia synthesis and offering a promising alternative to Haber-Bosch processes. However, Li-NRR is exceptionally sensitive to trace water, and even minor variations in water content can profoundly alter interfacial chemistry. Here, we systematically investigate how initial water concentration affects Li-NRR performance in a continuous-flow cell. Excess water drives the formation of a thick solid electrolyte interphase (SEI) layer, which may impede nitrogen access to metallic lithium and hinder lithium-ion transport. As a result, the ammonia Faradaic efficiency collapses from ~61% to …

Insertion Of Noble Metal Free Cathodic Catalyst Layer With Fe-N-C Catalyst For Boosted Performance Of Pemfc, Shi Zhou, Muhammad Tariq, Asif Nadeem Tabish, Muhammad Salman, Fan-Di Ning, Muhammad Rayyan Tayyab, Ran-Ran Peng, Meng-Geng Hao, Wen-Mu Li, Xiao-Chun Zhou

Journal of Electrochemistry

Economical Fe-N-C catalysts are considered as promising alternatives to platinum group metal catalysts for proton exchange membrane fuel cells (PEMFCs). Despite exhibiting robust activity on rotating disk electrodes, their performance within membrane electrode assemblies often experiences limitations, such as decreased O₂ diffusion, high H₂O₂ formation, low proton conduction, and a lower electron transfer number. In this study, key factors, including proton transport, electron conduction, and gas diffusion within air-breathing PEMFCs, have been investigated by adjusting cathode catalyst layer (CCL) compositions. From the experimental results, the optimal peak power density was obtained when the loading of Fe-N-C …

Finiteness Of Torsion Loci And Normal Functions Arising From Cycles On Hadamard Products, Devin Akman

Arts & Sciences Graduate Student Theses and Dissertations

The unifying theme of this dissertation is normal functions. In the first chapter, we study an invariant of knot exteriors and similar manifolds called the A-polynomial through the lens of vanishing loci of normal functions. Using a special case of the Zilber-Pink conjecture, proven in the second chapter, we show that only finitely many irreducible Laurent polynomials of bounded overgenus appear as factors of A-polynomials. In the third chapter, we construct normal functions from hypergeometric variations of Hodge structure and compute their regulators. Finally, we determine under which conditions incomplete motivic cohomology cycles on families complete to cycles on their …

Final Insufficiently Reclaimed Sites Quality Assurance Project Plan (Qapp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Exactly Solving One-Dimensional Models For The Fractional Quantum Hall Effect, Joseph R. Cruise

Arts & Sciences Graduate Student Theses and Dissertations

Nearly fifty years after its experimental debut, the quantum hall effect has produced perhaps more unique descriptions than any other subject of condensed matter physics. From topological band theory to non-commutative geometry, topological and conformal field theories, random matrix models and more, each approach captures a subset of the essential features, and compliments the others to produce a beautiful mosaic description. In this thesis, we revisit the fractional quantum hall (FQH) effect from its oldest vantage point, that of the trial wavefunctions and the parent Hamiltonians which stabilize them. Laughlin's observation of the importance of the theory of holomorphic functions …

Final Unreclaimed Sites Quality Assurance Project Plan (Qapp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.