Entire DC Network | Open Access Articles | Digital Commons Network™

Re: Approval Letter For The Diggings East 100% Remedial Design Package Submittal (Dated July 23, 2026), The 100% Sbcca Programmatic Technical Specifications Submittal (Technical Specifications) (Dated May 29, 2026), The Final Revised Silver Bow Creek Conservation Area (Sbcca) Construction Quality Assurance Plan (Cqap) (Dated July 29, 2026), The Final Sbcca Construction Monitoring Quality Assurance Project Plan (Qapp) (Dated July 29, 2026), And The Final Sbcca Materials Management Plan (Mmp) (Dated July 29, 2026), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Go to article

Clark Tailings Consolidated Waste Management Area Site Investigation Quality Assurance Project Plan (Qapp), Woodard & Curran

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Go to article

The Effectiveness Of Bacillus-Coated Npk Fertilizer On Broccoli Growth, Nutrient Status, Rhizosphere Bacillus Population, Yield, And Flower Quality, Betty Natalie Fitriatin, Rara Rahmatika Risanti, Reginawanti Hindersah, Fasa Aditya Hanindipto, Gita Bina Nugraha

Jurnal Kultivasi

Efforts to increase the availability of macronutrients in soil have relied on inorganic fertilization, particularly NPK fertilizers. However, global challenges in the fertilizer raw material supply chain, including price fluctuations and availability, are driving the need for more efficient and sustainable fertilizer alternatives. The field experiment was conducted in Baruajak, Lembang District, West Java, Indonesia, to evaluate the effectiveness of Bacillus-coated NPK fertilizer on broccoli (Brassica oleracea var. italica) growth, nutrient status, rhizosphere Bacillus population, yield, and flower quality. The experiment used a randomized complete block design with 15 fertilizer treatments and three replications. Treatments consisted of two coated NPK …

Go to article

Effects Of Ab Mix Hydroponic Solution And Ameliorant Mixtures On Cayenne Pepper Growth And Yield In An Inceptisols, Tien Turmuktini, Betty Natalie Fitriatin, Tualar Simarmata, Firda Widayanti, Nuryanti, Lia Amalia, Elly Roosma Ria, Linlin Parlinah

Jurnal Kultivasi

Cayenne pepper (Capsicum frustescens L.) cultivation faces several problems, including acidic pH and inadequate nutrient management. These problems can be addressed by using a mixture of various ameliorants and AB mix nutrient solutions. This study aimed to determine the effect of ameliorant mixture dosage and AB mix solution concentration on soil chemical properties, growth, and yield of cayenne pepper. The experiment used a randomized block design, consisting of two factors: nutrient solution concentration and ameliorant dosage. The factor of nutrient solution concentration consisted of four levels, i.e., 700, 1000, 1300, and 1600 ppm, while the factor of ameliorant dosage also …

Go to article

The Effect Of Silica-Enriched Rice Husk Biochar And Npk Fertilizer On Soil Total Nitrogen, Nitrogen Uptake, And Yield Of Lowland Rice, Emma Trinurani Sofyan, Oviyanti Mulyani, Ania Citraresmini, Meddy Rachmadi, Kharmelia Sandra Livia

Jurnal Kultivasi

Rice production in Indonesia increasingly relies on agricultural intensification, which can accelerate soil degradation and reduce fertilizer-use efficiency. Therefore, sustainable soil management is needed to maintain soil fertility. Silica-enriched rice husk biochar (biochar-silica) is a promising soil amendment because it improves soil properties and nutrient retention. This study evaluated the effects of biochar-silica and NPK fertilization on soil total nitrogen, nitrogen uptake, and yield of lowland rice (Oryza sativa L.). The experiment was conducted from September 2025 to January 2026 using a two-factor randomized block design with nine treatment combinations and three replications. The treatment consisted of three levels of …

Go to article

Development Of Robust Ratio Linear Fitting Method Of Temperature And Emissivity Separation For High-Temperature Data, Mitchell Manzardo, Michael L. Dexter, Shannon R. Young, John Bowlan, Anthony L. Franz

Faculty Publications

Accurate temperature and emissivity separation from thermal infrared radiance is essential for characterizing materials under high-temperature laboratory conditions. Existing temperature and emissivity separation methods have largely been developed for multispectral remote sensing applications, where long atmospheric path lengths require extensive atmospheric compensation. In contrast, the current work considers hyperspectral laboratory measurements acquired over a short optical path, where atmospheric effects are comparatively small but increased measurement uncertainty remains within portions of the measured spectrum. The ABB MR304 FTIR spectrometer used in this study exhibits reduced optical transmission below approximately 2.5 μm, producing increased measurement uncertainty within the spectral region containing …

Go to article

Palmnet: Confidence-Calibrated Edge-Cloud Ai For Field Diagnosis Of Date Palm Diseases, Muntadher Kareem, Raed Majeed

Karbala International Journal of Modern Science

Date palm (Phoenix dactylifera L.) is a cornerstone crop for Iraq and the wider MENA region, yet reliable in-field diagnosis of leaf disorders remains slow, labour-intensive, and constrained by a limited pool of agronomists. This paper presents PalmNet, a full-stack diagnostic system that classifies nine leaf conditions through a calibrated edge-cloud framework. The system is developed and evaluated on a public dataset of 3,089 field images spanning the nine classes, using a 70/15/15 stratified split. A ShuffleNetV2 student network, distilled from a ConvNeXt-Tiny teacher, is deployed on two complementary edge endpoints: a Raspberry Pi Zero 2 W field station …

Go to article

Modeling The Clonal Rosette Composition Of A Bromeliaceae Genet: A Combinatorial Approach, Layla K. Lammers, Erin N. Bodine

Spora: A Journal of Biomathematics

Bromeliaceae, a neo-tropical plant family encompassing over 3,000 species, exhibit two modes of reproduction: sexual reproduction via flowers and seeds, and asexual reproduction via genetically identical clonal rosettes. The vegetative bodies of bromeliads form rosettes with new leaves emerging from the center and clonal rosettes emerging above a single leaf in the rosette, resulting in a genetic individual consisting of a seed-grown rosette and multiple iterations of clonal rosettes. This research develops a combinatorial model of probability that a single genetic individual will include at least n clonal rosettes when a single rosette can produce at most 1 or 2 …

Go to article

Proactive Deep Q-Learning Approach For Anomaly Detection In Iot Idss, Hawraa A. Habeeb, Mehdi E. Manaa

Journal of Intelligent Informatics, Networking, and Cybersecurity

Breach rates and unparalleled vulnerabilities are a constant feature of the cyber landscape these days, and the increasing complexity of the proliferation of Internet of Things (IoT) nodes is to be expected. With these challenges, the conventional intrusion detection systems (IDS) are proven to be unable to deal with the extensive and varied data streams. Such systems can be fundamentally attributed to the classical nature of these systems, which are lacking in flexibility to analyze traffic in real-time and thus have no proactive capabilities of identifying patterns of unknown attacks. Considering these technical barriers, in this paper, an offensive-defensive system …

Go to article

Greenovation As A Strategic Leverage For Sustainable Competitive Advantage, Amiya Kumar Mohapatra, Anil Kumar, Yiğit Kazançoğlu

Management Dynamics

Greenovation is the new currency of the global economy which focuses on ‘green and innovation’ that generates environmental benefits through reduced resource consumption, lower carbon emissions, and improved ecological performance aligned with Sustainable Development Goals (SDGs). Greenovation focuses on long-term value creation by integrating environmental externalities, resource circularity, and multi-stakeholder governance and accountability structures. Greenovation integrated with corporate strategies can provide greater strategic competitive advantages which is also termed as green competitive advantage. By systematically integrating product and process improvements through Greenovation, firms can attain competitive advantage for long-term value creation for both internal and external stakeholders. It is imperative …

Go to article

Long-Term Effects Of Harvesting Disturbance And Site Preparation On Soil Properties And Tree Growth In A Loblolly Pine (Pinus Taeda) Forest, Kyle M. Caccamesi

LSU Master's Theses

Productivity decline between rotations has long been a concern in intensively managed forest plantations. Research done over the last century has provided greater clarity on why this phenomenon occurs. The objective of this research was to observe long-term soil and tree growth characteristics that are likely to be influenced by operational harvesting activities. The experiment was conducted in Washington Parish, Louisiana on a site dominated by Ruston soil series, a well-drained, fine loamy, siliceous and thermic typic Paleudult. A randomized complete block design with 2 x 2 x 2 factorial treatments was used. The treatment factors were harvesting method, fertilization, …

Go to article

Scattering Phase Shift In Quantum Mechanics On Quantum Computers: Non-Hermitian Systems And Imaginary-Time Simulations, Peng Guo, Paul Levan, Frank Lee, Yong Zhao

Research & Publications

To overcome the fast oscillatory behavior of correlation functions for extracting scattering phase shift in real-time quantum simulations encountered in the work of Guo et al. [Phys. Rev. D 113, 054512 (2026)], we propose and test two solutions in the present work. One is to simulate Hermitian systems in imaginary time, and the other is to simulate non-Hermitian systems in real time. We demonstrate that both approaches lead to the problem of nonunitary quantum evolution that can be solved by combining two quantum algorithms: block encoding and Hadamard test. The combined quantum algorithm does not require midcircuit …

Go to article

2026 August 13 - Tennessee Weekly Drought Summary, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Weekly Drought Summaries

No abstract provided.

Go to article

X-Ray Emission From V1674 Her (Nova Her 2021) And Characterization Of Cebr3 Detectors For Gamma-Ray Spectrometry, Tekeba Olbemo

Arts & Sciences Graduate Student Theses and Dissertations

Novae are thermonuclear explosions on the surface of the white dwarf in a close binary system. They are multi-wavelength transients emitting across the electromagnetic spectrum from radio to gamma-rays. This thesis primarily focuses on the X-ray emission from one particular nova, V1674 Her. V1674 Her (Nova Her 2021) is known for its ultra-fast decline time of ��2 ∼ 1 day. This under normal circumstances implies massive white dwarf potentially approaching the Chandrasekhar limit. We test this for V1674 Her by measuring its mass via X-ray spectroscopy method. The method calculates X-ray emission from physically motivated model of post-shock accretion column …

Go to article

Machine Learning Applications To Physical Processes, William Charles

Arts & Sciences Graduate Student Theses and Dissertations

This thesis demonstrates how machine learning techniques can solve computationally challenging problems across diverse areas of physics, from high-energy astrophysics to condensed matter systems, accelerating traditional computation. My first contribution addresses the computational expense of Monte Carlo calculations for radiative processes in relativistic plasmas. I develop a neural network sampling method that enables fast sampling from an arbitrary probability density, and demonstrate the method on inverse Compton scattering, achieving a speedup of up to an order of magnitude beyond standard methods. My second contribution addresses the structure and radiation of neutron star magnetospheres. I use physics-informed neural networks to model …

Go to article

Ai And The Environment: Solutions For Advancing Technology Safely, Kaitlynn Baker

Discovery Day - Daytona Beach

Since 2022, the world of Artificial Intelligence (AI) has boomed. AI went from a special and rare entity to a commonly used resource available to all through web sites, and phone apps. AI has benefitted everyday activities by making office, class, and personal tasks easier through grammar help, informational citations, and as someone to bounce ideas off of. Additionally, many companies have begun utilizing AI to improve customer service and experience, and train workers more efficiently, therefore, saving thousands of dollars. Despite the benefits humans reap from its use, AI has been harming our environment at growing rates. Data centers …

Go to article

Human-Centered Modeling Of Traﬀic As A Complex System, Poorendra P. Ramlall

Discovery Day - Daytona Beach

Traffic systems are driven not only by motion, but by interaction: vehicles influence one another, drivers continuously adapt to surrounding behaviour, and cognitive processes shape decisions that can propagate through the flow of traffic. Understanding these layered interactions is essential for improving traffic safety and for designing the next generation of intelligent, connected, and automated transportation systems. This PhD research develops a multiscale, data-driven framework for identifying, modelling, and ultimately interpreting interaction structure in traffic systems. The work first established an information-theoretic basis for this problem, demonstrating how information flow can uncover directional relationships in traffic dynamics and help infer …

Go to article

Assessing Attachment To Ai: Understanding The Theoretical Correlations And Consequences, Brianna Broderick

Discovery Day - Daytona Beach

Current research on Artificial Intelligence (AI) focuses on its capabilities and our understanding of it as an instrumental tool (i.e., utility completing tasks). However, as its ability to replicate natural language improves through both text and voice, an ever-growing number of users have turned to AI for emotional companionship. Concern grows as prior research on technology dependency suggests AI bonding may lead to less interaction with others and, in extreme circumstances, has already led to cases of suicide and divorce. Kasturiaratna & Hartanto (2025) developed the AI Attachment (AIA) scale, consisting of three factors, which include: emotional closeness (i.e., personal …

Go to article

Quantifying Grain Size In Scanning Electron Microscopy Images, Katherine Hoffsetz

Discovery Day - Daytona Beach

This project explores advanced image analysis techniques to assess the microstructure of highly strained austenitic stainless steel. Utilizing Python imaging libraries such as scikit-image and OpenCV, we aim to extract precise measurements for grain size from scanning electron microscopy (SEM) images. These metrics will be examined against the computed grain sizes of the sample from electron backscatter diffraction measurements. By automating the extraction of grain size measurements from SEM images, this study contributes to steamlining the quality assurance/ quality control of industrially processed materials.

Go to article

Learning Casual Structures From Aviation Accident Narratives Using Natural Language Processing And Graph-Based Knowledge Representation, Stephanie Ramsey, Katherine Hoffsetz, Madeline Gorman, Logan Lambeth

Discovery Day - Daytona Beach

Understanding the complex causal relationships underlying aviation accidents is critical for improving safety and preventing future incidents. However, much of this information exists in unstructured narrative reports, making large-scale analysis difficult. This project aims to automatically extract and model causal chains from National Transportation Safety Board (NTSB) accident narratives using a combination of traditional natural language processing (NLP) techniques, transformer-based architectures, and graph-based knowledge representation. Traditional NLP methods, including named entity recognition, dependency parsing, and rule-based pattern matching, will be used to identify structured cause–effect relationships. These approaches will be compared with transformer-based models, including a lightweight encoder for classification …

Go to article

Numerical Modeling Of A Secondary Breakup In The Veritas Asteroid Family, Jarrett Dieterle

Discovery Day - Daytona Beach

The Veritas asteroid family, located in the outer main belt, is believed to have formed from the catastrophic breakup of a parent body approximately 8.3 million years ago (e.g., Nesvorný et al., 2003). Larger fragments remained in the main belt, while smaller particles evolved inward under radiation forces, forming a toroidal dust structure observable in infrared data as paired bands. Previous studies (e.g., Dermott et al., 2001) have shown that these bands can be linked to their parent families and modeled from their initial disruptions. We propose that the 10° dust bands associated with Veritas may record evidence of a …

Go to article

High-Level Trajectory Learning For Non-Prehensile Object Manipulation With Hierarchical Reinforcement Learning, Gulsum Tuba Cibuk Girgin

Discovery Day - Daytona Beach

Site exploration requires in-situ resource utilization when the physical properties of resources are unknown. Therefore, a generalizable object manipulation method is crucial for extraterrestrial environments. Existing studies develop reinforcement learning policies that enable interaction with objects, in which quadruped robots learn to reach commanded goals with one foot while balancing with the remaining legs. However, in these studies, goal-oriented task execution relies on high-level trajectories provided by human experts, which limits autonomous robotic operations. In this study, we propose a hierarchical DRL in which a high-level pedipulation policy outputs commands for a low-level reach policy, enabling autonomous, smooth and affordable …

Go to article

Ai Race Between The Us And China, Kennedy Lyon-Lindersmith

Discovery Day - Daytona Beach

Technological leadership in AI and semiconductor manufacturing are both directly linked with military power and geopolitical influence. At the same time, the U.S. and China are currently defining the future of conflict in the cyber domain and are in strategic competition as China attempts to displace the U.S. as a global leader in AI. These factors contribute to an important national security threat that the U.S. is facing right now: An AI race between the U.S. and China, specifically regarding military cyber operations. This paper discusses some of the implications of a digital battlefield and analyzes international laws, international institutions, …

Go to article

Geometry-Conditioned Adversarial Defense For Sar Automatic Target Recognition Via Regime-Specialist Classification Heads, Skyler Fabre

Discovery Day - Daytona Beach

This project, titled Geometry-Conditioned Adversarial Defense for SAR Automatic Target Recognition via Regime-Specialist Classification Heads, addresses the critical vulnerability of deep neural networks deployed in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems to adversarial perturbations. This is where imperceptible pixel-level modifications cause confident misclassification, posing serious risks in defense and aerospace applications. The objective is to develop and evaluate RegimeResNet, a geometry-conditioned classification architecture that exploits sensor metadata unique to SAR collection systems. Rather than treating all images uniformly, RegimeResNet partitions the SAR capture space into nine geometric regimes defined by depression angle and target azimuth angle extracted …

Go to article

Sequential Causal Architecture For Multimodal Aviation Accident Prediction, Kaitlyn Cavanaugh, Isaac Morrison

Discovery Day - Daytona Beach

Aviation accidents are rarely the result of a single failure but rather from a complex causal chain of latent failures. While traditional data mining models often predict incident occurrence, they frequently overlook the sequential mechanics defined by known accident causation theoretical frameworks like the Swiss Cheese Model and the FAA's HFACS. This project addresses the need for interpretable, reliable, multi-stage forecasting by proposing a Sequential Causal Architecture that transforms theoretical causation models into a structured Directed Acyclic Graph (DAG) for multimodal accident causation chain prediction. Data from the NTSB and DOT is used and connected together in a meaningful way …

Go to article

Energy-Aware Bimodal Contact Detection For Leg Odometry, Emre Girgin

Discovery Day - Daytona Beach

Autonomous exploration of extraterrestrial environments using legged robots requires robust GNSS-free 3D state estimation. Standard leg odometry relies on Zero-Velocity Updates (ZUPT), which assume a grounded foot remains completely stationary. This assumption consistently fails on deformable granular terrain due to unobservable slippage, rapidly degrading state estimation. To mitigate this critical failure mode, we propose a dual contact-detection framework designed to robustly gate an Error-State Extended Kalman Filter (ESEKF) tracking pose, velocity, and IMU biases. The architecture isolates physical load and kinematics by modeling contact detection as two independent parallel Hidden Markov Models (HMMs). The Load HMM processes Ground Reaction Forces, …

Go to article

Motivational Outsourcing: Ai, Self-Determination, And The Changing Nature Of Adult Learning, Zoe Spanos

Discovery Day - Daytona Beach

Artificial intelligence (AI) is increasingly embedded into adult learning and higher education, serving not only as a support tool for cognitive aid but also as a system that can shape how learners regulate their motivation and engagement. This presentation examines how the use of AI in adult learning contexts may support or undermine self-determined motivation, drawing from Self-Determination Theory (SDT). It is a conceptual paper that draws on existing literature and theoretical analysis, examining AI reliance from minimal use to full automation across SDT's three basic psychological needs: autonomy, competence, and relatedness. Motivation is essential for learning, but the quality …

Go to article

Learning Motion Primitive Selection And Environment Abstraction, Edison Alberto Martinez Samaniego, Natalie Alexander, Kaelyn Weddle

Discovery Day - Daytona Beach

Learning Motion Primitive Selection and Environment Abstraction Advanced Air Mobility (AAM) is emerging as a transformative solution for short and medium range transportation; however, it introduces an operational model that differs significantly from conventional aviation. AAM vehicles are expected to operate closer to populated areas, with increased autonomy, in dense urban and suburban environments. These settings present constrained maneuvering conditions which highlights the importance of maintaining safe operation under degraded flight conditions. Abnormal conditions may endanger onboard passengers, people on the ground, and surrounding infrastructure, making rapid detection and mitigation essential to prevent loss of control. Recent research has explored …

Go to article

Modeling Aircraft Collision Risk Using Machine Learning And Traffic Density Data Ac, Jadia Renee Ewing, Alexander Van Baelan, Conrad Prisby, Rafal P. Smietana

Discovery Day - Daytona Beach

Air traffic congestion is an increasingly important factor in aviation safety as global flight activity continues to grow. This project investigates whether higher traffic density is associated with an increased risk of aviation incidents and identifies key contributing factors. Using publicly available flight (ADS-B) and incident (NTSB) data, we apply several machine learning models to analyze traffic patterns and predict risk. We begin with logistic regression to evaluate the relationship between density and incident probability, followed by decision trees to extract interpretable rules describing high-risk conditions. K-nearest neighbors (KNN) is used to examine similarity in traffic patterns among incident flights, …

Go to article

Low-Rank Spectral Analysis For The Reddening Of The Seven Sisters Star Cluster, Eric Rodarte, Angelina Scalice, Madison Warner, Kevin Numbe

Discovery Day - Daytona Beach

The Pleiades, also known as the Seven Sisters, is a stunning star cluster located approximately 440 light-years from Earth. This vibrant assemblage of hot blue stars in the Taurus constellation can be admired with the naked eye or through binoculars during early autumn. In this presentation, we utilize spectral theory to measure the reddening in the Pleiades star cluster. To evaluate the impact of interstellar dust on reddening, we employ principal component analysis (PCA) on a matrix representing color indices from various photometric bands linked to the cluster’s photometric data. This dataset was obtained from VIZIER. Our PCA analysis of …

Go to article

Full-Text Articles in Entire DC Network

Silver Bow Creek/Butte Area Superfund Site

Clark Tailings Consolidated Waste Management Area Site Investigation Quality Assurance Project Plan (Qapp), Woodard & Curran

Silver Bow Creek/Butte Area Superfund Site

The Effectiveness Of Bacillus-Coated Npk Fertilizer On Broccoli Growth, Nutrient Status, Rhizosphere Bacillus Population, Yield, And Flower Quality, Betty Natalie Fitriatin, Rara Rahmatika Risanti, Reginawanti Hindersah, Fasa Aditya Hanindipto, Gita Bina Nugraha

Jurnal Kultivasi

Effects Of Ab Mix Hydroponic Solution And Ameliorant Mixtures On Cayenne Pepper Growth And Yield In An Inceptisols, Tien Turmuktini, Betty Natalie Fitriatin, Tualar Simarmata, Firda Widayanti, Nuryanti, Lia Amalia, Elly Roosma Ria, Linlin Parlinah

Jurnal Kultivasi

The Effect Of Silica-Enriched Rice Husk Biochar And Npk Fertilizer On Soil Total Nitrogen, Nitrogen Uptake, And Yield Of Lowland Rice, Emma Trinurani Sofyan, Oviyanti Mulyani, Ania Citraresmini, Meddy Rachmadi, Kharmelia Sandra Livia

Jurnal Kultivasi

Development Of Robust Ratio Linear Fitting Method Of Temperature And Emissivity Separation For High-Temperature Data, Mitchell Manzardo, Michael L. Dexter, Shannon R. Young, John Bowlan, Anthony L. Franz

Faculty Publications

Palmnet: Confidence-Calibrated Edge-Cloud Ai For Field Diagnosis Of Date Palm Diseases, Muntadher Kareem, Raed Majeed

Karbala International Journal of Modern Science

Modeling The Clonal Rosette Composition Of A Bromeliaceae Genet: A Combinatorial Approach, Layla K. Lammers, Erin N. Bodine

Spora: A Journal of Biomathematics

Proactive Deep Q-Learning Approach For Anomaly Detection In Iot Idss, Hawraa A. Habeeb, Mehdi E. Manaa

Journal of Intelligent Informatics, Networking, and Cybersecurity

Greenovation As A Strategic Leverage For Sustainable Competitive Advantage, Amiya Kumar Mohapatra, Anil Kumar, Yiğit Kazançoğlu

Management Dynamics

Long-Term Effects Of Harvesting Disturbance And Site Preparation On Soil Properties And Tree Growth In A Loblolly Pine (Pinus Taeda) Forest, Kyle M. Caccamesi

LSU Master's Theses

Scattering Phase Shift In Quantum Mechanics On Quantum Computers: Non-Hermitian Systems And Imaginary-Time Simulations, Peng Guo, Paul Levan, Frank Lee, Yong Zhao

Research & Publications

2026 August 13 - Tennessee Weekly Drought Summary, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Weekly Drought Summaries

X-Ray Emission From V1674 Her (Nova Her 2021) And Characterization Of Cebr3 Detectors For Gamma-Ray Spectrometry, Tekeba Olbemo

Arts & Sciences Graduate Student Theses and Dissertations

Machine Learning Applications To Physical Processes, William Charles

Arts & Sciences Graduate Student Theses and Dissertations

Ai And The Environment: Solutions For Advancing Technology Safely, Kaitlynn Baker

Discovery Day - Daytona Beach

Human-Centered Modeling Of Traﬀic As A Complex System, Poorendra P. Ramlall

Discovery Day - Daytona Beach

Assessing Attachment To Ai: Understanding The Theoretical Correlations And Consequences, Brianna Broderick

Discovery Day - Daytona Beach

Quantifying Grain Size In Scanning Electron Microscopy Images, Katherine Hoffsetz

Discovery Day - Daytona Beach

Learning Casual Structures From Aviation Accident Narratives Using Natural Language Processing And Graph-Based Knowledge Representation, Stephanie Ramsey, Katherine Hoffsetz, Madeline Gorman, Logan Lambeth

Discovery Day - Daytona Beach

Numerical Modeling Of A Secondary Breakup In The Veritas Asteroid Family, Jarrett Dieterle

Discovery Day - Daytona Beach

High-Level Trajectory Learning For Non-Prehensile Object Manipulation With Hierarchical Reinforcement Learning, Gulsum Tuba Cibuk Girgin

Discovery Day - Daytona Beach

Ai Race Between The Us And China, Kennedy Lyon-Lindersmith

Discovery Day - Daytona Beach

Geometry-Conditioned Adversarial Defense For Sar Automatic Target Recognition Via Regime-Specialist Classification Heads, Skyler Fabre

Discovery Day - Daytona Beach

Sequential Causal Architecture For Multimodal Aviation Accident Prediction, Kaitlyn Cavanaugh, Isaac Morrison

Discovery Day - Daytona Beach

Energy-Aware Bimodal Contact Detection For Leg Odometry, Emre Girgin

Discovery Day - Daytona Beach

Motivational Outsourcing: Ai, Self-Determination, And The Changing Nature Of Adult Learning, Zoe Spanos

Discovery Day - Daytona Beach

Learning Motion Primitive Selection And Environment Abstraction, Edison Alberto Martinez Samaniego, Natalie Alexander, Kaelyn Weddle

Discovery Day - Daytona Beach

Modeling Aircraft Collision Risk Using Machine Learning And Traffic Density Data Ac, Jadia Renee Ewing, Alexander Van Baelan, Conrad Prisby, Rafal P. Smietana

Discovery Day - Daytona Beach

Low-Rank Spectral Analysis For The Reddening Of The Seven Sisters Star Cluster, Eric Rodarte, Angelina Scalice, Madison Warner, Kevin Numbe

Discovery Day - Daytona Beach