Physical Sciences and Mathematics Commons^™

Advective And Diffusive Gas Phase Transport In Vadose Zones: Importance For Defining Vapour Risks And Natural Source Zone Depletion Of Petroleum Hydrocarbons, Kaveh Sookhak Lari, Greg B. Davis, John L. Rayner, Trevor P. Bastow

Research outputs 2022 to 2026

Quantifying the interlinked behaviour of the soil microbiome, fluid flow, multi-component transport and partitioning, and biodegradation is key to characterising vapour risks and natural source zone depletion (NSZD) of light non-aqueous phase liquid (LNAPL) petroleum hydrocarbons. Critical to vapour transport and NSZD is transport of gases through the vadose zone (oxygen from the atmosphere, volatile organic compounds (VOCs), methane and carbon dioxide from the zone of LNAPL biodegradation). Volatilisation of VOCs from LNAPL, aerobic biodegradation, methanogenesis and heat production all generate gas pressure changes that may lead to enhanced gas fluxes apart from diffusion. Despite the importance of the gaseous …

Loneliness And Parental Relationships Among College Students, Alanna James, Charlie Barna

Symposium of Student Scholars

The transition to adulthood is a unique developmental period that involves rapid changes in youths’ individual development and social contexts, which can involve leaving behind familiar support networks (Eeske et al,. 2015). Common experiences during the transition to adulthood, like leaving home, pursuing college, and entering the workforce can leave individuals feeling marginalized and cutoff (Mathews et al,. 2022). Loneliness is a subjective feeling experienced by individuals in all age demographics (Matthews et al,. 2022). Despite a wide array of research on social connectedness interventions for older adults and people with physical disabilities (Zagic et al,. 2021), there is little …

Generating Channel Morphology Data Through Arcgis Pro, Ethan Manigbas

Symposium of Student Scholars

River cross-sections are often extracted using field surveys at measured intervals. This field-oriented approach allows for a tangible relationship between the data and its collector but at the expense of finance, time, labor, and potentially the environment. With the advancement of geospatial tools, such data can be found online, extracted, and even analyzed with contemporary Geographic Information Systems (GIS) in a completely virtual setting, transcending the need for fieldwork in select project topics. We tested this approach with the help of ArcGIS Pro software on the Vishnu Springs headwater stream located in the Western Illinois region of the Upper Mississippi …

The Classification Of Internet Memes Through Supervised And Unsupervised Machine Learning Algorithms, William H. Little

Symposium of Student Scholars

Memes, those captivating internet phenomena, effortlessly deliver online entertainment. By leveraging time-series data from Google Trends, we can vividly illustrate and dissect the dynamic trends in meme popularity. Previous studies have discerned four distinct post-peak popularity patterns— "smoothly decaying," "spikey decaying," "leveling off," and "long-term growth"—and elegantly modeled these using ordinary differential equations.

This research introduces a programmatic approach that harnesses both supervised and unsupervised machine learning algorithms. The dataset, now expanded to over 2000 elements, becomes the canvas for exploration. The K-means algorithm identifies clusters, which then serve as labels for the supervised SVC algorithm. The overarching goal is …

Latent Auto-Recursive Composition Engine: A Generative System For Creative Expression In Human-Ai Collaboration, Yenkai Huang

Computer Science Senior Theses

This thesis investigates the shifting boundaries of art in the era of Generative AI, crit-
ically examining the essence of art and the legitimacy of AI-generated works. Despite
significant advancements in the quality and accessibility of art through generative
AI, such creations frequently encounter skepticism regarding their status as authentic
art. To address this skepticism, the study explores the role of creative agency in var-
ious generative AI workflows and introduces an ”artist-in-the-loop” system tailored
for image generation models like Stable Diffusion. This system aims to deepen the
artist’s engagement and understanding of the creative process. Additionally, a novel
tool, …

Tree Recovery By Dynamic Programming, Gustavo Alberto Gratacos

McKelvey School of Engineering Theses & Dissertations

Tree-like structures are common, naturally occurring objects that are of interest to many fields of study, such as plant science and biomedicine. Analysis of these structures is typically based on skeletons extracted from captured data, which often contain spurious segments or cycles that need to be removed. We propose a dynamic programming algorithm which seeks to recover directed trees from these noisy skeletons. Our method recovers trees by removing edges and duplicating nodes while adhering to edge-label constraints. Our algorithm proceeds by iteratively merging graph nodes, such that the solution on the original graph can be obtained from those on …

Constructible Sandwich Cut, Philip A. Son

FIU Undergraduate Research Journal

In mathematical measure theory, the “Ham-Sandwich” theorem states that any n objects in an n-dimensional Euclidean space can be simultaneously divided in half with a single cut by an (n-1)-dimensional hyperplane. While it guarantees its existence, the theorem does not provide a way of finding this halving hyperplane, as it is only an existence result. In this paper, we look at the problem in dimension 2, more in the style of Euclid and the antique Greeks, that is from a constructible point of view, with straight edge and compass. For two arbitrary regions in the plane, there is certainly no …

An Examination Of The Ways In Which Transdisciplinary Research Could Be Used To Incentivize Local Communities To Combat The Illegal Wildlife Trade, Jessica Rios

FIU Undergraduate Research Journal

The illegal wildlife trade (IWT) is currently one of the most critical conservation concerns, given its direct impact on biodiversity loss, endangering local ecosystems, and adding pressure to all species at a point when they face dangers like deforestation and mass extinctions. This industry also significantly impacts local communities, many of which are compelled to engage in it as a result of their precarious socioeconomic conditions. While effective countermeasures to this global issue have been identified, successful implementation of these countermeasures require diverse disciplines and collaborators. This paper argues that a transdisciplinary approach that converges knowledge and skills from social …

The State Of Knowledge Of Cca Diversity In The Caribbean Coral Reefs, Danielle Macias, Alain Duran, Fabio Nauer

FIU Undergraduate Research Journal

Crustose coralline algae (CCA) are a diverse and ecologically important species found in most of the world’s oceans. The current lack of taxonomic knowledge and relative abundance compromises our ability to predict species diversity numbers and, thus, their ecological roles and impacts on coral reefs. To gather a better understanding of the state of knowledge of crustose coralline algae taxonomy in the Caribbean, 107 different research papers, and other primary and secondary literature were studied; any source with taxonomical information, species identification, or genetic markers for identification was recorded. All Genebank codes were collected and sorted by supposed species marker …

Understanding The Health Impacts Of Vehicular Emissions In South Florida: A Comprehensive Analysis, Janelle Ducheine, Noah Horesh, Jason C. Quinn

FIU Undergraduate Research Journal

South Florida is famous for its diverse cultural scene and year-round sunshine. This success, however, has not been without its consequences. While the region enjoys economic prosperity, the hidden cost of deteriorating air quality and adverse health effects from vehicle emissions necessitates urgent attention. Electric vehicles (EVs) have emerged as a potential solution, promising reduced emissions, and increased energy efficiency. However, the intricate life cycle emissions associated with EV energy production raise questions about their net benefits. Using predictive modeling and historical data, the study forecasts emissions trajectories and assesses their health implications. Results indicate a substantial reduction in pollutants …

Using Geochemical Tracers To Determine Seasonal Inputs Of Freshwater To A Coastal Estuary: Biscayne Bay, Fl, Melaney Lara, Rene M. Price

FIU Undergraduate Research Journal

Biscayne Bay is a coastal estuary that historically relied on rainfall and groundwater inputs from the karst Biscayne aquifer. The construction of major canals along the coastline has released point-source freshwater inputs into the bay, detrimentally affecting the Bay’s ecosystem balance. This project investigates the proportional inputs of freshwater between the wet and dry seasons in Deering Estate, adjacent to Biscayne Bay. The objective of this project was accomplished by analyzing the water chemistry of the bay using naturally occurring geochemical tracers. Water sampling occurred from May to August (wet season 2022) and January to March (dry season 2023); at …

Implications Of Microplastic Pollution For The Conservation Of Marine Protected Areas, Estefany D. Carvajal, Amanda Di Perna, Alain Duran

FIU Undergraduate Research Journal

Marine Protected Areas (MPAs) function as a tool for the protection and conservation of marine ecosystems. These designated areas should be free of any environmentally harmful pollutants. Microplastics (MPs) are plastic fragments measuring less than 5 mm (about 0.2 in). These fragments are an emerging threat to our oceans, and we are investigating the effectiveness of MPAs against these pollutants. We analyzed data gathered from research conducted on microplastic concentrations in MPAs and non-MPAs around the world. 53 MPAs and 53 non-MPAs around the world were used and the microplastic concentrations were deemed low, medium, or high by using the …

Exploring Neural Networks For Breast Cancer Tissue Classification, Stephen Jacobs, Md Abdullah Al Hafiz Khan

Symposium of Student Scholars

Last year, more than 240 thousand women in the United States were diagnosed with breast cancer. These patients are benefitting from decades of data that have been collected by cancer research institutions around the world. Tissue samples are analyzed and cataloged by these institutions, and several facilities like the University of Wisconsin are sharing this historical data to promote the advancement of new cancer treatments. Deep learning and neural network models are being built for this data to help doctors diagnose faster and design treatment options for patients by comparing their tissue samples with these historical datasets. We will use …

05-13--2024 Orsp Newsletter, Liz Williamson

ORSP Newsletter

Distinguished Research and Creative Achievement Award, Center for Practical Ethics, DARPA Connect, Air Force Internship

Analysis And Computation Of Constrained Sparse Coding On Emerging Non-Von Neumann Devices, Kyle Henke

Mathematics & Statistics ETDs

This dissertation seeks to understand how different formulations of the neurally inspired Locally Competitive Algorithm (LCA) represent and solve optimization problems. By studying these networks mathematically through the lens of dynamical and gradient systems, the goal is to discern how neural computations converge and link this knowledge to theoretical neuroscience and artificial intelligence (AI). Both classical computers and advanced emerging hardware are employed in this study. The contributions of this work include:

1. Theoretical Work: A comprehensive convergence analysis for networks using both generic Rectified Linear Unit (ReLU) and Rectified Sigmoid activation functions. Exploration of techniques to address the binary …

Thermal Physiology Of Sceloporus Olivaceus And Sceloporus Variabilis In South Texas, Ian Rockel

Masters Theses

This study explores the thermal physiology of two sympatric lizard species, Sceloporus olivaceus (Texas spiny lizard) and Sceloporus variabilis (rose-bellied lizard), in South Texas. I document the metabolic rate and its temperature sensitivity, thermal limits, and performance at different temperatures to better understand the impact of rising temperatures and urbanization on these ectotherms, whose body temperature and, consequently, metabolic rate, activity level, and reproductive success depend on their thermal environment. We hypothesize that S. olivaceus will exhibit greater thermal resilience owing to its broader latitudinal range, variable habitat usage within Texas, and presumed more versatile thermoregulatory strategies. Scelorporus olivaceus's higher …

Effects Of Ti Addition On The Characteristics Of Al-10zn-6mg-2si/Zro2 Composites Produced By Squeeze Casting, Qesha Diva Prameshvara, Pipin Indah Lestari, Bondan Tiara Sofyan

Journal of Materials Exploration and Findings

Metal matrix composite (MMC) with 7xxx aluminum matrix is potential for ballistic applications due to the combination of strength, toughness, and light weight. Previous study successfully produced aluminum-based composites with SiC particles which were able to stop type III bullet, however cracks remained on back of the plate. Therefore, in this research, SiC was replaced by zirconia (ZrO₂) due to its high fracture toughness. Ti-B grain refiner was added to further improve toughness through grain boundary strengthening mechanism. This research developed 5 vol.% ZrO₂ strengthened Al-10Zn-6Mg-2Si composite with addition of Al-5Ti-1B grain refiner produced through squeeze casting …

Integration Of The Ashby Technique And Pahl-Beitz Quantitative Ranking For Railway Axle Material Selection, Helya Chafshoh Nafisah, Jaka Fajar Fatriansyah, Siti Norasmah Surip

Journal of Materials Exploration and Findings

Railway axle serves as a vital connection between the train's wheels and its body. However, cyclic loading and high speed can induce fatigue in railway axle, which potentially leads to damage human safety. Therefore, it is important to find materials that have good mechanical properties with the lowest weight and cost. In this paper, a comprehensive method using Ashby chart has been performed to select candidate materials of railway axle. The methods begin with analyzing function by determining the problem, objective function, and constraints. After that, the results obtained are ranked using Pahl and Beitz quantitative weighting method. The results …

Toward Intuitive 3d Interactions In Virtual Reality: A Deep Learning- Based Dual-Hand Gesture Recognition Approach, Trudi Di Qi, Franceli L. Cibrian, Meghna Raswan, Tyler Kay, Hector M. Camarillo-Abad, Yuxin Wen

Engineering Faculty Articles and Research

Dual-hand gesture recognition is crucial for intuitive 3D interactions in virtual reality (VR), allowing the user to interact with virtual objects naturally through gestures using both handheld controllers. While deep learning and sensor-based technology have proven effective in recognizing single-hand gestures for 3D interactions, research on dual-hand gesture recognition for VR interactions is still underexplored. In this work, we introduce CWT-CNN-TCN, a novel deep learning model that combines a 2D Convolution Neural Network (CNN) with Continuous Wavelet Transformation (CWT) and a Temporal Convolution Network (TCN). This model can simultaneously extract features from the time-frequency domain and capture long-term dependencies using …

Characteristics Based Factor Models - Comparison Of Estimation Procedures, Henri Ohl

McKelvey School of Engineering Theses & Dissertations

Understanding cross-sectional and time series variation of asset returns is fundamental in finance, particularly in asset pricing. This thesis explores the integration of factor theory with machine learning to deepen our comprehension of these dynamics. Characteristics based factor models offer a systematic framework for quantifying an asset's underlying risk-return structure, leveraging time-varying conditional information on model parameters carried by firm-specific characteristics. These models serve as valuable tools for discerning the driving components of an asset's expected excess return. Recent research established a novel methodology for consistent parameter estimation within this framework, only requiring a large cross-section but not a long …

Assessing Reproducibility Of Brain-Behavior Associations Using Bootstrap Aggregation Methods, Zhetao Chen

Arts & Sciences Electronic Theses and Dissertations

在本论文中，随着越来越多地利用静息态功能连接 MRI (rs-fcMRI) 将神经活动与病理状况联系起来，我们面临着对此类数据可靠性的普遍担忧。我们的探索集中于提高人类连接组计划（HCP）数据集框架内大脑行为关联的可重复性。我们采用两种不同的引导聚合方法来研究功能连接可靠性的增强：使用循环块引导（CBB）的单独时间序列装袋和使用线性支持向量回归（LSVR）模型的主题级装袋。我们对 CBB 个体时间序列 bagging 的调查表明，这种方法并不能显着增强大脑行为关联的可重复性。这一发现指出了实现可靠的功能连接措施的复杂性以及某些聚合方法在克服这一挑战方面的局限性。相比之下，我们的学科水平考试 通过 LSVR 模型装袋呈现出更有希望的结果。这种方法显着增强了分析之间模型权重的可靠性，证明了其在提高数据稳健性和可重复性方面的功效。两种方法的这种不同影响强调了适当的分析策略在提高神经影像数据可靠性方面的关键作用。通过描述这两种方法的结果，本论文有助于对神经影像领域的数据可靠性进行更广泛的讨论。它强调了在不同数据集上持续进行方法创新和验证的必要性，以提高 rs-fcMRI 研究的可靠性和可解释性。

Analysis Of The Effect Of Different Surface Preparation Methods On Corrosion Resistance And Adhesion Strength Of Astm A36 Steel Substrate With Surface Tolerant Epoxy Paint As Coating Material, Irwan Wijaya Santoso, Daffa Aqila, Rini Riastuti, Rizal Tresna Ramadhani

Journal of Materials Exploration and Findings

In the industrial world, to extend the service life of materials, protection methods are carried out to slow down the material's corrosion rate. The protection method that is often used is the coating method. The coating method is a protection method by coating the substrate material using a coating material to prevent contact between the substrate material and the environment. In this research, the substrate material used is ASTM A36 steel and the coating material used is Surface Tolerant Epoxy paint. The independent variable used in this study lies in the surface preparation method which consists of: solvent cleaning, hand …

Accord: Constraint-Driven Mediation Of Multi-User Conflicts In Cloud Services, Abhiroop Tippavajjula, Primal Pappachan, Anna Squicciarini, Jose Such

Computer Science Faculty Publications and Presentations

When multiple users adopt collaborative cloud services like Google Drive to work on a shared resource, incorrect or missing permis- sions may cause conflicting or inconsistent access or use privileges. These issues (or conflicts) compromise resources confidentiality, integrity, or availability leading to a lack of trust in cloud services. An example conflict is when a user with editor permissions changes the permissions on a shared resource without consent from the orig- inal resource owner. In this demonstration, we introduce ACCORD, a web application built on top of Google Drive able to detect and resolve multi-user conflicts. ACCORD employs a simulator …

Building A Data Pipeline And Machine Learning Model For Insurance Data, Connor Weyers

Honors Theses

Insurance telematics is an emerging and exciting field. It combines the advancements in GPS tracking, computational analytics, data processing, and machine learning into a useful tool to help insurance companies make the best product for their consumers. This is why National Indemnity looked to implement a telematics portion to their business processes of underwriting insurance policies and sponsored a School of Computing Senior Design project. In this report, we will first review existing solutions that been used to solve problems and subproblems similar to that we are given in this project. We then propose designs for the data pipeline and …

Mending Trust In Ai: Trust Repair Policy Interventions For Large Language Models In Visual Data Journalism, Hangxiao Zhu

McKelvey School of Engineering Theses & Dissertations

Trust in Large Language Models (LLMs) emerged as a pivotal concern. This is because, despite the transformative potential of LLMs in enhancing the interpretability and interactivity of complex datasets, the opacity of these models and instances of inaccuracies or biases have led to a significant trust deficit among end-users. Moreover, there is a tendency for people to personify AI tools that utilize these LLMs, attributing abilities and sensibilities that they do not truly possess. This thesis exploits this personification and proposes a comprehensive framework of trust repair policies tailored to address the challenges inherent in LLM annotations within data journalism …

Development And Application Of Photoresponsive Small Molecule Probes To Modulate And Characterize Neuroreceptors, Spencer Kim

Dissertations - ALL

The field of neuroscience is rapidly evolving and so the development of novel tools that support innovative research is urgently needed. Two tools that have proven to be particularly impactful to the field of neuroscience are activity-based protein profiling (ABPP) and optogenetics, both of which have seen widespread application but with relatively limited advancement of the underlying technology. Limitations of ABPP and optogenetics have become increasingly apparent and research output is stalling due to lack of technological advancement. For example, few systems exist for targeting low-abundance and unstable proteins via ABPP and application of ABPP and optogenetic systems thus far …

On The Limitations And Restrictions Of The Hardy-Littlewood Circle Method, Daniel W. Havens

Mathematics & Statistics ETDs

We discuss herein the history, layout, and philosophy of the Hardy-Littlewood Circle method, as well as the more modern renditions thereof. The limitations and scope of each method presented is discussed in detail, providing examples of cases where the failure of the circle method is of relevance. We include a summary of famous problems which have been resolved using each methodology, as well as what limitations each methodology showcases.

Multimode Metamaterial Ring Resonator As An Entangling Bus For Artificial Atoms, Tianna Carroll

Dissertations - ALL

Circuit quantum electrodynamics (cQED) systems with superconducting qubits coupled to linear microwave resonators are a prominent platform for realizing scalable quantum information processors. Combining cQED architectures with multimode resonators leads to a broad set of applications for performing analog quantum simulation, implementing dense quantum memory, and generating multimode entangled states between physically distant qubits. Microwave resonators in cQED are typically formed from distributed transmission lines that exhibit conventional dispersion with harmonic mode spacing; in such systems, usually only a single resonant mode can be strongly coupled to a qubit. Superconducting metamaterial resonators comprised of lumped circuit elements can be designed …

Molecular Dynamics Insights Into The Structural Behavior And Interactions Of Complex Biological Systems, Shenghan Song

Chemistry and Chemical Biology ETDs

All-atom molecular dynamics (MD) simulations are essential for examining the structural and dynamic aspects of proteins at high resolution, complementing experiments limited by resolution, size, and complexity. Advancements in algorithms and hardware have enhanced the role of computational tools in scientific research, allowing for more accurate simulations of proteins with standard residues. However, simulating real biological systems, which include non-standard residues such as lipid membranes, remains challenging despite the assistance of tools like CHARMM-GUI. My research initially focused on simulating the PICK1 BAR dimer using steered MD simulations to investigate its structure and dynamics. Simulation works expanded to include systems …

Gauge-Invariant Correlators In Quantum Gravity, Kenneth Ratliff

Dissertations - ALL

Recent successes in Euclidean Dynamical Triangulations (EDT) motivate the further comparison of lattice observables to predictions of general relativity (GR) treated as an effective quantum theory. A particularly promising observable is the two-point function of the scalar curvature, which can be straightforwardly computed on the lattice and which in principle can also be computed from the Einstein-Hilbert path integral. Any such comparison should be between manifestly gauge-invariant observables, and will require that the GR predictions be analytically continued in a gauge-invariant manner to the Euclidean signature of the lattice. In this thesis I present my work toward this goal, namely: …