Physical Sciences and Mathematics Commons^™

3d Concrete Printing Material Prediction And Flow Simulation Using Physics-Informed Neural Network, Tianjie Zhang

Boise State University Theses and Dissertations

3D concrete printing (3DCP) is an innovative construction method that extrudes cementitious materials layer-by-layer to fabricate building components based on a digital model. 3DCP has gained increasing adoption globally for projects like buildings, bridges, retaining walls, and stormwater management systems. However, 3DCP places stringent demands on the rheological properties of the printable cementitious materials. Rheology is critical for the flow and buildability of fresh concrete during extrusion. The mixture must have a low yield stress for pumping, moderate viscosity to hold its shape after deposition, minimal bleeding and segregation of aggregates, as well as responsive rheology that can transition from …

Interpretation And Robustness Of Black-Box Ml Models For Secure Cyber Space, Qudrat E. Alahy Ratul

Boise State University Theses and Dissertations

The advent of sophisticated Machine Learning (ML) models has revolutionized the cybersecurity landscape, offering unprecedented capabilities to address complex challenges. Despite the ML model's capabilities, such models are still perceived as black boxes, the critical aspects of interpretability and robustness within these models often remain underexplored, rendering them susceptible to adversarial attacks and hindering their integration into sensitive applications. This dissertation focuses on interpretability and robustness of black-box ML models, aiming to fortify the cyberinfrastructure. Through a meticulous investigation of advanced techniques and methodologies, this work contributes towards elucidating and enhancing the interpretability and robustness of ML models in cybersecurity …

Developing A Convolutional Neural Network (Cnn) Model For Facial Expression Recognition (Fer), Danrlei Martins, Leonardo Diesel

ICT

This Capstone Project focused on developing an accurate Facial Expression Recognition (FER) model by leveraging deep learning techniques, specifically Convolutional Neural Networks (CNNs). The objective was to explore, design, and implement custom architectures and evaluate their performance against existing work. The process involved several stages, such as data preprocessing, data augmentation, architecture design, hyperparameter tuning, and performance assessment using metrics like accuracy and F1-score while utilizing the FER-2013 dataset for training. The resulting FER model exhibited competitive accuracy levels and generalization capabilities, opening up opportunities for real-time implementation and application across various domains.

Using Predictive Analytics To Identify Risk Of Heart Disease Based On Lifestyle Factors And Health Metrics., Luiza Cavalcanti Albuquerque Brayner, Edgard Pacheco

ICT

In this project, we will report an innovative application, for the healthcare sector usage, which basically is a health tracking and disease prevention application. The application will enable users to log their daily meals, exercise routines, and lifestyle habits, providing a comprehensive overview of the user's health status. By making use of Machine Learning and data analytics, our solution offers a personalised and automated insight and predictive analytics, which empowers users to proactively manage their well-being.

Through a detailed data analysis, users will gain valuable insights of potential diseases development and risk. This report will explore the development process, implementation …

Movie Recommendation System., Ingrid Menezes Castro, Robert Szlufik

ICT

This project is focused on implementing a Movie Recommendation System with the use of Machine Learning. The system was developed in Python and the datasets used were 'Movies' and 'Ratings' from MovieLens 25M. This project was developed with the CRISP-DM methodology and each of the phases is detailed in a report and Jupyter Notebook.

The system is a hybrid combining best qualities of collaboration filtering and user grouping. In the project we compare some models' accuracies, upgrade a chosen model and show the improved performance of our hybrid model that used the SVD algorithm. We are able to find recommended …

Survival Of Methanogens Desiccated On Mars Soil Simulants: Implications For Life On Mars, Lanee Knight

Biological Sciences Undergraduate Honors Theses

Because of Mars’s chemical features, it can be studied in the search for life and is a significant figure in the study of astrobiology (McKay, 2010). Dr. Kral’s laboratory studies methanogens, microorganisms in the domain Archaea, as possible life forms on Mars. Since some methanogens can withstand harsh environmental conditions and emit methane as a waste product, the growth of these microorganisms can be used as an indicator of potential life in the Mars subsurface (Kral et al., 1998). In this research, four methanogens, Methanothermobacter wolfeii, Methanobacterium formicicum, Methanosarcina barkeri, and Methanococcus maripaludi were desiccated in the …

Investigating The Impact Of Human-Centered Interface Design On The User Experience Of Mobile Device Users, Ruchir Gupta

Theses and Dissertations

In order to investigate the intricate interaction between interface design, user technological proficiency, and other components of the user experience, this research study used a mixed-method approach. The beginner user group—those with little experience or expertise with technology - were the main target audience. The important discovery emphasizes the substantial influence that careful design can have on improving the effectiveness and usability of interfaces for non-tech-savvy individuals. When using the suggested Interface B instead of the current Interface A, beginner participants' task completion times significantly improved, according to the user study. This underlines the significance of creating with the needs …

Deep Learning In Indus Valley Script Digitization, Deva Munikanta Reddy Atturu

Theses and Dissertations

This research introduces ASR-net(Ancient Script Recognition), a groundbreaking system that automatically digitizes ancient Indus seals by converting them into coded text, similar to Optical Character Recognition for modern languages. ASR-net, with an 95% success rate in identifying individual symbols, aims to address the crucial need for automated techniques in deciphering the enigmatic Indus script. Initially Yolov3 is utilized to create the bounding boxes around each graphemes present in the Indus Valley Seal. In addition to that we created M-net(Mahadevan) model to encode the graphemes. Beyond digitization, the paper proposes a new research challenge called the Motif Identification Problem (MIP) related …

Determination Of Common Drugs Of Abuse And Metabolites In Oral Fluid: Comparison Of Different Extraction Procedures, Melissa Koffer

Student Theses

Oral fluid has been gaining importance in forensic and clinical toxicology for many different drug testing scenarios. Oral fluid is composed of saliva, along with gingival fluid, bacteria, and food residues. Drug testing using oral fluid has many advantages over other matrices (blood, urine) such as the non-invasive collection, reduced risk of tampering, and shorter detection windows which show recent drug use. The purpose of this study was to develop an analytical method for the determination of 19 drugs of abuse and metabolites in oral fluid by liquid chromatography tandem mass spectrometry (LC-MS/MS) and compare three different extraction methods: liquid-liquid …

An Edge Computing System With Amd Xilinx Fpga Ai Customer Platform For Advanced Driver Assistance System, Tsun Kuang Chi, Tsung Yi Chen, Yu Chen Lin, Ting Lan Lin, Jun Ting Zhang, Cheng Lin Lu, Shih Lun Chen, Kuo Chen Li, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and …

Hilbert Reciprocity Over Number Fields, Dillon Snyder

Honors Scholar Theses

A Hilbert symbol has the value 1 or −1 depending on the existence of solutions to a certain quadratic equation in a local field, R, or C. Hilbert reciprocity states that for a number field F and two nonzero a and b in F, the product of Hilbert symbols associated to a and b at all the places of F is 1. That is, these Hilbert symbols are −1 for a finite, even number of places of F . Hilbert reciprocity when F = Q is equivalent to the classical quadratic reciprocity law, so Hilbert reciprocity in number fields can …

A Ui-Enhanced Approach To Generic Web-Based Scheduling, Tyler Hinrichs

Honors Scholar Theses

Administrative scheduling is a key aspect of a wide variety of systems, but despite being a widespread need, it is not a straightforward task. Organizational uniqueness introduces complexity when attempting to use algorithmic methods to automate scheduling, as individual organizations often have their own ways of determining various details and constraints of a schedule. However, in this paper, we assert that there are relevant commonalities that many different schedules fundamentally possess, allowing us to create a generic scheduling application that can be productively used for as many different scenarios as possible. After devising a schema that captures this generic representation, …

Database And Machine Learning Model For Classifying Autism Spectrum Disorder From Smartphone Based Electroretinography, Rory Harris

Honors Scholar Theses

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that negatively affects a patient’s cognitive and communication aptitude and, therefore, can severely impact that patient’s quality of life. Because of this, early diagnosis is paramount. In recent studies, electroretinography (ERG), which is a measure of the retina’s electrical response to a brief flash of light into the eye, has shown promise in detecting ASD. Access to these scans can provide early diagnosis, improving well-being. Current ERG devices are very expensive due to their on board processing capabilities. This paper aims to create an ERG device using a smartphone as the main …

3d-Printed Microfluidic Devices For Electrochemiluminescence Detection Of Mirna, Oscar Clement

Honors Scholar Theses

This thesis outlines the research conducted over the past two years on the production and application of microfluidic devices (MFDs) in electrochemiluminescent-based bioanalytical assays. The work is categorized into two main projects: designing and manufacturing MFDs and developing electrochemiluminescent (ECL) assays for detecting Alzheimer's disease (AD) associated microRNAs (miRNAs) using CRISPR technology. The process of learning to design MFDs involved acquiring proficiency in computer-aided design (CAD) software, stereolithography (SLA) 3D printing, and iterative design techniques. The development of the ECL-based assay for AD miRNA was a multidisciplinary endeavor, combining elements of inorganic and biological chemistry. Although the research on the …

How Does Hummock Creation In Submerging Salt Marshes Alter Nitrous Oxide Fluxes?, Juliette Doyle

Honors Scholar Theses

Climate change is altering ecosystems and the services they provide. Salt marsh ecosystems typically protect coastal areas and filter nitrogen out of water, but are rapidly submerging due to rising sea levels and human development that prevents landward migration. Recent restoration efforts to preserve salt marshes attempt to build elevation capital and promote vegetation and animal habitat, but it is unclear how such efforts affect salt marsh biogeochemistry and dynamics of nitrous oxide, a potent greenhouse gas. To better understand how adding sediment to submerging salt marshes may alter nitrous oxide fluxes, I leveraged a salt marsh hummock creation experiment …

An Educational Resource For Particle Identification (Pid) In Experimental Particle Physics Data, Richard Dube

Honors Scholar Theses

Although particle physics research is typically reserved for advanced undergraduates, algebra-based physics students can readily develop the foundational skills necessary to conduct particle physics research. Despite this, there is a shortage of educational resources that introduce particle physics to algebra-based physics students. This project, Particle Identification Playground, is a collection of Python-based activities that teach students about several fundamental topics in experimental particle physics research. Through these activities, students will learn about the common detectors used in particle physics, how they work, and how we can use these detectors to identify particles. Students are able to interact with 3D models …

Predicting 30-Day Unplanned Icu Readmissions Using Deep Learning And Natural Language Processing Techniques: A Mimic Iv Data Analysis, David Licerio

Computational and Data Sciences (MS) Theses

We design and implement a multi-stage modeling approach focused on predicting unplanned 30-day all- cause intensive care unit (ICU) hospital readmissions using the Medical Information Mart for Intensive Care (MIMIC IV) dataset. Structured data consisting of demographic information, comorbidities, lab results, and vital signs are combined with features extracted from medical text data consisting of patients’ diagnoses, procedures, and discharge notes and further engineered using several methods, including Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), and word embeddings.

We sequentially implement three distinct Dense Neural Networks (DNNs) combined with the LightGBM gradient-boosting framework. Our model attained a 5-fold cross-validated …

The Future Of Brain Tumor Diagnosis: Cnn And Transfer Learning Innovations, Shengyuan Wang

Mathematics, Statistics, and Computer Science Honors Projects

For the purpose of improving patient survival rates and facilitating efficient treatment planning, brain tumors need to be identified early and accurately classified. This research investigates the application of transfer learning and Convolutional Neural Networks (CNN) to create an automated, high-precision brain tumor segmentation and classification framework. Utilizing large-scale datasets, which comprise MRI images from open-accessible archives, the model exhibits the effectiveness of the method in various kinds of tumors and imaging scenarios. Our approach utilizes transfer learning techniques along with CNN architectures strengths to tackle the intrinsic difficulties of brain tumor diagnosis, namely significant tumor appearance variability and difficult …

Care-Teach: Proposing An Open-Source Approach To Personalized Learning, Jaime Augusto Alvarez Perez

Theses and Dissertations

Care-Teach is an algorithmic educational model that provides teachers and educators with the tools to create interactive, text-based lessons that address a student’s need for continuity and reinforcement. Care-Teach is built around two core components: a Student Behavior Profile and the Skill Tree. These models work together to give each student a personalized learning experience that reinforces their pre-existing strengths and inclinations. The Student Behavior Profile model keeps track of a learner’s inclinations and mood, utilizing metrics such as average response time and accuracy to categorize opportunities for educator involvement. The Skill Tree is an organizational …

Bayesian Estimation Of Reproduction Numbers From Distributions Of Outbreaks Sizes: Branching Process Approach, Alberta Araba Johnson

Theses and Dissertations

The Generalized Poisson distribution is useful in modeling epidemiological processes as a branching stochastic processes problem. Our goal is to construct accurate and reliable estimators for the reproduction number (R0) (i.e., the number of secondary infections), particularly in the context of disease outbreaks modeled by a Galton-Watson process. Towards this goal, we construct the classical Bayes estimator, the Maximum Likelihood estimator, and the Empirical Bayes (EB) estimator under the Square Error Loss function in Chapter II. We prove that the Empirical Bayes estimator is asymptotically optimal and estimate the rate of convergence. We then proceed to monotonize the Empirical Bayes …

Variational Bias Sampling For Collaborative Filtering Recommender Systems, Prisca Stephens

Theses and Dissertations

Advancements in digitalization has yielded enormous growth of data on online platforms, overwhelming users with multitude of options to choose from. Recommender systems narrow down these options to a few relevant ones thereby facilitating the decision-making processes for users. This study presents a framework for integrating variational bias sampling into model-based collaborative filtering techniques for recommender systems. Variational bias sampling is a novel and unique way to account for random factors that affect explicit ratings in collaborative filtering recommender systems. A Gaussian distribution is used to model all the possible random factors that could affect ratings. Sampling user and item …

Mitigation Methods For Instrumental Artifacts In Gravitational Wave Data, Raghav Girgaonkar

Theses and Dissertations

Strain data from ground-based gravitational wave detectors are regularly affected by instrumental artifacts known as glitches. Such glitches form the background of false alarms in the detection of gravitational waves from compact binary coalescences, in-turn reducing search sensitivity. If left unaccounted, these glitches along with other non-stationarity may also contribute towards a corrupted representation of the statistical properties of detector noise, subsequently affecting parameter estimation for detected gravitational wave candidates. Therefore, effective data analysis methods to veto glitches and identify non-stationarities in detector data are crucial to enhancing the sensitivity of a gravitational wave search. In this thesis, we …

Conditional Constrained And Unconstrained Quantization For A Uniform Distribution On A Hexagon, Christina Hamilton

Theses and Dissertations

In this thesis, we have considered a uniform distribution on a regular hexagon and the set of all its six vertices as a conditional set. For the uniform distribution under the conditional set first, for all positive integers n ≥ 6, we obtain the conditional optimal sets of n-points and the nth conditional quantization errors, and then we calculate the conditional quantization dimension and the conditional quantization coefficient in the unconstrained scenario. Then, for the uniform distribution on the hexagon taking the same conditional set, we investigate the conditional constrained optimal sets of n-points and the conditional constrained quantization errors …

Soil Carbon Sequestration Efforts In Arid And Semi-Arid Climates Under Conservation Agriculture And Reforestation, Samantha Lynn Colunga

Theses and Dissertations

Soil organic carbon (SOC) plays a vital role in the global carbon cycle and aids in climate change mitigation. However, deforestation and intensive agricultural practices threaten topsoil and carbon (C) pools through soil disturbances releasing vast amounts of carbon dioxide (CO2) by enhancing the decomposition of organic materials. Degraded soils typically lack stable aggregates, nutrient and water retention, and overall low fertility. In arid and semi-arid regions, soils are in an even more dire situation, as low precipitation and high temperatures slow down the accumulation of SOC. This thesis has two objectives: 1) to evaluate whether conservation tillage practices increase …

How Mathematics Instructors Foster The Development Of Black Students' Mathematics Identity In Undergraduate Active Learning Mathematics Courses, Ashly J. Olusanya

Theses and Dissertations

Black students must overcome unique challenges to succeed in mathematics. Educators are tasked with identifying equitable teaching practices to support these students. Active learning (AL) is a teaching pedagogy that engages students in rigorous mathematical activities and encourages student participation. This research study will explore the professors’ beliefs about how students learn mathematics and why they use active learning in their collegiate mathematics courses. The study explores the connections between these beliefs and their reported use of instructional practices. The study also identifies the instructors’ beliefs about developing students’ mathematics identities, particularly their Black …

A Multivariate Analysis Of The Gravitational Wave Signal Landscape From Core Collapse Supernovae, Raul Alberto Espinosa Perez

Theses and Dissertations

Core collapse supernovae (CCSN) are highly anticipated sources of gravitational waves (GW) during the on-going fourth observation run (O4) of GW detectors like LIGO and the future observation runs. The GW signal from the CCSN cannot be modeled mathematically. Several groups around the world have engaged in simulation of the predicted GW signals from CCSN sources. These simulations are carried out in supercomputers, and they incorporate general relativity, hydrodynamics, neutrino physics, mass and angular momentum of the stellar progenitor and nuclear equations of state (EoS). The output consists of simulated signals with varying duration, peak frequency, GW energy and time-frequency …

A Study On A Vector Complex Modified Korteweg-De Vries Equation, Changyan Shi

Theses and Dissertations

In this thesis, we systematically study a vector complex modified Kordeweg-de Vries equation by combining Hirota's bilinear method and the the Kadomtsev–Petviashvili (KP) reduction method. This vector nonlinear equation is a multi-component generalization of the well-known modified Kordeweg-de Vries (mKdV) equation and can be reduced to the known Hirota equation, Sasa-Satsuma (SS) equation, Sasa-Satsuma-mKdV equation as well as coupled Sasa-Satsuma equation. First, we bilinearize the vector complex mKdV equation under both the zero and nonzero boundary conditions by introducing auxiliary tau functions. Then, starting from two sets of bilinear equations of multi-component KP hierarchy and single-component KP-Toda …

The Perspectives Of Using Desmos For Students’ Conceptual Understanding And Procedural Fluency To Solve Linear Equations, Larmel Dimatulac Madrilejos

Theses and Dissertations

The study examines the perspectives of using the Desmos calculator of Algebra I students' conceptual understanding and procedural fluency to write, graph, and solve linear equations in Algebra I STAAR. While the students have continuously used technology for mathematics assessment, emergent bilingual students in South Texas still need help passing high-stakes testing. The framework of the study is grounded in the theory of mathematical education (knowledge of mathematics educators to teach), the theory of mathematical learning (understanding how students learn mathematics), and social constructivism. The study seeks ways to teach all students, mainly the minority, to learn …

Synthesis Of Nano-Microstructured Barium Titanate (Batio3) Incorporated In Pvdf-Hfp/Peo For Sustainable And Green Energy Generation, Rigobert Ybarra Jr.

Theses and Dissertations

The global reliance on fossil fuels and natural gas, has largely dominated the energy production field, but due to finite resource depletion and escalating greenhouse gas emissions, the immediate exploration of sustainable energy alternatives to mitigate climate change and ensure resource security has been a major concern. There has been extensive research into other more renewable methods of energy production such as wind and hydropower. Of these current energy generation types there are many areas of untapped potential from the mechanical movements generated ambiently not only in the large scale of power generation but also on a smaller scale. The …

Detection And Classification Of Diabetic Retinopathy Using Deep Learning Models, Aishat Olatunji

Electronic Theses and Dissertations

Healthcare analytics leverages extensive patient data for data-driven decision-making, enhancing patient care and results. Diabetic Retinopathy (DR), a complication of diabetes, stems from damage to the retina’s blood vessels. It can affect both type 1 and type 2 diabetes patients. Ophthalmologists employ retinal images for accurate DR diagnosis and severity assessment. Early detection is crucial for preserving vision and minimizing risks. In this context, we utilized a Kaggle dataset containing patient retinal images, employing Python’s versatile tools. Our research focuses on DR detection using deep learning techniques. We used a publicly available dataset to apply our proposed neural network and …