Theses and Dissertations

Permanent URI for this community

https://hdl.handle.net/10323/4415

Browse

Now showing 1 - 20 of 170

2-D Modeling of Moisture and Heat Diffusion in Adhesively Bonded Joints
(2021-11-15) Gerini Romagnoli, Marco; Nassar, Sayed A.; Yang, Lianxiang; Gu, Randy; Dembinski, Roman
In this dissertation research, a novel two-dimensional model is proposed for adhesively-bonded Single Lap Joints (SLJ) under combined mechanical loads and moisture/heat diffusion. Governing partial differential equations of the constitutive stress-diffusion model are formulated and solved numerically. Various scenarios for individual and combined diffusion of moisture and heat through the joint substrates and directly into the adhesive layer are analyzed. The resulting location-dependent material model is fed into the governing partial differential equations. Shear and peel stresses in the adhesive layer are investigated. Results are presented, with a focus on the improvements brought by the capability of the proposed model to predict the effects of diffusive patterns that are perpendicular to the axial tensile-shear load.Moisture diffusion across the joint width is found to have a significant effect on the shear stress distribution for structural epoxy adhesives with high elastic modulus. Numerical comparison with a linear Finite Elements Analysis is provided. Material properties are derived from experimental testing of commercially available two-component epoxy and polyurethane adhesives. This document is organized as follows: Chapter One "Introduction and Literature Review" includes a description of the analytical, numerical, and experimental work that serves as foundation for this dissertation. A previous one-dimensional analytical model is revised, and the motivations driving this research effort are illustrated. Chapter Two "Modeling of Heat and Moisture Diffusion" lays the groundwork for the analysis of diffusive patterns in the joint substrates and in the adhesive layer. Multiple scenarios of moisture and heat diffusion are explored, and their effect on the elastic properties of the adhesive is investigated. Governing partial differential equations are derived, and solution strategies are discussed. Chapter Three "Elastic Model" includes the formulation of coupled stress-diffusion partial differential equations for the shear and peel stresses in the adhesive layer, resulting from the application of an external tensile-shear load on the two adherends. Equilibrium considerations, stress-strain, and strain-displacement relationships are used to generate the constitutive equations, with adequate assumptions and simplifications. Chapter Four "Elastic Modulus of Structural Adhesives: Relationship to Bulk Material Temperature" contains the experimental procedure and results for the bulk adhesive tests. The elastic moduli of two-component epoxy and polyurethane adhesives are measured using a DMA Q800, and a linear law relating temperature to material properties is inferred. Chapters Five, Six, and Seven present the results of the shear and peel stress models for adhesive joints subjected to two-dimensional moisture-only, heat-only, and combined moisture/heat diffusion, respectively. A convergence study is performed on the two-dimensional solution to the heat equation governing moisture and heat diffusion in the adhesive layer. Stress gradients along the length and width of the bondline are analyzed, and the results are compared to the previous one-dimensional coupled stress-diffusion model. The results of the two-dimensional model are compared with a Finite Elements Analysis in Chapter Eight "FEA Comparison". Chapter Nine "Conclusions and Future Work" summarizes the major findings of this dissertation research, and outlines the potential for future work.
A Case Study of the Role of Universal Design for Learning in Impacting Teacher Professional Development and Instructional Design
(2022-01-01) Wozniak, Carrie Marie; Johnson, Eileen S.; Martin, Robert; Flummerfelt, Shannon
Educators continue to try to address the many different learning styles, disabilities, and intelligences of the students who enter their classroom. With each new year, the ask of our teachers becomes greater. Consequently, designing instruction from the start that addresses the many differences found in a classroom is a challenge for educators. Therefore, professional development centered around creating a flexible learning environment that reaches many types of learners has become the new expectation. The purpose of this mixed method case study was to understand the impact of professional development using the Universal Design for Learning framework on changing teacher practices, and thereby increasing perceived student engagement at three local middle schools.The findings of this study were analyzed through the lens of four data sources: Teacher Focus Groups, Teacher Lesson Plans, Teacher Formative Assessments - Understanding the Role of Lesson Design, and the Technology Usage Perception Survey. My study revealed the challenges that teachers had with identifying students unique learning needs and shows the importance of schools having a systemic learning framework. Teachers understood the importance of student engagement and good instructional design; however, designing the lessons to create a learner-centered classroom required them to completely rethink their lesson design process. This study concludes with recommendations for redesigning professional development, job embedded coaching, and systemic implementation of the UDL framework within a school district.
A Case Study of Trust and the Relationships between White Teachers and Their Black Students
(2022-01-01) Trobaugh, Joseph M.; Smith, Julia; Klein, Suzanne; Martin, Robert
The purpose of this study was to examine whether White teachers’ trust in theirBlack students would stimulate positive teacher-student relationships. One question guided my research: How do White teachers build trust with their Black students? This study used interviews of 5 White middle school teachers and 3 Black middle school students for the data set. Semi-structured interviews and a focus group were conducted to fully understand the lived experience of teachers and students within a middle school setting. All of the teacher participants worked in middle schools where they taught core content classes. Each teacher and student interviewed had at least 1 year experience teaching or attending middle school. Each interview and focus group was recorded via ZOOM and transcribed as soon as possible for accuracy and review. Important statements were extracted from the interview and focus group transcripts resulting in three major themes. Theme one, students and teachers’ definition of trust, presented how students and teachers viewed trust in developing positive relationships within the classroom. Theme two, teacher-student relationships, presented how trust impacts the interactions of students and teachers to effectively build strong relationships. Theme three, racial tension, presented how race and White norms impacted teaching practices and the development of teacher-student relationships. The researcher concluded that the participants had a perceived trust as an enabling factor for building positive relationships. However, students explained that building relationships could be improved by the teachers’ ability to adapt to their students.
A Different World: an Examination of the Relationship between Student Involvement and Student Well-Being in Black and White Students at a Predominantly White Institution
(2023-01-01) Millet, Mackenzie Janelle; Smith, Julia; Close, Stacey; Sulé, V. Thandi
The purpose of the study was to compare the relationship between student involvement and student well-being in Black and White students at a Predominately White Institution. Research has shown that students who are involved in organizations have more positive collegiate experiences. The primary areas of the study focused on student involvement, student well-being, and student racialized experiences and the relationship with anxiety, self-esteem, depression and psychological stress. Ultimately, this study examined the factors that contributed positively or negatively to the experiences of students on campus and provided recommendations for increasing the emotional well-being of Black students at Predominantly White Institutions. The methods for this study used a quantitative, cross-sectional approach. Data collection involved survey data which explored relationships between variables and the testing of differences between groups for significance. More specifically, it used descriptive statistics, cross tabulations (chi-square), one-way ANOVA, correlations between continuous measures and compared the size of correlations. The key findings of this study were that the majority of students were involved in at least one organization on campus. Both races of students stated their reasons for joining were due to enjoyment in the organizations, positive feelings of connectedness, sense of belonging, and celebration of cultural traditions. Additionally, students reported to have lower levels of depression and anxiety when they were involved in organizations on campus. This finding suggests that it is not the quality or number of organizations in which students are involved that impacts their emotional well-being; rather, it is the quality of experiences. Students indicated that negative experiences with microaggressions resulted in stress, anxiety, and depression, and a decrease in self-esteem. Furthermore, students reported having higher levels of self-esteem when they felt integrated within the university community
A High-Speed, Light Weight Hardware Architecture for H.264- Compatible Compression on an FPGA
(2023-01-01) Tayyebi, Azam; Hanna, Darrin M; Louie, Geoffrey; Alawneh, Shadi G; Cesmelioglu, Aycil
Video compression is a technique that reduces and removes spatial and temporal redundancy of video data, resulting in a reduction in transmission time and communication bandwidth across a network and efficient storage. H.264, a widely adopted video compression standard, is the result of collaborative efforts between the ISO (International Organization for Standardization) Moving Picture Experts Group (MPEG) and the ITU (International Telecommunication Union) Video Coding Experts Group (VCEG). H.264 uses an array of algorithms for coding digital video to achieve better compression efficiency compared to previous standards. However, this efficiency increase comes at the cost of higher computational complexity for H.264 encoders. This thesis design and implement an H.264-compatible intra-frame video encoder on FPGA. The encoding algorithms, like intra prediction, transform, quantization, and entropy coding, are initially implemented and tested in MATLAB. Later, these algorithms are translated into VHDL language and evaluated through timing simulations vi in Vivado. The FPGA implementation is then tested using various input pixel sizes and video resolutions across multiple FPGA devices. The encoder supports all video resolutions and frame rates.
A Hybridized Discontinuous Galerkin Scheme for the Coupled Stokes-Darcy Flow and Transport
(2022-03-22) Pham, Dinh Dong; Cesmelioglu, Aycil; Cheng, Eddie; Horvath, Tamas; Schmidt, Darrell; Shillor, Meir
The main focus of this thesis is on finding highly accurate and robust numerical methods to solve a complex flow and transport problem governed by the fully-coupled time-dependent Stokes-Darcy-transport equations. This problem has many applications one of which is groundwater contamination by pollutants transported via surface/subsurface flow. It consists of two main ingredients; the time-dependent Stokes-Darcy equations describing the flow, and the time-dependent advection-diffusion equation for the transport of chemicals via this flow. Therefore, the first part of this thesis is dedicated to studying the time-dependent Stokes-Darcy problem that describes the free flow and porous media flow on two different parts of a domain and their interaction at the common interface. We introduce a hybridized discontinuous Galerkin (HDG) method which provides exact mass conservation and pressure robustness and handles the interface conditions via facet unknowns. We prove well-posedness and a priori error estimates in the energy norm, and provide numerical experiments that show optimal convergence and robustness of the method with respect to the problem parameters. The second part deals with the time-dependent advection-diffusion equation where we again use an HDG method for the spatial discretization. We show the existence and uniqueness of the semi-discrete transport problem and prove a priori error estimates in the energy norm. A number of numerical experiments are presented for different boundary conditions and we observe optimal rates of convergence in each case. Combining the two parts by a sequential algorithm, we solve the fully coupled time-dependent Stokes-Darcy-transport problem. The coupling of the flow and transport is introduced by the dependence of the fluid viscosity and source/sink terms on the concentration and by the dependence of the dispersion/diffusion tensor in the porous media domain on the advective fluid velocity. Our sequential algorithm employs a linearizing decoupling strategy based on the backward Euler time-stepping where the Stokes-Darcy and the transport equations are solved sequentially by time-lagging the concentration. The well-posedness results and a priori error estimates for the velocity and the concentration in the energy norm are presented and numerical examples demonstrating optimal convergence and mass conservation are provided.
A Meta-Heuristic Algorithm Based on Modified Global Firefly Optimization: In Supply Chain Networks with Demand Uncertainty
(2022-03-15) Altherwi, Abdulhadi; Zohdy, Mohamed; Malik, Ali; Edwards, William; Cho, Seong-Yeon; Alwerfalli, Daw
Nowadays, many challenges affect global supply chain networks including disruptions, delays, and failures during shipment of products. These challenges also incur penalty costs due to customers’ unmet demands and failures in supply. In this dissertation, the model was developed as a multi-objective supply chain network under two risk factors including failure in supply and unmet demand based on three different scenarios. The objective of scenario I was to minimize the total expected transportation costs between stages for each supply chain and penalty costs associated with shortage of products. Supply chain with no failure in supply will communicate with supply chain with failure to deliver its product to the final customer. For scenario II, the objective was to maximize the profits of the supply chain that face extra inventory. This supply chain with surplus products will collaborate with supply chains with shortage of products to prevent any undesirable costs associated with extra inventory. The objective of scenario III was to develop a multi-objective function, which maximizes the profit and minimizes the total costs associated with production, holding, and penalties due to supplier failure of raw materials. Once a supply chain faces failure in supply of raw materials, other supply chains with no supply failure will collaborate to prevent any associated costs. This research investigates the applicability of the Modified Firefly Algorithm for a multi-stage supply chain network consisting of suppliers, manufacturers, storages, and markets under risks of failure. Commercial software cannot obtain the optimal results for these problems considered in this research. To achieve better findings, we applied a Modified Firefly Algorithm to solve the problem. Two case studies for a pipe and a steel manufacturing integrated supply chain demonstrated the efficiency of the model and the solutions obtained by the Firefly Algorithm. We used four optimization algorithms in ModeFRONTIER and MATLAB software to test the efficiency of the proposed algorithm. The results revealed that when compared with other four optimization algorithms, Firefly Algorithm can help achieve maximum profits and minimizing the total expected costs of supply chain networks.
A Multi-Pronged Approach to Studying Human-Animal Interactions in Zoo Settings
(2024-01-01) Truax, Jordyn Paige; Vonk, Jennifer; Escobar, Martha; Shackelford, Todd
Zoos of the past focused primarily on animal exhibition, yet the modern zoo has shifted to a focus on animal conservation and public education. This change has coincided with a negative shift in public opinion towards zoos after documentaries such as Blackfish, leading to comparatively more positive views of sanctuaries. These preferences seem to be influenced by the lack of animal exhibition at sanctuaries, suggesting that human influences on animals are important to public perceptions of zoos. Thus, human-animal interaction research is essential to understanding perceptions of zoos and human influences on captive animals. This dissertation addresses both factors. Study 1 assessed public opinions on zoos versus sanctuaries, and investigated how these opinions are impacted by knowledge of zoo practices. Highlighting any positive information, but particularly in relation to conservation, led to more positive public opinions. Studies 2 and 3 considered human-animal interactions through human impacts on captive animals, as further knowledge could both increase animal welfare, and then, positively influence public opinion. Study 2 examined the influence of visitors on the behaviors of zoo-housed parrots in an aviary. Birds engaged in decreased positive behaviors, increased negative behaviors, and more birds were present as visitor numbers increased. The increase in negative behaviors was minimal compared to the increase in birds present, which may indicate the birds were not negatively impacted by visitors. Study 3 evaluated the judgment biases of two ambassador animals after exposure to zoo visitors. The chicken displayed pessimism whether it was held or perched, but the tegu displayed pessimism only when no visitor touch occurred. This suggests negative effects of visitor interactions for the chicken, but touch interactions may not be aversive to the tegu. All three studies contribute to our understanding of human-animal interactions for the improvement of animal welfare and public perceptions of these facilities.
A Simulation-Based Fatigue Life Estimation Method for Nonlinear Systems under Non-Gaussian Loads
(2023-01-01) Mande, Onkar K; Mourelatos, Zissimos P.; Gu, Randy J; Monroe, Ryan; Drignei, Dorin
In the fields of durability and stochastic structural dynamics, it is customary to focus on linear structures subjected to Gaussian excitations. However, real-world engineering systems often exhibit nonlinear behavior and are exposed to non-Gaussian loads. Calculating fatigue life for such nonlinear systems under non-Gaussian loading presents many challenges such as complex nonlinear dynamics, multifaceted statistical characteristics, and time-dependent effects resulting in a very high computational effort. To overcome these hurdles, this research uses non-Gaussian Karhunen-Loeve expansion (NG-KLE) to not only predict the expected fatigue life but also obtain the Probability Density Function (PDF) of fatigue life. It integrates a sub-domain-based technique to significantly reduce the computational demands while preserving accuracy, by efficiently obtaining long time trajectories of random processes. This development is very useful for excitation signals that far exceed the process correlation length. The NG-KLE method serves as the main tool for characterizing the excitation process by estimating its non-Gaussian marginal distribution and autocorrelation function. A Karhunen-Loeve (KL) expansion is executed only for the first subdomain, and then extended to subsequent subdomains by establishing correlations between the KL expansion coefficients of adjacent subdomains. This innovative approach is adapted to non-Gaussian (NG) excitation, allowing for efficient characterization of both the input and output random processes using NG-KLE, enabling the generation of very long synthetic output random stress process samples. The fatigue life corresponding to each output stress trajectory contributes to the estimation of the PDF of fatigue life. The proposed generalized fatigue life estimation approach accommodates both Gaussian and non-Gaussian processes for both narrow and wide band signals. To demonstrate its effectiveness, we use a duffing oscillator system and a practical example involving a truck assembly modeled by the Finite Element Method (FEM).
A Vibro-acoustic CAE Approach for Active Noise Control Prediction
(2024-01-01) Abbas, Ahmad A; Mourelatos, Zissimos P; Latcha, Michael; Yang, Lianxiang; Drignei, Dorin; Sturla, Francisco
This research focuses on a comprehensive analysis and prediction of Active Noise Cancellation (ANC) system performance in vehicles, with particular emphasis on the structural and acoustic aspects. While acknowledging the significance of electronic components and control algorithms in ANC systems, this study focuses on predicting the ANC performance using a full vibro-acoustic vehicle model. Additionally, the integration of noise management supplier control systems with CAE ANC models is explored. The development of a predictive CAE methodology is demonstrated using a road noise cancellation example. The process involves several steps including the structural behavior of the Trim Body in White (TBIW) structure, the development of a vehicle cavity model, the derivation of accurate speaker models, the assessment of speaker integration with vehicle doors, and the development of Transfer Paths (TP) from speakers to microphones. A novel methodology is presented to quantify the door stiffness requirements for optimal speaker ANC performance, incorporating substructuring methods and physical testing. The accuracy of the developed CAE models is validated using physical testing of circular and oval-shaped speakers integrated into vehicle doors and the calculation of transfer paths between each door speaker and microphone locations is demonstrated. The significance of microphone and speaker locations relative to driver or passenger ear positions highlights their influence on ANC performance. Finally, a controller is developed to test the CAE model and illustrate its functionality using a supplier’s controller for sound management. Overall, this research establishes a reliable vibro-acoustic CAE ANC model capable of predicting ANC system performance accurately by integrating a full vehicle vibro-acoustic model with an ANC controller. Such a predictive capability enables optimization and enhancement of vehicle performance for noise cancellation, unlocking its full potential in mitigating vehicle noise.
Amplitude Method to Detect Debonding for Stack Bond Adhesive
(2024-01-01) Huang, Xiabao; Barber, Gary; Gu, Randy; Wang, Xia; Latcha, Michael; Qu, Hongwei; Zhou, Jun
Adhesively bonded joints have been applied in the automotive industry for the past few decades due to their advantages such as higher fatigue resistance, light weight, capability of joining dissimilar materials, good energy absorption and high torsional stiffness for overall body structure. They also provide an effective seal against noise and vibration at a low cost. There exists the challenge of defining the fatigue characteristics of adhesive joints under cyclic loading conditions and conventional methods have limitations in detecting the crack initiation of a bonded joint. This study introduces a method of detecting crack initiation by using the frequency method. It is found that stiffness change in the system is highly correlated to change in natural frequencies. By monitoring the change in natural frequencies, the crack initiation can be detected.
An Exploratory Study on Teachers’ Perceptions in Developing Leadership Skills in Four to Six-Year-Old Children
(2023-01-01) Joshi, Sweta; Groomes, Darlene; McNair, Shannan; Oden, Sherri; Wells, Caryn
When a child enters school, the learning environment, mainly the teacher, becomes one of the most significant adults in their life beyond the family (Anhert et al., 2013). Teachers can therefore be constructive in enhancing different skills in children, including leadership skills. Due to the fact teachers play a vital role in children’s development, the purpose of this study was to explore teachers’ perceptions of developing leadership in children and the strategies they use (if any) to instill leadership in them. The study used a qualitative approach by using interviews, observations, and a checklist as the tools to collect data and gain a deeper understanding of how teachers develop leadership in children with a focus on the strategies that teachers use in instilling leadership in them. Eleven teachers participated in this study from one private and three public schools. Data were collected in person and virtually due to the restrictions of the COVID-19 pandemic. Findings indicated teachers were well aware of the leaders in their classroom and their leadership characteristics. Furthermore, data showed teachers established developmentally appropriate teaching practices while applying various strategies and indicated teachers lack training in developing leadership in children. Better understanding the ways in which the COVID-19 pandemic impacted leadership contribute to the field of children’s development, and the development of leadership skills in children more specifically. The present study points to the importance of teachers’ training in developing leadership in children. Future research may focus on how and in what ways virtual settings can be effective in instilling leadership in children.
An Interactive Ecosystem of Music Learning: Individual Learning in Small Group Contexts in a Music Classroom
(2022-03-17) Grekin, Joshua David; Wiggins, Jacqueline H; Ricks-Doneen, Julie; Oden, Sherri L; Shambleau, Krista; Carver, Cynthia
In this qualitative study, I explored the relationships between individual and group learning in the context of music ensembles in the classroom. I sought to understand how groups and individuals construct and develop identities and search for power in this context and how the self-esteem, efficacy, and productivity of groups and individuals may be related. As a teacher-researcher (Kincheloe, 2003) in an interactive, interconnected multi-age, constructivist learning environment (Brooks & Brooks, 2001; Fosnot, 1996; Wiggins, 2015) where learners and groups of learners were encouraged to share ideas and knowledge, I examined the musical community from multiple perspectives; focusing separately on the entire school community, small musical ensembles, and individual learners. The relationships among these perspectives, and the experiences of these individuals and groups were the primary focus of this study. Data consisted of extant videos, audio recordings, teacher observation notes, and informal interviews, and were analyzed through a process of identifying and categorizing emergent themes. The findings of this study enabled me to conceptualize the entire musical community at the school as a constantly evolving ecosystem in which every individual and group was influenced by the evolution of the entire ecosystem, and the evolution of the entire ecosystem was influenced by every individual and group. Through this lens, musical groups and musical communities are seen as cohesive and developing entities separate from, and interacting with the individuals who constitute them. Further, I found that ideas, understandings, resources, and innovations resided within the ecosystem and that a robust, multi-perspective awareness of the ecosystem, both in its entirety and of its individual parts, by the learners and music teacher, positively influenced self-efficacy, creativity, development, and growth.
An Optimal Asynchrophasor in PMU Using Second Order Kalman Filter
(2021-11-15) Alqahtani, Nayef Mohammed S; Zohdy, Mohamed; Ganesan, Subramaniam; Kobus, Christopher; Elsayed, Suzan
Phasor Measurement Units (PMU) are very costly according to energy regulator and utility companies. Utility operators work on alternative solutions to reduce the error rate and operation costs of PMU. In this paper, we sought to optimize the PMU to reduce the level of error using Second-Order Kalman Filter (SOKF). Consequently, this optimization is based on minimizing the number of errors when receiving the signal from access points or from the main access point. We derived a simple mathematical model to estimate the phase coming from the PMU.PMUs provide Global Positioning System (GPS) time stamped synchronized measurements of voltage and currents with the phase angle of the system at certain points along the grid system. Those synchronized data measurements were extracted in form of amplitude and phase from various locations of the power grid to monitor and control the power system conditions. A PMU device is a crucial part of the power equipment in terms of the cost and operative point of view. However, such ongoing development and improvement to PMUs' principal work is essential to the network operators to enhance the grid quality and the operating expenses. A MATLAB model was created to implement the proposed method in the presence of Gaussian and non-Gaussian noise. It is based on an Asyncrhophasor technique resulting in a phase error minimization when receiving the signal from access point or from the main access point. The results show the proposed SOKF method outperforming the existing model as tested using Mean Square Error (MSE). The SOKF method was replaced with a synchronization unit into the PMU structure to clarify the significance of the proposed new PMU. This paper's proposed method leads to lower costs and less complex techniques to optimize the performance of PMU using SOKF
Analyzing the In Vivo Roles of Histone Acetyltransferases GCN5 And ESA1 in RSC Recruitment and Remodeling Activity Genome-Wide in Saccharomyces Cerevisiae
(2023-01-01) Biernat, Emily R.; Govind, Chhabi K; Chaudhry, Rasul; Blumer-Schuette, Sara
Transcription is important for gene expression and is a tightly-controlled process involving multiple mechanisms of regulation, including regulation by chromatin structure. Chromatin consists of nucleosomes, which are comprised of DNA wrapped around histone proteins. Chromatin remodelers such as the Remodels the Structure of Chromatin (RSC) complex play important roles in controlling DNA accessibility to the transcriptional machinery and organizing chromatin throughout the genome. Mutations in yeast and mammalian RSC orthologs have been linked to dysregulated cell cycle progression, chromosome segregation, stress response, and developmental processes. In this study, we investigated the mechanisms by which RSC associates with chromatin on a genome-wide scale and the impact of disrupted RSC-chromatin interactions on transcription. Previous studies have suggested that nucleosome acetylation via histone acetyltransferases (HATs) may facilitate RSC binding to chromatin. To explore this, we examined the effects of removing HATs Gcn5 and Esa1 on RSC occupancy. Surprisingly, our results revealed distinct effects of HAT loss on RSC occupancy at promoters and gene bodies. In promoters, the loss of HATs increased RSC association with promoter nucleosomes, particularly in promoters containing partially-unwrapped fragile nucleosomes. Additionally, we found that HAT-mediated acetylation is crucial for maintaining nucleosome depletion at promoters. Conversely, HAT loss decreased RSC occupancy in gene bodies of highly transcribed genes. This reduction in RSC binding was dependent on histone tails, as cells lacking these tails also showed a significant loss of RSC binding to gene bodies. High-resolution mapping and analyses demonstrated that RSC-bound nucleosomes, particularly in gene bodies, were highly accessible. Consistent with these findings, loss of HAT functions resulted in widespread transcriptional changes, impacting both transcription initiation and elongation. This work provides valuable insights into how HAT-mediated histone modifications regulate RSC association with chromatin and the consequent impact on global transcription.
Application of Several Machine Learning Algorithms for Multiple Stage Inference Data
(2024-01-01) Amen, Khalid A; Zohdy, Mohamed A; Rrushi, Julian; McDonald, Gary; Mahmoud, Mohammed
Historically, machine learning techniques have been dependent on utilizing data from two distinct phases to predict and identify particular occurrences. The outcomes of these studies may exhibit either validity or inaccuracy, represented by binary values of one or zero. An alternative term for this is a prognostication of one of two potential results. Several issues are present in this approach, which have the potential to yield inaccurate outcomes. The issues encompassed in this context consist of data imbalance, overfitting, and error propagation. This study aims to employ and use a multiple stage outcome approach to enhance accuracy and optimize the performance of outcomes. In this step of our research, we will be implementing the Multiclass Classification One-vs.-All methodology to analyze the data collected from various stages of the experiment's conclusion. In the subsequent phase, it is necessary to engage in the utilization or investigation of a diverse range of potential supervised models, which are trained through the application of machine learning algorithms. Subsequently, the determination of the model that exhibits a superior level of accuracy will be made by designating it as the victor. In our study, we employ and evaluate five distinct machine learning algorithms, namely Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Gradient Tree Boosting (GTB), and Extremely Randomized Trees (ERF). These algorithms are used within our machine learning framework to analyze multi-stage data and ascertain the technique that exhibits the highest accuracy in predicting outcome stages. This multi-stage conclusion would effectively narrow down the problem or difficulties at hand, reduce the potential for errors, and enhance the ability to accurately predict and diagnose medical diseases or cyber security threats. A Python-based model was developed to execute the proposed methodology. The utilized notion employs a binary format, which has been substantiated by empirical evidence and offers two potential outcomes. Upon the completion of our research, it was determined that the Logistic Regression and Support Vector Machine algorithms exhibited better performance compared to the other algorithms when a multiple stage outcome was employed. The results were assessed in terms of accuracy, precision, recall, and the F measure
Assessment of Frequency, Degradation, Normalization, Inhibition, and Potential Surrogates of the Sars-Cov-2 Gene Targets
(2023-01-01) Hunawill, Emily; Szlag, David; Wu, Colin; Avery, Adam; Wendell, Doug; Westrick, Randal
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus responsible for the coronavirus disease 2019 (COVID-19) pandemic. Our study focuses on identifying and quantifying the presence of SARS-CoV-2 in the sewage based on identifying three gene targets which target the nucleocapsid gene (N1 and N2) and the envelope gene (E). The first set of experiments looked at the degradation of these targets at 4C, 25C, and 35C. As temperature increased so did the rate constants. Based on the half-life data, N1 degraded faster than N2 at 4C, all targets degraded at the same rate at 25C, and E degraded faster than N1 and N2 at 35C. Frequency of the gene targets was then assessed. E occurred less frequently than N1 and N2 adding 2.3 more SARS-CoV-2 detections leading to its removal from testing. The N1 and N2 gene targets were both necessary with removal of one resulting in minimally 9.6 loss of SARS-CoV-2 detections. We investigated normalization of the N1 and N2 gene targets with four different fecal indicators (pepper mottled mild virus, HF183, lachno3, and crAssphage) to improve the correlation between the sewage signal and clinical cases. Normalizing the data did not result in an increased correlation between the clinical and sewage data. Bovine coronavirus (BCoV) and Phi6 viruses were evaluated as surrogates to estimate SARS-CoV-2 inhibition in a single duplex reaction. Duplexing these targets was successful without significant signal loss, and these targets were used to estimate SARS-CoV-2 inhibition. Compared to BCoV, Phi6 suggested over 10 more fully inhibited samples. Several workflow modifications including bovine serum albumin (BSA), dilution, polyvinylpyrrolidone, and pasteurization were applied to the sewage samples in an attempt to reduce sample inhibition. BSA was able to reduce inhibition for both N1 and N2. Lastly, BCoV, Phi6, and murine hepatitis virus (MHV) retrieval were compared to the retrieval of the SARS-CoV-2 gene targets in sewage to determine if they accurately depicted the amount of inhibition the SARS-CoV-2 targets exhibited. BCoV and MHV were better surrogates to assess SARS-CoV-2 inhibition. SARS-CoV-2 RNA concentration in sewage were shown to correlate with COVID-19 cases.
Assessment of Taxon Sampling on Phylogenetic Reconstructions and Timetrees: A New Methodology And Application
(2023-01-01) Powell, Christopher Lowell Edward; Battistuzzi, Fabia Ursula; Oleksyk, Taras K; Blumer-Schuette, Sara E
Over the past three decades, computational capabilities have grown at such a rapid rate that they have given rise to many computationally heavy science fields such as phylogenomics. As increasingly more genomes are sequenced in the three domains of life, larger and more species-complete phylogenetic tree reconstructions are leading to a better understanding of the Tree of Life and the evolutionary histories in deep times. However, these large datasets pose unique challenges from a modeling and computational perspective: accurately describing the evolutionary process of thousands of species is still beyond the capability of current evolutionary models while the computational burden limits our ability to exhaustively explore and test multiple hypotheses. These limitations become even more problematic when attempting to estimate the absolute times within these phylogenetic reconstructions (timetrees). These time estimations are not only constrained computationally by run times and resource requirements but also bound by the availability of fossil data to estimate divergence times for the evolution of species (primary calibrations). All of these issues are particularly severe in prokaryotes, because of the high number of species available in databases, their large evolutionary variability, and the few primary calibrations available. Yet, they represent two out of the three domains of life and are therefore key to reconstructing the Tree of Life. This combination of computational and data constraints is forcing researchers to make choices on the datasets being analyzed without a clear understanding of the consequences of these choices on the accuracy of the results obtained. This work presents an in-depth analysis of the effects of dataset choices on the reconstruction of phylogenetic histories using a newly developed tool (Phylogenetic Assessment of Taxon Sampling) that will enable fast, simple, and reproducible testing of taxon sampling. The PATS pipeline is available on GitHub: https://github.com/BlabOaklandU/PATS
A Bayesian Network Based Approach Toward An Anticipatory Safety Reasoning System Autonomous Vehicle Copilot
(2022-11-17) Frederick, Philip A.; Cheok, Ka C; Das, Manohar; Sengupta, Sankar; Lipták, László; Del Rose, Michael; Kania, Robert
Future ground vehicle transportation is expected to rely heavily on autonomous mobility. However, the technical progress required to ensure a completely safe autonomous vehicle for unlimited roadway use, and reliable ways to measure its safety, is behind expectations. It is believed that a research breakthrough is required to address this gap. This dissertation defines a novel method for addressing on-road autonomous vehicle safety, explicitly focusing on unsignalized intersections. A method is described to generate an anticipatory safety copilot to assist the autonomous system with motion decisions by combining data collected from global online sources and the local autonomous vehicle sensors. This anticipatory copilot reasons about the environment around the autonomous vehicle and projects the vehicle's real-time motion intent forward into a projected future version of the environment created via features from the combined local and global source information. Based on this processed information, the copilot anticipates the probabilistic success of the autonomous vehicle safely executing its intended action.
Benzodiazepine Coordination Chemistry And Nitrogen Heterocyclic Compounds From Reactions Of Carbonyl Alkynes With O-Phenylenediamines
(2022-07-19) Twardy, Dylan Joseph; Dembinski, Roman; Beyeh, Ngong; Chavez, Ferman; Wheeler, Kraig; Yang, Ziming
The presence of heterocyclic compounds in active pharmaceutical ingredients and natural products implicates their importance to synthetic chemistry. Moreover, their inherent structures offer potential as metal-chelators. This work involved the design of simple methods for the construction of new nitrogen-containing heterocycles and to explore examples of coordination complexes. Benzodiazepines and their derivatives are biologically active heterocycles often prescribed as a treatment for anxiety, epilepsy, and insomnia. In addition, benzimidazo[2,1-a]isoquinolines are another class of biologically active heterocycles that are composed of moieties inherent to a wide variety of active pharmaceutical ingredients. Herein, the microwave-assisted reaction in ethanol of o-phenylenediamines with either alk-2-ynones or 2-ethynyl benzaldehydes was found to yield 1,5-benzodiazepines and benzimidazo[2,1-a]isoquinolines, respectively. To facilitate selective coordination of benzodiazepines, new pyridine containing 1,5-benzodiazepine chelators were synthesized and combined with metal reagents to form new benzodiazepine metal complexes characterized by X-ray crystallography.

Browse

Browsing Theses and Dissertations by Title

Results Per Page

Sort Options