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Computational materials science plays a crucial role in advancing new and improved materials. To leverage the advantages of local and nonlocal methods and aid in the advancement of predictive capabilities for materials, multiscale models have been introduced. Many such methods have been proposed to overcome computational challenges in accuracy and efficiency. In this work, I begin by presenting a review of some multiscale methods for crystalline modeling to provide context for this dissertation.
Together with my advisor Dr. Xingjie Helen Li, we explore the static behavior of a bottom-up nonlocal-to-local coupling method, Atomistic-to-Continuum coupling, and explore the dynamic behavior of a nonlocal method, Peridynamics, to explore a bimaterial interface.
Inspired by the blending method developed by \cite{Seleson2013} for nonlocal-to-local coupling, we create a symmetric and consistent blended force-based Atomistic-to-Continuum (AtC) scheme for one-dimensional atomistic chains. AtC coupling schemes have been introduced to utilize the accuracy of atomistic models near known defects and the computational efficiency of continuum models elsewhere. The conditions for the well-posedness of the underlying model are established by analyzing an optimal blending size and blending type to ensure the stability of the $H^1$ seminorm for the blended force-based operator. We present several numerical experiments to test and confirm the theoretical findings.
Then, we create a Peridynamics-to-Peridynamics scheme to model a bimaterial bar in one dimension. Peridynamics (PD) naturally allows for the simulation of crack propagation in its model due to its use of integro-differentials and time derivatives instead of the spatial derivatives typical of classical models. Although PD can be computationally intensive, its ability to accurately model fracture behavior, especially at material interfaces, makes it a valuable tool for achieving high accuracy in simulations, especially due to the susceptibility of fracture where differing materials meet. We prove the conservation laws, derive the dispersion relation, and estimate the coefficient of reflection near the interface for this nonlocal-to-nonlocal problem. We seek an optimal nonlocal interaction kernel in the governing equation for the cross-material interaction to reduce spurious artifacts when the kernel is assumed to be constant.
Lastly, I discuss potential future development in Atomistic-to-Continuum coupling and Peridynamics.
The COVID-19 pandemic abruptly changed educational institutions globally and challenged teachers and students. This immediate shift was difficult for K12 teachers because they were required to teach their courses online or using a blended learning (BL) model. As BL use continues to grow, concerns about student-content engagement have emerged. This study used a single case study to investigate experiences with facilitating student-content engagement in BL environments. Eleven teachers from two districts were surveyed and individually interviewed with semi-structured interviews. The complex adaptive blended learning systems (CABLS) was used as the theoretical framework to guide the data collection, data analysis, and interpretation of results. Findings of this study revealed that the learner demographics showed a diversity in economic status and academic abilities, with socioeconomic status emerging as a potential indicator affecting students’ access to technology within BL environments. Digital literacy skills varied among students, influencing their student-content engagement in BL environments. Teacher experiences with BL varied from embracing the mix of technology-mediated instruction with traditional F2F methods. Additionally, results showed that the support systems such as instructional coaches and professional learning communities (PLCs) played a crucial role in facilitating student-content engagement and enhancing pedagogical practices. Furthermore, findings showed that districts and institutions have demonstrated commitment to supporting BL environments through multiple layers of support. As technology continues to evolve, addressing challenges and leveraging collaborative efforts will be essential in ensuring that BL environments thrive and promote meaningful student-content engagement. Implications of this study inform the development of best practices, guidelines, and resources to enhance student engagement and foster a culture of continuous improvement in BL environments to improve student learning outcomes.
Plasmonic metamaterials are artificially structured materials with the inclusion of metallic elements regarded as macroscopically uniform mediums. These materials showcase adaptable optical characteristics achieved through manipulation of the materials' intrinsic geometries at scales much finer than the wavelength of the incident electromagnetic radiation under consideration.
This dissertation focuses on the fabrication methodologies and applications of plasmonic metamaterials in perfect absorption and plasmonic sensing. Plasmonic metamaterials, distinguished by their ability to manipulate electromagnetic radiation through engineered subwavelength structures, have garnered significant attention for their potential in various fields, including photonics, sensing, and energy harvesting.
The dissertation examines current fabrication techniques for plasmonic metamaterials, focusing on additive manufacturing approaches. The advantages of two-photon polymerization for the fabrication of plasmonic metamaterials is discussed in detail along with more traditional techniques like electron beam vapor deposition and atomic layer deposition. The advantages and limitations of each approach are scrutinized, laying the groundwork for subsequent investigations into tailored designs for specific applications.
Building upon the foundation of fabrication techniques, two distinct applications of plasmonic metamaterials are examined. Firstly, the concept of perfect absorption, wherein the metamaterial is engineered to efficiently absorb incident electromagnetic radiation across a narrow spectral range. Through theoretical modeling and experimental validation, novel designs for achieving perfect absorption are proposed and characterized. The investigated designs leverage the unique optical properties of plasmonic metamaterials to enhance light-matter interactions.
Subsequently, the utilization of these architectures for sensing applications is demonstrated. By exploiting the sensitivity of surface plasmon resonance to changes in the local refractive index, plasmonic metamaterials offer unprecedented opportunities for label-free, real-time detection of biomolecules, gases, and other analytes.
This dissertation showcases the potential practical applications of plasmonic metamaterials in perfect absorption and plasmonic sensing. It contributes to the ongoing advancement of plasmonic metamaterials and their seamless integration into cutting-edge photonics and sensing technologies.
Rock weathering, or the mechanical and chemical breakdown of rock over time, creates the landscape on which all terrestrial life is built. Here, I quantify the rates and controls over mechanical weathering [rock cracking/fracturing] f surficial boulder deposits in Eastern California using by collecting rock and crack field measurements, clast size distribution data from the field, and rock elastic properties using laboratory testing. I used a chronosequence or space-for-time approach, whereby data are collected from rocks or sediments that have been exposed to natural weathering conditions for a range of times, using the properties of the stable deposits to represent the amount of weathering that occurs over the time span of exposure. I studied rocks at three sites, with rocks being exposed to Earth surface conditions from 0 to 148,00 years [148 ka].
I manually measured 8763 crack lengths, widths, and orientations from 2221 in situ boulders on Earth’s surface and found that that rock cracking is initially fastest when rocks are exposed to Earth’s surface conditions, with rocks accumulating cracks at a rate of 9-1502 mm of cracks per m^2 rock surface over a thousand years, or 0.1-36 individual cracks per m^2 rock surface over a thousand years. After this point, rocks continue to crack, but the rate of crack growth slows down. After about 30 ka, the growth rate is <36 mm of cracks/m^2 of rock surface per ka, or <1 individual cracks/m^2 of rock surface per ka. Using all rock and crack data I determined that age itself has the most consistent, positive, statistically significant correlation with the number of fractures per rock surface area [fracture number density] and the total length of fractures measured per rock surface area [fracture intensity].
From two sites, I collected a granitic boulder from each dated deposit for rock mechanics testing. These data show that rock compliance increases over time while mechanical weathering leads to an increase in microscale cracks, which alter the rock’s strength and elastic strain response under stress. Using the laboratory analyses and local weather station data, I implemented a simple daily stress model applying Paris’ law of subcritical crack growth to predict single crack growth after each day of weather conditions, for a period of 5000 years. Cracking occurred over only a limited number of unusually intense weather days when the daily range of air temperatures was the largest. In the two semi-arid sites, these cracking days were hot, dry summer days; in the arid site, the day when the most crack growth was predicted coincided with summer monsoonal rains. The model is highly sensitive to rock elastic properties, which supports the theory that a gradual increase in bulk compliance allows rocks to withstand stress without cracking over thousands of years.
Finally, I present clast size data to show that for volcanic and carbonate rocks, there is a correlation between the geometry of cracking observed on the rocks and the shape of sediments on older deposits: when many cracks are parallel to the rock surface, older deposits tend to have more flattened rocks on them. This shows that cracking rates and crack geometries can play a strong role in clast size and shape evolution over geologic time, and mechanical weathering should be considered when interpreting sediments in the geologic record.
These findings are directly applicable to geoscientists attempting to understand weathering, landscape evolution, and geological hazards. More broadly, the decreasing rock cracking rates that accompany slow mechanical property changes represent a real-world example of material fatigue vs. material failure.
The quality of life for those who support loved ones living with substance use disorder (SUD) is adversely affected due to destructive behaviors and the impact these behaviors have on the family system (Kaur, 2016). Consequently, primary support persons (PSP) often live their lives in silence and experience disenfranchised losses that impact not just the family unit but also impacts the human system, the most significant system among family units (Howard et al., 2010). This same researcher asserts this circular causality is almost always found among human and family systems as the actions of one person create responses or adaptions from other persons living within that same family unit. This is important because it highlights the way alcohol and other drugs (AOD) impact normal functioning of the addict, their loved ones, and society (Cudak & Pedagogika, 2015).
The purpose of this study was to examine variables that impact of quality of life of caregivers to people living with SUD. Perceived losses due to a loved one’s SUD, perceived social support, one’s own substance (ab)use, and stress were all examined to learn the impact these variables have on QOL. Multiple linear regression was utilized to examine the impact on QOL (n = 114) as predicted by losses, perceived support, substance use, and stress. Results indicated that support, losses, and stress are significantly associated with the dependent variable QOL (r2 = .815) to QOL. Results of this study postulate insight into future treatment approaches with PSP and highlight links to treatment that need to be addressed on behalf of PSP as well as the total family unit. These findings have implications for mental health and substance abuse counselors in terms of working with PSP and examining how improved QOL of support persons impacts those being treated for SUD. Future research is needed to examine how more thorough and more inclusive treatment approaches can include working with families of those who are addicted to substances.
Keywords: Quality of life, primary support person, substance use disorder, families, addiction, losses, depression and stress, support, family support, SUD treatment, family treatment involvement, support person
Sepsis is one of the leading causes of intensive care unit (ICU) transfers and mortality in the inpatient setting due to delayed recognition and untimely management of sepsis symptoms on non-ICU medical-surgical floors. Educating nurses on units with the highest rates of sepsis mortality and ICU transfers is important to increase confidence and knowledge to promote early recognition of sepsis and implementation of initial management guidelines. There is growing evidence of the effectiveness of escape rooms, however, most studies have been completed with students in academic settings. Additionally, sepsis education is traditionally targeted to the ICU and emergency department (ED) settings, not medical-surgical floors. The purpose of this project was to implement interactive escape room education with evidence-based sepsis content to prepare nurses to identify early warning signs of sepsis and clinical deterioration in medical-surgical patients. A two-group pre-/post-test quality improvement project was conducted with a sample of 17 nurses in the non-ICU medical-surgical units within the medicine service line at the project site. After the interactive escape room educational event, a statistically significant improvement in confidence and increased knowledge was demonstrated. Mean knowledge scores increased from 77.4 (SD=13.7) pre-intervention to 82.4 (SD=14.3) post-intervention. Significant improvements were seen in self-reported knowledge and confidence in identifying sepsis patients (z=2.33, p=.02), knowing how and what to monitor in sepsis patients (z=2.714, p=.007), and knowing initial management of patients with sepsis (z=2.646, p=.008). Mean ICU transfers decreased from 13 (SD=1.0) pre-intervention to 8.67 (SD=3.51) post-intervention indicating the project units performed better than the comparison units. Implementing an innovative escape room education intervention for non-ICU medical-surgical nurses is recommended to improve nurse knowledge and confidence in managing sepsis patients. By increasing nurse knowledge and confidence, earlier recognition of clinical signs of deterioration may assist with reducing ICU transfers related to clinical deterioration due to infections and sepsis.
This qualitative study explores the lack of African American students enrolled in AP Calculus courses in North Carolina public high schools. It considered the perception of student-counselor relationships, academic advising practices, and sense of identity of high school counselor participants. In-depth interviews were conducted with three, African American, female high school counselors with five to twenty-four years of experience in high school counseling. The data yielded five domains: characteristics of a school counselor, expected duties of a school counselor, criteria to become an AP Calculus student, student-counselor relationships, and academic advising practices and the outcomes. From the domains, twenty-seven themes were generated: empathetic, open-minded, organized, flexible, creative, knowledgeable, serving the holistic needs of students, classroom guidance activities, non-counselor duties, resource, enrollments, interventions, advocacy, completion of prerequisite courses, exceptions to the rule, teacher recommendation, AP agreement, importance, trust, connections, race, alternatives, methods, encouragement, benefits, awareness, and partnership. Recommendations include universal access to Math I for African American students in 8th grade, update all stakeholders of the role and purpose of school counselors, professional development for school counselors, and an integrated curriculum for school counselors and administrators.
The battery enclosures of current electric vehicles are made of metallic alloys, specifically aluminum or steel. Replacing these metallic alloys with a lightweight material, such as carbon fiber composite, may offer significant weight savings due to its comparable strength-to-weight ratio. Carbon fiber is corrosion-resistant and can be engineered for fire resistance and electrical insulation. It can also be fine-tuned for specific applications and performance needs, such as "crashworthiness".
Designing a carbon fiber-based battery enclosure for crash performance through trial-and-error experiments can be extremely laborious and inefficient. This inefficiency can be alleviated by using virtual manufacturing and structural analysis software. A simulation software chain allows for the virtual manufacturing and crash-testing of the battery enclosure in a single process. However, these numerical simulations are computationally expensive, time-consuming, and may require significant user interaction. Finding optimal design parameters within a reasonable time-frame can be extremely challenging.
The first part of this dissertation addresses the forward problem of accelerating the design of battery enclosures for crash performance. It involves developing a machine learning-based surrogate model of the simulation workflow that can provide quick, approximate results in a fraction of seconds. This can further support design space exploration studies.
Physical phenomena in engineering design are governed by differential equations, typically solved in a forward manner with known physical parameters, initial and/or boundary conditions, and a source term. However, there is often a need to reconstruct the source term from available measurement data, which may be corrupted with noise, along with the initial and/or boundary conditions, and physical parameters. These types of problems are known as inverse problems, more specifically, inverse source problems. Inverse source problems are often ill-posed and are usually solved by iterative schemes and optimization techniques with regularization, which can be time-consuming. In recent years, machine learning approaches have shown promise in managing ill-posed problems and handling noisy data.
The second part of this dissertation addresses a specific type of inverse source problem, known as the dynamic load identification problem, which involves determining the time-varying forces acting on a mechanical system from the sensor measurements. The study begins with the development of a deep learning model that leverages physics information to infer the forcing functions of both linear and nonlinear oscillators from observational data. Furthermore, the study leads up to a development of a physically consistent surrogate model that is capable of providing robust predictions from the noisy observations without the need to explicitly solve the differential equation.
Patients requiring admission to the Trauma Intensive Care Unit (TICU) represent some of
the most critically ill and complex cases within intensive care. These patients, often suffering
from significant trauma to vital areas, may necessitate prolonged enteral feeding, frequently
leading to the insertion of gastrostomy tubes. Despite the critical nature of gastrostomy tube
management for patients with severe trauma and the need for enteral feeding, there is a gap in
knowledge and confidence in this area. This gap necessitates targeted educational programs to
improve patient outcomes. This quality improvement project focused on the nursing staff in the
Trauma Intensive Care Unit (TICU) at a large academic medical center. The nurses received a
comprehensive education module developed according to Lippincott standards, which covered
the different types of gastrostomy tube types, nursing interventions, and documentation practices.
The module included a didactic component and hands-on practice with gastric tube models. A
pre-and post-test knowledge check was conducted to evaluate the learning outcomes. All 43
TICU staff registered nurses at the facility participated. After the educational module's
implementation, significant improvements were observed in nursing staff knowledge regarding
gastrostomy tubes. The median score for the pre-test was 70%, increasing to 100% on the post-
test. Wilcoxon sign-rank test showed a statistically significant difference between pre- and post-
test scores, z = 5.207, p < .001. The results demonstrate the effectiveness of the education
module in improving TICU nurses' knowledge of gastric tube care.
Sensor placement and Informative Path Planning (IPP) are fundamental problems that frequently arise in various domains. The sensor placement problem necessitates finding optimal sensing locations in an environment, enabling accurate estimation of the overall environmental state without explicitly monitoring the entire space. Sensor placement is particularly relevant for problems such as estimating ozone concentrations and conducting sparse-view computed tomography scanning. IPP is a closely related problem that seeks to identify the most informative locations along with a path that visits them while considering path constraints such as distance bounds and environmental boundaries. This proves useful in monitoring phenomena like ocean salinity and soil moisture in agricultural lands—situations where deploying static sensors is infeasible or the underlying dynamics of the environment are prone to change and require adaptively updating the sensing locations.
This thesis provides new insights leveraging Bayesian learning along with continuous and discrete optimization, which allow us to reduce the computation time and tackle novel variants of the considered problems. The thesis initially addresses sensor placement in both discrete and continuous environments using sparse Gaussian processes (SGP). Subsequently, the SGP-based sensor placement approach is generalized to address the IPP problem. The method demonstrates efficient scalability to large multi-robot IPP problems, accommodates non-point FoV sensors, and models differentiable path constraints such as distance budgets and boundary limits. Then the IPP approach is further generalized to handle online and decentralized heterogeneous multi-robot IPP. Next, the thesis delves into IPP within graph domains to address the methane gas leak rate estimation and source localization problem. An efficient Bayesian approach for leak rate estimation is introduced, enabling a fast discrete optimization-based IPP approach. Lastly, the thesis explores sensor placement in graph domains for wastewater-based epidemiology. A novel graph Bayesian approach is introduced, facilitating the placement of sensors in wastewater networks to maximize pathogen source localization accuracy and enable efficient source localization of pathogens.
