-
Prospective Students
-
Faculty and Staff
-
Current Students
-
Alumni and Friends
Global warming and climate change keep causing a catastrophic impact on the natural, social, economic, and political environment in many parts of the world. The urgency for the transition to a low-carbon economy through CO2 emissions reduction calls for innovative methods to harvest renewable energy sources to displace unsustainable fossil fuel power in North America. This work presents proposed methods for marine hydrokinetic and solar renewable power generation. On another front, since addressing the causes of global warming and climate change is not timely enough, this author proposes technologies to minimize their effects, which manifest through extreme weather events. Since renewables harvesting generates variable power profiles during extreme weather events, this work investigates high voltage interconnectors to smooth the total power variability of wind power farms far distant between themselves under hurricane events. Another effect of climate change is the increasing frequency of failures on overhead transmission lines due to extreme weather events. The author thus proposes a wide-area controller with phasor measurement and battery actuator to minimize the post-fault transients.
Beta-lactamase proteins are major contributors to antibiotic resistance, rendering beta-lactam antibiotics ineffective against bacterial infections. The emergence of novel beta-lactamases with expanded substrate specificity poses a global health threat. This study utilizes computational techniques to investigate the mechanisms by which beta-lactamases expand their substrate specificity, enabling bacteria to resist new antibiotics. By exploring the relationship between protein dynamics and function, the impact of enzyme motion on substrate specificity is elucidated.
Molecular dynamics simulations are conducted and analyzed to identify the functional dynamics involved in substrate recognition in beta-lactamase. Dynamic signatures are identified using a novel approach called Supervised Projective Learning with Orthogonal Completeness (SPLOC). Increased flexibility in loops neighboring the enzyme's active site facilitates optimal interactions with different antibiotics through local conformational flexibility. Notably, dynamic signatures differ between protein-antibiotic systems, highlighting the complexity of antibiotic binding mechanisms. These dynamic signatures are demonstrated as viable predictors of antibiotic resistance in beta-lactamase enzymes.
A proof-of-concept is presented for designing de-novo peptides that target these regions, offering a potential new class of beta-lactamase inhibitors capable of hindering the motions necessary for substrate recognition. This approach presents a promising strategy for controlling beta-lactamase-mediated antibiotic resistance.
We describe using detailed numerical simulations, the properties of detonation waves occurring in single-phase rotating detonation engines and the evolution of a shock-driven liquid fuel droplet. The studies span vastly different scales from the microscale at which the behavior of an isolated liquid fuel droplet has been investigated to device-scale simulations of a gas-phase rotating detonation engine.
Rotating Detonation Engines (RDEs) represent a relatively new concept in pressure gain combustion, where a detonation wave (DW) formed from injected mixture, travels circumferentially within an annular channel. The DW compresses the fuel to much higher pressures, resulting in the extraction of additional work and efficiencies not accessible through the conventional Brayton cycle. Mode transition in RDEs refers to an abrupt change in the number of detonation waves due to a change in inlet conditions such as the injected fuel reactivity and total pressure, and can affect engine performance. Through detailed numerical simulations in a 2D unrolled RDE geometry, an alternate mechanism for mode transition is proposed, along with a corresponding quantitative criterion that is validated using simulation data. A simple model to predict the number of DWs following mode transition is proposed and verified using simulation data.
In the second part of this thesis, we describe detailed numerical simulations of a liquid fuel droplet impacted by a Mach 5 shock wave, considering the effects of chemical reactions and phase change due to evaporation. The fuel droplet undergoes significant deformation and morphological changes following shock impingement, as the droplet surface becomes unstable to the Kelvin-Helmholtz instability. The production of fuel vapors by the droplet impairs the growth of such surface instabilities, leading to reduced growth of the droplet surface area when compared with a non-evaporating droplet. As the fuel vapors react, a diffusion flame is formed on the droplet-windward side, leading to intense droplet heating and enhanced vapor production in this region. Our results show significant spatial inhomogeneities are present in the droplet flowfield in all the cases investigated, which must be considered in the development of reduced order point-particle models for system-level simulations of detonation engines.
The increased reliance on Big Data Analytics (BDA) in society, politics, policy, and industry has catalyzed conversations related to the need for promoting ethical reasoning and decision-making in the mathematical sciences. While the majority of professional data scientists today come from privileged positions in society, those processed by the decisions made using data science are more often members of one or more marginalized social groups, translating into disproportionately negative outcomes for these individuals in society. Thus, it is argued that future citizens must develop an ethical mathematics consciousness (EMC) that human beings do mathematics; thus, there are potential ethical dilemmas and implications of mathematical work which may affect entities at the individual, group, societal, and/or environmental level. Drawing from this conjecture, the purpose of this Design-based research study was to develop a local instruction theory and materials that promote students’ ethical mathematics consciousness in a high school Ethical Data Science (EDS) course grounded in a feminist, relational ethic of caring and social response-ability. Outputs include the identification of design heuristics, including the task structures, participation structures, and discursive moves that supported students' development of EMC and equitable participation in classroom activities, an initial curriculum for the EDS course, and a student-use protocol and corresponding analytic framework for making critically conscious ethical decisions in data science.
The increased reliance on Big Data Analytics (BDA) in society, politics, policy, and industry has catalyzed conversations related to the need for promoting ethical reasoning and decision-making in the mathematical sciences. While the majority of professional data scientists today come from privileged positions in society, those processed by the decisions made using data science are more often members of one or more marginalized social groups, translating into disproportionately negative outcomes for these individuals in society. Thus, it is argued that future citizens must develop an ethical mathematics consciousness (EMC) that human beings do mathematics; thus, there are potential ethical dilemmas and implications of mathematical work which may affect entities at the individual, group, societal, and/or environmental level. Drawing from this conjecture, the purpose of this Design-based research study was to develop a local instruction theory and materials that promote students’ ethical mathematics consciousness in a high school Ethical Data Science (EDS) course grounded in a feminist, relational ethic of caring and social response-ability. Outputs include the identification of design heuristics, including the task structures, participation structures, and discursive moves that supported students' development of EMC and equitable participation in classroom activities, an initial curriculum for the EDS course, and a student-use protocol and corresponding analytic framework for making critically conscious ethical decisions in data science.
In contemporary society, individuals with substance abuse histories face a multitude of challenges that extend far beyond the physical and psychological effects of addiction. As they embark on the path of recovery and strive for reintegration into society, they are confronted with an additional formidable barrier: the pervasive stigma and discrimination that persistently accompany their past struggles. This dissertation seeks to illuminate the profound impact of stigma and discrimination on individuals with substance abuse histories, exploring the underlying factors that perpetuate these harmful attitudes, and proposing potential strategies to alleviate their burden. Comprised of three interconnected papers, this research analyzes trust dynamics, stigma, and social support towards this population, offering valuable insights for combating stigma and fostering a more inclusive and compassionate society.
The first paper focuses on the power of positive information to counteract negative stereotypes and enhance trust in everyday interactions involving individuals with substance abuse histories. By examining the ways in which positive information can mitigate stigmatizing perceptions, this paper uncovers strategies to promote understanding and empathy in social encounters, paving the way for more meaningful connections and reduced discrimination.
Moving forward, the second paper explores participants' perceptions of trust and trustworthiness when engaging with partners who possess varying substance abuse histories in a trust game. By investigating how participants' knowledge of their partners' backgrounds influences expectations of reciprocity and trustworthiness, this paper unravels the complex dynamics that shape interpersonal relationships. The findings shed light on the potential for shifting perceptions and dismantling biases, ultimately fostering an environment where trust can flourish. Lastly, the third paper investigates the social and relational factors that influence cooperation and support for individuals with substance abuse histories within familial and friendship networks. By identifying the barriers that hinder cooperation and providing recommendations for creating supportive environments, this paper aims to strengthen social support networks and facilitate a more compassionate and inclusive community for individuals in recovery.
Collectively, these three papers contribute to the broader goal of combating stigma, building trust, and fostering cooperation towards individuals with substance abuse histories. The findings underscore the pivotal role of positive information, perceptions of warmth and trustworthiness, and the significance of individual attitudes and social support networks in reducing stigma and cultivating an environment that embraces recovery. By revealing the complexities of stigma and discrimination, this dissertation aspires to inform policies, interventions, and societal attitudes that empower individuals with substance abuse histories to thrive and reintegrate into society with dignity and respect.
Additive manufacturing, specifically the Fused Deposition Modeling (FDM) method, has emerged as a promising technique for manufacturing. Using FDM, complex geometries can be created using precise layer-by-layer deposition of material. Among the advantages of this method are its cost-effectiveness, rapid prototyping capabilities, and ability to customize. Due to the similar melting point of ferroelectric polymers PVDF and PVDF-TrFE, which can be used for FDM printers, this study examined the possibility of using FDM for additive manufacturing of PVDF and PVDF-TrFE sensors with enhanced piezoelectric and pyroelectric properties. The resulting sensors can find applications in diverse fields such as biomedical engineering, robotics, energy harvesting, and sensing technologies, enabling advancements in various sectors that require sensitive and reliable sensor systems. Although both PVDF and PVDF-TrFE can be printed by FDM, the XRD result indicated that only PVDF-TrFE crystallized in the polar phase upon cooling from the melt while PVDF always crystallized in the nonpolar phase. Therefore, only PVDF-TrFE could be used for piezoelectric and pyroelectric samples. Using the corona discharge method, consistent responses from both piezoand pyroelectric sensors were observed. Using a 30 mW laser, samples were measured for pyroelectricity. Upon poling at 25 kV for 10 minutes at room temperature, the maximum pyroelectric response was 50 mV. Samples were clamped in one end and measured in deflection mode for their piezoelectric response. Upon stimulating the free end of a PVDF-TrFE sample printed on a PVDF layer as a substrate, 130 V of open circuit piezoelectric response was observed.
Recent advancements in manufacturing and post-processing of optics with sub-aperture methods have enabled greater degrees of freedom to realize complex optical surfaces. Introduction of residual mid-spatial frequency (MSF) surface errors is a consequence of sub-aperture manufacturing. MSF errors have spatial frequencies between ‘low frequency’ form and ‘high frequency’ roughness with ambiguous bounds and with distributions that range from nearly random to highly deterministic and complex, depending on manufacturing method and conditions. MSF errors degrade optical performance and present significant challenges for both specification and optical performance predictions. The lack of specifications that directly connect to optical performance and the lack of widely available capabilities and procedures for modeling of generalized MSF errors are significant impediments to understanding their impacts in imaging systems.
The primary goals of this dissertation are (1) to explore and expand the connections between MSF specifications and optical performance for complex MSF error distributions, and (2) to demonstrate the implementation of these concepts within commercial software packages to enable the exploration of generalized MSF errors in optical systems. Results are addressed through three articles. The first article addresses the mathematical development and benefits of pupil-difference probability distribution (PDPD) moments to specify MSF errors and connect them to optical performance. The second article builds from this work to provide modeling procedures and explores their application to both random and deterministic MSF errors. The third article demonstrates the integration of these new concepts and methods into MATLAB™ and CODE V™, and their use on sample refractive and reflective imaging systems.
From the perspective of manufacturing, it is not always ideal to use conventional methods of surface and optical metrology. Most optical metrology systems require a stable and pressure-temperature controlled environment in a laboratory setting as they are sensitive to such disturbances. The main contributing factor to these levels of sensitivity is the presence of a reference mirror. In most interferometric systems, interference patterns are generated by overlapping the wavefield generated from the object being measured with that generated from a reference mirror. Different algorithms can be used further by modulating the interference patterns to generate three-dimensional surface maps. Similar setups and algorithms could be used to not only generate the surface maps but also the object complex wavefield which extends its applications in digital holography. The goal of this research is to develop interferometric holography systems that are robust to environmental effects and suitable for in-situ metrology in manufacturing processes. Part 1 of this dissertation focuses on developing a lateral shear interferometric holography system using a pair of geometric-phase (GP) gratings. Two designs are proposed which allowed for different shear selection strategies. The proposed designs are robust to environmental effects by virtue of their design as a self-referenced and common-path configuration. A polarized camera sensor is used to record the interferograms with different phase shifts. Using an alternating projection algorithm, the recorded intensity maps are used to estimate the object wavefield. The errors generated by the algorithm are studied as a function of the shears selected to record the interferograms using synthetic intensity maps for both designs. The correlation is investigated using spatial and frequency information density functions and the errors generated by both designs are compared. Part 2 investigates the limitations of selected shears from the perspective of the spatial information density function. The major outcome from Parts 1 and 2 is proof that the shear selection strategies, the shear amounts, and the shear orientations affect the wavefield reconstruction. This leads to Part 3 of the dissertation which focuses on the optimal selection of shears for this system. Due to the complexity of the equations that govern the effect of shears on the reconstruction of different surface frequencies, a statistical approach was used to optimize the shears based on simulations that reconstructed a defocused point source wavefield. A point source wavefield is used for these simulations because it is the ideal wavefield demonstrating the reconstruction of all possible frequencies within the field of view. The results were compared to frequency information density maps to correlate the results. Parts 1,2 and 3 show a complete work starting from exploring designs to identifying optimal shear settings for a coherent digital holography system to measure transmissive and reflective samples. Part 4 shows a secondary application for this system that uses the GP grating pairs to make a fringe projection system that is suitable for diffused surfaces. The proposed system provides flexibility to adjust the characteristics of the projected fringes easily by changing the space between the gratings and the grating pair orientation. Example measurements are presented, and the capabilities of the setup are demonstrated. The proposed design can produce adjustable fringe patterns with fringe spacing varying from large values to as small as sub-millimeter distances. The fringe orientation can also be changed, and the patterns can be projected on objects of a wide range of sizes without losing the fringe contrast.
