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Beam Hardening and Scatter Artifact with Metal Objects Inside and Outside the Field of View in CBCT Imaging
No full textIntroduction: This study compares the objective and subjective appearance of artifacts produced by metal objects inside and outside the limited field of view (FOV) of a common region of interest (ROI).
Methods: Metal objects (titanium rods, zirconium rods, zirconium crucibles) were positioned in the human cadaver���s left maxilla and imaged using Accuitomo 170 CBCT with limited FOV. Sixteen scans (7 with metal inside the FOV, 7 with metal outside the FOV, and two controls) were obtained. The artifacts were objectively evaluated by calculating the standard deviation (SD) of grayscale values using ImageJ software. For the subjective test, two observers rated paired images for beam hardening/scatter artifacts.
Results: The Wilcoxon signed rank test showed no difference in the SD of grayscale values with the metal objects inside or outside the FOV (p=.2882). However, a subjective comparison of the two image groups (metal objects inside and outside FOV) showed subtle differences in beam hardening intensity in the region immediately adjacent to the metal objects. The SD of grayscale values for slices at the level of metal objects were higher than those apical to the level of metal objects (p<0.001). Conclusion: Strategic repositioning of a limited FOV CBCT to avoid metal objects did not significantly reduce the image noise. However, it reduced the beam hardening artifact on the area of the image immediately adjacent to the metal object. Additionally, selectively tilting the patient���s chin to keep metal objects in a different horizontal plane from ROI may reduce artifact appearance
A Multifaceted Investigation of Cloud Microphysics: From Improving Convective Cloud Microphysics Parameterizations to Revealing Aerosol-Cirrus Cloud Interactions
No full textCloud microphysical processes and their parameterizations are at the core of multi-scale atmospheric modeling but still remain one of the major sources of uncertainty in weather and climate simulations. The aims of this dissertation are twofold: advancing our understanding of the physical processes involved in aerosol-cloud interactions and developing new parameterizations for better representing cloud microphysics in global climate models (GCMs). This dissertation is divided into two main parts. Part I focuses on evaluating and improving cloud microphysics parameterizations for convective clouds in GCMs. Part II focuses on investigating the impacts of volcanic aerosol on cirrus cloud using satellite observations, detailed microphysical model simulations and two GCMs.
GCMs have started treating convective clouds with detailed cloud microphysics parameterizations. However, the representations of convective cloud microphysical processes are often based on those for large-scale stratiform clouds, warranting further model evaluation for the fidelity of microphysics treatments transferred among various cloud types. Here, we evaluate and improve several aspects of the convective cloud microphysics in the NCAR Community Atmosphere Model version 5.3 (CAM5.3), including processes of hydrometeor sedimentation, graupel production, convective snow detrainment, and rain generation against ground-based and satellite observations. Our model development efforts lead to substantial improvements in the simulations of cloud radiative forcing, graupel microphysics, convective cloud ice amount, and tropical precipitation over the default model settings. These improvements set a better stage for future studies of convective cloud processes and their interactions with large-scale environments and aerosols.
Explosive volcanic eruptions inject a large amount of sulfur dioxide and ash particles into the upper troposphere and lower stratosphere, where volcanic-origin aerosols (sulfate and ashes) may modify cirrus cloud microphysics through ice nucleation but to an unknown extent due to limited research on this topic. Here, we aim to narrow this knowledge gap with the advent of advanced satellite retrievals of aerosol and cloud capturing the episodes of enhanced stratospheric aerosol loadings produced by modern moderate-magnitude eruptions. An analysis of 10-yr satellite datasets shows a phenomenal decrease in number and increase in size of cirrus ice crystals in the midlatitude lower stratosphere in response to ash-rich volcanic eruptions (2008 Kasatochi, 2009 Sarychev), indicative of heterogeneous freezing on volcanic ash suppressing homogeneous freezing. Conversely, cirrus clouds for the ash-deficit scenario (2015 Calbuco) are found to have up to 2.2 times more ice crystals, implying a moderately enhanced homogeneous freezing on volcanic sulfate aerosols. These impacts of aerosol on cirrus cloud, disentangling influences from meteorological co-variability, are most likely. Cloud parcel model with detailed physical ice nucleation processes is employed to elucidate the mechanisms of aerosol effects and the modeling results corroborate the observational findings. Sensitivity modeling experiments are also performed using two GCMs, CESM2.2 and E3SM-PA. Impacts of sub-grid scale vertical velocity generated by gravity waves on cirrus ice formation and volcanic ash emissions are found absent, identifying models��� inability to accurately capture the response of cirrus clouds to volcanic emissions and areas for future model development.
The studies in this dissertation advance our understanding of volcanic aerosol-cirrus cloud interactions on the process-level, stress the necessity and importance of accurate representations of cloud microphysical processes in GCMs, and advocate iterative effort in improving parameterizations as GCMs are the only means by which we project future climate
Using Stimuli-Responsive Material for the Design and Fabrication of Artificial Muscles
No full textStimuli-responsive materials that change shape (i.e., elongate, contract, and/or twist) when exposed to an appropriate stimulus are promising candidates to replace traditional machines in biomedical devices. This dissertation explores the innovative use of stimuli-responsive materials in addressing the challenges of treating stress urinary incontinence (SUI), a condition that affects nearly 50% of women during their lifetime. Current treatments for SUI are associated with complications leading to undesirable outcomes such as postoperative voiding dysfunction. The research, divided into four key chapters, focuses on the development and application of artificial muscle devices based on two distinct stimuli-responsive materials ��� Liquid Crystal Elastomers (LCEs) and Magnetoactive Elastomers (MAEs) for the potential treatment of SUI.
In Chapter I, the dissertation commences with a comprehensive introduction to stimuli-responsive materials, elucidating their pivotal role in the design and fabrication of artificial muscles. Emphasizing the versatility of two materials (LCEs and MAEs), the chapter provides a foundation for their application in the subsequent chapters. Chapter II delves into the pathophysiology of SUI, providing a thorough overview of the condition, including its causes, prevalence, and impact. This section establishes the contextual framework for the subsequent exploration of the subsequent development of LCE and MAE-based devices for urethral support. We also provide relevant information that must be considered when designing in vitro models of the urinary tract and selecting appropriate animal models to evaluate devices. Chapter III focuses on the design, fabrication, and in vitro and in vivo evaluation of a dynamic urethral support device based on LCEs. In Chapter IV, I extend the exploration to MAEs and investigate their integration into a dynamic urethral support device. MAE-based devices were fabricated, characterized, and then evaluated using a simple in vitro urinary system simulating the effects of stress or cough.
Collectively, this dissertation contributes to the interdisciplinary field of biomedical engineering by integrating stimuli-responsive materials with innovative solutions for SUI. Investigating the potential of LCEs and MAEs in providing adaptive and customizable support, this dissertation presents a novel approach to addressing SUI through advanced materials. The findings presented herein pave the way for further advancements in the design and fabrication of artificial muscles, offering hope for improved therapeutic interventions for complex healthcare challenges
An Economic Analysis of Dynamic and Causal Impacts on the Red Meat Market in the United States
No full textThe red meat market has recently undergone several significant changes in response to changing market conditions, supply chain shocks, and policy impacts. The overall objective of this study is to contribute a better understanding of how segments of the red meat industry respond to shocks. Specifically, this study (1) uses a vector error correction model and directed acyclic graphs to analyze the dynamic interactions and causal effects between cold storage stocks, prices, imports, and exports in the red meat market (2) analyzes the impacts of California���s Proposition 12 animal welfare law on retail and wholesale prices using a difference-in-differences framework and (3) analyzes the dynamic interactions and causal inference patterns of U.S. pork exports to Mexico to further investigate the causes of changing pork export patterns and the impact of the price spread between bone-in and boneless hams on pork exports (volume) to Mexico.
The first essay finds that imports and exports are primary drivers of changes in pork and beef cold storage stocks. The second essay finds that California���s Proposition 12 and Massachusetts��� Question 3 have resulted in price increases ranging from 6% to 21% throughout the supply chain. Finally, the last essay finds that a shock in price spread would cause a long-term impact on bone-in ham exports to Mexico, that a shock in price spread would only account for a small amount of forecast error variation in bone-in exports, and that there is not a direct contemporaneous causal relationship between the two. Ultimately, this study provides valuable information to producers, policy-makers, consumers, and other stakeholders regarding how segments of the red meat industry responds to changing market conditions, supply chain shocks, and policy impacts
Reliability and Economics of Distribution Systems with Edge-Level Distributed Energy Resources
No full textElectrical power distribution systems are experiencing a pivotal transformation due to the increasing integration of edge-level behind-the-meter (BTM) distributed energy resources (DERs). This transformation introduces challenges in the planning and operation of distribution systems. Primary challenges include reliable delivery of electricity to the end user while maintaining the utility���s financial viability. This dissertation introduces a multi-level hierarchical framework that bridges the gap in the current distribution system reliability assessment by incorporating the complexities and stochastic nature of end-user DERs. This framework is adaptable to distribution systems with varying levels of DER penetration and addresses the technological diversity and unpredictability inherent in DERs, making it a significant advancement over existing methodologies. This work developed a modular general-purpose end-user reliability model that forms the basis for developing reliability assessment methods and revenue impact analysis. A bottom-up probabilistic approach is presented that integrates the end-user with BTM DER into the reliability assessment. A notable innovation in this work is the application of probabilistic distributions to quantify the end-user BTM DER penetration and integrate them into the probabilistic approach to assess distribution system reliability in various DER penetration scenarios. Economic impact assessment forms another crucial dimension of this dissertation, encompassing a comprehensive exploration of the implications of end-user BTM DER integration for utility revenue, customer costs, and overall system economics. The dissertation examines the cost-benefit dynamics of DERs and the influence of regulatory policies such as Net Energy Metering (NEM), quantifying the economic impacts under various DER adoption scenarios. The dissertation employs reliability test cases and simulation analyses to study the effectiveness of the developed framework and assessment methodologies. This dissertation contributes to the field of power systems by providing methods and tools for managing the challenges and opportunities presented by end-user BTM DER in system planning and integration
The Effects of a Structured Literacy Computer Program Implemented at Home on the Early Literacy Skills of Preschool Children
No full textThe purpose of the current study was to examine the effects of a computer program on the early literacy skills of preschool children, the relationship between fidelity to the intervention and improvement in literacy skills, and the reported satisfaction with the program. Forty-two four- and five-year-old children were randomly assigned to an intervention group, which used the OgStar Reading Early Reader iPad computer program/app, or a control group, which used the IXL Math computer program/app. The recommendation was to engage with the program for 15- 20 minutes per day for five days a week over a period of eight weeks in the summer prior to kindergarten. Three DIBELS measures were used to assess early literacy skills: Letter Naming Fluency (LNF), Phoneme Segmentation Fluency (PSF), and Nonsense Word Fluency (NWF). Parents submitted fidelity data about the number of lessons completed and completed a survey to assess their opinions regarding the use of the program. A total of 33 children completed posttests. Using linear regression and controlling for pretest score, students in the intervention group scored statistically significantly higher on LNF posttests (g = 0.41, p = .025) and NWF- correct letter sounds posttests (g = 0.52, p = .009) over the control group. No statistically significant differences were found between the groups for PSF (g = 0.19, p = .458) or NWF- words recoded correctly (g = 0.61, p = .057). Overall, fidelity to the planned intervention varied across participants. The number of lessons completed was moderately related to participant gains in LNF (r = 0.38), NWF- correct letter sounds (r = 0.31), and NWF- words recoded correctly (r = 0.36). Parent reported level of satisfaction with the app was generally positive. Parents reported they thought their child learned new skills (4.8/6.0) and would recommend it to other parents (4.5/6.0). These findings provide some initial support that the use of the OgStar Early Reader app may improve alphabetic knowledge for preschool children. Further study of the program and its effectiveness for a variety of participants and contexts is needed
Biological Roles of Bone Morphogenetic Protein 1 (BMP1) in Periodontium
No full textThis thesis explores the pivotal role of Bone Morphogenetic Protein 1 (BMP1) in the development and maintenance of the periodontium, employing Bmp1 conditional knockout (cKO) mouse models. The research involved creating a unique lineage of mice with a targeted deletion of the Bmp1 gene in dental follicle cells during early embryonic development, specifically in the progenitor cells destined to differentiate into alveolar bone osteoblasts, cementoblasts, and periodontal ligament (PDL) fibroblasts, known as Osr2-Cre;Bmp1^flox/flox mice. This study provides a detailed comparative analysis of the alveolar bone, cementum, and PDL structures in these Bmp1 cKO mice against normal control mice through various analytical methods, including plain x-ray radiography, histological examination, and immunohistochemical (IHC) analysis. Our findings reveal significant disruptions in PDL collagen fiber organization and bone matrix development, leading to substantial alveolar bone loss in Bmp1 cKO mice at both 6 and 24 weeks of age. Histological and IHC analyses highlighted a reduction in PDL integrity, abnormal protein distributions, and significant decreases in Dentin Matrix Protein 1 (DMP1) levels, suggesting BMP1's essential role in collagen synthesis and the overall mineralization process. Notably, irregular distribution patterns of periostin and fibrillin, underscore the profound impact of BMP1 deletion on dental morphology and periodontal health. The study conclusively demonstrates that BMP1 is critical for the structural integrity and functional maintenance of the periodontium, influencing various proteins involved in the development and health of the periodontal ligament and alveolar bone. The observed defects in Bmp1 cKO mice highlight the importance of BMP1 in collagen network maintenance and alveolar bone formation, with significant implications for periodontal disease pathology and treatment strategies. This research underlines the complex interplay between BMP1 and other proteins in periodontal development, providing invaluable insights into the mechanisms underpinning periodontal health and disease
Optimization-based Scheduling in Multi-service Appointment Systems with Application to College Counseling Centers
No full textAppointment scheduling is a crucial problem in various domains such as healthcare and logistics. In practice, several complicating factors make the decision-making process challenging. In this work, we study multi-service appointment scheduling systems having non-stationary arrival processes, with a special focus on college Counseling and Psychological Service (CAPS) centers. Given the increasing prevalence of mental health issues among college students and the resource constraints faced by CAPS centers in addressing the rise in demand, our goal is to propose data-driven solutions that improve students��� access to these vital services.
To achieve this, we adopt a two-step methodology. First, we develop a comprehensive discrete-event simulation (DES) model that accurately reflects the complexities of CAPS center operations. This model acts as a testing ground for evaluating the performances of different scheduling-related policies. Second, we construct optimization-based frameworks that leverage historical demand to identify data-driven schedules that lead to good-performing systems, while incorporating several realistic factors like multiple customer classes and their associated importance, time-varying demands, resource limitations, and implementability. To address the challenge of characterizing system performance for such complex stochastic systems, we develop stylized optimization models based on approximation schemes that capture the transient behavior of the original system. Further analysis leads to key structural properties, which we use to devise efficient globally convergent solution schemes for the stylized models. Our numerical experiments, based on data obtained from Texas A&M University���s CAPS center, demonstrate the benefits of the proposed scheduling methodologies, leading to policies that significantly enhance system performance compared to current scheduling practices
Modeling and Design Optimization of Thermal Hydraulic Systems for Advanced Reactor Applications
No full textThis study presents the thermal���hydraulic phenomena within advanced reactor applications for design and modeling. It is organized into three main sections, each tackling distinct yet interconnected aspects of thermal���hydraulic applications in advanced nuclear reactors.
In the second main section of this dissertation, artificial neural networks (ANNs) and advanced machine learning algorithms are used to predict the friction factors and flow regimes that change from laminar���to���transition and transition���to���turbulent in wire-wrapped fuel assemblies. The ensemble methods showed superior performance for classification of the flow regimes, with accuracies exceeding 95%. The ANN model for friction factor outperformed traditional correlations, with a mean error of 0.10%. This study represents a significant advance in understanding and predicting hydrodynamics in wire-wrapped fuel assemblies.
The third main section identifies the most promising phase change materials for latent heat thermal energy storage in high-temperature applications for heat pipe���cooled microreactors. Twenty-one eutectic salts were studied based on their thermophysical properties and performance metrics. The most promising candidates were MgCl2���NaCl and CaCl2���NaCl. In addition, copper and aluminum foams were investigated for compatibility and durability under extreme conditions. The mixture of copper foam and CaCl2���NaCl exhibited the least corrosion. After a 300-hour melting/solidification cycle, the melting temperature of CaCl2���NaCl remained stable, confirming its reliability for high-temperature thermal energy storage (TES) applications.
The fourth main section of the research delves into a new concept of a tree-shaped fin design for a latent heat thermal energy storage system. The research has made significant progress by utilizing a novel approach that goes beyond the traditional focus on (i) multi���objective considering both power density and energy density, (ii) investigating all variables freely varying using global searching optimization, and (iii) the constraint of the evenly distributed last branch. The study used surrogate���based multi���objective optimization, specifically the Random Forest model, to explore energy density in volume fractions ranging from 9% to 44%. The study achieved a 33% optimal volume fraction for the fin design, resulting in a 61.6% increase in power density and a 38.18% reduction in melting time compared to conventional plate fin designs.
In essence, the overall objective of this dissertation is to contribute valuable insights and methodologies to the ongoing research targeted at enhancing thermal efficiency, safety, and cost��� effectiveness in advanced reactor designs using computational techniques
Sequential Decision-Making Under Uncertainty in Multi-Robot Target Tracking Application
No full textA significant advance in robotics and automation is making robots handle uncertainty, a prerequisite to deploying robots outside the lab. To interact with a stochastic environment, the significant effort of evaluating possible conditions challenges the robotic system���s reaction and functionality. For example, how to meet the typical 1 Hz decision frequency with online planning to accommodate uncertainties in a multi-robot system, combined by robots maintaining multiple robotic layers in a single-board computer.
This thesis focuses on such trade-offs between computation and optimality in the world of uncertainty. We formalize planning tasks as Partial Observation Markov Decision Process (POMDP) as it models uncertainties in a horizon-based mathematical model.
The first topic is to plan under time-dependent stochastic constraints, specifically traffic signals. The vehicle���s pass at the intersection depends on the stochastic fixed-pattern traffic signal, introducing the time dimension to the planning space. We propose a spatial horizon-based search to avoid such a curse of additional dimensionality in the planning. Furthermore, the uncertainty of the future signal is overcome by the event of an initial signal change, after which the problem is no longer stochastic. With probabilistic modeling of traffic signal dynamics, we define such planning as an MDP and obtain the solution.
Uncertainty needs tackling in partially observable environments and multi-agent systems. Our second topic, multi-sensor active target tracking, involves interacting with objects of interest (OOI) in a multi-robot system. The objective is to deploy multiple robots with a limited field of view (FoV) to maximize the system-wise target tracking performance. We formulate the problem as a POMDP and apply the approximate dynamic programming (ADP) method to generate the receding horizon control policy for the POMDP. Planning horizon incorporates the estimation of OOIs to handle tricky target-tracking scenarios such as divergent OOI trajectories. On the other hand, extending the horizon increases resilience to the uncertainty of the world model, such as the unknown object of occlusion to the sensing.
The multi-agent aspect injects other robots��� actions, which become a consideration in the uncertainty of the ego agent. Agent-by-agent planning reduces the complexity of planning, and we found the performance boundary of such sequential decision-making based on the locally Greedy algorithm. Since the original locally Greedy algorithm does not consider agents��� policies, which are not updated, adding the intention of those agents shows improvement based on our empirical studies. From implementing multi-agent planning based on receding horizon and objective optimization, we observe the emergent behaviors of cooperation. We show the consistency between planning algorithms��� optimality and the frequency of cooperative behavior in active target-tracking scenarios.
In summary, this dissertation mainly includes algorithmic research in robotic planning in an environment with various uncertainties: sensing, world model, and other agents��� decisions. It also contributes to the eco-driving and multi-robot sensing system application in the problem statement, algorithm design, behavior analysis, and simulation