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Interview with Melissa Gentry; Can High School Counselors Help the Economics Pipeline?
No full textIn the Winter 2025 issue of PERCspectives on Researh, PERC's M.J. Grove Scholar Melissa Gentry shares her experience working at the Boston Federal Reserve and researching disability economics. A summary of her paper, "Can High School Counselors Help the Economics Pipeline," discusses the outcomes of providing economics-focused workshops for high school counselors on admissions and student major choice
Machine Learning-Based Automated Fault Detection and Diagnostics in Building Systems
No full textAutomated fault detection and diagnostics (AFDD) analysis in commercial building systems using machine learning (ML) can improve the building���s efficiency and conserve energy costs from inefficient equipment operation. Boolean rules-based analysis is standard in current AFDD solutions but limits analysis to the rules defined and calibrated by energy engineers. As part of this dissertation, an automated process was developed to provide ML-based building analytics to building engineers and operators with minimal training in ML. The process can be applied to buildings with a variety of configurations, which reduces time and manual effort required for fault analysis when compared to Boolean rule-based systems. The developed procedure introduces advanced diagnostics with automatically generated metrics to validate the ML model���s predictions and rank detected faults in order of fault severity. Explanations of the methodology used for the ML analysis include a description of the algorithms used.
The analysis was applied to a building on the Texas A&M University campus where the results are shown to illustrate the performance of the process using measured data from a commercial building. Three case studies which analyze the building���s equipment are presented to show ML���s advantages over rule-based analysis. ML can detect faults in the system caused by degrading components. ML can also detect faults in system components with missing sensors by modeling expected system operation and making comparisons to actual system operation. An example of ML detecting a failure in a building is shown along with a demonstration of the decision boundaries of ML-based FDD in comparison with Boolean rule-based analysis. The results from these examples are used to show the strengths and weaknesses of using ML for AFDD analysis
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
Amino Acid Type and Concentration Impact on Endothelial Nitric Oxide Synthase in Restructured Hams
No full textThis study investigated amino acid types, either singly or in combination, at varying concentration levels to determine which was most effective as a substrate for the endothelial nitric oxide synthase system (eNOS) enzyme to generate nitric oxide for its evaluation as an alternative curing system. Restructured hams were manufactured with pork semimembranosus muscle with a 20% brine consisting of salt, sugar, phosphate and sodium erythorbate and addition of either L-arginine (Arg), L-citrulline (Cit) or in combination (Arg/Cit) at concentrations of 1000, 3000 or 5000ppm. A nitrite (NaNO2) control (200 ppm) was also included. Hams were cooked to (71��C), chilled, vacuum packaged and analyzed on day 1, 7, 28 and 56 of refrigerated (4��C) storage for residual nitrate (RNO3) nitrite (RNO2) and nitroslyhemochromagen (NO-Heme). Sensory panel and textural attributes were analyzed on day 28. For RNO3 values an interaction (p=0.0001) was observed for amino acid type and concentration. Trends suggested 1000ppm concentration for amino acid treatment combinations resulted in higher NO3 values. The main effects of amin acid type and concentration did not affect RNO2 values in the restructured hams however all amino acid treatments produced ~2/3 of the amount of RNO2 than nitrite control. An amino acid type x concentration interaction (p=0.01) NO-Heme values. An amino acid x storage day interaction (p=0.001) NO-Heme values was observed, however, no amino acid treatment or concentration was more effective at generating NO-Heme values. Amino acid type influenced Cured Ham ID (p=0.004) and Ham Flavor Aftertaste (p=0.006) with Arg exhibiting scores closest in value to the nitrite control. Amino acid type also affected Soured Aromatic (p=0.03) and Chemical/Medicinal/Metallic (p=0.001) with Cit treated ham having the highest values for both attributes. An amino acid x concentration (p=0.0001) interaction was observed for objective Hardness values with Arg treated hams values increasing as concentration increased and exhibiting closest values to nitrite control. The data from this study suggests that Arg most effectively cures restructured hams by activation of the eNOS system to generate NO and that a concentration of 1000ppm may be sufficient
Deciphering Cell Systems: Machine Learning Perspectives and Approaches for the Analysis of Single-Cell Data
No full textThis doctoral dissertation delves into the application of machine learning techniques in molecular biology, exploring gene expression regulation at the single-cell level and navigating the intricacies of cellular biology. The study specifically focuses on the utilization of modern neural networks to address cell-cell communications, gene function inference, and decipher protein expression. These applications aim to elucidate the complex interactions governing cellular behavior, as evidenced by the analysis of single-cell RNA sequencing (scRNA-seq) data.
In pursuit of these goals, I have developed and implemented advanced computational methodologies that combine systems biology and modern neural networks techniques. These methods are specifically crafted to manage the high-dimensionality and complexity of single-cell data, facilitating a more nuanced comprehension of genotype-phenotype relationships.
This research makes a significant contribution to the field of computational biology by proposing the use of neural networks to tackle the longstanding optimization problem in manifold learning. Furthermore, the study investigates generative models for learning gene regulatory networks and simulates gene knockout at the single-cell resolution. Lastly, the research delves into enhancing the interpretability of black box neural network models, applying them to multimodality data.
This research also contributes to the cell biology field by first providing an in-depth analysis of cell-cell interactions, highlighting how these interactions shape cellular behavior and influence disease progression. In addition, this research investigates gene function prediction, focusing on how gene knockouts can affect cellular phenotypes and their potential therapeutic implications. Lastly, this research looks into how gene expression patterns translate into protein expression and how accurately and interpretably this translation process can be predicted. This aspect of this research yields important insights into the functional implications of gene expression, which may be applied to the understanding of disease mechanisms and drug responses.
This research serves as a valuable resource because, in addition to the three introduced tools, it provides a comprehensive overview of state-of-the-art methodologies and their respective applications in the analysis of single-cell data within the recent years.
In conclusion, this doctoral dissertation represents a significant contribution to the field of computational biology and cellular biology by providing novel methods and insights into the genotype-phenotype relationships at the single cell level. These methods and discoveries not only improve our understanding of cellular behavior, but also pave the way for the creation of novel therapeutic strategies, thereby potentially enhancing our ability to combat a wide range of diseases
Shame as an Affective Component of Pain Experiences
No full textThe experience of pain is a warning that potential harm has come to the body. Furthermore, pain is recognized as a biopsychosocial phenomenon, with a person���s traits, emotions, social interactions, and environments all encompassing pain outcomes. As such, identifying specific psychological components of the experience of pain may help further guide our understanding of the manifestation and persistence of pain. One potential aspect of pain may be the self-conscious emotion of shame, a feeling arising from the judgment of others acting as a warning that something about one���s self is socially unacceptable. Indeed, people living with chronic pain frequently express feeling shame because of how pain impacts their lives; however it has not been assessed in relation to the exacerbation of pain itself. Therefore, this dissertation employed four studies examining the relationship between shame and pain, specifically hypothesizing that greater feelings of shame would be associated with greater pain experiences. Studies 1a and 1b were correlational online studies, finding that shame was positively associated with clinically relevant self-report of pain. Study 2 found a similar relationship with self-reported measures of central sensitization, but not laboratory-based sensory measures central sensitization as assessed by mechanical temporal summation. Study 3 explored the relationship between shame and endogenous opioid response measured using conditioned pain modulation, and also did not find support for the overarching hypothesis. Study 4 utilized a daily diary design to observe actual lived experiences of shame and pain, and found that people experienced greater pain severity and interference on days they felt more shame. Although results were mixed across studies they provide a foundation for future understanding of the relationship between shame and pain outcomes with implications for clinical treatment and care
Effect of Bone Segementation Data of the Maxilla and Mandible on the Accuracy of Bone-Supported Surgical Guides
No full textPurpose: The purpose of this study was to identify whether the quantity of Cone Beam Computed Tomography Scan (CBCT) image slices included in the jaw segmentation process has a significant effect on the accuracy of bone-supported guides and whether there is a minimum number of slices that can be identified as being needed to produce a surface model of the mandible and the maxilla that will yield an accurate bone-supported guide.
Materials and Methods: Dried human edentulous mandibles and maxillae, three of each, were selected. Each bone model was digitized using an intraoral scanner, and each scan was saved as a Standard Tessellation Language (STL). A bone-supported surgical guide was created on the surface scan of each jaw using a 3D implant planning software program, each serving as a reference guide for each specimen. CBCT images were then acquired once for each bone model, which were then imported into an implant planning software in digital imaging in communication in medicine (DICOM) format. A CBCT image segmentation procedure was then performed for each jaw to create a surface model of each bone with varying numbers of CBCT slices included for each segmentation: 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100 slices. A surgical guide was then created on each surface model rendered from CBCT segmentations and saved as STL. Then the STL of each experimental guide and its corresponding reference guide were imported into an image analysis program and Root Mean Square (RMS) values and heat maps generated to study the internal fit deviation of each guide.
Results: There were statistically significant differences (P<0.001) between the guides created from different numbers of CBCT slices segmented. Additionally, there were statistically significant results between the mandibular and maxillary models, with the mandibular groups having a higher mean deviation (238 ��m) than the maxillary guides (230 ��m). For the mandibles combined, groups below 70 slices produced significant results, while for the maxillae combined, anything below 90 slices was significant.
Conclusions: The internal fit of bone-supported guides is affected by the number of CBCT slices used in the jaw segmentations process for rendering the bone model on which the guide is created. There is a difference in the minimum number of slices needed to create an accurate bone supported guide on the maxilla and mandible. The threshold number of slices is higher for the maxilla compared to the mandible. Although differences between the different guides were observed, the clinical implications are not as clear and should be further studied
Bifacial Passive Radiative Cooling of Silicon Solar Cells Using PDMS for Increased Efficiency
No full textWith the rising levels of pollution and environmental degradation due to fossil fuel energy, there is an urgent need for clean and renewable energy. To compete with the vastly greater generation capacity of fossil fuels, the sources of renewable energy need to be more efficient at lower cost. There has been an increasing amount of work in improving the efficiency of solar cells by using several different materials and methods to generate power more effectively. However, all standard solar cells heat up in the course of operation under the sunlight. This heat absorption comes with significant reductions in the efficiency, reliability, and overall lifespan of the solar cells. In this thesis research, we present a relatively inexpensive, energy efficient and simple way to increase the efficiency of a solar cell by using a high emissivity material showing the effectiveness of radiative cooling on the simplest monocrystalline silicon solar cell. We found that coating a high emissivity material, such as PDMS, on both top and bottom of a solar cell allows for a large amount of thermal radiation to be removed to outer space, which acts as the ultimate heat sink. With in-field tests, we found an average temperature reduction of 6��C of a 1.06% performance increase. Considering the limited power conversion efficiency of a typical solar cell, we anticipate that energy efficient radiative coolers will be ubiquitous in the upcoming generations of solar cells
Exploring the Impact of Catalyst Supports on Hydrogenolysis of Polyolefins over Cobalt-Based Catalyst
No full textThe persistent utilization of plastics and inadequate disposal methods post-consumption are responsible for causing numerous ecological challenges on a global scale. In recent years, hydrogenolysis has been studied as a promising route to chemically repurpose polypropylene and polyethylene, which are some of the most widely used plastics. In this study, polyethylene is subjected to hydrogenolysis, utilizing cobalt-based catalysts on three supports: Zinc Zirconium Oxide (ZnZrO), Cerium Oxide (CeO2), and Titania (TiO2). The reaction is conducted under batch conditions at 275��C and 30 bar H2 pressure, with reactions performed for periods ranging from 30 minutes to 32 hours. Our findings reveal that cobalt supported on ZnZrO exhibits a high yield of liquid phase alkanes (C5-C30) up to 67%. The evolution of products over time also aids us in understanding the influence of the support material on catalyst performance, we propose likely reaction routes followed for hydrogenolysis carried out in the case of Co/ZnZrO and Co/TiO2. A loading study is also carried out to assess the impact of active metal density on reaction product yield and selectivity. Further, the efficacy of Co/ZnZrO is examined by using it to carry out hydrogenolysis of a post-consumer LDPE bottle, yielding results largely consistent with those obtained from the model PE substrate employed in our investigation. These outcomes underscore the pivotal role of support materials in the hydrogenolysis reaction and contribute to the mitigation of challenges stemming from inefficient plastic disposal, providing a more feasible way of upcycling plastics
Raman Spectroscopy as a Diagnostic Tool for the Detection of Tomato Brown Rugose Fruit Virus
No full textTomatoes and peppers together constitute a billion-dollar industry in the United States alone, which speaks highly of their importance to growers and the United States Department of Agriculture (USDA) alike. These crops, however, are host to a destructive virus, tomato brown rugose fruit virus (ToBRFV). Due to its ability to overcome all tobomovirus resistance genes, including Tm-2��, there are no known resistant varieties for ToBRFV. Therefore, many regulations are in place in hopes of controlling its spread, yet it continues to spread to many areas around the world, constituting a global epidemic. Raman spectroscopy (RS) is a noninvasive tool that scans a sample and uses the interaction of light and molecules to give a resulting spectrum of scattered light that can be used for analysis. This tool for detecting chemical compounds is already popular in many fields and has recently even been a growing research endeavor for disease diagnostics in plant pathology, where it has shown great promise with a wide variety of diseases. In this study, we examine ToBRFV as well as tobacco mosaic virus (TMV) and aim to determine if RS can be used as a diagnostic method to aid in management practices. We hypothesize that we will be able to observe differences in spectral peaks between healthy and infected plant/seed tissue with control, TMV, and ToBRFV groups. After inoculation, scanning, and Matlab analysis, it was demonstrated that there were spectral differences between healthy and infected plants, as well as differences between TMV and ToBRFV-infected plants. Additionally, this experiment was done on pepper seeds and preliminary data was obtained which displayed spectral differences between pepper seeds infected with ToBRFV and control seeds, showing potential support for this notion on seed tissue as well. Results suggest that RS does show promise to be incorporated as a diagnostic method and may provide useful insight into virus distribution throughout plants. Based on the results obtained in this study, there is potential for future work to take place on the basis of this data