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Factors Influencing Community Assembly of The Amphibian Microbiome in Rochester, NY
Get PDFAmphibians are crucial in wetlands and, by extension, our planet. As predator and prey, amphibians serve as keystone species, vital for the continued equilibrium in their wetland ecosystems. However, the global amphibian population is currently threatened by the spread of a pathogenic fungus known as Batrachochytrium dendrobatidis (Bd). Over the past thirty years, Bd has spread worldwide, threatening hundreds of amphibian species. The environmental factors that influence microbial community assembly in amphibians are key to developing interventions that can protect amphibians from disease. This research examines the abiotic and biotic factors influencing microbial community composition in amphibians of Rochester, New York. Additionally, this research examines novel factors that may impact community composition, such as Bd\u27s environmental presence and metamorphosis\u27s role in structuring the adult amphibian microbiome. I demonstrate that factors such as host species, development stage, location, and pathogen presence significantly contribute to structuring the amphibian microbiome, highlighting their impact on an amphibian population that had not been previously studied. Additionally, I found that amphibian species that undergo typical metamorphosis have a more diverse microbiome, including many beneficial bacteria that have been putatively identified to have antifungal properties. The presence of Bd in amphibian breeding habitats was found to influence the diversity and composition of the microbiome significantly. This research demonstrates the effect of Bd on community structure in populations that were historically considered unaffected by the pathogen, highlighting a significant gap in the standard practices of the field
Bodies Unseen: Hysteria’s Stain on Women’s Healthcare
Get PDFBodies Unseen is a 5-minute motion graphics piece accompanied by a 12-page zine that explores the historical and ongoing dismissal of women’s pain within the medical system, rooted in the legacy of hysteria. Through the fictionalized stories of Ada, Rosa, and Mabel-women from different time periods who face misdiagnosis and neglect, the project illustrates the persistent gender bias in healthcare. Drawing from historical records, feminist theory, and contemporary research on medical inequity, this work connects past injustices to modern-day disparities in diagnosis and treatment. The animation and zine work in tandem to educate viewers and encourage dialogue about the systemic minimization of women’s health concerns, aiming to foster empathy and raise awareness around an issue often left unspoken. This thesis employs a multidisciplinary design approach, combining frame-by-frame animation, typographic animation, and hand-drawn illustration to communicate complex and emotionally charged content in an accessible, compelling format. The accompanying zine expands on these visual stories with educational context, statistics, and resource recommendations. Both components are unified by an intentional visual style that blends historical reference with contemporary design sensibilities. The design draws inspiration from 18th and 19th-century Gothic visual styles, connecting the aesthetics of hysteria’s historical documentation with modern design tools to reflect on the continued, haunting presence of these ideologies in current medical practices. This project serves as a visual communication strategy aimed at bridging heavy storytelling with data-driven awareness
Improving Reciprocating Compressor Valve Condition Monitoring via an Accelerated Lifetime Tester and Deep Learning
Get PDFThe goal of this research is to improve the current condition monitoring method for diagnosing reciprocating compressor valves. Leakage, seat wear, and spring degradation are three common valve faults that are seeded into all the valves of a Dresser-Rand ESH-1 dual-acting reciprocating compressor. The method converts raw vibration data into the time-frequency domain and determines if the Continuous Wavelet Transformation or the Smoothed Pseudo Wigner-Ville Spectrum can achieve a higher classification accuracy. The Continuous Wavelet Transformation can vary its resolution through two parameters while the Smoothed Pseudo Wigner-Ville Spectrum can vary its resolution through smoothing window sizes. Both time-frequency transforms are optimized and compared to determine which one can provide the highest classification accuracy using a linear discriminant classifier. A portion of the transformed data, the region of interest, is converted into a grey-scale image and a black-and-white image where distribution statistics are calculated and used as features for classifying valve health using a linear discriminant classifier. The region of interest is also examined, and the boundaries are tested to find the optimal region for each valve independently. This creates a methodology that can be used on all the valves in a Dresser-Rand ESH-1 reciprocating compressor and achieve a high classification accuracy. The classification accuracy can be improved further using deep learning which creates features automatically. This allows for a more complex feature space but requires more training data. The work includes transfer learning from GoogLeNet which is used for image classification allowing for faster training times but is limited by the input size. This takes longer to train than a statistical classifier but creates a larger separation between the classes. A second deep learning model is created which is a convolutional neural network with a depth of four. It is created from scratch and uses all the valve vibrations and outputs the valve that has a fault as well as the fault type. The convolutional neural network allows for one algorithm per compressor as opposed to one algorithm per valve. Both deep learning methods require data augmentation to increase the amount of data. Synthetic data is created from existing data which looks realistic to increase the classification accuracy and reduce overfitting which allows the algorithm to generalize to other compressors more easily. Wearing down poppets naturally takes a long time, so a poppet accelerator is designed and created to wear down poppets in a natural way but faster than a compressor. This system is used to understand better wear patterns in poppet valves as well as relate wear to the original feature space. The worn poppets are put in the compressor at progressively worn stages and plotted using the created algorithms
UVM BASED VERIFICATION OF EE621 RISC PROCESSOR MODULE
Get PDFIn the process of digital design, the majority of efforts are focused on verification. This is because a proper verification environment can help engineers discover bugs that otherwise would have flown under the radar. On the other hand, a poorly designed verification environment could fail to fully confirm the device’s functionality and leave the customer with a buggy mess. For obvious reasons, this should be avoided at all costs, hence the emphasis on functional verification and validation efforts. Throughout time, the complexity of the average design under test (DUT) has increased dramatically, making verification a significant challenge for today’s verification engineers. As such, the verification industry is always looking for more efficient methodologies. One such methodology is called the Universal Verification Methodology (UVM), which is a class library written in the SystemVerilog language. This paper discusses the framework of a self-checking testbench with contained random stimulus generation, created with the powerful tools of UVM, to verify the functionality of a Reduced Instruction Set Computer (RISC) Processor. The RISC processor was written with Verilog HDL and was the final project of the EE621: Design of Computer Systems class at RIT. The testbench was run for both RTL and Netlist simulations using Cadence Xcelium. It was found that the DUT would pass the self-checking testbench for approximately 90% of runs. Functional coverage metrics that outline the testbench’s effectiveness were extracted and reported. From these metrics, it was proven that the UVM testbench was able to generate random instructions that covered 100% of the possible opcode, source register, and destination register values
Urban Heat Island Mitigation: A GIS Based Analysis of Cool Roofing, Canopy Cover, and Imperviousness in Rochester, New York
Get PDFUrban Heat Islands (UHI’s) pose an increasing risk to public and environmental health. This phenomenon of heightened temperatures occurs in developed cities around the world, largely due to anthropogenic changes in the landscape. This study investigates the interactions of two UHI mitigation strategies, cool roofing, and altering vegetation cover and impervious surfaces, within the city of Rochester, New York. Using Landsat 9 band 10 imagery and land cover data, surface temperature patterns were analyzed in relation to roof color, imperviousness, and canopy cover across the neighborhoods within the city. GIS based statistical analyses were utilized to predict temperature changes resulting from increases in canopy cover, imperviousness, and roof color along with decreases in the same categories. Results indicate that an increase in canopy cover of 10% reduces temperature by an average of 2.5 °C, while an increase in imperviousness of 10% increases temperatures by an average of 1.87 °C. Cool roofs showed a potential decrease in temperature of 0.5 °C, although this was dependent on the specific neighborhood where cool roofs were implemented. Despite being influenced by satellite resolution and roof sample size limitations, this study provides important insights into the relationship between surface temperature and land cover on a city wide basis
Under the Mask: Labeling and Self-Perception in Augmented Reality
Get PDFUnder the Mask is a speculative AR experience that critically explores how social labels shape both perception and self-perception in everyday life. The project reimagines augmented reality not as a technological novelty, but as a conceptual mirror—a way to visualize how identity is continuously negotiated in social space. Users interact with two distinct labeling functions: when they assign a tag to themselves, it becomes visible to everyone, symbolizing the ways in which self-identification enters public discourse. In contrast, when users label others, those tags remain private—visible only to the individual user—highlighting how our assumptions primarily influence our own vision, not objective reality. The project draws attention to the emotional and psychological tension between internal identity and external judgment. By simulating this interaction in a playful and reflective way, Under the Mask encourages users to question both the power and the fragility of labeling systems. Rather than rejecting labels altogether, it reframes them as tools for social navigation—useful but incomplete, meaningful but mutable. This experience ultimately advocates for self-awareness, openness, and the recognition that while we live within a network of mutual influence, the choice of how to interpret and internalize labels remains our own
Ferroelectric Field-Effect Transistors in Analog Applications: Demonstrated in Time-Domain Neuromorphic Computing Cells
No full textIn response to diminishing improvements from transistor scaling, the semiconductor field has shifted its focus to architectural innovation and emerging device technologies. Ferroelectric field-effect transistors (FeFETs) are non-volatile memory elements which offer new opportunities for energy-efficient computing, particularly in compute-in-memory (CIM) architectures. This thesis presents a novel FeFET-based computing cell integrated into a time-domain (TD) computing architecture. The proposed design is evaluated using a device model derived from experimental data and compared with competing non-volatile memory implementations. Results demonstrate improvements in area efficiency and performance, highlighting the potential of the FeFET cell for next-generation TD-CIM systems
