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LibQUAL+ 2025 Survey : University of Central Oklahoma
Get PDFLibQUAL+ provides a protocol for bechmarking perceptions of library service quality. These reports contain the results and comments from participants providing feedback about the Max Chambers Library in 2025
Local Climate Change Messages in Oklahoma
Get PDFThe uneven recognition of climate change poses a challenge for those seeking to address related impacts. Framing theory provides a lens to understand what storylines and narratives help audiences recognize climate change challenges. In this experiment, climate change messages representing local or distant frames about policy or natural disaster topics are presented to Oklahoma audiences. Values, beliefs, and town size are examined to understand how individual characteristics impact message framing effects. Findings show that local message frames result in a slight increase in risk perception, but climate change engagement and concern are not significantly impacted by the frame used. Interaction effects between frames and attitudes are minimal. Differences in climate change perceptions are higher among Oklahomans residing in metropolitan areas than those in and small towns. Findings point to a need for more holistic approaches to effectively communicate climate change challenges
Landscape changes elevate the risk of avian influenza virus diversification and emergence in the East Asian–Australasian Flyway
Get PDFFinancial support was provided by the University of Oklahoma Libraries' Open Access Fund.Highly pathogenic avian influenza viruses (HPAIV) persistently threaten wild waterfowl, domestic poultry, and public health. The East Asian–Australasian Flyway plays a crucial role in HPAIV dynamics due to its large populations of migratory waterfowl and poultry. Over recent decades, this flyway has undergone substantial landscape changes, including both losses and gains of waterfowl habitats. These changes can affect waterfowl distributions, increase contact with poultry, and consequently alter ecological conditions that favor avian influenza virus (AIV) evolution. However, limited research has assessed these likely impacts. Here, we integrated empirical data and an individual-based model to simulate AIV transmission in migratory waterfowl and domestic poultry, including wild-to-poultry spillover and reassortment dynamics in poultry, across landscapes representing the years 2000 and 2015. We used the reassortment incidence as a proxy for ecological and transmission conditions that support viral diversification and the emergence of novel subtypes. Our simulations show that landscape change reshaped the waterfowl distribution, facilitated bird aggregation at improved habitats, increased coinfection, and raised reassortment rate by 1,593%, indicating a substantially higher potential for viral diversification and emergence. Model-generated risk maps show expanded and increased reassortment risk in southeastern China, the Yellow River Basin, and northeastern China. These findings suggest the importance of landscape change as a driver of potential AIV diversification and subtype emergence. This underscores the need for interdisciplinary approaches that integrate landscape dynamics, host movement, and viral evolution to better assess and mitigate future risk.Ye
MATRIX SCHEDULER FOR INSTRUCTION QUEUE IN OOO PROCESSORS
No full textMulti-core architectures and multi-threaded programs dominate today's world. In this world, delivering the highest IPC is of the utmost importance. Many mobile devices, including desktops and servers, require high performance and minimal power consumption. This has led to the RISC architecture revolution, led by ARM with the first iPhone. Since then, both Moore's law and new architectures have contributed to ever-faster and more power-efficient chips. What once were in-order processors are now being replaced with out-of-order designs to improve the throughput of instructions at the same power.
The instruction queue is one of the most power-hungry regions of an out-of-order processor. An instruction queue, in its essence, is an entity that dispatches instructions to the execution units as soon as their input data is ready. Most modern processors use a CAM (content-addressable-memory) to realize the instruction queue. In a traditional IQ using CAM, the source arguments are constantly checked every cycle to see if they are ready, and the instruction is dispatched if all of the instruction's source arguments are ready. This approach is inherently power-hungry. The matrix instruction queue is an alternative to the traditional IQ and avoids CAM. The techniques that can be used to compress the matrix IQ to run efficiently, as well as the details of the IPC of this compressed matrix IQ and its resource usage, are presented in this thesis
Faculty and Staff Publications Test Submission
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UCO Academic Calendar 2025-26
Get PDFNoAnnual publication of the University of Central Oklahoma's academic calendar with the official semester schedule, break times, and office closures for the designated school year along with a tentative calendar for the following school year. The calendar is prepared by the University Registrar who submits the calendar to the state regents for approval. This version of the calendar is published each summer by University Communications and distributed to campus offices
ANALYSIS OF THE AERODYNAMIC PERFORMANCE OF CARBON FIBER WINGS FABRICATED USING 3D PRINTED MOLDS WITH VARIED SURFACE FINISHES
Get PDFThis thesis investigates the aerodynamic impact of surface roughness in carbon fiber reinforced polymer (CFRP) wing skins fabricated using 3D-printed molds. Additive manufacturing via fused deposition modeling (FDM) offers a low-cost method for producing custom molds but often introduces surface imperfections that transfer to the final CFRP part. Mold surface quality is critical for airfoils, where roughness can alter boundary layer behavior. Three mold surface conditions—unsanded, sanded, and coated—were used to produce CFRP wing skins with varying roughness. Surface roughness was measured using a stylus profilometer and quantified through Ra, Rz, and RSm. Wing skins were fabricated using a wet layup process with vacuum bagging at room temperature. Wing skin roughness was then measured to assess fidelity to the mold. Using these values, computational fluid dynamics (CFD) simulations in ANSYS Fluent evaluated aerodynamic performance at Reynolds numbers of 300,000, 3,100,000, and 5,400,000. Higher surface roughness caused earlier boundary layer transition, increased drag, and reduced lift. The coated mold produced the smoothest surface and best aerodynamic results. This study shows that FDM induced surface roughness affects aerodynamic efficiency and emphasizes the value of post-processing in performance-sensitive, low-cost composite manufacturing
INVESTIGATION OF STRATEGIES FOR IMPROVING THE PERFORMANCE OF SMART THERMOSTAT-DRIVEN FDD METHODS
No full textThis dissertation presents an investigation of two strategies for evaluating the performance of smart thermostat-driven fault detection and diagnosis (FDD) methods that are intended for vapor compression air conditioning (AC) systems in residential homes. Though there have been some research efforts within the last decade on smart thermostat-driven FDD, there are several challenges which have limited the advancement of the concept into a marketable technology. Therefore, the investigation in this dissertation is to explore some of the ways these challenges can be addressed.For the first strategy, a novel dynamic co-simulation model is developed using EnergyPlus and Modelica to gain a fundamental understanding of the coupling between building thermal response and AC system. The model is then validated and used to investigate the effectiveness of using smart thermostats for low-cost FDD in residential AC systems. Smart thermostats mea¬sures indoor air conditions which reflect the indoor air responses to both AC operations and other factors like weather and building gains. As these other factors are typically unmeasurable by smart thermostats and yet prone to variability, they pose potential disturbances which can affect AC operation. This makes estimating the impact of these disturbances (referred to as uncontrollable building load disturbances (UBLDs)) on AC operation in buildings and the performance of FDD methods critical. Therefore, in this study, it is essential to couple the dynamics of AC operation and building response in the development of the co-simulation model. Additionally, a novel automated calibration framework is also developed in this research to validate the coupled model. The validated model was then used to simulate seven UBLD cases and two prevalent faults with different severities. The study also proposed a set of metrics for evaluating the performance of FDD methods. With the simulation results and the proposed metrics, impact analysis on AC duty factor and enthalpy change were carried out. Results of the analyses showed that severe UBLDs can cause almost 9% increase in energy consumption. The results also showed that these UBLDs can cause false alarm rates in FDD up to 70% if appropriate thresholds and post-processing strategies are not used. Meanwhile, results on the impact of faults showed that with the simultaneous impact of UBLDs, only low charge faults up to 30% severity and low indoor airflow fault up to 60% severity can be confidently detected using duty factor as FDD feature. Meanwhile, the analyses and results also led to the proposal of another FDD feature (enthalpy change) which offers better sensitivity and a potential for diagnosis which the duty factor feature lacks. With this new feature, an automated FDD algorithm was developed, validated and successfully deployed in four test-homes where it was able to detect 30% undercharge fault and 30% low indoor airflow, as well as installation mismatch. Overall, this research work creates a new pathway for promoting smart thermostat FDD technology in the residential market within the US and beyond
Impact of Host Diet and Lifestyle on Oral Microbial Ecologies Across Temporal and Geographic Scales
No full textMicrobiomes are complex host-associated microbial networks that play important roles in maintaining systemic health. These microbial communities are influenced by host behavior and environmental factors, while host health can be reciprocally affected by shifts in microbial composition and functioning. This dissertation investigates how the oral microbiome is impacted by factors such as host behavior, diet, geography, and time. This work uses calcified dental plaque, or dental calculus, which forms on the surfaces of the teeth throughout an individual’s life and traps microbes and their genetic information. Leveraging advances in shotgun metagenomic sequencing and ancient DNA techniques, oral microbiomes were recovered from a total of 91 individuals from diverse ancestral populations. Chapter one provides the framework of the dissertation and discusses the current state of the field of ancient metagenomics, including the findings from other studies on how oral microbiomes impact health and are shaped by human bioculture. Chapter two investigates how the adoption of maize as a dietary staple by ancestral Maya populations in Belize led to changes in their oral microbiome over a nearly 10,000-year time period. Here, it is revealed that an increase in the availability of fermentable carbohydrates was accompanied by an increased necessity for acid tolerance among oral microbes, leading to differential abundance of key microbes. Chapter three investigates whether intensive acorn consumption impacted the oral microbiome of ancient Muwekma Ohlone peoples from California, U.S. in comparison to those of other populations of varying subsistence patterns. This chapter also includes an analysis of metagenomically assembled genomes (MAGs) from ancient American oral microbiomes, providing a snapshot of oral microbial strain diversity in pre-European contact populations. The functional potential of metagenomes from those with this unique diet revealed a composition similar to agricultural groups, but without enrichment of specific organisms that mark the agricultural diet. Chapter four presents methods for extracting and interpreting non-human herbivorous oral metagenomes by examining dental calculus from ancient and modern bison in Oklahoma. This is the first investigation into oral microbiomes from bison dental calculus, and among the first to explore modern non-human dental calculus. This approach not only revealed prokaryotic organisms that mark bison oral microbiomes but also exhibited ecologically significant eukaryotic plant and fungal genomes accessible through bison dental calculus. Each project employed molecular techniques designated for ancient and modern DNA extraction and shotgun library preparation. Additionally, innovative computational methods for taxonomic and functional assessment, phylogenetic analysis, and de novo metagenome assembly expanded the breadth of knowledge that could be obtained from metagenomic data. By examining the interplay between diet and oral microbial composition and function, this dissertation summarizes the mechanisms driving the existence and prominence of oral microbes, especially some associated with oral disease. By reconstructing metagenomically assembled genomes, we uncover hidden strain diversity irrespective of host or clinical relevance. Finally, by expanding into non-human hosts, we demonstrate the acquisition of environmental eukaryotic DNA from dental calculus of a keystone mammalian species
TOWARDS BREATH-BASED DISEASE DIAGNOSIS: A MACHINE LEARNING PIPELINE ON PTR-MS DERIVED VOC SIGNATURES
No full textDiseases such as lung cancer are often difficult to diagnose in their early stages, and by the time detection occurs, the disease may have already progressed to an aggressive phase. Noninvasive methods like exhaled breath analysis offer a promising alternative that can facilitate early detection in a painless and accessible manner. Even established techniques such as Reverse Transcription Polymerase Chain Reaction (RT-PCR ), commonly used for COVID-19 detection, fall short in delivering comprehensive diagnostic coverage. In this study, we leverage Proton Transfer Reaction - Time of Flight - Mass Spectrometry (PTR-TOF-MS) to analyze exhaled breath samples, enabling high-resolution detection of volatile organic compounds (VOCs). By examining the concentration profiles of these VOCs, we aim to identify disease-specific molecular signatures, or “fingerprints,” that distinguish between health conditions. Each disease produces a unique VOC signature, making real-time, noninvasive diagnosis possible. A key contribution of this work is the development of a cascading classification model tailored for real-time disease prediction. Despite working with a relatively limited dataset, the model achieves classification accuracies of up to 96\%, highlighting the potential of breath-based diagnostics. In a separate approach, we employed Cohen’s dd effect size method to identify statistically significant VOCs that differentiate disease groups. This statistical technique serves as a powerful tool for biomarker discovery, enabling more interpretable models and guiding future research on disease-specific breath signatures. These results strongly suggest that expanding the dataset could further enhance model performance and generalizability, paving the way for scalable and rapid clinical screening tools