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OPERATIC REDUCTIONS AND THEIR PEDAGOGICAL IMPLICATIONS FOR USE IN LIBERAL ARTS COLLEGE AND UNIVERSITY LEVEL OPERA SCENES PERFORMANCES
No full textProviding enrichment and further learning opportunities within the opera scenes program at the collegiate level is paramount to this project. Generally, opera scenes programs are performed with a vocal score and piano reduction accompaniment. Students at smaller university settings, such as liberal arts or community colleges, may never have the chance to perform complete mainstage operatic works due to lack of resources. Since opera scenes are likely the only exposure to opera performance at these levels, providing something more than piano accompaniment opens a realm of possibilities for learning and performing. Within this document, I share reductions for scores of three commonly performed operas within the repertory that span the Baroque Era, Classical Era, and Contemporary Era. I have reduced scenes from Dido and Aeneas, Le nozze di Figaro, and A Midsummer Night’s Dream from full score orchestral writing to four-six piece chamber ensemble. I considered the limited resources that may be present at smaller collegiate settings and restricted my instrument choices to those found in most bands, as many smaller settings do not have a string program. Scenes from these operas were chosen based on criteria such as the demands on the singer, specific voice types, eras of musical history, and orchestration.
This document investigates the pedagogical aspects of programming these scenes in the repertoire as well as the difficulty level for the singers and instrumentalists. Finally, within each chapter I outline aspects which contribute to the difficulty rating of beginner, intermediate, or advanced, such as singer’s range, level of difficulty in musicianship, language, available voice types. I also outline various choices I made for creating the reductions themselves. I hope that these reductions will begin a body of repertory for scenes performance that foster collaboration between singers and instrumentalists and provides additional learning experience, which will in turn aid in overall professional development of these young musicians
We aren't so different: implicit bias & unintended stereotyping
Get PDFImplicit biases potentially influence attitudes and behaviors towards social in-groups and out-groups, such as racial minorities. In this empirical study, the strength of the implicit association measure (d) was obtained from 112 university students using the PsyToolKit version of the Implicit Association Test (IAT). These scores were then compared to d scores from various other sources, including the national-level IAT dataset provided by Project Implicit, a subset of the national dataset specific to Oklahoma, a new sample from a metropolitan university in Oklahoma, and a new sample from a nearby Historically Black College or University (HBCU). The IAT involves a computerized task where participants swiftly categorize words and pictures based on whether they convey negative or positive sentiments. In this experiment, participants used the "E" key for the left side of the screen and the "I" key for the right side. Pressing one key signifies that the stimulus belongs to the designated category (good/bad) while pressing the other key indicates that it does not. Key-press response times and error rates are then used to calculate d scores. Higher d scores suggest stronger associations with the particular group--thus, a high positive value implies a positive association with "White = Good" stimuli in this context. Conversely, a negative d score implies an association of "Black = Good". Analysis of variance and post-hoc tests unveiled disparities in d-scores among the various datasets and racial groups. Overall, state-level d scores surpassed national-level d scores, suggesting more positive associations between White individuals and "Good". At the local level, the distinction between the metropolitan university and the HBCU was notable: The metropolitan area exhibited a negative d score, indicative of an association with Black individuals and "Good", while the HBCU population demonstrated positive d scores, signifying an association with White individuals and "Good". These variations in d scores among Black, White, and other participants may indicate differences in the strength of implicit associations between race and valence across these distinct groups. Additionally, at the national level, d scores for Black participants were negative, while those for White participants were positive, and d scores for individuals of other races were positive. At the state level, d scores for Black participants remained negative, while those for White participants were positive, along with positive d scores for participants of other races. At the local level, both the metropolitan university and the HBCU exhibited differences in d scores among Black, White, and other participants, as previously mentioned. In the metropolitan area, Black participants exhibited positive d scores, White participants had negative d scores, and individuals of other races showed positive scores. Conversely, at the HBCU, Black participants had positive scores, White participants also had positive scores, and individuals of all other races similarly demonstrated positive scores. The intriguing results indicated that Black participants from the metropolitan area displayed a preference for White individuals and associated them with positive attributes, while White participants exhibited a preference for Black individuals, also associating them with positive traits. Moreover, at the HBCU, Black individuals similarly demonstrated a preference for White individuals and their associated positive attributes. The survey administered following the computerized task included several different Likert scales, encompassing a discrimination scale consisting of questions regarding racial preference, scenario questions concerning social interactions with individuals of other races, the Social Dominance scale (SDO), Belief in a Just World, Humanitarianism, the Big Five Personality traits, Right-Wing Authoritarianism, and the Bayesian racism scale. Upon running a Pearson correlation between the survey scores and d scores, the results did not reveal significant correlations for all the listed scales, except for SDO, which exhibited a significant correlation between the two. Survey scores were collected from both the metropolitan and HBCU populations
Fables in Natural History: An Examination of the Allegorical in the Development of Natural History Texts
Get PDFThe Latin bestiary is known not only for its striking illustrations but also for the extensive religious commentary which accompanies the description of nearly every animal. While there is debate over whether the bestiary was intended primarily as a religious tool or as a “scientific” encyclopedia, the most accurate image of the bestiary can only be gained by taking a holistic approach and treating all levels of the text as equally valid and important elements. A series of case studies in the premodern conceptualization of different animals—the elephant, the rhinoceros, and the unicorn—illustrates the ways in which medieval knowledge of animals was mythologized from earlier accounts and the ways in which factual truth, religious allegory, and medicinal and magical uses of animal products overlapped into a unified whole. The study of the bestiaries as works with both scientific and religious significance helps to give them a clear place in the history of science and the development of the field of natural history
MORE ATTRACTIVE THAN EXPECTED? EFFECTIVENESS OF THE EMBODIED VIRTUAL AGENT IN ONLINE SHOPPING RECOMMENDATION: MODERATION EFFECT OF CAPABILITY AND MEDIATION EFFECT OF TRUST
Get PDFThis study explores the growing field of Intelligent Virtual Agents (IVAs) in e-commerce, with a focus on embodied virtual agents (EVAs) that serve as virtual sales assistants. It examines how the physical attractiveness of EVAs influences consumer attitude and purchase behavior through two empirical studies grounded in expectancy violation theory. The first study assesses the impact of attractiveness-related expectancy violations on consumer attitudes, and purchasing intentions, incorporating novelty-seeking tendencies and the need for cognitive closure as moderating variables as well as trust as a mediating variable. The second study explores the interaction between EVAs’ attractiveness violations and their functional capabilities, examining whether an EVA’s effectiveness in influencing consumer response depends on its perceived capabilities. This research extends our understanding of how EVAs' appearance impacts digital consumer interactions and offers insights for enhancing online shopping experiences and business performance
HARNESSING DEEP REINFORCEMENT LEARNING: STUDIES IN ROBOTIC MANIPULATION, ENHANCED SEMANTIC SEGMENTATION, AND SECURING IMAGE CLASSIFIERS
Get PDFThis dissertation investigates the transformative potential of Deep Reinforcement Learning (DRL) in three critical domains: robotic manipulation, enhanced semantic segmentation, and the security of image classifiers. Through comprehensive exploration and analysis, this research addresses the challenges and limitations inherent in current DRL methodologies, offering novel insights and practical solutions.
In the domain of robotic manipulation, the study provides an in-depth examination of various DRL algorithms, including value-based, policy-based, and actor-critic methods. The findings highlight the specific strengths and limitations of each algorithm, guiding the selection of appropriate methods for diverse robotic applications. Additionally, the research proposes new directions for integrating multiple learning paradigms to enhance robotic adaptability and performance in complex environments.
For enhanced semantic segmentation, the dissertation develops a robust framework utilizing reinforced active learning methodologies. By integrating advanced techniques such as Dueling Deep Q-Networks (Dueling DQN), Prioritized Experience Replay, Noisy Networks, and Emphasizing Recent Experience, the framework addresses imbalanced datasets and optimizes annotation processes. Experimental results demonstrate the framework's robustness and efficiency across various domains, particularly under constrained annotation budgets.
In securing image classifiers, the research focuses on developing surrogate models capable of replicating proprietary image classification models under stringent constraints. An open-source framework integrating popular DQN extensions is introduced, demonstrating their effectiveness in enhancing attack methodologies. The evaluation of synthetic data generation techniques identifies best practices for training robust adversarial models, advancing the understanding of effective attack strategies in AI security.
This dissertation underscores the importance of improving sample efficiency, stability, generalization, and robustness in DRL algorithms. Ethical and practical considerations are addressed, ensuring the minimization of risks associated with model extraction attacks. The practical implications extend to various fields, including autonomous vehicles, robotics, and AI security, providing actionable insights for deploying DRL technologies.
The research paves the way for future work in integrating multi-paradigm learning, expanding evaluation frameworks, developing robust defense mechanisms, and leveraging advanced data generation techniques. The findings reinforce the transformative potential of DRL, shaping its future applications and ensuring its role as a critical tool in tackling complex decision-making tasks across diverse fields
“I Love What I Do; I Just Can’t”: Examining Job Burnout Among Generation Z Local Television Journalists
Get PDFResearch shows that burnout has plagued journalists for generations, yet there is a present lack of studies specifically investigating it among journalists in television news, which still remains a vital source of news for many, and Generation Z journalists, whom scholars have pinpointed as being at risk for burnout due to their young age and lesser years of experience. To address these research gaps, this thesis – to the researcher’s knowledge – makes a first attempt to examine burnout among these two populations. Interviews with 25 Generation Z local television journalists show that most of these journalists are burned out and felt its impact on their lives at work and at home. In following the job demands-resources (JD-R) theory of burnout, qualitative data reveal that doing more with decreased resources and issues with management drained the journalists the most, while making an impact on their communities and support from their co-workers were the biggest motivators. Being transparent about stress and burnout among journalists emerged as a key approach for news managers and journalism educators to better alleviate burnout in newsrooms and prepare journalism students for their workplaces respectively. Specific recommendations from the Generation Z local television journalists for newsroom and educational interventions to effectively minimize burnout among journalists are also discussed
Development of Algorithms to Determine Accurate Parameters for Eye Movement Detection For Visual Scanning Behavior Analysis
Get PDFOne way to investigate how humans interact with their environment is by studying how they visually search and gather relevant information in order to make decisions. Visual search can be analyzed through visual scan paths, the time ordered sequence of eye fixations and saccadic movements. To create visual scan paths, researchers often use eye fixation detection algorithms, many of which rely on threshold parameters set by the researcher, to automatically identify eye fixations from data collected by eye tracking devices.
However, the choice of threshold parameters used by eye movement detection algorithms is crucial, as many different factors, such as the participant population and the task to be completed, might affect what threshold values can accurately identify eye fixations. Inaccurate thresholds might result in visual scan paths that do not resemble the visual scan path carried out by an individual (i.e., the ideal visual scan path). For example, an inaccurate threshold might fail to identify eye fixations that took place, or combine multiple consecutive eye fixations together into a single eye fixation and place it somewhere in the environment that the individual never actually observed. As such, using inaccurate thresholds might affect our ability to understand and interpret an individual’s visual search (e.g., what information was observed, as well the order it was observed in) and decision-making process.
In this dissertation, novel procedures and algorithms are introduced to facilitate the identification and selection of accurate thresholds. First, an automated procedure was developed to automatically select accurate thresholds based on the impact of threshold values on eye movement metrics (e.g., number of eye fixation), expanding upon prior research efforts by automating a process that was previously largely manual. Second, two approaches are proposed to approximate the trend of similarities between ideal visual scan paths and visual scan paths created at different thresholds, used in prior studies to determine accurate thresholds, without the need to know or use ideal visual scan paths. Using ideal visual scan paths is not always feasible, as one needs to know the expected eye movements of individuals a head of time or needs to engage in the arduous and time consuming process of manually defining the ideal visual scan paths from the data collected. Third, and lastly, a classification framework was developed to identify similar visual scan paths that might showcase variations of a common visual scanning strategy using multiple similarity metrics
The Development of a Smart and Low Power Consumption Variable Speed Limit Sign for the Oklahoma Department of Transportation
No full textVariable Speed Limit Signs (VSLSs) play a crucial role in traffic management. Various studies have demonstrated that they can significantly reduce accidents and improve traffic flow, particularly on congested highways, during adverse weather conditions, in construction zones, and even during road accidents. However, existing technologies used to manufacture VSLSs, such as LED displays, mechanical rollers, and manually interchangeable cards, suffer from several drawbacks, including bulkiness, high cost, high power consumption, integration difficulties, and deployment complexity, thus impeding the implementation and expansion of such systems. This thesis addresses these challenges by proposing a novel approach using newer technologies. The proposed remotely managed variable speed limit traffic system offers a more efficient and cost-effective solution for traffic management. By implementing this new technology in VSLSs, we aim to reduce the size, weight, and power consumption of these devices while maintaining performance and visibility
Osteochondral Tissue Engineering for the Temporomandibular Joint Mandibular Condyle
Get PDFAn important goal for tissue engineering and regenerative medicine remains to to direct tissues regeneration with implantable scaffolds. Temporomandibular joint (TMJ) mandibular condyle tissue regeneration may require large scale scaffolds due to dramatic tissue loss. Unfortunately, there is a paucity of research on large-scale anatomically shaped scaffolds for osteochondral tissue regeneration. In the current dissertation, a human sized goat TMJ mandibular condylar prosthesis was developed with different phases for cartilage and bone regeneration. To regenerate cartilage, an acellular hydrogel was comprised of a light-cured pentenoate-modified hyaluronan (PHA) and devitalized cartilage matrix (DVC) based hydrogel. The hydrogel exhibited signs of potential chondrogenicity with upregulation of cartilage-specific genes (i.e., aggrecan and SOX-9) during in vitro cell culture with human bone marrow mesenchymal stem cells. An Ogden model was employed to improve the stiffness characterization of the cartilage-matrix hydrogel. In contrast with linear mechanical data, the hydrogel stiffness behavior was nonlinear. The nonlinear Ogden model fit exhibited a good fit of the nonlinear cartilage-matrix hydrogel mechanical data to failure (R2=0.998 ± 0.001). For the bone substrate, we developed an in-house custom filament for use with commercially available 3D-printers. A goat-sized anatomically shaped 3D-printed osteochondral scaffold was digitally designed, fabricated, and implanted for 6 months in a small animal TMJ study. The study demonstrated that cartilage-like structures could be regenerated on the condyle surface and that bone formation was possible, though precise spatial control of bone formation remains an important challenge for further investigation. In addition, the integration of a hydrogel chondral phase with a stiff osteal phase presented a challenging. The current thesis thus aimed to enhance furthermore aimed to develop a biomechanically interlocking structure to enhance the interface strength, and furthermore enhance the bioactive properties of 3D-printed PCL-based bone scaffolds. For the biomechanically interlocking interface structure, an hourglass tube shape was introduced. Interface biomechanics of the hourglass tube structure were investigated with both empirical experiments, and a computer model that simulated the experiment conditions. The hourglass tube computer model exhibited a shift in stress favoring compressive stresses. Empirically, the hourglass tube exhibited 54% higher ultimate interface shear stress, 49% higher nominal strain at failure, and 2.15-fold higher energy to failure than the crosshatch substrate’s 33 kPa, 19%, and 3.9 kJ · m3, respectively. To promote controlled bone growth, a series of potentially osteoinductive biomaterials, i.e., demineralized bone matrix (DBM), and devitalized tendon (DVT) were successfully incorporated into a PCL-based 3D-printing filament at concentrations of up to 50% w/w and 3D-printed to form scaffolds. 3D-printed PCL functionalized with 37.5% w/w HAp and 12.5% w/w DBM exhibited enhanced osteogenic gene expression for RUNX2 and OCN. Overall, the current dissertation demonstrated signs of functional TMJ restoration with an acellular prosthesis; therefore, the significance of the current dissertation was the development of a functional biomaterial scaffold that was 3D-printable and translatable to temporomandibular joint restoration
MODULATING LATTICE OXYGEN ACTIVITY OF CA2FE2-XMXO5 BROWNMILLERITE FOR PRODUCTION OF HIGH PURITY HYDROGEN FROM BIOMASS RESOURCES
No full textChemical looping (CL) technology offers a promising avenue for producing high-purity hydrogen with inherent CO2 separation. A critical challenge lies in identifying oxygen carriers with high performance and sustained activity across multiple redox cycles. This study investigates the influence of metal doping (Co, Cu, Ni) on the performance of brownmillerite-structured Ca2Fe2O5 for biomass gasification and hydrogen production. Carriers were synthesized via a citric acid-assisted sol-gel method and tested in a fixed-bed reactor under atmospheric conditions. The study systematically examined the effects of temperature, water injection rate (steam/biomass ratio), and catalysts on biomass conversion. An optimal water injection rate of 0.1 mL/min with Ni-doped Ca2Fe2O5catalyst significantly enhanced hydrogen yield by 82.4% compared to the undoped carrier. Increasing temperature consistently improved H₂ yield throughout the gasification process. Notably, doping with Ni and Co significantly increased H₂ yield from 27.82 g/mol biomass (undoped) to 33.15 g/mol biomass (Ni) and 32.05 g/mol biomass (Co), respectively. Furthermore, this research explored the valuable application of adding biochar to the asphalt mixture. The results revealed that incorporating biochar significantly improved the asphalt's performance against rutting and cracking, offering a promising and sustainable approach to enhance pavement longevity