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Chamber Music for Voice, Viola and Piano: An Analysis of Johannes Brahms’s “Geistliches Wiegenlied” from Zwei Gesänge, Op. 91
No full textThis research paper examines Johannes Brahms\u27s “Geistliches Wiegenlied” from Zwei Gesänge, Op. 91, an integral but often overlooked work in the chamber-music repertoire for voice, viola, and piano. The study begins with brief historical background information on the text and music. Following this, an analysis of “Geistliches Wiegenlied” is provided as well as practical advice on interpretation and ensemble performance. Also included is a list of selected recordings. By exploring the special qualities of the work, this study aims to deepen the understanding and appreciation of Brahms\u27s contribution to chamber music for this unique ensemble
Intersectional Embodiment: An Exploration of Women\u27s Bodily Experiences in Contemporary American Literature
No full textMy dissertation, “Intersectional Embodiment: An Exploration of Women’s Bodily Experiences in Contemporary American Literature,” examines contemporary American novels written by women of color in order to illuminate how women of color protagonists realize, protect, and embrace their bodies as a source of power in a deeply oppressive society. Specifically, I analyze Kindred (1979) and the Parable novels (1993, 1998) by Octavia Butler, Displacement (2019), a graphic novel by Kiku Hughes, and Severance (2018) by Ling Ma. In these novels, women protagonists must account for their bodies to understand their identity, combat oppressive bodily control, and, above all, survive. Both neo-historical novels and climate fiction show how the history of the present and the future is being [re]written on the bodies of women of color. These novels rewrite the past and envision different futures, underscoring women’s struggles and their resistance to a white supremacist capitalist patriarchy. These authors confront politically divisive and culturally urgent matters: whether their characters are fleeing to safety in a climate-affected world or are actively working to free themselves from enslavement, the narrative structure of each novel reflects a specific intersectional embodied experience, one that is both racialized and gendered. Ultimately, my research highlights individual and collective histories, emphasizing that embodiment, when implicated in stories steeped in trauma, becomes formed through the racialized and gendered aspects of societal subjugation via social hierarchies. In these works, the bodies of women are infused with experience, encapsulating conglomerations of histories and harboring sites of punishment—and yet, these women fight, resist, and survive as a means to sustain autonomy and protect others
Analysis of the Public Acceptance of Cryptocurrency in the Central African Republic
No full textThis dissertation explores the public acceptance of cryptocurrency in the Central African Republic (CAR), applying the Technology Acceptance Model (TAM) as the theoretical framework. The study adopts a research model and survey instrument derived from a previous published research paper. Data were collected through paper-based surveys from 357 respondents, mostly students and faculty members at the University of Bangui in the CAR. The responses were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with Smart PLS software. The results reveal that increased awareness of cryptocurrency significantly boosts its acceptance, with factors such as perceived ease of use and perceived usefulness playing critical roles in influencing users’ attitudes toward cryptocurrency. However, trust does not emerge as a significant predictor of the intention to accept cryptocurrency. The study results offer valuable insights for policymakers and government agencies by providing a foundation for developing strategies to facilitate the integration of cryptocurrency into the CAR\u27s financial and monetary systems
Augmentation and Hybridization of Conventional Forms of Process Control with Advanced Control Methods
No full textWithin the last decade in the field of process control, there has developed a distinct gap between the technological advancements of today and the well-established theory that has preceded it. This is most evident where the novel developments and structures often outpace the necessary guarantees and strong foundation required to ensure the possibility of widespread adoption. Therefore, the goals of this work are to broadly integrate those novel advanced control methods into the field of process control in such a way to increase the applicability and encourage implementation.
The first portion of this work seeks to further explore the nature of switched systems, which often create unconventional design criteria where standard approaches are limited in their approach. Most work applied creates the need for either multi-objective optimization or the introduction of discrete variables, with either method requiring manipulation and tuning of the problem. There is a defined gap in the literature for creating a method of continuous transition between operational regions with disparate objectives for control. Therefore, the the first portion of this work examines estimation-based model predictive control (E-MPC) to create an algorithm for objective prioritization such that distinct objectives may be defined for mutually exclusive operational regions. The objective prioritization algorithm is built by using logical conditions that define regions of operation which are incorporated into the objective function, thus allowing smooth transition between a single objective or bank of objectives. The control objective prioritization is cast in the framework of a model predictive controller that is coupled with an extended Kalman filter for estimation of critical yet unmeasured state and performance variables. This applied to the superheater-reheater system of a natural gas combined cycle (NGCC) power plant.
For the remainder of this work, the focus will be on the gap separating the continued advancements of reinforcement learning (RL) and the field of process control. Specifically, it is desired to streamline implementation of RL into control applications for complex dynamic systems in such a way that performance and safety criteria are met or exceeded. Within the last decade, advanced computational techniques have allowed the use of RL in conjunction with continuous systems, but developments have failed to accommodate the often stringent learning and performance requirements necessary for control of a plant environment. This work seeks to bridge that gap, establishing RL as a viable control method while also ensuring the safety and performance expected of any conventional process control.
An approach considered in this work is to make use of direct RL that can work in parallel with a conventional mode of process control which may already be in place. In this way, the RL may learn and be supported by the existing mode of control online while gradually assuming control of the system. In the event of a degradation of performance, the guarantees of safe operation by the existing control (under the assumption that the existing control does guarantee safety) are still in place. This approach has little been explored in open literature, and this gap is what this work seeks to fill. The parallel implementation of RL alongside more conventional process control (CPC) allows for the RL algorithm to learn from CPC. The past performance of both methods are assessed on a continuous basis, allowing for a transition from CPC to RL and, if needed, transitioning back to CPC from RL. This allows the RL algorithm to slowly and safely assume control of the process without significant degradation in control performance. It is shown that the RL can derive a near optimal policy even when coupled with a suboptimal CPC. It is also demonstrated that the coupled RL-CPC algorithm learns at a faster rate than traditional RL methods of exploration while the algorithm\u27s performance does not deteriorate below CPC, even when exposed to an unknown operating condition. This is applied to a benchmark nonlinear continuously stirred tank reactor (CSTR) as well as a flowsheet of a solid oxide fuel cell (SOFC).
A differing approach is then presented investigating the integration of RL with existing model predictive control (MPC) in order to provide a constrained policy for the RL while also creating an adaptable objective for the MPC. RL and MPC possess an inherent synergy in the manner in which they function, and selection of MPC for combination with RL is not arbitrary. Two specific aspects of MPC are advantageous for such a combination- the use of a value function and the use of a model. The use of a model in MPC is also useful since, by solving for the optimal trajectory, a projected view of the expected reward is gained. While this information can be inaccurate based on the current value function, it can allow for accelerated learning. By combining this with a correction for state transitions, an MPC formulation is derived that obeys the constraints set forth, but can adapt to changing dynamics and correct for plant-model mismatch without a required discrete update, an advantage over standard MPC formulations. We propose two algorithms for the value-function model predictive controller (VFMPC)- one denoted as VFMPC(0) where the one step return is utilized to learn the cost function, and the other denoted as VFMPC(n) where the optimal trajectory is used to learn the n-step return subject to the dynamics of the process model. An artificial neural network (ANN) model is introduced into VFMPC(n) to improve the controller performance under slowly changing dynamics and plant-model mismatch. The developed algorithms are applied to two applications, a double integrator and a selective catalytic reduction (SCR) unit.
Finally, the issue of sample inefficiency in the training of RL is approached by leveraging unsupervised machine learning (ML). This problem is two-fold in that both the quality of data as well as the selection for training is often difficult to determine quantitatively. The goal of this work is to leverage unsupervised learning tools, such as gaussian mixture models to help contribute to both the exploration policy and the training/learning done by the algorithm. Because of the availability of data over the course of the training period, it is desired to use such ML algorithms to evaluate the data in order to both form a prediction for the current state of the system, as well as sort data for quality in learning. Such a method would allow the screening of potentially risky actions, without the need for an internal prediction model for the RL algorithm, while also accelerating learning
Manualized Interventions For Individuals Experiencing or At-Risk for Adverse Life Experiences
No full textBackground: Occupational practitioners have a role in providing holistic and client-centered care to promote occupational performance, engagement, and satisfaction through occupations. Many individuals are impacted in the engagement of occupations secondary to adverse life experiences that have caused mental distress, difficulties in cognitive processing, limited social support, and disruptions in social determinants that have made accessing services complicated. Purpose: Due to the increased likelihood of individuals who have experienced adverse life experiences experiencing occupational distress and performance limitations, the capstone student aimed to develop an understanding of adverse life experiences, provide insight into the needs of the population, and develop an intervention strategy that can be utilized for young adolescents who have experienced an adverse life experiences and staff at transitional living programs that encounter these individuals to facilitate occupational performance and engagement in transitional living skills. Methods: The quantitative study with supplemental qualitative data using pre-/post- surveys was inclusive of staff at transitional living programs and clients involved in transitional living programs. Programs were recruited via email and telephone for virtual or in-person workshops focused on education and training on the use and effectiveness of a manualized intervention. Results: The results indicated that both staff and clients at transitional living programs across West Virginia found moderate to high value in the use and effectiveness of a manualized intervention to promote increased competence and confidence in performance and engagement in transitional living skills such as money management, home management, and driving/accessing public transportation. Conclusion: Those impacted by trauma and adversity in childhood are more likely to experience mental health disorders and disruptions in cognitive functions, making it more challenging to manage their activities and transitions of daily living. The Transitional Living Skills workbook serves as a tool to help individuals through self-guided or facilitator-guided training and activities to develop the skills needed for transitional living – ensuring that the individual will feel more comfortable and confident in their skills before transitioning to independent living
Tree Crown Economics of Broadleaf Deciduous Forests
No full textTree crown architecture, a critical determinant of forest ecosystem processes such as photosynthesis, evapotranspiration, and spectral reflectance, is shaped by adaptive trade-offs in resource use and environmental responses. However, significant gaps remain in our understanding of how these traits vary across species, environmental gradients, and temporal scales. This dissertation addresses these gaps by employing remote sensing data across three interconnected studies. Together, these studies advance tree crown economic theory, highlighting how crown traits mediate trade-offs between light capture and water-use efficiency and how these traits influence forest responses to global change. Collectively, this dissertation offer insights for improving models that can predict forest ecosystem responses to global change, monitoring forest health, and informing sustainable forest management strategies. This research underscores the critical role of crown architecture in shaping the resilience and functionality of forests in a rapidly changing world
Family Dynamics and Youth Delinquency A Sociological Perspective on Family Functioning, Atmosphere, And History
No full textABSTRACT
Family Dynamics and Youth Delinquency
A Sociological Perspective on Family Functioning, Atmosphere, And History
Gulzar Jalal
This dissertation critically revisits several well-established socio-criminological theories to present an emerging perspective on the sociological roots of youth delinquency in lost connections with others. The results provide a cross-sectional analysis of the Future Families and Child Wellbeing Study (FFCWS). This longitudinal mixed-method birth cohort dataset followed children from birth to age 15 across 4,898 families in 20 U.S. cities. The first study in this dissertation examines the strength of social bonds and the impact of our socially constructed self-image—i.e., the “looking-glass self”—on youth delinquency while controlling for measures of individual impulsivity. These findings lay the groundwork for the second study, which explores the relationship between youth delinquency and the social atmosphere in three living spaces: home, school, and neighborhood. The third study investigates the impact of broader structural inequities, historical patterns in family involvement in the criminal and juvenile justice systems, and the quality of personal youth-police interactions as influences on youth attitudes and behaviors. In total, these three studies highlight the limitations of punitive and personal-treatment models for preventing delinquency and advocate for more theoretically and empirically informed approaches that strengthen connections between youth and others
Psychophysiological Tools & Techniques to Predict Perceptual Motor Task Outcomes
Get PDFThe process of perceiving environmental information, analyzing it, formulating a plan of action, and carrying out said plan is vital to our day to day lives. This loop occurs for every motor task we perform, from actions as simple as elbow flexion to as complex as performing surgery. As such, it is imperative to understand what physiological, psychological, and neurocognitive factors most dictate success or failure in the motor performance loop.
Prior research has shown that measures from each of these facets have strong relationships with motor performance outcomes, though these findings tended to rely on artificially produced stress in laboratory environments. The present study investigated how these factors influence motor outcomes in real world performance, via a high-level rifle marksmanship competition.
Thirty-seven male participants of the Precision Rifle Series competed in multiple stages of fire running the gamut from stationary long-distance marksmanship to moving targets at unknown distances to repositioning and shooting multiple targets. Heart rate variability, competitive stress, perceived workload, mindfulness, and emotional regulation measures were taken throughout the competition, allowing for an evaluation of previous findings in a real-world setting. To ensure the collected data represented general motor performance and not solely precision marksmanship, a secondary study was conducted concurrently involving a fictional putting competition in which the same measures were collected.
In brief, frequently utilized measures of physiology, neurocognitive status, psychology, anxiety, and workload were assessed in both laboratory and real-world scenarios. Notably, both in-lab and real-world evaluations of these metrics agreed with the current literature, providing evidence that they are valid for use in real-world scenarios. Moreso, the use of commercially available wearable technology which allows for data collection during rigorous activities in austere environments was demonstrated.
Lastly, advancements in the Integrative Framework of Stress, Attention, and Visuomotor Performance were explored utilizing both in-lab and real-world competition data. In both settings, a challenge assessment, which indicates that the participant feels they have the resources necessary to meet task demands, was associated with greater performance, lower internal workloads, and lower neurocognitive fatigue
Deep Learning-Driven Biometric Security: Advancing Liveness Detection and Anti-Spoofing Techniques
No full textBiometric authentication has become a key part of our everyday lives—from unlocking smartphones with a fingerprint or face to verifying identities in banks and airports. These systems rely on our unique physical or behavioral traits, making them both convenient and secure. Unlike passwords, biometrics cannot be forgotten or stolen in the traditional sense. However, they are not without risk. One of the biggest concerns is spoofing: attempts by attackers to fool systems using fake biometric traits, such as silicone fingerprints or AI-generated videos.
As generative AI tools become more powerful and accessible, the ability to create convincing fake biometric data is easier than ever before. This raises serious concerns about the security and trustworthiness of biometric systems. Traditional anti-spoofing methods often struggle to keep up—they depend heavily on labeled data and manually designed features, and they often fail when facing new or unknown types of attacks.
To address these challenges, this research explores deep learning approaches that aim to make biometric systems smarter, more adaptable, and more secure. The focus is on two areas where spoofing is especially dangerous: fingerprint presentation attack detection (PAD) and face liveness detection using a physiological signal called remote photoplethysmography (rPPG).
For fingerprint spoof detection, this study introduces both supervised and unsupervised deep learning models. The unsupervised approach learns only from real fingerprints and detects anything unusual that might indicate a spoof, eliminating the need for collecting fake data. This is especially valuable when new attack types appear. The supervised method goes a step further, using CNNs, attention modules, and Transformers to identify fine-grained patterns that distinguish real from fake fingerprints. Both approaches achieve high accuracy on benchmark datasets and help reduce the time and effort needed for data collection.
In the second part of the work, a novel framework for face liveness detection is proposed. Unlike traditional methods that rely on texture or appearance, this approach uses rPPG—a signal derived from tiny changes in facial skin color caused by blood flow. These changes are difficult to fake and provide strong evidence that a person is real and alive. The Swin-AUnet model was developed to reconstruct high-quality rPPG signals from facial videos. It combines the strengths of U-Net, Swin Transformers, and a GAN-based training strategy with multiple discriminators. Self-supervised learning helps the model learn without needing large labeled datasets, and temporal modeling improves its ability to work in real-world scenarios where lighting and movement vary.
Extensive testing on five public datasets—PURE, UBFC-rPPG, OBF, MR-NIRP, and MMSE-HR—shows that the proposed methods work well under diverse conditions. The reconstructed signals not only support accurate liveness detection but also open doors for remote health applications, such as monitoring heart rate and stress.
By bridging deep learning with physiological signal understanding, this research makes an important step toward building more secure and reliable biometric systems. The proposed methods reduce reliance on large spoof datasets, adapt to evolving threats, and offer practical solutions for real-world security. As biometric technology becomes even more integrated into daily life, these contributions help ensure it remains trustworthy, ethical, and resilient
Disaster Supplies Kit
No full textThis collection of poems explores the theme of apocalypse. It frequently features religion, climate crisis, Michigan, West Virginia, crows, cryptids, survival, hope, and love. The majority of the poems within this collection are free verse, though many are written as prose poems. There is also a sonnet sequence, in which the titles are items from FEMA’s checklist for an emergency preparedness kit. The collection is organized into sections to reflect a rough timeline for an apocalypse: Before, Build a Disaster Supplies Kit, The Collapse, and After. The goal of this manuscript is to explore the hope and dread that one might face in an apocalyptic scenario