University at Albany, State University of New York
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Medios y (Narco)Cultura en Puerto Rico: Una Mirada Sociológica desde los Imaginarios y Representaciones
No full textThis dissertation offers an analysis of the emergence and configuration of narcoculture in Puerto Rico from the late twentieth century to the present. It focuses on the intersection of mass media, popular culture, and everyday life to examine how illicit economies and their representations have become integrated into processes of social meaning-making and the contemporary experience of economic modernity in the archipelago. The research is grounded in cultural studies and cultural sociology, drawing on theoretical frameworks such as actor-network theory, symbolic interactionism, and gore capitalism.
Using a qualitative, multi-method approach—including systematic analysis of journalistic coverage, audiovisual materials, music, television, and digital artifacts such as internet memes—the study traces the evolution of social imaginaries and public narratives surrounding drug trafficking, the figure of the bichote, and the police force. The latter is analyzed within the political context of leaderships elected under platforms of punitive populism. This research argues that media and cultural production not only reproduce but also actively shape imaginaries of criminality, security, and social mobility, revealing how narcoculture functions both as a structural symptom and a survival strategy in a context marked by colonial legacies, economic exclusion, and the restructuring of the Puerto Rican state.
Ultimately, this dissertation contributes to contemporary debates on violence, informality, and representation in the Caribbean and Latin America, offering a situated perspective that critically examines the centrality of visual and media discourses in legitimizing new forms of social order
I GET IT FROM MY MOMMA: EXPLORING THE PROCESS OF TRANSGENERATIONAL RESILIENCY AMONG TWO GENERATIONS OF SINGLE BLACK MOTHERS
No full textAbstract
Aim: This dissertation examines how resilience is transmitted across two generations of Black women (18 to 70 years of age). It aims to enhance our knowledge of the meaning of resilience in the context of single motherhood and how beliefs, values, faith-based approaches to coping, and culturally driven approaches to mothering influence outcomes for the next generation. Knowledge gaps regarding theoretical formulations of resilience and single Black mothers are discussed. Methodology: The researcher conducted in-depth interviews with ten women (five mother-daughter dyads). A phenomenological inquiry was conducted to explore the meaning-making process of resilience for mothers; human development, ecological theories, family systems and resilience theories guided the analysis. Findings: Single mothers (n=5, generation 2) described their conceptualizations of resilience and their perceptions of how their relationships with their mothers (n=5, generation 1) facilitated the transmission of resilience. Patterns across dyads (to the third generation) revealed the benefits gained by receiving guidance and support from great grandmothers to grandmothers to daughters as well as from friends, their church, their job, and advocacy work. Several described attachments to helping others, especially other mothers in need. Mechanisms such as modeling, faith-based living, social support and racial socialization were integral to the transmission of resilience. These findings support the need to further expand research and literature to encompass strengths-based perspectives when engaging Black single mothers in research or practice.
Conclusions: This dissertation contributes to theoretical formulations regarding family resilience patterns across generations and fills critical gaps in literature regarding Black women.
The findings suggest that practitioners and policy makers would benefit from developing holistic, culturally relevant, and strengths-based approaches to support single parent households
Age-of-Acquisition (AoA) Effects Reflect an Orthographic Processing Advantage for Early-Acquired Words
No full textAge-of-acquisition (AoA) effects, the processing advantage for words learned early in life, are well-documented across various tasks. While various theories attempt to explain these effects, the underlying mechanisms remain debated. This dissertation examined the role of orthographic processing in AoA effects through three experiments emphasizing orthographic rather than semantic processing. In Experiment 1, participants completed an eye-tracking and proofreading task to detect spelling errors in sentences containing early- versus late-acquired words, followed by a spelling dictation task to assess individual differences in spelling ability. Experiment 2 employed a spelling dictation task to test whether early-acquired words show better spelling accuracy than late-acquired words. Experiment 3 used a same-different judgment task to examine how quickly participants can discriminate between correct and misspelled versions of early- versus late-acquired words, along with a similarity ratings task to control for visual confounds. The stimuli across all experiments were carefully matched for potentially confounding variables, including word frequency, imageability, and OLD-20. Results from Experiments 1 and 2 revealed better performance for early-acquired words, providing support for theories that propose AoA effects arise from multiple levels of lexical processing, including orthography. However, Experiment 3 showed no AoA effects in same-different judgments, revealing boundary conditions for when these processing advantages emerge. Together, these findings provide convergent evidence that orthographic processing contributes to AoA effects while also establishing boundary conditions. Theoretical implications, limitations, and future directions are also discussed
Static Beam Stop Array Based on Interpolation Algorithms
Get PDFX-ray scatter in Cone Beam Computed Tomography (CBCT) considerably degrades image quality by introducing CT value shifts, low-contrast degradation, and distinctive artifacts, thereby limiting adoption in both clinical and industrial applications. This paper presents an innovative scatter correction method based on a Beam Stop Array (BSA) that combines optimized hardware design and advanced algorithmic interpolation techniques. By strategically positioning a BSA with periodically arranged lead columns between the X-ray source and the object, the approach selectively blocks the primary beam, allowing for pure scatter measurement. The resulting sparsely sampled scatter data are accurately interpolated using cubic spline and projection-domain techniques to reconstruct full-field scatter distributions while concurrently restoring missing primary beam information. An extensive experimental study, including phantom and pelvic model scans, demonstrates that the proposed method achieves HU accuracy, noise reduction (by approximately 10%), and artifact suppression comparable to or exceeding conventional anti-scatter grid (ASG) techniques. Furthermore, optimization strategies such as overexposure handling, secondary calibration, and exemplar-based inpainting for artifact repair further enhance image quality. These results highlight the potential of the proposed BSA-based scatter correction scheme to deliver low-dose, high-precision CBCT imaging and set the stage for future integration with deep learning algorithms for improved computational efficiency and artifact mitigation
Graph-Based Machine Learning Framework for Power Electronic Converter Circuit Analysis and Design
No full textTraditional methods for electronic circuit analysis and design face significant challenges in efficiency, computational cost, and scalability, often encountering convergence difficulties. machine learning (ML) models provides a powerful alternative, since it can predict performance metrics, explore design trade-offs (Pareto fronts), and inherently manage variations in circuit structure, thus significantly speeding up design/analysis iterations, reducing computational burden, and facilitating the optimization of complex electronic systems. While ML models offer potential solutions, its application in power electronics has largely focused on control or component-level tasks using surrogate models, neglecting the fundamental representation of circuit topology and component interconnectivity. The problem stems from the existing ML-based circuit modeling approaches, since they lack a systematic means to encode circuit topology and component values effectively. This thesis introduces a novel, systematic framework for representing electric circuits as graphs, specifically designed to enable graph-based ML applications. The framework provides a method to construct graph representations based on the bond graph modeling approach, which captures both the topology and the dynamics of electric circuits components. Building upon this representation, Graph Neural Network (GNN) models are developed and tailored for various circuit analysis tasks. The thesis starts with the systematic bond graph-based framework for graph construction suitable for circuits with varying parameters and operating modes, followed by the design and applications of GNN models for classification tasks (demonstrated on resonant circuits and DC-DC converter topologies) and multi-variable regression tasks (predicting DC-DC converter output voltage and efficiency across different configurations and operating conditions, including conduction modes). Additionally, a detailed characterization of the computational requirements and scalability of the framework is provided, validated on complex circuits like three-phase DC-AC inverter under diverse conditions. Furthermore, the framework is enhanced by incorporating heterogeneous graph properties, utilizing distinct node and edge types derived from the bond graph formalism to improve physical fidelity and representation accuracy compared to homogeneous approaches. Finally, a heterogeneous Physics-Informed GNN (PIGNN) is proposed, integrating fundamental circuit laws (KCL/KVL) via a custom loss function to enhance model generalization beyond the training data distribution. This establishes a robust methodology for interfacing physical electrical structures with machine learning, enabling a wide range of ML tasks such as classification and regression, and providing a foundational step towards more advanced applications like automated power electronics circuit synthesis and design
“Broken Things: An Archive”
Get PDFThis creative thesis starts from the idea that memory is shaped as much by the present as by the past. It explores the emotional and personal complexities of family estrangement, grief, survival, and love. Inspired by Saidiya Hartman’s idea that “every generation confronts the task of choosing its past,” the project moves away from more traditional storytelling and instead is constructed as a collection of archival elements, that includes prose. What originally intended to be a collection of short stories gradually became a fictional family archive—made up of letters, text messages, and other everyday items—that reflects the messy, often conflicting ways we remember. Influenced by writers like Toni Morrison, Christina Sharpe, Alice Walker, and Abdellah Taïa, the thesis draws from autofiction, memory work, and self-preservation practices to tell stories about characters shaped, but not entirely defined, by the histories they inherit and the silences that surround them. Rather than forcing clear endings, the form relies on fragmentation and muddiness, embracing the uncertainty and discomfort that come with our memories. In doing so, this project becomes both a form of storytelling and a way of sharing what might otherwise be lost. It honors what is the most fragile, overlooked, and unfinished, and makes a case that even imperfect memories are worth holding on to
Demonstration and Assessment of Dark Field and Phase X-ray Computed Tomography
Get PDFConventional computed tomography produces a 2D slice or 3D image from multiple conventional images to avoid overlapping structures and increase visualization of the internal features of the object. A set up, macro controls and processing for computed tomography was created for the Center for X-ray Optics for the first time, and demonstrated in this work. Adding differential phase contrast to conventional x-ray images highlights edges. Two more channels of information, dark field and integrated phase, improve the contrast and increase the signal-to-noise ratios of the image. Combining these channels with computed tomography is an important development. This work is the first demonstration of phase and dark field tomography for the Center.
Mesh-based x-ray phase imaging is a technique used to extract five computed channels: computed attenuation along with vertical and horizontal dark field and differential phase contrast images. The integrated phase image is computed from the differential phases. A periodic, metal, inexpensive mesh is employed to visualize the distortion of the x-ray beam when a sample present. Fourier transform techniques are utilized to produce those channels. A simple set up was developed to demonstrate computed attenuation, dark field and phase x-ray CT. The signal-to-noise ratios were quantified for the first time for mesh-based CT with a conventional source. The SNR for a strong scatterer was demonstrated to be a factor of 10 higher than the attenuation contrast for a slice through a CT image.
Geometric-flow x-ray phase imaging (also known as optical-flow) is another method used in this research for phase reconstruction and combined with computed tomography to produce a phase channel in addition to the conventional CT. This may be the first demonstration of this technique with a conventional, large spot, source. The differential phase images in orthogonal directions map the deformation of the reference pattern. A wire mesh or sandpaper can be used as a reference pattern in the optical-flow technique. The quality of the differential phase x-ray computed tomography was measured for the first time, by edge enhancement and the signal-to-noise ratio SNR. Optimization experiments were performed for the edge enhancement by varying the x-ray voltage and adjusting the distance between the sample to the detector. Similarly, the contrast SNR was examined versus the exposure time for the integrated phase CT
The Job Satisfaction of New York State’s Public School Business and Marketing Education Teachers
Get PDFThe Strengthening Career and Technical Education for the 21st Century (Perkins) Act defines career and technical education as organized educational activities in which students learn applied academics, professional skills, and career training in a specific technical field. Many United States students, and ,specifically, New York State students, do not have access to CTE coursework. New York has seven CTE content areas: agriculture, business and marketing, computer science, family and consumer science, health sciences, technology, and trade/technical education.
One barrier to accessing this coursework is the availability of teaching staff. According to data collected by the New York State Education Department about teachers teaching out of certification, CTE has the lowest retention of teachers in the same school year to year. Many CTE teaching positions are filled by individuals teaching out of certification or who entered teaching through alternative pathways. Having uncertified teachers can mean that potentially underqualified and untrained individuals are teaching hazardous CTE coursework.
This study focused on one of the seven CTE content areas, business and marketing education (BME), to examine the job satisfaction of current teachers. This study sought to determine the satisfaction of New York’s BME teachers in the profession. A study on New York’s teachers is significant as New York is one of the largest states in the country and has, for years, been an innovator in educational policy, including an early history in organized oversight of BME programs. Since the teacher is one of the most critical determinants of the success of an academic program, a focus on teacher job satisfaction allows examination of some of the greater issues impacting teacher recruitment and retention.
The theoretical framework utilized various job satisfaction and employee motivation theories, such as Maslow’s Hierarchy, Herzberg’s Two-Factor Theory, person-environment fit theories, and teacher agency. The conceptual framework proposes four primary factors of BME teacher satisfaction: career needs, individual factors, job environment, and relationships, and four secondary factors: BME content; job characteristics; job security; and students, colleagues, and administrators.
This study used a mixed-methods (QUAN-qual) survey approach to gather quantitative and qualitative data regarding BME teachers’ job satisfaction. A modified version of the Minnesota Satisfaction Questionnaire (short form) measured teachers’ satisfaction on twenty items. Eight independent variables: gender, professional work experience, years of teaching experience, sole business teacher status, school size, socioeconomic status of district, region of the state, and CTSO advisor status, were analyzed to determine if any statistically significant differences exist. Two qualitative open-response questions asked respondents about their perceived benefits and challenges of the profession.
A total of 237 BME teachers throughout New York State responded to this survey. Factor analysis was used to simplify the twenty MSQ study items for analysis. The study found slight differences between developing and experienced teachers on the individual factor, whether a business teacher is the only one in their department on the career factor, graduating class sizes between 201-400 and 401+ on the career factor, and CTSO advisor and the environment factor.
The open-response questions provided insights into several benefits of being a BME teacher, including instructional autonomy, business and marketing education content, the teaching environment, personal fulfillment, and supporting student growth. Some of the challenges identified include challenging work environments, relationships with students and families, a lack of a mandate for BME, and job security.
Some central themes are that not having a mandate for BME is both liberating and challenging, that BME teachers place a high value on instructional autonomy, that BME teachers value teaching real-life skills to students, and that relationships are essential for success as a BME teacher. As New York State endeavors to make CTE a part of the new graduation requirements, this study provides teachers, educational leaders, and policymakers insight into the current status of the BME profession in New York and how to improve teacher satisfaction in the face of a changing policy climate.
This study adds to the literature on teacher job satisfaction, in general terms and specific to New York State’s BME teachers. It also adds to the scholarly understanding of CTE and BME. Some recommendations include improving instructional environments to emphasize a positive instructional culture, including BME teachers in policy decisions, developing teacher agency, and uplifting instructional autonomy to prepare students for life beyond high school. As New York State looks to implement the portrait of a graduate in the coming years, this study highlights what BME content is in modern classrooms and what the satisfaction is of those teachers in an environment of policy change
Associations of Screen Time, Growth, and Sleep in the Upstate KIDS Study
Get PDFIn the modern digital era, children are increasingly engaging with digital media (i.e., screen time), and the health-related implications of increased screen time are not fully understood. Excessive screen time in early childhood is increasingly recognized as a potential contributing factor to adverse health outcomes, including increased body mass index (BMI) and disrupted sleep patterns. Despite American Academy of Child and Adolescent Psychiatry recommendations for limiting screen time, studies indicate that many children surpass these guidelines, leading to concerns about reduced physical activity and disrupted sleep. Sedentary behaviors associated with screen time have been linked to increased risk of obesity, compounded by exposure to unhealthy food advertising. Insufficient sleep, which is often attributed to screen time, can have detrimental effects on children’s cognitive and physical development and can act through time displacement and biological effects of blue light. However, findings remain mixed regarding the independent and interactive associations between screen time, sleep, and childhood obesity. This dissertation synthesizes findings from three distinct aims using data from the Upstate KIDS cohort to examine the multifaceted relationships between screen time, childhood obesity, and sleep patterns in toddlerhood and middle childhood. In Chapter 2, we investigated the association between screen time during toddlerhood (12-24 months of age) and BMI-for-age percentiles (24-36 months of age) in 1,691 children. Analyses showed that children in the highest screen time tertile exhibited a modest increase in BMI-for-age and 1.5 times higher odds of being overweight or obese compared to those in the lowest screen time tertile. However, these associations were attenuated after adjusting for confounders. Maternal pre-pregnancy BMI, parental age, and child race and ethnicity were strongly associated with BMI-for-age percentiles and increased odds of overweight and obesity, underscoring the influential role of family health and sociodemographic factors. Chapter 3 focused on the relationship between screen time and sleep in middle childhood (7-9 years of age) among 1,296 children. While there was not a significant prospective association between screen exposure in toddlerhood and sleep duration in mid-childhood, cross-sectional analyses indicated that additional screen time in middle childhood was associated with a reduction in sleep duration. Most notably, we observed a substantial 30.6-minute reduction in sleep duration per additional hour of screen time among children with consistently low screen use from toddlerhood through childhood, suggesting that children who are less accustomed to high screen exposure may be more sensitive to its sleep-disrupting effects. In Chapter 4, the investigation is extended to BMI-for-age percentiles in mid-childhood using data from 1,267 children. Although cross-sectional analyses at ages 7-9 years did not show a direct association between screen time and BMI or odds of overweight and obesity, higher screen time in toddlerhood was significantly associated with increased screen use later in childhood and was associated with a 1.52-2.11 percentage point increase in BMI-for-age percentiles. Collectively, these studies highlight that early screen exposure is linked to subsequent behavioral patterns and modest increases in BMI, with its impact on sleep varying. The attenuation of associations after controlling for familial and sociodemographic factors emphasizes the need for early interventions based on individual family context. Considering the rapidly evolving digital landscape, these findings underscore the necessity for ongoing research that adapts to new technologies and addresses a broader spectrum of health outcomes related to screen use in childhood
Impact of the New York State Gender Recognition Act on Mental Health Outcomes among Non-binary Adults Served by the New York Public Mental Health System
Get PDFThis dissertation explores the impact of the 2021 New York State Gender Recognition Act (GRA) on mental health outcomes and service utilization among non-binary adults in the public mental health system. The GRA introduced pivotal legal reforms—including the option for an “X” gender marker and self-attestation for gender identity changes on state-issued documents—designed to affirm gender diversity and reduce structural barriers for transgender and non-binary populations. Guided by gender affirmation theory and minority stress frameworks, this study investigates how these legal shifts relate to changes in mental health service engagement and psychological well-being among non-binary individuals.
Using a difference-in-differences (DD) approach, this study draws on data from the New York State Office of Mental Health’s Patient Characteristics Survey (PCS) and linked Medicaid service claims to analyze patterns in mental health service encounters between 2019 and 2022. Analyses focus on adults identified as “Non-Binary (X)” or “Unknown” and compare them to a matched reference population of cisgender, heterosexual adults. Key outcomes include utilization patterns across outpatient (OP), inpatient (IP), and emergency services, as well as shifts in diagnostic profiles.
Findings reveal a marked increase in the number of individuals identifying as non-binary between 2019 and 2022, alongside elevated rates of mood and anxiety disorders. The largest changes occurred in outpatient mental health services, where non-binary individuals used these services much more frequently. Increased identity recognition and legal affirmation may encourage earlier and more frequent community-based care engagement, according to this change. At the same time, there was a non-significant but noticeable decline in the use of inpatient services. This trend might indicate a shift away from acute or crisis-driven care and toward longer-term, preventative outpatient interventions, which could be made easier by enhanced psychological health and less stigma after legal gender recognition. The findings imply that policy measures like the GRA may help reduce the severity and escalation of mental health conditions among non-binary individuals by promoting access to affirming, lower-intensity care pathways.
This study contributes to a growing body of literature on non-binary health by offering empirical evidence on the intersection of legal policy and mental health systems. It emphasizes how important inclusive laws are in determining access to care and the ongoing need for outpatient services that are gender affirming and catered to the unique requirements of non-binary communities