University at Albany, State University of New York
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Influence of Cloud-Radiative Forcing on Tropical Cyclone Development in Moderate Vertical Wind Shear
Full text linkThis research seeks to better understand how cloud–radiative forcing (CRF) influences the structural evolution and intensity change of tropical cyclones (TCs) during the organizaional stage, with a particular focus on environments characterized by moderate vertical wind shear. While CRF has been shown to impact TC development during genesis and mature stages, its role during the transitional post-genesis phase—when TCs exhibit disorganized convection and are highly sensitive to environmental influences—remains poorly understood. Moreover, forecasting TC intensity in moderate shear environments continues to present substantial challenges, underscoring the need for focused investigation of TC development in the moderate-shear regime.
The Weather Research and Forecasting (WRF) model is used to examine how CRF influences TC development in moderate shear environments using idealized ensemble experiments with and without CRF.
For weak TCs in the organizational stage, the response to moderate vertical wind shear is indirectly modulated by CRF, particularly at the higher end of the moderate shear spectrum. Key findings reveal a bifurcation in TC structural and intensity evolution: CRF-on TCs exhibit greater resilience to shear-induced vortex tilt and undergo sustained intensification, while CRF-off TCs experience prolonged stagnation in development and ultimately reach lower peak intensities.
CRF-on TCs are marked by reduced vortex tilt, broader outer-core wind fields, and stronger secondary circulations, resulting in a more vertically aligned and dynamically robust vortex. In contrast, CRF-off TCs undergo a brief period of rapid spin-up and radius of maximum wind contraction, but this is followed by a breakdown in structural coherence due to enhanced vulnerability to shear-induced radial and downdraft ventilation. These effects delay intensification by up to 72 hours and limit TC intensity. The initial intensification in CRF-off TCs thus represents a short-lived phase, whereas CRF-on TCs achieve a more persistent spin-up, leading to broader and stronger wind fields.
Nighttime longwave radiative processes play a key role in driving these differences. In CRF-off TCs, enhanced longwave cooling promotes downdraft development via evaporative cooling and boundary-layer destabilization, resulting in stronger low-entropy air intrusion into the core that suppresses convection. Conversely, the greenhouse effect associated with CRF increases ascent, enhances core moistening in typically subsident upshear regions, and accelerates the spin-up of the tangential wind field. These findings underscore the indirect but relevant role of CRF in shaping TC structure, thermodynamic resilience, and intensity evolution under moderate vertical shear
Understanding How County Governments Strategically Plan and Strategize Technology: A Multiple Case Study in New York State
Full text linkEmbedded in the communities they serve, local governments increasingly leverage technology to modernize processes and operations to digitally transform public services and interactions with stakeholders to create and add value. Concomitantly, they must ensure that the use of technology does not threaten the rights and safety of individuals and organizations. Digital transformation requires alignment of technology with institutional, organizational, and environmental factors in addition to persistent innovations in technology, policy, and management. This balancing act hinges on governments’ capacity for strategic technology decision-making. As technology’s value propositions expand and local governments become more reliant on it, there is a growing need to strategically manage technology as a resource and investment. Such a need necessitates strategic planning and strategizing so that local governments can better plan and respond to change, navigate the complexities of digital transformation, and maximize technology’s value while minimizing its negative impacts. Previous research on local governments mostly examines technology-related strategic decision-making processes in cities and pays less attention to such decision processes in counties, which play a significant role as providers of a range of (inter)local and regional government services and programs. Given this role and the fact that counties are pervasive as there are about 3,143 of them across the country, there is a need for more insights into the internal and external factors shaping strategic decision-making processes in county governments. This dissertation addresses these gaps through multiple case studies in the unique context of New York State, where counties exercise substantial autonomy and have experienced considerable progress and challenges in digital transformation.
Through semi-structured interviews with stakeholders and a review of relevant documents and artifacts, the study identifies institutional, organizational, and environmental factors that influence technology strategic planning in New York State counties. The results of this dissertation also contribute to a better understanding of the continuous practice of strategy or strategizing as related to technology. In terms of implications, this research offers a grounded framework for policymakers, administrators, and technology professionals within the public sector tasked with navigating priorities and challenges of digital transformation in local governments
Investigation of the Binding Mechanism between the Supplemental and Tail Regions of miRNA and mRNA within Argonaute 2
Full text linkMicroRNAs (miRNA) are a class of small non-coding RNAs that play a key role in gene silencing. miRNAs form an RNA-induced silencing complex (RISC) with the argonaute 2 (AGO2) protein in order to control gene expression through silencing messenger RNAs (mRNA). The ability for a miRNA-mRNA complex to fit into AGO2 and form the RISC is based on the location and characteristics of a central bulge, but the mechanism and interactions that lead to a functional RISC remain unknown. Using available experimental data, we modeled the miR-34 and NOTCH1 duplex inside of AGO2 with only the seed region base paired. The resulting structure was then simulated in a series of all atom molecular dynamics (MD) simulations with distance restraints being applied to the base pairs after the central bulge to investigate how the supplemental and tail region of the miRNA moves from the P channel to the N channel to bind with the mRNA
Ribosome Hibernation in Mycobacterium abscessus and Its Effect on Antibiotic Resistance
Full text linkNon-tuberculosis mycobacteria (NTM) represent a diverse repertoire of species, most of which are ubiquitous in the environment. Some NTM species, such as Mycobacterium abscessus, can cause life-threatening infections in people with underlying medical conditions. The clinical incidence of such infections is on the rise, posing an emerging threat to public health. M. abscessus is characterized by intrinsic and acquired antibiotic resistance, complicating treatment strategies. Most chemotherapeutic approaches for M. abscessus infection target the bacterial ribosome, necessitating a better understanding of the physiological processes which confer this resistance. Multiple mycobacterial species respond to zinc-limiting conditions by inducing remodeling and hibernation of 70S ribosomes. Ribosome remodeling in this context refers to the replacement of zinc-binding proteins on the ribosomal surface with their paralogues. Ribosome hibernation involves binding of mycobacterial protein Y (Mpy) to the 30S subunit near the inter-subunit interface of the 70S ribosome encompassing the decoding center, thereby inactivating the ribosome in a stable state. We hypothesize that Mpy-dependent ribosome hibernation drives resistance to a clinically relevant aminoglycoside in M. abscessus, amikacin. We perform a systematic analysis of ribosome remodeling and hibernation in M. abscessus and determine the effect of ribosome hibernation on the susceptibility of M. abscessus to amikacin. Lastly, we offer new insights into the mycobacterial stringent response in a model in which Rsh surveils the amino acylation status of the tRNA during the first cycle of elongation to activate the stringent response. We note that this mechanism of Rsh activation is programmed to slow down upon induction of ribosome hibernation
Mobile Air Quality Assessment Using Public Transportation: A Community-Focused Study in the Capital Region
Full text linkThe monitoring of air quality plays a crucial role in establishment of concentrations of air pollutants over space and time in relation to human health, urban planning, and environmental conservation. This research offers a new strategy of monitoring the air quality in the Capital Region of New York State utilizing public transit buses as air quality moving sampling platforms. To assess the level of pollutants, a Capital District Transportation Authority (CDTA) bus network was used across the urban, suburban, and rural areas thus filling a research gap where public transit has most of the time been disregarded as a mobile monitoring platform.
The study was conducted in seven county’s of New York’s capital region: Schenectady, urban Troy, suburban Slingerlands and Glenmont, industrial Cohoes, rural Schodack and Cross gates Mall. Cities including Schenectady and Troy present information on pollution in congested, developed regions appreciated more for their industrial value as compared to less-urban areas such as Slingerlands and Glenmont. Cohoes, with its blend of industrial, commercial, and natural landscapes, and Schodack, a predominantly rural area, together demonstrate that both densely and sparsely populated regions can exhibit unexpected pollution trends, challenging the assumption that urban density alone drives high exposure levels. Moreover, Cross gates Mall, an area of commercial traffic density that attracted dense vehicular and pedestrian traffic, provided insights into emissions from densely travelled zones.
In order to assess the ambient air quality and pollution hotspots the study uses relatively low- cost sensors and monitors such as Observ Air, Duet Sensors, P-Trak, and Q-Trak technologies. To measure black carbon (BC), particulate matter (PM₂.₅), ultra-fine particles (UFP), carbon dioxide (CO₂) and carbon monoxide (CO) respectively. The study was conducted in the Fall 2024 and Spring 2025 to understand the seasonal variation in air quality across different routes.
In addition to the time variation, other key factors like heating emissions and increased vegetation in spring were also assessed. This temporal approach offers a better perspective of pollutant behavior and exposure within various kinds of weather and community structures.
The results of this study are comparable with other existing literature and seeks to contribute to the trends in air quality by identifying spatial and seasonal variability in the Capital Region. Therefore, by using public transportation as the mobile monitoring unit, the project argues the prospect of a real-time, cost-efficient, eco-friendly, and easily expandable air quality monitoring initiative. Therefore, the findings have important implications for public health, environmental justice, and urban and transportation planning and policies, and provide important information that could be used by various policymakers, authorities, and citizens to design specific and effective pollution reduction interventions and to enhance the environmental conditions in various urban areas
Can We See How It Works? The Interactions among Teacher, Student, and Content at a Living History Museum
Full text linkToday’s students need to be critical consumers of information. To that end, a thoughtful social studies curriculum provides students an opportunity to gain content knowledge and to practice thinking skills. However, elementary teachers, especially primary grade (K-2) teachers, often do not possess the skills and content knowledge to teach rich historical content and thinking skills. Teachers can partner with museum professionals and access museum content in this endeavor. Living history museums, immersive museums that interpret multiple buildings, trades, and heritage breed animals, hold the potential to be a strong partner for primary grade students, who are only 5-, 6- and, 7- years old.
Focusing on primary grade teachers, museum educators, and students, this qualitative, design-based, case study research sought to answer the following research questions: When a living history museum is used in primary grade social studies instruction how do the classroom teachers interact with the museum educators and students? How do the museum educators interact with the primary grade teachers and students? And, how is the Visual Thinking Strategies (VTS) protocol used during field trips to a living history museum and to what effect?
Using Hawkins (1967/1975/2003) I, Thou, It (Teacher-Student-Content) framework, I studied the interactions, questions, and responses of the museum educators, primary grade teachers, and students while visiting a living history museum in rural upstate New York that interprets a working 19th century sawmill. Findings indicate that the relationships and interactions among the primary grade teacher, museum educator, and students are complex—with the adults independently and collaboratively serving as the I, or teacher. Teachers also became learners, learners of both the museum artifacts and of primary grade students. In addition, one museum educator deployed the Visual Thinking Strategies thinking protocol. The VTS protocol allowed the interactions among the students and the content to be the primary focus of the experience, promoted student exploration, and honored the knowledge that students brought to the museum
Large Scale Machine Learning over Knowledge Graphs
No full textKnowledge graphs (KGs) have become popular across various fields, providing convenient access to web-based knowledge while storing and formalizing domain-specific information. By analyzing KGs, patterns, connections, and dependencies can be identified across different data sources, enabling the inference of new knowledge from given facts. As the use of KGs expands, the size of modern KGs has grown significantly, making them impossible to process within the main memory of a single computer. Distributed computing offers a viable solution to this challenge by leveraging the combined capabilities of multiple servers within a cluster. This thesis explores how distributed computing can be effectively utilized to perform machine learning over large knowledge graphs, with a focus on facilitating scalable analytics and accelerating downstream tasks. Specifically, this thesis addresses three core challenges: (i) how to facilitate analytics over Knowledge Graphs in Apache Spark, (ii) how to compute KG embeddings at scale, and (iii) how to accelerate and enhance a representative downstream task
Modeling Feature and Instance Interactions in Longitudinal Data for Improved Performance
No full textTabular data has been a core representation format in machine learning, widely used in high-stakes domains such as healthcare, finance, and social services. In many of these domains, data is collected longitudinally, capturing sequences of events and evolving feature values for the same entities over time. Despite its prevalence, most machine learning approaches assume mutual independence between features and independent and identically distributed (i.i.d.) instances. However, these assumptions limit effectiveness in settings where features and entities exhibit complex interdependencies. Moreover, existing approaches frequently overlook a critical dimension: the real-world impact of the decisions informed by model predictions. This thesis addresses these limitations with the broader goal of improving model performance—not only in terms of aligning with ground truth labels but also in terms of enhancing outcomes. To achieve this, we propose four complementary models tailored for longitudinal tabular data: TRACE, PREVISE, REPLETE, and SECURE. TRACE models high-stakes systems as event transition networks and defines a novel sequence similarity score based on event sequences to support personalized event prediction. PREVISE constructs Bayesian networks that capture both service transitions and their links to exit, providing structured probabilistic modeling for high-stakes longitudinal systems. Building upon these foundations, REPLETE captures feature interactions by modeling temporal patterns and functional relationships between events, while simultaneously capturing instance interactions through soft clustering of entities based on shared characteristics. These relationships are embedded into a unified optimization framework that learns low-dimensional representations, which are then used as input to predictive models. REPLETE\u27s generalizability and effectiveness are demonstrated across three domains—homeless service assignment, financial service recommendation, and ICU service prediction. Our experiments show that REPLETE performs significantly well in the healthcare and social service domains and achieves performance comparable to the best-performing baseline in the financial domain. Finally, SECURE complements predictive modeling with decision-making aligned to real-world outcomes. It jointly models the likelihood of a favorable event outcome and the risk of undesirable recurrence using a tripartite event-outcome-recurrence graph, while enforcing a similarity-based neighbor constraint to ensure feasible recommendations. SECURE is evaluated in the context of homeless service assignment, demonstrating that it provides outcome-aligned recommendations that outperform baseline approaches focused solely on predictive accuracy. Together, the models introduced in this thesis represent a step forward in building predictive systems that are both accurate and socially impactful
A Strength-Based Approach to Understanding the Impact of Experiences of Discrimination Related to Identity-Based Stressors on Disordered Eating among a Sample of Sexual and Gender Diverse Individuals
Full text linkEmpirical research investigating eating disorder (ED) behaviors among sexual and gender minorities has grown substantially in recent decades, though notable gaps call for further work to clarify mixed findings regarding the experiences of this heterogeneous population. This study sought to add to our understanding of disordered eating behaviors in LGBTQIA+ participants, with a specific focus on differences between lesbian and gay (LG) versus bisexual, transgender, queer, intersex, asexual, and other identities (BTQIA+), with the latter group remaining especially underrepresented in the literature. Study aims were firstly, to quantify ED incidence and the frequency of experiences of identity-based discrimination in sexual and gender minority respondents, and secondly, to examine the moderating roles of resilience and social support in the relationship between identity-based discrimination and eating pathology. Consistent with prominent theories of minority stress and eating pathology, it was hypothesized that BTQIA+ participants would exhibit similar or greater levels of discrimination and eating pathology than their LG peers. It was further hypothesized that intersecting minority identities, and specifically the intersection of race and gender and sexual minority status, would be associated with greater ED symptoms, and that resilience and social support would moderate the relationship between experiences of discrimination and eating pathology. As an exploratory aim, we also evaluated the factor structure of our primary measure of ED symptoms, the Eating Pathology Symptoms Inventory, in this diverse sample of sexual and gender minority individuals. Study participants were recruited via the online platform Prolific and grouped into two groups of LG (n = 164) and BTQIA+ (n = 233). They provided demographic information and completed measures of experiences of discrimination in daily life, disordered eating symptoms, resilience, perceived social support, and coping. Findings revealed that LG individuals reported greater symptoms of eating pathology compared to their BTQIA+ counterparts, but there were no significant differences in experiences of daily discrimination between the two groups. Holding more than one minority identity significantly impacted levels of ED symptoms in a subset of participants. Neither resilience nor social support moderated the relation between everyday discrimination and eating pathology. Furthermore, the EPSI eight-factor structure was found to be appropriate for use in LGBTQIA+ participants. This investigation provides a more nuanced understanding of the experiences of sexual and gender diverse groups regarding identity-based discrimination and eating pathology, encourages screening for EDs in this population, and may inform culturally-responsive healthcare practices that attend to the unique needs of LGBTQIA+ individuals
Framing Ethnicity: How Ideological Threats Shape State Policies towards the Ethnic Chinese in Southeast Asia
Full text linkPost-colonial multiethnic societies still face significant challenges in nation-building today. Not only do countries follow different paths in dealing with ethnic diversity, but many also shift their strategies across time. State policies towards the ethnic Chinese in Southeast Asia, including accommodation, integration, marginalization, and assimilation, vary along two dimensions: the state’s discrimination towards the ethnic Chinese and the room for negotiation between the state and the ethnic Chinese in formulating these policies. Based on puzzling empirical observations on differing levels of changes in state policies towards the ethnic Chinese in Indonesia, Malaysia, and Singapore in the 1960s and 1970s, my research aims to explain what drove the varied degrees of policy changes across the three cases. More broadly, what factors explain the different levels of policy changes across cases in the post-colonial contexts? To address these questions, my dissertation conducts a critical juncture analysis to investigate how the state’s ideological framing of the ethnic Chinese as communists in the formative years of the three cases—roughly from the 1950s to the 1960s—triggered varying levels of policy changes and how such framing strategy shaped their trajectories of nation-building in the long run. My arguments are composed of three parts. First, I argue that communist ideology plays a critical role in the formulation of state strategies toward ethnic Chinese. Second, I treat framing as the core step linking communism with Chineseness. By performing the core tasks of framing, state elites deliberately altered the nature of the “Chinese problem” to an ideological issue; their efforts to conflate Chinese ethnicity with communist ideology served as a master frame guiding the state’s moves to formulate policies towards the ethnic Chinese in the three cases. Third, I locate state elites’ ideological framing of the ethnic Chinese as communists during the critical juncture from the 1950s to 1960s as the main driving force generating the differing levels of changes in state policies towards the ethnic Chinese in Indonesia, Malaysia, and Singapore in the 1960s and 1970s. Specifically, variations in the framing strategy, driven by varying levels of threats posed by the ethnic Chinese as “communists,” give rise to the different levels of changes in state policies towards the ethnic Chinese across cases. Such causal effects are mediated through the combination of two key factors: the urgency of communist threats and the tractability of the Chinese ethnicity. Furthermore, I identify two subtypes of critical juncture across the three cases, namely, the generative type in Indonesia and the activating type in Malaysia and Singapore, given the different conditioning effects of critical antecedents on the critical juncture as well as a legitimation mechanism linking the critical juncture and the legacies it generates all three cases share in common. My research not only contributes to the existing political science literature on ethnic politics and sociological studies of group threats and framing but also sheds new light on our understanding of the effects of ideology on ethnicity in post-colonial multiethnic states