33760 research outputs found
Sort by
Search for Soft Unclustered Energy Patterns at the Large Hadron Collider and studies of radiation damage in plastic scintillators
No full textData collected by the Compact Muon Solenoid experiment at the Large Hadron Collider, which collides protons at a center-of-mass energy of approximately 14 TeV, enable tests of the Standard Model of Particle Physics and searches for physics beyond the Standard Model. This thesis presents studies of radiation damage to plastic scintillators, used to produce signals in the CMS calorimeter system, and a search for a beyond-the-Standard-Model particle, the Soft Unclustered Energy Pattern (SUEP).
The effects of ionizing radiation on the light output and the optical properties of plastic scintillators are assessed for a variety of materials, dopant concentrations, fluors, antioxidant concentrations, sample thicknesses, doses, and dose rates. Depending on the dose rate, the samples exhibit internal regions with different refractive indices separated by a visible boundary. Measurements of the boundary depth indicate compatibility with the expected oxygen penetration depth during irradiation. The refractive indices of the internal regions are higher than those of the outer regions, which match the indices of unirradiated samples.
Dark sectors with a large t'Hooft coupling can give rise to SUEP, which are final states characterized by large charged particle multiplicities. A search for SUEP producing muons in the final state is also presented. The expected 95% CL upper limits for the cross sections indicate that, once unblinded, the search will be able to set upper limits about 3-4 orders of magnitude lower than the theory predictions, meaning that it is capable of either excluding the theory or discovering new physics
Breath Analytics: A Sensor-Driven Study of Day-to-Day Human Respiration
No full textBreathing is a vital physiological process that serves as a window into both physical and mental health. This dissertation explores the feasibility of measuring and analyzing human breathing patterns using a wearable sensor in real-world, non-clinical environments across a full day. We focus on characterizing the temporal structure of individual breaths—composed of inhale, exhale, and hold periods—and identifying recurring characteristic breaths. These \textit{characteristic breaths} capture the diversity of respiratory behavior while revealing consistent patterns across individuals and their health-states.
Utilizing the Spire Tag, a wearable multi-sensor device equipped with a piezoelectric force sensor, accelerometer, and PPG, we collected high-resolution respiratory and movement data. We conducted a study to examine normal breathing, and include observations on coughing, sneezing, and other respiratory events, to gain deeper insights into respiratory physiology.
We analyzed real-world breathing data from a large public health study where participants wore Spire tags 24/7 in an uncontrolled setting to investigate the distinct changes in breathing patterns that emerge before, during, and after respiratory illness. We developed a system to process and segment the data into individual breaths and select one subject from this cohort and extract time-based features from over 17,000 unique breaths. Through clustering, we derive nine representative characteristic breath types that describe the underlying physiology and account for over 80\% of daily breathing patterns.
We provide a case-study on three subjects from this cohort by analyzing their full day respiratory behavior. Our findings suggest that shifts in the distribution of characteristic breath types specific to the individual as they transition between sick and non-sick days. Our work lays the foundation for a data-driven framework for personalized respiratory monitoring and real-time illness detection
PALS 2025 : Deliverable 5
No full textThis presentation board was used to illustrate the what took place at the Blooms and Beats Block Party from the UMD PALS 2025 Green Isles Project.Acknowledgements: School of Architecture and Urban Planning, Creative Placemaking Minor, PALS (Partnership for Action Learning in Sustainability), Purple Line Corridor Coalition, Takoma Langley Crossroads Development Authorityhttps://drive.google.com/file/d/1Ll1QDANgqG4Icr6Px7IQ8gci-c7nOXHO/view?usp=sharin
SYMBIOSIS, COGNITION, EMPATHY: SHAPING BEHAVIOR AND ENHANCING AI IN DIGITAL ECOSYSTEMS
No full textDigital platforms form complex ecosystems whose dynamics often evade traditional analyses focused on economic effects or simplified user behavior. This dissertation advances a more comprehensive understanding of these dynamics by integrating system-level dynamics and human psychology. Across three empirical studies addressing platform resilience, content engagement, and AI data integrity, it applies principles of ecological interdependence (symbiosis), analyzes cognitive responses to online discourse, and investigates empathy's role in data quality.The first study investigates Korea's gaming ecosystem during COVID-19 using panel vector autoregression, revealing significant indirect symbiotic effects between physical (PC café) and online (streaming) platforms. It further demonstrates 'symbiotic plasticity'—an adaptive reconfiguration where these indirect relationships fluctuate—as a key mechanism enhancing overall ecosystem resilience.
The second study examines online content engagement through causal forests, revealing nuanced effects of incivility. Mild uncivil content increases subsequent comments and novel ideas by triggering emotional arousal, while extreme incivility suppresses active participation and creativity as users experience cognitive dissonance, causing shifts toward passive voting or disengagement.
The third study demonstrates how Cognitive Empathy Priming (CEP)—a brief psychological intervention encouraging perspective-taking—significantly improves crowdsourced data quality for subjective AI tasks like detecting sexism. This approach enhances label accuracy, consistency between raters, and alignment with expert judgment, ultimately improving downstream AI model performance.
Together, these studies demonstrate that integrating systems thinking with psychological insights provides insight into how platforms adapt, how discourse evolves, and how AI data integrity can be enhanced. The findings offer practical guidance for building resilient ecosystems, designing effective content moderation, and improving AI reliability through targeted cognitive interventions—valuable insights for researchers, platform managers, AI developers, and policymakers
NOVEL MULTIVARIATE BAYESIAN VARIABLE SELECTION METHODS WITH APPLICATION TO GENETIC FINE MAPPING
No full textGenome Wide Association Studies (GWAS) and more recently, Transcriptome Wide Association Studies (TWAS) have been extensively used to identify genomic loci of strong association with complex human traits. However, extreme correlation between genetic variants (i.e. linkage disequilibrium (LD)) and the marginal nature of such studies can make it difficult to identify causal genes or genetic variants. Furthermore, these studies are often univariate in nature, whereas phenotypes of interest such as complex human diseases are also correlated with one another. There also exists the problem of pleiotropy, where a single variant or gene may be causal for multiple phenotypes. Statistical models for such data to more accurately identify causal variants and genes require a flexible framework known as fine mapping. In this dissertation, we propose several multivariate Bayesian variable selection models to perform fine mapping. The rest of the dissertation is presented as follows: In Chapter 1, we provide an introduction and review of molecular biology, fine mapping, variable selection and Bayesian methods. In Chapter 2 we propose a multivariate Bayesian variable selection model for multi-trait fine mapping for GWAS. In Chapter 3 we extend our model in Chapter 2 to allow for higher dimensional outcomes through the use of a latent infinite factor model for phenome-wide fine mapping for TWAS. In both chapters, we apply the proposed models to multiple real-data applications (e.g. fine mapping of heritable neuroimaging features, disease conditions from electronic health record data) and evaluate the models through extensive simulations compared against existing methods. In Chapter 4, we propose a Variational Bayes approach as an alternative way to estimate the models described in Chapters 2 and 3. Finally, in Chapter 5, we discuss the work we have done and provide potential future extensions
MECHANISTIC AND PHOTOLYTIC STUDIES OF CYCLOPROPYL, BENZIMIDAZOLE, AND BENZOTRIAZOLE NITRENIUM IONS
No full textNitrenium ions are short-lived, reactive intermediates that play an integral role in both biology and chemistry. These ions have been implicated as carcinogens in DNA-damaging reactions but have also been utilized as new electrophilic reagents in chemical synthesis. The work presented in this dissertation investigated three nitrenium ions using photolytic studies to understand their chemical and kinetic behaviors. Chapter 1 starts with an introduction to organic photochemistry and its use not only in synthetic research but also in the generation of short-lived intermediates. The chapter also presents laser-flash photolysis, a technique vital to studying reactive nitrenium ion intermediates generated in the heterolysis of N-aminopyridinium salts. Chapter 2 provides a background of nitrenium ions detailing the conception of the divalent cationic nitrogen species and early studies that led to the eventual proof of the short-lived, reactive intermediates through competitive trapping experiments and isolation of adducts. Chapter 3 explores two cyclopropyl nitrenium ions, one where the substituent is an aromatic ring capable of delocalizing the positive charge through pi-conjugation and another where the substituent is an alkyl group incapable of delocalizing positive charge. In the case of the aromatic cyclopropyl nitrenium ion, stable products result from a mix of ring expansion, nucleophilic addition and ethylene elimination. In contrast, the only observed product for the alkyl cyclopropyl nitrenium ion results from ethylene elimination. Chapter 4 examines the benzimidazolenium ion which DFT calculations predict to be a very reactive nitrenium ion intermediate. Experimental studies have shown that this nitrenium ion primarily decays through H-atom abstraction from the solvent and in-cage recombination with the pyridine leaving group. Chapter 5 probes the benzotriazolenium ion to see if it has similar reactivity to the previously studied benzimidazolenium ion. Initial experimental results have shown that the benzotriazolenium ion decays through H-atom abstraction from the solvent and in-cage recombination with the pyridine leaving group. In addition, another decay pathway appears to be nucleophilic trapping with alcohols (ROH) to form alkoxy adducts to the benzotriazole ring
From Bergman kernels to Polarity: Perspectives on the Mahler, Bourgain, and Blocki conjectures
No full textThis thesis investigates fundamental questions about convex bodies, polarity, and volume. By revisiting their connection to Bergman kernels, novel concepts of -polarity and Mahler volumes are introduced and studied extensively. This elucidates Nazarov's approach to the Mahler conjecture and simultaneously offers a new approach both to the Mahler Conjectures and Bourgain's Hyperplane Conjecture. Moreover, it unifies B\l{}ocki's and Mahler's Conjectures as the endpoint cases of new -Mahler Conjectures. Key contributions include refined bounds, new inequalities, the resolution of B\l{}ocki's conjecture for Bergman kernels of tube domains in dimension two, and the development of new links between the Mahler conjecture and Bourgain's conjecture.
In the first part of this thesis (Chapter 2), Nazarov's bound on the Bergman kernels of tube domains is revisited. A measure of asymetry for convex bodies is introduced, and Nazarov's proof of the Bergman kernel bound is reformualted to avoid the use of John's theorem and remove the symmetry assumption for convex bodies. A new bound for Bergman kernels of tube domains is developed, tailored to the symmetry of the body. This approach recovers Nazarov's bound for symmetric bodies and establishes a new bound applicable to all convex bodies.
Motivated by the study of Bergman kernels of tube domains, the third chapter introduces the concept of -polarity, a novel notion that provides a smooth approximation of classical polarity. -polarity is studied in detail; the existence and uniqueness of -Santal\'o points along with -Santal\'o inequalities, both in the symmetric and non-symmetric cases, are established. Steiner symmetrization is used as the main tool in proving the -Santal\'o inequalities. Using these notions, Bergman kernels of tube domains are now understood via the volume of the -polar body, leading to the derivation of sharp upper bounds as a corollary. Additionally, the -Mahler volumes and Santal\'o points are utilized in the derivation of an upper bound on the isotropic constant, leading to a novel approach to Bourgain's slicing problem.
The introduction of -Mahler volumes naturally leads to the formulation of -Mahler conjectures. Numerical evidence is provided to suggest that, unlike classical polarity, the cube and its linear images are the unique minimizers in the symmetric case. In Chapter 4, the -Mahler conjectures are verified in dimension two by adapting Mahler's sliding argument both in the symmetric and non-symmetric cases. In the latter case, triangles centered at the origin are proven to be the minimizers. As a corollary, sharp lower bounds on the Bergman kernels of tube domains are established in dimension two, verifying in the affirmative a conjecture of B\l{}ocki in that case.
Lastly, -Legendre transforms are introduced as the functional analogues of -polarity. Functional -Santal\'o points and -Santal\'o inequalities are established. This is achieved using the Fokker--Planck heat flow method introduced by Nakamura--Tsuji. Additionally, alternative approaches inspired by the work of Artstein--Klartag--Milman are explored by computing the asymptotics in dimension of the -Mahler volumes of the Euclidean ball
Smoking Cessation and Type 2 Diabetes: Application of Marginal Structural Model and G-Formula Method
No full textSmoking cessation has been proven to be associated with a reduced risk of chronic diseases, such as cardiovascular disease, chronic obstructive pulmonary disease, and cancer. However, its relationship with type 2 diabetes mellitus (T2DM) remains complex. Weight gain following smoking cessation has been identified as a key mediator that may offset some of the metabolic benefits of quitting. Previous studies have reported inconsistent findings, largely due to inadequate handling of time-varying exposures, confounders, mediators, and potential selection bias from informative censoring. This dissertation applies marginal structural models (MSMs) and the parametric mediational g-formula to address these methodological limitations and investigate the temporal relationships between smoking cessation, weight change, and T2DM risk. The dissertation is composed of three papers.Paper 1 presents a systematic review and meta-analysis of eleven cohort studies that examined the association between smoking cessation and T2DM risk, stratified by post-cessation weight change. Using random-effects models, we found that quitters who gained weight had a 71% higher T2DM risk compared to continuous smokers (HR = 1.71; 95% CI: 1.12–2.62), particularly among recent quitters (HR = 2.20; 95% CI: 1.27–3.82). In contrast, long-term quitters had a reduced risk (HR = 0.91; 95% CI: 0.87–0.95). Quitters without weight gain showed no increased risk if recently quit (HR = 0.99; 95% CI: 0.81–1.02), and a lower risk if long-term (HR = 0.84; 95% CI: 0.81–0.87). Compared to never smokers, recent quitters exhibited elevated T2DM risk regardless of weight change. These findings underscore the importance of both cessation duration and weight management in mitigating diabetes risk.
Paper 2 utilizes MSMs with inverse probability weighting to estimate the causal effect of smoking cessation on T2DM risk using data from the Framingham Offspring Study (1983–2014). Among 1,880 ever-smokers, those who quit smoking had a 17% lower risk of T2DM compared to those who never quit (HR = 0.83; 95% CI: 0.61–1.12). Notably, individuals who maintained cessation for more than 10 years experienced a 31% reduction in risk (HR = 0.69; 95% CI: 0.49–0.96). Additionally, never smokers had a 26% lower risk compared to current smokers (HR = 0.74; 95% CI: 0.55–0.99). Long-term quitters had risk levels comparable to never smokers, reinforcing the long-term benefits of sustained cessation.
Paper 3 applies the parametric mediational g-formula to estimate the interventional direct and indirect effects of smoking cessation on T2DM risk, with weight change as the mediator. The analysis included 416 current smokers at baseline (Exam 3) in the Framingham Offspring Cohort. Long-term cessation (20 years) was associated with a 6.77% absolute reduction in 30-year T2DM risk compared to continued smoking. Approximately 32% of the total effect at 1-year post-cessation was mediated through weight change, but this indirect effect diminished with longer cessation durations, approaching zero after 10 years. These findings suggest that the long-term metabolic benefits of quitting outweigh the short-term adverse effects of weight gain and support efforts to promote smoking cessation along with early weight management strategies.
This dissertation demonstrates that while post-cessation weight gain may temporarily elevate the risk ofT2DM risk, long-term smoking cessation ultimately reduces that risk. The protective effect becomes more pronounced with sustained cessation, and the influence of weight gain as a mediator diminishes over time. These findings support the promotion of smoking cessation alongside early weight management strategies
Essays on Technology, Decision-Making, and Economic Development
No full textUnderstanding how individuals make decisions under constraints is central to the study of economic development. Technological, financial, and social constraints shape economic behavior in ways that influence access to opportunities, policy effectiveness, and long-run welfare. This dissertation explores these constraints in Sub-Saharan Africa through three essays. The first examines technological constraints by developing a generalized framework for evaluating the causal impact of mobile network access, addressing measurement challenges in geospatial data and providing a method for studying steady-state impacts of mobile connectivity. The second examines financial constraints to clean energy adoption, using a randomized experiment in Cameroon to assess the effectiveness of subsidies in promoting biogas technology uptake and highlighting a disconnect between stated intent and actual adoption. The third analyzes social constraints by investigating how shifts in national over ethnic identity influence labor market outcomes in Sub-Saharan Africa, leveraging national football team victories as an exogenous shock to identity. Together, these essays contribute to our understanding of how constraints shape economic behavior in Sub-Saharan Africa and provide evidence to inform the design of policies that promote economic development.
Chapter 1 addresses a critical gap in the literature on mobile connectivity by developing a broadly applicable method to estimate its causal effects. While prior studies have largely focused on the rollout of telecom networks or relied on unusually high-quality data, this chapter introduces an approach that enables evaluation in steady-state conditions where network access has stabilized, using widely available data sources. I employ a Longley-Rice Irregular Terrain Model to predict mobile signal strength and implement a regression discontinuity design at the threshold for basic mobile access. A key challenge in geospatial analyses using Demographic and Health Surveys (DHS) data is coordinate displacement, which protects respondent privacy but introduces measurement error that can bias estimates. To address this, I construct a machine learning-based proxy that significantly improves signal strength estimation, reducing the root mean squared error by 25.56% to 52.31% at the margin. Monte Carlo simulations demonstrate that this correction improves classification around the treatment threshold and reduces bias in treatment effect estimates, particularly in noisy environments. Applying this framework to DHS data, I validate the approach using women's phone ownership rates and estimate the impact of network access on infant mortality. The results support the framework’s reliability and highlight its potential for future research on mobile network connectivity.
Chapter 2 (co-authored with Anna L. Berka, Cornelis Gardebroek, and Niccolò F. Meriggi), examines financial constraints in the adoption of complex energy technologies. While subsidies have been shown to facilitate the adoption of low-cost, intuitive technologies, their effectiveness in promoting more complex, capital-intensive innovations remains less understood. In this chapter, I present evidence from a clustered randomized controlled trial in rural Cameroon, where households were offered varying levels of subsidies for biodigester construction, a technology that enables biogas production. The results indicate that 25% and 45% subsidies increased contract signing by 15 and 20 percentage points, respectively, yet few of these agreements resulted in actual biodigester construction. The 45% subsidy increased completed constructions by 3 percentage points, while the 25% subsidy had no significant effect. The findings reveal a disconnect between initial adoption intent and actual follow-through, suggesting that financial incentives alone may be insufficient for promoting complex technology adoption. We identify household characteristics associated with successful adoption and argue that hyper-targeting of subsidies could enhance policy effectiveness in promoting capital-intensive technologies.
Chapter 3 explores how social identity influences economic behavior, particularly in ethnically diverse societies. Using national football team victories as an instrument, I examine how shifts in national over ethnic identification affect labor market outcomes in Sub-Saharan Africa. Previous research has documented that national victories increase nationalistic sentiment, but this chapter provides new evidence that these shifts are particularly pronounced in regions where ethnic majorities face higher relative unemployment. In addition, the observed shift persists for at least 45 days, suggesting longer-term effects on identity. Further, I present suggestive evidence that national identity is related to ethnic unemployment disparities, which I interpret through the lens of in-group favoritism in labor markets. These findings illustrate how collective experiences contribute to nation-building, shaping identity, economic behavior, and potentially mitigating labor market inequality in ethnically fractionalized societies.
Together, these essays illustrate how technological, financial, and social constraints shape economic behavior in developing contexts. By advancing empirical methods for assessing the impacts of technological constraints, refining our understanding of financial barriers to technology adoption, and uncovering the economic effects of identity shifts, this dissertation contributes to both economic theory and policy design. The findings enhance our understanding of development challenges and provide insights that can inform more effective interventions and strategies for fostering inclusive economic growth
Maryland Office of Outdoor Recreation: County-Level Insights
No full textFinal report for HBUS 205: Capstone in Interdisciplinary Business (Spring 2025). University of Maryland, College ParkThis report presents an in-depth county-level analysis of Maryland’s outdoor recreation economy, conducted by students in the Interdisciplinary Business Capstone in collaboration with the Maryland Department of Natural Resources (DNR) and the Office of Outdoor Recreation (OOR). Focusing on Cecil, Worcester, and Anne Arundel Counties, the project identifies critical opportunities and challenges related to outdoor recreation infrastructure, business support, data collection, and economic development. Through literature review, stakeholder interviews, and comparative case studies the team proposes actionable strategies to improve data consistency, expand public access to natural amenities, support small recreation-based businesses, and strengthen the state's trail and waterway networks. The report recommends a phased action plan emphasizing infrastructure upgrades, state-wide branding, automated rental solutions, and equitable support systems to enhance recreational access, resident satisfaction, and local economic impact across Maryland.Marylan