33760 research outputs found
Sort by
Understanding Linkages Between Coastal Environment & Community Health
No full textCoastal environments face unique challenges that profoundly affect human well-being. Stressors such as pollution, rising temperatures, flooding, harmful algal blooms (HABs), and emerging contaminants threaten ecosystems and disproportionately impact vulnerable communities. This report presents the findings from the Fall 2024 Coastal Environment and Community Health course, by graduate students in the Marine, Estuarine, and Environmental Science program at the University System of Maryland.https://ian.umces.edu/site/assets/files/32239/understanding-linkages-between-coastal-environment-and-community-health.pd
Charles County Climate Adaptation Report Card Methods Document
No full textThis document describes the scoring processes for the 27 indicators used in the Charles County Coastal Adaptation Report Card.https://ian.umces.edu/site/assets/files/32773/charles-county-climate-adaptation-report-card-methods-document.pd
The History of the Evangelical Churches of the Valleys of Piemont: Political Performance, and the Use of History in the Cromwellian Protectorate
No full textThe History of the Evangelical Churches of the Valleys of Piemont: Political Performance, and the Use of History in the Cromwellian Protectorate
In 1655, the Duke of Savoy ordered the violent removal of Protestants from his territory in Northern Italy in the basin of the Italian Alps. The horrific massacre that resulted provided vivid evidence of the arbitrary cruelty of Catholic European powers in the development of mid-seventeenth-century polemic literature. Specifically, it provided the Cromwellian Protectorate a useful tragedy in conceptualizing and actuating long-standing threads of religious history that provided the backbone justification of military action against their enemies. This presentation focuses on the book commissioned by the Protectorate and compiled by Sir Samuel Morland titled The History of the Evangelical Churches of the Valleys of Piemont and the complex composition of this book in terms of its religious and political uses. This talk will include descriptions of extant copies of the book in addition to its contents and some history of its use, dissemination, and idiosyncratic elements of specific copies.
Content warning: This presentation will contain images from Morland’s work which graphically depict atrocious imagery of extreme violence against men, women, and children
INSTRUMENTAL ADVANCES IN CAPILLARY ELECTROPHORESIS MASS SPECTROMETRY FOR TRACE-SENSITIVE PROTEOMICS
No full textProteomics plays a pivotal role in modern biology by qualitatively and quantitatively characterizing the proteome of biological systems and holistically reflecting the overall dynamics and heterogeneity in organisms. Capillary electrophoresis mass spectrometry (CE-MS) serves as a powerful analytical tool for proteomics and offers superior resolution and minimal sample consumption, yet its adoption has been limited by historical gaps in instrumental development. This dissertation progresses CE-MS proteomics by developing instrumentation and methodologies to promote sensitivity, robustness, scalability, automation, and throughput to facilitate analyses of low-amount proteome samples. The works in this dissertation achieved advances including detection of peptides at sub-picomolar levels (30 zmol), robust analysis of 100 nL proteome samples, and levitational sample enrichment by 4.4 folds. Collectively, this dissertation contributes to the leaping development of CE-MS for proteomics.Chapter 1 overviews the background, current state and challenges of capillary electrophoresis mass spectrometry proteomics and explains research motivation.
Chapter 2 introduces methodology combining large-volume sample stacking (LVSS) CE and trapped ion mobility spectrometry (TIMS) for ultra-high-sensitive analysis of low-abundance peptides from mouse brain tissues.
Chapter 3 presents a robotic CE-MS system (RoboCap) achieving robust and reproducible analysis of minimal sample volume of proteome.
Chapter 4 details an acoustic levitational sample enrichment method for trace-amount proteome.
Chapter 5 summarizes the research outcomes in this dissertation and provides potential directions to further improve CE-MS proteomics
PHENOMENOLOGY OF THE STANDARD MODEL AND BEYOND
No full textWhile the Standard Model (SM) of particle physics is the most precise theory everdiscovered, it is known to be incomplete. The SM cannot account for the observed
Dark Matter (DM) in the universe or that the neutrinos have masses. Furthermore,
theoretical puzzles such as the matter-antimatter asymmetry and the Planck-weak
hierarchy remain unexplained within the SM. These shortcomings have motivated
extensive efforts to search for physics beyond the SM (BSM).
One promising class of BSM theories involves “Hidden Sectors”, which are newparticles and interactions that are singlets under SM gauge symmetries. While minimal hidden sector scenarios have been widely studied, models with non-minimal field content and richer dynamics remain less explored. In the first part of this dissertation, we examine both minimal and non-minimal hidden sector models, addressing open
questions regarding their discovery potential. We evaluate the sensitivity of future
lepton colliders to vector portal models across a range of benchmark scenarios, and
demonstrate how neutrino telescopes can probe non-minimal hidden sectors using two
simplified models.
Precision measurements of SM observables, such as electroweak precision observables (EWPOs) and the anomalous magnetic moment of the muon (g − 2), offer a complementary approach to direct searches by probing virtual effects of new physics. A crucial input to global EW fits is the top quark mass mt, which must be knownwith high precision to fully exploit the sensitivity of these observables. However,
most current methods are limited by uncertainties in production modeling (including
possible BSM effects therein) and jet energy calibration. In the second part of this
dissertation, we propose a new method for measuring mt that reduces these sources of
systematic uncertainty while involving complementary systematics. If implemented,
this approach could significantly enhance the precision of current and future measurements, helping to sharpen indirect probes of BSM physics
Highly Constrained Kinetic Models for Quantitative Single-Cell Gene Expression Analysis
No full textCells are for the most part genetically identical, and it is the gene expression which determines different cell types in the body. Transcription, the first step in gene expression, is a complex, multi-step process initiated by transcription factors (TFs) binding to DNA promoter regions, followed by recruitment of proteins like chromatin remodelers, general transcription factors, and RNA polymerase II in eukaryotes. Variability in transcriptional outcomes arises from both regulatory mechanisms and stochastic events, making it essential to understand transcription dynamics to better grasp heterogeneity within gene regulatory networks. This dissertation investigates transcription from a kinetic biochemical perspective, leveraging steady-state and kinetic gene data. I hypothesize that single-cell variation can be utilized to infer dynamic mechanisms of transcriptional regulation. I employ single-cell RNA sequencing (scRNA-seq) and single-molecule Fluorescence in situ Hybridization (smFISH) to quantify mRNA levels, alongside live-cell imaging to measure TF dwell time and transcriptional bursting kinetics. By integrating these methods into a computational model, we aim to simulate transcriptional dynamics and assess existing models, such as the telegraph and kinetic proofreading models, in capturing regulatory steps. This research offers insights into transcriptional regulation and its implications for disease, while improving gene expression prediction models in molecular biology.
In conclusion, gene expression and regulation are highly dependent on the biochemical aspects of the proteins associated with transcription. A thorough study of transcriptional mechanisms will help us understand multiple diseases that are caused by mis-regulation of transcription. Furthermore, a robust model that can mimic the multiple-step transcription mechanism will also help us quantitatively analyze single-cell gene expression, which is an emerging problem in molecular biology
How Does Spectral Resolution Influence Net Doppler Shifts?
No full textExoplanets orbit stars beyond our Solar System. To investigate their atmospheres, astronomers analyze the light they emit or block, especially during transits. When spread into a spectrum, this light reveals spectral lines showing what gases are present. If the gases are moving, the lines shift slightly due to the Doppler effect. By measuring these shifts, astronomers can detect winds and other atmospheric motions.
At high spectral resolution, these subtle shifts are clear, allowing precise velocity measurements. But at lower resolution, the lines blur, making Doppler shifts harder to detect. This limits our ability to study atmospheric dynamics using low-resolution data.
In this project, I analyze simulated exoplanet spectra across spectral resolutions to investigate how Doppler shift retrieval changes. I also compare models with and without magnetic fields to assess their effects on measured velocities. Using cross-correlation techniques, I examine how resolution and magnetic fields impact the interpretation of atmospheric motion across orbital phases.
Preliminary analysis shows that magnetic fields can change the measured Doppler velocities, even at the same orbital phase. I’ve identified consistent velocity differences between magnetic-on and magnetic-off models and am currently analyzing how these differences vary with spectral resolution. This work will aid astronomers in detecting atmospheric motions and understanding how spectral resolution influences the interpretation of exoplanet spectra
Computational Framing Analysis: Proposing And Applying an Unsupervised Entity-Centric Semantic Relations Approach
No full textThis dissertation presents a novel computational approach to analyze how the news media construct frames around certain people or groups in their coverage. The approach centers on entity-centric emphasis frames, focusing on the language used to attribute key entities (e.g., shooters and victims in mass shooting incidents). The unsupervised method, named Semantic Relations-based Unsupervised Framing Analysis (SUFA), uses computational techniques to detect and analyze framing patterns based on semantic relations, moving beyond existing bag-of-words, co-occurrence, and frequency-based approaches. The dissertation includes three main projects, each building on the previous to develop, apply, and improve this new approach.
Project 1 (Chapter 2) provides a critical review of existing computational methods used for supervised and unsupervised framing analysis. The review highlights limitations in traditional unsupervised approaches, which largely rely on bag-of-words, frequency, and word co-occurrence methods, often failing to capture contextual meaning and relationships between words. The survey article recommends integrating semantic relations into unsupervised framing analysis, proposing a more nuanced method for detecting frames.
Project 2 (Chapter 3) builds on the recommendations of Project 1 and introduces Semantic Relations-based Unsupervised Framing Analysis (SUFA) as a new computational framing approach to explore entity-centric emphasis frames. This chapter presents a mixed-method study consisting of qualitative textual analysis and computational analysis applied to 100 news reports (600 paragraphs) on the Uvalde school mass shooting from four major U.S. media outlets, The New York Times, Cable News Network (CNN), Wall Street Journal, and Fox News. The qualitative analysis identifies how semantic relations contribute to frame construction, while the computational analysis employs natural language processing techniques, including dependency parsing, to extract and analyze entity-centric frames (e.g., shooter, victims, incident). The study outlines the strengths, limitations, and practical applications of SUFA, demonstrating its potential as a scalable and context-aware framing analysis approach.
Project 3 (Chapter 4) applies SUFA to a large-scale dataset of gun violence coverage in the United States and advances the methodological approach by automating the formation of frames from framing components. This study analyzes one month of news reports (N = 1334) from nine major U.S. news outlets covering the 2022 Uvalde elementary school mass shooting incident in Texas. Three of the outlets were selected from the left-centered bias category: The New York Times (n=227), The Washington Post (n=228), and The USA Today (n=128). Three were selected from the right-centered bias category: The Wall Street Journal (n=47), The New York Post (n=255), and The Dallas Morning News (n=155). And three others were included from the least-biased category: The Hill (n=227), The Indianapolis Star (n=39), and The Des Moines Register (n=28). The media outlets’ biases were determined by scores provided by Media Bias/Fact Check (MBFC), a non-partisan and independent site that provides bias scores for media outlets.
The research under Project 3 goes beyond existing topic modeling approaches, which primarily rely on bag-of-words, co-occurrence, and frequency-based methods that often fail to capture semantic relationships between words. Instead, SUFA leverages advanced NLP techniques such as dependency parsing and coreference resolution to identify how words modify or relate to key entities (e.g., shooter, victims). Furthermore, large language models (LLMs) such as OpenAI’s GPT-4o are incorporated to automate the clustering of framing components. This advances SUFA and improves its scalability for unsupervised frame detection. At the same time, it demonstrates how LLMs can be utilized in the coding of textual data and the clustering of framing components. This chapter provides how SUFA’s entity-centric emphasis framing focus offers deeper insights into media narratives and bias, particularly in the framing of shooters and victims in a mass shooting incident.
The results revealed that at least six frames were attributed to the shooter, and nine frames were attributed to the victims in different ways. The right-centered news media group deployed some frames, including action attribution, younger age, and allegation certainty, significantly higher compared to left-centered ones, to frame the shooter. As the regression analysis provides, left-centered media are significantly more likely to deploy personalized victim framing (our victims, your victims) and emphasize older victims. In contrast, right-centered media more frequently use dehumanization framing. At the same time, there is also a significant difference in how right-leaning and left-leaning news outlets use individual framing components. These results highlight news media’s ideological differences in how they deploy framing components and frames to attribute to victims and the shooter.
Guided by framing and attribution theory, the exploration of frames provides theoretical insights into how media frames assign responsibility for mass shootings through international or external attributions. For instance, one prominent frame, action attribution, highlights the shooter’s agency and responsibility, aligning with internal attribution and frequently used by right-leaning media.
This dissertation makes several important contributions to computational framing research. It advances the SUFA approach by applying it to a large-scale dataset and enhancing its methodological rigor through the integration of semantic relations, dependency parsing, coreference resolution, and large language models (LLMs) for automated clustering. The study applies SUFA to mass shooting coverage, offering one of the first large-scale, unsupervised analyses of shooter and victim frames across media bias groups. It bridges attribution theory with computational methods, demonstrating how internal and external attributions of responsibility are reflected in media frames. Additionally, two annotated mass shooting datasets, validated through human and GPT comparison, provide a valuable resource for future research. This dissertation also shows SUFA’s interdisciplinary potential, showing how it can support framing analysis across fields such as communication, political science, and computer science.
In terms of implications, SUFA can be potentially used as a powerful tool for real-time media monitoring and enhanced social media analytics by adding framing-based insights that move beyond surface-level metrics like sentiment analysis and mention frequency. It can support crisis communication by helping crisis practitioners understand how key entities are framed during crises, informing more effective response strategies. Computational- or data-driven journalists can use SUFA to uncover and visualize media bias and framing trends. The general public, educators, and activists can also apply the approach to strengthen their media literacy and, expectedly, hold media outlets accountable for biased or misleading representations.
Overall, this dissertation develops, applies, and advances a computational framing analysis approach grounded in semantic relations to explore entity-centric emphasis frames. Although SUFA has some methodological limitations, including its primary reliance on textual framing components and its focus on entity-centric frames, its successful application across multiple datasets underscores its potential as a powerful tool for computational media analysis, bridging the gap between social science theories and advanced computational methods.
Keywords: Computational framing analysis, natural language processing, machine learning, dependency parsing, semantic relations, method, attribution theory, framing, gun violence, mass shootings, public health crisis, computational strategic communication, social media analytics, media monitoring
MONITORING URBAN DYNAMICS USING HIGH-SPATIAL AND HIGH-TEMPORAL RESOLUTION SATELLITE IMAGES
No full textWorldwide economic development and population growth have led to unprecedented urban area dynamics. These changes have an impact on natural ecosystems and influence the development of human society. It manifests a long-term impact on urban sustainability at various scales in terms of economic consequences, environmental degradation, and societal impacts. With the availability of satellite data at higher spatial (3-10 m) and temporal (1-3 days) resolutions, new opportunities arise to monitor and characterize urban changes. This dissertation advances the utilization of high-spatial and high-temporal resolution satellite images to identify and monitor urban area changes, including gradual urban area changes and the recovery process after urban disturbance (a discrete event that disrupts the urban system). However, several knowledge gaps remain: insufficient studies monitoring gradual intra-urban changes, limited geolocation scope and predominately focus on large cities in mapping gradual urban dynamics, only a few studies differentiating multiple urban classes, and a lack of analysis on high temporal frequency especially in the context of urban disturbance. This dissertation addresses these issues targeting three individual case studies. Chapter 2 focuses on evaluating the accuracy of urban dynamics detection across diverse geographic locations globally using Sentinel 2 images based on a deep learning model. This chapter also detects and monitors gradual intra-urban changes in the Washington DC-Baltimore region from 2018 to 2019. The study reveals that in just one year almost 1% of the total urban area underwent changes with the majority coming from the construction of commercial buildings, followed by residential buildings. Almost 10% of changes were attributed to the construction of new or the renovation of existing schools. Chapter 3 expands the geolocation scope to the under-researched country of Ukraine, as well as smaller cities that are under-studies. This chapter analyzed and characterized changes in various urban land use subcategories across 30 Ukrainian cities utilizing Sentinel-2 images. Findings indicate that between 2016 and 2021, approximately 3.5% of the total urban area in Ukraine changed. Among all classes that underwent changes in 2016, the most significant transformation decrease was observed in green urban areas (25.63 〖km〗^2), natural vegetation (12.02 〖km〗^2), and agriculture land (3.85 〖km〗^2). Within urban sub-category transitions, the combined area of the urban fabric (16.61 〖km〗^2), construction sites (14.94 〖km〗^2), dumpsites (5.01 〖km〗^2), and industrial zones (4.42 〖km〗^2) experienced notable increases by 2021. Chapter 4 targets the lack of dense time-series image analysis, especially in assessing recovery after urban disturbances. This chapter examines housing recovery after Hurricane Maria in Puerto Rico utilizing time-series PlanetScope imagery to provide insights into the post-disaster recovery. The locations and duration of temporary protective blue roofs (tarps) are identified and linked with socio-economic data for further analysis. Results estimated 14,767±586 buildings with temporary roof installations across five cities in Puerto Rico, or 6.3% of the total number of buildings, with the average duration of installation 351±10 days (almost 1 year). Additionally, socially vulnerable populations, including people with lower income, were more likely to experience the need for temporary roofs for their damaged property and longer waiting times to substitute those with permanent roofs. Moreover, the in-situ survey in San Juan and Ponce provides a deep understanding of life experiences. The dissertation advances the applications of high-spatial and high-temporal resolution satellite images in urban dynamic monitoring, contributing to the development of inclusive, safe, resilient, and sustainable cities
Bridging the Gap Between Environmental and Social Justice
No full textFinal report for PLCY790: Capstone in Public Policy (Spring 2025). University of Maryland, College ParkDefensores de la Cuenca, a Latin@-led environmental justice nonprofit, works to empower Latino youth in the environmental job sector, create new green space through tree planting initiatives, and inform the community about the efforts to preserve the Chesapeake Bay watershed. Despite their work, social issues and determinants such as poverty, trash, and built environment have impeded the success of their long-term organizational goals. To combat these issues, Defensores wants to enter the realm of social justice while remaining an environmental justice organization centered on Latino advocacy.
This paper aims to aid that entrance by exploring three key themes: the intersection of environmental and social justice, how social justice can engage Latin@s, and how environmental and social justice can converge to serve Latin@s.
Using a literature review as well as case studies, the research made four key analytical findings: intersections of social and environmental justice, interorganizational movements, identifying issues that matter, and mobilizing Latin@s. From these findings, six recommendations were created for Defensores de la Cuenca: find a specific area of interest, connect current environmental and social priorities, understand the values of the Latin@ community, identify specific ways the Latin@ community is affected by the chosen issues, find alliances with like-minded organizations, and make initial goals small. By following these recommendations, Defensores de la Cuenca will be better equipped in their venture into social justice work paired with their environmental justice work.Marylan