Digital Commons Network^™

Preliminary Observations From The Relativistic Electron Atmospheric Loss (Real) Satellite Mission, Evzen Selvon, Robyn Millan

Wetterhahn Science Symposium Posters

The Relativistic Electron Atmospheric Loss (REAL) spacecraft (launched in July 2025) is a 3U cubesat designed to measure the precise energies (1 keV – 2MeV) and pitch angles of electrons entering the Earth’s ionosphere. The mission involves Dartmouth, BU, JHUAPL, MSU, and NASA. REAL carries three particle sensors measuring low, medium, and high energies. This work is focused on the ElectroStatic Analyzer (ESA) instrument, designed to measure lower energy electrons (1-40 keV) in the directions parallel and perpendicular to the Earth’s magnetic field. This research aims to identify notable events observed by the REAL spacecraft for future analysis.

Predicting Next-Day Eur/Usd Direction Using News Sentiment And Technical Indicators With Finbert And Xgboost, Darshan Sanjaybhai Khunt

Selected Full-Text Master Theses 2021-

Forecasting exchange-rate movements is a challenging task because currency prices are influenced not only by macroeconomic and financial variables but also by market sentiment reflected in financial news. This thesis examines whether financial news headlines can be used to predict the next-day directional movement of the EUR/USD exchange rate by applying finance-specific natural language processing and machine learning techniques.

The study uses a dataset of approximately 466,000 finance-related English-language news headlines collected between 2021 and 2025, aligned with daily EUR/USD closing prices. After preprocessing and temporal alignment, the data are used to construct a binary classification task in which the …

Forecasting Precipitation In Cuba Using Graph Based Deep Learning, Taufiqul Islam

Earth & Environmental Sciences Theses

Daily precipitation forecasting remained a challenging problem in regions characterized by strong spatial heterogeneity, nonlinear atmospheric dynamics, and intermittent rainfall behavior. Cuba represented a particularly complex case due to the combined influence of tropical cyclones, easterly waves, mesoscale convective systems, and orographic effects, which produced highly variable rainfall patterns in both space and time. Conventional statistical and machine-learning models typically treated stations independently and therefore overlooked spatial dependencies that strongly influenced rainfall variability across the island.

This study developed a spatiotemporal deep-learning framework for daily precipitation forecasting across 40 spatial nodes in Cuba using data from 1979 to 2023. The …

Investigation Of Reey Concentrations In Argillaceous Rocks Associated With Coal Beds In Southwestern West Virginia, Alyssa Cameron Long

Theses, Dissertations and Capstones

Rare earth elements (REEs) and Yttrium are classified as “critical minerals” that are used in many industries and are typically extracted from carbonatites and related alkaline plutonic rocks. The limited availability of REE+Y requires finding alternate sources such as coal fly ash, tonstein, fireclays, and shales. This study investigates the potential enrichment of shales, fireclays, and siltstones interbedded with coal from southwestern WV in REEY, and the mechanisms of such enrichment. Using ICP-AES analysis of various rock types shows that shales, silty shales, and siderite nodules interbedded with Fire Clay and Chilton Coal beds in the Kanawha Formation record the …

Game-Based Learning For Asynchronous Ai Literacy Course: Approach To Improve Students' Cognitive, Behavioural, Affective, And Ethical Learning Of Ai, Jinhee Kim, Guang Yang, Wing Sha Chan, Xi Lin, Yukyeong Song

STEMPS Faculty Publications

Educators in higher education face persistent challenges in scaling AI literacy across disciplines and helping novice learners understand abstract AI concepts. Although research on game-based learning (GBL) reports mixed outcomes, few studies have examined its large-scale use in mandatory, asynchronous AI literacy courses for diverse undergraduate populations. Addressing this gap, this study investigates a scalable GBL-based AI literacy course delivered to 4898 first-year undergraduates across disciplines. Using a mixed-methods design with 311 valid pre- and post-survey responses and 20 interviews, the study evaluates students' cognitive, behavioural, affective, and ethical learning of AI. Quantitative results show significant improvements in overall AI …

Floodwater Salinity And Flood Duration Regulate Greenhouse Gas Production In High- Latitude Coastal Wetland And Tundra Soils, Mia J. Dicianna

Electronic Theses and Dissertations

Coastal high-latitude ecosystems are increasingly flooded from storm surges associated with climate change, exposing soils that were historically infrequently inundated to higher-salinity waters for longer durations. Sub-Arctic wetlands and tundra store large amounts of carbon, yet how flooding characteristics, particularly flood duration, influence microbial mineralization and, consequently, greenhouse gas (GHG) production in these soils remains poorly understood. We conducted a full-factorial microcosm incubation experiment in which coastal wetland and tundra soils were subjected to simulated flooding with three durations (1, 3, and 10 days) and four salinity treatments (unflooded, freshwater, 3 ppt, and 12 ppt) and measured carbon dioxide (CO2) …

Simulating Social Attitudes With Llms: Accuracy, Demographic Effects, And Refusal Behavior In The Sensitive Domain Of Suicide Prevention, Cristina J. Perez, Michael P. Vasquez Jr., Philippe J. Giabbanelli, Patrick Y. Wu

VMASC Publications

Large language models (LLMs) are increasingly used to simulate public opinion, yet their validity in sensitive policy domains remains underexplored. We evaluate whether LLMs can reproduce attitudes toward suicide prevention policies using 32 questions drawn from seven nationally representative U.S. surveys (2023-2025). We systematically vary demographic conditioning (race/ethnicity, gender, age, education, income, party), prompt framing (direct elicitation, respondent embodiment, specialist embodiment), and model architecture (GPT-5 Nano, DeepSeek V3.2, Meta Llama 3.1 8B, Mistral Small 24B). Across 811,560 prompts, the mean absolute error—the average gap between predicted and human response distributions—is 23 percentage points. We also find that LLM responses to …

An Exploration Of Geomagnetically Induced Currents And The Accessibility Of Geomagnetic Data Collection, Richard Le

Physics Theses

Geomagnetically Induced Currents (GICs) are electrical currents induced in long grounded conductors during geomagnetic disturbances. They represent one of the primary space weather hazards to critical infrastructure, particularly high-voltage power transmission systems and other grounded conductor networks such as pipelines. As modern society becomes more dependent on these systems, understanding and mitigating the impacts of space weather has become increasingly important. This thesis examines the physical mechanisms that generate GICs, from the interaction of the solar wind with Earth’s Magnetosphere to Magnetosphere-Ionosphere-Thermosphere (MIT) coupling and the current systems that drive geomagnetic disturbances (GMDs). Case studies from Zimbabwe and Finland illustrate …

Computational Study Of Rotating Detonation Combustors, Aditya Balasubramaniam

Mechanical and Aerospace Engineering Theses

Rotating detonation combustors (RDCs) are pressure-gain combustion devices that sustain one or more continuously rotating detonation waves, offering potential thermodynamic and performance advantages over conventional deflagration-based systems. Their behavior depends strongly on combustor geometry and operating conditions. Understanding these effects is therefore essential for the design and optimization of practical RDCs. Accordingly, this thesis numerically investigates annular RDCs with two primary objectives: (1) to evaluate the effects of propellant mass flux and (2) to assess the influence of annular width on detonation-wave dynamics and combustor performance.

A finite-volume framework is used to solve the compressible reactive Euler equations with hydrogen–air …

Reliable And Label-Efficient Learning For Open-World Visual Perception And Robot Learning Under Uncertainty, Zongyao Lyu

Computer Science and Engineering Dissertations

Modern learning systems deployed in open-world environments must make reliable decisions despite predictive uncertainty, previously unseen classes, limited annotations, and distribution shifts. This dissertation develops methods for reliable and label-efficient learning in visual perception and robot control.

First, this work studies uncertainty in object detection by representing semantic and spatial predictions probabilistically. A deep-ensemble framework aggregates detections into class-probability distributions and probabilistic bounding boxes, while a subsequent extension combines deep ensembles with Monte Carlo dropout to further investigate predictive uncertainty. Second, this dissertation addresses open-set recognition, where classes absent during training may appear at inference time. An empirical study shows …

Advancing Hate Speech Detection: Binary And Multiclass Approaches From Traditional Methods To Parameter-Efficient And Ontology-Guided Language Models, Mahmoud Abusaqer

Graduate Theses/Dissertations

The widespread proliferation of hate speech on social media platforms poses significant challenges for content moderation and user safety, requiring automated systems that are simultaneously accurate, efficient, and capable of fine-grained distinctions. This thesis investigates hate speech detection through five published manuscripts organized into two complementary threads: binary detection (hateful vs. non-hateful) and multiclass detection across demographic targeting categories. The binary thread progresses from a broad 38-model baseline spanning traditional machine learning, deep learning, and transformer architectures (where RoBERTa reaches 91.48% accuracy and CatBoost remains competitive at 88.60%) to parameter-efficient adaptation, in which Low-Rank Adaptation (LoRA) of large language models …

Constraining The Tectono-Climatic Evolution Of The Northern Rocky Mountains: Insights From Volcanic Glass, Leaf Water, And Leaf Wax Hydrogen Isotopes, Rijumon Nandy

Earth & Environmental Sciences Dissertations

The Eocene-Oligocene period in the North American Cordillera represents a period of great intrigue with complex interactions among tectonics, climate, and surface processes. Although this period is generally associated with synorogenic extension associated with gravitational collapse of the Cordillera, the paleotopographic history remains debated. Existing studies offer conflicting interpretations, suggesting either sustained elevations or modest uplift during this time. Notably, recent reconstructions based on volcanic glass δD values have proposed renewed uplift during the early Oligocene, which needs to be tested with a more extensive and robust dataset. Concurrently, global cooling and aridification during this interval may have been amplified …

The Critical Role Of Near-Surface Dynamics In Identifying Spawning Areas Of Atlantic Bluefin Tuna In The Gulf Of Mexico, Donald R. Johnson, William J. Teague, Harriet Perry, James S. Franks

Gulf and Caribbean Research

Atlantic Bluefin Tuna, Thunnus thynnus (ABFT), migrate long distances to broadcast spawn in the Gulf of Mexico (GOM) basin, the Mediterranean Sea and the Slope Sea (NW Atlantic). These areas have commonalities including a highly dynamic upper ocean eddy environment that draws nutrients from shallow thermoclines and adjacent continental shelves and can provide temporal sanctuaries from some larval predators. In the GOM basin, the Loop Current (LC) and its energetic spin—off eddies sweep weak swimming predators such as jellyfish (Pelagia noctiluca) into lines and aggregations, leaving relatively open areas as temporal sanctuaries for ABFT larvae during the vulnerable …

Students-Generative Ai Interaction Patterns And Its Impact On Academic Writing, Jinhee Kim, Sang-Soog Lee, Rita Detrick, Jialin Wang, Na Li

STEMPS Faculty Publications

Considering both the transformative opportunities and challenges presented by generative AI (GenAI) in academic writing, effectively integrating GenAI into the academic setting becomes a significant need requiring prioritization. Yet, there is limited understanding regarding the nature of interactions between different types of students, what behavioral patterns students exhibit during a student-GenAI interaction (SAI) on a given task, and how these different SAI patterns relate to the actual writing task performance. This study, therefore, aimed to identify SAI patterns of academic writing tasks depending on students’ level of AI literacy and examine the differences in academic writing performance between the identified …

Foreword, Pedagogical Innovations In Computer Science Education, Helen Crompton

STEMPS Faculty Publications

[Introduction] Computer science education sits at a defining moment. Across schools and universities worldwide, computing is no longer a niche discipline reserved for a select few. It is a foundational literacy that shapes how learners understand the world, participate in society, and imagine their futures. At the same time, the rapid pace of technological change, particularly in artificial intelligence, data systems, and intelligent tools, has placed unprecedented pressure on educators to rethink not only what we teach, but why and how we teach it. This book arrives precisely when such reflection is most needed.

News Media Sentiment Toward Chinese Ai: A Comparative Analysis With Belt And Road Initiative Involvement And Public Opinion On China, Asya Vaisberg

Pomona Senior Theses

This study evaluates different countries' news media’s sentiment towards Chinese AI, between May 2023 and May 2024, by using Microsoft Azure NLP Sentiment Analysis. The results are then compared with the country’s public opinion on China and its involvement in the Belt and Road Initiative (BRI). For this study 12 countries have been selected which are USA, Australia, Pakistan, Peru, Russia, Romania, Italy, Greece, Portugal, Philippines, Brazil, and Egypt. For each one, GNews Application Programming Interface (API), which has access to more than 60,000 global news sources, was used to aggregate relevant news articles based on queried keywords. The collected …

An Llm-Driven System For Doctor-Patient Simulation, Akilan Amithasagaran

Computer Science Theses

Effective physician-patient communication is fundamental to clinical competence, yet traditional simulation-based training methods using standardized patients and high-fidelity manikins are costly, resource-intensive, and difficult to scale. This dissertation presents CLiVR (Conversational Learning system in Virtual Reality), an LLM-driven system that integrates large language models and 3D avatars to simulate doctor-patient interactions for medical communication training.

CLiVR addresses three key limitations in existing virtual reality medical training platforms. First, the system operates on standalone Meta Quest 3 hardware with realistic 3D patient avatars featuring synchronized lip movements and speech-based interaction. Second, CLiVR grounds LLM responses using a curated syndrome-symptom database, constraining …

Bail Reform, Large Language Model Risk And Reasoning, William Wyatt

CGU Theses & Dissertations

This dissertation contains three studies. Each asks how rules or language change the choices people and machines make when outcomes are uncertain. The first study, written with Kiran John, evaluates California’s 2020 cashless bail reform. We use propensity score matching on arrestee records from the windows before and after implementation, and we test whether the shift away from cash bail produced any effect on subsequent offending. It did not. Matched comparisons yield small, statistically insignificant differences across every window we examined. That null result cuts against both sides of the public argument. The reform did not drive a spike in …

Adaptive Electric Vehicle Routing And Charging With Deep Reinforcement Learning, Mandana Farhang Ghahfarokhi, Hyoshin Park, Venktesh Pandey, Gyugeun Yoon

Engineering Management & Systems Engineering Faculty Publications

As electric vehicles (EVs) gain popularity, efficient routing and charging solutions remain challenging due to time-dependent travel variability, sparse charging infrastructure, and heterogeneous user preferences. To address these challenges, this paper introduces a decision-support system that integrates three complementary methods: Temporal Multimodal Multivariate Learning (TMML) for real-time characterization of travel time uncertainty, Time-Dependent Shortest Path (TDSP) for reliability-aware route choice, and Deep Q-Network (DQN) reinforcement learning for adaptive charging decisions in sparse infrastructure environments. TMML updates link-level travel time distributions in real-time through Bayesian inference with cluster-based propagation, reducing uncertainties across the network. TDSP leverages these updated distributions to estimate …

A New Parallel-In-Time Direct Inverse Method For Nonlinear Differential Equations, Nail K. Yamaleev, Subhash Paudel

Mathematics & Statistics Faculty Publications

We propose a new method for parallelization of the first-order backward difference discretization (BDF1) of the first-order time derivative in nonlinear partial differential equations, such as conservation law equations. The time derivative term is discretized by using the method of lines based on the implicit BDF1 scheme, while the inviscid and viscous terms are approximated by conventional 2nd-order central discretizations of the 1st- and 2nd-order derivatives in each spatial direction. The global system of nonlinear discrete equations in the space-time domain is solved by the Newton method for all time levels simultaneously. For the BDF1 discretization, this all-at-once system at …

An Ensemble Classifier For Ordinal Outcomes In High-Dimensional Genomics Data, Heranga K. Rathnasekara, Sinjini Sikdar

Mathematics & Statistics Faculty Publications

Analysis of genomics data for predicting disease outcomes is a fast-growing field in medical research. There often exist categorical, specifically, ordinal outcomes that need to be predicted based on genomic profiles. This has led to recent development of some high-dimensional ordinal classification methods that can address the large dimensionality of the genomic covariate set. These high-dimensional ordinal models tend to vary widely in their performance depending on the data they are applied to and the evaluation criteria used. In this article, we outline an ensemble ordinal classifier that integrates different ordinal modeling approaches through bootstrap-based model evaluation, multi-metric performance assessment, …

Does Fishery Management For Groupers (Teleostei: Epinephelidae) Protect Them Effectively? Context From The Iucn's Red List Of Threatened Species, Sean T. Fennessy, Christi Linardich, Kevin Rhodes, Joao P. Barreiros, David Pollard, Eloy Sosa-Cordero, Felicia Coleman, Alfonso Aguilar-Perera, Christopher R. Malinowski, Thierry Brulé, Pedro Afonso, Kayan Ma, Min Liu, Muktha Menon, Colin Wen, Stanley K. H. Shea, Sean N. Porter, Matthew Craig, Yvonne Sadovy De Mitcheson

Biological Sciences Faculty Publications

Worldwide, groupers (Epinephelidae) are commercially valued fishes, which also play key ecological roles on tropical and subtropical reefs. In 2007 and 2016, the IUCN's Groupers and Wrasses Specialist Group assessed all 160+ grouper species, with 17 of these being identified as threatened in 2016 and the major threat factor being overexploitation. Our present study aimed to identify whether management measures (MMs) for previously assessed groupers were established, whether these measures aligned with IUCN's Red List categories, and whether they effectively protect grouper populations. Experts in grouper biology and management assigned scores per grouper species based on the extent to which …

Three Decades Of Classifying Threatened Species: Lessons Learned From And About The Iucn Red List Criteria For Quantifying Extinction Risk, H. Resit Akçakaya, E. J. Milner-Gulland, Mike Hoffmann, Helen M. Regan, Ilona Naujokaitis-Lewis, Andre E. Punt, Kevin J. Gaston, David A. Keith, Stuart H. M. Butchart, Simon N. Stuart, Nigel J. Collar, Moreno Di Marco, Monika Bohm, Alex J. Berryman, David P. Mallon, Axel Hochkirch, Nicholas K. Dulvy, Malin Rivers, Thomas M. Brooks, James R. S. Westrip, Jonathan Paul Rodriguez, Craig Hilton-Taylor, Janet Scott, Simon Tarr, Sophie H. Ledger, Christi Linardich

Biological Sciences Faculty Publications

The IUCN Red List of Threatened Species, the most widely used global system for assessing species' extinction risk, has become a foundational source of information for conservation management, policy and research. Since the adoption of quantitative extinction risk criteria more than three decades ago, the Red List has expanded substantially in scope and influence, informing decisions ranging from species conservation and protected area designation to international agreements, corporate risk assessments and global biodiversity indicators. Given its central role, maintaining scientific rigour, transparency and trust in the Red List system is essential. Feedback from users, emerging from evolving applications and scientific …

Characterization Of Phytoplankton-Excreted Metabolites Mediating Carbon Flux Through The Surface Ocean, Yuting Zhu, Hanna S. Anderson, Eli Salcedo, Samuel E. Miller, Krista Longnecker, Melissa C. Kido Soule, Sheean T. Haley, Gretchen J. Swarr, Rogier Braakman, Sonya T. Dyhrman, Elizabeth B. Kujawinski

Chemistry & Biochemistry Faculty Publications

The marine labile dissolved organic carbon (DOC) pool is a dynamic reservoir of thousands of molecules that cycles approximately one-quarter of Earth’s primary production within days to weeks. After excretion by phytoplankton and other microbes, metabolites are rapidly consumed, resulting in low standing concentrations (picomolar to low nanomolar). Despite the decades-long search for labile DOC sources and molecular identities, marine phytoplankton exometabolomes are not well characterized, largely due to difficulties in measuring small polar molecules in saline water. Here, we profiled the exometabolomes of six axenic phytoplankton species representing key functional groups including a diatom (Thalassiosira pseudonana CCMP1335), a …

Expanded Scorpionate And Siderophore-Inspired Ligands: From Foundational Designs To Modern Applications, Austin Winfield Medley, Trandon Allen Bender

Chemistry & Biochemistry Faculty Publications

Scorpionate ligands have been advanced significantly through systematic modifications of their apical atoms and heterocyclic arms, expanding their structural diversity and chemical reactivity. Recent biologically inspired variants now enable accurate modeling of complex bioinorganic motifs, such as the Fe₄S₄ clusters of nitrogenase, and support enzyme‐like reactivity under mild aqueous conditions. These developments have broadened the impact of scorpionate chemistry across bioinorganic modeling, homogeneous catalysis, and biorthogonal transformations. In particular, expanded tripodal scaffolds provide modular, tunable platforms for mimicking enzyme active sites and probing biological nitrogen fixation pathways. Beyond fundamental insight, these ligands present practical opportunities for sustainable catalysis by enabling …

M-Type Star Spectral Analysis: Sco Line List Including The B²Σ⁺-X²Σ⁺ Band System, Léo Lavy, Jacques Liévin, Peter F. Bernath

Chemistry & Biochemistry Faculty Publications

An analysis of the B²Σ⁺ − X²Σ⁺ band system of ⁴⁵Sc¹⁶O is presented. ScO was excited in a hollow cathode discharge, and its Doppler-limited resolution spectrum was measured with a Fourier transform spectrometer. The spectroscopic constants of the B²Σ⁺ state with v ≤ 3 are reported, and equilibrium constants are derived. Ab initio transition dipole moment functions are calculated for the A²Π − X²Σ⁺ and B²Σ⁺ − X²Σ⁺ transitions and scaled using lifetime measurements. We report new line lists of hyperfine-resolved transitions for ⁴⁵Sc¹⁶O with recalculated Einstein A coefficients and oscillator strengths of the …

The Hitran2024 Molecular Spectroscopic Database, I. E. Gordon, L. S. Rothman, R. J. Hargreaves, F. M. Gomez, T. Bertin, C. Hill, R. V. Kochanov, Y. Tan, P. Wcisło, V. Yu. Makhnev, P. F. Bernath, M. Birk, V. Boudon, A. Campargue, A. Coustenis, B. J. Drouin, R. R. Gamache, J. T. Hodges, D. Jacquemart, E. J. Mlawer, A. V. Nikitin, V. I. Perevalov, M. Rotger, S. Robert, J. Tennyson, G. C. Toon, H. Tran, V. G. Tyuterev, E. M. Adkins, A. Barbe, D. M. Bailey, K. Bielska, L. Bizzocchi, T. A. Blake, C. A. Bowesman, P. Cacciani, P. Čermák, A. G. Császár, L. Denis, S. C. Egbert, O. Egorov, A. Yu. Ermilov, A. J. Fleisher, H. Fleurbaey, A. Foltynowicz, T. Furtenbacher, M. Germann, E. R. Guest, J. J. Harrison, J. -M. Hartmann, A. Hjältén, S. -M. Hu, X. Huang, T. J. Johnson, H. Jóźwiak, S. Kassi, M. V. Khan, F. Kwabia-Tchana, T. J. Lee, D. Lisak, A. -W. Liu, O. M. Lyulin, N. A. Malarich, L. Manceron, A. A. Marinina, S. T. Massie, J. Mascio, E. S. Medvedev, V. V. Meshkov, G. Ch. Mellau, M. Melosso, S. N. Mikhailenko, D. Mondelain, H.S.P. Müller, M. O' Donnell, A. Owens, A. Perrin, O. L. Polyansky, P. L. Raston, Z. D. Reed, M. Rey, C. Richard, G. B. Rieker, C. Röske, S. W. Sharpe, E. Starikova, N. Stolarczyk, A. V. Stolyarov, K. Sung, F. Tamassia, J. Terragni, V. G. Ushakov, S. Vasilchenko, B. Vispoel, K. L. Vodopyanov, G. Wagner, S. Wójtewicz, S. N. Yurchenko, N. F. Zobov

Chemistry & Biochemistry Faculty Publications

The HITRAN database is a curated compilation of validated molecular spectroscopic parameters, established in the early 1970s. It is used by various computer codes to predict and simulate the transmission and emission of light in gaseous media (with an emphasis on terrestrial and planetary atmospheres). The HITRAN compilation is composed of six major components. These components include the line-by-line spectroscopic parameters required for high-resolution radiative-transfer codes, experimentally derived absorption cross-sections (for molecules where it is not yet feasible for representation in a line-by-line form), collision-induced absorption data, aerosol indices of refraction, and general tables (including partition sums) that apply globally …

Investigating The In Vitro Antimicrobial Potential And Comprehensive Computational Studies Of New Schiff Base Derivatives, Abrar Hussain, Shahzaib Akhter, Hammad Nasir, Khurram Shahzad, Muhammad Arfan, Sand Hyun Park

Chemistry & Biochemistry Faculty Publications

Antimicrobial resistance (AMR) is a growing global health threat driven by multidrug-resistant bacteria (Staphylococcus aureus, Pseudomonas aeruginosa), and fungi (Candida albicans, and C. parapsilosis). This study evaluated six novel Schiff base derivatives (HSB-1 to HSB-6) through integrated in vitro antimicrobial activity and comprehensive computational studies. In vitro disk diffusion assay demonstrated the largest zones of inhibition against S. aureus for HSB-6 and HSB-1 (15–17 mm), activity against P. aeruginosa for HSB-5 and HSB-6 (12 mm), and moderate antifungal activity for HSB-4 (8–11 mm). Molecular docking results correlated with the in vitro findings with the binding energy ΔG = −12.3 kcal/mol …

Mitochondrial Genome Of The Indo-Pacific Mesophotic Coral Leptoseris Columna (Scleractinia: Agariciidae) Assembled Using Pacbio Long-Read Sequencing, Nomita Rani Adhikary, Daniel J. Barshis, J. Antonio Baeza

Biological Sciences Faculty Publications

Leptoseris columna, a mesophotic coral species belonging to the family Agariciidae, is distributed throughout the Indo-Pacific region. This species is considered as of "Least concern" by the IUCN, yet, faces multiple local and global stressors. To support conservation plans, this study sequenced and characterized the complete mitochondrial genome of L. columna. The complete mitochondrial genome of Leptoseris columna was assembled using PacBio long-reads with a coverage of 459× per bp. The AT-rich mitochondrial genome of Leptoseris columna is 18,546 bp long and comprises 13 protein-coding genes (PCGs), 2 transfer RNA genes (trnM and trnW), and 2 ribosomal RNA …

The Role Of Education In Reducing Social Inequality: A Systems-Level Analysis Of Socio-Technical Infrastructures And Policy Governance, Aisling O'Shea, Batzorig Dashnyam, Ximena Quintanilla

Women's & Gender Studies Faculty Publications

Social inequality remains one of the most persistent challenges to global systemic stability, threatening the robustness of democratic institutions and economic sustainability. Education has long been theorized as the primary mechanism for social mobility and the mitigation of disparate life outcomes; however, its role within modern socio-technical infrastructures is increasingly complex and often contradictory. This paper provides a comprehensive systems-level analysis of the relationship between educational architecture and social stratification. By examining the structural trade-offs inherent in contemporary pedagogical deployment, the research evaluates how institutional governance, digital infrastructure, and policy mandates either facilitate or hinder the reduction of inequality. The …