arXiv subjects

Madison Smith

Publications and source records attributed to Madison Smith.

4 recordsLinked to original sources

arXiv2026

GLOW I: Comprehensive Measurements of Gas-Rich, Star-Forming, Low-Mass Galaxies in the Nearby Universe

Gas-rich, star-forming, low-mass galaxies in the nearby universe are powerful laboratories for studying baryonic physics in detail including: stellar mass assembly, stellar feedback, chemical enrichment, and the interplay of the interstellar medium with star formation. Investigating these disparate yet interconnected processes requires data obtained by myriad observatories. Here, we present a comprehensive atlas of uniformly processed data on 37 low-mass galaxies within 6 Mpc. The atlas includes archival data on (i) the HI from the Very Large Array observatory; (ii) resolved stars from Hubble Space Telescope optical imaging; (iii) Spitzer Space Telescope 3.6 micron imaging; and (iv) optical imaging from ground-based telescopes. We also compile measurements of (i) tip-of-the-red-giant-branch (TRGB) distances to the galaxies; (ii) direct method gas-phase oxygen abundances and nitrogen to oxygen abundance ratios; (iii) constraints on the local environment around each galaxy; and (iv) other measurements from the literature. We supplement the data with new observations where needed to complete the measurements for all galaxies in the sample. From these data, we find good agreement between stellar masses measured from color-magnitude diagrams and those estimated from 3.6 micron imaging by assuming a mass-to-light ratio. We also provide the first mapping of the HI profiles as a function of structural parameters. These data sets and measurements are the foundation for the Galaxies Losing Oxygen via Winds (GLOW) project whose main aim is to characterize the star formation - chemical enrichment cycle of low-mass galaxies by measuring the production, distribution, and retention of oxygen on a galaxy-by-galaxy basis.

astro-ph.GA↗

arXiv2025

Reverse Stress Testing for Supply Chain Resilience

Supply chains' increasing globalization and complexity have recently produced unpredictable disruptions, ripple effects, and cascading resulting failures. Proposed practices for managing these concerns include the advanced field of forward stress testing, where threats and predicted impacts to the supply chain are evaluated to harden the system against the most damaging scenarios. Such approaches are limited by the almost endless number of potential threat scenarios and cannot capture residual risk. In contrast to forward stress testing, this paper develops a reverse stress testing (RST) methodology that allows to predict which changes, with probabilistic certainty, across the supply chain network are most likely to cause a specified level of disruption at a specific entity in the network. The methodology was applied to the case of copper wire imports into the USA, a simple good which may have significant implications for national security. Results show that Canada, Chile, and Mexico are predicted to consistently be sources of disruptions at multiple loss levels. Other countries (e.g., Papua New Guinea) may contribute to small disruptions but be less important for the catastrophic losses of concern for decision makers. Other countries' disruptions would be catastrophic (e.g., Chile). The proposed methodology is the first case of reverse stress testing application in complex multilayered supply chains and can be used to address both risk and resilience.

physics.data-an↗

arXiv2024

The Convergence of AI and Synthetic Biology: The Looming Deluge

The convergence of artificial intelligence (AI) and synthetic biology is rapidly accelerating the pace of biological discovery and engineering. AI techniques, such as large language models and biological design tools, are enabling the automated design, build, test, and learning cycles for engineered biological systems. This convergence promises to democratize synthetic biology and unlock novel applications across domains from medicine to environmental sustainability. However, it also poses significant risks around reliability, dual use, and governance. The opacity of AI models, the deskilling of workforces, and the outdated nature of current regulatory frameworks present challenges in ensuring responsible development. Urgent attention is needed to update governance structures, integrate human oversight into increasingly automated workflows, and foster a culture of responsibility among the growing community of bioengineers. Only by proactively addressing these issues can we realize the transformative potential of AI-driven synthetic biology while mitigating its risks.

q-bio.OT↗

arXiv2024

Access to Emergency Services: A New York City Case Study

Emergency services play a crucial role in safeguarding human life and property within society. In this paper, we propose a network-based methodology for calculating transportation access between emergency services and the broader community. Using New York City as a case study, this study identifies 'emergency service deserts' based on the National Fire Protection Association (NFPA) guidelines, where accessibility to Fire, Emergency Medical Services, Police, and Hospitals are compromised. The results show that while 95% of NYC residents are well-served by emergency services, the residents of Staten Island are disproportionately underserved. By quantifying the relationship between first responder travel time, Emergency Services Sector (ESS) site density, and population density, we discovered a negative power law relationship between travel time and ESS site density. This relationship can be used directly by policymakers to determine which parts of a community would benefit the most from providing new ESS locations. Furthermore, this methodology can be used to quantify the resilience of emergency service infrastructure by observing changes in accessibility in communities facing threats.

physics.soc-ph↗