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Globally, rapid population growth in cities, regulatory and governance failures, poor infrastructure, inadequate funding for urban water systems, and the impacts of climate change are each rapidly reconfiguring regional hydrosocial relations. In the United States, these hydrosocial reconfigurations tend to reinforce racial inequalities tied to infrastructure, exacerbating environmental injustices. More generally, according to a framework of racial capitalism, infrastructural regions and hydrosocial relations are always already racialized and structured simultaneously by capitalism and racism. In this paper, we integrate hydrosocial and racial justice perspectives with the literature on infrastructural regionalism to examine Atlanta’s position in the so-called tri-state water wars between Alabama, Georgia and Florida. Combining analysis of academic, policy, and legal documents, journalistic accounts, and semi-structured interviews with water conservationists and managers working in Atlanta, we examine conflicts over water use in the infrastructural region of the Apalachicola–Chattahoochee–Flint (ACF) river system. We emphasize that the ACF conflict reworks regional hydrosocial relations through territorializations of racial capitalism. We demonstrate how particular discourses that reify Atlanta as a monolith overly simplify the regional dimensions of the crisis, diminishing the views, roles and interventions of diverse actors in the ACF region. We argue that work on infrastructure regionalism and water governance can be deepened through attention to the hydro-racial fix. 

We measure the sunspot areas of activity cycle 24 using ten years of continuum images from the Helioseismic and Magnetic Imager, and compare them with the peak flare soft X-ray flux from the Geostationary Operational Environmental Satellite. We find that the sunspot area in our sample is positively correlated with the magnitude of the largest flare they produce. Complex spot groups withβγδ magnetic classification tend to be larger and more likely to produce intense flares. Our findings are qualitatively consistent with previous studies.

 

Progress toward achieving Sustainable Development Goal 6, clean water and sanitation for all, is behind schedule and faces substantial financial challenges. Rigorous water, sanitation, and hygiene (WASH) interventions have underperformed, casting doubt on their efficacy and potentially undermining confidence in WASH funding and investments. But these interventions have leaned on a narrow set of WASH indicators—linear growth and diarrhea—that reflect a 20th‐century prioritization of microbiological water quality as the most important measurement of WASH intervention success. Even when water is microbiologically safe, hundreds of millions of people face harassment, assault, injury, poisoning, anxiety, exhaustion, depression, social exclusion, discrimination, subjugation, hunger, debt, or work, school, or family care absenteeism when retrieving or consuming household water. Measures of WASH intervention success should incorporate these impacts to reinforce the WASH value proposition. We present a way forward for implementing a monitoring and evaluation paradigm shift that can help achieve transformative WASH.

This article is categorized under:

Engineering Water > Water, Health, and Sanitation

Human Water > Value of Water

Human Water > Methods

 

We used a convolutional neural network to infer stellar rotation periods from a set of synthetic light curves simulated with realistic spot-evolution patterns. We convolved these simulated light curves with real TESS light curves containing minimal intrinsic astrophysical variability to allow the network to learn TESS systematics and estimate rotation periods despite them. In addition to periods, we predict uncertainties via heteroskedastic regression to estimate the credibility of the period predictions. In the most credible half of the test data, we recover 10% accurate periods for 46% of the targets, and 20% accurate periods for 69% of the targets. Using our trained network, we successfully recover periods of real stars with literature rotation measurements, even past the 13.7 day limit generally encountered by TESS rotation searches using conventional period-finding techniques. Our method also demonstrates resistance to half-period aliases. We present the neural network and simulated training data, and introduce the softwarebutterpyused to synthesize the light curves using realistic starspot evolution.