The intersecting negative effects of structural racism, COVID-19, climate change, and chronic diseases disproportionately affect racial and ethnic minorities in the US and around the world. Urban populations of color are concentrated in historically redlined, segregated, disinvested, and marginalized neighborhoods with inadequate quality housing and limited access to resources, including quality greenspaces designed to support natural ecosystems and healthy outdoor activities while mitigating urban environmental challenges such as air pollution, heat island effects, combined sewer overflows and poor water quality. Disinvested urban environments thus contribute to health inequity via physical and social environmental exposures, resulting in disparities across numerous health outcomes, including COVID-19 and chronic diseases such as cancer and cardiovascular diseases (CVD). In this paper, we build off an existing conceptual framework and propose another conceptual framework for the role of greenspace in contributing to resilience and health equity in the US and beyond. We argue that strategic investments in public greenspaces in urban neighborhoods impacted by long term economic disinvestment are critically needed to adapt and build resilience in communities of color, with urgency due to immediate health threats of climate change, COVID-19, and endemic disparities in chronic diseases. We suggest that equity-focused investments in public urban greenspaces are needed to reduce social inequalities, expand economic opportunities with diversity in workforce initiatives, build resilient urban ecosystems, and improve health equity. We recommend key strategies and considerations to guide this investment, drawing upon a robust compilation of scientific literature along with decades of community-based work, using strategic partnerships from multiple efforts in Milwaukee Wisconsin as examples of success.
The need for operators of Oil and Gas assets to maintain depleting reserves have led to the development of new productive zones in mature fields. With new technology and improved understanding of the existing reservoirs, more discoveries within mature fields have been made requiring drilling activities to be performed below the existing mature fields. Drilling across previously produced intervals can be challenging as the depleted layers (or low pressure zones) have narrow margins between the pore pressure and fracture pressure gradients resulting in drilling problem as lost circulation. To drill through such intervals successfully, loss prevention materials are incorporated in the drilling fluid as a preventive measure as against a corrective approach after experiencing losses. The sizing of loss prevention materials is however hinged on the accurate prediction of the induced fracture widths. In this study, Artificial Neural Network (a subset of artificial intelligence) is utilised to construct a tool to predict the width of induced fractures and determine the sizing for loss prevention materials. The Artificial Neural Network (ANN) is trained and validated using a limited 30-point input data set. This resulted in a squared correlation of 79%. The results from the ANN is compared with existing 2D fracture models and benchmarked against experimental results published in literature.
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