Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates.

We assess which waters the Clean Water Act protects and how Supreme Court and White House rules change this regulation. We train a deep learning model using aerial imagery and geophysical data to predict 150,000 jurisdictional determinations from the Army Corps of Engineers, each deciding regulation for one water resource. Under a 2006 Supreme Court ruling, the Clean Water Act protects two-thirds of US streams and more than half of wetlands; under a 2020 White House rule, it protects less than half of streams and a fourth of wetlands, implying deregulation of 690,000 stream miles, 35 million wetland acres, and 30% of waters around drinking-water sources.

Identifying snowfall elevation patterns by assimilating satellite-based snow depth retrievals.

Precipitation in mountain regions is highly variable and poorly measured, posing important challenges to water resource management. Traditional methods to estimate precipitation include in-situ gauges, Doppler weather radars, satellite radars and radiometers, numerical modeling and reanalysis products. Each of these methods is unable to adequately capture complex orographic precipitation.

Comparative Study with and without the Use of 3D Prototyping of an Unconventional Technique in the Surgical Planning of Revision of Total Hip Arthroplasty.

This article presents a comparison between two cases in which there was a need for revision of total hip arthroplasty due to aseptic acetabular failure. We used 3D prototyping in one of the cases to perform an unconventional technique of molding synthesis material before the procedure to evaluate the time saved in the transoperative period in complex cases.