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Using network science to evaluate landslide hazards on Big Sur Coast, California, USA
Abstract. Landslides, ranging from slips to catastrophic failures, pose significant challenges for prediction. This study employs a physically inspired framework to assess landslide hazard at a regional scale (Big Sur Coast, California). Our approach integrates techniques from the study of complex systems with multivariate statistical analysis to identify areas prone to landslide hazards. We successfully apply a technique originally developed on the 2017 Mud Creek landslide and refine our statistical metrics to characterize landslide hazard within a larger geographical area. Our method is compared against factors such as landslide location, slope, displacement, precipitation, and InSAR coherence using multivariate statistical analysis. Our network analyses, which incorporates spatiotemporal dynamics, perform better as a monitoring technique than traditional methods. This approach has potential for real-time monitoring and evaluating landslide hazard across multiple sites.
I'm in my 40s, single, and childless, so I moved in with my 90-year-old grandma. It isn't always easy being her caregiver.
At 41 years old, I moved in with my 88-year-old grandmother when it became more difficult for her to live alone. I slipped into the role of caregiver.
Scientists find a hidden obesity trigger in soybean oil
Researchers at UC Riverside have uncovered why soybean oil, one of America's most widely consumed ingredients, drives significant weight gain—at least in mice. The findings point not to the oil itself but to the fat-derived molecules it produces inside the body, called oxylipins, which can trigger inflammation, alter liver function, and influence genes tied to metabolism.