Content moderation decisions can have variable impacts on the events and discourses they aim to regulate.This study analyzes Twitter data from before and after the removal of key Arizona Election Audit Twitter accounts in March of 2021.After collecting tweets that refer to the election audit in Arizona Snacks in this designated timeframe, a before/
Peer-to-peer loan acceptance and default prediction with artificial intelligence
Logistic regression (LR) and support vector machine algorithms, together with linear and nonlinear deep neural networks (DNNs), are applied to lending data in order to replicate lender acceptance of loans and predict the likelihood of default of issued loans.A two-phase model is proposed; the first phase predicts loan rejection, while the second on
Study protocol: a mixed-methods study to evaluate which health visiting models in England are most promising for mitigating the harms of adverse childhood experiences
Introduction Exposure to adverse childhood experiences (ACEs) is associated with poorer health outcomes throughout life.In England, health visiting is a long-standing, nationally implemented service that aims to prevent and mitigate the impact of adversity in early childhood, including for children exposed to ACEs.A range of health visiting service
Investigating Efficiency of Vector-Valued Intensity Measures in Seismic Demand Assessment of Concrete Dams
The efficiency of vector-valued intensity measures for predicting the seismic demand in gravity dams is investigated.The Folsom gravity dam-reservoir coupled system is selected and numerically analyzed under a set of two-hundred actual ground motions.First, the well-defined scalar IMs are separately investigated, and then Eyebrow Stencils they are