Shifting from traditional landslide occurrence modeling to scenario estimation with a "glass-box" machine learning.

Extreme rainfall events represent one of the main triggers of landslides. As climate change continues to reshape global weather patterns, the frequency and intensity of such events are increasing, amplifying landslide occurrences and associated threats to communities. In this contribution, we analyze relationships between landslide occurrence and extreme rainfall events by using a "glass-box" machine learning model, namely Explainable Boosting Machine.

A methodological approach of QRA for slow-moving landslides at a regional scale.

Landslides represent a serious worldwide hazard, especially in Italy, where exposure to hydrogeological risk is very high; for this reason, a landslide quantitative risk assessment (QRA) is crucial for risk management and for planning mitigation measures. In this study, we present and describe a novel methodological approach of QRA for slow-moving landslides, aiming at national replicability. This procedure has been applied at the basin scale in the Arno River basin (9100 km, Central Italy), where most landslides are slow-moving.

[Uterine rupture on unscarred uterus following labor induction for 2(nd) trimester termination of pregnancy with oral misoprostol: conservative management].

Uterine rupture occurred at 25 weeks amenorrhea during pregnancy termination by misoprostol for fetal polykystosis. Rupture occurred on an unscared uterus after administration of significant accumulated doses of oral misoprostol (1400 g). The patient was managed conservatively and carried a subsequent pregnancy six months after the intervention.