Long-term investigation of methane and carbon dioxide emissions in two Italian landfills.

Landfills play a key role as greenhouse gas (GHGs) emitters, and urgently need assessment and management plans development to swiftly reduce their climate impact. In this context, accurate emission measurements from landfills under different climate and management would reduce the uncertainty in emission accounting. In this study, more than one year of long-term high-frequency data of CO and CH fluxes were collected in two Italian landfills (Giugliano and Case Passerini) with contrasting management (gas recovery VS no management) using eddy covariance (EC), with the aim to i) investigate the relation between climate drivers and CO and CH fluxes at different time intervals and ii) to assess the overall GHG balances including the biogas extraction and energy recovery components.

Changes in Revolving-Door Mental Health Hospitalizations during the COVID-19 Pandemic: A 5-Year Chart Review Study.

This study assessed changes in revolving-door (RD) mental health hospitalizations during the COVID-19 pandemic. A 5-year retrospective hospital chart review was performed, collecting revolving-door hospitalization, sociodemographic, and clinical data. Out of 1036 patients, 5.

Real-time air concentrations and turbulent fluxes of volatile organic compounds (VOCs) over historic closed landfills to assess their potential environmental impact.

For the first time, emission/deposition fluxes of volatile organic compounds (VOCs) and HS from a historic closed landfill site in Southern Italy were determined by Eddy Covariance (EC) using Proton Transfer Reaction Time-of-Flight Mass Spectrometry (PTR-TOF-MS). This was done in two field campaigns of one week performed in July and October 2016, where fluxes of CO and CH were also measured. Many compounds not previously identified in the biogas were detected by PTR-TOF-MS, but only in July some of them produced positive fluxes exceeding the flux limit of detection.

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records.

Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electronic health record (EHR) data. The protocol comprises five main stages: formal problem definition, data pre-processing, architecture selection, calibration and uncertainty, and generalizability evaluation.