Dissolution of simulated nuclear waste glass at high surface area to solution volume, high pH and 70 °C: comparison of international simple glass and SON68 glass.

Long-term static dissolution experiments, lasting up to ∼1500 days, were conducted on International Simple Glass (ISG) and SON68 glass under hyperalkaline pH, at 70 °C, and at a very high glass surface area to solution volume ratio. The study compared (1) glass dissolution kinetics, (2) secondary phase formation, and (3) the microstructure of the altered glass and secondary phase interface. Boron release indicated rapid initial dissolution followed by a slowdown mainly due to a significant pH drop.

Teaching and Learning Clinical Reasoning in Nursing Education: A Student Training Course.

Clinical reasoning is an essential component of nursing. It has emerged as a concept that integrates the core competencies of quality and safety education for nurses. In cooperation with five European partners, Instituto Politécnico de Setúbal (IPS) realized the "Clinical Reasoning in Nursing and Midwifery Education and Practice" project as part of the Erasmus+ project.

A multi-task and multi-channel convolutional neural network for semi-supervised neonatal artefact detection.

. Automated artefact detection in the neonatal electroencephalogram (EEG) is crucial for reliable automated EEG analysis, but limited availability of expert artefact annotations challenges the development of deep learning models for artefact detection. This paper proposes a semi-supervised deep learning approach for artefact detection in neonatal EEG that requires few labelled data by training a multi-task convolutional neural network (CNN).

Automated detection and removal of flat line segments and large amplitude fluctuations in neonatal electroencephalography.

Background: Artefact removal in neonatal electroencephalography (EEG) by visual inspection generally depends on the expertise of the operator, is time consuming and is not a consistent pre-processing step to the pipeline for the automated EEG analysis. Therefore, there is the need for the automated detection and removal of artefacts in neonatal EEG, especially of distinct and predominant artefacts such as flat line segments (mainly caused by instrumental error where contact between electrodes and head box is lost) and large amplitude fluctuations (related to neonatal movements).

Method: A threshold-based algorithm for the automated detection and removal of flat line segments and large amplitude fluctuations in neonatal EEG of infants at term-equivalent age is developed.