Hydrogen-Assisted Aging Applied to Storage and Sealing Materials: A Comprehensive Review.

Hydrogen is a possible alternative to fossil fuels in achieving a sustainable energy future. Unlike other, older energy sources, the suitability of materials for storing, distributing, and sealing systems in a hydrogen environment has not been comprehensively studied. Aging, the extended exposure of a material to an environmental condition, with hydrogen causes degradation and damage to materials that differ from other technologies.

An Overview of Challenges for the Future of Hydrogen.

Hydrogen's wide availability and versatile production methods establish it as a primary green energy source, driving substantial interest among the public, industry, and governments due to its future fuel potential. Notable investment is directed toward hydrogen research and material innovation for transmission, storage, fuel cells, and sensors. Ensuring safe and dependable hydrogen facilities is paramount, given the challenges in accident control.

Deep transfer learning approaches for Monkeypox disease diagnosis.

Monkeypox has become a significant global challenge as the number of cases increases daily. Those infected with the disease often display various skin symptoms and can spread the infection through contamination. Recently, Machine Learning (ML) has shown potential in image-based diagnoses, such as detecting cancer, identifying tumor cells, and identifying coronavirus disease (COVID)-19 patients.

Machine Learning-based Voice Assessment for the Detection of Positive and Recovered COVID-19 Patients.

Many virological tests have been implemented during the Coronavirus Disease 2019 (COVID-19) pandemic for diagnostic purposes, but they appear unsuitable for screening purposes. Furthermore, current screening strategies are not accurate enough to effectively curb the spread of the disease. Therefore, the present study was conducted within a controlled clinical environment to determine eventual detectable variations in the voice of COVID-19 patients, recovered and healthy subjects, and also to determine whether machine learning-based voice assessment (MLVA) can accurately discriminate between them, thus potentially serving as a more effective mass-screening tool.

Identifying Insomnia From Social Media Posts: Psycholinguistic Analyses of User Tweets.

Background: Many people suffer from insomnia, a sleep disorder characterized by difficulty falling and staying asleep during the night. As social media have become a ubiquitous platform to share users' thoughts, opinions, activities, and preferences with their friends and acquaintances, the shared content across these platforms can be used to diagnose different health problems, including insomnia. Only a few recent studies have examined the prediction of insomnia from Twitter data, and we found research gaps in predicting insomnia from word usage patterns and correlations between users' insomnia and their Big 5 personality traits as derived from social media interactions.

Optimization of 5' Untranslated Region of Modified mRNA for Use in Cardiac or Hepatic Ischemic Injury.

Modified mRNA (modRNA) is a gene-delivery platform for transiently introducing a single gene or several genes of interest to different cell types and tissues. modRNA is considered to be a safe vector for gene transfer, as it negligibly activates the innate immune system and does not compromise the genome integrity. The use of modRNA in basic and translational science is rising, due to the clinical potential of modRNA.

Apnoea in relation to neonatal temporal lobe haemorrhage.

Background: Six term newborns presented with apnoea and temporal lobe haemorrhage.

Aim: In this observational study, we described involvement of the limbic system in apnoeic term newborns with temporal lobe injury.

Methods: Six term newborns presented to the neonatal care unit with apnoea within the first 2 days of life.