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. Addressing material compatibility issues within hydrogen systems remains a critical focus. Challenges, roadmaps, and scenarios steer long-term planning and technology outlooks. Strategic visions align actions and policies, encompassing societal and ecological dimensions. The confluence of hydrogen's promise with material progress holds the prospect of reshaping our energy landscape sustainably. Forming collective future perspectives to foresee this emerging technology's potential benefits is valuable. Our review article comprehensively explores the forthcoming challenges in hydrogen technology. We extensively examine the challenges and opportunities associated with hydrogen production, incorporating CO2 capture technology. Furthermore, the interaction of materials and composites with hydrogen, particularly in the context of hydrogen transmission, pipeline, and infrastructure, are discussed to understand the interplay between materials and hydrogen dynamics. Additionally, the exploration extends to the embrittlement phenomena during storage and transmission, coupled with a comprehensive examination of the advancements and hurdles intrinsic to hydrogen fuel cells. Finally, our exploration encompasses addressing hydrogen safety from an industrial perspective. By illuminating these dimensions, our article provides a panoramic view of the evolving hydrogen landscape.

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. Improved understanding of the physical and chemical mechanisms of degradation due to a gaseous hydrogen atmosphere allows us to better select and develop materials that are best suited to carrier and sealing applications. Damage to materials from aging is inevitable with exposure to high-pressure hydrogen. This review discusses the specific mechanisms of different categories of aging of storage and sealing materials in a hydrogen environment. Additionally, this article discusses different laboratory test methods to simulate each type of aging. It covers the limitations of current research in determining material integrity through existing techniques for aging experiments and explores the latest developments in the field. Important improvements are also suggested in terms of material development and testing procedures.

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. Thus, ML could potentially be used to diagnose Monkeypox as well. In this study, we developed a Monkeypox diagnosis model using Generalization and Regularization-based Transfer Learning approaches (GRA-TLA) for binary and multiclass classification. We tested our proposed approach on ten different convolutional Neural Network (CNN) models in three separate studies. The preliminary computational results showed that our proposed approach, combined with Extreme Inception (Xception), was able to distinguish between individuals with and without Monkeypox with an accuracy ranging from 77% to 88% in Studies One and Two, while Residual Network (ResNet)-101 had the best performance for multiclass classification in Study Three, with an accuracy ranging from 84% to 99%. In addition, we found that our proposed approach was computationally efficient compared to existing TL approaches in terms of the number of parameters (NP) and Floating-Point Operations per Second (FLOPs) required. We also used Local Interpretable Model-Agnostic Explanations (LIME) to explain our model's predictions and feature extractions, providing a deeper understanding of the specific features that may indicate the onset of Monkeypox.

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. Three different subpopulations were consecutively recruited: positive COVID-19 patients, recovered COVID-19 patients and healthy individuals as controls. Positive patients were recruited within 10 days from nasal swab positivity. Recovery from COVID-19 was established clinically, virologically and radiologically. Healthy individuals reported no COVID-19 symptoms and yielded negative results at serological testing. All study participants provided three trials for multiple vocal tasks (sustained vowel phonation, speech, cough). All recordings were initially divided into three different binary classifications with a feature selection, ranking and cross-validated RBF-SVM pipeline. This brough a mean accuracy of 90.24%, a mean sensitivity of 91.15%, a mean specificity of 89.13% and a mean AUC of 0.94 across all tasks and all comparisons, and outlined the sustained vowel as the most effective vocal task for COVID discrimination. Moreover, a three-way classification was carried out on an external test set comprised of 30 subjects, 10 per class, with a mean accuracy of 80% and an accuracy of 100% for the detection of positive subjects. Within this assessment, recovered individuals proved to be the most difficult class to identify, and all the misclassified subjects were declared positive; this might be related to mid and short-term vocal traces of COVID-19, even after the clinical resolution of the infection. In conclusion, MLVA may accurately discriminate between positive COVID-19 patients, recovered COVID-19 patients and healthy individuals. Further studies should test MLVA among larger populations and asymptomatic positive COVID-19 patients to validate this novel screening technology and test its potential application as a potentially more effective surveillance strategy for COVID-19.

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. OBJECTIVE: The purpose of this study is to build an insomnia prediction model from users' psycholinguistic patterns, including the elements of word usage, semantics, and their Big 5 personality traits as derived from tweets. METHODS: In this paper, we exploited both psycholinguistic and personality traits derived from tweets to identify insomnia patients. First, we built psycholinguistic profiles of the users from their word choices and the semantic relationships between the words of their tweets. We then determined the relationship between a users' personality traits and insomnia. Finally, we built a double-weighted ensemble classification model to predict insomnia from both psycholinguistic and personality traits as derived from user tweets. RESULTS: Our classification model showed strong prediction potential (78.8%) to predict insomnia from tweets. As insomniacs are generally ill-tempered and feel more stress and mental exhaustion, we observed significant correlations of certain word usage patterns among them. They tend to use negative words (eg, "no," "not," "never"). Some people frequently use swear words (eg, "damn," "piss," "fuck") with strong temperament. They also use anxious (eg, "worried," "fearful," "nervous") and sad (eg, "crying," "grief," "sad") words in their tweets. We also found that the users with high neuroticism and conscientiousness scores for the Big 5 personality traits likely have strong correlations with insomnia. Additionally, we observed that users with high conscientiousness scores have strong correlations with insomnia patterns, while negative correlation between extraversion and insomnia was also found. CONCLUSIONS: Our model can help predict insomnia from users' social media interactions. Thus, incorporating our model into a software system can help family members detect insomnia problems in individuals before they become worse. The software system can also help doctors to diagnose possible insomnia in patients.