Design and development of multilayer cotton masks via machine learning

Leow, Yihao; Shi, J.K.; Liu, W.; Ni, Xi; Yew, Pek Yin Michelle; Liu, S.; Li, Z.; Xue, Yanbing; Kai, Dan; Loh, Xian Jun

doi:10.1016/j.mtadv.2021.100178

Cited by 4 publications

(2 citation statements)

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“…The models were successful, even with limited training data, in predicting all three properties. ML algorithms are highly desirable as they are economical and improve experiment results in mask design [ 143 ]. The respirator burdens the wearer’s breathing ability, which leads to a decline in physical activity.…”

Section: Omicronmentioning

confidence: 99%

Artificial Intelligence: A Next-Level Approach in Confronting the COVID-19 Pandemic

et al. 2023

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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which caused coronavirus diseases (COVID-19) in late 2019 in China created a devastating economical loss and loss of human lives. To date, 11 variants have been identified with minimum to maximum severity of infection and surges in cases. Bacterial co-infection/secondary infection is identified during viral respiratory infection, which is a vital reason for morbidity and mortality. The occurrence of secondary infections is an additional burden to the healthcare system; therefore, the quick diagnosis of both COVID-19 and secondary infections will reduce work pressure on healthcare workers. Therefore, well-established support from Artificial Intelligence (AI) could reduce the stress in healthcare and even help in creating novel products to defend against the coronavirus. AI is one of the rapidly growing fields with numerous applications for the healthcare sector. The present review aims to access the recent literature on the role of AI and how its subfamily machine learning (ML) and deep learning (DL) are used to curb the pandemic’s effects. We discuss the role of AI in COVID-19 infections, the detection of secondary infections, technology-assisted protection from COVID-19, global laws and regulations on AI, and the impact of the pandemic on public life.

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Section: Omicronmentioning

confidence: 99%

Artificial Intelligence: A Next-Level Approach in Confronting the COVID-19 Pandemic

et al. 2023

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“…Donning of personal protective equipment such as surgical masks and N95 were also encouraged in increased risks areas such as hospitals and patient care facilities to contain the virus [ 11 , 12 ]. The pandemic has thrown up a lot of fresh research in different areas from the understanding of how masks serve to protect us and our surrounding people from viruses, to how sanitizing agents can help in preventing transmission through the fomite route, to the study of how aerosols facilitate the spread of the virus, to the design of a smart mask that can observe a patient's health conditions during infection [ [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] ].…”

Section: Introductionmentioning

confidence: 99%

SARS-CoV-2 in wastewater: From detection to evaluation

Zhang

Duran

Lim

et al. 2022

Materials Today Advances

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SARS-CoV-2 presence in wastewater has been reported in several studies and has received widespread attention among the Wastewater-based epidemiology (WBE) community. Such studies can potentially be used as a proxy for early warning of potential COVID-19 outbreak, or as a mitigation measure for potential virus transmission via contaminated water. In this review, we summarized the latest understanding on the detection, concentration, and evaluation of SARS-CoV-2 in wastewater. Importantly, we discuss factors affecting the quality of wastewater surveillance ranging from temperature, pH, starting concentration, as well as the presence of chemical pollutants. These factors greatly affect the reliability and comparability of studies reported by various communities across the world. Overall, this review provides a broadly encompassing guidance for epidemiological study using wastewater surveillance.

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