An Intelligent Classification Diagnosis Based on Blood Oxygen Saturation Signals for Medical Data Security Including COVID-19 in Industry 5.0

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is gradually valued due to its high prevalence, high risk, and high mortality. Alternative to the polysomnography (PSG) diagnosis, the proposed method assesses the subject's degree of illness considering the supply chain and Industry 5.0 requirement efficiently and accurately. This article uses the blood oxygen saturation (SpO(2)) signal count of the number of apnea or hypoventilation events during the sleep of the subject, calculating the apnea-hypopnea index (AHI) and the subject's disease level. SpO(2) signals are used to extract 35-D features based on the time domain, including approximate entropy, central tendency measure, and Lempel-Ziv complexity to accelerate the diagnosis process in supply chains. The feature selection process is reduced from 35 to 7 dimensions that benefits to the implementation in the practical supply chains in Industry 5.0 by extracting the extracted features. This article applies Pearson correlation coefficient selection, based on minimum redundancy-maximum correlation algorithm selection, and a wrapper based on the backward search algorithm. The accuracy rate is 86.92%, and the specificity is 90.7% under the selected random forest classifier. A random forest classifier was used to calculate the AHI index, and a linear regression analysis was performed with the AHI index obtained from the PSG. The result reaches a 92% accuracy rate in assessing the prevalence of OSAHS, satisfying the industrial deployment.

Reconfiguring a hierarchical supply chain model under pandemic using text mining and social media analysis

Purpose: This study is to reconfigure a hierarchical supply chain model utilizing databases and text files to understand future pathways due to COVID-19 pandemic has had a bullwhip effect, disrupting the global supply chain, and a mechanism is needed to address this disruptive event under pandemic uncertainties. Design/methodology/approach: To address this mechanism, this study employs bibliometric analysis and text mining to reconfigure a hierarchical supply chain model under pandemic conditions and associates it with social media to conduct an intuitive visual analysis. Findings: The current academic concerns are related to an overconcentration on risk management and a data-driven approach, generating an enormous gap between the concerns of academics and those of the public. The evidence shows that for both countries with outstanding performance and those that need improvement, the efficiency in terms of preventing the spread of the pandemic should be promoted. Originality/value: This study contributes to (1) reconfiguring a hierarchical supply chain model under pandemic uncertainties and (2) bridging theory and practice by offering comparable interrelated attributes to guide post-COVID-19 strategies in the supply chain. The findings are that the supply management approach and big data are attributes that involve the concerns of world public and academics under pandemic uncertainties. © 2021, Emerald Publishing Limited.

Circular economy needs the resilient systems in covid-19 pandemic

This note reveals the gaps from circular economy (CE) and resilience in the literature during COVID-19 pandemic. The disruptive event affects the circularity in the supply chain due to numerous single-use-products in food, health, plastic industries. The unsustainable production and consumption is harmful for social, ecological, and economic systems. The industrial practices need a highly resilient network to have better visibility and agility to shift sourcing. Prior studies reveal the gap that CE needs the resilient systems. Still, there is a need to conceptualize and models resilience in CE studies using quantitative and qualitative approaches. © 2020 Penerbit Universiti Kebangsaan Malaysia. All rights reserved.