Characterizations and Optimization for Resilient Manufacturing Systems With Considerations of Process Uncertainties

The recent COVID-19 pandemic reveals the vulnerability of global supply chains: the unforeseen supply crunches and unpredictable variability in customer demands lead to catastrophic disruption to production planning and management, causing wild swings in productivity for most manufacturing systems. Therefore, a smart and resilient manufacturing system (S & RMS) is promised to withstand such unexpected perturbations and adjust promptly to mitigate their impacts on the system's stability. However, modeling the system's resilience to the impacts of disruptive events has not been fully addressed. We investigate a generalized polynomial chaos (gPC) expansion-based discrete-event dynamic system (DEDS) model to capture uncertainties and irregularly disruptive events for manufacturing systems. The analytic approach allows a real-time optimization for production planning to mitigate the impacts of intermittent disruptive events (e.g., supply shortages) and enhance the system's resilience. The case study on a hybrid bearing manufacturing workshop suggests that the proposed approach allows a timely intervention in production planning to significantly reduce the downtime (around one-fifth of the downtime compared to the one without controls) while guaranteeing maximum productivity under the system perturbations and uncertainties.

[Transmission routes of 2019-novel coronavirus (2019-nCoV)].

Since the outbreak of COVID-19 in Wuhan, China, at the end of 2019, it has demonstrated China's ability to identify unknown pathogens. At present, reports showed that the main transmission routes are respiratory droplets and indirect contact, other vertical transmission routes have yet to be confirmed. This review discusses the possible transmission routes of 2019-novel coronavirus (2019-nCoV), based on currently research, the main transmission routes are respiratory droplets and indirect contact, fecal-oral might bepossible, while aerosol, tear (conjunctival) and mother-to-fetus still have yet to be confirmed, providing a reference basis for 2019-nCoV prevention and control and public protection.

Virtual screening of small molecular inhibitors of SARS-CoV-2 3CL hydrolase based on high-throughput molecular docking and prediction of Chinese materia medica and its compound against COVID-19

Objective: Based on the systematic pharmacological database of traditional Chinese medicine (TCM) and the analysis platform TCMSP, the computer virtual screening technique was used to screen the small molecule inhibitors of SARS-CoV-2 3CL hydrolase from Chinese materia medica (CMM), and speculate the potential anti-COVID-19 novel coronavirus pneumonia TCMs and its compounds. Methods: SARS-CoV-2 3CL hydrolase protein was targeted in this study. Autodock Vina software and Python script were used to realize high-throughput molecular docking. Combined with “ADME-Lipinski” rules, the re-screening was carried out to optimize the active ingredients and speculate the key TCMs and compound prescriptions. Based on the perspective of network pharmacology, a component-target-pathway network was constructed to infer the mechanism of action of core drug pairs. Results: Taking the reference ligand as positive control, 66 natural micromolecule compounds with good pharmacokinetic properties were obtained. Twelve single TCMs, two Chinese medicine pairs of Glycyrrhizae Radix et Rhizoma-Mori Cortex and Lonicerae Japonicae Flos-Forsythiae Fructus, and 12 TCM prescriptions including Sangju Drink and modified Sangju Drink and Yinqiao Powder were selected as candidate schemes to fight against novel coronavirus pneumonia. Conclusion: This study is based on high-throughput molecular docking technology to virtually screen small molecule inhibitors of SARS-CoV-2 3CL hydrolase of CMM and Chinese medicines, innovatively analyze the potential molecular mechanism in combination with network pharmacology, and provide scientific guidance and theoretical basis for TCM to resist novel coronavirus pneumonia.