COVID-19 Challenges: Can Industry 4.0 Technologies Help with Business Continuity?

The COVID-19 pandemic has halted economic activities and made business dynamics much more challenging by introducing several additional operational, structural, and managerial constraints. The problem has affected global supply chains in many ways, and has questioned their long-term continuity. On the other hand, Industry 4.0 is an emerging phenomenon. However, there is a need to investigate how Industry 4.0 technologies may play a potential role in sustaining business operations to ease unprecedented causalities. The current research aims to investigate the potentiality of Industry 4.0 technologies to solve the COVID-19 challenges for long term sustainability. From an exploratory literature analysis coupled with the Delphi method, keeping in view the situation of the pandemic, ten challenge groups that have affected global business dynamics were identified. A questionnaire was developed with the aim of accumulating industrial and academic experts to evaluate the degree of influence and interrelationship among the identified challenges. The Decision Making, Trial and Evaluation Laboratory (DEMATEL) approach was deployed to further analyze the challenges for the categorization of these into causes and effects, further prioritizing them for better decision making. The prioritized challenges from the list of causes were governmental policies and support, followed by real access to customers and a lack of infrastructure. Additionally, these challenges were further evaluated through the expert opinion of Industry 4.0 systems experts and strategic-level supply chain experts to potentially gauge the potency of Industry 4.0 technologies to solve COVID-19-induced challenges. The outcomes of this research (which used Delphi integrated with a DEMATEL approach) are expected to support businesses in formulating strategies with the aim of business continuity in combating future disruptions caused by COVID-19-like pandemics.

Deep Learning Approach for Discovery of In Silico Drugs for Combating COVID-19.

Early diagnosis of pandemic diseases such as COVID-19 can prove beneficial in dealing with difficult situations and helping radiologists and other experts manage staffing more effectively. The application of deep learning techniques for genetics, microscopy, and drug discovery has created a global impact. It can enhance and speed up the process of medical research and development of vaccines, which is required for pandemics such as COVID-19. However, current drugs such as remdesivir and clinical trials of other chemical compounds have not shown many impressive results. Therefore, it can take more time to provide effective treatment or drugs. In this paper, a deep learning approach based on logistic regression, SVM, Random Forest, and QSAR modeling is suggested. QSAR modeling is done to find the drug targets with protein interaction along with the calculation of binding affinities. Then deep learning models were used for training the molecular descriptor dataset for the robust discovery of drugs and feature extraction for combating COVID-19. Results have shown more significant binding affinities (greater than -18) for many molecules that can be used to block the multiplication of SARS-CoV-2, responsible for COVID-19.

A COVID-19 Supply Chain Management Strategy Based on Variable Production under Uncertain Environment Conditions.

The management of a controllable production in the manufacturing system is essential to achieve viable advantages, particularly during emergency conditions. Disasters, either man-made or natural, affect production and supply chains negatively with perilous effects. On the other hand, flexibility and resilience to manage the perpetuated risks in a manufacturing system are vital for achieving a controllable production rate. Still, these performances are strongly dependent on the multi-criteria decision making in the working environment with the policies launched during the crisis. Undoubtedly, health stability in a society generates ripple effects in the supply chain due to high demand fluctuation, likewise due to the Coronavirus disease-2019 (COVID-19) pandemic. Incorporation of dependent demand factors to manage the risk from uncertainty during this pandemic has been a challenge to achieve a viable profit for the supply chain partners. A non-linear supply chain management model is developed with a controllable production rate to provide an economic benefit to the manufacturing firm in terms of the optimized total cost of production and to deal with the different situations under variable demand. The costs in the model are set as fuzzy to cope up with the uncertain conditions created by lasting pandemic. A numerical experiment is performed by utilizing the data set of the multi-stage manufacturing firm. The optimal results provide support for the industrial managers based on the proactive plan by the optimal utilization of the resources and controllable production rate to cope with the emergencies in a pandemic.

Evaluating lubricant performance to reduce COVID-19 PPE-related skin injury.

BACKGROUND: Healthcare workers around the world are experiencing skin injury due to the extended use of personal protective equipment (PPE) during the COVID-19 pandemic. These injuries are the result of high shear stresses acting on the skin, caused by friction with the PPE. This study aims to provide a practical lubricating solution for frontline medical staff working a 4+ hours shift wearing PPE. METHODS: A literature review into skin friction and skin lubrication was conducted to identify products and substances that can reduce friction. We evaluated the lubricating performance of commercially available products in vivo using a custom-built tribometer. FINDINGS: Most lubricants provide a strong initial friction reduction, but only few products provide lubrication that lasts for four hours. The response of skin to friction is a complex interplay between the lubricating properties and durability of the film deposited on the surface and the response of skin to the lubricating substance, which include epidermal absorption, occlusion, and water retention. INTERPRETATION: Talcum powder, a petrolatum-lanolin mixture, and a coconut oil-cocoa butter-beeswax mixture showed excellent long-lasting low friction. Moisturising the skin results in excessive friction, and the use of products that are aimed at 'moisturising without leaving a non-greasy feel' should be prevented. Most investigated dressings also demonstrate excellent performance.