Artificial Intelligence against COVID-19 Pandemic: A Comprehensive Insight.

COVID-19 is a pandemic initially identified in Wuhan, China, which is caused by a novel coronavirus, also recognized as the Severe Acute Respiratory Syndrome (SARS-nCoV-2). Unlike other coronaviruses, this novel pathogen may cause unusual contagious pain, which results in viral pneumonia, serious heart problems, and even death. Researchers worldwide are continuously striving to develop a cure for this highly infectious disease, yet there are no well-defined absolute treatments available at present. Several vaccination drives using emergency use authorisation vaccines have been held across many countries; however, their long-term efficacy and side-effects studies are yet to be studied. Various analytical and statistical models have been developed, however, their outcome rate is prolonged. Thus, modern science stresses the application of state-of-the-art methods to combat COVID-19. This paper aims to provide a deep insight into the comprehensive literature about AI and AI-driven tools in the battle against the COVID-19 pandemic. The high efficacy of these AI systems can be observed in terms of highly accurate results, i.e., > 95%, as reported in various studies. The extensive literature reviewed in this paper is divided into five sections, each describing the application of AI against COVID-19 viz. COVID-19 prevention, diagnostic, infection spread trend prediction, therapeutic and drug repurposing. The application of Artificial Intelligence (AI) and AI-driven tools are proving to be useful in managing and fighting against the COVID-19 pandemic, especially by analysing the X-Ray and CT-Scan imaging data of infected subjects, infection trend predictions, etc.

Prioritization of lower back pain risk factors among industrial workers using the best-worst method.

Although lower back pain (LBP) is reported to be the most prevalent musculoskeletal disorder related to work amongst workers, little systematic effort has been made to identify the factors and to determine their priority for the onset of LBP. In this article, an attempt has been made to determine the priority of LBP risk factors using a relatively new, simple and effective multicriteria decision-making method, i.e., the best-worst method. The best-worst method has been used in this research as it makes use of relatively fewer data for comparisons and it provides comparisons with better consistency leading to more reliable results. Results of the study revealed that the occupational factor is the most prominent main risk factor for LBP, followed by psychosocial and personal factors. Further, physical load and genetics are the most and least dominating LBP risk factors, respectively.

Analyzing barriers for implementation of public health and social measures to prevent the transmission of COVID-19 disease using DEMATEL method.

BACKGROUND AND AIMS: COVID-19 is a contagious disease caused by a new coronavirus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Till now there are no vaccines and explicit medicines available for the treatment of COVID-19 disease, therefore the initiatives like public health and social measures are helpful in limiting the quantity of contaminations and saving lives. Keeping this in view, an attempt has been made in the present work to identify the barriers which hinder the implementation of these public health and social measures METHODS: The barriers to implement public health and social measures for preventing transmission of COVID-19 are identified using systematic literature review and these barriers are then categorized using Decision-making trial and evaluation laboratory (DEMATEL) method. RESULTS: Results reveal that among ten identified barriers, Lack of resources for implementing public health and social measures is found to be the most influential barrier to implement public health and social measures for preventing transmission of COVID-19. CONCLUSIONS: The successful implementation of public health and social measures depends on the adequate availability of resources which include medical equipment, personnel and financial dealings. The outcome of this research work will benefit and motivate the higher authorities to consider and overcome various challenges to prevent the transmission of COVID-19.

Investigation on Effect of Strain Rate and Heat Generation on Traverse Force in FSW of Dissimilar Aerospace Grade Aluminium Alloys.

The emergence of the aerospace sector requires efficient joining of aerospace grade aluminium alloys. For large-scale industrial practices, achievement of optimum friction stir welding (FSW) parameters is chiefly aimed at obtaining maximum strain rate in deforming material with least application of traverse force on the tool pin. Exact computation of strain rate is not possible due to complex and unexposed material flow kinematics. Estimation using micro-structural evolution serves as one of the very few methods applicable to analyze the yet unmapped interdependence of strain rate and traverse force. Therefore, the present work assessed strain rate in the stir zone using Zener Holloman parameter. The maximum and minimum strain rates of 6.95 and 0.31 s-1 were obtained for highest and least traverse force, respectively.