Analyzing the barriers for aquaponics adoption using integrated BWM and fuzzy DEMATEL approach in Indian context.

Aquaponic system in greenhouses which can recycle and reuse the water and nutrients is gaining importance across the world to counter the uncertainties due to weather fluctuations. However, there is a slow pace of growth in aquaculture practices around the globe in general and India in particular. There are many barriers to adopt the aquaponic culture. In this study an analysis of the barriers for aquaponics culture in Indian context during the COVID-19 period is presented. Literature review and interactions with various stakeholders help to find out the list of potential factors while gauging the success of their prospective aquaponics project. The "best-worst" methodology (BWM) is employed for ranking of barriers, whereas categorizing of barriers is carried out with the help of fuzzy DEMATEL. Furthermore, the results of this research work are of great value to corporations or start-up companies looking to invest in this technology as well as to farmers who wish to adopt this farming technique.

Hyperglycemia and steroid use increase the risk of rhino-orbito-cerebral mucormycosis regardless of COVID-19 hospitalization: Case-control study, India.

BACKGROUND: In the ongoing COVID-19 pandemic, an increased incidence of ROCM was noted in India among those infected with COVID. We determined risk factors for rhino-orbito-cerebral mucormycosis (ROCM) post Coronavirus disease 2019 (COVID-19) among those never and ever hospitalized for COVID-19 separately through a multicentric, hospital-based, unmatched case-control study across India. METHODS: We defined cases and controls as those with and without post-COVID ROCM, respectively. We compared their socio-demographics, co-morbidities, steroid use, glycaemic status, and practices. We calculated crude and adjusted odds ratio (AOR) with 95% confidence intervals (CI) through logistic regression. The covariates with a p-value for crude OR of less than 0·20 were considered for the regression model. RESULTS: Among hospitalised, we recruited 267 cases and 256 controls and 116 cases and 231 controls among never hospitalised. Risk factors (AOR; 95% CI) for post-COVID ROCM among the hospitalised were age 45-59 years (2·1; 1·4 to 3·1), having diabetes mellitus (4·9; 3·4 to 7·1), elevated plasma glucose (6·4; 2·4 to 17·2), steroid use (3·2; 2 to 5·2) and frequent nasal washing (4·8; 1·4 to 17). Among those never hospitalised, age ≥ 60 years (6·6; 3·3 to 13·3), having diabetes mellitus (6·7; 3·8 to 11·6), elevated plasma glucose (13·7; 2·2 to 84), steroid use (9·8; 5·8 to 16·6), and cloth facemask use (2·6; 1·5 to 4·5) were associated with increased risk of post-COVID ROCM. CONCLUSIONS: Hyperglycemia, irrespective of having diabetes mellitus and steroid use, was associated with an increased risk of ROCM independent of COVID-19 hospitalisation. Rational steroid usage and glucose monitoring may reduce the risk of post-COVID.

Artificial Intelligence-Based Data-Driven Strategy to Accelerate Research, Development, and Clinical Trials of COVID Vaccine.

The global COVID-19 (coronavirus disease 2019) pandemic, which was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a significant loss of human life around the world. The SARS-CoV-2 has caused significant problems to medical systems and healthcare facilities due to its unexpected global expansion. Despite all of the efforts, developing effective treatments, diagnostic techniques, and vaccinations for this unique virus is a top priority and takes a long time. However, the foremost step in vaccine development is to identify possible antigens for a vaccine. The traditional method was time taking, but after the breakthrough technology of reverse vaccinology (RV) was introduced in 2000, it drastically lowers the time needed to detect antigens ranging from 5-15 years to 1-2 years. The different RV tools work based on machine learning (ML) and artificial intelligence (AI). Models based on AI and ML have shown promising solutions in accelerating the discovery and optimization of new antivirals or effective vaccine candidates. In the present scenario, AI has been extensively used for drug and vaccine research against SARS-COV-2 therapy discovery. This is more useful for the identification of potential existing drugs with inhibitory human coronavirus by using different datasets. The AI tools and computational approaches have led to speedy research and the development of a vaccine to fight against the coronavirus. Therefore, this paper suggests the role of artificial intelligence in the field of clinical trials of vaccines and clinical practices using different tools.

Measuring Circular Supply Chain Risk: A Bayesian Network Methodology

The world is facing economic, as well as social, crisis due to the COVID-19 pandemic. Implementing sustainable practices is one of the possible ways to address these issues. Adopting circular oriented techniques throughout the supply chain not only guarantees economic profitability, but also provides an edge to the organization in the market of fierce global competition. The concept of implementing circularity in the supply chain is novel and dynamic in nature, and it involves certain risk. In this study, a Bayesian Network methodology is adopted to analyze how the risk propagation takes place in a circular supply chain network of an automobile organization. The circular supply chain network consists of a group of manufacturers, retailers and recyclers, located in the Delhi–NCR region. Economic, environmental, social, technological, waste management, agile vulnerability, and risk of cannibalization are the major risk categories that were identified through an extensive literature review. Further, the impact of risk on the performance of the circular supply chain is analyzed by considering performance parameters such as lost sales, impact on supply chain revenue, and inventory holding cost. Risk exposure index is incorporated into the study to analyze the vulnerability of each node. The findings of the study reveal that the reverse side of the circular supply chain can be a source of risk propagation during the implementation of the circularity concept. This work is carried out under a single industry domain. In the future, risk propagation analysis can be examined in the supply chain of other sectors. The findings of the study can assist the supply chain managers and the risk experts to focus on the areas that are more vulnerable to risk.