Scarcity of natural resources and increasing level of pollution have put the survival of human in trouble. This situation has compelled the world to think about the solutions to maintain environmental harmony along with industrial and economic growth. Adopting sustainable consumption and production (SCP) linked with circular economy (CE) can work as a key solution. But adopting SCP linked with CE is not a simple task, as it may lead to heavy losses to organization, if not adopted properly. So there is a requirement of developing full understanding about the various critical success factors (CSFs) to achieve the goal of SCP. In this research study, the efforts have been made to identify various CSFs to adopt SCP linked with CE. As a result, total twenty-seven CSFs have been identified with the help of literature and experts' inputs. All the twenty-seven CSFs were analyzed using fuzzy DEMATEL methodology to quantify interrelationship among them. The results revealed that population control, firm committed government, firm focused top management, full financial backup, policies formation and technology up-gradation are the main causal factors and improved safety, reliability, quality, repairing-remanufacturing, full utilization of resources and maintaining ecological-economic balance are the main factors under effect group in adoption of SCP linked with CE. This framework will be helpful for government, policy makers and managers in maintaining societal and environmental harmony along with economic growth.Keywords Sustainable consumption and production (SCP) • Circular economy (CE) • Critical success factors • Fuzzy decision-making trial and evaluation laboratory (fuzzy DEMATEL)

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The threats like increasing pollution level and scarcity of natural resources are showing that Sustainable Consumption and Production (SCP) is one of the key solutions and the main contributor driver of Sustainable Development Goals (SDGs). Eventually, a mysterious disease named as COVID-19 started spreading very fast around the globe and it was declared a pandemic. To protect citizens from this pandemic, the government announced complete lockdown in the country. This lockdown due to COVID-19 has put its impact on every aspect of life. In this research study, the efforts have been made to identify various impacts of COVID-19 on SCP practices and propose various solutions to overcome these impacts of COVID-19. In the Indian perspective, a total fifteen impacts of COVID-19 under five different heads along with eighteen solutions to overcome these impacts have been identified with the help of literature, various reports and experts’ inputs. All these impacts and solutions were analyzed using a hybrid framework. Results revealed that COVID-19 majorly put its impact on nation, business and behavior followed by the impact on society and environment. All these impacts can be overcome by adopting solutions like strong and clear policies formation and its implementation, financial packages to industries to boost up the economy, firm determination of top management, safety of the workforce and tax reduction on sustainable products. The final results will be very helpful for all the actors focused on SCP.

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Abstract-To gain improved performance in multiple issue superscalar processors, the increment in instruction fetch and issue rate is pretty necessary. Evasion of control hazard is a primary source to get peak instruction level parallelism in superscalar processors. Conditional branch prediction can help in improving the performance of processors only when these predictors are equipped with algorithms to give higher accuracy. The Increment in single miss-prediction rate can cause wastage of more than 20% of the instructions cycles, which leads us to an exploration of new techniques and algorithms that increase the accuracy of branch prediction. Alloying is a way to exploit the local and global history of different predictors in the same structure and sometimes also called hybrid branch prediction. In this paper, we aim to design a more accurate and robust two-level alloyed predictor, whose behavior is more dynamic on changing branch direction.