Fundamental information about the chemistry of sludge, published rates of sludge generation, and models of paper production and wastepaper recycling were combined to create a predictive model. The goal of the modeling exercise was to determine and project global sludge production until the year 2050. It was predicted that a global shift in paper and paperboard production would result in the Asia-Pacific region emerging as a major producer of papermill sludge. Global production of papermill sludge was predicted to rise over the next 50 years by between 48 and 86% over current levels. Sludge was found to contain a large amount of woody organic material, but the proportion of this material in the sludge was found to drop as recycling programs were implemented. Sludge was also found to contain a large amount of woody carbon, which comprised about 30% of the total sludge solids. The presence of such a large proportion of woody carbon may become important if a system of carbon crediting is implemented for the forest industry. Key words: carbon cycle, forests, papermill sludge, modeling, life cycle analysis.

After the World War II, every country throughout the world is experiencing the biggest crisis induced by the devastating Coronavirus disease (COVID-19), which initially arose in the city of Wuhan in December 2019. This global pandemic has severely affected not only the health of billions of people but also the economy of countries all over the world. It has been evident that novel virus has infected a total of 20,674,903 lives as on 12 August 2020. The dissemination of the virus can be regulated by detecting the positive COVID cases as soon as possible. The reverse-transcriptase polymerase chain reaction (RT-PCR) is the basic approach used in the identification of the COVID-19. As RT-PCR is less sensitive to determine the novel virus at the beginning stage, it is worthwhile to develop more robust and other diagnosis approaches for the detection of the novel coronavirus. Due to the accessibility of medical datasets comprising of radiography images publicly, more robust diagnosis approaches are contributed by the researchers and technocrats for the identification of COVID-19 images using the techniques of deep leaning. In this paper, we proposed VGG16 and MobileNet-V2, which makes use of ADAM and RMSprop optimizers for the automatic identification of the COVID-19 images from other pneumonia chest X-ray images. Then, the efficiency of the proposed methodology has been enhanced by the application of data augmentation and transfer learning approach which is used to overcome the overfitting problem. From the experimental outcomes, it can be deduced that the proposed MobileNet-V2 model using ADAM and RMSprop optimizer achieves better accomplishment in terms of accuracy, sensitivity and specificity when contrasted with the VGG 16 using ADAM and RMSprop optimizers.

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