ABSTRACT
The outbreak of coronavirus disease in 2019 (COVID-19) caused by the SARS-CoV-2 virus and its pandemic effects have created a demand for essential medical equipment. To date, there are no specific, clinically significant licensed drugs and vaccines available for COVID-19. Hence, mapping out COVID-19 problems and preventing the spread with relevant technology are very urgent. This study is a review of the work done till October, 2020 on solving COVID-19 with 3D printing. Many patients who need to be hospitalized because of COVID-19 can only survive on bio-macromolecules antiviral respiratory assistance and other medical devices. A bio-cellular face shield with relative comfortability made of bio-macromolecules polymerized polyvinyl chloride (BPVC) and other biomaterials are produced with 3D printers. Summarily, it was evident from this review study that additive manufacturing (AM) is a proffered technology for efficient production of an improved bio-macromolecules capable of significant COVID-19 test and personal protective equipment (PPE) to reduce the effect of COVID-19 on the world economy. Innovative AM applications can play an essential role to combat invisible killers (COVID-19) and its hydra-headed pandemic effects on humans, economics and society.
ABSTRACT
As a threat for transportation system, traffic crashes have a wide range of social consequences for governments. Traffic crashes are increasing in developing countries and Iran as a developing country is not immune from this risk. There are several researches in the literature to predict traffic crash severity based on artificial neural networks (ANNs), support vector machines and decision trees. This paper attempts to investigate the crash injury severity of rural roads by using a hybrid clustering and classification approach to compare the performance of classification algorithms before and after applying the clustering. In this paper, a novel rule-based genetic algorithm (GA) is proposed to predict crash injury severity, which is evaluated by performance criteria in comparison with classification algorithms like ANN. The results obtained from analysis of 13,673 crashes (5600 property damage, 778 fatal crashes, 4690 slight injuries and 2605 severe injuries) on rural roads in Tehran Province of Iran during 2011-2013 revealed that the proposed GA method outperforms other classification algorithms based on classification metrics like precision (86%), recall (88%) and accuracy (87%). Moreover, the proposed GA method has the highest level of interpretation, is easy to understand and provides feedback to analysts.
