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https://www.selleckchem.com/products/iodoacetamide.html The coronavirus disease (COVID-19) outbreak has raised consumer concerns about health. By employing 306 online questionnaires, we identify COVID-19's effect on online organic agriculture product consumption and rural health tourism intention based on stimulus-organism-response theory and event system theory by incorporating risk information disclosure of COVID-19 as the moderating variable and health consciousness and risk perception as the mediating variables. These findings suggest that considering the impact of COVID-19 can help focus the production and online sales of organic agricultural products, the establishment and improvement of rural health facilities, and the marketing of rural health tourism.The novel 2019 coronavirus disease (COVID-19) has infected over 141 million people worldwide since April 20, 2021. More than 200 countries around the world have been affected by the coronavirus pandemic. Screening for COVID-19, we use fast and inexpensive images from computed tomography (CT) scans. In this paper, ResNet-50, VGG-16, convolutional neural network (CNN), convolutional auto-encoder neural network (CAENN), and machine learning (ML) methods are proposed for classifying Chest CT Images of COVID-19. The dataset consists of 1252 CT scans that are positive and 1230 CT scans that are negative for COVID-19 virus. The proposed models have priority over the other models that there is no need of pre-trained networks and data augmentation for them. The classification accuracies of ResNet-50, VGG-16, CNN, and CAENN were obtained 92.24%, 94.07%, 93.84%, and 93.04% respectively. Among ML classifiers, the nearest neighbor (NN) had the highest performance with an accuracy of 94%.Over the two last decades, coronaviruses have affected human life in different ways, especially in terms of health and economy. Due to the profound effects of novel coronaviruses, growing tides of research are emerging in various research fi

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