Drying is one of the ways to reduce postharvest waste and processing in agricultural products. Drying with hot air is one of the most popular drying methods in the food industry. The purpose of this study is to investigate the effect of ultrasound and temperature on the quality and thermodynamic properties in the process of drying nectarine slices in a hot air dryer. The drying process was performed at four levels of ultrasonic pre-treatment of 0 min (control sample), 10, 20, and 40 min and three temperature levels of 50, 60, and 75°C. Experiments were performed on 5 mm thick How to cite this article: Jahanbakhshi A, Yeganeh R, Momeny M. Influence of ultrasound pre-treatment and temperature on the quality and thermodynamic properties in the drying process of nectarine slices in a hot air dryer.

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Chest X-ray images are used in deep convolutional neural networks for the detection of COVID-19, the greatest human challenge of the 21st century. Robustness to noise and improvement of generalization are the major challenges in designing these networks. In this paper, we introduce a strategy for data augmentation using the determination of the type and value of noise density to improve the robustness and generalization of deep CNNs for COVID-19 detection. Firstly, we present a learning-to-augment approach that generates new noisy variants of the original image data with optimized noise density. We apply a Bayesian optimization technique to control and choose the optimal noise type and its parameters. Secondly, we propose a novel data augmentation strategy, based on denoised X-ray images, that uses the distance between denoised and original pixels to generate new data. We develop an autoencoder model to create new data using denoised images corrupted by the Gaussian and impulse noise. A database of chest X-ray images, containing COVID-19 positive, healthy, and non-COVID pneumonia cases, is used to fine-tune the pre-trained networks (AlexNet, ShuffleNet, ResNet18, and GoogleNet). The proposed method performs better results compared to the state-of-the-art learning to augment strategies in terms of sensitivity (0.808), specificity (0.915), and F-Measure (0.737). The source code of the proposed method is available at https://github.com/mohamadmomeny/Learning-to-augment-strategy .

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