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In recent months, a novel virus named Coronavirus has emerged to become a pandemic. The virus is spreading not only humans, but it is also affecting animals. First ever case of Coronavirus was registered in city of Wuhan, Hubei province of China on 31st of December in 2019. Coronavirus infected patients display very similar symptoms like pneumonia, and it attacks the respiratory organs of the body, causing difficulty in breathing. The disease is diagnosed using a Real-Time Reverse Transcriptase Polymerase Chain reaction (RT-PCR) kit and requires time in the laboratory to confirm the presence of the virus. Due to insufficient availability of the kits, the suspected patients cannot be treated in time, which in turn increases the chance of spreading the disease. To overcome this solution, radiologists observed the changes appearing in the radiological images such as X-ray and CT scans. Using deep learning algorithms, the suspected patients’ X-ray or Computed Tomography (CT) scan can differentiate between the healthy person and the patient affected by Coronavirus. In this paper, popular deep learning architectures are used to develop a Coronavirus diagnostic systems. The architectures used in this paper are VGG16, DenseNet121, Xception, NASNet, and EfficientNet. Multiclass classification is performed in this paper. The classes considered are COVID-19 positive patients, normal patients, and other class. In other class, chest X-ray images of pneumonia, influenza, and other illnesses related to the chest region are included. The accuracies obtained for VGG16, DenseNet121, Xception, NASNet, and EfficientNet are 79.01%, 89.96%, 88.03%, 85.03% and 93.48% respectively. The need for deep learning with radiologic images is necessary for this critical condition as this will provide a second opinion to the radiologists fast and accurately. These deep learning Coronavirus detection systems can also be useful in the regions where expert physicians and well-equipped clinics are not easily accessible.

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The theory previously formulated for the equation of state (PVT) is now extended to the free energy change AGm by including the requisite additional contributions to the free energies of components and mixtures. As an example of a compatible binary mixture with experimentally known PVT and chemical potentials, we consider the n-hexane + n-hexadecane pair. The former data set had previously been discussed in terms of the theory. Without introduction of additional parameters, both sets of properties are quantitatively described. The very minor corrections in the scaling pressure arise from two facts. First, this parameter is predicted by the theory without adjustment to pressure data of the mixture, and second, AGm is computed as a difference between large quantities. This correction leaves the predicted equation of state practically unaltered. General procedures for the combined analysis of chemical potentials and PVT are outlined. As an example of a polymer solution we consider the polyethylene + n-hexane pair for a low ( = 8000) and high (Af = 177 000) uniform molecular weight. The equation of state of both components and thus their scaling parameters are known, but sufficient information for the mixture is not available, to define the corresponding parameters. Here a relative cross-interaction parameter becomes the important quantity to be explored. Lower critical solution temperatures (TJ and cloud point curves are computed. Small variations in the above parameters within the bounds suggested by the equation of state analysis of the C6 + C16 pair have a significant effect on Tc. In view of the success of the equation of state it is of interest to examine the effect of pressure. The computed increments of Tc conform to an iso-free volume difference condition, where free volume is defined in terms of the hole fraction inherent in the theory. Similarly a reduced cloud point curve can be established.

Efficient locating the fruit on the tree is one of the major requirements for the fruit harvesting system. This paper presents the fruit detection using improved multiple features based algorithm. To detect the fruit, an image processing algorithm is trained for efficient feature extraction. The algorithm is designed with the aim of calculating different weights for features like intensity, color, orientation and edge of the input test image. The weights of different features represent the approximate locations of the fruit within an image. The Detection Efficiency is achieved up to 90% for different fruit image on tree, captured at different positions. The input images are the section of tree image. The proposed approach can be applied for targeting fruits for robotic fruit harvesting.