A three-dimensional model for simulating the maldistribution of liquid flow in random packed beds

The hydrodynamic characteristics of liquid flow in a packed column are studied using model equations that are based on the volume average concept. The objective is to track radial variations in flow rates (or maldistributions) on a scale smaller than the equipment scale, but larger than the scale of the packing. Such models remain valid on a wide range of scales. Once a number of parameters such as voidage and permeability are determined, the model could be used for scale up purpose. We present the results of two and three dimensional simulation using a computational fluid dynamics solver. For several different inlet distributions, the development of flow patterns and the formation of the natural flow patterns near the wall are simulated. The simulation results on wall flow patterns are in reasonably good agreement with published experimental data.     

Detection of Diabetic Retinopathy from Retinal Images Using DenseNet Models

A prevalent diabetic complication is Diabetic Retinopathy (DR), which can damage the retina’s veins, leading to a severe loss of vision. If treated in the early stage, it can help to prevent vision loss. But since its diagnosis takes time and there is a shortage of ophthalmologists, patients suffer vision loss even before diagnosis. Hence, early detection of DR is the necessity of the time. The primary purpose of the work is to apply the data fusion/feature fusion technique, which combines more than one relevant feature to predict diabetic retinopathy at an early stage with greater accuracy. Mechanized procedures for diabetic retinopathy analysis are fundamental in taking care of these issues. While profound learning for parallel characterization has accomplished high approval exactness’s, multi-stage order results are less noteworthy, especially during beginning phase sickness. Densely Connected Convolutional Networks are suggested to detect of Diabetic Retinopathy on retinal images. The presented model is trained on a Diabetic Retinopathy Dataset having 3,662 images given by APTOS. Experimental results suggest that the training accuracy of 93.51% 0.98 precision, 0.98 recall and 0.98 F1-score has been achieved through the best one out of the three models in the proposed work. The same model is tested on 550 images of the Kaggle 2015 dataset where the proposed model was able to detect No DR images with 96% accuracy, Mild DR images with 90% accuracy, Moderate DR images with 89% accuracy, Severe DR images with 87% accuracy and Proliferative DR images with 93% accuracy.