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This paper starts by reviewing the most significant previous studies on fluid-structure interaction of flows past stationary and moving circular cylinders, including those undergoing fluid dynamic excitation in the form of vortex-

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The aim of this research is to examine the methods of nursing students to deal with future anxiety and stress.

Advancements in deep learning and availability of medical imaging data have led to the use of CNN-based architectures in disease diagnostic assisted systems. In spite of the abundant use of reverse transcription-polymerase chain reaction-based tests in COVID-19 diagnosis, CT images offer an applicable supplement with their high sensitivity rates. Here, we study the classification of COVID-19 pneumonia and non-COVID-19 pneumonia in chest CT scans using efficient deep learning methods to be readily implemented by any hospital. We report our deep network framework design that encompasses Convolutional Neural Networks and bidirectional Long Short Term Memory architectures. Our study achieved high specificity (COVID-19 pneumonia: 98.3%, non-COVID-19 pneumonia: 96.2% Healthy: 89.3%) and high sensitivity (COVID-19 pneumonia: 84.0%, non-COVID-19 pneumonia: 93.9% Healthy: 94.9%) in classifying COVID-19 pneumonia, non-COVID-19 pneumonia and healthy patients. Next, we provide visual explanations for the Convolutional Neural Network predictions with gradient-weighted class activation mapping (Grad-CAM). The results provided a model explainability by showing that Ground Glass Opacities, indicators of COVID-19 pneumonia disease, were captured by our convolutional neural network. Finally, we have implemented our approach in three hospitals proving its compatibility and efficiency.

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There are various equations and models for assessing erosion for both single phase and multiphase flow scenarios. Consequently, this study was performed to shed light on the issue of which models provide better predictions. For the single phase situation (air-sand flow), Computational Fluid Dynamics (CFD) erosion modeling in a direct impact geometry was conducted, and the performance of four different erosion equations was scrutinized. On the other hand, for the multiphase flow scenario, churn/annular multiphase flow regimes, that can be highly erosive when sand is present within the piping system, were selected to examine the accuracy of six different widely-used multiphase flow erosion models. For both cases, the modeling results were compared to available experimental data. For the first part of the study, ANSYS FLUENT 16.1 was used to perform CFD-based erosion modeling including flow modeling, particle tracking, and erosion calculation. For the other part, several codes were employed to investigate six empirical/mechanistic erosion models including: 1D churn model, 2D mixture model, multiphase model, annular model, DNV GL model, and Salama model. The University of Tulsa SPPS software and the DNV GL Erosion Tool were used for this part. Furthermore, the Salama model was coded. It was revealed that under air-sand flow conditions, Arabnejad et al. (2015a) mechanistic model provided the best results (15% error compared to experimental data). For the multiphase flow part of the study, it was found that regardless of particle size, the multiphase erosion model underpredicts all erosion data, and the 1D churn, Salama and 2D mixture erosion models significantly overpredicted the experimental data. Moreover, the annular and DNV GL models provided the best predictions among all six models. A literature survey reveals that there is a limited number of comparative studies for erosion prediction models. This study, therefore, should be of interest to a broad readership including but not limited to erosion, corrosion, multiphase flow, and flow assurance.

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