Aerodynamic analysis of Speedo Fastskin-I Swimsuit

Swimming is one of the most energy intensive sporting events, where a winner is decided by a short margin. The winning time margin can be increased by various means, including engineered outfits within the game's regulations. In swimming, apart from optimisation of the swimmer's body, an appropriately devised swimsuit can play a significant role in reducing the drag, thereby enhancing the winning time margin. The main motivation for undertaking this study stems from the increasing levels of technical sophistication in the swimsuits that are claimed by the manufacturers for performance enhancement. Therefore, the goal of this paper is to undertake an experimental study with microscopic illustration of the swimsuit fabric, and its effects on aerodynamic properties. The study utilised a commercial swimsuit under stretched and un-stretched conditions of fabric morphology, and their impact on aerodynamic drag. This study was conducted using a wind tunnel for a range of Reynolds numbers. The simplified body shape was used to determine the aerodynamic drag. The finding of this study illustrates that there is a significant difference between the aerodynamic drag for the stretched and un-stretched surface morphology of the Speedo FS-I swimsuit. Furthermore, the microscopic analysis of the stretched and un-stretched fabric was undertaken to extend our undertstanding

Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray

Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon in the right time and thus an early diagnosis of pneumonia is vital. The aim of this paper is to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances made in making accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. 5247 Bacterial, viral and normal chest x-rays images underwent preprocessing techniques and the modified images were trained for the transfer learning based classification task. In this work, the authors have reported three schemes of classifications: normal vs pneumonia, bacterial vs viral pneumonia and normal, bacterial and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3% respectively. This is the highest accuracy in any scheme than the accuracies reported in the literature. Therefore, the proposed study can be useful in faster-diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.Comment: 13 Figures, 5 tables. arXiv admin note: text overlap with arXiv:2003.1314