Wavelet Transform and Convolutional Neural Network Based Techniques in Combating Sudden Cardiac Death

Sudden cardiac death (SCD) is a global threat that demands our attention and research. Statistics show that 50% of cardiac deaths are sudden cardiac death. Therefore, early cardiac arrhythmia detection may lead to timely and proper treatment, saving lives. We proposed a less complex, fast, and more efficient algorithm that quickly and accurately detects heart abnormalities. Firstly, we carefully examined 23 ECG signals of the patients who died from SCD to detect their arrhythmias. Then, we trained a deep learning model to auto-detect and distinguish the most lethal arrhythmias in SCD: Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF), from Normal Sinus Rhythm (NSR). Our work combined two techniques: Wavelet Transform (WT) and pre-trained Convolutional Neural Network (CNN). WT was used to convert an ECG signal into scalogram and CNN for features extraction and arrhythmias classification. When examined in the MIT-BIH Normal Sinus Rhythm, MIT-BIH Malignant Ventricular Ectopy, and Creighton University Ventricular Tachyarrhythmia databases, the proposed methodology obtained an accuracy of 98.7% and an F-score of 0.9867, despite being less expensive and simple to execute

Element Geochemical Analysis of the Contribution of Aeolian Sand to Suspended Sediment in Desert Stream Flash Floods

The interaction of wind and water in semiarid and arid areas usually leads to low-frequency flash flood events in desert rivers, which have adverse effects on river systems and ecology. In arid zones, many aeolian dune-fields terminate in stream channels and deliver aeolian sand to the channels. Although aeolian processes are common to many desert rivers, whether the aeolian processes contribute to fluvial sediment loss is still unknown. Here, we identified the aeolian-fluvial cycling process responsible for the high rate of suspended sediment transport in the Sudalaer desert stream in the Ordos plateau of China. On the basis of element geochemistry data analysis, we found that aeolian sand was similar to suspended sediment in element composition, which suggests that aeolian sand contributes to suspended sediment in flash floods. Scatter plots of some elements further confirm that aeolian sand is the major source of the suspended sediment. Factor analysis and the relation between some elements and suspended sediment concentration prove that the greater the aeolian process, the higher the suspended sediment concentration and the greater the contribution of aeolian sand to suspended sediment yield. We conclude that aeolian sand is the greatest contributor to flash floods in the Sudalaer desert stream

Social Networks in Online Peer-to-Peer Lending: The Case of Event-Type Ties as Pipes and Prisms

A considerable amount of academic research on crowdfunding has highlighted the importance of online social networks to crowdfunding success. Despite findings from these early studies, the focus of the extant literature has been on more persistent state-type ties such as friendship. In the current research, we examine how borrower-partner and borrower-team event-type ties affect lender behavior and loan success in online peer-to-peer (P2P) lending. Our empirical results using a multilevel mixed effects model reveal that borrower-team networks function as pipes that facilitate the flow of information and prospective lenders while borrower-partner ties function as prisms that signal borrowers’ pressing financial need. Our results highlight the importance of establishing lending teams on crowdfunding platforms to enhance lender contribution

R-L-MS-L Filter Function for CT Image Reconstruction

In X-ray computer tomography (CT), convolution back projection is a fundamental algorithm for CT image reconstruction. As filtering plays an important part in convolution back projection, the choice of filter has a direct impact upon the quality of reconstructed images. Aim at improving reconstructed image quality, a new mixed filter based on the idea of “first weighted average then linear mixing” is designed in this article, denoted by R-L-MS-L. Here, R-L filter is relied on to guarantee the spatial resolution of reconstructed image and S-L filter is processed via 3-point weighted averaging to improve the density resolution, thus enhancing the overall reconstruction quality. Gaussian noise of different coefficients is added to the projection data to contrast the noise performance of the new and traditional mixed filters. The simulation and experiment results show that the new filter is better in anti-noise performance and produces reconstructed images with notably improved quality