Detecting flood-induced changes using synthetic aperture radar (SAR) is crucial for crisis management and damage assessment. Nevertheless, current methodologies predominantly focus on changes in buildings within optical images, struggling with the complex structures of floods. These structures are marked by widespread speckle noise and are accompanied by an increase in computational cost. These challenges hinder their success in real-world applications, necessitating a novel approach. This article proposes LiST-Net, a lightweight SAR transformer network with dimension-wise attention to improve flood detection accuracy. LiST-Net offers three key advantages. First, the graph neighbor module (GNM) is designed to enhance both detailed information of neighboring pixels and multidate features within the encoder. Second, the dimension-wise interactive attention (DIA) module is proposed to effectively reduce computational complexity while enhancing feature representation. Third, an attentive supervised learning module (ASLM) is incorporated to mitigate noise through a pixel mask gate, allowing change water information to pass through and improving the accuracy of water edge delineation. The effectiveness of LiST-Net is evaluated on two flood detection datasets, S1GFloods and ETCI-2021. Experimental results demonstrate that LiST-Net outperforms existing methods, showcasing a 94.7% improvement in $F1$ and an 88.7% enhancement in intersection over union (IoU) on the S1GFloods datasets, with lower computational costs (11.78G) and fewer parameters (7.34M). This underscores LiST-Net as a promising strategy for precise and effective mapping of floods within SAR images in real-world applications. A public release of the demo code will be available at https://github.com/Tamer-Saleh .

Background: The aim of this study was to compare the short-term outcomes of mechanical valve replacement versus tissue valve replacement in cases of rheumatic mitral valve disease in Minia governorate in Upper Egypt known to have a relatively low socioeconomic status. Methods: Over the period of 12 months January 2013 and January 2014, 60 patients with rheumatic mitral valve diseases (stenosis, regurgitation or both) were admitted to Cardiothoracic Surgery Unit, Minia University Hospital, and underwent MVR. These patients were classified into two groups; group A (30 patients underwent MVR with mechanical valves, female to male ratio was 2:1 and mean age was 39.6 ± 10 years), and group B (30 patients underwent MVR with tissue valves, female to male was 4:1 and mean age was 38 ± 10.5 years). Results: There was no overall significant difference between the two groups regarding preoperative patient characteristics, perioperative and postoperative outcomes except for thrombo-embolic complications. Six patients (20%) developed postoperative complications in group A and four patients (13.3%) in group B. Cardiac tamponade rates were similar in mechanical and tissue valve recipients (3.3%). Thromboembolism rates, prolonged ventilation, new-onset of arrhythmia, wound infection; bleeding rates and low cardiac output state (LCOS) were significantly higher in mechanical valve recipients. Conclusion: Tissue mitral valve offers excellent early postoperative results and less complication rate than mechanical mitral valve. The EOA is significantly bigger in the tissue mitral valve in sizes 27–29 thus offering less patient prosthesis mismatch. Tissue valves are suitable for populations with lower socioeconomic status as Minia Governorate