Nanoparticle additions promote outstanding fracture toughness and fatigue strength in a cast Al–Cu alloy

Fatigue resistance and fracture toughness are essential to the reliability of castings during service. Reducing microporosity in cast alloys is a crucial issue for improving their fatigue strength and fracture toughness. Here we report a significant reduction in microporosity of a cast aluminum-copper alloy developed using in situ TiC nanoparticle additions coupled with a stir-casting method. The addition of TiC nanoparticles is found to improve the interdendritic feedability of liquid metal and as a result reduces the volume fraction of microporosity by two orders of magnitude compared to a conventionally cast Al–Cu alloy. This method, which is mechanistically associated with a pore-dominated process, acts to significantly improve the fatigue strength, fracture toughness and uniform elongation of the cast Al–Cu alloy to values which, to the best of our knowledge, represent the highest ever achieved for a cast aluminum alloy

NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization

For iris recognition in non-cooperative environments, iris segmentation has been regarded as the first most important challenge still open to the biometric community, affecting all downstream tasks from normalization to recognition. In recent years, deep learning technologies have gained significant popularity among various computer vision tasks and also been introduced in iris biometrics, especially iris segmentation. To investigate recent developments and attract more interest of researchers in the iris segmentation method, we organized the 2021 NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization (NIR-ISL 2021) at the 2021 International Joint Conference on Biometrics (IJCB 2021). The challenge was used as a public platform to assess the performance of iris segmentation and localization methods on Asian and African NIR iris images captured in non-cooperative environments. The three best-performing entries achieved solid and satisfactory iris segmentation and localization results in most cases, and their code and models have been made publicly available for reproducibility research