Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data can significantly reduce MR scan times, leading to improved equipment utilization and enhanced patient comfort. Applying federated learning (FL) in MR image reconstruction enables multiple hospitals to collaboratively train a model in a distributed manner without aggregating local data, thereby protecting patient privacy. Prototype-based FL further optimizes communication costs by sharing lightweight representatives as global knowledge for multisite collaboration. However, to our knowledge, existing prototypebased FL methods have not been explored in the context of image generation tasks. Moreover, tackling domain shifts in multi-site MR image data due to differences in sequences, scanners, and diseases presents a significant challenge. To address these issues, we propose FedPMR, the first personalized prototype-based FL framework for accelerated MR image reconstruction. FedPMR collaboratively searches semantic features in the approximate direction across multiple sites and improves generalization of local models by integrating common knowledge. Extensive experiments conducted on three public datasets and one private dataset demonstrate that FedPMR not only reduces communication costs ($236.70 \text{Mb} \rightarrow 8 ~\text{Kb}$), but also achieves approximately a 3.0 % improvement in SSIM and a 4.9 % improvement in PSNR for clients experiencing greater domain shift. Additionally, we explore the optimal selection of collaborative prototypes in image generation, including the effects of location, scale, dimensionality reduction methods, and multi-scale fusion.

Systems safety is becoming increasingly challenging due to the presence of ever-more complex applications. Safety analysis is an important aspect of Safety-Critical Systems Engineering (SCSE) to discover problems in system design that can potentially lead to hazards with risks that may lead to accidents. Performing safety analysis requires significant manual effort — its automation has become the research focus in the critical system domain due to the increasing complexity of systems and the emergence of open adaptive systems. In this paper, we propose a novel methodology in which automated safety analysis drives the design of safety-critical systems. We delve into the specifics of our approach and the supporting tools. Additionally, we discuss the method to integrate our approach into the current practice of SCSE. The experimental results reveal that the proposed approach with its supporting tool promotes the efficiency of safety analysis significantly, whilst maintaining high degrees of correctness, coverage and scalability.

Safety analysis is an important aspect in Safety-Critical Systems Engineering (SCSE) to discover design problems that can potentially lead to hazards and eventually, accidents. Performing safety analysis requires significant manual effort --- its automation has become the research focus in the critical system domain due to the increasing complexity of systems and emergence of open adaptive systems. In this paper, we present a methodology, in which automated safety analysis drives the design of safety-critical systems. We discuss our approach with its tool support and evaluate its applicability. We briefly discuss how our approach fits into current practice of SCSE.

The vertical structure of wind is a key factor in modulating air quality, from which the determination of boundary layer height (BLH) remains a major challenge. In this paper, we developed an improved threshold method to determine the BLH from radar wind profiler (RWP) measurements. The normalized signal-to-noise ratio (SNR) profiles were used instead of the original SNR profiles to avoid instrumental inconsistencies. Additionally, a peak filter was designed to indicate the BLH based on the maximum SNR by taking into account the multiple peaks in the SNR profile. This algorithm was then applied to the RWP measurements taken in the summer (June-July-August) of 2018 in Beijing to obtain the BLHs. Validation analyses suggested that the BLH retrievals from RWP exhibited high consistency with those from radiosondes, with an average correlation coefficient of 0.69 (0.66) and a root mean squared error of 0.39 (0.41) in the daytime (nighttime). Additionally, the major features of summertime BLHs in Beijing were examined. In particular, a distinct diurnal variation in BLH was observed with a peak (1630 ± 510 m) occurring at 0600 universal time coordinated (UTC) and a minimum (587 ± 343 m) at 2300 UTC. Therefore, the algorithm presented here has great potential to be applied to other regions to obtain reliable BLHs. The findings obtained here highlight the importance of vertical wind structure in air quality studies.