SAIC-GM (China)

Abstract Linear gyrokinetic simulations have been performed using the GTC code to investigate the effects of fast ions on dominant instability changes from TEM to ITG in the EAST. The effects of fractions (5%, 7.5%, and 10%) and temperatures (10 keV, 20 keV, and 30 keV) of fast ions are investigated. The dominant mode transitions from trapped electron mode (TEM) to ion temperature gradient (ITG) mode under different plasma parameters. A transition of the dominant instability is observed at 0.7 ≤ kθρs ≤ 0.8 and shifts to kθρs ≈ 0.7 when fast ions are considered. In addition, fast ions stabilize ITG modes, and the effect becomes more significant as the fraction of fast ions increases, reducing the growth rate by approximately 10% when the fraction of fast ions is 10%. The stabilizing effect increases with increasing temperature of fast ions, but varies little for Tf > 20 keV. Fast ions reduce the ion temperature gradient R/LTi threshold of the dominant instability changes from TEM to ITG. The threshold decreases with decreasing temperature of fast ions, with a reduction of about 10%-20% when the fraction reaches 10% in hydrogen plasmas, and about 3%-12% when deuterium is the main ion. Furthermore, the normalized electron temperature gradient R/LTe for the dominant instability changes from ITG to TEM is lower in hydrogen plasmas, decreasing from 5.4 to 4.1 as Tf increases from 10 keV to 30 keV, while in deuterium plasmas it decreases from 10.5 to 9.5. Moreover, electron–electron collisions lead to the dominant instability changes from TEM to ITG at 0.4 ≤ kθρs ≤ 0.8 and strongly stabilize the ITG mode, reducing its growth rate about 50%, while ion–ion collisions have little influence on TEM and ITG.

The powerful computing capabilities of supercomputers enable them to execute a wide variety of high performance computing (HPC) applications. However, when executing HPC applications, the low hardware utilization prevents the efficiency of supercomputers from being fully exploited. Performance analysis is an effective way to discover performance bottlenecks. Existing research focuses on the profiling or modeling of HPC application performance, lacking systematic automatic performance analysis and tuning methods. To address the above challenge, in this paper, we propose a composable suite for HPC applications that can achieve low-overhead performance profiling, fine-grained performance modeling and automatic performance tuning. Specifically, a low-overhead profiling method is first proposed to accurately characterize the performance of HPC applications while minimizing the interference of measurement. Then, fine-grained performance models are built to predict the performance of HPC applications based on the profiling sampling. Finally, according to the performance models, an automatic tuning method is designed to improve the performance of coupled HPC applications. The experimental results show that the overhead of our profiling method is less than 15% for the benchmarks and the real-world applications. The average relative error of our modeling method is less than 10%. The optimal layouts contribute to the total running time savings of 16.33% and the total computing fee savings of 31.44%. The effectiveness of our methods is superior to the baselines.

The biomimetic mechanisms of cat tongue licking and bristle adhesion of fly legs are explored, and a flexible end effector scheme for manipulators with non-sensory agile adhesion is proposed. Experimental methods are used to prepare high aspect ratio magnetic control cilia array structures based on magnetically controlled nanoparticles, microscopic adhesion surface structures where the base material and magnetic fluid are integrated, and high-voltage electrostatic flexible adhesion film structures that can be dissipated for use. Three types of actuators, namely magnetic control cilia adhesion device, magnetic fluid microscopic surface adhesion device, and electrostatic flexible film adhesion device, are innovatively developed, providing key technical support for flexible end effectors of manipulators in space environments.

Multi-agent collaborative perception mitigates the issues of limited perception range and occlusion in single-vehicle perception through shared sensing information, resulting in improved perception performance. However, current collaborative strategies often fail to differentiate moving objects from the background in complex scenarios. This leads to critical object data being obscured by excessive background information during data fusion, complicating effective integration. Additionally, efficiently leveraging historical perception data remains a significant challenge. To address these issues, we propose a parameter-free motion attribute extraction module that captures motion features between consecutive frames, enabling more efficient utilization of historical information to enhance current perception. Specifically, we design two temporal modules to comprehensively explore both ego and collaborators’ historical information. Furthermore, we propose a method that simultaneously considers the relationships between global appearance features and local motion features of shared information, thereby enabling better integration of foreground object data during collaboration. We validate our algorithm through experiments conducted on both simulated and real-world collaborative benchmarks. Both qualitative and quantitative results demonstrate that our proposed MHCPP significantly outperforms state-of-the-art (SOTA) methods in terms of 3D object detection and prediction tasks.

Abstract The extreme high temperature in western North America (WNA) exerts profound impacts on industrial and agricultural production, and trigger catastrophic wildfires. Exploring the underlying mechanisms influencing extreme hot days over WNA (WEHDs) and improving the seasonal prediction are of great scientific and social significance. This study reveals that two independent precursor signals, the persistent negative sea surface temperature (SST) anomalies in tropical eastern Pacific and the cooling tendency in tropical North Atlantic SST during springtime exhibit significant influence on WEHDs. A physics‐based empirical model constructed using these two predictors exhibits robust independent prediction skills. Guided by the underlying physical mechanisms, we integrate SST tendency fields as critical input features into convolutional neural network (CNN) to further enhance the prediction accuracy. The physically informed CNN achieves significantly improved performance and successfully predicts the extreme WEHD events of 2021. The results emphasize the pivotal role of physical cognition in advancing deep learning‐based climate prediction.