Addressing the shortcomings of the Sparrow Search Algorithm (SSA), such as low accuracy of convergence and tendency of falling into local optimum, a Multi-strategy Integrated Sparrow Search Algorithm (MISSA) is proposed. In this method, by improving the black-winged kite algorithm and applying it to the producer’s position update formula, an improved search strategy (ISS) is firstly proposed to enhance search ability. Secondly, a new strategy inspired by the Coot algorithm, called the group follow strategy (GFS), is proposed to improve the ability to jump out of the local optimum. Finally, a proposed random opposition-based learning strategy (ROBLS) is applied to the population after each iteration to enhance its diversity. To verify MISSA’s effectiveness, extensive testing is conducted on 24 benchmark functions as well as CEC 2017 functions. The experimental results, complemented by Wilcoxon rank-sum tests, conclusively demonstrate that MISSA outperforms SSA and other advanced optimization algorithms, exhibiting superior overall performance.

Network embedding algorithms learn a mapping from the discrete representation of nodes to continuous vector spaces that preserve the proximities of nodes. The techniques have a wide range of applications in various downstream tasks such as node classification, link prediction, and network alignment. Despite recent efforts to the design of novel models, little attention has been paid to understanding the instability of network embedding. In this paper, we fill this gap by investigating several state-of-the-art network embedding methods. Node embeddings form a geometric shape in the latent space. Characterizing the geometry is critical to figure out the variance of network embedding. Hence, we define two metrics to characterize the geometric properties and find that node embeddings tremble in different instantiations of an embedding space. Then, we formally define the stability of node embeddings as the invariance of the nearest neighbors of nodes. Experimental results show that existing embedding approaches have significant amounts of instability. We explore the influence factors that affect the stability of different methods and find that both network structure and algorithm models affect the stability of node embeddings significantly. Finally, we examine the implications of embedding instability for downstream tasks and find remarkable impacts on the performance.

Rocks on the Martian surface exhibit signatures of geological activities. Rock distribution is also an important factor for landing site selection for exploration missions. This study investigated the rock abundance and erosion rate at the landing site of the Chinese Mars rover, Zhurong, in southern Utopia Planitia. A convolutional neural network based method was developed for rock detection from the images (0.7 m/pixel) obtained by using the high‐resolution imaging camera (HiRIC) onboard the Tianwen‐1 orbiter. Approximately 10,000 rocks with a minimum diameter of 1.4 m (2 pixels on the image) were extracted from the HiRIC images covering the Zhurong landing site and its neighboring region. According to the results, the rock abundance at the Zhurong landing site was approximately 5%. Measurements from the ground images acquired by the Zhurong rover after landing confirmed a rock abundance of approximately 5% at the landing site, consistent with the orbital measurement before landing. Furthermore, we investigated the rock erosion rate at the Zhurong landing region. Rock abundance was negatively correlated with the crater retention age and positively correlated with crater diameter. The obtained rock breakdown rate (0.054 nm/y to 0.074 nm/y) and surface denudation rate by filled volume of craters (0.104 nm/y to 0.209 nm/y) were in agreement with the values previously reported for the Amazonian period on Mars. The low erosion rate implies a dry climate and the absence of fluvial erosion processes in the Amazonian period.

This paper performs numerical simulation on the decompression process of a Mars rover using FLUENT. The pressure differential between the inside and outside of the Mars rover resulting from changes in ambient pressure of the rocket fairing is investigated. In terms of numerical simulation, PROFILE outlet boundary conditions are developed and the impacts of ambient pressure settings, time steps, and mesh density are investigated to improve the accuracy of simulation results. The decompression process of the separate large module, large and small modules under two types of ambient pressures are simulated. The results show that the largest pressure differential between the inside and outside of the module body is less than 2200 Pa. Because of the small size of the small module, the results for the separate large module and the large/small modules are consistent. The pressure differential between the inside and outside of the rover is mainly influenced by the variation in ambient pressure.