High chromium cast iron (HCCI) is widely used in the manufacturing of equipment parts in the fields of mining, cement, electric power, metallurgy, the chemical industry, and paper-making because of its excellent wear and corrosion resistance. Although the microstructure and properties of HCCI can be modified by controlling the casting and heat treatment process, alloying is still the most basic and important method to improve the properties of HCCI. There are about 14 common alloying elements in HCCI, among which nickel, copper, and manganese are typical austenite stabilizing elements, which can increase austenite content and matrix electrode potential. The addition of elements such as silicon, nitrogen, boron, and rare earth (RE) is often small, but it has a significant effect on tailoring the microstructure, thereby improving wear resistance and impact toughness. It was thought that after years of development, the research on the role of the above elements in HCCI was relatively complete, but in the past 5 to 10 years, there has been a lot of new research progress. Moreover, the current development situation of HCCI is still relatively extensive, and there are still many problems regarding the alloying of HCCI to be further studied and solved. In this paper, the research results of austenitic stabilizing elements and modifying elements in HCCI are reviewed. The existing forms, distribution law of these elements in HCCI, and their effects on the microstructure, hardness, wear resistance, and corrosion resistance of HCCI are summarized. Combined with the current research situation, the future research and development direction of HCCI alloying is prospected.

To effectively mitigate failures in interdependent systems during targeted-attack scenarios, a common approach is to pre-store repair resources. The question arises: what constitutes an appropriate quantity of these pre-stored repair resources? The paper introduces a novel recovery strategy aimed at providing guidance for this issue. Current recovery strategies frequently emphasize the dynamic interplay between cascading failures and recovery processes, indicating that interventions during the recovery phase are permissible. In this context, the recovery strategy focus on recovering a predetermined number of failed nodes that are adjacent to the largest connected component of each individual network, along with their dependent nodes, at each recovery stage. Simulation results demonstrate that this strategy significantly enhances the capacity to prevent system breakdowns for interdependent networks subjected to targeted attacks. Therefore, by determining the necessary recovery steps to prevent system failures and the appropriate repair resources required for each step, this novel strategy can serve as a valuable reference for the pre-storage of repair resources. Significantly, the strategy can be effectively applied to interdependent networks associated with critical infrastructure, such as power grids and communication networks.

Citation networks are fundamental for analyzing the mechanisms and patterns of knowledge creation and dissemination. While most studies focus on pairwise attachment between papers, they often overlook compound relational structures, such as co-citation. Combining two key empirical features, superlinear node inflow and the temporal decay of node influence, we propose the Triangular Evolutionary Model of Superlinear Growth and Aging (TEM-SGA). The fitting results demonstrate that the TEM-SGA reproduces key structural properties of real citation networks, including degree distributions, generalized degree distributions, and average clustering coefficients. Further structural analyses reveal that the impact of aging varies with structural scale and depends on the interplay between aging and growth, one manifestation of which is that, as growth accelerates, it increasingly offsets aging-related disruptions. This motivates a degenerate model, the Triangular Evolutionary Model of Superlinear Growth (TEM-SG), which excludes aging. A theoretical analysis shows that its degree and generalized degree distributions follow a power law. By modeling interactions among triadic closure, dynamic expansion, and aging, this study offers insights into citation network evolution and strengthens its theoretical foundation.

Mining relatively important nodes with respect to known significant nodes in complex networks is a pivotal branch of node importance research, with promising applications ranging from epidemic risk inference to disease-gene prioritization. Among existing approaches, integrating network embedding with the gravity model offers an attractive framework, as its multiplicative form aggregates pairwise proximity and influence without iterative convergence. Nevertheless, the aforementioned approaches evaluate the seed set strictly on a per-node basis, thereby neglecting the collective influence of the seed set as an integrated entity—namely, its aggregate multi-hop reachability relative to a given candidate node. To address these issues, this paper proposes NSPG (Network Semantic PageRank Gravity), which refines the gravity model in two aspects. First, it replaces mass construction with a temperature-scaled exponential of the cosine similarity. This term is derived from the same embedding geometry as the Euclidean distance in the denominator, yet it captures only the relative aspect of similarity, while the denominator captures the absolute aspect. Second, it employs personalized PageRank as a seed-conditioned gravitational coefficient, which quantifies the candidate node’s aggregate multi-hop proximity to the seed set as a whole. Experiments on undirected, unweighted, static networks show that NSPG achieves superior or comparable performance. Moreover, the model remains robust across various experimental settings, owing to the complementarity of the numerator and denominator: they form a static embedding-geometric layer that anchors the scores, while the PPR coefficient acts as a seed-conditioned layer that adapts to seed variation.