Fluctuations in groundwater inflow induced by periodic fracturing of overlying strata with mining - Shuning Dong; Zhenfang Zhou; Yang Dong; Qiang Ji; Jiankun Xue; Tiantian Wang; Yutong Wang; Le Han; Peiqiang Gao; Shenghu Luo; Yingying Yao (Bulletin of Engineering Geology and the Environment, 2026)

Analysis of Rockfall Motion Characteristics Based on a 3D DDA Stochastic Model: A Case Study of the Houyu Tunnel in Fujian Province - Lu Zheng; Zhiyuan Zhu; Yihan Wu; Shuning Zhang; Zhiyuan Li; Zishuang Han; Manchao He; Guangqi Chen (Rock Mechanics and Rock Engineering, 2026)

Revealing Structural Brain-Cognition Relationships in Children: A Comparison of Morphometric Similarity and INverse Divergence Networks - Shuning Han; Hao Jia; Gemma Vilaseca; Núria Vilaró; Feng Duan; Zhe Sun; Cesar F. Caiafa; Jordi Solé-Casals (Neuroinformatics, 2026)
The study of structural brain networks (SBNs) offers critical insights into brain-cognition relationships. However, a comprehensive comparison of these methods in terms of their topological properties, cognitive relevance, and sensitivity to connection density remains lacking. This study compares two types of individual-level SBNs–morphometric similarity networks (MSNs) and morphometric inverse divergence (MIND) networks–by analyzing their associations with cognitive performance using sMRI data from 29 male children. Group- and individual-level analyses were conducted to evaluate differences in hemispheric connectivity, topological features, and their correlations with cognitive performance across different connection densities. In our analyses, a connection density of $$p=0.05\sim 0.15$$ appeared optimal for stabilizing network properties and maximizing cognitive correlations in both MSN and MIND. Moreover, advanced network segregation and integration metrics (such as local efficiency and node versatility, along with their global summaries) demonstrated greater sensitivity to cognitive performance. However, MSNs appeared to provide a more reliable framework, demonstrating more stable associations across connection densities in topological and hemispheric dimensions. Specifically, higher cognitive performance may be linked to stronger left intra-hemispheric connectivity, weaker inter-hemispheric connectivity, and more modular network organization–consistent with established theories of hemispheric specialization and efficient modularity. In contrast, MIND networks exhibit reduced effectiveness and stability across metrics and densities in our data. These preliminary insights enhance our understanding of brain-cognition relationships and provide practical guidelines for parameter selection and metric identification in network-based cognitive analyses.

Corrigendum to “Effects of carboxymethyl chitosan oligosaccharide on regulating immunologic function and inhibiting tumor growth” [Carbohydrate Polymers 250 (2020) 116994] - Zhiwen Jiang; Shuning Wang; Jun Hou; Jinhua Chi; Shuo Wang; Kai Shao; Wanshun Liu; Rongju Sun; Baoqin Han (Carbohydrate Polymers, 2025)

A resonance energy transfer electrochemiluminescence sensor based on the synergistic effect of luminol@β-cyclodextrin and Ni-MOF@Pt NPs for the oxytetracycline detection - Li Tian; Shuning Yang; Guangping Ma; Pengfei Han; Hanyue Jiang; Wenzhuo Wang; Juan Lu (Food Chemistry, 2025)

Highly efficient removal of Aflatoxin B1 employing a flower-like covalent organic framework-based fiber membrane - Shuning Li; Kang Qin; Yixue Fu; Defu He; Dianpeng Han; Shuang Li; Yu Wang; Shuyue Ren; Yuan Peng; Zhixian Gao (Journal of Environmental Chemical Engineering, 2023)