Microbial mechanisms mediate divergent ecosystem responses to nitrogen and phosphorus enrichment - Zehao Lv; Gangsheng Wang; Daifeng Xiang; Xiankai Lu; Mingkai Jiang; Donghai Wu; Qinggong Mao; Shuhao Zhou; Shanshan Qi; Zirui Mu; Yilei Zhong; Mianhai Zheng; et al. (Soil Biology and Biochemistry, 2026)

A satellite-based assessment of alder pollen exposure across 51 cities in Bavaria, Germany - Zirui Tang; Yang Mu; Monica Gonzalez-Alonso; Qingyu Li (Urban Forestry & Urban Greening, 2026)

Nonlinear microbial thresholds drive non-additive soil greenhouse gas responses to compound climate extremes - Zehao Lv; Gangsheng Wang; Daifeng Xiang; Shuhao Zhou; Wanyu Li; Shanshan Qi; Yajing Han; Zirui Mu; Yilei Zhong; Shuang Li; Yuhao Pan; Jiahe Liu (Agricultural and Forest Meteorology, 2026)

Target Detection in Ship Remote Sensing Images Considering Cloud and Fog Occlusion - Xiaopeng Shao; Zirui Wang; Yang Yang; Shaojie Zheng; Jianwu Mu (Journal of Marine Science and Engineering, 2026)
The recognition of targets in ship remote sensing images is crucial for ship collision avoidance, military reconnaissance, and emergency rescue. However, climatic factors such as clouds and fog can obscure and blur remote sensing image targets, leading to missed and false detections in target detection. Therefore, it is necessary to study ship remote sensing target detection that considers the impact of cloud and fog occlusion. Due to the large scale and vast amount of information in remote sensing images, in order to achieve high-precision target detection based on limited resource platforms, a comparison of the detection accuracy and parameter quantity of the YOLO series algorithms was first conducted. Based on the analysis results, the YOLOv8s network model with the least number of parameters while ensuring detection accuracy was selected for lightweight network model improvement. The FasterNet was utilized to replace the backbone feature extraction network of YOLOv8s, and the detection accuracy and lightweight level of the resulting FN-YOLOv8s network model were both improved. Furthermore, structural improvements were made to the AOD-Net dehazing network. By introducing a smoothness loss function, the halo artifacts often generated during the image dehazing process were addressed. Meanwhile, by integrating the atmospheric light value and transmittance, the accumulation error was effectively reduced, significantly enhancing the dehazing effect of remote sensing images.

Coupled natural-social water system in a subwatershed of the Tennessee River Basin - Zirui Mu; Shanshan Qi; Gangsheng Wang (Science of The Total Environment, 2024)

Physiological characteristics, rhizosphere soil properties, and root-related microbial communities of Trifolium repens L. in response to Pb toxicity - Meiqi Mu; Zicheng Wang; Zirui Chen; Yuchen Wu; Wanting Nie; Siwen Zhao; Xiujie Yin; Xiaohua Teng (Science of The Total Environment, 2024)

A comprehensive observation on the pollution characteristics of peroxyacetyl nitrate (PAN) in Beijing, China - Hailiang Zhang; Shengrui Tong; Wenqian Zhang; Yanyong Xu; Mingzhu Zhai; Yucong Guo; Xin Li; Lili Wang; Guiqian Tang; Zirui Liu; Bo Hu; Chengtang Liu; et al. (Science of The Total Environment, 2023)

Artificial intelligence–based method for the rapid detection of fish parasites (Ichthyophthirius multifiliis, Gyrodactylus kobayashii, and Argulus japonicus) - Jiadong Li; Zirui Lian; Zhelin Wu; Lihua Zeng; Liangliang Mu; Ye Yuan; Hao Bai; Zheng Guo; Kangsen Mai; Xiao Tu; Jianmin Ye (Aquaculture, 2023)