PMTFIM: Integrating machine learning with nutrient balance theory to estimate multi-stage paddy fertilization information at field scale over large regions - Housheng Wang; Xiang Gao; Wei Jiang; Xuerong Lang; Xian Hu; Meihua Qiu; Qiankun Guo; Yonghong Liang; Xuelei Wang; Yue Mu; Rui Ren; Ganghua Li; et al. (ISPRS Journal of Photogrammetry and Remote Sensing, 2025)

Enhancing regional-scale simulation accuracy of paddy runoff by coupling water balance models with remote sensing - Housheng Wang; Rui Ren; Xiang Gao; Huazhan Zhang; Wei Jiang; Xuerong Lang; Xian Hu; Xiaosan Jiang; Kun Zhao; Bin Zhang; Zhipeng Liu; Wenjuan Zheng; et al. (Journal of Hydrology, 2025)

The Spatial–Temporal Characteristics of Wave Energy Resource Availability in the China Seas - Rui-Zhe Shen; Cheng-Tao Yi; Yu-Nuo Liu; Lei Wang; Kai Wu; Mu-Yu Chen; Chong-Wei Zheng (Journal of Marine Science and Engineering, 2025)
For coastal nations and regions, wave energy provides a localized energy solution, decreasing dependency on external energy sources and fostering the sustainable development of local economies. Effective wave height occurrence (EWHO) represents the availability of wave energy and is a crucial parameter for site selection for optimal wave energy. This paper systematically analyzes the distribution of EWHO in China seas areas using significant wave height (SWH) data in the fifth generation of ECMWF atmospheric reanalysis (ERA5) and key climate indices. Employing methods such as climate statistical analysis, linear regression, significance testing, and trend analysis, the study highlights the temporal and spatial distribution characteristics, variation trends, and correlations with climate indices of EWHO. This research aims to provide technical assistance and decision support for the development of wave energy at sea. The results indicate the following conclusions: (1) The high EWHO in the China seas is predominantly located in northern Nanhai, southern Donghai, and the eastern waters of the Philippine Islands. The EWHO is highest in winter. (2) The growth trend of EWHO is most notable in the sea area east of the line connecting the Ryukyu Islands, Taiwan, and the northeastern Philippines, peaking in spring and being relatively weak in winter. (3) The correlation between NINO3 and EWHO is most significant in Nanhai and the northeastern waters of the Philippines, peaking in February with correlation coefficients ranging from −0.30 to −0.50.

Distribution, risk evaluation, and source allocation of cesium and strontium in surface soil in a mining city - Jia-li Li; Chun-dan Gan; Xin-yue Du; Xue-ying Yuan; Wen-lin Zhong; Meng-qi Yang; Rui Liu; Xiao-yu Li; Hao Wang; Yu-liang Liao; Zheng Wang; Mu-cheng Xu; et al. (Environmental Geochemistry and Health, 2024)

Social determinants of health and their relation to suboptimal health status in the context of 3PM: a latent profile analysis - Lai Kun Tong; Yue Yi Li; Yong Bing Liu; Mu Rui Zheng; Guang Lei Fu; Mio Leng Au (EPMA Journal, 2024)

Machine learning improves prediction of severity and outcomes of acute pancreatitis: a prospective multi-center cohort study - Jia-Ning Li; Dong Mu; Shi-Cheng Zheng; Wei Tian; Zuo-Yan Wu; Jie Meng; Rui-Feng Wang; Tian-Lei Zheng; Yue-Lun Zhang; John Windsor; Guo-Tao Lu; Dong Wu (Science China Life Sciences, 2023)

Short-term effects of individual exposure to PM2.5 on hospital admissions for myocardial infarction and stroke: a population-based case-crossover study in Guangzhou, China - Jie-Qi Jin; Guo-Zhen Lin; Shuang-Ying Wu; Mu-Rui Zheng; Hui Liu; Xiang-Yi Liu; Min-Qian Yan; Zhao-Yue Chen; Chun-Quan Ou (Environmental Science and Pollution Research, 2023)