Synergizing machine learning and interpolation methods: A Stacking framework for global-scale satellite soil moisture gap filling - Jiaming Rong; Jiangyuan Zeng; Kun-Shan Chen; Hongliang Ma; Pengfei Shi; Husi Letu; Xiang Zhang; Xihui Gu; Haiyun Bi; Chunlin Zhang (Remote Sensing of Environment, 2025)

Enhancing vessel trajectory prediction with bi-state space and squeeze excitation attention - Caiquan Xiong; Jiaming Li; Xinyun Wu; Donghua Liu; Qi Wang; Rong Gao; Jinjia Ruan (Ocean Engineering, 2025)

Global-scale gap filling of satellite soil moisture products: Methods and validation - Chunlin Zhang; Jiangyuan Zeng; Pengfei Shi; Hongliang Ma; Husi Letu; Xiang Zhang; Panshan Wang; Haiyun Bi; Jiaming Rong (Journal of Hydrology, 2025)

Informer-Based Model for Long-Term Ship Trajectory Prediction - Caiquan Xiong; Hao Shi; Jiaming Li; Xinyun Wu; Rong Gao (Journal of Marine Science and Engineering, 2024)
Ship trajectory prediction is a complex time series forecasting problem that necessitates models capable of accurately capturing both long-term trends and short-term fluctuations in vessel movements. While existing deep learning models excel in short-term predictions, they struggle with long-sequence time series forecasting (LSTF) due to difficulties in capturing long-term dependencies, resulting in significant prediction errors. This paper proposes the Informer-TP method, leveraging Automatic Identification System (AIS) data and based on the Informer model, to enhance the ability to capture long-term dependencies, thereby improving the accuracy of long-term ship trajectory predictions. Firstly, AIS data are preprocessed and divided into trajectory segments. Secondly, the time series is separated from the trajectory data in each segment and input into the model. The Informer model is utilized to improve long-term ship trajectory prediction ability, and the output mechanism is adjusted to enable predictions for each segment. Finally, the proposed model’s effectiveness is validated through comparisons with baseline models, and the influence of various sequence lengths Ltoken on the Informer-TP model is explored. Experimental results show that compared with other models, the proposed model exhibits the lowest Mean Squared Error, Mean Absolute Error, and Haversine distance in long-term forecasting, demonstrating that the model can effectively capture long-term dependencies in the trajectories, thereby improving the accuracy of long-term vessel trajectory predictions. This provides an effective and feasible method for ensuring ship navigation safety and advancing intelligent shipping.

An injectable antibacterial hydrogel with bacterial-targeting properties for subcutaneous suppuration treatment - Yongchang Tian; Rong Zhang; Jiaming Cui; Yaowei Zhu; Minghui Sun; Ian W. Hamley; Chunsheng Xiao; Li Chen (Chemical Engineering Journal, 2024)

A novel method for efficiently recycling platinum group metals and copper by Co-smelting spent automobile catalysts with waste-printed circuit boards - Rong Huang; Songshan Zhou; Jiaming Chen; Xiangfei Zeng; Yunhui Han; Jinchuan Qin; Yao Huang; Fan Lin; Xi Yu; Shushu Liao; Zhiqiang Chen; Yubin Han; et al. (Journal of Cleaner Production, 2024)

Using geodetic measurements derived terrestrial water storage to investigate the characteristics of drought in Yunnan, China - Chao Chen; Rong Zou; Zhiwei Fang; Jiaming Cao; Qi Wang (GPS Solutions, 2024)
The use of the global navigation satellite system (GNSS) for monitoring changes in terrestrial water storage (TWS) is growing. However, the density of GNSS stations is sparse in most areas, and the widely used Green’s function (GF) method cannot provide a satisfactory resolution for inversion problems. As the Slepian basis function (SBF) method has been successfully used for gravity inversions, GNSS geodesists have recently applied the SBF method to GNSS displacements. However, the evaluation of TWS differences inferred from GNSS using the SBF and the GF has been rarely assessed. In this study, we use both the GF and the SBF methods to investigate the TWS in Yunnan by using more than ten years (2010–2021) of GNSS observations. We observe a remarkable consistency between the two inversion methods employed for GNSS data, as well as a strong agreement with equivalent water heights (EWH) inferred from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO), hydrological model (GLDAS-NOAH), and precipitation data, despite variations in the amplitude. Furthermore, affected by monsoon climate and topography, Yunnan is prone to drought. We analyze the main nine drought events, evaluate the connection between GNSS-inverted TWS during 2010–2021, and find that almost all droughts in Yunnan occurred during the "trough period" within the GNSS-DSI.

Low genetic differentiation among altitudes in wild Camellia oleifera, a subtropical evergreen hexaploid plant - Xiaomao Huang; Jiaming Chen; Xiaoqiang Yang; Shihua Duan; Chuan Long; Gang Ge; Jun Rong (Tree Genetics & Genomes, 2018)

An efficient method for developing polymorphic microsatellite markers from high-throughput transcriptome sequencing: a case study of hexaploid oil-tea camellia (Camellia oleifera) - Xiangyan Cui; Xiaomao Huang; Jiaming Chen; Xiaoqiang Yang; Jun Rong (Euphytica, 2018)

Rapid and highly efficient callus induction and plant regeneration in the starch-rich duckweed strains of Landoltia punctata - Meng Huang; Lili Fu; Xuepiao Sun; Rong Di; Jiaming Zhang (Acta Physiologiae Plantarum, 2016)