Tunicamycin-induced endoplasmic reticulum stress triggers ZBP1-Mediated PANoptosis in macrophages and mouse spleen - Hongkun Jiang; Chen Qian; Xiaoting Lin; Ximei Huang; Zila Wang; Faquan Lin (Molecular Biology Reports, 2026)

Storm surge forecasting using TCWiSE synthetic typhoons with an LSTM model - Cheng Chen; Jianxiang Mo; Hongkun Lin; Fengyi Li (Ocean Engineering, 2026)

Linking Pore Structure Parameters with Coal Strength and Initial Gas Desorption for Outburst Risk Identification: A Preliminary, Region-Specific Threshold Framework - Hongkun Wang; Lin Hong; Dameng Gao; Dan Zheng; Ye Zhang (Rock Mechanics and Rock Engineering, 2026)

Pressure-Driven Pore Structure Evolution in Coal: Insights from Low-Field NMR - Lin Hong; Hongkun Wang; Dan Zheng; Dameng Gao (Rock Mechanics and Rock Engineering, 2026)

Optimizing typhoon wave height forecasting with LSTM model using SWAN-BP model output and TCWiSE synthetic typhoons - Hongkun Lin; Zhiqiang Song; Cheng Chen (Ocean Engineering, 2026)

Reduction mediated iodide antioxidation: “Chemical Preservation” of precursor solution facilitates construction of high efficiency and stable wide bandgap inverted perovskite solar cells - Chao Liu; Yingchen Li; Hongkun Cai; Boyan Li; Shuping Lin; Qi Sun; Dalong Zhong; Jifeng Liu; Qinwen Guo; Zhiwen Xu; Juan Li; Jian Ni; et al. (Chemical Engineering Journal, 2026)

Ketamine's brain spatial distribution and metabolic effects in a mouse model of anxiety: Insight into in situ mass spectrometry imaging and metabolomics methods - Hong Yang; Weihao Fan; Xinyu Yang; Ying Wei; Li Xiao; Hongkun Yang; Linzhi Jiang; Jian Li; Kaiting Shi; Shuang Zhao; Lin Yang; Yi Ye; et al. (NeuroToxicology, 2026)

Engineering odor control in algal foods: Machine learning for quality enhancement - Yilan Sun; Qinhua Zhang; Hongkun Lin; Juehan Lu; Huiyue Zhang; Che Su; Shiguo Huang; Jie Pang; Xiaolin Li (Journal of Food Engineering, 2026)

Spatiotemporal dynamics of sea ice volume and their potential drivers in Liaodong Bay, China, using Sentinel-3 data over five ice seasons - Hongkun Ling; Jialing Wang; Lintai Zheng; Yue Lin; Jiashen Tian; Zhen Wang; Zhichuang Lu; Jiabo Han; Wenhua Liu; Liangliang Yang (Marine Geodesy, 2025)

Estimation of rice yield using multi-source remote sensing data combined with crop growth model and deep learning algorithm - Jian Lu; Jian Li; Hongkun Fu; Wenlong Zou; Junrui Kang; Haiwei Yu; Xinglei Lin (Agricultural and Forest Meteorology, 2025)

Effect of metal-modified sewage sludge biochar tubule on immobilization of chromium in unsaturated soil: Groundwater table fluctuations induced by rainfall - Lin Chen; Yinger Deng; Pengjie Li; Hongkun Yang; Hu Su; Ning Wang; Rui Yang (Chemosphere, 2024)

Enhancing typhoon wave hindcasting with random forests and BP neural networks in the SWAN model - Cheng Chen; Hongkun Lin; Dawei Guan; Feng Cai; Qiaoyi Wang; Qingchun Liu (Frontiers in Marine Science, 2024)
Forecasting typhoon waves during typhoons is crucial. In this paper, the numerical wave model SWAN was enhanced through integration with two machine learning methods: the Back Propagation Neural Network and Random Forest. This integration facilitated the development of two distinct models, namely SWAN-BP and SWAN-Tree. Through correlation analysis, key input features were identified for the machine learning models. The forecasts from the SWAN model were subsequently utilized as inputs to enhance further wave prediction. These hybrid models were validated using data from Typhoon Doksuri (2023) and Typhoon Nesat (2017). The results indicated significant improvements in predicting typhoon-induced wave heights with both the SWAN-BP and SWAN-Tree models compared to the original SWAN model. Specifically, the SWAN-BP model demonstrated a 33% improvement in accuracy for the Typhoon Doksuri, whereas the SWAN-Tree model exhibited a 24% improvement. For Typhoon Nesat, the accuracy improvements were 23% for the SWAN-BP model and 21% for the SWAN-Tree model. These findings demonstrate that integrating wave numerical models with machine learning techniques can significantly enhance the predictive accuracy of numerical models. This approach offers a cost-effective means to improve the existing wave forecasting database. Traditionally, the direct use of meteorological and oceanographic data for typhoon wave prediction might be compromised by biases inherent in the numerical wave models. However, the SWAN-BP and SWAN-Tree models effectively reduce these biases, thereby providing more accurate and robust predictions. In conclusion, this paper enhances the predictive accuracy of the SWAN model and establishes a crucial foundation for more precise typhoon wave forecasting through the application of machine learning techniques.

The study on the shortcut approach for cooperative disposal of arsenic-rich copper dust and waste acid to prepare high-purity copper - Feng Yan; Hongkun Dai; Yu Wang; Maoguang Ruan; Shanrong Zhao; Xiaohua Yu; Yan Lin (Journal of Cleaner Production, 2024)

Delays in heading and improvements in both spikelet number and spike length are associated with the Aegilops tausschii photoperiod-sensitive ppd-D1b allele - Shunzong Ning; Laibin Zhao; Shijie Li; Shengke Li; Tianqing Zang; Yu’e Liu; Hongkun Yang; Xuejiao Chen; Xue Chen; Yingjin Yi; Ming Hao; Lianquan Zhang; et al. (Cereal Research Communications, 2023)