Development and experimental assessment of continuous-flow radio frequency enzyme inactivation systems using a multiphysics model of an S-shaped tube - Bolin Fu; Shulong Chu; Xuepeng Yuan; Shaojin Wang; Lixia Hou (Journal of Food Engineering, 2027)

Fusion of hyperspectral and multisource remote sensing data using intelligent optimized ensemble learning to estimate the SOC content of seven mangrove species in China - Jun Sun; Weiguo Jiang; Ziyan Ling; Peng Hou; Zhijie Xiao; Zhe Yang; Ze Zhang; Bolin Fu; Lu Liu; Yuyang Li; Xu He (ISPRS Journal of Photogrammetry and Remote Sensing, 2026)

Underestimated climate contributions: Dynamic attribution of vegetation changes in China incorporating soil moisture and vapor pressure deficit - Hui Zheng; Yuhang Huang; Rong Li; Xiaoshan Yin; Bolin Fu; Junling Jin (Agricultural and Forest Meteorology, 2026)

Thermally driven self-assembly of triterpenoid-fructan supramolecular particles contributes significantly to the efficacy of Zexie Tang against MASH - Senjie Chen; Yongbin Xu; Dewen Liu; Lijuan Xie; Chenyang Zhou; Bolin Wu; Lingyan Zhu; Guosheng Fu; Siqi Gao; Wei Liu; Caihua Sun; Luping Qin; et al. (Carbohydrate Polymers, 2026)

Safety evaluation of pasteurized Akkermansia muciniphila YGMCC2645 - Jie Tian; Zhili He; Minlei Zhao; Xianghong Ke; Yinjing Zhang; Jingjing Qu; Ying Xia; Shaohua Fu; Wenxiang Yang; Lanjie Pei; Jun Fan; Jianguo Chen; et al. (Food and Chemical Toxicology, 2026)

A large-scale framework for deriving tidal flat topography from SWOT data - Hao Xu; Nan Xu; Wenyu Li; Kai Tan; Chunpeng Chen; Huan Li; Lucheng Zhan; Pei Xin; Jiaqi Yao; Peng Li; Zhen Zhang; Haipeng Zhao; et al. (Remote Sensing of Environment, 2026)

Synergistic construction of an annual 2 m mangrove species dataset from 2016 to 2023 using structural features and hybrid stacked deep learning models—Beibu Gulf, Guangxi, China - Jun Sun; Weiguo Jiang; Ziyan Ling; Bolin Fu; Ze Zhang; Zhijie Xiao; Xingling Mu (ISPRS Journal of Photogrammetry and Remote Sensing, 2025)

Cross-scenario transfer learning for estimating mangrove nitrogen and phosphorus content from field hyperspectral data to SDGSAT-1 and Sentinel-2 images - Bolin Fu; Yan Wu; Li Zhang; Weiwei Sun; Yeqiao Wang; Tengfang Deng; Hongchang He; Keyue Huang (Remote Sensing of Environment, 2025)

Safety evaluation of Nannochloropsis gaditana oil as a novel food - Jingjing Qu; Taiping Zhu; Xiaoya Du; Yingjing Zhang; Jie Tian; Ying Xia; Xianghong Ke; Shaohua Fu; Bolin Fan (Regulatory Toxicology and Pharmacology, 2025)

Integrating remote sensing and mechanistic model for spatial evaluation of shelterbelt porosity and windbreak effectiveness in agricultural landscapes - Xing Zhang; Bolin Fu; Rongxin Deng; Ying Li; Jingwen Li; Jianwu Jiang; Jing Tang (Agricultural and Forest Meteorology, 2025)

Improving RF heating performance for granular products through vibration assistance - Kangning Yang; Zhiyu Chen; Bolin Fu; Shaojin Wang; Lixia Hou (Innovative Food Science & Emerging Technologies, 2025)

Numerical analysis of thermal behavior of CMC solutions in various S-shaped tubes during radio frequency pasteurization - Shulong Chu; Bolin Fu; Qingshi Zhang; Shaojin Wang; Lixia Hou (Innovative Food Science & Emerging Technologies, 2025)

A novel hybrid machine learning approach for accurate retrieval of ocean surface chlorophyll-a across oligotrophic to eutrophic waters - Ting Qin; Tianlong Liang; Donglin Fan; Hongchang He; Guiwen Lan; Bolin Fu (Environmental Research, 2025)

Green synthesis of silver nanoparticles using persimmon polysaccharides for enhanced polysaccharide-based film performance - Yadong Zhu; Xiaohui Pang; Wenlin Zhang; Chen Zhang; Bolin Zhang; Jianmin Fu; Hongfei Zhao; Weijuan Han (Food Research International, 2025)

Accurately estimating and revealing the patterns of leaf C:N:P stoichiometry with remote sensing and neural network methods in a karst area - Wen He; Yuefeng Yao; Yanqiong Li; Ling Yu; Longkang Ni; Bolin Fu; Jinjun Huang; Donxing Li (Computers and Electronics in Agriculture, 2025)

Safety evaluation of alpha-glycerylphosphorylcholine as a novel food - Jie Tian; Xianghong Ke; Yinjing Zhang; Jingjing Qu; Shaohua Fu; Ying Xia; Wenxiang Yang; Yanhua Zeng; Jun Fan; Yanmei Li; Bolin Fan (Food and Chemical Toxicology, 2025)

Reversal of gentamicin sulfate resistance in avian pathogenic Escherichia coli by matrine combined with berberine hydrochloride - Jinwu Meng; Jinxue Ding; Weiran Wang; Bolin Gu; Fanting Zhou; Desheng Wu; Xiang Fu; Jiaguo Liu (Archives of Microbiology, 2024)

A novel hierarchical approach to insight to spectral characteristics in surface water of karst wetlands and estimate its non-optically active parameters using field hyperspectral data - Bolin Fu; Sunzhe Li; Zhinan Lao; Yingying Wei; Kaishan Song; Tengfang Deng; Yeqiao Wang (Water Research, 2024)

Quantifying vegetation species functional traits along hydrologic gradients in karst wetland based on 3D mapping with UAV hyperspectral point cloud - Bolin Fu; Liwei Deng; Weiwei Sun; Hongchang He; Huajian Li; Yong Wang; Yeqiao Wang (Remote Sensing of Environment, 2024)

Exploring the hydrologic regulation and pollutant release of complex paddy systems in a large-scale agricultural catchment - Weixiang Li; Ziqian Zhu; Weiping Xiong; Bolin Zhou; Hongliang Hu; Jing Tong; Xin Li; Xiang Gao; Shuai Li; Wenxin Fu; Lan Lu; Zhaohui Yang; et al. (Journal of Hydrology, 2024)

Interlayer manipulation of bio-inspired Ti3C2Tx nanocontainer through intercalation of amino acid molecules to dramatically boosting uranyl hijacking capability from seawater - Chang Liu; Ye Li; Miao Lei; Dongxue Liu; Bolin Li; Chengbin Fu; Junpeng Guo (Journal of Hazardous Materials, 2024)

Comparison of leaf chlorophyll content retrieval performance of citrus using FOD and CWT methods with field-based full-spectrum hyperspectral reflectance data - Bin Xiao; Sunzhe Li; Shiqing Dou; Hongchang He; Bolin Fu; Tengxu Zhang; Weiwei Sun; Yanli Yang; Yuankang Xiong; Jinke Shi; Jie Zhang (Computers and Electronics in Agriculture, 2024)

Multi-sensor and multi-platform retrieval of water chlorophyll a concentration in karst wetlands using transfer learning frameworks with ASD, UAV, and Planet CubeSate reflectance data - Bolin Fu; Sunzhe Li; Zhinan Lao; Bingyan Yuan; Yiyin Liang; Wen He; Weiwei Sun; Hongchang He (Science of The Total Environment, 2023)

Systemic subchronic toxicity and comparison of four selenium nutritional supplements by 90-day oral exposure in Sprague-Dawley rats - Lu Yu; Yanmei Li; Wen Qu; Yanhua Zheng; Xuejiao Chen; Shaohua Fu; Jingjing Qu; Jie Tian; Shuiyuan Cheng; Xin Cong; Bolin Fan; Chunhong Wang (Food and Chemical Toxicology, 2023)

In suit establishment of Ti3C2 MXenes exchanger via Na+, K+ and NH4+ intercalation: Exceptionally UO22+ loading capability and hijacking mechanism - Chang Liu; Ye Li; Dongxue Liu; Miao Lei; Bolin Li; Chengbin Fu; Jun Zhang (Chemical Engineering Journal, 2023)

Constructing of a stable Ti3C2 MXenes nano-traps with ultrahigh and selectively uranyl encapsulation capability by polyethyleneimine intercalation - Chang Liu; Ye Li; Dongxue Liu; Miao Lei; Bolin Li; Chengbin Fu; Jun Zhang (Chemical Engineering Journal, 2023)

Synergistic multi-altimeter for estimating water level in the coastal zone of Beibu Gulf using SEL, ALES + and BFAST algorithms - Jiaoling Qin; Sunzhe Li; Hang Yao; Bolin Fu; Hongchang He; Feng Wang; Lilong Liu; Donglin Fan; Xu He; Yuyang Li (Frontiers in Marine Science, 2023)
Accurately monitoring and predicting the large-scale dynamic changes of water levels in coastal zones is essential for its protection, restoration and sustainable development. However, there has been a challenge for achieving this goal using a single radar altimeter and retracking technique due to the diversity and complexity of coastal waveforms. To solve this issue, we proposed an approach of estimating water level of the coastal zone in Beibu Gulf, China, by combination of waveform classifications and multiple sub-waveform retrackers. This paper stacked Random Forest (RF), XGBoost and CatBoost algorithms for building an ensemble learning (SEL) model to classify coastal waveforms, and further evaluated the performance of three retracking strategies in refining waveforms using Cryosat-2, SARAL, Sentinel-3 altimeters. We compared the estimation accuracy of the coastal water levels between the single altimeter and synergistic multi-altimeter, and combined Breaks for Additive Season and Trend (BFAST), Mann-Kendall mutation test (MK) with Long Short-Term Memory (LSTM) algorithms to track the historical change process of coastal water levels, and predict its future development trend. This paper found that: (1) The SEL algorithm achieved high-precision classification of different coastal waveforms with an average accuracy of 0.959, which outperformed three single machine learning algorithms. (2) Combination of Threshold Retracker and ALES+ Retracker (TR_ALES+) achieved the better retracking quality with an improvement of correlation coefficient (R, 0.089~0.475) and root mean square error (RMSE, 0.008∼ 0.029 m) when comparing to the Threshold Retracker & Primary Peak COG Retracker and Threshold Retracker & Primary Peak Threshold Retracker. (3) The coastal water levels of Cryosat-2, SARAL, Sentinel-3 and multi-altimeter were in good agreement (R>0.66, RMSE<0.135m) with Copernicus Climate Change Service (C3S) water level. (4) The coastal water levels of the Beibu Gulf displayed a slowly rising trend from 2011 to 2021 with an average annual growth rate of 8mm/a, its lowest water level focused on May-August, the peak of water level was in October-November, and the average annual growth rate of water level from 2022-2031 was about 0.6mm/a. These results can provide guidance for scientific monitoring and sustainable management of coastal zones.

Synthesis of Lanthanum-Modified Attapulgite and Its Phosphorus-Fixing Mechanism for Urban Eutrophic Lake - Lingfeng Ye; Ye Li; Cong Liu; Bolin Li; Dongxue Liu; Chang Liu; Jie Wan; Chengbin Fu (Water, Air, & Soil Pollution, 2022)

CHLNET: A novel hybrid 1D CNN-SVR algorithm for estimating ocean surface chlorophyll-a - Donglin Fan; Hongchang He; Ruisheng Wang; You Zeng; Bolin Fu; Yuankang Xiong; Lilong Liu; Yong Xu; Ertao Gao (Frontiers in Marine Science, 2022)
Developing a unified chlorophyll-a (Chla) inversion algorithm for cross-water types is a significant challenge owing to the insufficiency of input features and training samples. Although machine learning algorithms can build a consistent model for different trophic waters, the accuracy of the inversion is dependent on the quality of the extended features. Here, we designed a novel hybrid framework called CHLNET, which combines a one-dimensional convolutional neural network (1D CNN) and support vector regression (SVR). The 1D CNN is used to extract features from the original band features, and the SVR is used to perform a fit of Chla. CHLNET is trained and tested using match-up pairs of SeaWiFS remote sensing reflectance [Rrs(λ)] in situ with Chla ranging from 0.009 mg/m³ to 138.046 mg/m³, which covers mostly ocean water types. Performance metrics in the log space of CHLNET were better than those of the state-of-the-art algorithms on the testing dataset, and CHLNET had the best overall performance with the largest cover area in the star plot. The frequency distribution of predicted Chla by CHLNET was more consistent with that of in situ Chla. While the spatial pattern was not smooth in low Chla concentration waters, CHLNET demonstrated excellent mapping ability at the global and local scales in high Chla concentration waters. Through the band-shift method, which transfers the Rrs(λ) of MERIS and MODIS-Aqua to the Rrs(λ) of SeaWiFS in the visible spectral range, CHLNET obtained better accuracy than the blended algorithm of OCx and CI on MERIS and MODIS-Aqua matchups, which validates the generalization of CHLNET on cross-sensor types. The results indicate that CHLNET avoids the drawbacks of manually constructing extended features and the need for merging water type-appropriate algorithms for Chla retrieval, as well as provides a new idea for unified Chla concentration inversion across water types. Thus, CHLNET may serve as an alternative approach for Chla inversion.

Evaluation of LAI Estimation of Mangrove Communities Using DLR and ELR Algorithms With UAV, Hyperspectral, and SAR Images - Bolin Fu; Jun Sun; Yeqiao Wang; Wenlan Yang; Hongchang He; Lilong Liu; Liangke Huang; Donglin Fan; Ertao Gao (Frontiers in Marine Science, 2022)
The high-precision estimation of mangrove leaf area index (LAI) using a deep learning regression algorithm (DLR) always requires a large amount of training sample data. However, it is difficult for LAI field measurements to collect a sufficient amount of sample data in mangrove wetlands. To tackle this challenge, this paper proposed an approach for expanding training samples and quantitatively evaluated the performance of estimating LAI for mangrove communities using Deep Neural Networks (DNN) and Transformer algorithms. This study also explored the effects of unmanned aerial vehicle (UAV) and Sentinel-2A multispectral, orbital hyper spectral (OHS), and GF-3 SAR images on LAI estimation of different mangrove communities. Finally, this paper evaluated the LAI estimation ability of mangrove communities using ensemble learning regression (ELR) and DLR algorithms. The results showed that: (1) the UAV images achieved the better LAI estimation of different mangrove communities (R 2 = 0.5974–0.6186), and GF-3 SAR images were better for LAI estimation of Avicennia marina with high coverage (R 2 = 0.567). The optimal spectral range for estimating LAI for mangroves in the optical images was between 650–680 nm. (2) The ELR model outperformed single base model, and produced the high-accuracy LAI estimation (R 2 = 0.5266–0.713) for different mangrove communities. (3) The average accuracy (R 2 ) of the ELR model was higher by 0.0019–0.149 than the DLR models, which demonstrated that the ELR model had a better capability (R 2 = 0.5865–0.6416) in LAI estimation. The Transformer-based LAI estimation of A. marina (R 2 = 0.6355) was better than the DNN model, while the DNN model produced higher accuracy for Kandelia candel (KC) (R 2 = 0.5577). (4) With the increase in the expansion ratio of the training sample (10–50%), the LAI estimation accuracy (R 2 ) of DNN and Transformer models for different mangrove communities increased by 0.1166–0.2037 and 0.1037–0.1644, respectively. Under the same estimation accuracy, the sample enhancement method in this paper could reduce the number of filed measurements by 20–40%.

Impacts of land use change on ecosystem service value in Lijiang River Basin, China - Ning Li; Jinye Wang; Haoyu Wang; Bolin Fu; Jianjun Chen; Wen He (Environmental Science and Pollution Research, 2021)

Isolation and characterization of an antimicrobial peptide from bovine hemoglobin α-subunit - Jianhe Hu; Minglu Xu; Bolin Hang; Lan Wang; Qing Wang; Junjie Chen; Tao Song; Dengfeng Fu; Ziliang Wang; Sanhu Wang; Xingyou Liu (World Journal of Microbiology and Biotechnology, 2011)