Brønsted base-pillared cobalt-nickel layered double hydroxides for aqueous calcium-ion batteries - Mengting Shen; Yuning Peng; Xin Wang; Huan Liu; Xiang Cai (Chemical Engineering Journal, 2026)

Development and testing of a scaled floating wind turbine in the wind tunnel for dMATCH framework - Yuning Gao; Xinliang Tian; Yitang Liu; Xin Li; Peng Wang; Binrong Wen; Eddie Yin Kwee Ng (Ocean Engineering, 2026)

A flexible bionic bimodal sensor with distinguishing temperature and humidity signals - Xuan Yao; Chang Gao; Mingjian Pan; Tongkun Wei; Weihe Shen; Qing You; Hanyu Wang; Jingru Peng; Danke Chen; Xiaoqiu Tang; Xue Li; Yuning Li; et al. (Chemical Engineering Journal, 2025)

Study on mechanism of in-situ hydrogen generation based on molecular dynamics simulation from pyrolysis of heavy oil - Houfeng He; Yuning Gong; Mingguo Peng; Haoran Zheng; Tian Feng; Ming Zhang; Qiu Li; Pengcheng Liu (Fuel, 2025)

Regional Wave Spectra Prediction Method Based on Deep Learning - Yuning Liu; Rui Li; Wei Hu; Peng Ren; Chao Xu (Journal of Marine Science and Engineering, 2025)
The wave spectrum, as a key statistical feature describing wave energy distribution, is crucial for understanding wave propagation mechanisms and supporting ocean engineering applications. This study, based on ERA5 reanalysis spectrum data, proposes a model combining CNN and xLSTM for rapid gridded wave spectrum prediction over the Bohai and Yellow Seas domain. It uses 2D gridded spectrum data rather than a spectrum at specific points as input and analyzes the impact of various input factors at different time lags on wave development. The results show that incorporating water depth and mean sea level pressure significantly reduces errors. The model performs well across seasons with the seasonal spatial average root mean square error (SARMSE) of spectral energy remaining below 0.040 m2·s and RMSEs for significant wave height (SWH) and mean wave period (MWP) of 0.138 m and 1.331 s, respectively. At individual points, the spectral density bias is near zero, correlation coefficients range from 0.95 to 0.98, and the peak frequency RMSE is between 0.03 and 0.04 Hz. During a typical cold wave event, the model accurately reproduces the energy evolution and peak frequency shift. Buoy observations confirm that the model effectively tracks significant wave height trends under varying conditions. Moreover, applying a frequency-weighted loss function enhances the model’s ability to capture high-frequency spectral components, further improving prediction accuracy. Overall, the proposed method shows strong performance in spectrum prediction and provides a valuable approach for regional wave spectrum modeling.

Aerodynamic load extraction in the real-time hybrid model test for floating wind turbine - Zhihao Jiang; Yuning Gao; Xinliang Tian; Gang Chen; Binrong Wen; Zhike Peng; Wei Zhang; Mindong Yang; Yang Gao (Ocean Engineering, 2025)

dMATCH: A novel integrated testing method for floating wind turbine dynamics-concept, framework and delay influence - Yuning Gao; Binrong Wen; Zhihao Jiang; Xin Li; Zhike Peng; Guiyong Zhou; Yirong Xia; Xinliang Tian (Ocean Engineering, 2025)

dMATCH: A novel integrated testing method for floating wind turbine dynamics – Potential error and its impact - Yuning Gao; Xinliang Tian; Guangqi Liu; Zhihao Jiang; Da Li; Tao Peng; Guiyong Zhou; Binrong Wen (Ocean Engineering, 2024)

On the Nearshore Significant Wave Height Inversion from Video Images Based on Deep Learning - Chao Xu; Rui Li; Wei Hu; Peng Ren; Yanchen Song; Haoqiang Tian; Zhiyong Wang; Weizhen Xu; Yuning Liu (Journal of Marine Science and Engineering, 2024)
Accurate observation of nearshore waves is crucial for coastal safety. In this study, the feasibility of extracting wave information from wave video images captured by shore-based cameras using deep learning methods was explored, focusing on inverting nearshore significant wave height (SWH) from instantaneous wave video images. The accuracy of deep learning models in classifying wind wave and swell wave images was investigated, providing reliable classification results for SWH inversion research. A classification network named ResNet-SW for wave types with improved ResNet was proposed. On this basis, the impact of instantaneous wave images, meteorological factors, and oceanographic factors on SWH inversion was evaluated, and an inversion network named Inversion-Net for SWH that integrates multiple factors was proposed. The inversion performance was significantly enhanced by the specialized models for wind wave and swell. Additionally, the inversion accuracy and stability were further enhanced by improving the loss function of Inversion-Net. Ultimately, time series inversion results were synthesized from the outputs of multiple models; the final inversion results yielded a mean absolute error of 0.04 m and a mean absolute percentage error of 8.52%. Despite certain limitations, this method can still serve as a useful alternative for wave observation.

MFNet: Multi-scale feature enhancement networks for wheat head detection and counting in complex scene - Yurong Qian; Yugang Qin; Hongyang Wei; Yiguo Lu; Yuning Huang; Peng Liu; Yingying Fan (Computers and Electronics in Agriculture, 2024)

Change of soil bacterial communities in chemically stabilized chromium contaminated soils in an accelerated aging experiment - Linying Yao; Yuning He; Peng Zhao; Zhihao Xie; Longfei Jiang; Danni Li; Yueqiao Liu; Rui Ma; Jianli Jia; Dayi Zhang (Journal of Environmental Chemical Engineering, 2024)

Experimental study on crashworthiness and lightweight of cutting-type energy-absorbing structure of magnesium alloy for trains - Tao Li; Yong Peng; Yuning Qiao; Wanying Zhu; Jing Zhang; Kui Wang; Guoquan Xie; Honghao Zhang (Engineering Structures, 2024)

Metabarcoding of microeukaryotes in surface sediments from the Pacific Arctic and adjacent sea areas: The role of diatoms in the biological pump - Liang Peng; Changliang Xie; Maoting Wang; Junning Gu; Yuning Zhang; Tao Jiang; Yingchun Cui; Zhaohui Wang (Global and Planetary Change, 2023)

Recovering valuable metals from spent hydrodesulfurization catalysts by co-leaching, dissociation, and stepwise precipitation - Bingxu Chen; Hao Du; Yuning Guo; Shaona Wang; Jianping Peng; Yi Zhang; Lanjie Li; Congmei Xu (Journal of Environmental Chemical Engineering, 2023)

Stitching electron localized heptazine units with “carbon patches” to regulate exciton dissociation behavior of carbon nitride for photocatalytic elimination of petroleum hydrocarbons - Qi Guo; Yuning Wu; Linhong Xia; Xue-Fang Yu; Kaisheng Zhang; Yujie Du; Lixue Zhang; Hua Tang; Jianbo Cheng; Jianpeng Shang; Yanhua Peng; Zhuo Li; et al. (Chemical Engineering Journal, 2023)