Performance comparison of machine learning and GPU accelerated solvers in high-fidelity simulation of Cambridge stratified flames - Min Zhang; Guanlin Dang; Runze Mao; Yuqing Cai; Ruixing Yang; Han Li; Zhi X. Chen (Fuel, 2026)

How plant diversity and nitrogen deposition affect the net nutrient retention on temperate extensive green roofs: A case study - Guang Hao; Yihan Wang; Hechen Li; Shaobo Gao; Runze Wu; Zhuolin Li; Jiale Han; Hongyuan Li; Rui Hua; Nan Yang (Ecological Engineering, 2026)

A Dual-Pathway Degradation-Aware Network for Label-Free Remaining Useful Life Prediction of Offshore Wind Turbines - Yutong Qian; Weiqian Xu; Runze Mao; Peihua Han; Ning Yuan; Guoyuan Li; Houxiang Zhang (Journal of Marine Science and Engineering, 2026)
Remaining useful life (RUL) prediction for offshore wind turbines is important for predictive maintenance. However, its practical use is limited by the lack of reliable RUL labels, the small number of fault events, and the complex degradation links among turbine components. Most existing data-driven methods require labeled failure data and cannot fully capture how the degradation of different components is related. To address these limitations, a Dual-Pathway Degradation-Aware Network for label-free RUL prediction is proposed. The proposed method first constructs component-level health indicators (HIs) from SCADA data in a self-supervised manner and generates pseudo-RUL labels through a linear countdown strategy. It then jointly models the global degradation evolution and cross-component interactions using a global temporal branch and a fully connected graph branch, where residual aggregation is adopted to fuse node representations for RUL estimation. Experiments are conducted on the German North Sea Farm B dataset under a leave-one-out cross-validation (LOOCV) protocol. From the obtained results, the proposed method achieves an MAE of 111.3 h and an RMSE of 122.6 h, reducing the average MAE by approximately 19% compared with the strongest baseline. The proposed method achieves the best performance on three of the five turbines with fault events. Furthermore, the proposed method demonstrates superior capability in capturing component interaction patterns and provides an effective label-free solution for offshore wind turbine RUL prediction under limited fault data.

Sea state estimation for ship operations: A systematic review of methods, datasets, and challenges - Xiaorong Li; Runze Mao; Peihua Han; Houxiang Zhang (Ocean Engineering, 2026)

A holistic fault diagnosis and early warning method for offshore wind turbines - Ning Yuan; Runze Mao; Peihua Han; Weiqian Xu; Houxiang Zhang (Ocean Engineering, 2026)

Corrigendum to “A ready-to-use RNA delivery platform modulated by metal ions” [Chem. Eng. J. 506 (2025) 1–15 (160170)] - Zhaojian Qin; Yongzhuo Jiang; Peng Tao; Haifeng Liu; Han Zhong; Xiaoyi Ding; Runze Zhao; Yi Lu; Lifeng Zhang; Xiaomin Chen; Hongxin Zhuang; Jiao Wu; et al. (Chemical Engineering Journal, 2026)

A Sea State Estimation and Uncertainty Awareness Model Integrating Multi-Scale Convolution and DropKey-Transformer - Ting Cui; Runze Mao; Xinyu Guo; Peihua Han; Houxiang Zhang (Journal of Marine Science and Engineering, 2026)
Accurate sea state estimation is of great significance to marine engineering safety and disaster prevention and mitigation. This paper proposes a new sea state estimation architecture integrating multi-scale 1D-CNN, transformer encoder, and the MC-DropKey mechanism. First, the front-end multi-scale 1D-CNN is used in sequence modeling to extract high-quality local features. Then, the back-end transformer encoder is used to capture global long-range temporal dependencies. In addition, the MC-DropKey mechanism is introduced. It achieves dynamic uncertainty quantification while maintaining high-precision estimation. Comprehensive comparative and ablation experiments show that the proposed model significantly outperforms the baseline models in wave height and wave direction estimation tasks, achieving the lowest MAE of 0.117 m and CAE of 5.212°. Moreover, the uncertainty intervals output by the model effectively quantify estimation confidence, reaching a 96.67% prediction interval coverage probability. The obtained results provide strong technical support for scientific scheduling and decision-making in complex marine environments.

A Physics-Informed Non-Markovian Deep Learning Model for Robust Ship Motion Prediction Under Non-Ideal Observations - Xinyu Guo; Runze Mao; Peihua Han; Zhicheng Li; Houxiang Zhang (Journal of Marine Science and Engineering, 2026)
High-fidelity ship dynamics models are essential for the reliable operation of maritime autonomous systems. However, existing Markov-based maneuvering models and purely data-driven predictors struggle to capture hydrodynamic memory and degrade under non-ideal sensing. To address these challenges, this paper proposes a novel approach for robust ship motion prediction, the Non-Markovian Memory-Augmented Environment-Perceived and Physics-Informed Network (NMA-EPIN). This method explicitly models long-term hydrodynamic dependencies through a memory-augmented architecture. Within NMA-EPIN, a Control-Physics-Informed Neural Network (CPINN) paradigm enforces velocity–position kinematic consistency and control-logic alignment as soft constraints, suppressing cumulative drift under degraded observations. Experiments on a high-fidelity simulated dataset show that NMA-EPIN attains an average coefficient of determination R2=0.977 under nominal conditions, effectively eliminating the position drift observed in baselines. Under extreme compound perturbations (50% sensor noise, packet loss, and delays), NMA-EPIN retains R2≈0.91, which significantly outperforms the baselines.

Involvement of gut microbiota in sub-chronic chlorantraniliprole-induced metabolic alteration in chironomid larvae (Propsilocerus akamusi): Evidence from multi-omics, histopathological and biochemical analysis - Zeyang Sun; Anqi Han; Jiani Li; Wei Chen; Jian Mao; Runze Li; Jian Shi; Xiaorui Yan; Chuncai Yan (Pesticide Biochemistry and Physiology, 2026)

A systematic review of fault diagnosis methods for offshore wind turbines: Approaches, features, and emerging directions - Xinyan Wang; Runze Mao; Peihua Han; Ning Yuan; Yuanjiang Li; Yuting Guo; Shen Yin; Houxiang Zhang (Ocean Engineering, 2026)

DFODE-Kit: Deep learning package for solving flame chemical kinetics with high-dimensional stiff ordinary differential equations - Han Li; Ke Xiao; Yangchen Xu; Haoze Zhang; Zhenyi Chen; Runze Mao; Zhi X. Chen (Computer Physics Communications, 2026)

Integrating eDNA metabarcoding and functional trait analysis to uncover fish community structure and assembly mechanism in the Taiwan strait - Weiyi He; Hao Huang; Lei Wang; Danyun Ou; Jiaqiao Wang; Runze Han; Yanxu Zhang; Jinli Qiu; Weiwen Li; Kar-Hoe Loh; Sze-Wan Poong (Frontiers in Marine Science, 2026)
Marine fish communities in coastal systems are increasingly shaped by environmental gradients associated with anthropogenic pressures and climate variability, yet how these factors influence community assembly and functional traits remain poorly understood. In this study, environmental DNA (eDNA) metabarcoding using the 12S MiFish primer with 33 water samples was integrated with taxonomic and functional diversity analyses, co-occurrence network analysis, the neutral community model, hierarchical modeling of species communities, and single-trait-based community-weighted means to investigate fish communities in the Taiwan Strait (TWS). This interdisciplinary approach provides both a unique perspective and a comprehensive framework for fish biodiversity assessment. Fish communities exhibited high modularity and weak interspecific interactions, with an uneven distribution of species within functional space, indicating elevated ecological vulnerability. Community assembly was jointly governed by stochastic and deterministic processes, with NO₂⁻ identified as a significant environmental driver shaping fish community assembly. Notably, fish communities in the inshore and principal axis regions exhibited higher clustering coefficient but lower network modularity compared to other regions. Hierarchical modeling of species communities and community-weighted means further indicated clear distributional and trait differences among fish from the estuarine to offshore areas, consistent with species-specific responses to environmental gradients from inshore to offshore areas. Overall, TWS fish communities are dominated by species with very small and small body-length traits, while reef-associated fishes are mainly concentrated in inshore and midshore regions. Fish community assembly and functional traits in TWS exhibited systematic variation along the offshore distance gradient and were jointly modulated by key environmental factors.

DeepFlame 2.0: A new version for fully GPU-native machine learning accelerated reacting flow simulations under low-Mach conditions - Runze Mao; Xinyu Dong; Xuan Bai; Ziheng Wu; Guanlin Dang; Han Li; Zhi X. Chen (Computer Physics Communications, 2025)

Revealing the entanglement between pectin and silver carp myofibrillar protein molecular chains: A new perspective on the effect of solubilization mode on gel properties - Han Yu; Xiyu Yu; Xin Guo; Runze Li; Hui Li; Jian Zhang (Food Research International, 2025)

Experimental comparison of air entrapment capacity in sandy soils for liquefaction mitigation: Evaluating air injection method efficiency in soil desaturation - Saeed Sarajpoor; Yumin Chen; Yi Han; Runze Chen; Zhongling Fu; Ke Ma (Soil Dynamics and Earthquake Engineering, 2025)

Corrigendum to “Combined measurement of roots, δ18O and δ2H, and a Bayesian mixed model capture the soil profiles of wheat water uptake in a deep loamy soil” [Soil Tillage Res. 246 (2025) 106359] - Runze Zhang; Jiaxing Xu; Panxin Zhang; Yan Han; Changlu Hu; Victor Sadras; Xueyun Yang; Shulan Zhang (Soil and Tillage Research, 2025)

Combined measurement of roots, δ18O and δ2H, and a Bayesian mixed model capture the soil profiles of wheat water uptake in a deep loamy soil - Runze Zhang; Jiaxing Xu; Panxin Zhang; Yan Han; Changlu Hu; Victor Sadras; Xueyun Yang; Shulan Zhang (Soil and Tillage Research, 2025)

A ready-to-use RNA delivery platform modulated by metal ions - Zhaojian Qin; Yongzhuo Jiang; Peng Tao; Haifeng Liu; Han Zhong; Xiaoyi Ding; Runze Zhao; Yi Lu; Lifeng Zhang; Xiaomin Chen; Hongxin Zhuang; Jiao Wu; et al. (Chemical Engineering Journal, 2025)

Manure replacing synthetic fertilizer improves crop yield sustainability and reduces carbon footprint under winter wheat–summer maize cropping system - Jincan Niu; Qudsia Saeed; Wenning Wang; Runze Zhang; Lin Liu; Fenglian Lv; Jiaxing Xu; Yan Han; Panxin Zhang; Changlu Hu; Hu Xu; Benhua Sun; et al. (Journal of Environmental Management, 2024)

Effect of apple high-methoxyl pectin on heat-induced gelation of silver carp myofibrillar protein - Han Yu; Yunfeng Zhao; Runze Li; Xin Guo; Pingping Liu; Jian Zhang (Food Chemistry, 2024)

Carborane-based multiple-effect coupling protection mechanism assists the design of new high-temperature resistant hybrid resin - Zichun Ding; Lishuai Zong; Weizhao Han; Runze Liu; Siying Wang; Yuhang Wang; Qing Wang; Xigao Jian; Jinyan Wang (Chemical Engineering Journal, 2024)

Clinical Translation of the LevelCheck Decision Support Algorithm for Target Localization in Spine Surgery - Amir Manbachi; Tharindu De Silva; Ali Uneri; Matthew Jacobson; Joseph Goerres; Michael Ketcha; Runze Han; Nafi Aygun; David Thompson; Xiaobu Ye; Sebastian Vogt; Gerhard Kleinszig; et al. (Annals of Biomedical Engineering, 2018)