Integrated physiological and molecular responses of Chlorella sorokiniana to high-sulfate starch processing wastewater: Balancing growth promotion and metabolic adaptation - Ruixin Yan; Shuang Qiu; Xiaofan Li; Ayesha Algade Amadu; Shijian Ge (Bioresource Technology, 2026)

Conserved sulfate removal pathway in microalgae: A cross-species investigation for high-sulfate wastewater treatment - Xiaofan Li; Shuang Qiu; Ruixin Yan; Wenju Shan; Shijian Ge (Journal of Environmental Management, 2026)

Study on Antioxidant Capacity and Apoptosis in Liver of Chicks Following in Ovo Feeding of Selenized Glucose and Methylselenised Glucose - Minmeng Zhao; Qiao Shi; Ruixin Su; Kang Wen; Jing Ge; Daoqing Gong; Yun Zheng (Biological Trace Element Research, 2026)

MF-GCN: Multimodal Information Fusion Using Incremental Graph Convolutional Network for Ship Behavior Anomaly Detection - Ruixin Ma; Jinhao Zhang; Weizhi Nie; Naiming Ge; Hao Wen; Aoxiang Liu (Journal of Marine Science and Engineering, 2026)
Ship behavior anomaly detection is critical for intelligent perception and early warning in complex inland waterways, where single-source sensing (e.g., AIS-only or vision-only) is often fragile under occlusion, illumination variation, and signal noise. This study proposes MF-GCN, a multimodal (heterogeneous) information fusion framework based on an Incremental Graph Convolutional Network (IGCN) to detect and warn anomalous ship behaviors by jointly modeling AIS, video imagery, LiDAR point clouds, and water level signals. We first extract modality-specific features and enforce temporal–spatial consistency via timestamp and geo-referencing alignment, then construct an evolving graph in which nodes represent multimodal features and edges encode temporal dependency and semantic similarity. MF-GCN integrates a Semantic Clustering-based GCN (S-GCN) to inject historical semantic context and an Attentive Fusion-based GCN (A-GCN) to learn dynamic cross-modal correlations using multi-head attention. Experiments on our constructed real-world datasets demonstrate that MF-GCN achieves accuracies of 93.8%, 93.8%, and 93.3% with F1-scores of 93.6%, 93.6%, and 93.3% for ship deviation warning, bridge-crossing warning, and inter-ship collision warning, respectively, consistently outperforming representative baselines. These results verify the effectiveness of the proposed method for robust multimodal anomaly detection and early warning in inland-waterway scenarios.

Kinetic stability and transformation pathway of halogenated nitrophenols in drinking water - Jiacheng Ge; Xiaoyan Chen; Zhenqi Du; Yingying Zhou; Ruixin Qu; Jiangdong Cui; Shunke Ding; Wenhai Chu (Journal of Hazardous Materials, 2025)

An engineered DNA aptamer-based PROTAC for precise therapy of p53-R175H hotspot mutant-driven cancer - Lingping Kong; Fanlu Meng; Ping Zhou; Ruixin Ge; Xiaoshan Geng; Zhihao Yang; Guo Li; Linlin Zhang; Jing Wang; Jinfeng Ma; Cheng Dong; Jun Zhou; et al. (Science Bulletin, 2024)

Occurrence, Migration, and Transformation of Black Carbon in Environmental Matrix and Its Influence on the Environmental Fate of Coexisting Pollutants: A Review - Yaqian Gao; Yuhui Ge; Yunfeng Ma; Hailun Zhao; Guixing Xiao; Pau Loke Show; Jianqiu Chen; Ruixin Guo; Yanhua Liu (Reviews of Environmental Contamination and Toxicology, 2023)

Sensor Placement with Two-Dimensional Equal Arc Length Non-Uniform Sampling for Underwater Terrain Deformation Monitoring - Chunying Xu; Junwei Hu; Jiawang Chen; Yongqiang Ge; Ruixin Liang (Journal of Marine Science and Engineering, 2021)
Sensor placement plays an important role in terrain deformation monitoring systems and has an essential effect on data collection. The difficulty of sensor placement entails obtaining the most adequate and reliable information with the fewest number of sensors. Most sensor placement schemes are currently based on randomized non-uniform sampling and probability statistics, such as structural modality and optimization methods, which are difficult to directly apply due to the randomness and spatial heterogeneity of terrain deformation. In this study, the placement conditions of two-dimensional non-uniform sampling with equal arc length were deduced for underwater terrain deformation monitoring based on the MEMS accelerometer network. In order to completely reconstruct the underwater terrain, the arc length interval of the sensors should be less than 12Ω (Ω is the maximum frequency of the detected terrain). The maximum MRE and maximum RMSE were both less than seven percent in a terrain deformation monitoring experiment and a water tank test. The research results help technicians apply contact sensor arrays for underwater terrain monitoring.

An Adaptive Supervised Nonlinear Feature Extraction for Hyperspectral Imagery Classification - Haimiao Ge; Liguo Wang; Cheng Li; Yanzhong Liu; Ruixin Chen (Journal of the Indian Society of Remote Sensing, 2018)