Heterologous expression of DobHLH25 from Dendrobium officinale enhances drought tolerance in Arabidopsis - Qingxia Liu; Jian Wang; Yan Zhang; Rong Wang; Youya Liu; Jingzhong Chen; Bo Wang; Ningmei Chen; Qingwen Sun; Ronghui Gu; Yuan Huang (Functional & Integrative Genomics, 2026)

Pillar stability in horizontal salt cavern energy storage: Model development and field application - Dong Chen; Hongling Ma; Wei Liang; Xuan Wang; Wentao Li; Siheng Li; Mengjiao Wang; Ronghui Xu; Xiaomeng Sun; Tao Xiong; Changdong Shao (International Journal of Rock Mechanics and Mining Sciences, 2026)

DSTGT-Net: Inductive Dynamic Spatio-Temporal Graph Transformer for multi-vessel long-term trajectory prediction in high-density congested waterways - Jiawen Li; Jiahua Sun; Liang Cao; Tingjun Liu; Ronghui Li (Ocean Engineering, 2026)

Targeted co-delivery of curcumin and TRAIL via engineered extracellular vesicles: a synergistic therapy against resistant cancers - Lang Liu; Jiahong Jiang; Zelin Wu; Xiaoli Wu; Jun Peng; Nuo Xu; Xudong Lin; Ronghui Sun; Yuyi Kang; Changhong Ke; Caijun Zhao; Qilin Huang; et al. (Drug Delivery and Translational Research, 2026)

Synergistic phosphorus-carboxyl co-modification of dolomite for enhanced immobilization and long-term stabilization of Pb in acidic contaminated soils - Jiaxue Sun; Fanmin Zhu; Ronghui Zhang; Chongqing Mei; Shijie Zhao; Zhanbin Huang; Chuanfu Li (Environmental Research, 2026)

Chicoric acid combined with Clostridium leptum regulates IBS-induced intestinal dysfunction and depressive-like behavior through 5-HT signaling pathway - Ye Wang; Ronghui Chen; Youkang Chen; Wanting Zhang; Chunjian Wang; Lijun Sun; Huixin Yu; Min Zhang; Xuebo Liu; Yutang Wang (Food Bioscience, 2026)

An ethyl cellulose-coated bacterial cellulose based hydrophobic and degradable straw-like materials towards drinking straws - Ronghui Zhang; Bianjing Sun; Longyi Chen; Chuntao Chen; Dongping Sun (Carbohydrate Polymers, 2025)

Dynamic evolution of volatile and non-volatile metabolic profiles in black tea during fermentation on an industrial scale - Qian Pu; Mingjin Li; Anlan Qu; Yanan Liu; Minghui Qi; Tianci Shen; Ronghui Sun; Shuang Wu; Wangnian Qin; Jingyi Xiao; Yu Wang; Youyi Huang (Food Chemistry, 2025)

A study of ENSO magnitude and lifespan in CMIP6 models - Siyu Ou; De-Zheng Sun; Ronghui Zhang (Acta Oceanologica Sinica, 2025)

Investigation of temperature and humidity impact on thermal comfort in deep underground tunnel during construction in hot climate: A case study in an underground hydropower station - Shengyu Liu; Wentao Wu; Xueqing Sun; Hongxin Tian; Jianwei Li; Ronghui Zhou; Bezoun Deborah Dembele; Xiong Shen (Tunnelling and Underground Space Technology, 2025)

Electrochemical enhancement of the accumulation of photosensitive components in anoxygenic phototrophic bacteria extracellular: A new insight into the preparation of degradable microbial photosensitizer for water treatment - Ronghui He; Jian Sun; Yong Yuan; Xiaoyan Bai; Qintie Lin; Yaping Zhang; Kang Dai; Zhenbo Xu (Journal of Hazardous Materials, 2025)

Addressing unfamiliar ship type recognition in real-scenario vessel monitoring: a multi-angle metric networks framework - Jiahua Sun; Jiawen Li; Ronghui Li; Langtao Wu; Liang Cao; Molin Sun (Frontiers in Marine Science, 2025)
Intelligent ship monitoring technology, driven by its exceptional data fitting ability, has emerged as a crucial component within the field of intelligent maritime perception. However, existing deep learning-based ship monitoring studies primarily focus on minimizing the discrepancy between predicted and true labels during model training. This approach, unfortunately, restricts the model to learning only from labeled ship samples within the training set, limiting its capacity to recognize new and unseen ship categories. To address this challenge and enhance the model’s generalization ability and adaptability, a novel framework is presented, termed MultiAngle Metric Networks. The proposed framework incorporates ResNet as its foundation. By employing a novel multi-scale loss function and a new similarity measure, the framework effectively learns ship patterns by minimizing sample distances within the same category and maximizing distances between samples of different categories. The experimental results indicate that the proposed framework achieves the highest level of ship monitoring accuracy when evaluated on three distinct ship monitoring datasets. Even in the case of unfamiliar ships, where the detection performance of conventional models significantly deteriorates, the framework maintains stable and efficient detection capabilities. These experimental results highlight the framework’s ability to effectively generalize its understanding beyond the training samples and adapt to real-world scenarios.

Metal-support interaction in supported Pt single-atom catalyst promotes lattice oxygen activation to achieve complete oxidation of acetone at low concentrations - Tian Tang; ShunZheng Zhao; YunPeng Liu; XiaoLong Tang; Long Sun; YiMing Ma; RongHui Zhu; HongHong Yi (Journal of Hazardous Materials, 2024)

Constructing ZrO2@UiO-66 heterostructure nanoparticles to significantly improve energy storage density of PEI-based nanocomposites at high temperature - Zhuo Wang; Zhuang Liu; Zeyu Ning; Jinteng Kang; Ting Zhao; Ronghui Ye; Ying Xue; Caidan Hou; Zixiong Sun (Chemical Engineering Journal, 2024)

Advancements in electrothermal heterogeneous catalytic pollutants: Efficient conductor selection, novel device design and synergistic mechanism studies - Ronghui Zhu; Shunzheng Zhao; Xiaolong Tang; Fengyu Gao; Weixiao Wang; Long Sun; Honghong Yi (Journal of Environmental Chemical Engineering, 2024)

A novel alginate-embedded magnetic biochar-anoxygenic photosynthetic bacteria composite microspheres for multipollutant removal: Mechanisms of photo-bioelectrochemical enhancement and excellent reusability performance - Ronghui He; Jian Sun; Xiaoyan Bai; Qintie Lin; Yong Yuan; Yaping Zhang; Kang Dai; Zhenbo Xu (Environmental Research, 2024)

Engineered nanomaterials display a variable interaction with anoxygenic photoelectrogenic biofilm for pharmaceutical pollutants degradation - Ronghui He; Xubin Zhang; Jian Sun; Xiaoyan Bai; Qintie Lin; Yong Yuan; Yaping Zhang; Kang Dai; Zhenbo Xu (Chemical Engineering Journal, 2024)

LFLD-CLbased NET: A Curriculum-Learning-Based Deep Learning Network with Leap-Forward-Learning-Decay for Ship Detection - Jiawen Li; Jiahua Sun; Xin Li; Yun Yang; Xin Jiang; Ronghui Li (Journal of Marine Science and Engineering, 2023)
Ship detection in the maritime domain awareness field has seen a significant shift towards deep-learning-based techniques as the mainstream approach. However, most existing deep-learning-based ship detection models adopt a random sampling strategy for training data, neglecting the complexity differences among samples and the learning progress of the model, which hinders training efficiency, robustness, and generalization ability. To address this issue, we propose a ship detection model called the Leap-Forward-Learning-Decay and Curriculum Learning-based Network (LFLD-CLbased NET). This model incorporates innovative strategies as Leap-Forward-Learning-Decay and curriculum learning to enhance its ship detection capabilities. The LFLD-CLbased NET is composed of ResNet as the feature extraction unit, combined with a difficulty generator and a difficulty scheduler. The difficulty generator in LFLD-CLbased NET effectively expands data samples based on real ocean scenarios, and the difficulty scheduler constructs corresponding curriculum training data, enabling the model to be trained in an orderly manner from easy to difficult. The Leap-Forward-Learning-Decay strategy, which allows for flexible adjustment of the learning rate during curriculum training, is proposed for enhancing training efficiency. Our experimental findings demonstrate that our model achieved a detection accuracy of 86.635%, approximately 10% higher than other deep-learning-based ship detection models. In addition, we conducted extensive supplementary experiments to evaluate the effectiveness of the learning rate adjustment strategy and curriculum training in ship detection tasks. Furthermore, we conducted exploratory experiments on different modules to compare performance differences under varying parameter configurations.

Knowledge-Transfer-Based Bidirectional Vessel Monitoring System for Remote and Nearshore Images - Jiawen Li; Yun Yang; Xin Li; Jiahua Sun; Ronghui Li (Journal of Marine Science and Engineering, 2023)
Vessel monitoring technology involves the application of remote sensing technologies to detect and identify vessels in various environments, which is critical for monitoring vessel traffic, identifying potential threats, and facilitating maritime safety and security to achieve real-time maritime awareness in military and civilian domains. However, most existing vessel monitoring models tend to focus on a single remote sensing information source, leading to limited detection functionality and underutilization of available information. In light of these limitations, this paper proposes a comprehensive ship monitoring system that integrates remote satellite devices and nearshore detection equipment. The system employs ResNet, a deep learning model, along with data augmentation and transfer learning techniques to enable bidirectional detection of satellite cloud images and nearshore outboard profile images, thereby alleviating prevailing issues such as low detection accuracy, homogeneous functionality, and poor image recognition applicability. Empirical findings based on two real-world vessel monitoring datasets demonstrate that the proposed system consistently performs best in both nearshore identification and remote detection. Additionally, extensive supplementary experiments were conducted to evaluate the effectiveness of different modules and discuss the constraints of current deep learning-based vessel monitoring models.

Extracting copper and cobalt from non-ferrous residues by iron- and sulfur-oxidizing bacteria - Jianxing Sun; Wenxian Liu; Ruichang Tang; Haina Cheng; Ronghui Liu; Kang Liu; Wenhao Zhan; Yang Ge; Hongbo Zhou; Yuguang Wang (Environmental Science and Pollution Research, 2021)

Physiological and antioxidant responses of Basella alba to NaCl or Na2SO4 stress - Jianfeng Ning; Shaoying Ai; Shaohai Yang; Lihua Cui; Yong Chen; Lili Sun; Ronghui Wang; Mengjun Li; Zhaobing Zeng (Acta Physiologiae Plantarum, 2015)