Exogenous organic matter and clay mineral additions regulate microbial-mineral mediated soil organic carbon accumulation in alkaline-calcareous soils - Xi Yang; Donghao Ma; Lin Chen; Ting Yang; Yongqi Zhang; Hongwei Jiang; Nan Zhang; Hongtao Zou; Congzhi Zhang; Guixiang Zhou; Yanfang Zhou; Yu Luo; et al. (Applied Soil Ecology, 2026)

Interdecadal intensification of late-spring precipitation over the Hengduan Mountains since the late 1990s - Meiying Zheng; Huizhi Liu; Shengyuan Liu; Haoran Li; Xiaoyang Liu; Lijun Xie; Guotao Zhang; Ting Yang; Fei Wang; Yunhao Gu; Junjie Zhao; Shuo He (Climate Dynamics, 2026)

Adaptive N and P fertilization ratio based on successional stages promotes synergistic enhancement of grassland functions on the Loess Plateau - Zhifei Chen; Junjie Zhou; Yuting Yang; Lingbin Yan; Bingcheng Xu (Plant and Soil, 2026)

A Novel Evidence of Consistency Between the Upper and Lower Airways in Central Regulation - Hanrui Wang; Xiaoyu Song; Ting Yang; Hui Shen; Tongpeng Chu; Yakui Mou; Chao Ren; Xicheng Song (Neuroscience Bulletin, 2026)

Compound microbial-composite carrier inoculant boosts sugarcane growth and reshapes rhizosphere bacterial community assembly - Dangsheng Pang; Tingting Yang; Jie Mo; Changju Liang; Changning Li; Yangrui Li; Dengfeng Dong; Yongxiu Xing (Applied Soil Ecology, 2026)

Coastal marsh conversion to paddy fields leads to a more pronounced stimulation of temperature sensitivity of soil microbial respiration in higher-latitude regions - Xiaochen Yao; Weiqi Wang; Yuling Yang; Wangting Yang; Bingjie Ren; Qinan Hu; Lidong Shen (Marine Pollution Bulletin, 2026)

Unraveling the size-dependent toxicity mechanisms of polystyrene microplastics on coral symbiotic Symbiodiniaceae: Integrated physiological and transcriptomic perspectives - Jiayuan Liang; Yating Yang; Tianyi Niu; Zhicong Li; Zhuqing Liang; Mingyao Lu; Li Zhang; Yihe Feng; Sanqiang Gong; Kefu Yu (Marine Pollution Bulletin, 2026)

A novel oral vaccine system for sustainable aquaculture based on live feed diatom: With white spot syndrome virus as an example - Wenting Yang; Shihao Li; Xin Li; Xi Qiang; Xuehua Liu; Songcui Wu; Xiujun Xie; Shan Gao; Guangce Wang (Aquaculture, 2026)

Distinct dose-response patterns between methane production and oxidation to urea or (NH4)2SO4 input in Chinese paddy fields - Wang-ting Yang; Bing-jie Ren; Si-le Wen; Yi-zhu Zhu; Jiang-bing Xu; Li-dong Shen; Wei-qi Wang; Jordi Sardans; Josep Peñuelas (Applied Soil Ecology, 2026)

Co-occurrence networks dominated by rare and abundant fungal species influence the potential function of fungal communities in Eucalyptus urophylla plantations under successive planting - Yuting Yang; Aoyu Ling; Lingyue Zhu; Qinzhan Wu; Kangting Huang; Dexu Zhang; Zhengye Wang; Zuoyu Qin; Lichao Wu; Jian Tang (Applied Soil Ecology, 2025)

Enhanced soil ecosystem multifunctionality and microbial community shifts following spent mushroom substrate application in vineyards - Xiaochi Ma; Feng Han; Guiting Yang; Jinggui Wu; Yan Ma (Applied Soil Ecology, 2025)

CH4 emissions from a double-cropping rice field in subtropical China over seven years - Xiao Liu; Xiaoqin Dai; Fengting Yang; Shengwang Meng; Huimin Wang (Agricultural and Forest Meteorology, 2023)

Towards a universal evapotranspiration model based on optimality principles - Shen Tan; Han Wang; Iain. Colin Prentice; Kun Yang; Rodolfo. L.B. Nóbrega; Xiaomang Liu; Yong Wang; Yuting Yang (Agricultural and Forest Meteorology, 2023)

Deep learning for smart fish farming: applications, opportunities and challenges - Xinting Yang; Song Zhang; Jintao Liu; Qinfeng Gao; Shuanglin Dong; Chao Zhou (Reviews in Aquaculture, 2021)
The rapid emergence of deep learning (DL) technology has resulted in its successful use in various fields, including aquaculture. DL creates both new opportunities and a series of challenges for information and data processing in smart fish farming. This paper focuses on applications of DL in aquaculture, including live fish identification, species classification, behavioural analysis, feeding decisions, size or biomass estimation, and water quality prediction. The technical details of DL methods applied to smart fish farming are also analysed, including data, algorithms and performance. The review results show that the most significant contribution of DL is its ability to automatically extract features. However, challenges still exist; DL is still in a weak artificial intelligence stage and requires large amounts of labelled data for training, which has become a bottleneck that restricts further DL applications in aquaculture. Nevertheless, DL still offers breakthroughs for addressing complex data in aquaculture. In brief, our purpose is to provide researchers and practitioners with a better understanding of the current state of the art of DL in aquaculture, which can provide strong support for implementing smart fish farming applications.

Intelligent feeding control methods in aquaculture with an emphasis on fish: a review - Chao Zhou; Daming Xu; Kai Lin; Chuanheng Sun; Xinting Yang (Reviews in Aquaculture, 2018)
In aquaculture, feeding is the primary factor determining efficiency and cost, so it is important to know when to stop feeding to maximize efficiency. Until now, fish feeding has been mostly based on artificial discrimination, which is usually time‐consuming and laborious. In recent years, intelligent feeding control according to changes in behaviour and growth status has gained increasing attention. This approach involves many methods as well as monitoring and feedback equipment and can automatically determine the feeding demands of fish. This review summarizes the development of intelligent feeding control methods, such as mathematical models, acoustic methods and computer vision, in aquaculture over the past three decades. All methods have unique application scenarios and models for the culture to which they are most suitable, and the advantages and disadvantages of each method in the laboratory as well as in pond, cage and recirculating aquaculture systems are analysed. Studies show that improvements in sensor accuracy and hardware and software processing speed have promoted the development of new technologies and methods, providing effective or potential support for intelligent feeding control. However, its accuracy and intelligent are still need to be improved to meet the needs of actual feeding scenarios. Through close collaborations between engineers and fish behaviourists, the feeding machine and system will be more elaborate and precise on the basis of the above methods, and the level of intelligence will be further improved.