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Interaction of soil microbial communities and phosphorus fractions under long-term fertilization in paddy soil

Journal of Integrative Agriculture 2022年第7期21卷 2134-2144页

作者： Muhammad QASWAR Waqas AHMED HUANG Jing LIU Kai-lou ZHANG Lu HAN Tian-fu DU Jiang-xue Sehrish ALI Hafeez UR-RAHIM HUANG Qing-hai ZHANG Hui-min National Engineering Laboratory for Improving Quality of Arable Land/lnstitute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural SciencesBeijing 100081P.R.China Department of Environment Faculty of Bioscience EngineeringGhent UniversityGhent 9000Belgium Guangdong Provincial Key Laboratory for Radionuclides Pollution Control and Resources School of Environmental Science and EngineeringGuangzhou UniversityGuangzhou 510006P.R.China National Observation Station of Qiyang Agri-ecology System/lnstitute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural SciencesQiyang 426182P.R.China National Engineering and Technology Research Center for Red Soil Improvement/Jiangxi Institute of Red Soil Nanchang 331717P.R.China Key Laboratory of Industrial Ecology and Environmental Engineering Ministry of Education/School of Environmental Science and TechnologyDalian University of TechnologyDalian 116024P.R.China

Understanding the impact of biological activities on the soil phosphorus(P)distribution under long-term fertilizer application can facilitate better soil P fertility ***,the primary objectives of this study were to investigate the effect of long-term(since 1981)fertilizer application on the soil P fractions and microbial community and to evaluate correlations between the microbial community structure and P *** following treatments were implemented in a long-term field trial:no fertilization(CK),inorganic N and K(NK),inorganic P and K(PK),inorganic N,P and K(NPK)and manure+NPK(MNPK)*** study showed that the soil pH,soil organic carbon and total and available N and P concentrations were considerably higher in the MNPK treatment than in the CK *** soil microbial biomass C,N and P concentrations were also significantly higher in the MNPK treatment than in the CK *** fertilization treatments,theβ-1,4-glucosidase,α-1,4-glucosidase,urease,acid phosphatase and phosphodiesterase activities were the highest in the MNPK *** to inorganic fertilization,the MNPK treatment increased the labile soil P fractions and decreased the residual soil P *** fertilization significantly affected the soil microbial *** total phospholipid fatty acid(PLFA)concentrations in the NK,PK,NPK and MNPK treatments were 23.3,43.1,48.7 and 87.7%higher,respectively,than in the CK treatment.A significant correlation was observed between the microbial community and soil P ***,the aggregated boosted tree(ABT)model showed that among the various soil biochemical properties,the total PLFA concentration was the factor that most influenced the active P pool,accounting for 35.4%of the relative influence of all soil biochemical properties *** findings reveal that combined manure and inorganic fertilizer application is a better approach than applying inorganic fertilizer alone fo

关键词： enzyme activities long-term fertilization microbial community microbial biomass phosphorus fraction

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Asymptotic Theory for Regularized system Identification Part I: Empirical Bayes Hyper-parameter Estimator

arXiv

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arXiv 2022年

作者： Ju, Yue Mu, Biqiang Ljung, Lennart Chen, Tianshi The School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and System Science Chinese Academy of Sciences Beijing100190 China The Division of Automatic Control Department of Electrical Engineering Linköping University LinköpingSE-58183 Sweden

Regularized techniques, also named as kernel-based techniques, are the major advances in system identification in the last decade. Although many promising results have been achieved, their theoretical analysis is far from complete and there are still many key problems to be solved. One of them is the asymptotic theory, which is about convergence properties of the model estimators as the sample size goes to infinity. The existing related results for regularized system identification are about the almost sure convergence of various hyper-parameter estimators. A common problem of those results is that they do not contain information on the factors that affect the convergence properties of those hyper-parameter estimators, e.g., the regression matrix. In this paper, we tackle problems of this kind for the regularized finite impulse response model estimation with the empirical Bayes (EB) hyper-parameter estimator and filtered white noise input. In order to expose and find those factors, we study the convergence in distribution of the EB hyper-parameter estimator, and the asymptotic distribution of its corresponding model estimator. For illustration, we run Monte Carlo simulations to show the efficacy of our obtained theoretical results. Copyright © 2022, The Authors. All rights reserved.

关键词： Monte Carlo methods

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engineering grain boundaries in monolayer molybdenum disulfide for efficient water-ion separation

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Science (New York, N.Y.) 2025年第6735期387卷 776-782页

作者： Shen, Jie Aljarb, Areej Cai, Yichen Liu, Xing Min, Jiacheng Wang, Yingge Wang, Qingxiao Zhang, Chenhui Chen, Cailing Hakami, Mariam Fu, Jui-Han Zhang, Hui Li, Guanxing Wang, Xiaoqian Chen, Zhuo Li, Jiaqiang Dong, Xinglong Shih, Kaimin Huang, Kuo-Wei Tung, Vincent Shi, Guosheng Pinnau, Ingo Li, Lain-Jong Han, Yu Physical Sciences and Engineering Division (PSE) King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore Department of Physics King Abdulaziz University Jeddah Saudi Arabia KAUST Catalysis Center King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Key Laboratory of Flexible Electronics (KLOFE) and Institute of Advanced Materials (IAM) Nanjing Tech University (NanjingTech) Nanjing China Shanghai Applied Radiation Institute Shanghai Key Laboratory of Atomic Control and Application of Inorganic 2D Supermaterials Shanghai University Shanghai China Department of Mechanical Engineering University of Hong Kong Pok Fu Lam Road Hong Kong China Department of Civil Engineering University of Hong Kong Pok Fu Lam Road Hong Kong China Imaging and Characterization Core Lab King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Department of Chemical System Engineering School of Engineering University of Tokyo Tokyo Japan Center for Electron Microscopy South China University of Technology Guangzhou China School of Emergent Soft Matter South China University of Technology Guangzhou China Catalyst Center of Excellence (CCoE) Research and Development Center Saudi Aramco Dhahran Saudi Arabia Center for Sustainability and Energy Technologies Chang Gung University Taoyuan Taiwan Key Laboratory of Comprehensive and Highly Efficient Utilization of Salt Lake Resources Qinghai Institute of Salt Lakes Chinese Academy of Sciences Xining Qinghai China University of Chinese Academy of Sciences Wenzhou China

Two-dimensional (2D) materials have long been considered as ideal platforms for developing separation membranes. However, it is difficult to generate uniform subnanometer pores over large areas on 2D materials. We report that the well-defined eight-membered ring (8-MR) pores, typically formed at the boundaries of two antiparallel grains of monolayer molybdenum disulfide (MoS2), can serve as molecular sieves for efficient water-ion separation. The density of grain boundaries and, consequently, the number of 8-MR pores can be tuned by regulating the grain size. Optimized MoS2 membranes outperformed the state-of-the-art membranes in forward osmosis tests by demonstrating both ultrahigh water/sodium chloride selectivity and exceptional water permeance. Creating precise pore structures on atomically thin films through grain boundary engineering presents a promising route for producing membranes suitable for various applications.

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Solarsam: Building-Scale Photovoltaic Potential Assessment Based on Segment Anything Model (Sam) and Remote Sensing for Emerging City

SSRN

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SSRN 2024年

作者： Li, Guanglei Wang, Guohao Luo, Tengqi Hu, Yuxiao Wu, Shouyuan Gong, Guanghui Song, Chenchen Guo, Zhiling Liu, Zhengguang Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan250061 China Department of Power and Electrical Engineering Northwest A&F University Yangling712100 China The Hong Kong Polytechnic University Hong Kong Eastern Institute of Technology Ningbo315200 China Higher Information Industry Technology Research Institute Beijing Information Science and Technology University Beijing100192 China Department of Chemical Engineering The University of Manchester ManchesterM13 9PL United Kingdom

Driven by advancements in photovoltaic (PV) technology, solar energy has emerged as a promising renewable energy source, due to its ease of integration onto building rooftops, facades, and windows. For the emerging cities, the lack of detailed street-level data presents a challenge for effectively assessing the potential of building-integrated photovoltaic (BIPV). To address this, this study introduces SolarSAM, a novel BIPV evaluation method that leverages remote sensing imagery and deep learning techniques, and an emerging city in northern China is utilized to validate the model performance. During the process, SolarSAM segmented various building rooftops using text prompt guided semantic segmentation. Separate PV models were then developed for Rooftop PV, Facade-integrated PV, and PV windows systems, using this segmented data and local climate information. The potential for BIPV installation, solar power generation, and city-wide power self-sufficiency were assessed, revealing that the annual BIPV power generation potential surpassed the city’s total electricity consumption by a factor of 2.5. Economic and environmental analysis were also conducted, including levelized cost of electricity and carbon reduction calculations, comparing different BIPV systems across various building categories. These findings demonstrated the model’s performance and reveled the potential of BIPV power generation in the future. © 2024, The Authors. All rights reserved.

关键词： Solar power generation

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Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study

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Journal of Medical Internet research 2024年第1期26卷 e48443页

作者： Lee, You-Qian Chen, Ching-Tai Chen, Chien-Chang Lee, Chung-Hong Chen, Peitsz Wu, Chi-Shin Dai, Hong-Jie Dialogue System Technical Department Asustek Computer Inc Taipei Taiwan Intelligent System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan Center for Precision Health Research Asia University Taichung Taiwan Electromagnetic Sensing Control and AI Computing System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Knowledge Discovery and Data Mining Lab Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Chemical Engineering Feng Chia University Taichung Taiwan National Center for Geriatrics and Welfare Research National Health Research Institutes Zhunan Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan School of Post-Baccalaureate Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan

Background: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. Objective: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. Methods: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models’ outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. Results: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of cod

关键词： ChatGPT code mixing deidentification electronic health record large language model pretrained language model

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An Efficient Planning and control Framework for Quadrotors Based on Heuristic Search and Data-Driven Model Predictive control

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IEEE Transactions on Industrial Electronics 2025年

作者： Wang, Sifei Wang, Yaonan Miao, Zhiqiang Wang, Xiangke He, Wei Wang, Hesheng Hunan University College of Electrical and Information Engineering Changsha 410082 China National Engineering Research Center for Robot Visual Perception and Control Changsha 410082 China National University of Defense Technology College of Intelligence Science and Technology Changsha 410073 China Beijing Information Science and Technology University School of Automation Institute of Artificial Intelligence Beijing 100083 China University of Science and Technology School of Intelligence Science and Technology Beijing 100083 China Shanghai Jiao Tong University Department of Automation Shanghai 200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai 200240 China Harbin Institute of Technology State Key Laboratory of Robotics and System Harbin 150001 China

This article presents a framework for quadrotors that integrate planning and control, which employs a heuristic depth-first search (HDFS) with data-driven model predictive control (MPC). The proposed framework intends to enable multiquadrotor systems to traverse cluttered environments containing obstacles and disturbances. The heuristic function enables quadrotors to identify a secure route in a timely manner. The heuristic function setup employed by HDFS results in a reduction in the number of rasters accessed, particularly in scenarios that are not labyrinthine. This consequently leads to an enhancement in computational speed for multiquadrotor systems when compared to the A* algorithm. Furthermore, data-driven MPC enhances the nominal model of the quadrotor by modeling the disturbance while tracking the desired states. Consequently, data-driven MPC is capable of attaining superior tracking precision in a disturbance environment when compared to MPC without data-driven. The transformation from the path to the desired states of the quadrotor is achieved through the utilization of B-splines and differential flatness. Simulations and experiments substantiate the efficacy of the proposed approach. © 1982-2012 IEEE.

关键词： Collision avoidance data-driven depth-first search model predictive control multiquadrotor systems

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DC fault ride-through for wind power integration via MMC-HVDC based on precise control of energy storage configured inside wind farm

DC fault ride-through for wind power integration via MMC-HVD...

引用

Renewable Power Generation and Future Power systems Conference 2023 (RPG 2023 UK)

作者： Y. Sun Y. Guo F. Fan J. Fan G. Li Z. Wang X. Wu Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin People's Republic of China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin People's Republic of China Department of Electronic and Electrical Engineering University of Strathclyde Glasgow UK State Grid Smart Grid Research Institute Beijing People's Republic of China

The modular multilevel converter-based high voltage direct current (MMC-HVDC) overhead lines transmission scheme is the preferred option for wind power integration, though it is susceptible to DC faults. To ensure the connection of a wind farm (WF) to the grid during DC faults, this paper proposes a coordinated control strategy for DC fault ride-through (FRT) based on the energy storage system configured inside the WF for wind power integration through a bipolar MMC-HVDC. In the case of an instantaneous DC fault, the non-fault pole is regulated to operate at full power state to maximise wind power transmission. The exceedance of available wind power outputs over the transmission capacity of the non-fault pole is then accurately allocated to the internal energy storage units (ESUs) of wind generators (WGs) in order to ensure the power balance of the system and reduce the state of charge variance of ESUs as soon as possible. In order to reduce the energy capacities of ESUs required to store the excessive wind power in the event of a permanent DC fault, the method of step-by-step removing WGs is adopted to ensure the system power remaining balanced when the internal ESUs of the disconnected WGs are out of operation. The model is developed and carried out on the PSCAD/EMTDC simulation platform to verify the effectiveness of the proposed control strategies.

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An IMU Error Correction Method Based on Multithreshold ZV Detection and SRCKF

An IMU Error Correction Method Based on Multithreshold ZV De...

引用

Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Jialiang Luo Shichao Chen Yisheng Lv Wenqiao Sun Fan Yang Ruijie Xu The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Information Engineering China University of Geosciences Beijing China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Transportation and Economics Research Institute and the Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited China The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences College of Rail Transit Shan Dong JiaoTong University Jinan China Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic University Hong Kong China

With the rapid development and widespread application of Inertial Measurement Units (IMUs), IMU-based human localization has become critically important in environments lacking Global Navigation Satellite systems (GNSS) signals. Methods such as UWB/WIFI and other anchor-based localization have been employed to enhance the positioning accuracy. However, these methods are susceptible to the environmental interference and increased costs. In contrast, SINS-based localization methods eliminate reliance on the external data. However, this method can suffer from drift errors due to cumulative integration errors. Inertial navigation error correction techniques like Zero Detection Update Technology (ZUPT) provide potential solutions to address drift errors. This paper proposes an error correction method based on the multithreshold zero-velocity detection and Square Root Cubature Kalman Filter (SRCKF) updates. Compared to the single fixed threshold detection, the multi-threshold zero-velocity detection reduces the likelihood of missed zero velocity detection and false alarms, providing accurate time intervals for error correction. Experimental results demonstrate that the SRCKF-based algorithm reduces position deviation to approximately 0.3146 meters, corresponding to an error ratio of1.14%. The proposed method enables cost-effective and accurate pedestrian localization, driving advancements in the field.

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Secure and Privacy-preserving Data-sharing Framework based on Blockchain Technology for Al-Najaf/Iraq Oil Refinery 19

Secure and Privacy-preserving Data-sharing Framework based o...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Umran, Samir M. Lu, SongFeng Abduljabbar, Zaid Ameen Lu, Zhi Feng, Bingyan Zheng, Lu Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Iraqi Cement State Company Ministry of Industry and Minerals Baghdad10011 Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China University of Basrah College of Education for Pure Sciences Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Industrial Internet Research Institute Wuhan Huazhong Numerical Control Co. Ltd Wuhan430074 China South-Central University for Nationalities College of Computer Science Wuhan430074 China

ISBN: (纸本)9798350346558

The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network;provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality. © 2022 IEEE.

关键词： Digital storage

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The DRTS-Net Framework: Enhanced Probabilistic Health Perception for Wind Turbine Bearings Under Complex Operating Conditions With Improved Interpretability

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Yang, Tongguang Li, Guanchen Huo, Jinzhao Li, Xiaoyong Zhou, Xianwen Han, Qingkai Northeastern University School of Mechanical Engineering and Automation Shenyang110819 China Johns Hopkins University Department of Mechanical Engineering BaltimoreMD21218 United States Foshan University School of Mechatronic Engineering and Automation Foshan528225 China SCNU Environmental Research Institute Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety MOE Key Laboratory of Theoretical Chemistry of Environment South China Normal University School of Environment Guangzhou510006 China SCNU Qingyuan Institute of Science and Technology Innovation Company Ltd. Qingyuan511517 China Northeastern University Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Shenyang110819 China

Health perception in mechanical systems is a crucial aspect of prognostics and health management (PHM). Deep learning models have been successfully applied to health perception in wind turbine bearings. However, due to the time-varying, redundant, and noisy characteristics of time-series data representing bearing wear, existing models face significant challenges, particularly in terms of generalization and interpretability. Moreover, although most current models have provided health perceptions, they overlook the strong stochastic nature of bearing degradation data, and thus fail to deliver reliable predictions of uncertainty information. To address these technical challenges, this study introduces a novel Deep residual temporal-spatial framework (DRTS-Net) for probabilistic health perception of wind turbine bearing wear states. This article creatively designs spatiotemporal information fusion module for extracting features from bearing degradation data. Inspired by improvements in residual network structures, the DRTS-Net integrates an attention mechanism and a soft thresholding mechanism from the deep residual shrinkage network to optimize the feature extraction process. This integration aims to mitigate the interference from minor redundant features in the degradation data. The framework incorporates Gaussian process regression in the output layer to yield the probability distribution of single-health perception values for bearing features. A comparative study was conducted using the same bearing health state dataset against current mainstream deep learning methods for bearing health assessment. Results demonstrate that the proposed DRTS-Net achieves superior performance in high-precision health perception, high-reliability probabilistic health perception. © 1963-2012 IEEE.

关键词： Stochastic systems

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