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Environmental dynamics of nitrogen and phosphorus release from river sediments of arid areas

Journal of Arid Land 2024年第5期16卷 685-698页

作者： SU Wenhao WU Chengcheng Sun Xuanxuan LEI Rongrong LEI Li wang ling ZHU Xinping College of Bioscience and Resources Environment Beijing University of AgricultureBeijing 102206China Xinjiang Tianxi Environmental Protection Technology Co.Ltd. Urumqi 830000China College of Resources and Environment Sciences Xinjiang Agricultural UniversityUrumqi 830052China

Human activities lead to the accumulation of a large amount of nitrogen and phosphorus in sediments in rivers.Simultaneously,nitrogen and phosphorus can be affected by environment and re-enter the upper water body,causing secondary pollution of the river water.In this study,laboratory simulation experiments were conducted initially to investigate the release of nitrogen and phosphorus from river sediments in Urumqi City and the surrounding areas in Xinjiang Uygur Autonomous Region of China and determine the factors that influence their release.The results of this study showed significant short-term differences in nitrogen and phosphorus release characteristics from sediments at different sampling points.The proposed secondary kinetics model(i.e.,pseudo-second-order kinetics model)better fitted the release process of sediment nitrogen and phosphorus.The release of nitrogen and phosphorus from sediments is a complex process driven by multiple factors,therefore,we tested the influence of three factors(pH,temperature,and disturbance intensity)on the release of nitrogen and phosphorus from sediments in this study.The most amount of nitrate nitrogen(NO_(3)^(–)-N)was released under neutral conditions,while the most significant release of ammonia nitrogen(NH_(4)^(+)-N)occurred under acidic and alkaline conditions.The release of nitrite nitrogen(NO_(2)^(-)-N)was less affected by pH.The dissolved total phosphorus(DTP)released significantly in the alkaline water environment,while the release of dissolved organic phosphorus(DOP)was more significant in acidic water.The release amount of soluble reactive phosphorus(SRP)increased with an increase in pH.The sediments released nitrogen and phosphorus at higher temperatures,particularly NH_(4)^(+)-N,NO_(3)^(–)-N,and SRP.The highest amount of DOP was released at 15.0℃.An increase in disturbance intensity exacerbated the release of nitrogen and phosphorus from sediments.NH_(4)^(+)-N,DTP,and SRP levels increased linearly with the intensity of disturbance,while NO_(3)^(–)-N and NO_(2)

关键词： sediment nitrogen and phosphorus environmental dynamics pseudo-second-order kinetics model dissolved organic phosphorus(DOP) Urumqi City

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Application of Large Language Models in Data Governance in the Petroleum and Petrochemical Industry

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China Oil & Gas 2024年第2期31卷 27-32页

作者： wang ling PetroChina Liaohe Oilfield Company

This paper delves into the current application, challenges, and demands of data governance in the oil and gas industry, as well as methods and implementation strategies to enhance data governance efficacy through the latest technologies such as large language models and algorithm optimization. Through the analysis of the current application status of large language models in intelligent data classification, data quality improvement, knowledge extraction and management, and intelligent search and information retrieval, the main challenges and demands faced by the petroleum and petrochemical industry in data governance have been revealed. A set of data governance optimization solutions tailored for the oil and gas industry has been proposed. These solutions aim to address the current data management challenges in the industry through advanced data processing technologies and algorithmic optimization, enhance the efficiency and quality of data governance, and thereby support the digital transformation and development of the oil and gas industry.

关键词： petrochemical thereby industry

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Effects of Nitrogen-Regulating Gene AreA on Growth,Pathogenicity,and Fumonisin Synthesis of Fusarium proliferatum

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Rice science 2024年第1期31卷 10-13,I0013-I0017页

作者： SUN Lei CHEN Xu ZHOU Qianya ZHANG Tianlei YU Qian LIU Lianmeng HUANG Shiwen wang ling Key Laboratory of Research and Utilization of Ethnomedicinal Plant Resources of Hunan Province College of Biological and Food EngineeringHuaihua UniversityHuaihua 418000China State Key Laboratory of Rice Biology and Breeding China National Rice Research InstituteHangzhou 310006China

Rice spikelet rot disease not only threatens rice yields but also poses risks to humans and animals due to the production of the category 2B carcinogen fumonisins by the pathogen Fusarium proliferatum.Nitrogen(N)metabolism is known to have a significant influence on fungal growth and the synthesis of secondary metabolites.AreA is a global N regulatory gene belonging to the GATA transcription factor family.In this study,we observed that theΔAreA mutant exhibited a notable reduction in growth rate and conidium production.Pathogenicity experiments revealed thatΔAreA had almost lost its ability to infect rice spikelets.

关键词： metabolism Fusarium Growth

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Consensus of Chinese experts on glioma multidisciplinary team management (2nd edition)

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中华医学杂志(英文版) 2024年第11期137卷 1267-1270页

作者： Wu Jinsong Zhou Zhirui Wu Zanyi Tang Zhiwei Li Ming Yin Siyuan Luo Xufei wang ling Chen Yaolong Zhao Guoguang Jiang Tao Mao Ying The National Glioma MDT Alliance of National Center for Neurological Diseases Department of Neurosurgery Beijing Tiantan Hospital Capital Medical University Beijing China Department of Neurosurgery Huashan Hospital Shanghai Medical College Fudan University Shanghai China Department of Radiation Oncology Huashan Hospital Shanghai Medical College Fudan University Shanghai China Department of Neurosurgery The First Affiliated Hospital of Fujian Medical University Fuzhou Fujian China Neurosurgery Department The First Affiliated Hospital of Kunming Medical University Kunming Yunnan China Department of Neurosurgery Henan Provincial People’s Hospital People’s Hospital of Zhengzhou University Zhengzhou Henan China State Key Laboratory of Pharmaceutical Biotechnology Institute for Brain Sciences Advanced Institute for Life Sciences School of Life Sciences Nanjing University Nanjing Jiangsu China Evidence-Based Medicine Center School of Basic Medical Sciences Lanzhou University Lanzhou Gansu China School of Public Health Lanzhou University Lanzhou Gansu China Department of Neurosurgery Xuanwu Hospital Capital Medical University Beijing China

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Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning

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World Journal of Clinical Cases 2024年第7期12卷 1235-1242页

作者： ling wang Deng-Yan Long Intensive Care Unit People's Hospital of Qiandongnan Miao and Dong Autonomous PrefectureKaili 556000Guizhou ProvinceChina

BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective preventive measures.AIM To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.METHODS Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission.Relevant data from the initial 14 d of ICU stay,such as age,comorbidities,sedative dosage,vasopressor dosage,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,were gathered.The relationships between these variables and ICU-AW were examined.Utilizing iterative machine learning techniques,a multilayer perceptron neural network model was developed,and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve.RESULTS Within the ICU-AW group,age,duration of mechanical ventilation,lorazepam dosage,adrenaline dosage,and length of ICU stay were significantly higher than in the non-ICU-AW group.Additionally,sepsis,multiple organ dysfunction syndrome,hypoalbuminemia,acute heart failure,respiratory failure,acute kidney injury,anemia,stress-related gastrointestinal bleeding,shock,hypertension,coronary artery disease,malignant tumors,and rehabilitation therapy ratios were significantly higher in the ICU-AW group,demonstrating statistical significance.The most influential factors contributing to ICU-AW were identified as the length of ICU stay(100.0%)and the duration of mechanical ventilation(54.9%).The neural network model predicted ICU-AW with an area under the curve of 0.941,sensitivity of 92.2%,and specificity of 82.7%.CONCLUSION The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.A primary preventive strategy,when feasible,involves minimizing both ICU stay and mechanical ventilation duration.

关键词： Intensive care unit-acquired weakness Risk factors Machine learning Prevention Strategies

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Evaluation of 3D crustal seismic velocity models in southwest China:Model performance,limitation,and prospects

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Science China Earth Sciences 2024年第2期67卷 604-619页

作者： Xin wang ling CHEN Qi-Fu CHEN Key Laboratory of Earth and Planetary Physics Institute of Geology and GeophysicsChinese Academy of SciencesBeijing100029China College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijing100049China State Key Laboratory of Lithospheric Evolution Institute of Geology and GeophysicsChinese Academy of SciencesBeijing100029China

Southwest China is a tectonically and seismically active region,witnessing strong deformation due to the collision between the Indian and Eurasian plates.Constraining the subsurface velocity structure of this region is thus important in understanding the tectonics and geodynamic processes of continental collision and in mitigating seismic hazards.Numerous studies have provided various 3D seismic velocity models in southwest China.However,discrepancies exist among these models,and less effort has been made to quantify the reliability and accuracy of these existing velocity models.In this study,we use regional 3D waveform simulation to evaluate the performance of various regional crustal 3D velocity models in reproducing observed seismograms.We particularly focus on two recent earthquake sequence in the region,the 2021 Yunnan Yangbi MS_(6.4) earthquake sequence and the 2022 Sichuan Luding MS_(6.8) earthquake sequence.The tested 3D velocity models include the Southwest China Community Velocity model V1.0,the Unified Seismic Tomography Models for Continental China Lithosphere V2.0,the adjoint full waveform tomography model of the crustal and upper mantle beneath Eastern Tibetan Plateau,and the shallow seismic structure model beneath continental China.Our results show that the tested 3D velocity models generally capture well long-period(ling.The full-waveform tomography model shows a slighter better performance than the other models,especially for the body-waves,highlighting the advantages of full-waveform method in achieving sub-wavele

关键词： Southwest China Seismic tomography models Shallow Structures Waveform simulation Model evaluation

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Applications of mercury stable isotopes for tracing volcanism in the geologic record

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Science China Earth Sciences 2024年第5期67卷 1436-1458页

作者： Qing GONG Ming-Xing ling wang ZHENG State Key Laboratory of Nuclear Resources and Environment East China University of TechnologyNanchang 330013China School of Earth System Science Institute of Surface-Earth System ScienceTianjin UniversityTianjin 300072China

Mercury(Hg)enrichments in ancient sediments have been used as a proxy of volcanism,especially large igneous province(LIP)eruptions.However,considering the existence of other potential Hg sources besides volcanoes and the diverse factors(e.g.,organic matters,clay minerals,sulfide minerals and Fe oxides)that can affect Hg sequestration,there are considerable uncertainties to simply regard sedimentary Hg anomalies as signatures of volcanic activities.Mercury stable isotopes,a promising tool for tracing the origins and transformations of Hg,have been increasingly used for determining the causes of Hg spikes and understanding the geochemical behavior of Hg in the geologic record.To date,lots of researches have applied Hg concentrations and Hg isotopes to identify LIP volcanisms linking with significant geological events such as mass extinctions,ocean anoxic events and other environmental perturbations that mainly occurred in the Phanerozoic.However,the results in previous studies clearly show that not all Hg enrichments are derived from volcanic inputs,which emphasize the need for more caution in using Hg as a fingerprint of volcanism.With a better understanding of Hg isotopes in the future,there will be important implications for Hg isotopes to reconstruct volcanic activities in the rock records and their impacts on biological evolution.

关键词： Mercury stable isotopes Large igneous province Mass extinction event Ocean anoxic event

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Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 919-931页

作者： Fei Ming Wenyin Gong ling wang Yaochu Jin the School of Computer Science China University of GeosciencesWuhan 430074China the Department of Automation Tsinghua UniversityBeijing 100084China the Faculty of Technology Bielefeld UniversityNorth Rhine-Westphalia33619 BielefeldGermany

Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention.Various constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with the use of different algorithmic strategies,evolutionary operators,and constraint-handling techniques.The performance of CMOEAs may be heavily dependent on the operators used,however,it is usually difficult to select suitable operators for the problem at hand.Hence,improving operator selection is promising and necessary for CMOEAs.This work proposes an online operator selection framework assisted by Deep Reinforcement Learning.The dynamics of the population,including convergence,diversity,and feasibility,are regarded as the state;the candidate operators are considered as actions;and the improvement of the population state is treated as the reward.By using a Q-network to learn a policy to estimate the Q-values of all actions,the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance.The framework is embedded into four popular CMOEAs and assessed on 42 benchmark problems.The experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.

关键词： Constrained multi-objective optimization deep Qlearning deep reinforcement learning(DRL) evolutionary algorithms evolutionary operator selection

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Reinforcement Learning-Based Dynamic Order Recommendation for On-Demand Food Delivery

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Tsinghua Science and Technology 2024年第2期29卷 356-367页

作者： Xing wang ling wang Chenxin Dong Hao Ren Ke Xing Department of Automation Tsinghua UniversityBeijing 100080China School of Mechanical and Automotive Engineering Qingdao Hengxing University of Science and TechnologyQingdao 266100China Meituan Beijing 100015China

On-demand food delivery(OFD)is gaining more and more popularity in modern society.As a kernel order assignment manner in OFD scenario,order recommendation directly influences the delivery efficiency of the platform and the delivery experience of riders.This paper addresses the dynamism of the order recommendation problem and proposes a reinforcement learning solution method.An actor-critic network based on long short term memory(LSTM)unit is designed to deal with the order-grabbing conflict between different riders.Besides,three rider sequencing rules are accordingly proposed to match different time steps of the LSTM unit with different riders.To test the performance of the proposed method,extensive experiments are conducted based on real data from Meituan delivery platform.The results demonstrate that the proposed reinforcement learning based order recommendation method can significantly increase the number of grabbed orders and reduce the number of order-grabbing conflicts,resulting in better delivery efficiency and experience for the platform and riders.

关键词： on-demand food delivery order recommendation reinforcement learning actor-critic network long short term memory

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Promising use of metformin in treating neurological disorders:biomarker-guided therapies

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Neural Regeneration Research 2024年第5期19卷 1045-1055页

作者： Allison Loan Charvi Syal Margarita Lui ling He Jing wang Regenerative Medicine Program Ottawa Hospital Research InstituteOttawaONCanada Department of Biology Faculty of ScienceUniversity of OttawaOttawaONCanada Department of Pediatrics and Medicine Johns Hopkins Medical SchoolBaltimoreMDUSA Department of Cellular and Molecular Medicine Faculty of MedicineUniversity of OttawaOttawaONCanada University of Ottawa Brain and Mind Research Institute OttawaONCanada

Neurological disorders are a diverse group of conditions that affect the nervous system and include neurodegenerative diseases(Alzheimer’s disease,multiple sclerosis,Parkinson’s disease,Huntington’s disease),cerebrovascular conditions(stroke),and neurodevelopmental disorders(autism spectrum disorder).Although they affect millions of individuals around the world,only a limited number of effective treatment options are available today.Since most neurological disorders express mitochondria-related metabolic perturbations,metformin,a biguanide type II antidiabetic drug,has attracted a lot of attention to be repurposed to treat neurological disorders by correcting their perturbed energy metabolism.However,controversial research emerges regarding the beneficial/detrimental effects of metformin on these neurological disorders.Given that most neurological disorders have complex etiology in their pathophysiology and are influenced by various risk factors such as aging,lifestyle,genetics,and environment,it is important to identify perturbed molecular functions that can be targeted by metformin in these neurological disorders.These molecules can then be used as biomarkers to stratify subpopulations of patients who show distinct molecular/pathological properties and can respond to metformin treatment,ultimately developing targeted therapy.In this review,we will discuss mitochondria-related metabolic perturbations and impaired molecular pathways in these neurological disorders and how these can be used as biomarkers to guide metformin-responsive treatment for the targeted therapy to treat neurological disorders.

关键词： Alzheimer’s disease Huntington’s disease metformin mitochondrial perturbation multiple sclerosis neural degenerative diseases Parkinson’s disease stroke targeted therapy

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