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Origin and tectonic setting of low-Si alkaline magma

Science China Earth Sciences 2024年第5期67卷 1417-1435页

作者： Qiushi ZHOU rui wang State Key Laboratory of Geological Processes and Mineral Resources and Institute of Earth SciencesChina University of GeosciencesBeijing 100083China Frontiers Science Center for Deep-time Digital Earth China University of Geosciences(Beijing)Beijing 100083China

Despite of the relatively rare occurrence of alkaline igneous rocks on Earth, they are the most diverse group of igneous rocks due to variations in their mineral assemblages and geochemical compositions. Alkaline igneous rocks are generally characterized by Si undersaturation, and K-Na(and in some cases C) enrichment. Although there is no unified alkaline igneous rock classification scheme, diagrams such as total alkalis-SiO_(2) or K_(2)O-SiO_(2) can be used to classify alkaline igneous rocks. The origins of silica-saturated alkaline rocks(SiO_(2)>52 wt.%) are relatively complex because multiple episodes of magmatism are often involved, therefore most studies have focused on the origins of silica-undersaturated alkaline magmas.Numerous experimental petrological studies have been conducted in the past few decades. Initially, it was considered that enrichment of trace elements was only achieved by low-degree partial melting of mantle peridotite, but the experimental melts could not reproduce the geochemical composition of natural alkaline igneous rocks. Subsequent studies have focused on carbonate-bearing eclogites that represent the average component of subducted oceanic crust. Although experimental studies indicate that silica-undersaturated and alkali-oversaturated melts can be generated from eclogites, some studies have considered that natural silica-undersaturated alkaline igneous rocks are the result of multi-stage source enrichment by incompatible elements. Low-degree partial melts of the mantle can be emplaced in the lithospheric mantle, forming veins consisting of phlogopite, amphibole, and pyroxene. Melting experiments including these components show that the produced melts better reproduce the naturally occurring silica-undersaturated alkaline magmas. Silica-deficient alkaline igneous rocks occur in various tectonic settings. Those in intraplate and divergent settings(i.e., mainly continental rifts) normally involve contributions from recycled components such as subducted slabs at the mantle transition z

关键词： Alkaline igneous rock Tectonic setting Petrogenesis Big data

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Microsnoop: A generalist tool for microscopy image representation

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The Innovation 2024年第1期5卷 109-117页

作者： Dejin Xun rui wang Xingcai Zhang Yi wang Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China State Key Lab of Computer-Aided Design&Computer Graphics Zhejiang UniversityHangzhou 310058China John A.Paulson School of Engineering and Applied Sciences Harvard UniversityCambridgeMA 02138USA Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University Hangzhou 310018China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314100China

Accurate profiling of microscopy images from small scale to high throughput is an essential procedure in basic and applied biological research.Here,we present Microsnoop,a novel deep learning–based representation tool trained on large-scale microscopy images using masked self-supervised learning.Microsnoop can process various complex and heterogeneous images,and we classified images into three categories:single-cell,full-field,and batch-experiment images.Our benchmark study on 10 high-quality evaluation datasets,containing over 2,230,000 images,demonstrated Microsnoop’s robust and state-ofthe-art microscopy image representation ability,surpassing existing generalist and even several custom algorithms.Microsnoop can be integrated with other pipelines to perform tasks such as superresolution histopathology image and multimodal analysis.Furthermore,Microsnoop can be adapted to various hardware and can be easily deployed on local or cloud computing platforms.We will regularly retrain and reevaluate the model using communitycontributed data to consistently improve Microsnoop.

关键词： hardware image representation

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Neurotoxicity of tetramethylammonium ion on larval and juvenile zebrafish:Effects on neurobehaviors and multiple biomarkers

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Journal of Environmental Sciences 2024年第9期143卷 138-147页

作者： ruixin Zhang rui wang Jiajun Chang GDaniel Sheng Daqiang Yin Key Laboratory of Yangtze River Water Environment Ministry of EducationCollege of Environmental Science and EngineeringTongji UniversityShanghai 200092China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092China

Tetramethylammonium hydroxide(TMAH)is an important compound that utilized and released by the rapidly expanding semiconductor industry,which could hardly be removed by the conventional wastewater treatment techniques.As a cholinergic agonist,the tetramethylammonium ion(TMA^(+))has been reported to induce toxicity to muscular and respiratory systems of mammals and human,however the toxicity on aquatic biota remains poorly known.We investigated the neurotoxic effects of TMA^(+)exposure on zebrafish,based on neurobehavior tests and a series of biomarkers.Significant inhibitions on the swimming distance of zebrafish larvae were observed when the exposure level exceeded 50 mg/L,and significant alterations on swimming path angles(straight and deflective movements)occurred even at 10 mg/L.The tested neurobehavioral endpoints of zebrafish larvae were significantly positively correlated with reactive oxygen species(ROS)and malondialdehyde(MDA),significantly negatively related with the activities of antioxidant enzymes,but not significantly correlated with the level of acetylcholinesterase(AChE).Such relationship indicates that the observed neurotoxic effects on swimming behavior of zebrafish larvae is mainly driven by oxidative stress,rather than the alterations of neurotransmitter.At the highest exposure concentration(200 mg/L),TMA^(+)evoked more severe toxicity on zebrafish juveniles,showing significantly stronger elevation on the MDA activity,and greater inhibitions on the activities of antioxidant enzymes and ACh E,suggesting juveniles were more susceptible to TMA^(+)exposure than larval zebrafish.

关键词： Tetramethylammonium Zebrafish Oxidative stress Acetylcholinesterase Neurobehavior

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Manipulating fast Li_(2)S redox via carbon confinement and oxygen defect engineering of In_(2)O_(3)for lithium-sulfur batteries

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Nano Research 2024年第6期17卷 5179-5187页

作者： Jinlei Qin rui wang Zilong Yuan Pei Xiao Deli wang Key Laboratory of Material Chemistry for Energy Conversion and Storage Ministry of EducationHubei Key Laboratory of Material Chemistry and Service FailureSchool of Chemistry and Chemical EngineeringHuazhong University of Science and TechnologyWuhan 430074China

Lithium-sulfur(Li-S)batteries have been considered as promising energy storage systems due to the merits of high energy density and low cost.However,the lithium polysulfides(LiPSs)diffusion and sluggish redox kinetics hamper the battery performance.In this work,low-bandgap indium oxide(In_(2)O_(3))with dense oxygen vacancies(In_(2)O_(3−x),0

关键词： Li-S batteries nitrogen-doped carbon confinement oxygen vacancies bidirectional Li_(2)S redox

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Fully distributed dynamic event-triggered formation-containment tracking for multiagent systems with multiple types of disturbances

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Science China(Information Sciences) 2024年第1期67卷 168-186页

作者： Weihua LI Huaguang ZHANG Juan ZHANG rui wang College of Information Science and Engineering Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

This paper is dedicated to addressing the time-varying output formation-containment tracking(TVOFCT) problem for heterogeneous linear multiagent systems(MASs) with multiple types of disturbances under directed communication topology.The agents are divided into tracking leaders,formation leaders,and followers.In the output space,the formation leaders are required to form an expected geometric formation while tracking the tracking leader,and the followers are required to enter the convex hull spanned by formation leaders.First,a disturbance observer is designed to observe the disturbance with deterministic dynamics.Then,by combining adaptive technology with event-triggered technology,an independent fully distributed dynamic event-triggered(FDDET) compensator is designed to estimate the state of the tracking leader.The main advantages of this compensator lie in the following three aspects:(1) the adaptive weight does not increase unboundedly;(2) the interval time between events is expanded;(3) the design does not rely on any global information.Similar to the design of this compensator,an independent FDDET convex hull observer is further designed to observe the convex hull spanned by formation leaders.Subsequently,based on the designed disturbance observer,compensator,and convex hull observer,the adaptive disturbance rejection control input which can make the MASs achieve TVOFCT is formulated.Finally,a numerical simulation is provided to clearly verify the validity of the theoretical results.

关键词： formation-containment tracking disturbance rejection multiagent systems fully distributed control dynamic event-triggered control

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A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1342-1357页

作者： Sheng Qi rui wang Tao Zhang Xu Yang ruiqing Sun Ling wang College of Systems Engineering National University of Defense TechnologyChangsha 410073China Xiangjiang Laboratory Changsha 410205China College of Computer National University of Defense TechnologyChangsha 410073China Department of Automation Tsinghua UniversityBeijing 100084China IEEE

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

关键词： Evolutionary algorithms learning swarm optimiza-tion sparse large-scale optimization sparse large-scale multi-objec-tive problems two-layer encoding.

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Evaluating the stability and volumetric flowback rate of proppant packs in hydraulic fractures using the lattice Boltzmann-discrete element coupling method

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第6期16卷 2052-2063页

作者： Duo wang Sanbai Li rui wang Binhui Li Zhejun Pan National Key Laboratory of Continental Shale Oil Northeast Petroleum UniversityDaqing163318China Institute of Unconventional Oil&Gas Northeast Petroleum UniversityDaqing163318China School of Environmental Science and Engineering Southern University of Science and TechnologyShenzhen518055China National Center for Applied Mathematics Shenzhen(NCAMS) Southern University of Science and TechnologyShenzhen518055China National Key Laboratory of Continental Shale Oil Daqing Oilfield of CNPCDaqing163002China Daqing Oilfield Exploration and Development Research Institute DaqingHeilongjiang163712China

The stability and mobility of proppant packs in hydraulic fractures during hydrocarbon production are numerically investigated by the lattice Boltzmann-discrete element coupling method(LB-DEM).This study starts with a preliminary proppant settling test,from which a solid volume fraction of 0.575 is calibrated for the proppant pack in the fracture.In the established workflow to investigate proppant flowback,a displacement is applied to the fracture surfaces to compact the generated proppant pack as well as further mimicking proppant embedment under closure stress.When a pressure gradient is applied to drive the fluid-particle flow,a critical aperture-to-diameter ratio of 4 is observed,above which the proppant pack would collapse.The results also show that the volumetric proppant flowback rate increases quadratically with the fracture aperture,while a linear variation between the particle flux and the pressure gradient is exhibited for a fixed fracture aperture.The research outcome contributes towards an improved understanding of proppant flowback in hydraulic fractures,which also supports an optimised proppant size selection for hydraulic fracturing operations.

关键词： Numerical simulation Hydraulic fracturing Proppant flowback Closure stress Particulate flow

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Learning to Branch in Combinatorial Optimization With Graph Pointer Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 157-169页

作者： rui wang Zhiming Zhou Kaiwen Li Tao Zhang Ling wang Xin Xu Xiangke Liao IEEE the Xiangjiang Laboratory and the College of Systems Engineering National University of Defense TechnologyChangsha 410073China the Institute of Automation Chinese Academy of SciencesBeijing 100190China the College of Systems Engineering National University of Defense TechnologyChangsha 410073 the Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology Changsha 410073China the Department of Automation Tsinghua UniversityBeijing 100084China the College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410073China the College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China

Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex problems.Given the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.

关键词： Branch-and-bound(B&B) combinatorial optimization deep learning graph neural network imitation learning

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Evolutionary Multi-Tasking Optimization for High-Efficiency Time Series Data Clustering

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Tsinghua Science and Technology 2024年第2期29卷 343-355页

作者： rui wang Wenhua Li Kaili Shen Tao Zhang Xiangke Liao Xiangjiang Laboratory Changsha 410205China College of Systems Engineering National University of Defense TechnologyChangsha 410073China Ant Group Co. Ltd.Hangzhou 310000China College of Computer Science and Technology NUDTChangsha 410073China

Time series clustering is a challenging problem due to the large-volume,high-dimensional,and warping characteristics of time series data.Traditional clustering methods often use a single criterion or distance measure,which may not capture all the features of the data.This paper proposes a novel method for time series clustering based on evolutionary multi-tasking optimization,termed i-MFEA,which uses an improved multifactorial evolutionary algorithm to optimize multiple clustering tasks simultaneously,each with a different validity index or distance measure.Therefore,i-MFEA can produce diverse and robust clustering solutions that satisfy various preferences of decision-makers.Experiments on two artificial datasets show that i-MFEA outperforms single-objective evolutionary algorithms and traditional clustering methods in terms of convergence speed and clustering quality.The paper also discusses how i-MFEA can address two long-standing issues in time series clustering:the choice of appropriate similarity measure and the number of clusters.

关键词： time series clustering evolutionary multi-tasking multifactorial optimization clustering validity index distance measure

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A combination of straw incorporation and polymer-coated urea offsets soil ammonia and nitrous oxide emissions in winter wheat fields

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Journal of Integrative Agriculture 2024年第5期23卷 1718-1736页

作者： Xiaoyun wang Yajie Tian Qianhui Zhang Zhengxin Zhao rui wang Huanjie Cai Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education Northwest A&F UniversityYangling712100China College of Water Resources and Architectural Engineering Northwest A&F UniversityYangling 712100China

The combined effects of straw incorporation(SI)and polymer-coated urea(PCU)application on soil ammonia(NH_(3))and nitrous oxide(N_(2)O)emissions from agricultural fields have not been comprehensively evaluated in Northwest China.We conducted a two-year field experiment to assess the effects of combining SI with either uncoated urea(U)or PCU on soil NH_(3)emissions,N_(2)O emissions,winter wheat yields,yield-scaled NH_(3)(/NH_(3)),and yield-scaled N_(2)O(/N_(2)O).Five treatments were investigated,no nitrogen(N)fertilizer(N_(0)),U application at 150 kg N ha-1 with and without SI(SI+U and S_(0)+U),and PCU application at 150 kg N ha^(-1) with and without SI(SI+PCU and S_(0)+PCU).The results showed that the NH_(3);emissions increased by 20.98-34.35%following Sl compared to straw removal,mainly due to increases in soil ammonium(NH_(4)^(+)-N)content and water-flled pore space(WFPS).SI resulted in higher N_(2)O emissions than under the So scenario by 13.31-49.23%due to increases in soil inorganic N(SIN)contents,WFPS,and soil microbial biomass.In contrast,the PCU application reduced the SIN contents compared to the U application,reducing the NH_(3)and N_(2)O emissions by 45.99-58.07 and 18.08-53.04%,respectively.Moreover,no significant positive effects of the SI or PCU applications on the winter wheat yield were observed.The lowest /NH_(3) and /N_(2)O values were observed under the S_(0)+PCU and SI+PCU treatments.Our results suggest that single PCU applications and their combination with straw are the optimal agricultural strategies for mitigating gaseous N emissions and maintaining optimal winter wheat yields in Northwest China.

关键词： straw incorporation polymer-coated urea NH_(3)and N_(2)O emissions winter wheat yields

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