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A Cooperated Imperialist Competitive Algorithm for Unrelated Parallel Batch Machine Scheduling Problem

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Computers, Materials & Continua 2024年第5期79卷 1855-1874页

作者： Deming Lei Heen Li College of Automation Wuhan University of TechnologyWuhan430070China

This study focuses on the scheduling problem of unrelated parallel batch processing machines(BPM)with release times,a scenario derived from the moulding process in a foundry.In this process,a batch is initially formed,placed in a sandbox,and then the sandbox is positioned on a BPM formoulding.The complexity of the scheduling problem increases due to the consideration of BPM capacity and sandbox volume.To minimize the makespan,a new cooperated imperialist competitive algorithm(CICA)is introduced.In CICA,the number of empires is not a parameter,and four empires aremaintained throughout the search process.Two types of assimilations are achieved:The strongest and weakest empires cooperate in their assimilation,while the remaining two empires,having a close normalization total cost,combine in their assimilation.A new form of imperialist competition is proposed to prevent insufficient competition,and the unique features of the problem are effectively utilized.Computational experiments are conducted across several instances,and a significant amount of experimental results show that the newstrategies of CICAare effective,indicating promising advantages for the considered BPMscheduling problems.

关键词： Release time assimilation imperialist competitive algorithm batch processing machines scheduling

Machine Learning With Data assimilation and Uncertainty Quantification for Dynamical Systems:A Review

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IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1361-1387页

作者： Sibo Cheng César Quilodrán-Casas Said Ouala Alban Farchi Che Liu Pierre Tandeo Ronan Fablet Didier Lucor Bertrand Iooss Julien Brajard Dunhui Xiao Tijana Janjic Weiping Ding Yike Guo Alberto Carrassi Marc Bocquet Rossella Arcucci Data Science Institute Department of ComputingImperial College LondonSW72AZ London Department of Earth Science and Engineering Imperial College LondonSW72AZ London Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China the IMT Atlantique Lab-STICCUMR CNRS 6285France and OdysseyInria/IMTFrance.P.Tandeo is also with RIKEN Center for Computational ScienceKobeJapan the CEREA École des Ponts and EDF R&Dîle-de-FranceFrance the Laboratoire Interdisciplinaire des Sciences du Numérique CNRSParis-Saclay UniversityF-91403OrsayFrance the Electricitéde France(EDF) 78401 ChatouFranceInstitut de Mathématiques de Toulouse31062 ToulouseFrance and SINCLAIR AI LabSaclayFrance the Sorbonne University ParisFranceand also with Nansen Environmental and Remote Sensing Center(NERSC)BergenNorway the School of Mathematical Sciences Tongji UniversityShanghai 200092China the Mathematical Institute for Machine Learning and Data Science KU Eichstaett-IngolstadtBavariaGermany the School of Information Science and Technology Nantong UniversityNantong 226019China the Department of Physics and Astronomy“Augusto Righi” University of Bologna40124 BolognaItaly

Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.Typical applications span from computational fluid dynamics(CFD)to geoscience and climate systems.Recently,much effort has been given in combining DA,UQ and machine learning(ML)techniques.These research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive guide.This paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of applications.This review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems.Therefore,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice versa.Some exciting perspectives of this rapidly developing research field are also discussed.Index Terms-Data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).

关键词： assimilation overcome apply

Transcriptomics reveals the effect of ammonia nitrogen concentration on Pseudomonas stutzeri F2 assimilation and the analysis of amtB function

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Synthetic and Systems Biotechnology 2023年第2期8卷 262-272页

作者： Wei-lai Fu Pei-feng Duan Qiang Wang Ya-xin Liao Yun-shuang Wang Mei-juan Xu Hui-hui Jiang Xian Zhang Zhi-ming Rao The Key Laboratory of Industrial Biotechnology Ministry of EducationSchool of BiotechnologyJiangnan UniversityWuxi214122PR China The Key Laboratory of Functional Aquafeed and Culture Environment Control Fujian DBN Huayou Aquatic Science and Technology Co.LtdZhangzhou363500PR China School of Biological and Environmental Engineering Chaohu UniversityHefei238024PR China

The biological treatment of wastewater with high concentrations of ammonia nitrogen has become a hot research issue,but there are limited reports on the mechanism of ammonia nitrogen utilization by microorganisms.In this paper,a transcriptomic approach was used to investigate the differences in gene expression at 500.0 mg/L(Amo 500)and 100.0 mg/L(Amo 100)ammonium concentrations to reveal the mechanism of ammonia nitrogen removal from water by Pseudomonas stutzeri F2.The transcriptome data showed 1015(459 up-regulated and 556 down-regulated)differentially expressed genes with functional gene annotation related to nitrogen source metabolism,glycolysis,tricarboxylic acid cycle,extracellular polysaccharide synthesis,energy conversion and transmembrane transport,revealing the metabolic process of ammonium nitrogen conversion to biological ni-trogen in P.stutzeri F2 through assimilation.To verify the effect of ammonium transporter protein(AmtB)of cell membrane on assimilation,a P.stutzeri F2-ΔamtB mutant strain was obtained by constructing a knockout plasmid(pK18mobsacB-ΔamtB),and it was found that the growth characteristics and ammonium removal rate of the mutant strain were significantly reduced at high ammonium concentration.The carbon source components and dissolved oxygen conditions were optimized after analyzing the transcriptome data,and the ammonium removal rate was increased from 41.23%to 94.92%with 500.0 mg/L ammonium concentration.The study of P.stutzeri F2 transcript level reveals the mechanism of ammonia nitrogen influence on microbial assimilation process and improvement strategy,which provides a new strategy for the treatment of ammonia nitrogen wastewater.

关键词： Ammonia nitrogen Pseudomonas stutzeri F2 Transcriptomic assimilation amtB

Effect of 2-m Temperature Data assimilation in the CMA-MESO 3DVAR System

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Journal of Meteorological Research 2023年第2期37卷 218-233页

作者： Zhifang XU Lin ZHANG Ruichun WANG Jiandong GONG CMA Earth System Modeling and Prediction Centre China Meteorological Administration(CMA)Beijing100081 State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesChina Meteorological AdministrationBeijing100081 National Meteorological Centre China Meteorological AdministrationBeijing100081

assimilation of surface observations including 2-m temperature(T_(2m))in numerical weather prediction(NWP)models remains a challenging problem owing to differences between the elevation of model terrain and that of actual observation stations.NWP results can be improved only if surface observations are assimilated appropriately.In this study,a T_(2m)data assimilation scheme that carefully considers misrepresentation of model and station terrain was established by using the three-dimensional variational data assimilation(3DVAR)system of the China Meteorological Administration mesoscale model(CMA-MESO).The corresponding forward observation operator,tangent linear operator,and adjoint operator for the T_(2m)observations under three terrain mismatch treatments were developed.The T_(2m)data were assimilated in the same method as that adopted for temperature sounding data with additional representative errors,when station terrain was 100 m higher than model terrain;otherwise,the T_(2m)data were assimilated by using the surface similarity theory assimilation operator.Furthermore,if station terrain was lower than model terrain,additional representative errors were stipulated and corrected.Test of a rainfall case showed that the observation innovation and analysis residuals both exhibited Gaussian distribution and that the analysis increment was reasonable.Moreover,it was found that on completion of the data assimilation cycle,T_(2m)data assimilation obviously influenced the temperature,wind,and relative humidity fields throughout the troposphere,with the greatest impact evident in the lower layers,and that both the area and the intensity of rainfall were better forecasted,especially for the first 12hours.Long-term continuous experiments for 2–28 February and 5–20 July 2020,further verified that T_(2m)data assimilation reduced deviations not only in T_(2m)but also in 10-m wind forecasts.More importantly,the precipitation equitable threat scores were improved over the two experimental periods.In summary,this study confirme

关键词： 2-m temperature China Meteorological Administration mesoscale model(CMA-MESO) assimilation three-dimensional variational(3DVAR)data assimilation kilometer-scale

Preface to the Special Issue:Predictability,Data assimilation,and Dynamics of High Impact Weather-In Memory of Dr.Fuqing ZHANG

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Advances in Atmospheric Sciences 2022年第5期39卷 673-675页

作者： Zhiyong MENG Department of Atmospheric and Oceanic Sciences School of PhysicsPeking UniversityBeijing 100871China

It has been two and a half years since Fuqing ZHANG,a distinguished professor in the Department of Meteorology and Atmospheric Science at the Pennsylvania State University(PSU),passed away unexpectedly on 19 July 2019.This special issue is to commemorate Prof.Fuqing ZHANG,who has made tremendous contributions on predictability,data assimilation,and the dynamics of high impact weather.

关键词： ZHANG weather assimilation

Some progress on ocean data assimilation in China: Introduction of the special section “Ocean Data assimilation”

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Acta Oceanologica Sinica 2022年第2期41卷 1-2页

作者： Huizan Wang Yan Chen Weimin Zhang College of Meteorology and Oceanography National University of Defense TechnologyChangsha 410073China

This special section is the scientific legacy of the 13th National Ocean Data assimilation and Numerical Simulation Conference of China,which was held in Changsha,China during December 3–4,2020 with more than 160 participants from 35 units in China.It continued a series of National Ocean Data assimilation conferences which began in 2003,which played an important role in the development of ocean data assimilation in China.In this 13th conference,there were 41 oral presentations and 15 posters,which focused on ocean data assimilation methods,ocean reanalysis data,developments and operational application of ocean data assimilation systems,assimilation of new type ocean observations,ocean targeted observation analysis,coupled data assimilation,ocean models and parameter estimation,etc.This conference was hosted by National University of Defense Technology,and cohosted by the Institute of Atmospheric Physics of Chinese Academy of Sciences,the South China Sea Institute of Oceanology of Chinese Academy of Sciences,the Second Institute of Oceanography of the Ministry of Natural Resources,Ocean University of China,Tianjin University and Hohai University.

关键词： China ocean assimilation

assimilation of Satellite Altimetry into a Western North Pacific Operational Model

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Advances in Atmospheric Sciences 2001年第5期18卷 767-786页

作者： Masafumi Kamachi, Tsurane Kuragano, Noriya Yoshioka, Jiang Zhu, and Francesco Uboldi Meteorological Research Institute, 1-1 Nagamine, Tsukubo 305-0052 Japan Japan Meteorological Agenry, 1-3-4 Otemachi, Tokyo 100-8122 Japan Institute of Atmospheric Physics Meteorological Research Institute Tsukuba Japan Japan Meteorological Agency Tokyo Japan Institute of Atmospheric Physics Chinese Academy of Science Beijing China Magritte SNC Milano Italy

An ocean data assimilation system, COMPASS-K (the Comprehensive Ocean Modeling, Prediction, Analysis and Synthesis System in the Kuroshio-region), has been developed at the Meteorological Research Institute (MRI). The purposes of the development are understanding ocean variability in the Kuroshio region as a local response to a global climate change with assimilated four-dimensional data sets, development of an operational system in the Japan Meteorological Agency, and for the GODAE (Global Ocean Data assimilation Experiment) project. The model is an eddy permitting version of an MRI-OGCM. Space-time decorrelation scales of ocean variability are estimated with TOPEX / POSEIDON (T/P) altimeter data. Subsurface temperature and salinity fields are projected from the T / P altimeter data with a statistical correlation method and are assimilated into the model with a time-retrospective nudging scheme. Seasonal variation in the western North Pacific is investigated. Realistic space-time distribution of the physical quantities, the path of Kuroshio and its separation from Honshu are captured well. The Kuroshio volume transport is well reproduced in a reanalysis experiment of 1993. Preliminary predictability experiments are done in February and March, 1994. Predictability diagram shows the time scale of the predictability for temperature field is about 17 days in the Kuroshio south of Japan. This time scale is smaller than that in the North Atlantic.

关键词： assimilation Kuroshio predictability

Towards reliable Arctic sea ice prediction using multivariate data assimilation

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Science Bulletin 2019年第1期64卷 63-72页

作者： Jiping Liu Zhiqiang Chen Yongyun Hu Yuanyuan Zhang Yifan Ding Xiao Cheng Qinghua Yang Lars Nerger Gunnar Spreen Radley Horton Jun Inoue Chaoyuan Yang Ming Li Mirong Song Department of Atmospheric and Environmental Sciences University at Albany State University of New York College of Ocean and Meteorology Guangdong Ocean University Department of Atmospheric and Oceanic Sciences School of Physics Peking University College of Global Change and Earth System Science Beijing Normal University Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies and School of Atmospheric Sciences Sun Yat-sen University Alfred-Wegener-Institut Helmholtz Zentrum für Polar-und Meeresforschung University of Bremen Institute of Environmental Physics Lamont-Doherty Earth Observatory Columbia University Earth Institute National Institute of Polar Research Polar Research and Forecasting Division National Marine Environmental Forecasting Center State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences

Rapid declines in Arctic sea ice have captured attention and pose significant challenges to a variety of stakeholders. There is a rising demand for Arctic sea ice prediction at daily to seasonal time scales, which is partly a sea ice initial condition problem. Thus, a multivariate data assimilation that integrates sea ice observations to generate realistic and skillful model initialization is needed to improve predictive skill of Arctic sea ice. Sea ice data assimilation is a relatively new research area. In this review paper, we focus on two challenges for implementing multivariate data assimilation systems for sea ice forecast. First, to address the challenge of limited spatiotemporal coverage and large uncertainties of observations, we discuss sea ice parameters derived from satellite remote sensing that(1) have been utilized for improved model initialization, including concentration, thickness and drift, and(2) are currently under development with the potential for enhancing the predictability of Arctic sea ice, including melt ponds and sea ice leads. Second, to strive to generate the ‘‘best" estimate of sea ice initial conditions by combining model simulations/forecasts and observations, we review capabilities and limitations of different data assimilation techniques that have been developed and used to assimilate observed sea ice parameters in dynamical models.

关键词： Arctic sea ice prediction Remote sensing Data assimilation

assimilation of carbonate country rock by the parent magma of the Panzhihua Fe-Ti-V deposit(SW China):Evidence from stable isotopes

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Geoscience Frontiers 2013年第5期4卷 547-554页

作者： Clement Ganino Chris Harris Nicholas T.Arndt Stephen A.Prevec Geoffrey H.Howarth Geoazur Universite de Nice-Sophia AntipolisCNRS/IRD250 rue Albert Einstein 06560 ValbonneFrance Department of Geological Sciences University of Cape TownRondebosch 7701South Africa ISTerre Grenoble UniversitesCNRS/IRD BP 53 38041 Grenoble CEDEX 9France Department of Geology Rhodes UniversityGrahamstown 6140South Africa

The Panzhihua intrusion in southwest China is part of the Emeishan Large Igneous Province and host of a large Fe-Ti-V ore deposit.During emplacement of the main intrusion,multiple generations of mafic dykes invaded carbonate wall rocks,producing a large contact aureole.We measured the oxygen-isotope composition of the intrusions,their constituent minerals,and samples of the country rock.Magnetite and plagioclase from Panzhihua intrusion haveδ18O values that are consistent with magmatic equilibrium, and formed from magmas withδ18O values that were 1-2‰higher than expected in a mantle-derived magma.The unmetamorphosed country rock has highδ18O values,ranging from 13.2‰（sandstone） to 24.6-28.6‰（dolomite）.The skarns and marbles from the aureole have lowerδ18O andδ13C values than their protolith suggesting interaction with fluids that were in exchange equilibrium with the adjacent mafic magmas and especially the numerous mafic dykes that intruded the aureole.This would explain the alteration ofδ18O of the dykes which have significantly higher values than expected for a mantle-derived magma.Depending on the exactδ18O values assumed for the magma and contaminant, the amount of assimilation required to produce the elevatedδ18O value of the Panzhihua intrusion was between 8 and 13.7 wt.%,assuming simple mixing.The exact mechanism of contamination is unclear but may involve a combination of assimilation of bulk country rock,mixing with a melt of the country rock and exchange with CO2-rich fluid derived from decarbonation of the marls and dolomites.These mechanisms,particularly the latter,were probably involved in the formation of the Fe-Ti-V ores.

关键词： Panzhihua Stable isotopes Carbonates assimilation Contamination