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Application of multi-attribute matching technology based on geological models for sedimentary facies:A case study of the 3rd member in the Lower Jurassic Badaowan Formation,Hongshanzui area,Junggar Basin,China

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Petroleum Science 2022年第1期19卷 116-127页

作者： Zong-Quan Yao Fan Yang Deleqiati Jianatayi Wei Wang Yang Gao Le Sun Wen-Feng Wang Bo-Yu Mu Pin-Bai Pan School of Geology and Mining Engineering Xinjiang UniversityUrumqiXinjiang 830047China Key Laboratory of Central Asian Orogenic Belts and Continental Dynamics Xinjiang UniversityUrumqiXinjiang 830047China Research Institute of Peleoleum Exploration and Development PetroChinaBeijing100083China National 305 Project Office UrumqiXinjiang 830000China Beijing Sunshine Geo-Tech Co.Ltd. Beijing100080China CNOOC Research Institute Co. Ltd.Beijing100028China

Regarding high drilling costs,an effort should be made to substantially reduce the drilling operation.To achieve this goal,exploration and development stages should be carried out precisely with maximum information acquired from the reservoir.The use of multi-attribute matching technology to predict sedimentary system has always been a very important but challenging task.To resolve the challenges,we utilized a quantitative analysis method of seismic attributes based on geological models involving high resolution 3D seismic data for sedimentary facies.We developed a workflow that includes core data,seismic attribute analysis,and well logging to highlight the benefit of understanding the facies distribution in the 3 rd Member of the Lower Jurassic Badaowan Formation,Hongshanzui area,Junggar Basin,China.1)Data preprocessing.2)Cluster analysis.3)RMS attribute based on a normal distribution constrains facies boundary.4)Mapping the sedimentary facies by using MRA(multiple regression analysis)prediction model combined with the lithofacies assemblages and logging facies assemblages.The confident level presented in this research is 0.745,which suggests that the methods and data-mining techniques are practical and efficient,and also be used to map facies in other similar geological settings.

关键词： clustering analysis Mapping the sedimentary facies Normal distribution Multi-attribute matching Hongshanzui area

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Slope deformation partitioning and monitoring points optimization based on cluster analysis

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山地科学学报(英文) 2023年第8期20卷 2405-2421页

作者： LI Yuan-zheng SHEN Jun-hui ZHANG Wei-xin ZHANG Kai-qiang PENG Zhang-hai HUANG Meng State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of TechnologyChengdu 610059China School of Resources and Environmental Engineering Hefei University of TechnologyHefei 230009China Inner Mongolia First Machinery Group Co. LtdBaotou 014030China School of Civil Engineering Chongqing UniversityChongqing 400045China China Communications Construction Fourth Harbor Engineering Institute Co. LtdGuangzhou 510230China

The scientific and fair positioning of monitoring locations for surface displacement on slopes is a prerequisite for early warning and forecasting.However,there is no specific provision on how to effectively determine the number and location of monitoring points according to the actual deformation characteristics of the slope.There are still some defects in the layout of monitoring points.To this end,based on displacement data series and spatial location information of surface displacement monitoring points,by combining displacement series correlation and spatial distance influence factors,a spatial deformation correlation calculation model of slope based on clustering analysis was proposed to calculate the correlation between different monitoring points,based on which the deformation area of the slope was divided.The redundant monitoring points in each partition were eliminated based on the partition's outcome,and the overall optimal arrangement of slope monitoring points was then achieved.This method scientifically addresses the issues of slope deformation zoning and data gathering overlap.It not only eliminates human subjectivity from slope deformation zoning but also increases the efficiency and accuracy of slope monitoring.In order to verify the effectiveness of the method,a sand-mudstone interbedded CounterTilt excavation slope in the Chongqing city of China was used as the research object.Twenty-four monitoring points deployed on this slope were monitored for surface displacement for 13 months.The spatial location of the monitoring points was discussed.The results show that the proposed method of slope deformation zoning and the optimized placement of monitoring points are feasible.

关键词： Excavation slope Surface displacement monitoring Spatial deformation analysis clustering analysis Slope deformation partitioning Monitoring point optimization

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analysis of CLARANS Algorithm for Weather Data Based on Spark

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Computers, Materials & Continua 2023年第8期76卷 2427-2441页

作者： Jiahao Zhang Honglin Wang College of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China

With the rapid development of technology,processing the explosive growth of meteorological data on traditional standalone computing has become increasingly time-consuming,which cannot meet the demands of scientific research and business.Therefore,this paper proposes the implementation of the parallel clustering Large Application based upon RANdomized Search(CLARANS)clustering algorithm on the Spark cloud computing platformto cluster China’s climate regions usingmeteorological data from1988 to 2018.The aim is to address the challenge of applying clustering algorithms to large datasets.In this paper,the morphological similarity distance is adopted as the similarity measurement standard instead of Euclidean distance,which improves clustering accuracy.Furthermore,the issue of local optima caused by an improper selection of initial clustering centers is addressed by utilizing the max-distance criterion.Compared to the k-means clustering algorithm already implemented in the Spark platform,the proposed algorithm has strong robustness,can reduce the interference of outliers in the dataset on clustering results,and has higher parallel performance than the frequently used serial algorithms,thus improving the efficiency of big data analysis.This experiment compares the clustered centroid data with the annual average meteorological data of representative cities in the five typical meteorological regions that exist in China,and the results show that the clustering results are in good agreement with the meteorological data obtained from the National Meteorological Science Data Center.This algorithm has a positive effect on the clustering analysis of massive meteorological data and deserves attention in scientific research activities.

关键词： clustering analysis cloud computing platform parallel algorithm

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Physical and chemical characterization of sediments from an Andean river exposed to mining and agricultural activities: The Moquegua River, Peru

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International Journal of Sediment Research 2022年第6期37卷 780-793页

作者： Luis de Los Santos Valladares Juan Luis Ccamapaza Renato Arturo Valencia-Bedregal Luis Enrique Borja-Castro Jose Velazquez-Garcia Dehiwalage Harshani Nimalika Perera Adrian Ionescu David Arvidsson Eustace Peregrine Barnes Peter Newton Hugo Lepage Patrick Byrne Angel Guillermo Bustamante Dominguez Crispin H.W.Barnes Cavendish Laboratory Department of Physics University of Cambridge Escuela Acad emico Profesional de Ingeniería Ambiental Universidad Nacional de Moquegua Provincia de Mariscal Nieto Laboratorio de Cer amicos y Nanomateriales Facultad de Ciencias Físicas Universidad Nacional Mayor de San Marcos School of Biological and Environmental Science Liverpool John Moores University

This study presents an analysis of sediments in an Andean river impacted by both natural conditions and anthropogenic activities. Fifty samples were collected from selected sites throughout the Moquegua River drainage basin, and Tambo River headwaters at Pasto Grande, in Peru, and analysed with X-ray diffraction, energy dispersive X-ray spectroscopy, scanning electron microscopy, Fourier-transform infrared spectroscopy and thermogravimetric analysis. Si, Ca, Al, Fe, and O, common constituents of soils, were dominant, along with K, N, and P. The latter originating as primary macronutrients or chemical residuals from fertilizers used in agriculture. Higher concentrations of macronutrients and organic components were found in agricultural and urban areas, respectively. Arsenic minerals were also detected, which occur naturally, but possibly at levels augmented by anthropogenic activity. The application of cluster analysis revealed clustering between arsenic, arsenolite, and potassium but no significant geospatial correlation between sample sites in the drainage basin.

关键词： Andean river Moquegua river Sediments Fourier-transform infrared spectroscopy TG-DTG/DTA clustering analysis

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Jensen-Renyi’s-Tsallis Fuzzy Divergence Information Measure with its Applications

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Communications in Mathematics and Statistics 2022年第3期10卷 451-482页

作者： Ratika Kadian Satish Kumar Department of Mathematics Maharishi Markandeshwar(Deemed to be)UniversityMullana-Ambala 133207India

In this paper,we have characterized the sum of two general measures associated with two distributions with discrete random variables.One of these measures is logarithmic,while others contains the power of variables,named as joint representation of Renyi’s-Tsallis divergence measure.Then,we propose a divergence measure based on Jensen-Renyi’s-Tsallis entropy which is known as a Jensen-Renyi’s-Tsallis divergence measure.It is a generalization of J-divergence information measure.One of the silent features of this measure is that we can allot the equal weight to each probability distribution.This makes it specifically reasonable for the study of decision problems,where the weights could be the prior probabilities.Further,the idea has been generalized from probabilistic to fuzzy similarity/dissimilarity measure.Besides the validation of the proposed measure,some of its key properties are also studied.Further,the performance of the proposed measure is contrasted with some existing measures.At last,some illustrative examples are solved in the context of clustering analysis,financial diagnosis and pattern recognition which demonstrate the practicality and adequacy of the proposed measure between two fuzzy sets(FSs).

关键词： Fuzzy sets Divergence measure Similarity measure clustering analysis Financial diagnosis

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Conventional Breeding and Molecular Markers for Blast Disease Resistance in Rice (Oryza sativa L.)

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Phyton-International Journal of Experimental Botany 2023年第3期92卷 725-746页

作者： Adel A.Rezk Mohamed M.El-Malky Hossam S.El-Beltagi Mohammed Al-daej Kotb AAttia Agricultural Biotechnology Department College of Agricultural and Food ScienceKing Faisal UniversityAl-Ahsa31982Saudi Arabia Plant Pathology Research Institute Agriculture Research CenterGiza12619Egypt Rice Research and Training Center SakhaField Crops Research InstituteAgricultural Research CenterKafr El-Sheikh33717Egypt Biochemistry Department Faculty of AgricultureCairo UniversityGiza12613Egypt Center of Excellence in Biotechnology Research King Saud UniversityRiyadh11451Saudi Arabia Rice Biotechnology Laboratory Rice Research DepartmentField Crops Research InstituteARCKafr EL-Sheikh33717Egypt

Monogenic lines,which carried 23 genes for blast resistance were tested and used donors to transfer resistance genes by crossing method.The results under blast nursery revealed that 9 genes from 23 genes were susceptible to highly susceptible under the three locations(Sakha,Gemmeza,and Zarzoura in Egypt);Pia,Pik,Pik-p,Piz-t,Pita,Pi b,Pi,Pi 19 and Pi 20.While,the genes Pii,Pik-s,Pik-h,Pi z,Piz-5,Pi sh,Pi 3,Pi 1,Pi 5,Pi 7,Pi 9,Pi 12,Pikm and Pita-2 were highly resistant at the same locations.clustering analysis confirmed the results,which divided into two groups;the first one included all the susceptible genes,while the second one included the resistance genes.In the greenhouse test,the reaction pattern of five races produced 100%resistance under artificial inoculation with eight genes showing complete resistance to all isolates.The completely resistant genes:Pii,Pik-s,Piz,Piz-5(=bi2)(t),Pita(=Pi4)(t),Pita,Pi b and Pi1 as well as clustering analysis confirmed the results.In the F1 crosses,the results showed all the 25 crosses were resistant for leaf blast disease under field conditions.While,the results in F2 population showed seven crosses with segregation ratio of 15(R):1(S),two cross gave segregated ratio of 3 R:1 S and one gave 13:3.For the identification of blast resistance genes in the parental lines,the marker K3959,linked to Pik-s gene and the variety IRBLKS-F5 carry this gene,which was from the monogenic line.The results showed that four genotypes;Sakha 105,Sakha 103,Sakha 106 and IRBLKS-F5 were carrying Pik-s gene,while was absent in the Sakha 101,Sakha 104,IRBL5-M,IRBL9-W,IRBLTACP1 and IRBL9-W(R)genotypes.As for Pi 5 gene,the results showed that it was present in Sakha 103 and Sakha 104 varieties and absent in the rest of the genotypes.In addition,Pita-Pita-2 gene was found in the three Egyptian genotypes(Sakha 105,Sakha 101 and Sakha 104)plus IRBLTACP1 monogenetic.In F2 generation,six populations were used to study the inheritance of blast resistance and specific primers to confirm the ratio and identif

关键词： Biotechnology tools clustering analysis monogenic lines resistance genes breeding Oryza sativa L.

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Tower-Based Greenhouse Gas Measurement Network Design——The National Institute of Standards and Technology North East Corridor Testbed

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Advances in Atmospheric Sciences 2017年第9期34卷 1095-1105页

作者： Israel LOPEZ-COTO Subhomoy GHOSH Kuldeep PRASAD James WHETSTONE National Institute of Standards and Technology

The North-East Corridor （NEC） Testbed project is the 3rd of three NIST （National Institute of Standards and Technology） greenhouse gas emissions testbeds designed to advance greenhouse gas measurements capabilities. A design approach for a dense observing network combined with atmospheric inversion methodologies is described. The Advanced Research Weather Research and Forecasting Model with the Stochastic Time-Inverted Lagrangian Transport model were used to derive the sensitivity of hypothetical observations to surface greenhouse gas emissions （footprints）. Unlike other network design algorithms, an iterative selection algorithm, based on a k-means clustering method, was applied to minimize the similarities between the temporal response of each site and maximize sensitivity to the urban emissions contribution. Once a network was selected, a synthetic inversion Bayesian Kalman filter was used to evaluate observing system performance. We present the performances of various measurement network configurations consisting of differing numbers of towers and tower locations. Results show that an overly spatially compact network has decreased spatial coverage, as the spatial information added per site is then suboptimal as to cover the largest possible area, whilst networks dispersed too broadly lose capabilities of constraining flux uncertainties. In addition, we explore the possibility of using a very high density network of lower cost and performance sensors characterized by larger uncertainties and temporal drift. analysis convergence is faster with a large number of observing locations, reducing the response time of the filter. Larger uncertainties in the observations implies lower values of uncertainty reduction. On the other hand, the drift is a bias in nature, which is added to the observations and, therefore, biasing the retrieved fluxes.

关键词： greenhouse gases network design clustering analysis Kalman filter OSSE

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From demand response to integrated demand response: review and prospect of research and application

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Protection and Control of Modern Power Systems 2019年第1期4卷 150-162页

作者： Wujing Huang Ning Zhang Chongqing Kang Mingxuan Li Molin Huo Department of Electrical Engineering Tsinghua UniversityHaidian DistrictBeijing100084China State Grid(Suzhou)City&Energy Research Institute SuzhouChina

In the traditional power system demand response, customers respond to electricity price or incentive and change their original power consumption pattern accordingly to gain additional benefits. With the development of multi-energy systems (MES) in which electricity, heat, natural gas and other forms of energy are coupled with each other, all types of energy customers are able to participate in demand response, leading to the concept of integrated demand response (IDR). In IDR, energy consumers can response not only by reducing energy consumption or opting for off-peak energy consumption but also by changing the type of the consumed energy. Taking the traditional demand response in power system as a starting point, the studies of the fundamental theory, framework design and potential estimation of demand response in power system are reviewed, and the practical cases and software development of demand response are introduced. Finally, the current theoretical research and application of IDR are assessed.

关键词： Demand response Integrated demand response Multi-energy systems Integrated energy systems Energy hub Energy storage Microgrid Renewable energy Gas network clustering analysis Review

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analysis on herbal medicines utilized for treatment of COVID-19

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Acta Pharmaceutica Sinica B 2020年第7期10卷 1192-1204页

作者： Lu Luo Jingwen Jiang Cheng Wang Martin Fitzgerald Weifeng Hu Yumei Zhou Hui Zhang Shilin Chen Institute of Chinese Materia Medica China Academy of Chinese Medical SciencesBeijing 100700China West China Biomedical Big Data Center West China HospitalSichuan UniversityChengdu 610041 China Institute of Basic Medical Sciences Chinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing 100006China Ashdale Clinic Cork T12 T2VKIreland The Third Clinical Medical College Zhejiang Chinese Medical UniversityHangzhou 310000China The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine Guangzhou 518033China Akupunktur Akademiet Aabyhoej 8230Denmark

As coronavirus disease 2019(COVID-19)pandemic poses a substantial global public health threat,traditional Chinese medicine(TCM)was used in 91.50%of the COVID-19 cases in China,showing encouraging results in improving symptom management and reducing the deterioration,mortality,and recurrence rates.A total of 166 modified herbal formulae consisting of 179 single herbal medicines were collected for treating COVID-19 in China.Glycyrrhizae Radix et Rhizome,Scutellariae Radix,and Armeniacae Semen Amarum are the most frequently utilized in clinics,most of which are antipyretic(47,26.26%),expectorant and cough-suppressing(22,12.29%),and dampness-resolving(21,11.73%)from traditional descriptions.A total of 1212 chemical components containingβ-sitosterol,stigmasterol,and quercetin were primarily selected.Additionally,using complex system entropy and unsupervised hierarchical clustering,8 core herbal combinations and 10 new formulae emerged as potentially useful candidates for COVID-19.Finally,following scaffold analysis,self-organizing mapping(SOM)and cluster analysis,12 clusters of molecules yielded 8 pharmacophore families of structures that were further screened as pharmacological targets in human metabolic pathways for inhibiting coronavirus.This article aims to make more easily accessible and share historical herbal knowledge used in contemporary treatments in a modern manner to assist researchers contain the global spread of COVID-19.

关键词： COVID-19 Traditional Chinese medicine(TCM) Herbal medicines clustering analysis Scaffold analysis

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Modified micro-mechanics based multiscale model for progressive failure prediction of 2D twill woven composites

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Chinese Journal of Aeronautics 2020年第7期33卷 2070-2087页

作者： Meng WANG Peiwei ZHANG Qingguo FEI Fei GUO Institute of Aerospace Machinery and Dynamics Southeast UniversityNanjing 211189China Department of Engineering Mechanics Southeast UniversityNanjing 211189China

To consider fiber random distribution at the microscale for the multiscale model based on the micro-mechanics failure(MMF)theory,clustering method is used for the extraction of amplification factors.As the clustering method is a kind of unsupervised machine learning method,the elements with similar mechanical behavior under external loading can be included in a cluster automatically at the microscale.With this modification,the fiber random distribution model can be used for multiscale damage analysis in the framework of MMF theory.To validate the modified multiscale analysis method,progressive damage analysis of a kind of 2D twill woven composites is conducted based on different microscale models.The stress values for microscale models with fiber hexagonal and random distribution patterns are compared first.Much higher stress concentration is generated in the fiber random distribution model due to the smaller inter-fiber distance especially under longitudinal shear loading.The obtained cluster distribution results exhibit the characters of the stress distribution in the two microscale models.Thereafter,tensile and compressive responses of the 2D twill woven composite are predicted with the modified multiscale analysis method and accuracy of the method is verified through comparison with published experimental results.From the simulation results,it can be found that the matrix damage initiation from the model based on the fiber random distribution model is premature compared with that from the model based on the fiber hexagonal distribution model.Besides,under tensile loading,the damage all initiates from the fill tows and propagates to the wrap tows.However,under compressive loading,the matrix damage initiates from the wrap tows in the model based on the fiber random distribution model.

关键词： 2D twill woven composites clustering analysis Fiber distribution patterns Micro-mechanics failure theory Multiscale damage model

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