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新疆洪海沟矿床酸法地浸采铀试验

有色金属（冶炼部分） 2024年第8期 105-112页

作者：张青林黄群英贾志远陈立范龙黎广荣周义朋新疆中核天山铀业有限公司新疆伊宁835000 东华理工大学核资源与环境国家重点实验室南昌330013

洪海沟矿床是我国新疆千吨铀矿大基地的重要后备资源。为确立该矿床开发的适宜浸出工艺,在前期实验室研究基础上,开展了“四注一抽”规模的酸法地浸采铀试验。结果表明:该矿床微酸和低酸浸出均未达到预期浸出效果,采用3.0~4.0 g/L酸度... 详细信息

洪海沟矿床是我国新疆千吨铀矿大基地的重要后备资源。为确立该矿床开发的适宜浸出工艺,在前期实验室研究基础上,开展了“四注一抽”规模的酸法地浸采铀试验。结果表明:该矿床微酸和低酸浸出均未达到预期浸出效果,采用3.0~4.0 g/L酸度酸化、7.0~7.5 g/L酸度浸出是适宜工艺;300 mg/L剂量的H_(2)O_(2)能加速铀的浸出,但受限于溶出的Fe含量较低,氧化剂效能受限;尽管矿床碳酸盐含量不高,但浸出液Ca^(2+)、SO_(4)^(2-)持续增加易形成石膏沉淀导致抽注流量下降,需要在后续生产中予以关注。

关键词：砂岩铀矿地浸采铀酸法浸出条件试验

A geospatial hybrid cloud platform based on multi-sourced computing and model resources for geosciences

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International Journal of Digital Earth 2018年第12期11卷 1184-1204页

作者： qunying huang Jing Li Zhenlong Li Department of Geography University of Wisconsin–MadisonMadisonWIUSA Department of Geography and the Environment University of DenverDenverCOUSA Department of Geography University of South CarolinaColumbiaSCUSA

Cloud computing has been considered as the next-generation computing platform with the potential to address the data and computing challenges in ***,only a limited number of geoscientists have been adapting this platform for their scientific research mainly due to two barriers:1)selecting an appropriate cloud platform for a specific application could be challenging,as various cloud services are available and 2)existing general cloud platforms are not designed to support geoscience applications,algorithms and *** tackle such barriers,this research aims to design a hybrid cloud computing(HCC)platform that can utilize and integrate the computing resources across different organizations to build a unified geospatial cloud computing *** platform can manage different types of underlying cloud infrastructure(e.g.,private or public clouds),and enables geoscientists to test and leverage the cloud capabilities through a web ***,the platform also provides different geospatial cloud services,such as workflow as a service,on the top of common cloud services(e.g.,infrastructure as a service)provided by general cloud ***,geoscientists can easily create a model workflow by recruiting the needed models for a geospatial application or task on the fly.A HCC prototype is developed and dust storm simulation is used to demonstrate the capability and feasibility of such platform in facilitating geosciences by leveraging across-organization computing and model resources.

关键词： Cloud computing Big Data geospatial cloud services workflow as a service(WaaS) geoprocessing as a service(GaaS) model as a service(MaaS) high-performance computing parallel computing

Geographic context-aware text mining:enhance social media message classification for situational awareness by integrating spatial and temporal features

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International Journal of Digital Earth 2021年第11期14卷 1721-1743页

作者： Christopher Scheele Manzhu Yu qunying huang Spatial Computing and Data Mining Lab Department of GeographyUniversity of Wisconsin-MadisonMadisonWIUSA Department of Geography Pennsylvania State UniversityUniversity ParkPAUSA

To find disaster relevant social media messages,current approaches utilize natural language processing methods or machine learning algorithms relying on text only,which have not been perfected due to the variability and uncertainty in the language used on social media and ignoring the geographic context of the messages when ***,a disaster relevant social media message is highly sensitive to its posting location and ***,limited studies exist to explore what spatial features and the extent of how temporal,and especially spatial features can aid text *** paper proposes a geographic context-aware text mining method to incorporate spatial and temporal information derived from social media and authoritative datasets,along with the text information,for classifying disaster relevant social media *** work designed and demonstrated how diverse types of spatial and temporal features can be derived from spatial data,and then used to enhance text *** deep learning-based method and commonly used machine learning algorithms,assessed the accuracy of the enhanced text-mining *** performance results of different classification models generated by various combinations of textual,spatial,and temporal features indicate that additional spatial and temporal features help improve the overall accuracy of the classification.

关键词： Spatial data science spatially enabled text mining situational awareness deep learning GeoAI spatial features

球磨转速对含钆ODS钢中M_(23)C_(6)析出的影响研究

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材料导报 2023年第17期37卷 232-237页

作者：杨新异黄群英中国科学院合肥物质科学研究院核能安全技术研究所合肥230031 中国科学技术大学研究生院科学岛分院合肥230026

以“结构/屏蔽一体化”为研发目标的含钆ODS合金具有较优的中子屏蔽性能与高温力学性能,可作为小型模块化铅冷快堆中子屏蔽材料的研发方向之一。在机械合金化-放电等离子体烧结工艺制备含钆ODS-316L钢的研究中发现,球磨转速影响材料的... 详细信息

以“结构/屏蔽一体化”为研发目标的含钆ODS合金具有较优的中子屏蔽性能与高温力学性能,可作为小型模块化铅冷快堆中子屏蔽材料的研发方向之一。在机械合金化-放电等离子体烧结工艺制备含钆ODS-316L钢的研究中发现,球磨转速影响材料的析出相种类,如在220 r/min低球磨转速下,ODS-316L钢中仅存在纳米尺寸的Gd-Si-O析出相,而在300 r/min高球磨转速下,除纳米尺寸的Gd-Si-O析出相外,材料内还分布着大量百纳米尺寸的片层堆叠状M_(23)C_(6)型碳化物,且M_(23)C_(6)内同样存在纳米含钆氧化物颗粒。高球磨转速使球磨粉内元素的偏析与内应力的累积促进了M_(23)C_(6)的形核,随后的粉末烧结温度则为M_(23)C_(6)的生长提供了驱动力。此研究可为粉末冶金含钆ODS-316L钢的微观组织调控奠定一定的实验与理论基础。

关键词：中子屏蔽合金含钆氧化物弥散强化(ODS)钢粉末冶金球磨转速 M_(23)C_(6)型碳化物

Big Data and cloud computing:innovation opportunities and challenges

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International Journal of Digital Earth 2017年第1期10卷 13-53页

作者： Chaowei Yang qunying huang Zhenlong Li Kai Liu Fei Hu NSF Spatiotemporal Innovation Center George Mason UniversityFairfaxVAUSA Department of Geography University of Wisconsin-MadisonMadisonWIUSA Department of Geography University of South CarolinaColumbiaSCUSA

Big Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business,sciences and *** the same time,Big Data presents challenges for digital earth to store,transport,process,mine and serve the *** computing provides fundamental support to address the challenges with shared computing resources including computing,storage,networking and analytical software;the application of these resources has fostered impressive Big Data *** paper surveys the two frontiers–Big Data and cloud computing–and reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science *** the aspects of a general introduction,sources,challenges,technology status and research opportunities,the following observations are offered:(i)cloud computing and Big Data enable science discoveries and application developments;(ii)cloud computing provides major solutions for Big Data;(iii)Big Data,spatiotemporal thinking and various application domains drive the advancement of cloud computing and relevant technologies with new requirements;(iv)intrinsic spatiotemporal principles of Big Data and geospatial sciences provide the source for finding technical and theoretical solutions to optimize cloud computing and processing Big Data;(v)open availability of Big Data and processing capability pose social challenges of geospatial significance and(vi)a weave of innovations is transforming Big Data into geospatial research,engineering and business *** review introduces future innovations and a research agenda for cloud computing supporting the transformation of the volume,velocity,variety and veracity into values of Big Data for local to global digital earth science and applications.

关键词： Spatiotemporal computing digital earth geospatial cyberinfrastructure geoinformatics CyberGIS Smart cities

Introduction to social sensing and big data computing for disaster management

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International Journal of Digital Earth 2019年第11期12卷 1198-1204页

作者： Zhenlong Li qunying huang Christopher T.Emrich Geoinformation and Big Data Research Laboratory Department of GeographyUniversity of South CarolinaColumbiaSCUSA Department of Geography University of Wisconsin-MadisonMadisonWIUSA School of Public Administration and National Center for Integrated Coastal Research University of Central FloridaOrlandoFLUSA

Traditional data collection methods such as remote sensing and field surveying often fail to offer timely information during or immediately following disaster *** sensing enables all citizens to become part of a large sensor network,which is low cost,more comprehensive,and always broadcasting situational awareness ***,data collected with social sensing is often massive,heterogeneous,noisy,unreliable from some aspects,comes in continuous streams,and often lacks geospatial reference ***,these issues represent a grand challenge toward fully leveraging social sensing for emergency management decision making under extreme ***,big data computing methods and technologies such as high-performance computing,deep learning,and multi-source data fusion become critical components of using social sensing to understand the impact of and response to the disaster events in a timely *** special issue captures recent advancements in leveraging social sensing and big data computing for supporting disaster *** analyzed within these papers are some of the promises and pitfalls of social sensing data for disaster relevant information extraction,impact area assessment,population mapping,occurrence patterns,geographical disparities in social media use,and inclusion in larger decision support systems.

关键词： Social media VGI big data natural hazards spatial computing

Deep learning for real-time social media text classification for situation awareness-using Hurricanes Sandy,Harvey,and Irma as case studies

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International Journal of Digital Earth 2019年第11期12卷 1230-1247页

作者： Manzhu Yu qunying huang Han Qin Chris Scheele Chaowei Yang Department of Geography and Geoinformation Science George Mason UniveristyFairfaxVAUSA Department of Geography University of Wisconsin-MadisonMadisonWIUSA Ankura WashingtonDCUSA

Social media platforms have been contributing to disaster management during the past several *** mining solutions using traditional machine learning techniques have been developed to categorize the messages into different themes,such as caution and advice,to better understand the meaning and leverage useful information from the social media text ***,these methods are mostly event specific and difficult to generalize for cross-event *** other words,traditional classification models trained by historic datasets are not capable of categorizing social media messages from a future *** research examines the capability of a convolutional neural network(CNN)model in cross-event Twitter topic classification based on three geo-tagged twitter datasets collected during Hurricanes Sandy,Harvey,and *** performance of the CNN model is compared to two traditional machine learning methods:support vector machine(SVM)and logistic regression(LR).Experiment results showed that CNN models achieved a consistently better accuracy for both single event and crossevent evaluation scenarios whereas SVM and LR models had lower accuracy compared to their own single event accuracy *** indicated that the CNN model has the capability of pre-training Twitter data from past events to classify for an upcoming event for situational awareness.

关键词： Text mining deep learning hurricanes Twitter convolutional neural network situational awareness

Utilize cloud computing to support dust storm forecasting

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International Journal of Digital Earth 2013年第4期6卷 338-355页

作者： qunying huang Chaowei Yang Karl Benedict Songqing Chen Abdelmounaam Rezgui Jibo Xie Joint Center for Intelligent Spatial Computing Geography and Geoinformation ScienceGeorge Mason UniversityFairfaxVAUSA Earth Data Analysis Center(EDAC) University of New MexicoAlbuquerqueNMUSA Department of Computer Science George Mason UniversityFairfaxVAUSA Center for Earth Observation and Digital Earth BeijingChina

The simulations and potential forecasting of dust storms are of significant interest to public health and environment *** storms have interannual variabilities and are typical disruptive *** computing platform for a dust storm forecasting operational system should support a disruptive fashion by scaling up to enable high-resolution forecasting and massive public access when dust storms come and scaling down when no dust storm events occur to save energy and *** the capability of providing a large,elastic,and virtualized pool of computational resources,cloud computing becomes a new and advantageous computing paradigm to resolve scientific problems traditionally requiring a large-scale and high-performance *** paper examines the viability for cloud computing to support dust storm *** a holistic study by systematically comparing cloud computing using Amazon EC2 to traditional high performance computing(HPC)cluster,we find that cloud computing is emerging as a credible solution for(1)supporting dust storm forecasting in spinning off a large group of computing resources in a few minutes to satisfy the disruptive computing requirements of dust storm forecasting,(2)performing high-resolution dust storm forecasting when required,(3)supporting concurrent computing requirements,(4)supporting real dust storm event forecasting for a large geographic domain by using recent dust storm event in Phoniex,05 July 2011 as example,and(5)reducing cost by maintaining low computing support when there is no dust storm events while invoking a large amount of computing resource to perform high-resolution forecasting and responding to large amount of concurrent public accesses.

关键词： spatial cloud computing CyberGIS cloud GIS loosely coupled nested model Amazon EC2

Adopting cloud computing to optimize spatial web portals for better performance to support Digital Earth and other global geospatial initiatives

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International Journal of Digital Earth 2015年第6期8卷 451-475页

作者： Jizhe Xia Chaowei Yang Kai Liu Zhipeng Gui Zhenlong Li qunying huang Rui Li Department of Geography and GeoInformation Science NSF Spatiotemporal Innovation CenterGeorge Mason UniversityFairfaxVAUSA School of Remote Sensing and Information Engineering Wuhan UniversityWuhanHubeiP.R.China LIESMARS Wuhan UniversityWuhanHubeiP.R.China

A spatial web portal(SWP)provides a web-based gateway to discover,access,manage,and integrate worldwide geospatial resources through the Internet and has the access characteristics of regional to global interest and *** various technologies have been adopted to improve SWP performance,enabling high-speed resource access for global users to better support Digital Earth remains challenging because of the computing and communication intensities in the SWP operation and the dynamic distribution of end *** paper proposes a cloud-enabled framework for high-speed SWP access by leveraging elastic resource pooling,dynamic workload balancing,and global *** results demonstrate that the new SWP framework outperforms the traditional computing infrastructure and better supports users of a global system such as Digital *** methodologies and framework can be adopted to support operational geospatial systems,such as monitoring national geographic state and spanning across regional and global geographic extent.

关键词： CyberGIS/WebGIS GeoPortal spatial cloud computing spatiotemporal thinking and computing GEO national geographic state