Two criteria are chosen to determine the boundaries of mountains in China. One is the domestic criterion (DC) in China; the other is an international criterion (IC). According to the DC, there are 4 000 265 km2 mountain areas; while according to the IC, there are 4 426 130 km2 mountain areas. The mountains are classified into six categories: C1.300～1 000 m;C2.1 000～1 500 m;C3.1 500～2 500 m;C4.2 500～3 500 m;C5.3 500～4 500 m;C6.≥4 500 m. The areas of mountains in the two criteria over 3 500 m are equal (C5 & C6). Except the area of C1 in DC is larger than international criterion (the value is 324 508 km2), the areas of C2, C3 and C4 in IC are larger than DC (the values are 2 273 km2, 336 186 km2 and 133 432 km2, separately).
在中国，确定山脉边界的标准有两种。一个是中国的国内标准(DC);二是国际标准(IC)。根据DC，有4 000 265平方公里的山区;而根据国际生态系统，山区面积为4 426 130平方公里。山脉分为6类:C1.300~1 000 m, C2.1 000~1 500 m, C3.1 500~2 500 m, C4.2 500~3 500 m, C5.3 500~4 500 m, C6。≥4 500m。在3 500 m以上的两个标准中，山的面积相等(C5和C6)。除DC中C1面积大于国际标准(324 508 km2)外，IC中C2、C3和C4面积均大于DC(分别为2 273 km2、336 186 km2和133 432 km2)。
{"title":"The quantification of mountains in China Based on Geographic Information System","authors":"Xiaobo Jiang, W. Ji, H. Zeng, Leiting Chen","doi":"10.1109/MCSE.2009.163","DOIUrl":"https://doi.org/10.1109/MCSE.2009.163","url":null,"abstract":"Two criteria are chosen to determine the boundaries of mountains in China. One is the domestic criterion (DC) in China; the other is an international criterion (IC). According to the DC, there are 4 000 265 km2 mountain areas; while according to the IC, there are 4 426 130 km2 mountain areas. The mountains are classified into six categories: C1.300～1 000 m;C2.1 000～1 500 m;C3.1 500～2 500 m;C4.2 500～3 500 m;C5.3 500～4 500 m;C6.≥4 500 m. The areas of mountains in the two criteria over 3 500 m are equal (C5 & C6). Except the area of C1 in DC is larger than international criterion (the value is 324 508 km2), the areas of C2, C3 and C4 in IC are larger than DC (the values are 2 273 km2, 336 186 km2 and 133 432 km2, separately).","PeriodicalId":100659,"journal":{"name":"IMPACT of Computing in Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74270719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Karpatne, S. Liess, James H. Faghmous, Vipin Kumar
Recent growth in the scale and variety of climate data has provided unprecedented opportunities to big data analytics research for understanding the Earth's climate system. There has been an upsurge of climate datasets in the past few decades that are collected using various modes of acquisition (e.g. local sensor recordings or remote sensing instruments), at different scales of observation (both in space and time), and in diverse data types and formats. Climate datasets however exhibit some unique characteristics (e.g. adherence to physical properties and spatio-temporal constraints) that makes it challenging to use traditional data-centric approaches for climate science applications. In this paper, we present a brief introduction of the different categories of climate datasets that are available from various sources. We further describe some of the major data-centric challenges in analyzing climate data.
{"title":"A Guide to Climate Datasets: Summary and Research Challenges","authors":"A. Karpatne, S. Liess, James H. Faghmous, Vipin Kumar","doi":"10.1109/MCSE.2015.130","DOIUrl":"https://doi.org/10.1109/MCSE.2015.130","url":null,"abstract":"Recent growth in the scale and variety of climate data has provided unprecedented opportunities to big data analytics research for understanding the Earth's climate system. There has been an upsurge of climate datasets in the past few decades that are collected using various modes of acquisition (e.g. local sensor recordings or remote sensing instruments), at different scales of observation (both in space and time), and in diverse data types and formats. Climate datasets however exhibit some unique characteristics (e.g. adherence to physical properties and spatio-temporal constraints) that makes it challenging to use traditional data-centric approaches for climate science applications. In this paper, we present a brief introduction of the different categories of climate datasets that are available from various sources. We further describe some of the major data-centric challenges in analyzing climate data.","PeriodicalId":100659,"journal":{"name":"IMPACT of Computing in Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85635677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Bosilca, Aurélien Bouteiller, Anthony Danalis, Mathieu Faverge, T. Hérault, J. Dongarra
New HPC system designs with steeply escalating processor and core counts, burgeoning heterogeneity and accelerators, and increasingly unpredictable memory access times, call for one or more dramatically new programming paradigms. These new approaches must react and adapt quickly to unexpected contentions and delays, and they must provide the execution environment with sufficient intelligence and flexibility to rearrange the execution to improve the resource utilization. Some candidates in this area have already begun to emerge. Here we present an approach based on task parallelism, one which reveals the application’s parallelism by expressing its algorithm as a task flow, with data dependencies in-between. This strategy allows the algorithm to be decoupled from the data distribution and the underlying hardware, since the algorithm is entirely expressed as flows of data. This kind of layering provides a clear separation of concerns among architecture, algorithm, and data distribution. Developers benefit from this separation because they can focus solely on the algorithmic level without the constraints involved with programming for current and future hardware trends.
{"title":"PaRSEC: A programming paradigm exploiting heterogeneity for enhancing scalability","authors":"G. Bosilca, Aurélien Bouteiller, Anthony Danalis, Mathieu Faverge, T. Hérault, J. Dongarra","doi":"10.1109/MCSE.2013.98","DOIUrl":"https://doi.org/10.1109/MCSE.2013.98","url":null,"abstract":"New HPC system designs with steeply escalating processor and core counts, burgeoning heterogeneity and accelerators, and increasingly unpredictable memory access times, call for one or more dramatically new programming paradigms. These new approaches must react and adapt quickly to unexpected contentions and delays, and they must provide the execution environment with sufficient intelligence and flexibility to rearrange the execution to improve the resource utilization. Some candidates in this area have already begun to emerge. Here we present an approach based on task parallelism, one which reveals the application’s parallelism by expressing its algorithm as a task flow, with data dependencies in-between. This strategy allows the algorithm to be decoupled from the data distribution and the underlying hardware, since the algorithm is entirely expressed as flows of data. This kind of layering provides a clear separation of concerns among architecture, algorithm, and data distribution. Developers benefit from this separation because they can focus solely on the algorithmic level without the constraints involved with programming for current and future hardware trends.","PeriodicalId":100659,"journal":{"name":"IMPACT of Computing in Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87621343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
How likely is it that neuroscientists could map your brain and place your consciousness into a virtual world?
神经科学家绘制你的大脑并将你的意识置于虚拟世界的可能性有多大?
{"title":"Computing Hell","authors":"C. Day","doi":"10.1109/MCSE.2013.4","DOIUrl":"https://doi.org/10.1109/MCSE.2013.4","url":null,"abstract":"How likely is it that neuroscientists could map your brain and place your consciousness into a virtual world?","PeriodicalId":100659,"journal":{"name":"IMPACT of Computing in Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88173835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As science becomes increasingly computational, reproducibility has become increasingly difficult, perhaps surprisingly. In many contexts, virtualization and cloud computing can mitigate the issues involved without significant overhead to the researcher, enabling the next generation of rigorous and reproducible computational science.
{"title":"CDE: A Tool for Creating Portable Experimental Software Packages","authors":"B. Howe","doi":"10.1109/MCSE.2012.36","DOIUrl":"https://doi.org/10.1109/MCSE.2012.36","url":null,"abstract":"As science becomes increasingly computational, reproducibility has become increasingly difficult, perhaps surprisingly. In many contexts, virtualization and cloud computing can mitigate the issues involved without significant overhead to the researcher, enabling the next generation of rigorous and reproducible computational science.","PeriodicalId":100659,"journal":{"name":"IMPACT of Computing in Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78116638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Is it possible to use computers to "read" the mind or simulate the brain? Here the author considers current research and possible scenarios.
是否有可能用电脑“读懂”人的思想或模拟大脑?在这里，作者考虑了当前的研究和可能的情况。
{"title":"Mind-Reading Computers","authors":"C. Day","doi":"10.1109/MCSE.2012.79","DOIUrl":"https://doi.org/10.1109/MCSE.2012.79","url":null,"abstract":"Is it possible to use computers to \"read\" the mind or simulate the brain? Here the author considers current research and possible scenarios.","PeriodicalId":100659,"journal":{"name":"IMPACT of Computing in Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83959145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
What will happen if academia disrupts traditional teaching methods by providing students with online and free academic materials?
如果学术界通过向学生提供在线和免费的学术资料来颠覆传统的教学方法，会发生什么?
{"title":"Accelerating Learning with Distance Education and Open Courseware","authors":"G. Thiruvathukal","doi":"10.1109/MCSE.2012.70","DOIUrl":"https://doi.org/10.1109/MCSE.2012.70","url":null,"abstract":"What will happen if academia disrupts traditional teaching methods by providing students with online and free academic materials?","PeriodicalId":100659,"journal":{"name":"IMPACT of Computing in Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73343339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Service-oriented workflow provides a mechanism to automate the coordination of a sequence of tasks within a process, whereas each task is presented as a Web service and integrated into the workflow. It has become increasingly important to have an appropriate workflow platform to manage the scientific workflow lifecycle. In order to address this need, we propose to use Pipeline Pilot to manage the lifecycle of grid-based scientific workflow using SOA approach. Pipeline Pilot is a commercial software package dedicated to scientific research. It is reliable, lightweight and comprehensive. We then describe a case study of how we employ Pipeline Pilot to manage a workflow lifecycle for calculating material dielectric properties in grid environment. We also discuss lessons we learned in managing lifecycle of a service-oriented scientific workflow within e-Science.
{"title":"Developing an End-to-End Scientific Workflow: a Case Study of Using a Reliable, Lightweight, and Comprehensive Workflow Platform in e-Science","authors":"Xiaoyu Yang","doi":"10.1109/MCSE.2009.211","DOIUrl":"https://doi.org/10.1109/MCSE.2009.211","url":null,"abstract":"Service-oriented workflow provides a mechanism to automate the coordination of a sequence of tasks within a process, whereas each task is presented as a Web service and integrated into the workflow. It has become increasingly important to have an appropriate workflow platform to manage the scientific workflow lifecycle. In order to address this need, we propose to use Pipeline Pilot to manage the lifecycle of grid-based scientific workflow using SOA approach. Pipeline Pilot is a commercial software package dedicated to scientific research. It is reliable, lightweight and comprehensive. We then describe a case study of how we employ Pipeline Pilot to manage a workflow lifecycle for calculating material dielectric properties in grid environment. We also discuss lessons we learned in managing lifecycle of a service-oriented scientific workflow within e-Science.","PeriodicalId":100659,"journal":{"name":"IMPACT of Computing in Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83877190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Textbooks on a topic such as C programming are evolutionary, not revolutionary. In contrast, Harry Cheng's entry is an innovative textbook and programming environment that defines a new interactive paradigm for learning and using C and C++ that's particularly well suited for scientists and engineers. And in Multiphysics Modeling Using COMSOL, author Roger W. Pryor has identified and filled the missing link in the existing literature on finite element modeling (FEM) using COMSOL Multiphysics. This hands-on introduction lets scientists and engineers quickly start building and solving models of different physical device structures to see how they behave on a computer before attempting to build physical prototypes.
关于某个主题(如C编程)的教科书是渐进的，而不是革命性的。相比之下，Harry Cheng的入口是一个创新的教科书和编程环境，它定义了一个新的交互范例，用于学习和使用C和c++，特别适合科学家和工程师。在使用COMSOL的多物理场建模中，作者Roger W. Pryor已经确定并填补了使用COMSOL Multiphysics进行有限元建模(FEM)的现有文献中缺失的环节。这个动手介绍让科学家和工程师快速开始建立和解决不同的物理设备结构的模型，看看他们如何在试图建立物理原型之前在计算机上的行为。
{"title":"Books [Two books reviews]","authors":"Thomas M. Huber, C. Schroder","doi":"10.1109/MCSE.2010.82","DOIUrl":"https://doi.org/10.1109/MCSE.2010.82","url":null,"abstract":"Textbooks on a topic such as C programming are evolutionary, not revolutionary. In contrast, Harry Cheng's entry is an innovative textbook and programming environment that defines a new interactive paradigm for learning and using C and C++ that's particularly well suited for scientists and engineers. And in Multiphysics Modeling Using COMSOL, author Roger W. Pryor has identified and filled the missing link in the existing literature on finite element modeling (FEM) using COMSOL Multiphysics. This hands-on introduction lets scientists and engineers quickly start building and solving models of different physical device structures to see how they behave on a computer before attempting to build physical prototypes.","PeriodicalId":100659,"journal":{"name":"IMPACT of Computing in Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87220411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The article describes recent progress in atomic and molecular level modeling and simulation of nanoscale materials and processes, as well as efforts by the US National Science Foundation's Network ...
本文描述了纳米材料和工艺的原子和分子水平建模和模拟的最新进展，以及美国国家科学基金会网络的努力。
{"title":"Cyber-Enabled Simulations in Nanoscale Science and Engineering","authors":"StrachanA., KlimeckG., LundstromM.","doi":"10.5555/2220076.2220205","DOIUrl":"https://doi.org/10.5555/2220076.2220205","url":null,"abstract":"The article describes recent progress in atomic and molecular level modeling and simulation of nanoscale materials and processes, as well as efforts by the US National Science Foundation's Network ...","PeriodicalId":100659,"journal":{"name":"IMPACT of Computing in Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75699494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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