Research relevance, 'knowledge transfer' and the geographies of CASE studentship collaboration

The Co-operative Awards in Science and Engineering (CASE) studentship programme of the UK Research Councils provide one example of wider efforts internationally to encourage so-called 'knowledge transfer' and thereby harness publicly supported university research more closely to the goals of national competitiveness, regional economic development and local regeneration. In this paper we describe the implications of how the various UK research councils have interpreted the objectives and beneficiaries of 'knowledge transfer', both for the relative opportunities available to human and physical geographers for collaboration through CASE and for the sorts of values that their research must serve. Then, we draw on unpublished data from the Economic and Social Research Council (ESRC) to explore the geographies of CASE studentship allocation and participation. The broad regional and institutional patterns of participation we describe have important implications for ongoing debates in the UK about research selectivity and the role of the university as an engine of local development, while the striking disciplinary patterns of CASE participation, and in particular the overwhelming success of geographers in this competitive programme, provide an opportunity to reassess claims about whether and for whom geographical research is relevant.     

Clustering and visualization of earthquake data in a grid environment

We present a web client-server service WEB-IS, which we have developed for remote analysis and visualization of seismic data consisting of both small magnitude events and large earthquakes. We show that the problem-solving environment (PSE) intended for prediction of large magnitude earthquakes can be based on this WEB-IS idea. The clustering schemes, feature generation, feature extraction techniques and rendering algorithms form a computational framework of this environment. On the other hand, easy and fast access both to the seismic data distributed among distant computing resources and to computational and visualization resources can be realized in a GRID framework. We discuss the usefulness of NaradaBrokering (iNtegrated Asynchronous Real-time Adaptive Distributed Architecture) as a middleware, allowing for flexibility and high throughput for remote visualization of geophysical data. The WEB-IS functionality was tested both on synthetic and the actual earthquake catalogs. We consider the application of similar methodology for tsunami alerts.     

Improved algorithms for generalized finite element simulations of three-dimensional hydraulic fracture propagation

This paper reports improvements to algorithms for the simulation of 3-D hydraulic fracturing with the Generalized Finite Element Method (GFEM). Three optimizations are presented and analyzed. First, an improved initial guess based on solving a 3-D elastic problem with the pressure from the previous step is shown to decrease the number of Newton iterations and increase robustness. Second, an improved methodology to find the time step that leads to fracture propagation is proposed and shown to decrease significantly the number of iterations. Third, reduced computational cost is observed by properly recycling the linear part of the coupled stiffness matrix. Two representative examples are used to analyze these improvements. Additionally, a methodology to include the leak-off term is presented and verified against asymptotic analytical solutions. Conservation of mass is shown to be well satisfied in all examples.     

智慧城市时空信息云平台建设研究

以完善的云环境建设为支撑,以构建标准安全的防护体系为保障,以时空信息云平台建设为基础,以智慧应用建设为重点,促成跨域、跨部门、跨层级的数据融合,提高城市综合智能化水平,使城市运转更加高效、敏捷、低碳与和谐。     

PMODTRAN: a parallel implementation based on MODTRAN for massive remote sensing data processing

MODerate resolution atmospheric TRANsmission (MODTRAN) is a commercial remote sensing (RS) software package that has been widely used to simulate radiative transfer of electromagnetic radiation through the Earth's atmosphere and the radiation observed by a remote sensor. However, when very large RS datasets must be processed in simulation applications at a global scale, it is extremely time-consuming to operate MODTRAN on a modern workstation. Under this circumstance, the use of parallel cluster computing to speed up the process becomes vital to this time-consuming task. This paper presents PMODTRAN, an implementation of a parallel task-scheduling algorithm based on MODTRAN. PMODTRAN was able to reduce the processing time of the test cases used here from over 4.4 months on a workstation to less than a week on a local computer cluster. In addition, PMODTRAN can distribute tasks with different levels of granularity and has some extra features, such as dynamic load balancing and parameter checking.     

A variable precision rough set approach to knowledge discovery in land cover classification

This paper presents a granular computing approach to spatial classification and prediction of land cover classes using rough set variable precision methods. In particular, it presents an approach to characterizing large spatially clustered data sets to discover knowledge in multi-source supervised classification. The evidential structure of spatial classification is founded on the notions of equivalence relations of rough set theory. It allows expressing spatial concepts in terms of approximation space wherein a decision class can be approximated through the partition of boundary regions. The paper also identifies how approximate reasoning can be introduced by using variable precision rough sets in the context of land cover characterization. The rough set theory is applied to demonstrate an empirical application and the predictive performance is compared with popular baseline machine learning algorithms. A comparison shows that the predictive performance of the rough set rule induction is slightly higher than the decision tree and significantly outperforms the baseline models such as neural network, naïve Bayesian and support vector machine methods.     

高性能计算技术在气象领域的应用

高性能计算通过应用超级计算机与并行处理技术解决复杂的计算问题,是信息技术发展比较迅猛的领域之一。气象应用始终是高性能计算的重要领域,高性能计算技术有效地解决了高分辨率、高精度气象数值预报模式发展限制,在气象预报预测业务中发挥着核心支撑作用。数十年以来,由于数值天气模式研究和业务运行对计算资源的强烈需求,国内外气象领域高性能计算机及应用迅速发展起来。气象领域对高性能计算能力及系统的可靠性需求日益提升。高性能计算技术将与气象预报应用日益融合,相互影响促进,不断创新发展。为满足气象预报预测业务和科研工作需求,中国气象部门将进一步提升高性能计算能力,并致力于优化集约高性能计算系统布局,高效管理计算资源,发挥最大效益。     

Application of ICTs in Airspace Management and Collaborative Decision-Making—Analyzing the Risk Avoidance in the Condition of Risky Weather as an Example

The application of ICTs makes structural change of the development and effective utilization of airspace. Next generation air transportation system (NextGen) includes new automation concepts with automated information to support the traffic control decision-making. As a result, in the field of academia and industry, air traffic controllers integrate information automatically while making decisions to change the previous manually integrated and decided pattern. The safety ability of airspace is reduced and airspace system is endangered under risky weather conditions of airspace. So there is an urgent demand for new information and communication technologies. The paper is an overview of the information constitution and support of NextGen and provides the study of the development of technique of airspace collaborative decision-makings to confirm the new features based on ICTs. It contains basic application-the input of data and output of the routes of airspace management and collaborative decision-making, and general application-the choose of probability nets of avoiding risky weather, and special application-the affection in the management of the air routes, which are made up of position and direction. The research shows the accurate schedule characteristics of airspace management and collaborative decision-making based on ICTs, which made the space accurate by time accurate. Second, the visualization of airspace management and collaborative decision-making based on ICTs made the maps of flight path under mobile data quickly generated. This could make the fully development and utilization of national airspace, ensure safety, and reduce air traffic controllers’ workload and the costs in delaying and operating in risky weather.     

基于神经网络的页岩微纳米孔隙微结构分析的正则化和最优化方法

页岩气成藏机理与页岩内部孔隙结构紧密相关,对页岩孔隙结构的研究成为页岩气勘探开发技术中至关重要的一环。页岩内部不同结构体组分对X-射线的吸收能谱不一样,这样就导致观测数据是由不同页岩组分衰减不同波段的X-射线构成的。经过对CT图像分割,能够获得页岩微孔结构的图像,尤其是获得有机质中孔隙类别、形状、尺寸、空间分布、连通特性。本文利用同步辐射X射线扫描重构的页岩CT数据,研究并设计基于多能CT图像的神经网络图像分割技术和算法,以期得到页岩体三维结构特征及空间分布,可以为建立有机质种类和无机矿物组成与微纳孔隙特征的联系以及最终实现页岩气的资源储量评估和勘探开发提供技术支持。